In the rapidly evolving Industry 4.0 landscape, the integration of industrial robots and Artificial Intelligence (AI) is revolutionizing the processes involved in storing and managing goods. While these advancements hold the promise of enhancing operational efficiency, they necessitate a robust and high-performing indoor network infrastructure. This demo paper introduces a dynamic network slicing mechanism tailored for Wi-Fi networks, capitalizing on readily available Commercial Off-The-Shelf (COTS) devices, and seamlessly incorporating In-Band Network Telemetry (INT) within a Software Defined Networking (SDN) framework. To effectively navigate the intricacies and uncertainties of network environments, we employ Fuzzy Logic to oversee queueing disciplines (qdisc), which directly influence air-time—the duration a device allocates to transmitting or receiving data over a wireless channel. Through a series of experimental demonstrations, we highlight the effectiveness of our proposed mechanism in maintaining stringent Quality of Service (QoS) standards even in conditions of network saturation. Our solution guarantees uninterrupted and streamlined operations, even in high-demand scenarios

The paper demonstrates the VITAL-5G platform capabilities to improve the adoption and effectiveness of 5G and beyond solutions within the Transport & Logistic domain by bridging the knowledge gap between industry stakeholders, network experts, and service developers. Therefore, in this paper, we present four distinct capabilities the VITAL-5G platform offers i) Facilitating deployment of vertical service in the 5G network, ii) Real-time monitoring of network and service performance and iii) Advanced failure diagnostics. IV) Utilization of 5G slices. To enhance the adoption and effectiveness of 5G and beyond solutions within the T&L domain, we showcase how the VITAL-5G platform hides the operational complexity from the experimenters, allowing them to express their network, application, and hardware requirements in a human-readable format, while in turn deploying complex services on the 5G SA infrastructure.

The diversity of Beyond 5G (B5G)/Sixth-Generation (6G) technologies and the increasing density of Internet-of-Things (IoT) clients are pushing the traditional MANagement and Orchestration (MANO) into more complex scenarios. The European Telecommunications Standards Institute (ETSI) Industry Specification Group (ISG) Zero-touch Network and Service Management (ZSM) has published several documents that present ZSM as a visionary concept, which promises to deliver benefits for BSG and 6G networks, such as increased efficiency in MANO and for tackling the challenges and limitations endured in the transition from human-intervention assisted network management to fully autonomous network management. This work-in-progress paper provides insights into the recent trends in defining and developing ZSM, the efforts towards standardization of its guidelines, coverage of existing solutions that apply the ZSM principles, a discussion about the imperative of its adoption in the domains of the evolving mobile generations and a proposal for its application in a vehicular use case of Smart Traffic Management (STM), where B5G/6G applications are enhanced by a robust ZSM capability of edge resources, to improve the effectiveness of STM.

ABSTRACT Background Young migrants face multiple challenges that can affect their mental, sexual and reproductive health. Objective To assess the prevalence of self-reported poor mental health and its associated demographic, post-migration and sexual risk behaviour factors among young migrants (aged 15–25) in Sweden. Methods Data were drawn from a cross-sectional survey conducted with migrants aged 15–65 years old in Sweden between December 2018 and November 2019 (n = 6449). Among these, 990 participants aged 15–25 were eligible for the study. Mental health was measured using the Refugee Health Screener-13. Missing data indicator analysis and multivariable logistic regression models were conducted to estimate the association between mental health, sexual risk behaviour, demographic and migration-related variables. Results Of the 990 participants, 59% reported poor mental health. Participants reporting poor mental health were more likely to be female (AOR:1.63, 95% CI:1.18–2.25), to have lived in Sweden more than three years (AOR:2.16, 95% CI:1.17–3.97), to engage in any sexual risk behaviour (AOR:1.99, 95% CI:1.25–3.17), and to live alone (AOR:1.95, 95% CI:1.25–3.03) or with friends they already knew (AOR:1.60, 95% CI:1.37–4.91). People arriving from the Americas (AOR:0.54, 95% CI:0.33–0.88), Asia (AOR:0.44, 95% CI:0.22–0.86), Europe (AOR:0.30, 95% CI:0.14–0.61) and Africa (AOR 0.37, 95% CI: 0.23–0.60) had lower odds of poor mental health than those arriving from Syria. Conclusion The prevalence of poor mental health among young migrants in Sweden was high, with specific subgroups (women, asylum seekers, people arriving from Syria, and those residing longer in Sweden) being particularly vulnerable. Our results indicate the interconnectedness between poor mental health and sexual risk behaviour in this population. Thus, policies targeting young migrants should ensure that healthcare services screen for both poor sexual and mental health at the same time.

: This report outlines a strategic approach to introduce pay-for-performance (P4P) incentives for improved noncommunicable disease (NCD) care in Republika Srpska (RS) and the Federation of Bosnia and Herzegovina (FBiH). Developed under the Health Systems Improvement Project (HSIP) and Multi-Donor Trust Fund (MDTF) for Health Systems Reform, the approach focuses on evidence-based, technically sound, and politically feasible strategies. Participatorily developed, the report synthesizes global lessons and analyzes the policy environment in RS and FBiH. It proposes key design features, addressing strategic opportunities and operational challenges. Behavioral economics insights and political economy factors inform the approach, identifying key levers, opportunities, and challenges affecting P4P implementation capacity. To enhance NCD care quality, the report recommends changes in the provider payment mix, tailored reforms at entity and cantonal levels, and active service user engagement. Emphasizing the importance of linking payment incentives to performance, the proposed design spans dimensions such as performance measures, basis of payment, payment attributes, recipient of payment, and targeted outcomes. An enabling environment is deemed critical. Relatedly, effective implementation requires robust data systems, stakeholder engagement, adapted legal frameworks, and suitable institutional arrangements. Technical assistance and budgetary support needs are identified. It is expected that P4P implementation will enhance NCD care coverage and quality, thereby improving health outcomes and overall health system performance in RS and FBiH.

Priority sequencing criteria are of utmost importance in the determination of the sequence in which jobs are processed at workstations in parallel machine scheduling. The utilization of diverse priority rules can result in varied sequencing arrangements, hence requiring more experimentation to ascertain the optimal rule. Hence, it is imperative to formulate a thorough approach for the selection of the most suitable priority sequencing rule from the standpoint of management decision-making. The objective of this research is to analyze and compare six different priority sequencing rules in the context of parallel machine scheduling. Additionally, a methodology is proposed for the assessment and selection of the most suitable rule. This methodology combines the full consistency method (FUCOM) with the measurement of alternatives and ranking according to compromise solution (MARCOS) method, which are both multi-criteria decision-making techniques. When reviewing and selecting the optimal priority sequencing rule, seven parameters are taken into consideration. The weights of these criteria are computed using the FUCOM method, while the relative proximity values of all priority sequencing rules are derived by the MARCOS method. The data indicate that the priority sequencing rules are prioritized according to their level of importance. The approach outlined in this study is essential for workstation management to make well-informed decisions regarding the choice of the most advantageous priority sequencing rule for parallel machine scheduling.

Introduction Recent studies showed that the myelin of the brain changes in the life span, and demyelination contributes to the loss of brain plasticity during normal aging. Diffusion-weighted magnetic resonance imaging (dMRI) allows studying brain connectivity in vivo by mapping axons in white matter with tractography algorithms. However, dMRI does not provide insight into myelin; thus, combining tractography with myelin-sensitive maps is necessary to investigate myelin-weighted brain connectivity. Tractometry is designated for this purpose, but it suffers from some serious limitations. Our study assessed the effectiveness of the recently proposed Myelin Streamlines Decomposition (MySD) method in estimating myelin-weighted connectomes and its capacity to detect changes in myelin network architecture during the process of normal aging. This approach opens up new possibilities compared to traditional Tractometry. Methods In a group of 85 healthy controls aged between 18 and 68 years, we estimated myelin-weighted connectomes using Tractometry and MySD, and compared their modulation with age by means of three well-known global network metrics. Results Following the literature, our results show that myelin development continues until brain maturation (40 years old), after which degeneration begins. In particular, mean connectivity strength and efficiency show an increasing trend up to 40 years, after which the process reverses. Both Tractometry and MySD are sensitive to these changes, but MySD turned out to be more accurate. Conclusion After regressing the known predictors, MySD results in lower residual error, indicating that MySD provides more accurate estimates of myelin-weighted connectivity than Tractometry.

ABSTRACT This study explores attitudes toward sustainable-timber resource management and climate-change mitigation by extending the life cycles of wood products and their cascading in Central and Southeastern Europe. A comprehensive survey involving the general public and professional organizations in Bosnia and Herzegovina, Croatia, Serbia, and Slovenia, reveals significant support for wood product reuse in construction, emphasizing ecological aspects and sustainability. Despite doubts about wood product quality, motivation for reuse remains high. Challenges, including limited availability and cost, are acknowledged. Reusing and recycling wood products at the end of their life cycle can extend the life of the wood resource and reduce carbon emissions. Cascading is a promising way to combat climate change and prolong the life cycle of wood products. This study highlights the potential of cascading wood use, underscores the necessity to improve the quantification of wood usage through material intensity analysis of buildings, and emphasizes the requirement for more comprehensive education and explanations to promote sustainable practices.

The number of loan requests is rapidly growing worldwide representing a multi-billion-dollar business in the credit approval industry. Large data volumes extracted from the banking transactions that represent customers’ behavior are available, but processing loan applications is a complex and time-consuming task for banking institutions. In 2022, over 20 million Americans had open loans, totaling USD 178 billion in debt, although over 20% of loan applications were rejected. Numerous statistical methods have been deployed to estimate loan risks opening the field to estimate whether machine learning techniques can better predict the potential risks. To study the machine learning paradigm in this sector, the mental health dataset and loan approval dataset presenting survey results from 1991 individuals are used as inputs to experiment with the credit risk prediction ability of the chosen machine learning algorithms. Giving a comprehensive comparative analysis, this paper shows how the chosen machine learning algorithms can distinguish between normal and risky loan customers who might never pay their debts back. The results from the tested algorithms show that XGBoost achieves the highest accuracy of 84% in the first dataset, surpassing gradient boost (83%) and KNN (83%). In the second dataset, random forest achieved the highest accuracy of 85%, followed by decision tree and KNN with 83%. Alongside accuracy, the precision, recall, and overall performance of the algorithms were tested and a confusion matrix analysis was performed producing numerical results that emphasized the superior performance of XGBoost and random forest in the classification tasks in the first dataset, and XGBoost and decision tree in the second dataset. Researchers and practitioners can rely on these findings to form their model selection process and enhance the accuracy and precision of their classification models.

BACKGROUND: Metabolic syndrome (MetS) is a group of comorbidities related to regulating hyperglycemia and acute cardiovascular incidents and complications. With the increasing prevalence in individuals with type 2 diabetes mellitus (T2DM), MetS represents an increasing public health problem and clinical challenge, and early diagnosis is necessary to avoid the accelerated development of diabetic complications. OBJECTIVE: To investigate the role of Complete Blood Count-derived Inflammation Indexes (CBCIIs) in predicting MetS in T2DM individuals. METHODS: The study was designed as a two-year prospective study and included 80 T2DM individuals divided into MetS and non-MetS groups based on MetS development over two years. The sera samples were analyzed for complete blood count parameters and C-reactive protein (CRP). Based on the laboratory test results, 13 CBCIIs were calculated and analyzed. The receiver operating characteristic (ROC) curve and their corresponding areas under the curve (AUC) were used to determine prognostic accuracy. RESULTS: There were significant differences between T2DM participants with Mets and those without MetS concerning Neutrophil to Platelet Ratio (NPR) values ( p < 0.001), Neutrophil to Lymphocyte and Platelet Ratio (NLPR) ( p < 0.001), Platelet to Lymphocyte Ratio (PLR) ( p < 0.001), Lymphocyte to C-reactive protein Ratio (LCR) ( p < 0.001), C-reactive protein to Lymphocyte Ratio (CRP/Ly) ( p < 0.001), Systemic immune inflammation index (SII) ( < 0.001), and Aggregate Index of Systemic Inflammation (AISI) ( p = 0.005). The results of ROC curve analysis have shown that the LCR (AUC of 0.907), CRP/Ly (AUC of 0.907) can serve as excellent predictors, but NPR (AUC of 0.734), NLRP (AUC of 0.755), PLR (AUC of 0.823), SII (AUC of 0.745), and AISI (AUC of 0.688) as good predictors of MetS in T2 DM individuals. CONCLUSION: This study confirms the reliability of the CBCIIs as novel, simple, low cost and valuable predictors of MetS developing in T2DM.