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Product category management (PCM) plays a pivotal role in today’s large stores. PCM manages to answer questions such as assortment planning (AP) and shelf space allocation (SSA). AP problem seeks to determine a list of products and suppliers, while SSA problem tries to design the layout of the selected products in the available shelf space. These problems aim to maximize the retailer sales under different constraints, such as limited purchasing budget, limited space of classes for displaying the products, and having at least a certain number of suppliers. This paper makes an attempt to develop an integrated mathematical model to optimize integrated AP, SSA, and inventory control problem for the perishable products. The objective of the model is to maximize the sales and retail profit, considering the costs of supplier contracting/selecting and ordering, assortment planning, holding, and procurement cost. GAMS BARON solver is hired to solve the proposed model in small and medium scales. However, because the problem is NP-hard, an evolutionary genetic algorithm (GA), and an efficient local search vibration damping optimization (VDO) algorithm are proposed. A real case study is considered to evaluate the effectiveness and capabilities of the model. Besides, some test problems of different sizes are generated and solved by the proposed metaheuristic solvers to confirm the efficient performance of proposed algorithms in solving large-scale instances.

Background Selecting and prioritizing candidate disease genes is necessary before conducting laboratory studies as identifying disease genes from a large number of candidate genes using laboratory methods, is a very costly and time-consuming task. There are many machine learning-based gene prioritization methods. These methods differ in various aspects including the feature vectors of genes, the used datasets with different structures, and the learning model. Creating a suitable feature vector for genes and an appropriate learning model on a variety of data with different and non-Euclidean structures, including graphs, as well as the lack of negative data are very important challenges of these methods. The use of graph neural networks has recently emerged in machine learning and other related fields, and they have demonstrated superior performance for a broad range of problems. Methods In this study, a new semi-supervised learning method based on graph convolutional networks is presented using the novel constructing feature vector for each gene. In the proposed method, first, we construct three feature vectors for each gene using terms from the Gene Ontology (GO) database. Then, we train a graph convolution network on these vectors using protein–protein interaction (PPI) network data to identify disease candidate genes. Our model discovers hidden layer representations encoding in both local graph structure as well as features of nodes. This method is characterized by the simultaneous consideration of topological information of the biological network (e.g., PPI) and other sources of evidence. Finally, a validation has been done to demonstrate the efficiency of our method. Results Several experiments are performed on 16 diseases to evaluate the proposed method's performance. The experiments demonstrate that our proposed method achieves the best results, in terms of precision, the area under the ROC curve (AUCs), and F1-score values, when compared with eight state-of-the-art network and machine learning-based disease gene prioritization methods. Conclusion This study shows that the proposed semi-supervised learning method appropriately classifies and ranks candidate disease genes using a graph convolutional network and an innovative method to create three feature vectors for genes based on the molecular function, cellular component, and biological process terms from GO data.

The motion of the fluid due to the swirling of a disk/sheet has many applications in engineering and industry. Investigating these types of problems is very difficult due to the non-linearity of the governing equations, especially when the governing equations are to be solved analytically. Time is also considered a challenge in problems, and times dependent problems are rare. This study aims to investigate the problem related to a transient rotating angled plate through two analytical techniques for the three-dimensional thin film nanomaterials flow. The geometry of research is a swirling sheet with a three-dimensional unsteady nanomaterial thin-film moment. The problem's governing equations of the conservation of mass, momentum, energy, and concentration are partial differential equations (PDEs). Solving PDEs, especially their analytical solution, is considered a serious challenge, but by using similar variables, they can be converted into ordinary differential equations (ODEs). The derived ODEs are still nonlinear, but it is possible to approximate them analytically with semi-analytical methods. This study transformed the governing PDEs into a set of nonlinear ODEs using appropriate similarity variables. The dimensionless parameters such as Prandtl number, Schmidt number, Brownian motion parameter, thermophoretic parameter, Nusselt, and Sherwood numbers are presented in ODEs, and the impact of these dimensionless parameters was considered in four cases. Every case that is considered in this problem was demonstrated with graphs. This study used modified AGM (Akbari–Ganji Method) and HAN (Hybrid analytical and numerical) methods to solve the ODEs, which are the novelty of the current study. The modified AGM is novel and has made the former AGM more complete. The second semi-analytical technique is the HAN method, and because it has been solved numerically in previous articles, this method has also been used. The new results were obtained using the modified AGM and HAN solutions. The validity of these two analytical solutions was proved when compared with the Runge–Kutta fourth-order (RK4) numerical solutions.

Comorbidities are common in chronic obstructive pulmonary disease (COPD) patients. This study was conducted to determine the prevalence of common comorbidities in patients with COPD compared with people without COPD. This cross-sectional, population-based study was performed on 6961 adults aged 35–70 years enrolled in the Shahrekord PERSIAN cohort study. Data (demographic and clinical characteristics, comorbidities, anthropometric and blood pressure measurements, laboratory, and spirometry tests) collection was performed according to the cohort protocol from 2015 to 2019. In the present study, 215 (3.1%) patients were diagnosed with COPD and 1753 (25.18%) ones with restrictive lung patterns. The mean age of COPD patients was 52.5 ± 9.76 years. 55.8% of patients were male, 17.7% were current smokers and 12.1% had a history of smoking or were former smokers. 5.6% of patients had no comorbidity and 94.5% had at least one comorbidity. The most common comorbidities in COPD patients were dyslipidemia (70.2%), hypertension (30.2%), metabolic syndrome (22.8%), and diabetes (16.7%). The most common comorbidities in individuals with a restrictive spirometry pattern were dyslipidemia (68.9%), metabolic syndrome (27.2%), hypertension (26.1%), depression (17.6%), and fatty liver (15.5%). The logistic regression analysis with 95% confidence interval (95%CI) of odds ratio (OR) showed that comorbidities of chronic lung diseases (OR = 2.12, 95% CI 1.30–3.44), diabetes (OR = 1.54, 95%CI 1.03–2.29), cardiovascular disease (OR = 1.52, 95%CI 1.17–2.43), and hypertension (OR = 1.4, 95%CI 1.02–1.99) were more likely to occur in COPD patients than in healthy individuals. Knowing these prevalence rates and related information provides new insights on comorbidities to reduce disease burden and develop preventive interventions and to regulate health care resources to meet the needs of patients in primary health care.

The purpose of this theoretical study is to explore the behavior of an electrically conducting micropolar fluid when subjected to a uniform magnetic field along the vertical axis between two stretching disks as the structure of the problem changes. In this context, structural changes refer to alterations in the distance between the two discs or the stretching rate of the two discs. The governing equations of this problem are a set of nonlinear coupled partial differential equations, which are transformed into a nonlinear coupled ordinary differential equation set by a similarity transformation. The transformation results in four dimensionless quantities and their derivatives that appear in the equations. Nine dimensionless parameters are derived via similarity variables, including stretching Reynolds number, magnetic parameter, radiation parameter, Prandtl number, Eckert number, Schmidt number, and three micropolar parameters. Previous similarity solutions focused on analyzing the effect of changes in each parameter on the four dimensionless quantities. However, this type of analysis is mainly mathematical and does not provide practical results. This study’s primary novelty is to redefine the magnetic parameter, Eckert number, stretching Reynolds number, and two micropolar parameters to analyze physical parameters that depend on the stretching rate of the two discs or the distance between them. The semi-analytical hybrid analytical and numerical method (HAN-method) is used to solve the equations. The results demonstrate that structural changes affect all five quantities of radial velocity, axial velocity, microrotation, temperature, and concentration. The study’s most significant finding is that an increase in the stretching rate of the two disks causes a sharp increase in temperature and Nusselt number. Conversely, increasing the distance between the two disks causes a sharp decrease in micro-rotation and wall couple stress. They were compared to a previous study in a specific case to validate the results’ accuracy.

Breast cancer poses a significant global health challenge, with Iran experiencing particularly high incidence and mortality rates. Understanding the adaptation process of Iranian breast cancer survivors’ post-treatment is crucial. This study explores the health perceptions, barriers, and coping mechanisms of Iranian survivors by integrating Stress-Coping Theory (SCT) and the Health Belief Model (HBM). Semi-structured interviews were conducted with 17 survivors, and a grounded theory approach guided the deductive content analysis of the data. The findings reveal key themes, including perceived susceptibility, benefits, barriers to care, cues to action, self-efficacy, and appraisal of action. Perceived susceptibility highlights diagnostic challenges stemming from practitioner errors and symptom misconceptions. Perceived benefits underscore the importance of early detection and support from healthcare providers and families. Barriers include cultural and financial obstacles, while cues to action reflect the influence of media, family, and personal experiences on healthcare-seeking behavior. The study also examines coping strategies, such as problem-focused and emotion-focused approaches, along with family support and external stressors. To address these barriers and enhance support systems, the study suggests specific strategies for healthcare providers, including targeted training to improve diagnostic accuracy and patient communication. Culturally sensitive awareness campaigns can correct symptom misconceptions, while financial counseling can mitigate economic barriers. Establishing community-based support groups and involving family members in care plans can enhance emotional and psychological support. These strategies aim to overcome the identified barriers and improve support systems for Iranian breast cancer survivors, ultimately fostering better recovery outcomes.

Background Despite extensive global research on mothers’ intentions regarding human milk banking (HMB), its acceptability remains underexplored in non-Western contexts, particularly in Muslim-majority countries. This study investigates barriers to HMB acceptability among nursing mothers in Iran through the lens of Social Cognitive Theory (SCT), emphasizing how cultural, religious, and contextual factors intersect with maternal decision-making. Methods A qualitative study was conducted in Tehran, Iran, between August and October 2024. Semi-structured interviews were held with twelve nursing mothers of premature infants unable to breastfeed. Data were analyzed thematically using Braun and Clarke’s six-step approach, guided by SCT to capture the interplay between personal, behavioral, and environmental influences on mothers’ decision-making regarding HMB. Rigorous strategies, including iterative coding and peer debriefing, were employed to ensure trustworthiness of the analysis. Results Three overarching themes emerged. Personal factors included emotional states, risk perceptions, self-efficacy, outcome expectations, and religious beliefs and ethics. Behavioral factors comprised trust-based decision-making and past behavioral patterns, which shaped willingness to engage with HMB. Environmental factors involved institutional accessibility, social support systems, authoritative influence, and cultural norms. Findings revealed that mothers experienced emotional conflict, mistrust in milk safety, and religious concerns about milk kinship and halal practices. Institutional and logistical barriers, coupled with lack of family and community support, further reduced HMB acceptability. Nevertheless, participants emphasized that religious endorsements, transparent regulations, health professional guidance, and improved service accessibility could enhance trust and participation. Conclusions This study highlights how reciprocal interactions among personal beliefs, behavioral patterns, and environmental contexts shape the acceptability of HMB among Iranian mothers. To improve uptake, culturally sensitive interventions are essential particularly those involving religious authorities, healthcare professionals, and awareness campaigns to address misconceptions and build trust. Strengthening institutional accessibility and transparency can further promote HMB as a viable feeding option. Future research should also examine the roles of socioeconomic status, healthcare access, and generational differences to broaden the evidence base for culturally adapted HMB policies in Muslim-majority contexts.

Background This study evaluated the prevalence of sick building syndrome (SBS) in Rasht, Iran, a subtropical climate with wetter cold season city, during the autumn and winter months of 2020, focusing on the effects of noise and ventilation. Methods A total of 420 residents completed the indoor air climate questionnaire (MM040EA), and a walk-through survey of 45 randomly selected residential units assessed environmental noise, ventilation rate, and luminous conditions. Results Approximately 38.2% reported SBS symptoms in the past three months. Significant associations were found between SBS and dim light ( P -value = 0.012, OR = 2.1, CI = 1.09-4), noise ( P -value = 0.031, OR = 1.75, CI = 1.1–2.9), passive smoking ( P -value < 0.01, OR = 2.6, CI = 1.22–5.4), static electricity ( P -value < 0.01, OR = 3.8, CI = 1.15–12.6), bad air ( P -value < 0.01, OR = 4.6, CI = 1.6–13), and high room temperature ( P -value = 0.039, OR = 2.6, CI = 1.13–5.95) at α = 0.05. The field survey revealed that 75.5% of units exceeded the national noise threshold of 55 dBA. The average ventilation rate was 20 lit/(p.sec), while 32% of the units reported low or moderate lighting during daytime hours. No significant association was found between the type of interior wall finishing or heating systems and SBS. Stronger correlation was observed between noise and SBS in districts with higher traffic-induced noise. Conclusion Considering high noise levels in residential areas, local authorities must prioritize noise insulation policies in building design and construction.

A biosensor can be defined as a compact analytical device or unit incorporating a biological or biologically derived sensitive recognition element immobilized on a physicochemical transducer to measure one or more analytes. Microfluidic systems, on the other hand, provide throughput processing, enhance transport for controlling the flow conditions, increase the mixing rate of different reagents, reduce sample and reagents volume (down to nanoliter), increase sensitivity of detection, and utilize the same platform for both sample preparation and detection. In view of these advantages, the integration of microfluidic and biosensor technologies provides the ability to merge chemical and biological components into a single platform and offers new opportunities for future biosensing applications including portability, disposability, real-time detection, unprecedented accuracies, and simultaneous analysis of different analytes in a single device. This review aims at representing advances and achievements in the field of microfluidic-based biosensing. The review also presents examples extracted from the literature to demonstrate the advantages of merging microfluidic and biosensing technologies and illustrate the versatility that such integration promises in the future biosensing for emerging areas of biological engineering, biomedical studies, point-of-care diagnostics, environmental monitoring, and precision agriculture.

Each year, unwanted plant pests and diseases, such as Hendel or potato soft rot, cause damage to crops and ecosystems all over the world. To continue to feed the growing population and protect the global ecosystems, the surveillance and management of the spread of these pests and diseases are crucial. Traditional methods of detection are often expensive, bulky and require expertise and training. Therefore, inexpensive, portable, and user-friendly methods are required. These include the use of different gas-sensing technologies to exploit volatile organic compounds released by plants under stress. These methods often meet these requirements, although they come with their own set of advantages and disadvantages, including the sheer number of variables that affect the profile of volatile organic compounds released, such as sensitivity to environmental factors and availability of soil nutrients or water, and sensor drift. Furthermore, most of these methods lack research on their use under field conditions. More research is needed to overcome these disadvantages and further understand the feasibility of the use of these methods under field conditions. This paper focuses on applications of different gas-sensing technologies from over the past decade to detect plant pests and diseases more efficiently.