Search Results (5,354)

Serotonin (5-hydroxytryptamine: 5-HT), a neurotransmitter that regulates mood in the brain and signaling in the gut, has receptors throughout the body that serve various functions, especially in the gut and brain. Selective serotonin reuptake inhibitors (SSRIs) are used to treat depression, but their efficacy is uncertain. Depression is often associated with early gastrointestinal symptoms. Gut disorders such as functional dyspepsia (FD), irritable bowel syndrome (IBS) and inflammatory bowel disease (IBD), including ulcerative colitis (UC) and Crohn’s disease (CD), are linked to elevated serotonin levels. In this review, we would like to discuss the approach of using serotonin as a biomarker for gut–brain, and body-wide organ communication may lead to the development of preventive and innovative treatments for gut–brain disorders, offering improved visibility and therapeutic monitoring. It could also be used to gauge stress intensity for self-care and mental health improvement. Full article

Self-organizing networks of small satellites have gradually gained attention in recent years. However, self-organizing networks of small satellites have high topological change frequency, large transmission delay, and complex communication environments, which require appropriate networking and routing methods. Therefore, this paper, considering the characteristics of satellite networks, proposes the shortest queue length-cluster-based routing protocol (SQL-CBRP) and has built a satellite self-organizing network simulation platform based on OMNeT++. In this platform, functions such as the initial establishment of satellite self-organizing networks and cluster maintenance have been implemented. The platform was used to verify the latency and packet loss rate of SQL-CBRP and to compare it with Dijkstra and Greedy Perimeter Stateless Routing (GPSR). The results show that under high load conditions, the delay of SQL-CBRP is reduced by up to 4.1%, and the packet loss rate is reduced by up to 7.1% compared to GPSR. When the communication load is imbalanced among clusters, the delay of SQL-CBRP is reduced by up to 12.7%, and the packet loss rate is reduced by up to 16.7% compared to GPSR. Therefore, SQL-CBRP performs better in networks with high loads and imbalance loads. Full article

At the global level, significant efforts have been made to address societal challenges and improve the lives of people and restore the planet’s ecosystems through sustainability and resilience programs. These programs, however, tend to be driven by governments, private sectors, and financial institutions, and therefore often lack a process of empowerment to ensure that the local communities can participate actively in co-designing and implementing these programs. More knowledge is needed on how to develop such programs and how the process of empowerment can be organized so that it supports in the long run sustainability transformation. Against this background, the paper explores the role of community empowerment programs as a critical tool for sustainability management strategies and practices. A semi-systematic review of 21 community empowerment programs for sustainability and resilience is conducted. The analysis reveals that the programs mostly aimed to address challenges such as the lack of education and capacity, limited access to basic services and resources, and poor governance and management. The programs initiators involve a diverse set of actors, especially through established partnerships and networks. Most of the programs address the specific needs of vulnerable or marginalized groups or communities. The structure of the programs typically follows a phased methodological approach, beginning with awareness-raising and problem identification, followed by capacity building that allows for making decisions collaboratively and for co-creating innovative solutions based on local knowledge and values. Also, monitoring and evaluation of transformative impact are mentioned as important structural elements. Specifically, the analysis highlights four main focus areas of empowerment: (1) capacity building, (2) self-reliance, control, ownership, responsibility, and independence, (3) participation, engagement, and collective action, and (4) integration of local knowledge and values. However, there is no one-size-fits-all approach to such programs. Instead, successful empowerment programs towards sustainability depend on a deep understanding of local contexts and the ability to tailor strategies to meet specific community needs. The review also identified knowledge gaps that require further investigation to enhance the effectiveness of empowerment programs for both people and nature. Full article

Breast cancer remains a global health problem requiring effective diagnostic methods for early detection, in order to achieve the World Health Organization’s ultimate goal of breast self-examination. A literature review indicates the urgency of improving diagnostic methods and identifies thermography as a promising, cost-effective, non-invasive, adjunctive, and complementary detection method. This research explores the potential of using machine learning techniques, specifically Bayesian networks combined with convolutional neural networks, to improve possible breast cancer diagnosis at early stages. Explainable artificial intelligence aims to clarify the reasoning behind any output of artificial neural network-based models. The proposed integration adds interpretability of the diagnosis, which is particularly significant for a medical diagnosis. We constructed two diagnostic expert models: Model A and Model B. In this research, Model A, combining thermal images after the explainable artificial intelligence process together with medical records, achieved an accuracy of 84.07%, while model B, which also includes a convolutional neural network prediction, achieved an accuracy of 90.93%. These results demonstrate the potential of explainable artificial intelligence to improve possible breast cancer diagnosis, with very high accuracy. Full article

Background and Objectives: Millions of people worldwide suffer from diabetes. The ever-increasing number of patients poses a huge challenge to healthcare systems. The purpose of this study was to evaluate the lifestyle and self-monitoring of type 2 diabetes patients using the Healthy Lifestyle and Self-Monitoring Questionnaire. Material and Methods: The analyses conducted were based on data collected using the Polish version of the Healthy Lifestyle and Self-Control Questionnaire among 104 patients diagnosed with type 2 diabetes who were treated at the Diabetes Outpatient Clinic. The in-house study also included an analysis of the relationship between lifestyle habits and disease acceptance and chronic disease functioning. Results: Respondents scored statistically significantly higher for the Healthy Lifestyle and Self-Monitoring Questionnaire than the norms assume, and the largest differences were observed in terms of the healthy dietary choices subscale (t = 8.07; p < 0.05). Only for the subscale of organized exercise were no statistically significant differences found (t = 0.50; p = 0.620). Conclusions: Type 2 diabetes is one of the diseases in which lifestyle not only contributes to its development but is also associated with its course and treatment outcomes. Reinforcing a health-promoting lifestyle is one of the cornerstones of treating patients with type 2 diabetes. Full article

Photocatalytic degradation of organic pollutants is one of the green ways to solve environmental problems. In this study, the PDI-COOH/PDINH composite photocatalysts were successfully synthesized by electrostatic self-assembly. Under visible light irradiation, the degradation efficiency of the optimal PDI-COOH/PDINH sample reached 67%, which was 1.7 and 1.6 times higher than that of the self-assembled PDINH supramolecule and PDI-COOH supramolecule, respectively. The excellent photocatalytic performance of PDI-COOH/PDINH can be attributed to the enhancement of the separation and transport efficiency of photogenerated carriers by the construction of a heterojunction and the expanded electronic conjugated structure by the combination of organic–organic semiconductors. This study offers a new idea for the preparation of organic–organic composite photocatalysts. Full article

Integrating ecosystem services (ESs) to delineate ecological functional zones (EFZs) is fundamental in terrestrial spatial planning and ecosystem management. However, existing studies have largely overlooked the refinement of EFZs at local scales, which hinders targeted and multi-scale ecosystem management. This study introduced a “two-step refinement zoning method” to address this gap, first using a self-organizing feature mapping method to delineate EFZs at a township scale, and then applying a hotspot overlay analysis to refine the resulting EFZs by designating them with different grades at the village scale. The proposed method was applied in Wuhan City, dividing it into five types of EFZs with different ES combinations and land use compositions. Furthermore, 5.23% of villages were identified as level I areas of EFZs, serving as advantageous areas of dominant ESs in the study area. On this basis, diversified management strategies and conservation priorities were proposed. This study provides a theoretical and methodological reference for terrestrial spatial planning and sustainable ecosystem management. Full article

Background: Adverse life occurrences (e.g., severe accidents, violence/abuse, organic disorders such as COVID-19) can elicit traumatic responses that heighten fear, anxiety, and depression. However, scientific research has shown that certain variables, such as self-esteem, based on theories like terror management theory (TMT) and the anxiety-buffering hypothesis (ABH), can mitigate the negative effects of trauma. This study aimed to test the ABH by assessing the buffering role of self-esteem in the relationships among the impact of traumatic experiences, fear, anxiety, and depression. Method: An observational research design was used. This study involved 321 participants who experienced COVID-19 as a traumatic experience. A sequential multiple-mediation model with observed variables (path analysis) was used to test the impact of the traumatic experience on fear, anxiety, and depression, examining the protective role of self-esteem. Results: A path analysis revealed that fear and anxiety mediated the relationship between the impact of the traumatic experience of COVID-19 and depression. Additionally, in line with the ABH, self-esteem was found to mediate the relationship between the predictors and their adverse psychological consequences. This suggests that self-esteem played a buffering role, mitigating the negative impact of traumatic experiences on mental health outcomes. Conclusions: These findings underscore the central mediating role of self-esteem, as well as fear and anxiety, in the pathway from trauma-related factors to depression. These insights advocate for evidence-based interventions aimed at alleviating the psychological suffering associated with traumatic experiences, fostering adaptation, and supporting psychological health. Full article

Funaria hygrometrica, a cosmopolitan moss species known for its remarkable dispersal capacity, was selected as the focal organism to investigate the relationship between landscape features and genetic diversity. Our study encompassed samples collected from two distinct regions: the Spanish Sierra Nevada Mountains (SN), characterized by a diverse landscape with an altitudinal difference of nearly 3500 m within a short distance, and the Murcia Region (MU) in Southeast Spain, characterized by a uniform landscape akin to the lowlands of Sierra Nevada. Genotyping analysis targeted three genetic regions: the nuclear ribosomal internal transcribed spacer (nrITS), the chloroplast rps3-rpl16 region, and the mitochondrial rpl5-rpl16 spacer. Through this analysis, we aimed to assess genetic variability and population structure across these environmentally contrasting regions. The Sierra Nevada populations exhibited significantly higher haplotype diversity (Hd = 0.78 in the highlands and 0.67 overall) and nucleotide diversity (π% = 0.51 for ITS1) compared to the Murcia populations (Hd = 0.35, π% = 0.14). Further investigation unveiled that samples from the lowlands of Sierra Nevada showed a closer genetic affinity to Murcia than to the highlands of Sierra Nevada. Furthermore, the genetic differentiation between highland and lowland populations was significant (Φ_ST = 0.55), with partial Mantel tests and ResistanceGA analysis revealing a strong correlation between ITS1-based genetic diversity and landscape features, including altitude and bioclimatic variables. Our study elucidated potential explanations for the observed genetic structuring within F. hygrometrica samples’ populations. These included factors such as a high selfing rate within restricted habitats, a limited average dispersal distance of spores, hybrid depression affecting partially incompatible genetic lineages, and recent migration facilitated via human activities into formerly unoccupied areas of the dry zones of Southeast Spain. Full article

Swarm Intelligence (SI) represents a paradigm shift in artificial intelligence, leveraging the collective behavior of decentralized, self-organized systems to solve complex problems. This study provides a comprehensive review of SI, focusing on its application to multi-robot systems. We explore foundational concepts, diverse SI algorithms, and their practical implementations by synthesizing insights from various reputable sources. The review highlights how principles derived from natural swarms, such as those of ants, bees, and birds, can be harnessed to enhance the efficiency, robustness, and scalability of multi-robot systems. We explore key advancements, ongoing challenges, and potential future directions. Through this extensive examination, we aim to provide a foundational understanding and a detailed taxonomy of SI research, paving the way for further innovation and development in theoretical and applied contexts. Full article

The health of road transport workers is affected by working conditions and life. However, there is a lack of studies on the level of stress and health of the families of these workers. This study aimed to evaluate the perceived stress level and family effectiveness of family members of road transport workers. A quantitative study was carried out with the family members of road transport workers in the southern region of Brazil. For data collection, a sociodemographic form, the Perceived Stress Scale (PSS), and the Evaluation of Family Effectiveness Strategies were used. The data were analyzed by simple frequency, Spearman correlation coefficient (ρ) (p < 0.05), and descriptive analysis from the perspective of Systemic Organization. The sample was composed of 49 family members of road transport workers. Perceived stress was higher in family members who had more than nine years of education (p = 0.0403). Family members who scored higher in Family Effectiveness scored high on the targets of Control (p = 0.0353) (Control aims to reduce anxiety and prevent and eliminate events that threaten family stability) and Growth (p = 0.0360) (represented by attitudes that promote new roles in response to critical situations experienced by families, which require re-adaptation processes and adjustments). The Control target was significant (p = 0.0353) in families that had more than three people. The Coherence dimension (concerning self-esteem, body image, personal identity, self-confidence, and sexual identity) presented positive significance (p = 0.0244) in families with health problems and whose income was less than USD 792.00 per month (p = 0.0072). The Individuation dimension (including functions and responsibilities, where talents are reinforced, as well as initiatives that allow for the incorporation of knowledge to assume behaviors against personal/family and environmental pressures), was significant (p = 0.0138) in families with incomes over USD 792.00. The Maintenance System (strategies for decision-making, problem negotiation, ritual and traditional roles, communication patterns, standards, financial management, and approaches to maintaining family harmony) presented positive significance (p = 0.0151) in families where drivers worked as intercity drivers, as did the Stability target (p = 0.0196) (concerning the continuity of routines, structure, organization, traditions, and values assumed by the family and transmitted from generation to generation, which promote unity and the development of values, attitudes, and beliefs). In conclusion, social factors, such as education, income, diseases, type of worker activity in road transport, and number of people in the family, influenced perceived stress and family effectiveness, which demonstrates the need to increase the promotion of health care for the families of road transport workers. Full article

This study explores the determinants of the intention to adopt blockchain technology for sustainable supply chain management in Indian micro, small, and medium enterprises. Different from existing studies that advocate the use of socio-technical theory for blockchain technologies, we develop a new theoretical framework, called “SOS,” based on a review of the existing literature. This is an adaptation of the technology–organization–environment framework that examines the measures and scales from socio-technical, organizational, and sustainability contexts. We use ADANCO 2.3.2 for variance-based structural equation modeling. The results show that two of the nine hypotheses are negatively significant, while the rest are positive. In our context, social sustainability and computer self-efficacy are strongly negatively significant for the adoption intention of blockchain technology in our context. Software quality and environmental sustainability are strongly positively significant. Meanwhile, collaboration, economic sustainability, and relative advantage mediated by experience are positively significant. Our study contributes to the literature by offering a new theoretical framework, fresh insights from the Indian industry, and several recommendations to practitioners. Full article

To deal with the task assignment problem of multi-AUV systems under kinematic constraints, which means steering capability constraints for underactuated AUVs or other vehicles likely, an improved task assignment algorithm is proposed combining the Dubins Path algorithm with improved SOM neural network algorithm. At first, the aimed tasks are assigned to the AUVs by the improved SOM neural network method based on workload balance and neighborhood function. When there exists kinematic constraints or obstacles which may cause failure of trajectory planning, task re-assignment will be implemented by changing the weights of SOM neurals, until the AUVs can have paths to reach all the targets. Then, the Dubins paths are generated in several limited cases. The AUV’s yaw angle is limited, which results in new assignments to the targets. Computation flow is designed so that the algorithm in MATLAB and Python can realize the path planning to multiple targets. Finally, simulation results prove that the proposed algorithm can effectively accomplish the task assignment task for a multi-AUV system. Full article

Self-driving cars are one of the main areas of research today, but it has to be acknowledged that the information from the sensors (the perceptron) is a huge amount of data, which is now unmanageable even when projected onto a single traffic junction. In the case of self-driving, the nodes have to be sequenced and organized according to the planned route. A self-driving car in Hungary would have to be able to interpret more than 70,000 traffic junctions to be able to drive all over the country. Besides the huge amount of data, another problem is the issue of validation and verification. For self-driving cars, this implies a level of complexity using traditional methods that calls into question the economics of the already existing system. Fuzzy Petri nets provide an alternative solution to both problems. They allow us to obtain a model that accurately describes the reliability of a node through its dynamics, which is essential in perception since the more reliable a node is, the smaller the deep learning mesh required. In this paper, we outline the analysis of a traffic node’s safety using Petri nets and fuzzy analysis to gain information on the reliability of the node, which is essential for the modeling of self-driving cars, due to the deep learning model of perception. The reliability of the dynamics of the node is determined by using the modified fuzzy Petri net procedure. The need for a fuzzy extension of the Petri net was developed by knowledge of real traffic databases. Full article

Driven by the growing energy crisis and environmental concerns regarding the utilization of fossil fuels, biomass liquefaction has emerged as a highly promising technology for the production of renewable energy and value-added chemicals. However, due to the high oxygen content of biomass materials, biocrude oil produced from liquefaction processes often contains substantial oxygenated compounds, posing challenges for direct downstream applications. Catalytic hydrodeoxygenation (HDO) upgrading with hydrogen donors is crucial for improving the quality and applicability of biomass-derived fuels and chemicals. The costs, safety, and sustainability concerns associated with high-pressure gaseous hydrogen and organic molecule hydrogen donors are driving researchers to explore alternative and innovative biomass hydrodeoxygenation approaches without exogenous hydrogen donors. This review offers an overview of the recent developments in catalytic hydro-liquefaction and hydrodeoxygenation methods for biomass valorization without external hydrogen donation, including catalytic self-transfer hydrogenolysis using endogenous hydrogen in biomass structure, in situ catalytic hydrodeoxygenation employing water as the hydrogen donor, and in situ hydrodeoxygenation via water splitting assisted by zero-valent metals. The in situ hydrogen supply mechanisms and the impact of various hydrodeoxygenation catalysts on hydrogen donation efficiency using endogenous hydrogen are summarized in detail in this work. Furthermore, the current obstacles and future research demands are also discussed in order to provide valuable recommendations for the advancement of biomass utilization technologies. Full article