Search Results (134)

Recent research has increasingly emphasized the synergies between Supply Chain Management Practices (SCMPs) and Quality Management Principles (QMPs), particularly through the emerging concept of Supply Chain Quality Management (SCQM). Despite this recognition, empirical evidence on how these practices interact to influence performance remains very limited, especially in the context of developing countries. This study addresses the gap by interviewing 70 Tunisian agri-food companies to investigate the relationships between five dimensions of SCMP, strategic supplier partnerships, customer relationship, information sharing, information quality and postponement, and the seven principles of ISO9001 QMP: leadership, engagement of people, improvement, customer focus, process approach, evidence-based decision making, and relationship management. Using factor analysis and structural equation modelling, the study explores the mediating role of competitive advantage (CA): price/cost, product quality, product innovation, delivery dependability and time-to-market—on operational performance. The findings indicate that analyzing SCMP, QMP, and CA as aggregated blocks does not produce significant explanatory correlations. Instead, judiciously reorganizing their sub-constructs into five integrated groups provides a more effective model: (1) information and decision capacity, (2) customer-centric innovation, (3) process management and agility, (4) supplier and network management, and (5) leadership and workforce engagement. This integrated classification offers managers a coherent framework for implementing SCMP and QMP to enhance competitiveness results. Full article

This study tackles the complex challenge of dynamic multi-objective vehicle routing optimization in large-scale equipment manufacturing, where routing operations significantly impact both economic performance and environmental sustainability. We develop an innovative Dynamic Multi-Objective Vehicle Routing Problem (DMOVRP) model that uniquely integrates three competing objectives: environmental impact reduction, delivery timeliness, and operational robustness. The proposed algorithm combines a dynamic event handler with the NSACOWDRL algorithm—an adaptive multi-objective optimization algorithm with dynamic event handling capability. The proposed system features adaptive mechanisms for handling real-time disruptions through specialized event classification and dynamic rescheduling protocols. Extensive computational experiments demonstrate the algorithm’s superior performance with statistically significant improvements using the Wilcoxon signed-rank test (p < 0.05, n = 30 runs per instance), achieving average relative gains of 15.2% in HV, 12.8% in IGD, and 8.9% in GD metrics compared to established methods. This research makes theoretical contributions through its feasibility quantification metric and practical advancements in routing schedule systems. By successfully reconciling traditionally conflicting objectives through dynamic JIT adjustments and robustness-aware optimization, this work provides manufacturers with a versatile decision-support tool that adapts to unpredictable workshop conditions while maintaining sustainable operations. Full article

Postal traffic and transport face challenges related to the rapid growth of parcel volumes, increasing demands for sustainability, and the need for integration into the smart transportation concept. This study explores the application of machine learning (ML) models for the classification of postal delivery times, with the aim of improving service efficiency and quality. As a case study, the Postal Center Zenica, one of the seven organizational units of the Public Enterprise “BH Pošta” in Bosnia and Herzegovina, was analyzed. The available dataset comprised 11,138 instances, which were cleaned and filtered, then expanded through two iterations of data augmentation using an autoencoder neural network. Five ML models, Random Forest, Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), k-Nearest Neighbors (kNN), and Multi-Layer Perceptron (MLP), were developed and compared, with hyperparameters optimized using the Bayesian method and evaluated through standard classification metrics. The results indicate that the data augmentation method significantly improves model performance, particularly in the classification of delayed shipments, with ensemble, especially Random Forest and XGBoost, emerging as the most robust solutions. Beyond contributions in the context of postal traffic and transport, the proposed methodological framework demonstrates interdisciplinary relevance, as it can also be applied in telecommunication traffic classes, where similar network dynamics require reliable predictive models. Full article

Research into oral mucosa-targeted drug delivery systems (DDS) is rapidly evolving, with growing emphasis on enhancing bioavailability and precision targeting while overcoming the unique anatomical and physiological barriers of the oral environment. Despite considerable progress, challenges such as enzymatic degradation, limited mucosal penetration, and solubility issues continue to hinder therapeutic success. Recent advancements have focused on innovative formulation strategies—including nanoparticulate and biomimetic systems—to improve delivery efficiency and systemic absorption. Simultaneously, smart and stimuli-responsive materials are emerging, offering dynamic, environment-sensitive drug release profiles. One particularly promising area involves the application of glycosaminoglycans, a class of naturally derived polysaccharides with excellent biocompatibility, mucoadhesive properties, and hydrogel-forming capacity. These materials not only enhance drug residence time at the mucosal site but also enable controlled release kinetics, thereby improving therapeutic outcomes. However, critical research gaps remain: standardized, clinically meaningful mucoadhesion/permeation assays and robust in vitro–in vivo correlations are still lacking; long-term stability, batch consistency of GAGs, and clear regulatory classification (drug, device, or combination) continue to impede scale-up and translation. Patient-centric performance—palatability, mouthfeel, discreet wearability—and head-to-head trials versus standard care also require systematic evaluation to guide adoption. Overall, converging advances in GAG-based films, hydrogels, and nanoengineered carriers position oral mucosal delivery as a realistic near-term option for precision local and selected systemic therapies—provided the field resolves standardization, stability, regulatory, and usability hurdles. Full article

Background/Objectives: In the United States, Black infants are twice as likely as infants of all other races and ethnicities to die by one year of age. Mediation modeling predicted that preventing low birthweight could alleviate 75% of this disparity. However, the potential confounding and interacting role of preterm birth remains a question. The goal of this study was to determine how birthweight and length of gestation interact in causing racial disparity. Methods: Records from more than 25 million singleton births were retrieved from the United States National Natality Database for the years 2016 to 2022. Two interaction models were evaluated using Bayesian estimation of potential outcomes. The first modeled the interaction between birthweight and length of gestation with both mediators measured as binary (normal/abnormal). The second modeled the interaction using five classifications for both birthweight and length of gestation. Results: Eliminating either abnormal birthweights or abnormal lengths of gestation would reduce racial disparity in infant mortality by approximately 75%. There was no additional reduction of racial disparity by normalizing both. Modeling the combinations of specific categories of birthweight and length of gestation showed Black infants were 2.76 (2.72, 2.79) times more likely to be born with extremely low birthweight and extremely preterm delivery. This single combination explained over 60% of the racial disparity in infant mortality. Conclusions: The current study clarifies how birthweight and preterm birth contribute to racial disparity and illustrates how Bayesian estimation of potential outcomes enables complex mediational investigations. Full article

Classification of video traffic is crucial for network management, enforcing quality of service, and optimising bandwidth. Feature selection plays a vital role in traffic identification by reducing data volume, enhancing accuracy, and reducing computational cost. This paper presents a comparative study of three feature selection approaches applied to video traffic identification: filter, wrapper, and embedded. Real-world traffic traces are collected from three popular video streaming platforms: YouTube, Netflix, and Amazon Prime Video, representing diverse content delivery characteristics. The main contributions of this work are (1) the identification of traffic generated by these streaming services, (2) a comparative evaluation of three feature selection methods, and (3) the application of previously untested algorithms for this task. We evaluate the examined methods using F1-score and computational efficiency. The results demonstrate distinct trade-offs among the approaches: the filter method offers low computational overhead with moderate accuracy, while the wrapper method achieves higher accuracy at the cost of longer processing times. The embedded method provides a balanced compromise by integrating feature selection within model training. This comparative analysis offers insights for designing video traffic identification systems in modern heterogeneous networks. Full article

High-Voltage Alternating Current (HVAC) transmission systems form the backbone of modern power grids, enabling efficient long-distance and high-capacity power delivery. In Saudi Arabia, ongoing initiatives to modernize and strengthen grid infrastructure demand advanced solutions to ensure system reliability, operational stability, and the minimization of economic losses caused by faults. Traditional fault detection and classification methods often depend on the manual interpretation of voltage and current signals, which is both labor-intensive and prone to human error. Although data-driven approaches such as Artificial Neural Networks (ANNs) and Deep Learning have been applied to automate fault analysis, their performance is often constrained by the quality and size of available training datasets, leading to poor generalization and physically inconsistent outcomes. This study proposes a novel hybrid fault detection and classification framework for the 380 kV Marjan–Safaniyah HVAC transmission line by integrating Deep Learning with Physics-Informed Neural Networks (PINNs). The PINN model embeds fundamental electrical laws, such as Kirchhoff’s Current Law (KCL), directly into the learning process, thereby constraining predictions to physically plausible behaviors and enhancing robustness and accuracy. Developed in MATLAB/Simulink using the Deep Learning Toolbox, the proposed framework performs fault detection and fault type classification within a unified architecture. A comparative analysis demonstrates that the hybrid PINN approach significantly outperforms conventional Deep Learning models, particularly by reducing false negatives and improving class discrimination. Furthermore, this study highlights the crucial role of balanced and representative datasets in achieving a reliable performance. Validation through confusion matrices and KCL residual histograms confirms the enhanced physical consistency and predictive reliability of the model. Overall, the proposed framework provides a powerful and scalable solution for real-time monitoring, fault diagnosis, and intelligent decision-making in high-voltage power transmission systems. Full article

Extracellular vesicles (EVs) are mediators of intercellular communication and serve as promising tools for drug discovery and targeted therapies. These lipid bilayer-bound nanovesicles facilitate the transfer of functional proteins, RNAs, lipids, and other biomolecules between cells, thereby influencing various physiological and pathological processes. This review outlines the molecular mechanisms governing EV biogenesis and cargo sorting, emphasizing the role of key regulatory proteins in modulating selective protein packaging. We explore the critical involvement of EVs in various disease microenvironments, including cancer progression, neurodegeneration, and immunological modulation. Their ability to cross biological barriers and deliver bioactive cargo makes them desirable candidates for precise drug delivery systems, especially in neurological and oncological disorders. Moreover, this review highlights advances in engineering EVs for the delivery of RNA therapeutics, CRISPR-Cas systems, and targeted small molecules. The utility of EVs as diagnostic tools in liquid biopsies and their integration into personalized medicine and companion diagnostics are also discussed. Patient-derived EVs offer dynamic insights into disease states and enable real-time treatment stratification. Despite their potential, challenges such as scalable isolation, cargo heterogeneity, and regulatory ambiguity remain significant hurdles. Recent studies have reported novel pharmacological approaches targeting EV biogenesis, secretion, and uptake pathways, with emerging regulators showing promise as drug targets for modulating EV cargo. Future directions include the standardization of EV analytics, scalable biomanufacturing, and the classification of EV-based therapeutics under evolving regulatory frameworks. This review emphasizes the multifaceted roles of EVs and their transformative potential as therapeutic platforms and biomarker reservoirs in next-generation precision medicine. Full article

Despite widespread adoption, traditional Agile project management practices often fail to ensure successful delivery of enterprise-scale software projects. One key limitation lies in the absence of a conceptually defined structure for the various types of Agile activities and their interactions. As a result, Agile methodologies typically lack formal indicators for evaluating the semantic content and progress status of project activities. Although widely used tools for Agile project management, such as Atlassian Jira, capture operational data, project status assessment interpretation remains largely subjective—relying on the experience and judgment of managers and team members rather than on a formal knowledge model or well-defined semantic attributes. As Agile project activities continue to grow in complexity, there is a pressing need for a modeling approach that captures their causal structure in order to describe the essential characteristics of the processes and ensure systematic monitoring and evaluation of the project. The complexity of the corresponding model must correlate with the causality of processes to avoid losing essential properties and to reveal the content of causal interactions. To address these gaps, this paper introduces a causal Agile process model that formalizes the internal structure and transformation pathways of Agile activity types. To our knowledge, it is the first framework to integrate a recursive, causally grounded structure into Agile management, enabling both semantic clarity and quantitative evaluation of project complexity and progress. The aim of the article is, first, to describe conceptually different Agile activity types from a causal modeling perspective, its internal structure and information transformations, and, second, to formally define the causal Agile management model and its characteristics. Each Agile activity type (e.g., theme, initiative, epic, user story) is modeled using the management transaction (MT) framework—an internal model of activity that comprises a closed-loop causal relationship among management function (F), process (P), state attribute (A), and control (V) informational flows. Using this framework, the internal structure of Agile activity types is normalized and the different roles of activities in internal MT interactions are defined. An important feature of this model is its recursive structure, formed through a hierarchy of MTs. Additionally, the paper presents classifications of vertical and horizontal causal interactions, uncovering theoretically grounded patterns of information exchange among Agile activities. These classifications support the derivation of quantitative indicators for assessing project complexity and progress at a given point in time, offering insights into activity specification completeness at hierarchical levels and overall project content completeness. Examples of complexity indicator calculations applied to real-world enterprise application system (EAS) projects are included. Finally, the paper describes enhancements to the Jira tool, including a causal Agile management repository and a prototype user interface. An experimental case study involving four Nordic EAS projects (using Scrum at the team level and SAFe at the program level) demonstrates that the Jira tool, when supplemented with causal analysis, can reveal missing links between themes and initiatives and align interdependencies between teams in real time. The causal Agile approach reduced the total number of requirements by an average of 13% and the number of change requests by 14%, indicating a significant improvement in project coordination and quality. Full article

The early detection of neurological and psychiatric disorders is critical for optimizing patient outcomes and improving the efficacy of healthcare delivery. This study presents a novel multiclass machine learning (ML) framework designed to classify epilepsy, migraine, and schizophrenia simultaneously using electroencephalography (EEG) signals. Unlike conventional approaches that predominantly rely on binary classification (e.g., healthy vs. diseased cohorts), this work addresses a significant gap in the literature by introducing a unified artificial neural network (ANN) architecture capable of discriminating among three distinct neurological and psychiatric conditions. The proposed methodology involves decomposing raw EEG signals into constituent frequency subbands to facilitate robust feature extraction. These discriminative features were subsequently classified using a multilayer ANN, achieving performance metrics of 95% sensitivity, 96% specificity, and a 95% F1-score. To enhance clinical applicability, the model was optimized for potential integration into real-time diagnostic systems, thereby supporting the development of a rapid, reliable, and scalable decision support tool. The results underscore the viability of EEG-based multiclass models as a promising diagnostic aid for neurological and psychiatric disorders. By consolidating the detection of multiple conditions within a single computational framework, this approach offers a scalable and efficient alternative to traditional binary classification paradigms. Full article

Background: The U.S. Medicare Annual Wellness Visit (AWV) offers a structured opportunity for cognitive screening and personalized prevention planning among older adults. Yet, implementation of AWVs, particularly for individuals with cognitive impairment, remains inconsistent across primary care or other diverse care settings. Methods: We conducted a scoping review using the Consolidated Framework for Implementation Research (CFIR) to explore multilevel factors influencing the implementation of the Medicare AWV’s cognitive screening component, with a focus on how these processes support the detection and management of cognitive impairment among older adults. We searched four databases and screened peer-reviewed studies published between 2011 and March 2025. Searches were conducted in Ovid MEDLINE, PubMed, EBSCOhost, and CINAHL databases. The initial search was completed on 3 January 2024 and updated monthly through 30 March 2025. All retrieved citations were imported into EndNote 21, where duplicates were removed. We screened titles and abstracts for relevance using the predefined inclusion criteria. Full-text articles were then reviewed and scored as either relevant (1) or not relevant (0). Discrepancies were resolved through consensus discussions. To assess the methodological quality of the included studies, we used the Joanna Briggs Institute critical appraisal tools appropriate to each study design. These tools evaluate rigor, trustworthiness, relevance, and risk of bias. We extracted the following data from each included study: Author(s), year, title, and journal; Study type and design; Data collection methods and setting; Sample size and population characteristics; Outcome measures; Intervention details (AWV delivery context); and Reported facilitators, barriers, and outcomes related to AWV implementation. The first two authors independently coded and synthesized all relevant data using a table created in Microsoft Excel. The CFIR guided our data analysis, thematizing our findings into facilitators and barriers across its five domains, viz: (1) Intervention Characteristics, (2) Outer Setting, (3) Inner Setting, (4) Characteristics of Individuals, and (5) Implementation Process. Results: Among 19 included studies, most used quantitative designs and secondary data. Our CFIR-based synthesis revealed that AWV implementation is shaped by interdependent factors across five domains. Key facilitators included AWV adaptability, Electronic Health Record (EHR) integration, team-based workflows, policy alignment (e.g., Accountable Care Organization participation), and provider confidence. Barriers included vague Centers for Medicare and Medicaid Services (CMS) guidance, limited reimbursement, staffing shortages, workflow misalignment, and provider discomfort with cognitive screening. Implementation strategies were often poorly defined or inconsistently applied. Conclusions: Effective AWV delivery for older adults with cognitive impairment requires more than sound policy and intervention design; it demands organizational readiness, structured implementation, and engaged providers. Tailored training, leadership support, and integrated infrastructure are essential. These insights are relevant not only for U.S. Medicare but also for global efforts to integrate dementia-sensitive care into primary health systems. Our study has a few limitations that should be acknowledged. First, our scoping review synthesized findings predominantly from quantitative studies, with only two mixed-method studies and no studies using strictly qualitative methodologies. Second, few studies disaggregated findings by race, ethnicity, or geography, reducing our ability to assess equity-related outcomes. Moreover, few studies provided sufficient detail on the specific cognitive screening instruments used or on the scope and delivery of educational materials for patients and caregivers, limiting generalizability and implementation insights. Third, grey literature and non-peer-reviewed sources were not included. Fourth, although CFIR provided a comprehensive analytic structure, some studies did not explicitly fit in with our implementation frameworks, which required subjective mapping of findings to CFIR domains and may have introduced classification bias. Additionally, although our review did not quantitatively stratify findings by year, we observed that studies from more recent years were more likely to emphasize implementation facilitators (e.g., use of templates, workflow integration), whereas earlier studies often highlighted systemic barriers such as time constraints and provider unfamiliarity with AWV components. Finally, while our review focused specifically on AWV implementation in the United States, we recognize the value of comparative analysis with international contexts. This work was supported by a grant from the National Institute on Aging, National Institutes of Health (Grant No. 1R01AG083102-01; PIs: Tzeng, Kuo, & Raji). Full article

Critical Internet of Things (IoT) data in Fifth Generation Vehicular Ad Hoc Networks (5G VANETs) demands Ultra-Reliable Low-Latency Communication (URLLC) to support mission-critical vehicular applications such as autonomous driving and collision avoidance. Achieving the stringent Quality of Service (QoS) requirements for these applications remains a significant challenge. This paper proposes a novel framework integrating Software-Defined Networking (SDN) and Network Functions Virtualisation (NFV) as embedded functionalities in connected vehicles. A lightweight SDN Controller model, implemented via vehicle on-board computing resources, optimised QoS for communications between connected vehicles and the Next-Generation Node B (gNB), achieving a consistent packet delivery rate of 100%, compared to 81–96% for existing solutions leveraging SDN. Furthermore, a Software-Defined Wide-Area Network (SD-WAN) model deployed at the gNB enabled the efficient management of data, network, identity, and server access. Performance evaluations indicate that SDN and NFV are reliable and scalable technologies for virtualised and distributed 5G VANET infrastructures. Our SDN-based in-vehicle traffic classification model for dynamic resource allocation achieved 100% accuracy, outperforming existing Artificial Intelligence (AI)-based methods with 88–99% accuracy. In addition, a significant increase of 187% in flow rates over time highlights the framework’s decreasing latency, adaptability, and scalability in supporting URLLC class guarantees for critical vehicular services. Full article

Cutaneous and oral mucosal adverse events (AEs) are among the most common non-hematologic toxicities observed during breast cancer treatment. These complications arise across various therapeutic modalities including chemotherapy, targeted therapy, hormonal therapy, radiotherapy, and immunotherapy. Although often underrecognized compared with systemic side effects, dermatologic and mucosal toxicities can severely impact the patients’ quality of life, leading to psychosocial distress, pain, and reduced treatment adherence. In severe cases, these toxicities may necessitate dose reductions, treatment delays, or discontinuation, thereby compromising oncologic outcomes. The growing use of precision medicine and novel targeted agents has broadened the spectrum of AEs, with some therapies linked to distinct dermatologic syndromes and mucosal complications such as mucositis, xerostomia, and lichenoid reactions. Early detection, accurate classification, and timely multidisciplinary management are essential for mitigating these effects. This review provides a comprehensive synthesis of current knowledge on cutaneous and oral mucosal toxicities associated with modern breast cancer therapies. Particular attention is given to clinical presentation, underlying pathophysiology, incidence, and evidence-based prevention and management strategies. We also explore emerging approaches, including nanoparticle-based delivery systems and personalized interventions, which may reduce toxicity without compromising therapeutic efficacy. By emphasizing the integration of dermatologic and mucosal care, this review aims to support clinicians in preserving treatment adherence and enhancing the overall therapeutic experience in breast cancer patients. The novelty of this review lies in its dual focus on cutaneous and oral complications across all major therapeutic classes, including recent biologic and immunotherapeutic agents, and its emphasis on multidisciplinary, patient-centered strategies. Full article

Indoor navigation poses persistent challenges due to the limitations of traditional positioning systems within buildings. In this study, we propose a novel approach to address this issue—not by continuously tracking the user’s location, but by estimating their position based on how closely their observed behavior matches the expected progression along a predefined route. This concept, while not universally applicable, is well-suited for specific indoor navigation scenarios, such as guiding couriers or delivery personnel through complex residential buildings. We explore this idea in detail in our paper. To implement this behavior-based localization, we introduce an LSTM-based method for classifying user behavior patterns, including standing, walking, and using stairs or elevators, by analyzing velocity sequences derived from smartphone sensors’ data. The developed model achieved 75% accuracy for individual activity type classification within one-second time windows, and 98.6% for full-sequence classification through majority voting. These results confirm the viability of real-time activity recognition as the foundation for a navigation system that aligns live user behavior with pre-recorded patterns, offering a cost-effective alternative to infrastructure-heavy indoor positioning systems. Full article

Driven by the rapid development of e-commerce and intelligent logistics, the volume of express delivery services has surged, making the efficient and accurate identification of shipping information a core requirement for automatic sorting systems. However, traditional Optical Character Recognition (OCR) technology struggles to meet the accuracy and real-time demands of complex logistics scenarios due to challenges such as image distortion, uneven illumination, and field overlap. This paper proposes a three-level collaborative recognition method based on deep learning that facilitates structured information extraction through regional normalization, dual-path parallel extraction, and a dynamic matching mechanism. First, the geometric distortion associated with contour detection and the lightweight direction classification model has been improved. Second, by integrating the enhanced YOLOv5s for key area localization with the upgraded PaddleOCR for full-text character extraction, a dual-path parallel architecture for positioning and recognition has been constructed. Finally, a dynamic space–semantic joint matching module has been designed that incorporates anti-offset IoU metrics and hierarchical semantic regularization constraints, thereby enhancing matching robustness through density-adaptive weight adjustment. Experimental results indicate that the accuracy of this method on a self-constructed dataset is 89.5%, with an F1 score of 90.1%, representing a 24.2% improvement over traditional OCR methods. The dynamic matching mechanism elevates the average accuracy of YOLOv5s from 78.5% to 89.7%, surpassing the Faster R-CNN benchmark model while maintaining a real-time processing efficiency of 76 FPS. This study offers a lightweight and highly robust solution for the efficient extraction of order information in complex logistics scenarios, significantly advancing the intelligent upgrading of sorting systems. Full article