Search Results (5,277)

Pathfinding is the process of finding the lowest cost route between a pair of points in space. The aforementioned cost can be based on time, distance, the number of required turns, and other individual or complex criteria. Pathfinding in dynamic environments is a complex issue, which has a long history of academic interest. An environment is considered dynamic when its topology may change in real time, often due to human interference. Influence mapping is a solution originating from the field of video games, which was previously used to solve similar problems in virtual environments, but achieved mixed results in real-life scenarios. The purpose of this study was to find whether the algorithm could be used in real indoor environments when combined with information collected by remote sensors. Full article

Background/Objectives: Obstructive sleep apnea (OSA) is marked by repetitive occurrences of upper airway (UA) obstruction during sleep. Morbidities impacting the metabolic, cardiovascular (CV) and neurological systems are correlated with OSA. Only a few studies have described the existence of different characteristics depending on sex and, to date, the girl phenotype in OSA pediatrics is not well known. The objective of this systematic review is to identify the specific phenotype of OSA in pediatric and adolescent females compared to males. Methods: A systematic review was performed. The terms “pediatric sleep apnea” and “sex differences” were used to look for publications using PubMed, the Cochrane Library and Web of Science. Inclusion criteria: (1) peer-reviewed journal articles written in English; (2) investigations conducted on individuals diagnosed with OSA; and (3) investigations providing information about sex differences. Exclusion criteria: (1) studies carried out with individuals aged 18 years and older; (2) studies involving a sample size of fewer than 10 patients; and (3) editorials, letters and case reports. Results: Fifteen studies were included and classified in sections related to sex-based differences. Conclusions: Limited information related to sex-based OSA differences in the pediatric population exists. These differences are conditioned by hormonal status, and are minimal in the premenarcheal period. Moreover, adolescent women present a lower prevalence of obesity and craniofacial alterations, lower OSA severity related to higher UA area and earlier tonsil regression. Hyperactivity is more frequent in boys. Some studies pointed to a higher risk of high diastolic blood pressure in girls than in boys. Full article

Rasmussen encephalitis (RE) is a rare and progressive form of chronic encephalitis that typically affects one hemisphere of the brain and primarily occurs in pediatric individuals. The current study aims to narratively review the literature about RE, including historical information, pathophysiology, and management of this condition. RE often occurs in individuals with normal development, and it is estimated that only a few new cases are identified each year in epilepsy centers. Approximately 10% of cases also occur in adolescents and adults. The hallmark feature of RE is drug-resistant focal seizures that can manifest as epilepsia partialis continua. Also, patients with RE usually develop motor and cognitive impairment throughout the years. Neuroimaging studies show progressive damage to the affected hemisphere, while histopathological examination reveals T-cell-dominated encephalitis with activated microglial cells and reactive astrogliosis. The current therapy guidelines suggest cerebral hemispherotomy is the most recommended treatment for seizures in RE, although significant neurological dysfunction can occur. Another option is pharmacological management with antiseizure medications and immunomodulatory agents. No significant progress has been made in understanding the pathophysiology of this condition in the last decades, especially regarding genetics. Notably, RE diagnosis still depends on the criteria established by Bien et al., and the accuracy can be limited and include genetically different individuals, leading to unexpected responses to management. Full article

Beliefs are information-processing structures formed along an individual’s developmental pathway. Beliefs can legitimize involvement in inappropriate or violent behaviors, particularly when they crystallize into cognitive schemas. While beliefs aid individuals in interpreting the surrounding world, overly rigid and inflexible beliefs can constrain the individual’s ability to process available information. This Systematic Review, carried out according to the PRISMA norms and guidelines, aims to understand the most prevalent beliefs regarding relationships among young adults and to examine their associations with violent or deviant behaviors. Articles included in this review were retrieved from the EBSCO, PubMed, and Web of Science databases in July 2022, resulting in a total of 594 studies, which were subsequently screened by two independent reviewers. A total of 51 studies were then selected for full reading, but 36 were excluded based on pre-defined eligibility criteria, leaving a final sample of 18 studies published between 2014 and 2022. The main objectives, country of origin, instruments used, sample composition and age, main results and conclusions were extracted from each study. Findings point toward the presence of related and legitimate beliefs about violence in intimate relationships, domestic violence, sexual violence, acceptance of the rape myth, or consent to engage in sexual activities. Full article

Background/Objectives: Individuals are turning increasingly towards online resources, such as TikTok, to educate themselves on their medical conditions. Polycystic ovary syndrome (PCOS) is a prominent example, as women report dissatisfaction with the diagnosis process and treatment options. This study aims to provide a content analysis of the quality of PCOS health information on TikTok. Methods: A total of 325 TikTok videos were screened. Pertinent data, including likes, comments, shares, and views, along with video content concerning symptoms, interventions, and provider interactions were analyzed. Two reviewers independently used a modified DISCERN criteria to assess the quality of information for each video. A logistic regression was also utilized to model the probability of healthcare professionals creating educational videos. Results: A total of 238 videos met the eligibility criteria for analysis. Videos had a median of 468,400 views (Q1 = 146,400, Q3 = 1,100,000) and 18,000 likes (Q1 = 5631, Q3 = 65,100). The mean modified DISCERN scores were 3.6 ± 0.9 for physicians (n = 23), 2.0 ± 1.1 for non-physician healthcare providers (n = 52), and 1.0 ± 0.2 for non-healthcare professionals (n = 141) (p < 0.001). Healthcare professionals were 10.9 times more likely to create educational videos in comparison to non-healthcare professionals (p < 0.001). Conclusions: TikTok videos related to PCOS attract considerable engagement but provide low-quality information. Most videos were made by non-healthcare professionals, who discussed treatment options with limited or no research. Healthcare providers should be aware of the impacts on patients’ preconceived notions and help to improve patient education. Full article

In the context of the construction of new liberal arts, the integration and intersection of disciplines have become a new trend in the development of higher education. How to promote the teaching reform of big data technology and application courses in the new liberal arts construction scenario has become an important issue in enhancing students’ digital talent literacy and social adaptability. In this study, an extended probabilistic linguistic TODIM (an acronym in Portuguese for interactive multi-criteria decision making) with probabilistic linguistic entropy weight and Hamming distance is presented for teaching reform plan evaluation for the core course “big data technology and applications” in the digital economy major. Firstly, probabilistic linguistic entropy weight, based on the entropy of the additive linguistic term set, is applied to generate weight information. Secondly, parameter sensitivity analysis is carried out to prove the stabilization and effectiveness of the extended TODIM approach. Thirdly, this extended approach can integrate the psychological factors and cognitive behaviors of decision-makers for effectively responding to education management in the new liberal arts construction scenario. Finally, a case study on teaching reform plan evaluation is carried out, and a comparative analysis with different criteria weights and different methods is conducted to verify the extended approach. The results indicate that the extended approach can provide an effective technical tool for scientific decision-making, especially in the teaching reform plan evaluation scenario in order to promote high-quality development of education. Full article

Background: Weight regains after bariatric surgery presents a significant challenge to long-term weight management and overall health outcomes. While clinical and surgical factors influencing bariatric outcomes have been extensively reviewed, there remains a notable gap in understanding how behavioral factors—specifically dietary habits, psychological influences, and lifestyle choices—impact long-term weight maintenance. This systematic review addresses this gap, highlighting the unique role of these behavioral factors in sustaining weight loss post-bariatric surgery. Aim: This systematic review aims to explore the behavioral factors—including dietary, psychological, and lifestyle influences—associated with weight regain after bariatric surgery. Methods: A comprehensive search was conducted across multiple databases, focusing on peer-reviewed articles published in English. We included observational studies exclusively focused on adults who experienced weight regain after bariatric surgery, with an emphasis on behavioral factors. Searches were conducted in MEDLINE via PubMed, Embase, Web of Science, Scopus, and Google Scholar, with the last search completed on 10 July 2024. Studies published within the past 10 years were considered. The Joanna Briggs Institute (JBI) critical appraisal tool for cross-sectional studies was used to assess the risk of bias. A total of 16 studies met the inclusion criteria: 12 cross-sectional studies, 3 cohort studies, and 1 in-depth interview study. Results: The review found inconsistencies in the definitions of weight regain across studies. Nonetheless, three primary behavioral factors were identified as potentially contributing to weight regain: dietary non-adherence and eating patterns, psychological determinants, and lifestyle behaviors. Dietary non-adherence included high-calorie food consumption, excessive intake of sweets, carbohydrates, and sweetened beverages, and low protein intake. Psychological factors such as depression, anxiety, emotional eating, and binge eating were prevalent among individuals experiencing weight regain. Lifestyle behaviors, including physical inactivity, smoking, and sedentary habits, also played significant roles. Conclusions: Post-bariatric weight management is multifaceted, necessitating a comprehensive approach that addresses dietary, psychological, and lifestyle factors. Most studies on behavioral factors linked to weight regain were cross-sectional with small sample sizes, limiting the ability to infer causality. Future research should include detailed information on dietary adherence, standardized tools to assess physical activity and sedentary behavior, and validated measures for psychological health. Tailored interventions and continuous support from healthcare professionals are essential for maximizing the effectiveness of bariatric surgery, promoting sustainable weight loss, and enhancing overall health outcomes. Full article

Large corporations have recently demonstrated an increasing propensity to enhance the sustainability and reliability of their supply chains in order to comply with environmental regulations and improve customer satisfaction through on-time demand fulfillment. There are two phases to this study: mathematical modeling and model solution using precise techniques. In the first step, a mixed-integer linear programming model is developed. This model is an improvement of an existing supply chain model. Further, our suggested strategy is verified by using numerical data based on three criteria and four suppliers. The goals of the proposed model are to maximize supply chain reliability, economic profit, and social responsibilities by taking suppliers’ priorities into account. Modeled as a mixed-integer linear programming problem, the constraints on the problem include budget, emission, demand, allocation, facility, and shipping capacity. Power symmetry and information symmetry are incorporated in order to perform symmetric analysis. The weighted sum method (WSM) and the technique for order of preference by similarity to ideal solution (TOPSIS) are the two methods used in the second step of solving the model to identify the best supplier. In order to evaluate how well the proposed methodology was applied, a practical case was considered and implemented. Full article

Phishing email assaults have been a prevalent cybercriminal tactic for many decades. Various detectors have been suggested over time that rely on textual information. However, to address the growing prevalence of phishing emails, more sophisticated techniques are required to use all aspects of emails to improve the detection capabilities of machine learning classifiers. This paper presents a novel approach to detecting phishing emails. The proposed methodology combines ensemble learning techniques with various variables, such as word frequency, the presence of specific keywords or phrases, and email length, to improve detection accuracy. We provide two approaches for the planned task; The first technique employs ensemble learning soft voting, while the second employs weighted ensemble learning. Both strategies use distinct machine learning algorithms to concurrently process the characteristics, reducing their complexity and enhancing the model’s performance. An extensive assessment and analysis are conducted, considering unique criteria designed to minimize biased and inaccurate findings. Our empirical experiments demonstrates that using ensemble learning to merge attributes in the evolution of phishing emails showcases the competitive performance of ensemble learning over other machine learning algorithms. This superiority is underscored by achieving an F1-score of 0.90 in the weighted ensemble method and 0.85 in the soft voting method, showcasing the effectiveness of this approach. Full article

Wind power has emerged as a crucial substitute for conventional fossil fuels. The combination of advanced technologies such as the internet of things (IoT) and machine learning (ML) has given rise to a new generation of energy systems that are intelligent, reliable, and efficient. The wind energy sector utilizes IoT devices to gather vital data, subsequently converting them into practical insights. The aforementioned information aids among others in the enhancement of wind turbine efficiency, precise anticipation of energy production, optimization of maintenance approaches, and detection of potential risks. In this context, the main goal of this work is to combine the IoT with ML in the wind energy sector by processing weather data acquired from sensors to predict wind power generation. To this end, three different regression models are evaluated. The models under comparison include Linear Regression, Random Forest, and Lasso Regression, which were evaluated using metrics such as coefficient of determination (R²), adjusted R², mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE). Moreover, the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) were taken into consideration as well. After examining a dataset from IoT devices that included weather data, the models provided substantial insights regarding their capabilities and responses to preprocessing, as well as each model’s reaction in terms of statistical performance deviation indicators. Ultimately, the data analysis and the results from metrics and criteria show that Random Forest regression is more suitable for weather condition datasets than the other two regression models. Both the advantages and shortcomings of the three regression models indicate that their integration with IoT devices will facilitate successful energy prediction. Full article

While the wine industry has already adopted some sustainable and environmentally friendly practices, special attention should be paid to the greenness of the analytical methodologies used. In this study, a new tool called “Green Wine Analytical Procedure Evaluation” (GWAPE) was developed to fulfill these “green” requirements. This framework offers a structured approach to evaluating the environmental impact of wine analysis processes, covering all the analytical steps from sample collection to the final results. GWAPE provides quantitative information, uses schematic representations, assigns varying levels of importance to green chemistry principles, and conducts detailed evaluations of hazard structures. Since wine analytical methods typically involve fewer highly hazardous reagents, certain criteria previously applied in green analytical chemistry should be integrated or omitted. In summary, GWAPE offers a customized and precise solution to help the laboratories of enology and the wineries assess their analytical methodologies’ environmental impact. As an example of application, the proposed metric was used to evaluate the greenness of three different standard analytical methodologies to determine sugars in wine, showing good discrimination ability. Full article

Opioid-induced constipation (OIC) is a very common and troublesome gastrointestinal side effect following the use of opioids. Despite existing international guidelines, OIC is largely underdiagnosed and undertreated. ECHO OIC is a European project designed to improve the diagnosis and management of OIC at the primary care level. The next phase of the ECHO OIC project is to review and adapt the proposed European pathway at national level, considering the local patient journey and clinical practice. A multidisciplinary group of 12 Italian experts reviewed and discussed the European path and formulated a seven-step guide for the practical management of OIC that is also easily applicable in primary care: 1. When prescribing long-term opioids, the physician should inform the patient of the possibility of the onset of OIC; 2. At opioid prescription, doctors should also prescribe a treatment for constipation, preferably macrogol or stimulant laxatives; 3. The patient should be evaluated for OIC within the second week of initiating opioid treatment, by clinical history and Rome IV criteria; 4. In the presence of constipation despite laxatives, prescription of a PAMORA (Peripherally Acting Mu Opioid Receptor Antagonist) should be considered; 5. When prescribing a PAMORA, prescribing information should be carefully reviewed, and patients should be accurately instructed for appropriate use; 6. Efficacy and tolerability of the PAMORA should be monitored regularly by Bowel Function Index, considering a cut-off of 30 for the possible step-up of OIC treatment; 7. After 4 weeks of treatment, if the efficacy of PAMORA is deemed inadequate, discontinuation of the PAMORA, addition of an anti-constipation drugs, change of opioid type, or referral to a specialist should be considered. Spreading knowledge about the OIC problem as much as possible to the health community is crucial to obtain not only an early treatment of the condition but also to promote its prevention. Full article

Diet is one modifiable risk factor for hypertension. The low-sodium DASH (Dietary Approaches to Stop Hypertension) eating plan has been shown to significantly reduce the risk of hypertension and cardiovascular disease. However, there is a lack of available health information on the economic feasibility and cultural acceptability of DASH for low-income African American (AA) populations who are at the most risk for hypertension. An integrative review was conducted to summarize empirical literature on the economic feasibility and cultural acceptability of the DASH plan for low-income AAs using these databases: PubMed, EMBASE, CINAHL Complete, AGRICOLA, Web of Science Core Collection, ProQuest’s Dissertations, Theses Citation Index, and Google Scholar. Study elements from articles in the final analysis were extracted. Eleven (11) published works met the study’s inclusion criteria. Major themes were the availability and access of healthy foods, economic impact of obtaining healthy foods, material resources for cooking, food literacy, and the cultural acceptability of the DASH plan. These findings suggest that cost and cultural familiarity inhibit low-income AAs from benefiting from the DASH plan. Additional research is needed to develop and pilot test low-cost, culturally sensitive DASH eating plans for low-income AAs. Full article

Background/Objectives: The rational use of medicines, in accordance with the World Health Organization (WHO) guidelines, is crucial for optimizing healthcare outcomes. This cross-sectional study aimed to evaluate drug prescribing patterns and assess prescription completeness based on the WHO core drug use criteria. A comprehensive analysis was conducted at the University Clinic in the Northern region, Kingdom of Saudi Arabia (KSA). Methods: The study assessed drug prescribing patterns and examined prescription completeness by analyzing various parameters recommended by the WHO core drug use criteria. Results: Upon analyzing the 615 prescriptions, it was observed that each prescription had a mean of 2.56 prescribed drugs. Multiple medicines per prescription were prevalent in 71.4%, whereas polypharmacy was evident in 2.9%. Analgesics were the most frequently prescribed medication, accounting for 50.4% of the prescriptions, followed by supplements (31.7%), decongestants (16.1%), cough syrup (12%), and antihypertensive and diabetes treatments at 17%. Furthermore, antimicrobials were prescribed in 21.5% of the prescriptions. During the evaluation, it was found that 19.8% of the prescriptions were incomplete, lacking important information on dosing, duration, and drugs not suited to the diagnosis. Conclusions: Most of the parameters evaluated in this study were determined to fall outside the range of recommended guidelines criticized by the WHO. As a result, the implementation of efficient intervention programs, such as education initiatives, is recommended to enhance the practice of rational drug use. Contribution: This study highlights the importance of improving prescription indicators at the national level, focusing on both medication prescribing characteristics and prescription quality as a practice. Full article

The planning of greener, more accessible, and safer cities is the focus of several strategies that aim to improve the population’s quality of life. This concern for the environment and the population’s quality of life has led to the implementation of active mobility policies. The effectiveness of the mobility solutions that are sought heavily depends on the identification of the main factors that favor their use, as well as how adequate urban spaces are in minimizing existing difficulties. This study presents an automated geographic information system (GIS) decision support tool that allows the identification of the level of suitability of urban transportation networks for the use of active modes. The tool is based on the determination of a set of mobility indices: walkability, bikeability, e-bikeability, and active mobility (a combination of walking and cycling suitability). The indices are obtained through a spatial multi-criteria analysis that considers the geometric features of roads, population density, and the location and attractiveness of the city’s main trip-generation points. The treatment, representation, and study of the variables considered in the analysis are carried out with the aid of geoprocessing, using the spatial and network analysis tools available in the GIS. The Model Builder functionality available in ArcGIS^® was used to automate the various processes required to calculate walking, cycling, and e-biking travel times, as well as the mobility indices. The developed tool was tested and validated through its application to a case study involving the road network of the urban perimeter of the medium-sized city of Covilhã, Portugal. However, the tool is designed to be applied with minimal adaptation to different scenarios and levels of known input information, providing average or typical values when specific information is not available. As a result, a flexible and automated GIS-based tool was obtained to support urban space and mobility managers in the implementation of efficient measures compatible with each city’s scenario. Full article