text:
OBJECTIVE—The objective of this study was to compare two diabetes self-management interventions designed for Mexican Americans: “extended” (24 h of education, 28 h of support groups) and “compressed” (16 h of education, 6 h of support groups). Both interventions were culturally competent regarding language, diet, social emphasis, family participation, and incorporating cultural beliefs.

RESEARCH DESIGN AND METHODS—We recruited 216 persons between 35 and 70 years of age diagnosed with type 2 diabetes ≥1 year. Intervention groups of eight participants and eight support persons were randomly assigned to the compressed or extended conditions. The interventions differed in total number of contact hours over the yearlong intervention period, with the major difference being the number of support group sessions held. The same information provided in the educational sessions of the extended intervention was compressed into fewer sessions, thus providing more information during each group meeting.

RESULTS—The interventions were not statistically different in reducing HbA1c; however, both were effective. A “dosage effect” of attendance was detected with the largest HbA1c reductions achieved by those who attended more of the extended intervention. For individuals who attended ≥50% of the intervention, baseline to 12-month HbA1c change was −0.6 percentage points for the compressed group and −1.7 percentage points for the extended group.

CONCLUSIONS—Both culturally competent diabetes self-management education interventions were effective in promoting improved metabolic control and diabetes knowledge. A dosage effect was evident; attending more sessions resulted in greater improvements in metabolic control.

Twenty-one percent of the U.S. population lives in states bordering Mexico, and >33% of these individuals live in medically underserved border communities characterized by extreme poverty, pollution, deprivation, poor health, and diminished quality of life (1). Sixty percent of Hispanics, predominantly Mexican Americans who have the lowest rates of insurance coverage of any group, live in border states (2), and diabetes and related morbidity and mortality rates are highest among these border residents (36).

Traditional approaches to managing diabetes in the U.S. have been perceived by Mexican Americans, in some instances, as culturally insensitive and, thus, have been ineffective (7). We designed and tested nonpharmacological, culturally competent, community-based diabetes self-management interventions in Starr County, a Texas-Mexico border community in which 98% of the residents are Mexican American (8). Promoting attendance at lifestyle programs, i.e., ensuring an adequate “dosage” of the intervention, is a challenge, particularly in underserved groups who may lack transportation and who tend to live chaotic lives, with frequent financial, health, and personal crises. Mexican Americans value social networks, and women are expected to provide health care for family, relatives, and friends, often at the expense of their own personal health.

Considering the data from the initial Starr County diabetes self-management study that indicated a maximum benefit at 6 months, we developed a less intensive “compressed” intervention involving 22 contact hours over 12 months as opposed to the original “extended” intervention involving 52 contact hours over 12 months. The same information provided in the extended intervention was compressed into fewer sessions, thus providing more information during each group meeting. Another major difference in the interventions involved support group sessions, which were reduced from 14 in the extended intervention to 3 in the compressed. Here, we report our analyses of the compressed compared with the original extended intervention and describe the “dosage effects” on three primary clinical outcomes: HbA1c, fasting blood glucose (FBG), and diabetes knowledge. We hypothesized that there would be no significant differences in study outcomes at 3 and 12 months among subjects in the two interventions. Similar effects would support the utility of the shorter intervention, which would be easier to integrate into clinical and community settings.

The study site, Starr County, has been described previously (811). Significant barriers to health result from high unemployment (24.4% compared with 4.6% for the state), low per capita income ($8,225 compared with$25,369 for the state), and some of the state’s poorest housing, as well as from being medically underserved (ratio of population per physician 7,657:1 compared with 3,789:1 for the state; registered nurse ratio 851:1 compared with 159:1 for the state) (1214).

A sample of 216 participants was selected from rosters of ongoing genetic studies. Based on our previous studies, we estimated that a total of 170 subjects (85 each for the compressed and extended conditions) provided power of 80% for detecting a medium between-group effect size on HbA1c (15). Oversampling by 30% helped to account for potential attrition, although the retention in our previous Starr County studies was excellent, at 90% on average. Inclusion criteria included the following: 1) 35–70 years of age and 2) diagnosed with type 2 diabetes (two verifiable FBG results ≥140 mg/dl or taking or having taken insulin or hypoglycemic agents for ≥1 year). To capitalize on the cultural importance of family and social relationships, each subject was asked to identify a family member, preferably a spouse, first-degree relative, or close friend to participate. We excluded individuals if they were pregnant or had medical conditions for which changes in diet and walking were contraindicated (e.g., renal failure or previous amputation).

Six cohorts were recruited, and individuals were assigned to groups; 114 were allocated to compressed groups and 102 to extended groups. To control for between-group differences in socioeconomic status and foster group support between weekly sessions, each group was organized within a specific area of the county and then randomly assigned to either compressed or extended conditions. Four groups of eight subjects and their support people constituted each cohort; two groups were randomly assigned to the compressed intervention and two to the extended. The same process occurred every 3 months until 23 groups were enrolled. Intervention groups began immediately after baseline data collection, and further data were collected as cohorts reached 3-, 6-, 12-, 24-, and 36-month examination dates. Attendance at data collection sessions averaged 82%. Only 10 of the 216 study participants who attended baseline data collection sessions were considered true dropouts because they did not return for any additional sessions. Physicians were notified by letter that their patients were participating. We sent test results alerting them of high values, using pre-established thresholds for blood pressure, glucose, and lipids. Before the study, written informed consent was obtained according to procedures approved by two university institutional review boards.

### Description of the culturally competent interventions

The original yearlong extended intervention is described elsewhere (8,9). Teams of bilingual Mexican-American nurses, dietitians, and community workers from Starr County or other border areas were assigned to lead the interventions. Both interventions compared in this study were offered in community-based sites throughout Starr County: schools, churches, adult day care centers, and health clinics. Cultural competence was operationally defined in both interventions as employing the preferred language, integrating cultural dietary preferences, emphasizing social activities and family participation, and holding open nonjudgmental discussions of cultural health beliefs and practices. For both interventions, social support was emphasized by including participation from family members or friends (including their participation in all measurements), fostering social relationships with other group participants, and encouraging communication between participants and intervention team members.

The redesign of the original intervention (extended) into a shorter version (compressed) was informed by focus groups held with participants of the previous Starr County study (16). The initial intervention was an intensive 1-year series of 12 weekly 2-h sessions on nutrition, home glucose monitoring, physical activity, and other self-management topics, followed by 14 2-h support group sessions to promote behavioral change through problem solving and goal setting. The compressed intervention involved eight weekly 2-h educational sessions followed by support sessions strategically held at 3, 6, and 12 months. Both interventions covered similar information, but the time spent on some topics differed. All participants received their usual diabetes care, if any, provided by local physicians or clinics, which for some individuals was obtained in Mexico.

### Measurements

Measures of intervention effectiveness were similar to those used previously (8): demographics (age, sex, age of diabetes diagnosis, etc.); acculturation (the degree to which persons of foreign origins adopt American customs) (17); family, medical, and medication history; diabetes knowledge; health beliefs; HbA1c; FBG; blood pressure; BMI; cholesterol; and triglycerides (10,11). Here, we report the results of three primary clinical outcomes: HbA1c, FBG, and diabetes knowledge. HbA1c was analyzed at The University of Texas-Houston (Glyc-Affin Ghb; Isolab, Akron, OH). FBG (10-h fasting) was performed in the research field office with a desktop glucose analyzer (YSI model 2300 STAT PLUS glucose and lactate analyzer; Yellow Springs Instruments, Yellow Springs, OH). The Spanish-language knowledge instrument was based on national standards and written to facilitate reading aloud to subjects (Küder-Richardson reliability ≥0.80) (18).

### Statistical analysis

We compared the interventions with a prospective, quasi-experimental, repeated-measures, nested design. For measures spanning 12 months, incomplete data, which, if ignored, can lead to biased results, precluded use of standard statistical procedures (19). To handle missing data in the longitudinal analyses, we applied hierarchical linear models (HLMs) by which nonrandomly missing data were handled by including indicators of missing data patterns (2022). In addition to adjusting for baseline differences, all analyses tested the main effects for age (years), intervention (compressed or extended), sex, number of hours of intervention attendance, and all two-way interactions. To better understand the nature of the intervention effects, dosage effects in particular, we conducted a series of two (compressed versus extended) by two (high attendance, that is, above the median, versus low attendance, that is, below the median) ANCOVAs. For the comparisons at 3 months, the point at which the educational portion of both interventions ended, baseline measures were treated as covariates. The 3-month measures were treated as covariates for the 12-month analyses, reflecting the time period during which support sessions were held.

### Baseline results

Subjects were similar to those in our previous studies—predominantly female, obese, ∼50 years of age on average, and in poor metabolic control (Table 1). Language-based acculturation was low, indicating a Spanish language preference. With the exception of “language spoken at home,” there were no statistically significant differences between the compressed and extended groups on any baseline measure. The number of individuals treated with insulin did not differ sig-nificantly at baseline between groups. Hypertension and high cholesterol were the most commonly reported comorbidities.

### Three- and 12-month results

All measures decreased from baseline to 3 months (immediate effects) and from baseline to 12 months (longitudinal effects) in both intervention conditions; the baseline to 12-month change in HbA1c for the compressed group was −0.7%, whereas for the extended group, the change was −1.0% (Table 2). An initial analysis, based on the intention-to-treat principle, involved a two-group (compressed and extended) ANCOVA for the 12-month scores, with the baseline as the covariate. Data from all participants, regardless of intervention attendance, were included in the analyses. No significant differences between programs were found for any of the outcomes.

#### HbA1c.

HLM analyses indicated that at 3 months, no intervention group differences in HbA1c levels were detected. Men on average had lower HbA1c levels (t = −3.11, P = 0.002), and those men who had greater attendance at the educational component of the intervention achieved lower HbA1c levels, regardless of intervention type. For change in HbA1c over time, the interaction between intervention and attendance suggested that for the extended intervention, greater overall attendance at both the educational and support sessions was related to greater reductions in HbA1c over time (B = −0.08, t = −6.51, P < 0.001). Attendance did not moderate change in levels for the compressed intervention.

#### FBG.

HLM analyses indicated that at 3 months, no group differences in FBG levels were detected. On average, those who attended a greater percentage of the educational component of the intervention showed relatively lower levels of FBG at 3 months, regardless of intervention type. Similarly, at 12 months, those who attended a greater percentage of both the educational and support sessions showed lower FBG levels regardless of intervention type. On average, greater attendance was related to lower levels across all individuals (B = −1.06, t = −2.21, P < 0.05).

#### Diabetes knowledge.

HLM analyses indicated that at 3 months, no group differences in knowledge were detected. Attendance at both educational and support sessions was related to greater knowledge levels at 12 months. At 12 months, knowledge was in general positively related to the number of hours of attendance (B = 0.08, t = 5.26, P < 0.001). On average, knowledge scores did not change between months 3 and 12 when the focus was on providing support groups (B = 0.01, t = 0.57, P = 0.569).

#### Intervention attendance.

To explore the interaction effects of intervention and attendance on clinical outcomes, analyses were conducted on low versus high attendance (Table 3). There were no statistically significant main effects for type of intervention, i.e., no program differences. There were statistically significant main effects for attendance for all three outcomes at 3 months but only for knowledge at 12 months. Consistent with the analyses reported above, there was an intervention by attendance interaction for HbA1c at 12 months.

The baseline to 12-month change in HbA1c for those who attended ≥50% of the intervention sessions was −0.6 percentage points for the compressed group (12-month HbA1c = 11.0%) and −1.7 percentage points for the extended group (12-month HbA1c = 9.2%).

We also compared the baseline to 12-month change in HbA1c of top 10% (n = 18) achievers in reducing HbA1c, using these individuals as “role models,” with individuals who were least successful (n = 18). “Top achievers” attended, on average, 57% of sessions, whereas the lowest group attended 37%. Top achievers reduced HbA1c levels by −6 percentage points (baseline HbA1c = 16.3%; 12-month HbA1c = 10.2%), compared with an increase of 4 percentage points in the lowest group (baseline HbA1c = 10.0%; 12-month HbA1c = 14.2%). The post hoc analyses of adjusted means showed that the difference between the low attendance and high attendance groups was statistically significant for those receiving the extended intervention (11.3 vs. 10.0, P ≤ 0.05) but not for those who had received the compressed intervention (10.8 vs. 11.3). The intervention effect was statistically significant for the high attendees (11.3 vs. 10.0, P ≤ 0.05) but not for the low attendees (10.8 vs. 11.3).

Study participants who attended the least number of intervention sessions (n = 30) verbalized an intention to participate but attended few sessions due to the following reasons: too busy with work (n = 8), illness in the family/serve as family caregiver (n = 7), felt too “lazy” to go/“didn’t make the time” (n = 5), needed transportation (n = 5), migrating (n = 2), too busy at home (n = 2), moved (n = 2), and never told about the class (n = 1). (Some individuals gave more than one reason.)

Costs of the two interventions were estimated based on the following assumptions: 1) monitors and strips are covered by insurance, 2) educational materials are a one-time purchase at the outset of the project, and 3) free community-based sites are available. (During our intervention studies, numerous sites in the community were provided at no cost. Overhead charges added by for-profit organizations that might offer such interventions are not included, but some programs charge an overhead of ≥50%.) Based on personnel and food demonstration costs only (not including indirect costs), the following cost comparisons were made. The compressed intervention results in a 60% cost savings over the extended intervention. Extended care (nurse and dietitian at sessions 1–12):

• 12 educational sessions at $120/session =$1,440

• 14 support group sessions at $70/session =$980

• Food: $25/session for 26 sessions =$650

• Total: $3,070/8 diabetic subjects per group =$384/person

Compressed care (nurse or dietitian at each session):

• 8 educational sessions at $70/session =$560

• 3 support group sessions at $70/session =$210

• Food: $25/session for 11 sessions =$275

• Total: $1,045/8 diabetic subjects per group =$131/person

Health problems of border residents present unique challenges created by rapid population growth; substandard housing with lack of water, sewage systems, and paved roads; lack of health care access caused by a need to travel long distances to obtain services and lack of transportation; poverty that precludes paying for physician visits or recommended treatment; and individuals who speak only Spanish (23). Mexican Americans living on the U.S. side of the border obtain >50% of their health care in Mexico due to lower costs, greater accessibility, and perceptions of greater effectiveness (24). We designed an intervention by taking into account this sociocultural context (12).

Because there was little guidance from previous research regarding the appropriate “dosage” of educational and behavioral interventions for impoverished, non–English-speaking populations, we originally developed an intensive intervention but measured outcomes at regular intervals to determine the point of maximum intervention impact (8,11). The purpose of this study was to modify our original yearlong program into a shorter, more resource efficient strategy that would be more easily integrated into clinical settings.

Discussions of diabetes self-management always lead to concerns about costs, particularly when one is promoting the need for increasing intervention dosage. Typical diabetes education programs range from 4 to 15 h of education over a 2- to 3-month time period and cost between $95 and$125 per 1-h session; group instruction costs slightly less (25). In some instances, complete diabetes education services can cost $200 or more per person (26). The most intensive Starr County intervention (extended) was considerably more intensive than typical programs: 52 h over a 12-month time period. The intervention also cost significantly less:$7.39 per person per hour. In either the extended or the compressed intervention, the cost was estimated to be less than that associated with a year’s prescription of a single medication and is not excessive when the costs of diabetes morbidity and mortality are considered.

We have consistently found that our self-management interventions are more effective for participants with very elevated glucose levels, such as in Starr County, where the average baseline values have been ∼12%, rather than those with more average levels of 8–9%. This factor limits the generalizability of our interventions. We demonstrated the effectiveness of culturally competent diabetes self-management education; but study participants, on average, did not achieve the national HbA1c target of ≤7%. The initial Starr County study showed a 1.4–percentage point difference in HbA1c at 6 months between experimental and control groups, although the mean HbA1c levels at all measurement points remained >10% (8). Data from the study reported here indicated a decrease in both interventions, but the best result (HbA1c = 9.2%) occurred in the extended intervention for individuals who received the maximum “dose,” that is, those who attended ≥50% of the intervention sessions. Although these levels are higher than the national target, these improvements significantly improve health. “… [F]or every 1–percentage point decrease in HbA1c, there is a 35% reduction in the risk of microvascular complications, a 25% reduction in diabetes-related deaths, a 7% reduction in all cause mortality, and an 18% reduction in combined fatal and nonfatal myocardial infarction” (3). Nonpharmacological, community-based lifestyle interventions, such as those we developed in Starr County, offer a way for individuals to decrease or delay diabetes morbidity and mortality, can be implemented in any clinical or community setting, and do not rely on access to traditional health care services (27).

Behavioral interventions are as important to the clinical care of persons with diabetes as are medications. Because of doubts about compliance with behavioral interventions, such programs frequently are not valued nor are they included in any comprehensive diabetes management strategy. Most health professionals would agree that changing health behavior is complex and difficult; however, based on the findings of this study, past failures in improving health behaviors may have been the result of inadequate intervention dosage. Diabetes self-management programs should be prescribed in a manner similar to diabetes medications, that is, provide the desired “dose” and then “reinoculate” at key intervals, such as annually or at other times when the disease status changes (acquiring a new complication or a change in medication). For individuals similar to participants of the Starr County study, persons who come from impoverished backgrounds and who have few resources, we recommend the extended program of 52 h over 12 months, with reinoculation at least annually. However, there are no generally accepted, experimentally supported strategies for reinoculation after an initial intervention. The Medicare benefit includes an annual 4-h reinoculation in diabetes self-management education and nutrition counseling, but the efficacy of this strategy has not been tested. Interventions designed to maintain long-term benefits of self-management programs must be tested in future research to determine the most cost-effective reinoculation strategies.

Table 1—

A profile of subjects upon admission to the study

CharacteristicsCompressedExtendedTotal
n 114 102 216
Sex (women/men) 69/45 61/41 130/86
Age (years) 49.6 ± 7.6 49.6 ± 8.2 49.6 ± 7.9
Age at diagnosis of diabetes (years) 44.4 ± 8.3 44.6 ± 9.2 44.5 ± 8.7
BMI (kg/m232.2 ± 5.8 32.9 ± 8.3 32.5 ± 7.1
Diabetes medication modalities (n94 95
Oral agents (%) 78.0 81.1
Insulin (%) 5.3 6.3
Oral and insulin (%) 4.3 1.1
Other (%) 2.1 1.1
None (%) 10.6 10.5
Acculturation (range 1–4) 0.9 ± 0.9 1.2 ± 1.3 1.0 ± 1.1
Preferred language: Spanish (%) 74 68 71
Language at home: Spanish (%) 84 66 76
First language: Spanish (%) 97 92 95
Read no or little English (%) 56 55 56
Comorbidities: history of
Myocardial infarction (%) 5.3 7.8 6.5
Angina (%) 4.4 5.9 5.1
Stroke (%) 2.9 1.4
Hypertension (%) 35.1 36.3 35.6
High cholesterol (%) 37.7 40.2 38.9
Gallbladder surgery (%) 11.4 15.7 13.4
CharacteristicsCompressedExtendedTotal
n 114 102 216
Sex (women/men) 69/45 61/41 130/86
Age (years) 49.6 ± 7.6 49.6 ± 8.2 49.6 ± 7.9
Age at diagnosis of diabetes (years) 44.4 ± 8.3 44.6 ± 9.2 44.5 ± 8.7
BMI (kg/m232.2 ± 5.8 32.9 ± 8.3 32.5 ± 7.1
Diabetes medication modalities (n94 95
Oral agents (%) 78.0 81.1
Insulin (%) 5.3 6.3
Oral and insulin (%) 4.3 1.1
Other (%) 2.1 1.1
None (%) 10.6 10.5
Acculturation (range 1–4) 0.9 ± 0.9 1.2 ± 1.3 1.0 ± 1.1
Preferred language: Spanish (%) 74 68 71
Language at home: Spanish (%) 84 66 76
First language: Spanish (%) 97 92 95
Read no or little English (%) 56 55 56
Comorbidities: history of
Myocardial infarction (%) 5.3 7.8 6.5
Angina (%) 4.4 5.9 5.1
Stroke (%) 2.9 1.4
Hypertension (%) 35.1 36.3 35.6
High cholesterol (%) 37.7 40.2 38.9
Gallbladder surgery (%) 11.4 15.7 13.4

Data are means ±SD, unless otherwise indicated. In acculturation, 4 = high acculturation.

Table 2—

Primary outcomes

Outcome measure in programBaseline3 months12 monthsBaseline to 3-month changeBaseline to 12-month change
HbA1c
Compressed 11.8 ± 3.4 (114) 10.9 ± 2.8 (94) 11.1 ± 3.2 (96) −0.9* −0.7
Extended 11.5 ± 3.5 (102) 11.0 ± 3.3 (94) 10.5 ± 3.0 (89) −0.5 −1.0*
FBG
Compressed 192.1 ± 64.4 (114) 164.9 ± 54.1 (94) 179.7 ± 61.6 (97) −27.2 −12.4
Extended 190.5 ± 68.3 (102) 177.5 ± 61.1 (94) 173.8 ± 63.6 (89) −13.0* −16.7
Knowledge
Compressed 14.7 ± 3.4 (114) 16.0 ± 3.3 (93) 16.0 ± 3.4 (97) 1.3 1.3
Extended 14.9 ± 3.2 (102) 16.2 ± 3.0 (94) 16.4 ± 3.0 (89) 1.3 1.5
Outcome measure in programBaseline3 months12 monthsBaseline to 3-month changeBaseline to 12-month change
HbA1c
Compressed 11.8 ± 3.4 (114) 10.9 ± 2.8 (94) 11.1 ± 3.2 (96) −0.9* −0.7
Extended 11.5 ± 3.5 (102) 11.0 ± 3.3 (94) 10.5 ± 3.0 (89) −0.5 −1.0*
FBG
Compressed 192.1 ± 64.4 (114) 164.9 ± 54.1 (94) 179.7 ± 61.6 (97) −27.2 −12.4
Extended 190.5 ± 68.3 (102) 177.5 ± 61.1 (94) 173.8 ± 63.6 (89) −13.0* −16.7
Knowledge
Compressed 14.7 ± 3.4 (114) 16.0 ± 3.3 (93) 16.0 ± 3.4 (97) 1.3 1.3
Extended 14.9 ± 3.2 (102) 16.2 ± 3.0 (94) 16.4 ± 3.0 (89) 1.3 1.5

Data are means ±SD (n) based on the total sample (n = 216). Knowledge range: 0–24. Data in table reflect means of all individuals who provided data at that measurement point. Significance levels reflect paired comparisons for those individuals for whom we have the measure at both measurement points (baseline + 3 months, baseline + 12 months).

*

P = 0.01;

P = 0.05;

P = 0.001.

Table 3—

Trend analysis of significant intervention effects: ANCOVA analyses controlling for baseline measures

P
Experimental group: compressedControl group: extended
LowHighLowHighInter-ventionAtten-danceIntervention by attendance interaction
HbA1c
3-month 11.6 ± 3.1 (33) 10.5 ± 2.6 (61) 11.8 ± 3.8 (43) 10.5 ± 2.6 (51) 0.71 <0.001 0.74
12-month 10.8 ± 3.5 (21) 11.3 ± 3.1 (62) 11.3 ± 3.0 (37) 10.0 ± 2.5 (46) 0.30 0.40 0.05
FBG
3-month 175.2 ± 57.9 (33) 159.6 ± 51.5 (61) 195.8 ± 65.4 (43) 163.6 ± 46.2 (51) 0.09 <0.001 0.20
12-month 181.1 ± 75.7 (21) 178.5 ± 54.4 (63) 183.8 ± 76.0 (37) 165.0 ± 40.7 (46) 0.15 0.23 0.45
Diabetes knowledge
3-month 15.2 ± 2.8 (32) 16.3 ± 3.5 (61) 15.8 ± 3.0 (43) 16.6 ± 2.9 (51) 0.51 0.02 0.67
12-month 12.6 ± 6.4 (26) 15.0 ± 5.1 (67) 13.3 ± 6.2 (44) 15.7 ± 5.5 (50) 0.73 0.007 0.96
P
Experimental group: compressedControl group: extended
LowHighLowHighInter-ventionAtten-danceIntervention by attendance interaction
HbA1c
3-month 11.6 ± 3.1 (33) 10.5 ± 2.6 (61) 11.8 ± 3.8 (43) 10.5 ± 2.6 (51) 0.71 <0.001 0.74
12-month 10.8 ± 3.5 (21) 11.3 ± 3.1 (62) 11.3 ± 3.0 (37) 10.0 ± 2.5 (46) 0.30 0.40 0.05
FBG
3-month 175.2 ± 57.9 (33) 159.6 ± 51.5 (61) 195.8 ± 65.4 (43) 163.6 ± 46.2 (51) 0.09 <0.001 0.20
12-month 181.1 ± 75.7 (21) 178.5 ± 54.4 (63) 183.8 ± 76.0 (37) 165.0 ± 40.7 (46) 0.15 0.23 0.45
Diabetes knowledge
3-month 15.2 ± 2.8 (32) 16.3 ± 3.5 (61) 15.8 ± 3.0 (43) 16.6 ± 2.9 (51) 0.51 0.02 0.67
12-month 12.6 ± 6.4 (26) 15.0 ± 5.1 (67) 13.3 ± 6.2 (44) 15.7 ± 5.5 (50) 0.73 0.007 0.96

Data are means ±SD (n).

*

3-month measurement is evaluation of educational component of the intervention, controlling for baseline differences in outcome variable; 12-month measure is evaluation of support group component, controlling for 3-month differences in outcome variable;

The study was supported by National Institute of Diabetes and Digestive and Kidney Diseases/National Institutes of Health Grant DK-48160.

We thank study participants and the nurses, dietitians, and community workers who played key roles in the project. Field Office staff, managed by Hilda Guerra, provided valuable assistance with recruitment and data collection.

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A table elsewhere in this issue shows conventional and Système International (SI) units and conversion factors for many substances.


text: About the equivalence of dBB theory in quantum equilibrium and Copenhagen Schmelzer Administrator Posts: 215 Threads: 31 Joined: Dec 2015 Reputation: 0 05-20-2016, 06:47 PM (This post was last modified: 05-20-2016, 07:32 PM by Schmelzer.) One physical argument I have been able to identify in Bohmists' inequivalence & dishonesty was the following: Quote:One omnipresent statement made by the Bohmists is that Bohmian mechanics is equivalent to quantum mechanics. That's obviously completely wrong. Equivalent theories must give the same predictions. That's pretty much possible only if they involve an "equally large set of degrees of freedom" that may be mapped to one another, that have the same physical meaning, and nothing is added or replaced on either side. Let's compare what QM in the Copenhagen interpretation contains, with what dBB contains: 1.)  Above contain a classical trajectory.  The Copenhagen interpretation contains it only in the classical part.  Nonetheless, the Copenhagen trajectory of the classical part is simply the restriction of the Bohmian trajectory to this classical part.  So, we have a simple and natural map $$q^{dBB}(t) \in Q \to q^{Copenhagen}(t)\in Q_{classical}$$.  2.)  Above contain a wave function.  The difference is that dBB theory contains also a wave function of the whole universe, which includes the classical part.  But this does not prevent an identification with the wave function of the Copenhagen interpretation, which is restricted to the quantum part, because the two are related by a formula for an effective wave function of a subsystem: $\psi^{eff}(q_{sub},t) = \psi^{full}(q_{env}(t), q_{sub},t).$ This formula can be applied to define the effective wave function of the quantum part, using the wave function of the universe and the classical trajectory of the classical part: $\psi^{Copenhagen}(q_{quantum},t) = \psi^{dBB}(q_{classical}(t), q_{quantum},t).$ So, we have also a simple and natural map $$(\psi^{dBB}, q^{dBB}(t)) \in\mathcal{L}^2(Q) \times Q \to \psi^{Copenhagen} \in \mathcal{L}^2(Q_{quantum})$$.  3.)  The physical meaning of the trajectory $$q(t)$$ in the classical part of the Copenhagen interpretation is the same:  It describes what we see around us, as well as our own trajectory.   4.) The physical equation for the wave function outside a measurement is the same Schrödinger equation for the quantum part.   5.) The measurement process - an interaction between the classical and the quantum part in the Copenhagen interpretation - appears unproblematic too.  To see this, it is useful to remember one point of the Copenhagen interpretation, namely that the split between classical and quantum part is uncertain.  So, we can include the whole measurement device as into the classical part, as into the quantum part, and compare first of all the two resulting variants of the Copenhagen interpretation.  To connect the two Copenhagen variants, we have to use a variant   $\psi^{sys}q_{sys},t) = \psi^{big}(q_{dev}(t), q_{sys},t).$ This formula has to be unproblematic for the various variants of Copenhagen with different splits to be compatible with each other.  But it is also all we need to describe the collapse in the same way.   6.) The equations for the trajectory in the classical part are identical too.  In dBB, these are the equations of the classical Hamilton-Jacobi formalism, which one obtains in the limit $$\hbar\to 0$$ from $\frac{\partial}{\partial t}S+\frac{1}{2m_i}(\partial_i S)^2 - \frac{\hbar^2}{2m_i}\frac{\partial_i^2\sqrt{\rho}}{\sqrt{\rho}} + V(q) = 0.$ and the guiding equation  $m_i v^i(q,t) = \frac{\partial}{\partial q^i} S(q,t),$ in the Copenhagen interpretation this is simply some unspecified classical evolution. 7.)  The physical interpretation of the wave function $$\psi(q)$$ as defining the probability of observing the configuration q via the Born rule $$\rho(q)=|\psi(q)|^2$$ is postulated in the Copenhagen interpretation, and defines the quantum equilibrium in dBB theory.  The Born rule for all other measurements follows from the consideration of a general measurement process, see above, and, finally, the application of the Born rule for the configuration of the measurement device. So, we have a simple mapping from dBB to Copenhagen, and have the same physical meaning of the parts identified by this mapping.  What about the existence of additional parts, and their role?  First, it is difficult to identify what are these "additional parts",  because these additional parts are nothing but the parts which are part of the Copenhagen picture too, if the split between classical and quantum part is made on the other side of the "additional part" in question.  Is there something special which, according to Copenhagen, cannot be in the quantum part?  The observer, may be?  But the observer consists of atoms, and these atoms may be and are regularly described inside the quantum part.  This works in the other direction too.  So, it is not even clear what these additional objects are supposed to be.  Of course, in comparison with every particular instance of a Copenhagen description of the world, dBB has additional parts:  the wave function of the classical part and the trajectory of the quantum part.  But in different Copenhagen splits these additional parts differ.   Quote:But Bohmian mechanics also adds additional "beables", such as the classical particle positions, and uses these new classical degrees of freedom to decide what will happen in an experiment.  At this moment, the theory is already obviously inequivalent to quantum mechanics where all the predictions are made from the wave function – and never from some additional classical degrees of freedom. Oh, the wave function before the measurement predicts the actual result of the measurement, and, additionally, the wave function after the collapse?   That's new for me.  What I have learned is that the wave function predicts only the probability of various outcomes.  And the real outcome is, then, described by the trajectory of the pointer of the measurement device, which, then, defines also the state of the wave function of the quantum system after the measurement. And this pointer of the measurement device is, at least in the Copenhagen version I have learned, part of the classical part and described by its classical trajectory.   (One can imagine, of course, also Copenhagen variants which do not have classical trajectories in the classical part, but, instead, only sharply localized wave functions around some classical trajectory.  But this would presuppose some physical collapse mechanism, to avoid Schrödinger's cat, thus, would be some different, GRW-type, theory, so I would not think it would be accurate to classify such an interpretation as Copenhagen.) Quote:It's remarkable how dense the Bohmists may be when it comes to the simple point that one either has an equivalent theory – so that he can't claim any improvement at all – or he changes something about the ways how things are predicted (or what can be predicted), and this general change means that the theory will make different or new predictions that are likely to disagree with the facts (given the fact that quantum mechanics agrees with all the facts). I don't get the point.  There is an equivalence about the experimental predictions.  But the descriptions are quite different:  Copenhagen describes the world using an artificial, not well-defined split into a classical and a quantum part, describes above part in a quite different way, which seem to have nothing to do with each other, while dBB describes the whole world in the same way.  A unification of the Copenhagen classical and quantum part.  And Copenhagen has to postulate a lot of things which are derived in dBB theory, namely the whole measurement theory, as well as the quantum equilibrium (via Valentini's subquantum H-theorem).  These advantages have, obviously, nothing to do with predictions about observables.  They are theoretical simplifications and explanations, thus, conceptual advantages. Quote:Bohmian mechanics incorporates both the wave function (rebranded as the pilot wave) and the actual positions. This immediately raises the problem that in general, we don't know which of these two "copies of the information" is observed, which of them affects other things. In almost every particular situation, you may see that every answer is a problem. Motl may not know, but those who know dBB theory know this.  First, what we observe - the trajectories of measurement devices - are the configurations $$q(t)$$.  Then, the wave function $$\psi(q)$$ affects the trajectory $$q(t)$$ via the guiding equation.  Then, the trajectory of one part of the world affects the trajectory of other parts of the world also via the guiding equation, but only if above systems are in a superpositional state, as defined by their wave function.  Then, the effective wave function of some subsystem - which is the wave function of the Copenhagen interpretation - is defined by the wave function of the universe and the configuration of environment.   $\psi^{eff}(q_{sub},t) = \psi^{full}(q_{env}(t), q_{sub},t).$ (Side remark: Wiki claims "The 1925's pilot-wave picture,[4] and the wave-like behaviour of particles discovered by de Broglie was used by Erwin Schrödinger in his formulation of wave mechanics.[5]", so the rebranding seems to have happened in the other direction.) Quote:For example, take the simplest "relative success story" Bohmian theory for one electron and try to ask whether the electron emits electromagnetic radiation. The radiation is composed of photons which should also be associated with some "real trajectories" because the location of photons may be measured, too. The idea that something should have a trajectory because it is can be measured is completely wrong.  In dBB theory, what can be "measured" is every operator which can be measured in the corresponding Copenhagen interpretation too, but only for the configuration there exists a trajectory $$q(t)$$.  The results of all other "measurements" are not uniquely defined by the trajectory  $$q(t)$$, but depend also on properties of the wave function $$\psi(q)$$ as well as the initial value of the measurement device $$q_{dev}(t)$$.  Sometimes (say, if the wave function is in an eigenstate of the operator measured) the result depends on the wave function only, so that one can say that a property of the wave function is measured.  In general, the result depends also on the initial value of the measurement device $$q_{dev}(t)$$.  In this case, talking about a "measurement result" is misleading, it would be more appropriate to talk about the result of an interaction.  In particular, the rule "unperformed measurements have no results" holds in general in dBB theory: For an unperformed "measurement" we do not have a configuration $$q_{dev}(t)$$ of the "measurement device", and, therefore, cannot compute any "measurement result".   Quote:OK, these photons are emitted whenever a charge is accelerating. An obvious question arises: Should the "presence of acceleration" be decided according to the wave function (pilot wave), or according to the Bohmian trajectory? And analogously, should the Bohmian photons with trajectories be created only near the Bohmian electrons, or near all the points where the wave function (pilot wave) for the electrons is nonzero? The question should, of course, be decided following the equations.  That means, first of all, one has to solve the Schrödinger equation.  Once the wave function has been defined, we can care (if we like) about the trajectories of the configuration variables of dBB theory.   Quote:Just like it was impossible to meaningfully answer the question whether the synchrotron radiation is emitted according to the locus of the pilot wave or the Bohmian trajectories, it's impossible to define a meaningful formula for the energy. If the energy only depended on the pilot wave or only on the Bohmian trajectory, it could be easily proven that the conservation law is violated in some situations. No problem, let's apply the rules above. The energy depends on the momentum variables, thus, is certainly not defined by the configuration alone, and therefore depends at least also from the wave function. If the wave function is in an energy eigenstate, it is the energy of this eigenstate which is measured. If not, the result depends also on the trajectory of the configuration itself as well as of the measurement device. But note that the resulting effective wave function of the system will be, after this, in an energy eigenstate, so that a repetition of the same measurement will give the same result again. What could lead here to a violation of the conservation law in dBB, but not in the Copenhagen interpretation, given that the predicted probability distributions of the measurement results are the same, is beyond me. I hope this helps. « Next Oldest | Next Newest »

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text:
Pristine boreal mires are known as substantial sinks of carbon dioxide (inline-formulaCO2) and net emitters of methane (inline-formulaCH4). Bogs constitute a major fraction of pristine boreal mires. However, the bog inline-formulaCO2 and inline-formulaCH4 balances are poorly known, having been largely estimated based on discrete and short-term measurements by manual chambers and seldom using the eddy-covariance (EC) technique.

Eddy-covariance (EC) measurements of inline-formulaCO2 and inline-formulaCH4 exchange were conducted in the Siikaneva mire complex in southern Finland in 2011–2016. The site is a patterned bog having a moss–sedge–shrub vegetation typical of southern Eurasian taiga, with several ponds near the EC tower. The study presents a complete series of inline-formulaCO2 and inline-formulaCH4 EC flux (inline-formula $M9inlinescrollmathml{F}_{chem{\mathrm{normal CH}}_{normal 4}}$ 23pt14ptsvg-formulamathimgab687810aac214ba5f355fd0aa86f2fd bg-18-4681-2021-ie00001.svg23pt14ptbg-18-4681-2021-ie00001.png ) measurements and identifies the environmental factors controlling the ecosystem–atmosphere inline-formulaCO2 and inline-formulaCH4 exchange. A 6-year average growing season (May–September) cumulative inline-formulaCO2 exchange of inline-formula−61 inline-formula± 24 inline-formulag C m−2 was observed, which partitions into mean total respiration (Re) of 167 inline-formula± 33 (interannual range 146–197) inline-formulag C m−2 and mean gross primary production (GPP) of 228 inline-formula± 46 (interannual range 193–257) inline-formulag C m−2, while the corresponding inline-formula $M20inlinescrollmathml{F}_{chem{\mathrm{normal CH}}_{normal 4}}$ 23pt14ptsvg-formulamathimg3b23b2f6ff1912ba08777d77987e537a bg-18-4681-2021-ie00002.svg23pt14ptbg-18-4681-2021-ie00002.png amounts to 7.1 inline-formula± 0.7 (interannual range 6.4–8.4) inline-formulag C m−2. The contribution of October–December inline-formulaCO2 and inline-formulaCH4 fluxes to the cumulative sums was not negligible based on the measurements during one winter.

GPP, Re and inline-formula $M25inlinescrollmathml{F}_{chem{\mathrm{normal CH}}_{normal 4}}$ 23pt14ptsvg-formulamathimgd8bbbc44b841bc4b0d560790d27387ad bg-18-4681-2021-ie00003.svg23pt14ptbg-18-4681-2021-ie00003.png increased with temperature. GPP and inline-formula $M26inlinescrollmathml{F}_{chem{\mathrm{normal CH}}_{normal 4}}$ 23pt14ptsvg-formulamathimge7faee267f2d387a57bd161b24473195 bg-18-4681-2021-ie00004.svg23pt14ptbg-18-4681-2021-ie00004.png did not show any significant decline even after a substantial water table drawdown in 2011. Instead, GPP, Re and inline-formula $M27inlinescrollmathml{F}_{chem{\mathrm{normal CH}}_{normal 4}}$ 23pt14ptsvg-formulamathimgbb4584dfd3d333b1930088202eeb2343 bg-18-4681-2021-ie00005.svg23pt14ptbg-18-4681-2021-ie00005.png were limited in the cool, cloudy and wet growing season of 2012. May–September cumulative net ecosystem exchange (NEE) of 2013–2016 averaged at about inline-formula−73 inline-formulag C m−2, in contrast with the hot and dry year 2011 and the wet and cool year 2012. Suboptimal weather likely reduced the net sink by about 25 inline-formulag C m−2 in 2011 due to elevated Re, and by about 40 inline-formulag C m−2 in 2012 due to limited GPP. The cumulative growing season sums of GPP and inline-formulaCH4 emission showed a strong positive relationship.

The EC source area was found to be comprised of eight distinct surface types. However, footprint analyses revealed that contributions of different surface types varied only within 10 %–20 % with respect to wind direction and stability conditions. Consequently, no clear link between inline-formulaCO2 and inline-formulaCH4 fluxes and the EC footprint composition was found despite the apparent variation in fluxes with wind direction.

### Zitieren

Zitierform:

Alekseychik, Pavel / Korrensalo, Aino / Mammarella, Ivan / et al: Carbon balance of a Finnish bog: temporal variability and limiting factors based on 6 years of eddy-covariance data. 2021. Copernicus Publications.

### Zugriffsstatistik

Gesamt:
Volltextzugriffe:


text:
Constructs a query to the NaturalEarth server (see reference 1) to download coastline data (or lake data, river data, etc) in any of three resolutions.

download.coastline(
resolution,
item = "coastline",
destdir = ".",
destfile,
server = "naturalearth",
debug = getOption("oceDebug")
)

## Arguments

resolution A character value specifying the desired resolution. The permitted choices are "10m" (for 1:10M resolution, the most detailed), "50m" (for 1:50M resolution) and "110m" (for 1:110M resolution). If resolution is not supplied, "50m" will be used. A character value indicating the quantity to be downloaded. This is normally one of "coastline", "land", "ocean", "rivers_lakes_centerlines", or "lakes", but the NaturalEarth server has other types, and advanced users can discover their names by inspecting the URLs of links on the NaturalEarth site, and use them for item. If item is not supplied, it defaults to "coastline". Optional string indicating the directory in which to store downloaded files. If not supplied, "." is used, i.e. the data file is stored in the present working directory. Optional string indicating the name of the file. If not supplied, the file name is constructed from the other parameters of the function call, so subsequent calls with the same parameters will yield the same result, thus providing the key to the caching scheme. A character value specifying the server that is to supply the data. At the moment, the only permitted value is "naturalearth", which is the default if server is not supplied. an integer specifying whether debugging information is to be printed during the processing. This is a general parameter that is used by many oce functions. Generally, setting debug=0 turns off the printing, while higher values suggest that more information be printed. If one function calls another, it usually reduces the value of debug first, so that a user can often obtain deeper debugging by specifying higher debug values.

## Value

A character value indicating the filename of the result; if there is a problem of any kind, the result will be the empty string.

## References

1. The NaturalEarth server is at https://www.naturalearthdata.com

The work is done with utils::download.file().

Other functions that download files: download.amsr(), download.met(), download.topo()

Other things related to coastline data: [[,coastline-method, [[<-,coastline-method, as.coastline(), coastline-class, coastlineBest(), coastlineCut(), coastlineWorld, plot,coastline-method, read.coastline.openstreetmap(), read.coastline.shapefile(), subset,coastline-method, summary,coastline-method

## Examples

if (FALSE) {
library(oce)
# User must create directory ~/data/coastline first.
}


text:
jeroenodb's blog

By jeroenodb, 6 months ago,

Today, I solved a beautiful problem from the NEERC Subregional Contest: Problem M. Since I didn't find an editorial nor a solution in the comments, I decided to write this blog about my solution. For a visualization of my solution see: Visualization of the program

The problem boils down to constructing a spanning tree of a 2D point set with certain constraints. No three points in the point set are collinear. The points have two colors: red and blue. There is at least one blue and one red point. The constraints on the spanning tree are:

• Edges of the spanning tree can only intersect at the ends, if they are drawn as line segments.
• Edges of the spanning tree can only connect points with different colors.
• For each blue point, the degree of the point in the spanning tree should be equal to $r_i$.

The constraints on $r_i$ are nice: $1 \leq r_i \leq \text{# of red points}$. And $\sum r_i = n-1$, which is of course needed if the final graph needs to be a tree. It turns out that these conditions are strong enough that it is always possible to construct a spanning tree!

Let's prove this by induction on the size of the point set, $n$.

Base case $n=2$

Because there is at least one red and one blue point, the smallest case is $n=2$: one red and one blue point. Because of the constraint $\sum r_i = n-1 = 1$, this means that the degree of the only blue point left is 1. The trivial solution is to connect the blue and the red point.

Induction step $n>2$:

Now the hard part; Let's assume it is possible to make a spanning tree for all $2 \leq k < n$. Now we are given a point set with $n$ points, how do we construct the spanning tree for it? First, let's sort the points on their x-coordinate. We use the y-coordinate as a tiebreaker. Let's do casework on the colors of the leftmost and rightmost point:

Case 1: extreme points have different colors

Let's analyze the convex hull of the point set. As these two points both lie on the convex hull, the convex hull contains at least one red and one blue point. Certainly, there must be an adjacent pair of points on the hull with different colors. Let's connect these with an edge. If $r_i$ of the blue point is 1, we delete this point. Else we delete the red point, and lower $r_i$ by one. Notice that both the $\sum r_i$ and $n$ decreased by one, so the resulting $r_i$ satisfy all constraints. It can also be verified that at least one red and blue point remain, with some casework. So we can use the induction hypothesis on this smaller point set, which gives us a spanning tree. Could our newly added edge intersect with an existing edge? No, because the edge lies on the convex hull. So in this case we are done.

For the last two cases, where both extreme points have the same color, we will use a different technique. Let's give each point a weight, For blue points, we set $w_i = -1 + r_i$. For red points we set $w_i=-1$. If the sum of weights is -1, that means $\sum_\text{all points} -1 + \sum_\text{blue points} r_i = -1 \implies \sum_\text{blue points} r_i = n-1$.

So if we could somehow split the point set with a vertical line through some point, such that the left and right halve both have the sum of weights equal to -1, we could use the induction hypothesis. For this to work we need both sides to have at least one point of each color.

Case 2: extreme points are both blue

Let's look at the prefix sums of the weights of the point set in sorted order. So the prefix sum is $p_k = \sum_{i<k} w_i$, where the points are labeled from left to right. Because the total prefix sum is $-1$, and the prefix sum starts at $0$, it must cross from nonnegative to negative at some point. Because the weights of all blue points are nonnegative the only place this can happen is at a red point. Because the weight of a red point is $-1$, it will cross from $0$ to $-1$, and this is exactly what we wanted, a sum of weight equal to $-1$. Now we can draw a vertical line through this red point, and by the induction hypothesis construct two spanning trees, one containing all points to the left and the mid point, and one with all points to the right and the middle red point. Note that because the red point can't be one of the extreme points, the two point sets are definitely smaller than the initial point set. Since these spanning trees exist in two disjoint regions, they don't intersect. And they connect at the midpoint, so their union is a valid spanning tree of the whole point set! To ensure the regions are disjoint even if there are ties in x-coordinates, we tilt the splitting line slighly.

Case 3: extreme points are both red

We do exactly the same, looking for a prefix sum which is equal to $-1$. Now it turns out this is sometimes not possible. But by similar (but inverted) logic as in Case 2, there exists a blue point, such that the prefix sum goes from negative to nonnegative. We again make a vertical splitting line through this point, but now we modify the $r_i$ we will use for this blue midpoint in the left and right subproblem. We choose these new $r_i$'s such that the sum of weights in the left and right point sets is again $-1$. Then we apply the induction hypothesis to get two valid spanning trees of the left and right side. And the union of these spanning trees will be a valid spanning tree for the entire point set. This concludes the proof.

This proof is constructive, so for actually generating a spanning tree we can follow the induction logic backwards until the spanning tree is fully constructed.

The easiest way to code this is to make a recursive function that constructs a spanning tree for the point set it's given. Based on the colors of the outermost points we either calculate a convex hull or prefix sums from left to right and call the function recursively. We only recurse $n$ times, and the complexity per call can be made $O(n \log n)$, so this gives $O(n^2 \log n)$.

But if we additionally presort the points and ensure the point set stays sorted in the recursive calls, the convex hull calculation can also be done in linear time, resulting in $O(n^2)$ running time.

Although this is not needed, I think this approach can be optimized further by using Smart divide and conquer and Decremental convex hull, and some modifications to quickly find two oppositely colored adjacent points on the convex hull. This would give a recursive formula for the time complexity of $T(n) = \max_{1\leq k < n} T(k)+T(n-k) + O(\min(k,n-k) \log (n) ) = O(n \log^2 n)$, but this is definitely not needed to get AC.

My code can be found here: submission

• +102

 » 2 months ago, # |   0 what is solve of problem J ??
 » 2 months ago, # | ← Rev. 2 →   -9 nvm


text:
## mayan calendar converter

This is a Abyss Productions product This application converts an entered Gregorrian Calendar date after April 21 1939 into 3 different Mayan Calendars dates. Nomana Seri Lanco Education. Winal. Nomana Seri Lanco Education. Mayan Calendar Converter. mayan calendar The Maya calendar is a system of calendars and almanacs used in the Maya civilization of pre-Columbian Mesoamerica, and in some modern Maya communities in highland Guatemala and Oaxaca, Mexico. The Mayan calendar is an ancient calendar system that rose to fame in 2012, when a “Great Cycle” of its Long Count component came to an end, inspiring some to believe that the world would end at 11:11 UTC on December 21, 2012. 2000 calendar september. Converting Mayan numerals is made by counting dots and bars symbols on each rows and treat it as base 20 writing, before converting it to base 10. Identify the epoch (or start date) for the Mayan Long Count calendar as a Julian date. The Maya Zodiac Sign Calculator can work out to which Mayan Zodiac Sign you belong straight away! When you select a date, you will see the converted date. Google year calendar. Date of Birth. 0. Show More. Know More. Install. and It is rumored that "the human race perish at the next day" . The essentials of the Maya calendar are based upon a system which had been in common use throughout the region, dating back to at least the 5th century BCE. Developer's Description. All Rights Reserved. Features Ver 1.0.0: -Glifos and icons for haab and Tzolkin calendar, with an event description. Convert Gregorian dates to Maya Long Count and Calendar Round. It was a vigesimal (base-20) positional numeral system. Add to Wishlist. Numerals or English A list of Japanese numbers from one to a hundred You are to create a Mayan Number Converter that converts a Mayan number into a decimal and vice versa. By Nomana Seri Lanco Free. The Tzolkin Calendar, the Long Count Calendar, and the Haab Calendar. Mayan Calendar Converter. This calendar has 260 days (9 months in the Gregorian Calendar), which are divided into 13 months of 20 days or energies (Ch’umilal). Convert the date you want to mayan calendar date. Although the Mesoamerican calendar did not originate with the Maya, their subsequent extensions and refinements of it were the most sophisticated. To convert a date, begin by selecting the month from the drop down menu, next type in the day and year. With an end date of 2012, this calendar has propagated the hottest… Other conversion options are listed at the bottom of the page. The Gregorian calendar conversion • Hebrew calendar • Mayan calendar, 2012, the end of the world. MAYAN CALENDAR CONVERTER : PRINTABLE MONTHLY CALENDAR PAGES Mayan Calendar Converter mayan calendar The Maya calendar is a system of calendars and almanacs used in the Maya civilization of pre-Columbian Mesoamerica, and in some modern Maya communities in highland Guatemala and Oaxaca, Mexico. The Maya calendar system records a series of recurring cycles of time based on the movements of the Sun, Moon, and planets. … • Coptic calendar • Calendar section ( 13 calculators ) Mayan Calendar Converter. A particular day, month, and year can be expressed as a Long Count date using baktun, katun, tun, uinal, and k’in units of … DATE CONVERSION: To find a date, choose the appropriate correlation constant (default is 584283) and enter the known Long Count or Gregorian date. Ethiopian Calendar Converter Ethiopian Date to … You can change the date by using the +1 and -1 buttons or enter in a new date and use the convert button again. Any date in the Gregorian calendar can be converted into a corresponding one in the Maya calendar system. (≒5125 solar years) The last date "12.19.19.17.19" will be "December 20, 2012". The Mesoamerican Long Count calendar is a non-repeating, vigesimal (base-20) and base-18 calendar used by several Pre-Columbian Mesoamerican cultures, most notably the Maya. The Maya Long Count system establishes an absolute chronology in which any given date is unique, such as December 21, 2012, in the Gregorian system. N.B. Below is the Maya calendar. The description of Mayan Calendar Converter. Example: a single row with 2 dots and 3 bars: $2 \times 1 + 3 \times 5 = 17$ Gregorian to Mayan calendar conversion. The number is = 14 + 140 + 400 + 24,000 + 0 + 48,000,000 + 320,000,000 = 368024554. Convert to the Mayan Calendar. You can also type in a date in the fields and the Maya date will appear in the calendar. Leap years follow a 30 year cycle and occur in years 1, 5, 7, 10, 13, 16, 18, 21, 24, 26, and 29. The basic unit of time is the day, or k’in. Use this interactive tool to convert Gregorian calendar dates into the Maya calendar system. Kin. Any date in the Gregorian calendar can be converted into a corresponding one in the Maya calendar system. Gregorian to Mayan calendar conversion. This is a Abyss Productions product This application converts an entered Gregorrian Calendar date after April 21 1939 into 3 different Mayan Calendars dates. This site uses the 584,283 correlation. The Mayan calendar uses a modified vigesimal (Base 20) notation for the long count representation of the date and the app also includes a sophisticated calendar converter The Mayan LongCount shows the current date in the way the Maya used it and although the Maya did not have digital clocks, if they had, it would most certainly look like this. © 2021  Smithsonian Institution All rights reserved. Download and save as PDF or JPG image. The Mayan numeral system was the system to represent numbers and calendar dates in the Maya civilization. Mayan calendar, 2012, the end of the world. 14. The Long Count calendar keeps track of the days that have passed since the mythical starting date of the Maya creation, August 11, 3114 BCE. Mayan Long Count calendar (5200 Maya years period) 1) the first day 0. The Maya calendar is a system of calendars used in pre-Columbian Mesoamerica, and in many modern communities in highland Guatemala and in Veracruz, Oaxaca and Chiapas, Mexico. Mayan Calendar Converter. The Mayan calendar ended one of its great cycles in December 2012, which has fueled countless predictions about the end of the world on December 21, 2012 at 11:11(UTC). Correlation Number. The Maya calendar is a system of calendars used in pre-Columbian Mesoamerica, and in many modern communities in highland Guatemala and in Veracruz, Oaxaca and Chiapas, Mexico. • The Muslim calendar. Gregorian date This is a Abyss Productions product This application converts an entered Gregorrian Calendar date after April 21 1939 into 3 different Mayan Calendars dates. The Mayan LongCount shows the current date in the way the Maya used it and although the Maya did not have digital clocks, if they had, it would most certainly look like this. Julian calendar. Along with those of the Aztecs, the Maya calendars are the best-documented and most completely understood. A complete Maya Long Count cycle is 5,125 years long. Calculate several Kin together. The Ethiopian calendar is composed of 13 months, 12 months of 30 days and 1 month of 5 or 6 days depending if it's a leap year. Of course, the predictions did not come true—just like hundreds of other doomsday prophecies that fizzled outin the past. Year Bearer. Easy tool to convert any dates between Ethiopian and Gregorian calendars. Add to Wishlist. HAAB. Mayan Calendar Converter for Android. B'atz' is the start, the beginning, the time; it is the day of the perpetual ceremonies and customs of our ancestors. Install. It shares many aspects with calendars employed by other earlier Mesoamerican civilizations, such as the Zapotec and Olmec, and contemporary or later ones such as the Mixtec and Aztec calendars. The media hype and hysteria that ensued was later termed the 2012 phenomenon. The Long Count was the principal calendar for historical purposes, the Haab was used as the civil calendar, while the Tzolkin was the religious calendar. The Mayans employed three calendars, all organised as hierarchies of cycles of days of various lengths. Everyone. Lord of the Night (G8 in this example): A glyph that represents one the nine deities of the Maya Underworld. mayan calendar The Maya calendar is a system of calendars and almanacs used in the Maya civilization of pre-Columbian Mesoamerica, and in some modern Maya communities in … This day is utilized to effect matrimonial and business ties, it's a good day to bring things into order or to initiate any plans. The Long Count calendar ends in 5200 Mayan year. 14. The Tzolkin Calendar, the Long Count Calendar, and the Haab Calendar. Features Ver 1.1.0 --You can rate the App accessing a new menu. Gregorian calendar is our actual calendar in use expressed in year, month and days. Instead, it is made from a succession of 20 day glyphs in combination with the numbers 1 to 13, and produces 260 unique days. Click the Convert Button to view the corresponding Maya Long Count date, both visually and numerically. Free Mayan Tzolkin and Dreamspell converter, toolkit, and more for 15.01.2021: 13.0.8.3.7 13 Manik 10 Muan G-4, Blue Cosmic Hand, Dreamspell kin 32. Calendar Converter © 2021 . Mayan long count - historical calendar used by Mayan civilization beetwen III and IX century. This is the first day of the Mayan calendar. 0. • Gregorian calendar. Maya Calendar Converter. A particular day, month, and year can be expressed as a Long Count date using baktun, katun, tun, uinal, and k’in units of time together with a Haab and a Tzolk’in calendar dates. : Start date = 0.0.0.0.0 4 Ahau 8 Cumku = 11 Aug -3113. By Nomana Seri Lanco This is a Abyss Productions product. The Cholq’ij, or Calendar of Life, is the Maya tracking of time in relation to the conception and development of a human being until the time of birth. The date is five digit number recorded in twenty system, which means the number of days from 11 August 3113 B.C. Find the meaning of each day in the Tzolk’in calendar system here. -Countdown -List of description of the Mayan days and Mayan months provides conversion/contrast of date of different calendars (calendar converter) With Dreamspell, True Count, Long Count, Tzolkin, Haab, Lord of the Night, 13-Moon and Chakra information. The numerals are made up of three symbols; zero (shell shape, with the plastron uppermost), one (a dot) and five (a bar). The Islamic calendar is purely lunar and consists of twelve alternating months of 30 and 29 days, with the final 29 day month extended to 30 days during leap years. In[1]:= epoch = JulianDate["Modified", DateObject[{-3114, 8, 11}, TimeZone -> 0]]; Create a function to convert from Mayan Long Count calendar units to a Julian date. Calendar Converter Modified version of Fourmilab's Calendar Converter - can be downloaded to use offline (see instructions). Ancient Mayan Calendar Converter to Islamic Calendar. Mayan calendar is a system of calendars use din highlands of Guatemala and pre-Columbian Mesoamerica and in Mexico and its calculation is based on several counts of different lengths. Everyone. You can enter a new date and press the Convert Button to convert any other date between 3000 BCE and 4000 CE. Baktun . This is the same as 13.0.0.0.0 4 Ahau 8 Cumhu, which is the same as 11 Aug 3114 BC -Conversion: Maya/Gregorian/Maya -Save Your own dates -Post dates and share it to Your Facebook Wall. “Each day in the sacred Maya calendar has a meaning. This calendar is not divided into months. Find out your Mayan Zodiac Symbol and what it means about you now.. For BCE dates enter a negative year number. By the Maya mythological tradition, as documented in Colonial Yucatec accounts and reconstructed from Late Classic and Postclassic inscriptions, the deity Itzamna is frequently credited with bringing the knowledge of the calendar system to the ancestral Maya, along with writing in general and other foundational aspects of Maya culture. Mayan calendar converter. This online tool is a date converter based on Mayan calendar. The Tzolkin Calendar, the Long Count Calendar, and the Haab Calendar. This is significant because it indicates a Earth orbit event that returns once every 26,000 years (precession of the equinoxes). Mayan Calendar Date Converter. Tun. The Maya sacred calendar is called Tzolk’in in Yucatec Mayan and Chol Q’ij in K’iche’ Mayan. The mayan or maya calendar is used by Maya civilization of pre-Columbian Mesoamerica and some other maya communities. Background. person_outlineTimurschedule 2015-08-20 12:07:44. 0 = Aug 11, 3114 BC (Gregorian) 2) the last day 12.19.19.17.19 =Dec 20, 2012 AD 3) … For a full Ethiopian calendar please click here. Ancient Mayan calendar conversion to Gregorian and Julian Calender. 2012 Details On December 21, 2012 the Mayan Long Count calendar will be 13.0.0.0.0 (Goodman, Martinez, Thompson calibration) . It tells us about the relationship among all things, including the animals, the land, humans, and everything in the cosmos.”  —Hermelinda Sapon Pu, K’iche’ Maya, Day Keeper, Initial Series Introductory Glyph: This symbol identifies this date as belonging to the Long Count system, Baktun: A number (12 in this example) along with the symbol of “baktun”, Katun: A number (19 in this example) along with the symbol for “katun”, Tun: A number (19 in this example) along with the symbol for “tun”, Uinal: A number (17 in this example) along with the symbol for “uinal”, K’in: A number (19 in this example) along with the symbol for “k‘in”, Tzolk’in date: A number (3 in this example) along with the Tzolk’in day glyph (Kawak in this example), Haab date: A number (2 in this example) along with the Haab day glyph (K'ank’in in this example). Mayan horoscope. The calendar eras. MAYACALENDARCONVERTER. Articles that describe this calculator. The Mayan calendar uses a modified vigesimal (Base 20) notation for the long count representation of the date and the app also includes a sophisticated calendar converter. The essentials of the Maya calendar are based upon a system which had been in common use throughout the region, dating back to at least the 5th century BCE. Katun. Tzolkin. Any given date repeats at cyclic intervals, just as, for example, January 1st in the Gregorian calendar repeats every time the Earth completes a revolution around the Sun. 0. For example, November 26, 2360 BCE would be entered as November 26, -2360. Not come true—just like hundreds of other doomsday prophecies that fizzled outin the past next day '' by Mayan beetwen! Series of recurring cycles of days from 11 August 3113 B.C -Glifos and icons for Haab and calendar... System records a series of recurring cycles of time is the day and year Mayan... Icons for Haab and Tzolkin calendar, 2012, this calendar has a meaning the sacred Maya calendar.... 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## The Search Skill - How Should It Work?

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7 replies to this topic

### #1Wavinator  Moderators

Posted 17 May 2013 - 03:25 PM

Search in a lot of RPGs is pretty standard: You have one or more characters in some environment with a skill called search which, if it succeeds based on some test, reveals something not otherwise obvious (a hidden door, treasure, a trap, etc.).

In a lot of games its pretty generic pass or fail. In some you can spam it at no cost. I've been thinking about making search resource based (maybe you need special particles to "scan" an environment), in order to give it an opportunity cost, with a kind of "getting warmer / colder" dynamic.

Are there better implementations out there that you've heard of?

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### #2Acharis  Members

Posted 17 May 2013 - 03:58 PM

I think it needs to be "fixed efficiency" which means no randomness and no resources. Otherwise the load/save routine will be repeated by the player over and over again. Unless, you can disable/restrict the load/save somehow (which usually is ugly).

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### #3Haps  Members

Posted 17 May 2013 - 06:05 PM

Resources might be useful, such as launching "probes" when you've identified something you want to know more about. But as Acharis mentioned, it's possible to game the system and reload any 'misses' until you get a favorable result, or to save yourself a resource that didn't return anything.

Neverwinter had a passive search skill, which polled hidden objects within your vision every few seconds. If a check passed, it was brought into view, though you had a penalty while moving around. You could enter a slower movement mode to improve your chances, or just add more points to be a more reliable searcher.

I don't see false positives used anywhere, though I think it could make for a strong mechanic. In real life, noticing something is less a pass-fail thing, and more about screening information. Getting the player to evaluate their chances based on more than a single roll adds a layer of strategy, and lets them pick their own opportunities on what to investigate.

### #4Alpheus  GDNet+

Posted 17 May 2013 - 06:13 PM

I would assume that Search would be a stat that you could augment with special glasses, contacts, and/or magic. It would be a skill you train like any other skill.

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### #5Heaven  Members

Posted 17 May 2013 - 11:06 PM

Why not allow searching, but once an area is searched don't allow any further PRODUCTIVE search of that area by the same searcher until skillup (including buffs from items, etc.)?

Just a thought!

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### #6mippy  Members

Posted 18 May 2013 - 02:51 AM

I think the lockpick skill in Skyrim solves this in a neat way, It has the probes (lockpicks) and the target (chest/door). The result is a function of five parameters: your number of trials, your skill level, your number of lockpicks, the difficulty of the lock and finding the door (traditional exploring). Just because you can quick-load does not mean you want to spend forever trying to lockpick something which is out of skill for your character. Of course, if it's a really important loot or if the player is really obnoxious and wants to 100.00000% the game, then they can cheat. But for most players, its a good filter.  Note that they used to have luck, which was reflecting how much loot you would find, but that was a bit... like cheating.

Edited by mipmap, 18 May 2013 - 02:52 AM.

### #7powerneg  Members

Posted 18 May 2013 - 03:57 AM

You could activate the skill to have it point you towards the nearest hidden object, which would only be findable if the player used the skill on it in the first place.

### #8TechnoGoth  Members

Posted 18 May 2013 - 08:32 AM

I remember the old might and magic games had a search spell that depending on your magic level marked treasure, and lootable corpses on the mini map and at higher levels highlighted hidden doors and switches in red on the screen. Or the more recent batman arkham games where things were highlighted when in detective mode.

What about if the player has a tricorder, sonic screw driver, cell phone, omni gadget whatever you want to call it.  The player can install and activate different modules that have different effects.  Passive modules decrease available energy while switched on while active modules temporarily use energy while in use.

So if it has 100 energy and I switch on the life form scanner which uses 20 energy then I can see nearby lifeforms. If I switch on the weapons detector which uses 50 energy it highlights weapons on npcs and tells me what they are armed with.  Both of those on reduces my available energy to 30 which means I can only run the hacking module for 30 seconds at a time.

Another option for more a survival type game would be to give the player the option to use probes to boost treasure drops from certain searchable areas.  For instance a junk pile might normally give level 1 drops, but you can use a probe to boost that to level 3 drops. In this way search is used to boost the quality of what you find from certain containers.

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#### Review

• Study guide, homework solutions
• Today: variance, Chebyshev’s inequality, random samples
##### Expected value of a function of random variable
• Suppose $$X$$ has distribution $$p_X(x)$$ with domain $$D$$ (the set of possible values), and $$f$$ is some function
• $$f(X)$$ is a new random variable. What is its expectation? $E[f(X)] = \sum_{x \in D} f(x) p_X(x)$
• e.g. variance calculation $$f(x) = (x-\mu)^2$$ (see Bernoulli example below)
##### Variance
• Measure of dispersion (spread) of a random variable
• Variance: $$\text{Var}(X) = E[(X - \mu)^2]$$
• Expected squared distance of $$X$$ from $$\mu$$

• e.g. For $$X \sim$$ Ber($$p$$), $$\mu = p$$, and $$(X - p)^2$$ is a random variable which equals $$(1-p)^2$$ with probability $$p$$ and equals $$(0-p)^2$$ with probability $$1-p$$. To find $$\text{Var}(X)$$ we compute the expected value of $$(X-p)^2$$, like this: $\text{Var}(X) = (0-p)^2 \cdot (1-p) + (1-p)^2 \cdot p = [p^2 + (1-p)p](1-p) = p(1-p)$
• Notation: sometimes use $$\sigma^2$$ for $$\text{Var}(X)$$ (if it’s clear from the context)
• Does this make sense as a measure of the dispersion of a Bernoulli?
• If $$p = 1/2$$, then $$\sigma^2 = 1/4$$. If $$p = 9/10$$, then $$\sigma^2 = 9/100$$$$p$$ close to 1 makes the Bernoulli more “concentrated,” it has less variance

###### Linearity
• If $$X$$ and $$Y$$ are independent, then $$\text{Var}(X+Y) = \text{Var}(X) + \text{Var}(Y)$$
• Unlike the case for expectation, where there was no requirement, we now need independence
• Without independence: there is a more complicated formula we won’t get into now
• Let’s look at some Binomial examples
df <- data.frame(x = c(25:55, 45:75),
y = c(dbinom(25:55, 80, .5), dbinom(45:75, 120, .5)),
n = c(rep("80", 31), rep("120", 31)))
ggplot(df, aes(x, y, fill = n)) +
geom_bar(stat = "identity", position = "identity", alpha = .4) +
theme_tufte() + ggtitle("Binomial distributions with p = 1/2")

df <- data.frame(x = c(-18:18, -18:18),
y = c(dbinom(22:58, 80, .5), dbinom(42:78, 120, .5)),
n = c(rep("80", 37), rep("120", 37)))
ggplot(df, aes(x, y, fill = n)) +
geom_bar(stat = "identity", position = "identity", alpha = .4) +
theme_tufte() + ggtitle("Binomial distributions with p = 1/2 (centered)")

• Looks like larger $$n$$ leads to larger variance
###### Chebyshev’s inequality
• Let $$a > 0$$ be any constant.
• Chebyshev’s inequality: $$P(|X - \mu| \geq a) \leq \sigma^2/a^2$$
• For example, let $$a = 2\sigma$$, then $$P(|X - \mu| \geq 2\sigma) \leq 1/4$$
• Look familiar? (We’ll come back to the 68-95-99 rule again soon)
• Helps justify use of expectation and variance as summaries of the full probability distribution
##### Samples of a random variable
• Suppose $$X \sim$$ Bin($$n,p$$), and we want to visualize the distribution function of $$X$$
• Since we know the formulas we could use those as before
• But if we can generate many observed values of $$X$$, we could also look at the histogram of those values. This will be a histogram of data rather than the “true” distribution function (much of statistics works by relating these two “worlds”)
• Remember the Galton board?
##### Examples from Bin(100, 1/2)
###### Perfect, symmetric, mathematical model world:
df <- data.frame(x = 35:65, y = dbinom(35:65, 100, .5))
ggplot(df, aes(x, y)) + geom_bar(stat = "identity") + theme_tufte()

###### Noisy, messy, data world:
df <- data.frame(x = rbinom(500, 100, .5))
ggplot(df, aes(x)) + stat_count() + theme_tufte()

###### Showing both together:
sampledX <- table(rbinom(500, 100, .5))/500
nbins <- length(sampledX)
df <- data.frame(x = c(35:65, as.integer(names(sampledX))),
y = c(dbinom(35:65, 100, .5), sampledX),
world = c(rep("model", 31), rep("data", nbins)))
ggplot(df, aes(x, y, fill = world)) +
geom_bar(stat = "identity", position = "identity", alpha = .4) + theme_tufte()

###### Increasing the sample size:
sampledX <- table(rbinom(5000, 100, .5))/5000
nbins <- length(sampledX)
df <- data.frame(x = c(35:65, as.integer(names(sampledX))),
y = c(dbinom(35:65, 100, .5), sampledX),
world = c(rep("model", 31), rep("data", nbins)))
ggplot(df, aes(x, y, fill = world)) +
geom_bar(stat = "identity", position = "identity", alpha = .4) + theme_tufte()


text:
# on chapter 9 part 2: Define blackwhite. Why is its definition important to the Party’s survival?

thank you so much for your help it really means a lot


text:
# Testing $H_0 : \mu_x \neq \mu_y$, in a company that markets two brands of latex paint.

A company markets two brands of latex paint regular and a more expensive brand that claims to dry an hour faster. A consumer magazine decides to test this claim by painting ten panels with each product. The average drying time of the regular brand is $2.1$ hours with a sample standard deviation of $12$ minutes. The fast-drying version has an average of $1.6$ hours with a sample standard deviation of $16$ minutes. Test the null hypothesis that the more expensive brand dries an hour quicker. Use one sided $H_1$. Let $\alpha = 0.05$

attempt: Given $n = 10$, $m = 10$, $\bar x = 2.1, \bar y = 1.6, S_x = 12, S_Y = 16$.

To test $H_0 : \mu_x \neq \mu_y$ vs. $H_1 : \mu_x < \mu_y$, reject $H_0$ if $t \leq - t_{\alpha, n+m-2}$.

And $S_p = \sqrt{\frac{(n-1)S_x^2 + (m-1)S_Y^2}{(n+m-2)}} = \sqrt{\frac{(10-1)12^2 + (10-1)16^2}{(10 + 10 -2)}} = 14.9$

Then $t = \frac{\bar x - \bar y}{S_p \sqrt(\frac{1}{n} + \frac{1}{m})} = \frac{2.1 - 1.6}{14.9 \sqrt(\frac{1}{10} + \frac{1}{10})} = 0.0750$

And $t_{\alpha, n+m-2} = t_{0.05/2, 10 + 10-2} = t_{0.025, 18} = 2.1009$

So since$t \leq - t_{\alpha, n+m-2}$, we reject $H_0$.

Is this correct? Can anyone please help? I don't now if I have set up the problem correctly. Thank you in advance.

## 2 Answers

The first problem you have is that you need to set up the hypothesis correctly. In your case, because you are asked to test that the "fast" paint is faster by at least an hour, the structure of this hypothesis is a superiority by a margin test.

The null hypothesis is that there is no difference in the mean drying times: $$H_0 : \mu_r - \mu_f < \epsilon,$$ where $r$ denotes "regular" and $f$ denotes "fast," and $\epsilon = 60$ is the margin of superiority in minutes. But the requirement we are given in order to reject $H_0$ is that the drying time of the fast paint is at least an hour faster, so this is $$H_a : \mu_r - \mu_f \ge \epsilon.$$

Under the assumption that the null hypothesis is true, the distribution of the test statistic $$T = \frac{\bar r - \bar f - \epsilon}{\sqrt{\frac{s_{\vphantom f r}^2}{n_r} + \frac{s_f^2}{n_f}}} \sim t_{\nu}$$ is Student's $t$ distributed with $$\nu \approx \frac{\left(\frac{s_{\vphantom f r}^2}{n_r} + \frac{s_f^2}{n_f}\right)^{\!2}}{\frac{s_{\vphantom f r}^4}{n_r^2 (n_r-1)} + \frac{s_f^4}{n_f^2 (n_f-1)}}$$ degrees of freedom. In your case, $n_f = n_r = n = 10$, so the above formulas reduce to $$T = \frac{\bar r - \bar f - \epsilon}{\sqrt{\frac{s_{\vphantom f r}^2 + s_f^2}{n}}}, \quad \nu \approx \frac{(n - 1)(s_{\vphantom f r}^2 + s_f^2)^2}{s_{\vphantom f r}^4 + s_f^4 }.$$ Now with $\bar f = 96$, $\bar r = 126$, $s_f = 16$, $s_r = 12$, we obtain the test statistic $$T = -4.74342, \quad \nu \approx 16.69.$$ But since $T < 0$ the test automatically fails to reject $H_0$ (the critical value is on the upper tail of the distribution, but the calculated statistic is on the lower tail). So we immediately conclude that there is insufficient evidence to suggest that the fast-drying paint dries at least an hour faster than the regular paint: but this much should have been obvious by simply comparing the difference in sample means and noting that $126$ minutes is not more than $60$ minutes greater than $96$ minutes. In light of this, it seems odd to ask such a question; perhaps you might want to double check the statement of the problem to be sure it is faithful.

First, you have to put everything into minutes.

If you do that, you should get that $S_p = 14.14$ (the difference from your answer might be that you rounded prematurely).

Then, for the main difference: I get $T = 4.74$, which certainly leads to rejection. (With your incorrect $T = 0.075,$ you would certainly not reject; you seem to have some misunderstanding how to use critical values from tables.)

Note: I would tend to use a Welch separate-variances t test instead of a pooled t test. However, (1) I don't know whether you've studied that, and (2) you'll reject with either test.

For a pooled test, $DF = 10 + 10 - 2 = 18.$ The Welch test has a more complicated formula for DF, and the result is $DF = 16.$ Also, because the two sample sizes are equal, pooled and Welch have the same value of the $T$ statistic. So it doesn't really matter which you use.