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The seniors' lifestyle is an important public-health issue. Hence, assessing the association of cell phone use as a rapidly spreading technology on older adults' lifestyle can be useful for planning prevention and health-promotion policies. This cross-sectional study was conducted from March 2020 to March 2021 in Isfahan Comprehensive Health Care Centers to investigate the impact of cell phone use on the lifestyle of the elderly. The Cell-Phone Over-Use Scale and the lifestyle checklist were the assessment tools employed for this purpose. Of the 300 participants with a mean age of 67.93 ± 5.14 years, 46% were male and 54% female. The average value of cell phone usage score was 47.88 ± 26.56, which was categorized into low 36%, moderate 41%, and overuse 23%. A positive and significant correlation was reported between the level of cell phone use in males (β =10.711, [0.95 confidence interval {CI} =4.262-17.160]) and people who have used a substance or opium in the past year (β =10.819, [0.95 CI = 3.677-17.961]). The present results found no significant association between cell phone use patterns and age, level of education, body mass index, television time, sports time, smoking in the past year, marital relationship, and living alone. In this study, we demonstrated a correlation between cell phone use and some demographic and lifestyle variables, namely sex and drug abuse.

Background: It is assumed that doctor-patient relationship plays an effective role in patients' satisfaction, medication adherence, and health outcomes since exploring different aspects of this relationship, such as addressing medication adherence, has rarely been investigated. Therefore, the main aim of the present study was to assess the impact of patients' satisfaction derived from communicating with doctors on medication adherence in hypertensive patients. Materials and Methods: This cross-sectional survey was conducted on three hundred patients with hypertension, using multistage sampling technique in health care centers in Isfahan, Iran. Data were collected by two questionnaires comprised (1) patients' satisfaction derived from the relationship with doctors and (2) medication adherence named \"Morisky Medication Adherence Scale\" with 8 items. Multivariate logistic regression model was applied to test the odds ratio (OR) of patients' satisfaction resulting from the relationship with physicians in numerous aspects in two groups: appropriate and inappropriate medication adherence. Results: A lower level of satisfaction derived from building the relationship (confidence interval [CI] =0.95, 0.06-0.71 and OR = 0.20) and empathy subscales (CI = 0.95, 13-0.80 and OR = 0.33) was associated with nonadherence to treatment after controlling the physicians' gender and patients' age, gender, education, and duration of disease. Conclusion: Patients' satisfaction resulting from building the relationship and empathy with physicians appeared to be associated with medication adherence among hypertensive patients.

The present study investigated the prevalence of illness anxiety disorder and its relationship with social health in the elderly population.BackgroundThe present study investigated the prevalence of illness anxiety disorder and its relationship with social health in the elderly population.This cross-sectional study was conducted on 400 elderly people in Isfahan referred to the educational health service in 2020. In addition to demographic characteristics, they answered the 36-item Evans hypochondriasis questionnaire and the 28-item social health questionnaire.Materials and MethodsThis cross-sectional study was conducted on 400 elderly people in Isfahan referred to the educational health service in 2020. In addition to demographic characteristics, they answered the 36-item Evans hypochondriasis questionnaire and the 28-item social health questionnaire.Four hundred elderly participants with a mean age of 68.1 ± 6.6 (range: 60-89) were included in the study. One hundred and ninety-nine (49.8%) were female and 201 (50.3%) were male. Only 24.3% of the geriatrics were in the healthy group and 21.2% in the borderline category. Seventy-two of the geriatric population (18%) had poor social health and 299 of this population (74.8%) had moderate social health, and the social health status of 29 elderlies (7.3%) was favorable. There was an inverse relationship between hypochondriasis score and total social health score and its dimensions (P < 0.05).ResultsFour hundred elderly participants with a mean age of 68.1 ± 6.6 (range: 60-89) were included in the study. One hundred and ninety-nine (49.8%) were female and 201 (50.3%) were male. Only 24.3% of the geriatrics were in the healthy group and 21.2% in the borderline category. Seventy-two of the geriatric population (18%) had poor social health and 299 of this population (74.8%) had moderate social health, and the social health status of 29 elderlies (7.3%) was favorable. There was an inverse relationship between hypochondriasis score and total social health score and its dimensions (P < 0.05).High prevalence of hypochondriasis was observed in the geriatrics compared to global studies. Most of the elderly population had moderate social health. Gender and education do not affect hypochondriasis and social health in the elderly. People with higher social health scores were clearly less likely to get hypochondriasis. Therefore, by improving the various dimensions of social health of the geriatrics, we can help reduce the prevalence of hypochondriasis in this age group.ConclusionHigh prevalence of hypochondriasis was observed in the geriatrics compared to global studies. Most of the elderly population had moderate social health. Gender and education do not affect hypochondriasis and social health in the elderly. People with higher social health scores were clearly less likely to get hypochondriasis. Therefore, by improving the various dimensions of social health of the geriatrics, we can help reduce the prevalence of hypochondriasis in this age group.

Background Assessing quality indicators in colorectal cancer (CRC) screening via the fecal immunochemical test (FIT) is crucial. However, data on developing countries with lower CRC rates, lifestyle transitions, newly established screening programs, and no consensus on optimal targets are scarce. Methods This analysis evaluated 2,209 average-risk individuals aged 50–70 years in Iran between 2017 and 2022. The polyp detection rate (PDR), adenoma detection rate (ADR), sessile serrated lesion detection rate (SDR), and adenomas per colonoscopy (APC) were calculated. Patient relationships with the center, colonoscopist, bowel preparation, and comorbidities were assessed. Additionally, potential risk factors for bowel preparation quality were examined. Results The analysis revealed a PDR of 34.5%, an ADR of 25.3%, an advanced ADR of 10%, a proximal ADR of 13.0%, an SDR of 1%, and a CRC rate of 2.7%. After adjustment, adenoma risk was greater in men (OR 1.84; 95% CI 1.49–2.69; p  < 0.01) and those aged 60–69 (OR 1.28; 95% CI 1.04–1.58; p  = 0.02), with similar trends for other detection rates. Polyps but not adenomas were more common in public centers (OR 1.28; 95% CI 1.04–1.57; p  = 0.02) and among the academic group (OR 1.34; 95% CI 1.07–1.68; p  = 0.01). PDR and APC were higher in colonoscopies with cecal intubation ( p  < 0.05). Bowel preparation quality was higher in private centers, with academic physicians, and among patients with a BMI < 30 ( p  < 0.05). Conclusion The results provide early evidence of proposed ADR targets in developing countries. Given that the ADR depends on background epidemiology, implementing a dynamic, standard living point system based on extensive data is crucial. We recommend updating physicians on screening guidelines, prioritizing men, older adults, diabetic patients, and obese individuals.

A mixed-model assembly line is a type of production line where different models of a product are assembled on. Mixed-model assembly lines can respond to unanticipated changes in product demands quickly without keeping so many inventories. Designing mixed-model assembly line involves solving the traditional problems of the assembly line design (consists of balancing problem, determining cycle time, and the number and sequence of stations) in addition of determining the sequence of products in assembly line. The main goal of this paper is presenting a method in order to determine the sequence of products in mixed-model assembly line by considering Just-in-Time systems. Moreover, supplying some required components from feeding lines is considered. A mathematical model is presented which is capable of specifying the sequence of products in the mixed-model assembly line by considering main criteria and keeping feeding lines balanced. Mathematical model can be used for solving small-size problems. Because the combinatorial nature of sequencing problems typically provides an intractable search space for problems of “real world” size, the search heuristics of simulated annealing and ant colony algorithms are presented and used to find solutions for several problem sets. Experimentations show that the simulated annealing approach outperforms the ant colony approach in objective function performance.

Background: Participation of Iranian women with a family history of breast cancer in breast cancer screening programs is low. This study evaluates the compliance of women having a family history of breast cancer with clinical breast exam (CBE) according to the stage of transtheoretical model (TTM) and health belief model (HBM). Materials and Methods: In this cross-sectional study, we used Persian version of champion's HBM scale to collect factors associated with TTM stages applied to screening from women over 20 years and older. The obtained data were analyzed by SPSS, using descriptive statistics, Chi-square test, independent t-test, and analysis of covariance. Results: Final sample size was 162 women. Thirty-three percent were in action/maintenance stage. Older women, family history of breast cancer in first-degree relatives, personal history of breast disease, insurance coverage, and a history of breast self-examination were associated with action/maintenance stage. Furthermore, women in action/maintenance stages had significantly fewer perceived barriers in terms of CBE in comparison to women in other stages (P < 0.05). There was no significant difference in other HBM subscales scores between various stages of CBE screening behavior (P > 0.05). Conclusion: The finding indicates that the rate of women in action/maintenance stage of CBE is low. Moreover, results show a strong association between perceived barriers and having a regular CBE. These clarify the necessity of promoting national target programs for breast cancer screening, which should be considered as the first preference for reducing CBE barriers.

Variation graph representations are projected to either replace or supplement conventional single genome references due to their ability to capture population genetic diversity and reduce reference bias. Vast catalogs of genetic variants for many species now exist, and it is natural to ask which among these are crucial to circumvent reference bias during read mapping. This dissertation research takes a holistic approach to develop a novel mathematical framework for variant selection in genome graphs subject to preserving sequences of length α with at most δ differences. This framework leads to a rich set of problems based on the types of variants (e.g., SNPs, indels, or structural variants) and whether the goal is to minimize the number of positions at which variants are incorporated or to minimize the total number of variants incorporated. We classify the computational complexity of these problems and provide efficient algorithms along with their software implementations. We also empirically evaluate run-time performance and reduction in number of variants achieved by the multiple algorithms proposed in this dissertation research. The designed mathematical framework aims to address the following prominent problem cases.First, we develop novel algorithms and complexity results for preserving sequences of length α represented by paths in the complete variation graph, under the Hamming distance and edit distance metrics. We also assess the extent of variant reduction achieved in human chromosome variation graphs using multiple α and δ parameter values corresponding to characteristics of short and long-read resequencing technologies. Additionally, we establish benchmark data sets and tools to empirically evaluate the impact of variant selection on read-to-graph mapping.Next, we consider the problem of preserving α-long substrings of the input haplotypes, the so-called haplotype-aware variant selection problem, under the Hamming distance metric. We show that this problem is NP-hard and develop an Integer Linear Programming (ILP) solution for it. The solution is effective in finding optimal solutions even on human chromosome-scale graphs and for a variety of sequence lengths and error percentage thresholds. In addition to ensuring optimality, our results demonstrate that a substantial additional reduction in the number of selected variants can be achieved when restricting preserved sequences to individual haplotypes. Finally, we develop the haplotype-aware variant selection problem under the edit distance metric, where the input haplotypes may contain SNPs, indels, and structural variants.We designed a two-level ILP formulation for the problem. We implement a software framework for this problem and empirically evaluate its effectiveness in finding optimal solutions on human chromosome-scale graphs and for a variety of sequence lengths and error percentage thresholds. We also experimentally evaluate the impact of the variant reduction obtained here on sequence-to-graph mapping accuracy. Taken together, these formulations and results are expected to significantly advance our knowledge on the creation and use of genome graphs.

This paper introduces a two-stage deep learning-based methodology for clustering time series data. First, a novel technique is introduced to utilize the characteristics (e.g., volatility) of the given time series data in order to create labels and thus enable transformation of the problem from an unsupervised into a supervised learning. Second, an autoencoder-based deep learning model is built to model both known and hidden non-linear features of time series data. The paper reports a case study in which the selected financial and stock time series data of over 70 stock indices are clustered into distinct groups using the introduced two-stage procedure. The results show that the proposed methodology is capable of achieving 87.5% accuracy in clustering and predicting the labels for unseen time series data. The paper also reports an important finding in which it is observed that the performance of both techniques (i.e., autoencoder and Kmeans) are comparable. However, there are a few instances of time series data that are classified differently by the autoencoder-based methodology compared to the Kmeans algorithm. The results may indicate that the proposed deep learning-based approach is taking into account additional hidden features that might be overlooked by conventional Kmeans. The finding raises the question whether the explicit features of data should be analyzed for clustering or more advanced techniques such as deep learning need to be adapted by which hidden features and relationships are explored for clustering purposes.

Mixed-model assembly line is known to be a special case of production lines where various and different models of the same products are inter-mixed to be assembled on the same line. Using mixed model assembly line involves not only the traditional problems of assembly line design (i.e., determining the line cycle time, the number and sequence of stations of the line, and balancing the line) but also determining the sequence in which products will be scheduled for assembly. In each station, some components are assembled in the final products. Under just in time (JIT) approach; an objective is to have a fairly constant usage rate for each component going into final assembly to facilitate the use of Kanban or other JIT shop-floor systems. In this paper, a mixed approach, the sequencing mixed-model assembly line problem with a JIT-supply of required materials, ‌is studied and a mathematical model is proposed for solving it. The results are compared with a traditional approach. Some experiments are used to evaluate the performance of the proposed model.

Background: It is assumed that doctor-patient relationship plays an effective role in patients' satisfaction, medication adherence, and health outcomes since exploring different aspects of this relationship, such as addressing medication adherence, has rarely been investigated. Therefore, the main aim of the present study was to assess the impact of patients' satisfaction derived from communicating with doctors on medication adherence in hypertensive patients. Materials and Methods: This cross-sectional survey was conducted on three hundred patients with hypertension, using multistage sampling technique in health care centers in Isfahan, Iran. Data were collected by two questionnaires comprised (1) patients' satisfaction derived from the relationship with doctors and (2) medication adherence named \"Morisky Medication Adherence Scale\" with 8 items. Multivariate logistic regression model was applied to test the odds ratio (OR) of patients' satisfaction resulting from the relationship with physicians in numerous aspects in two groups: appropriate and inappropriate medication adherence. Results: A lower level of satisfaction derived from building the relationship (confidence interval [CI] =0.95, 0.06-0.71 and OR = 0.20) and empathy subscales (CI = 0.95, 13-0.80 and OR = 0.33) was associated with nonadherence to treatment after controlling the physicians' gender and patients' age, gender, education, and duration of disease. Conclusion: Patients' satisfaction resulting from building the relationship and empathy with physicians appeared to be associated with medication adherence among hypertensive patients.