Multidisciplinary Digital Publishing Institute (Switzerland)
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Prediction of Chronic Obstructive Pulmonary Disease Using Machine Learning, Clinical Summary Notes, and Vital Signs: A Single-Center Retrospective Cohort Study in the United States
No full textIntroduction: Chronic obstructive pulmonary disease (COPD) is a leading cause of morbidity and mortality. Early identification and timely intervention for COPD exacerbations can reduce hospitalizations and complications, as well as improve patient outcomes. Methods: To develop and evaluate predictive models for COPD exacerbations using machine learning (ML), we performed a retrospective study using intensive care unit patient records. Records including 31,667 clinical notes and 10,489 vital signs were used to train and validate two machine learning models to predict COPD exacerbations in patients with known or suspected COPD. Predictive performance was evaluated for support vector machine, quadratic discriminant analysis, and adaptive boosting algorithms using area under the receiver operating characteristic curve (AUC). Results: The clinical note-based support vector machine model achieved an AUC of 0.81 and accuracy of 84.0% in predicting COPD exacerbations. Data from patient monitors and hospital information systems provided sufficient information for accurate prediction, demonstrating the utility of combining physiological signals with clinical text data. Discussion: Clinically available patient data and vital signs can effectively predict COPD exacerbations, potentially enabling earlier interventions, improved outcomes, and reduced healthcare burden. These findings suggest that integrating unstructured clinical notes with structured vital signs using ML frameworks may improve early detection of exacerbation risk, thus enabling appropriate patient counseling, triage, and treatment based on COPD severity
The Relationship Between Employee Satisfaction and Perceived Organizational Performance
No full textEmployee satisfaction remains a central theme in management, with substantial evidence linking it to organizational performance. This study examines the nature and strength of that relationship and investigates whether its magnitude varies across different performance indicators. Using existing data from 23 organizations where organization culture was assessed through the Denison Organizational Culture Survey (DOCS) between 2015 and 2022, the study analyzes 1532 employee responses. Organizational performance was evaluated across five dimensions: growth, profitability, quality of products and services, employee satisfaction, customer satisfaction, and overall performance. Bivariate regression analyses reveal positive and statistically significant relationships between employee satisfaction and all other performance indicators. The strongest associations were observed for overall performance and customer satisfaction, while moderate relationships emerged for profitability, growth, and quality of products and services. Employee satisfaction accounted for approximately 36% of the variance in overall performance. The findings support the view that employee satisfaction functions both as a driver and as an outcome of organizational performance. They further indicate that the strength of this relationship is greater for human-centered outcomes than for financial indicators. Practically, the results underscore the importance of cultivating intrinsic motivation, trust, and employee participation to enhance both satisfaction and perceived performance. Future research should investigate the causal direction between employee satisfaction and customer satisfaction and explore how organizational culture moderates this relationship across different sectors
Microplastic Contamination of Surface Water and Sediments in Lake Kotokel (Eastern Cisbaikalia)
No full textLake Kotokel is one of the largest lakes along the eastern shore of Lake Baikal. Increasing anthropogenic pressure combined with climate variability led to a sharp decline in its ecological condition, culminating in an outbreak of Gaff disease in 2009. Moreover, Lake Kotokel may serve as a source of pollutants to Lake Baikal due to its hydrological connection via a system of rivers. In light of these factors, ongoing research seeks to identify the drivers of ecological degradation in the lake ecosystem and provide a comprehensive assessment of its current environmental status and potential adverse processes. In this study, we report, for the first time, the occurrence of microplastic particles in the surface water and sediments of Lake Kotokel. Mean microplastic concentrations were 0.59 ± 0.25 items/m3 in water and 280 ± 162 items/kg dry weight in sediments, with corresponding average microplastic masses of 2.6 ± 1.4 μg/m3 in water and 1.33 ± 1.21 mg/kg dry weight in sediments. In surface waters, microplastic were predominantly 1–3 mm in size, whereas sediments were dominated by 0.1–1 mm particles. Fibers were the most abundant morphological form, comprising 79.5% and 82.8% of particles in water and sediments, respectively. Five polymer types were identified, with polyethylene terephthalate being the most prevalent. Based on particle abundance and polymer composition, the ecological risk of microplastic in Lake Kotokel was assessed as low to moderate
Development and Validation of a GPS Error-Mitigation Algorithm for Mental Health Digital Phenotyping
No full textMobile Global Positioning System (GPS) data offer a promising approach to inferring mental health status through behavioural analysis. Whilst previous research has explored location-based behavioural indicators including location clusters, entropy, and variance, persistent GPS measurement errors have compromised data reliability, limiting the practical deployment of smartphone-based digital phenotyping systems. This study develops and validates an algorithmic preprocessing method designed to mitigate inherent GPS measurement limitations in mobile health applications. We conducted comprehensive evaluation through controlled experimental protocols and naturalistic field assessments involving 38 participants over a seven-day period, capturing GPS data across diverse environmental contexts on both Android and iOS platforms. The proposed preprocessing algorithm demonstrated exceptional precision, consistently detecting major activity centres within an average 50-metre margin of error across both platforms. In naturalistic settings, the algorithm yielded robust location detection capabilities, producing spatial patterns that reflected plausible and behaviourally meaningful traits at the individual level. Cross-platform analysis revealed consistent performance regardless of operating system, with no significant differences in accuracy metrics between Android and iOS devices. These findings substantiate the potential of mobile GPS data as a reliable, objective source of behavioural information for mental health monitoring systems, contingent upon implementing sophisticated error-mitigation techniques. The validated algorithm addresses a critical technical barrier to the practical implementation of GPS-based digital phenotyping, enabling the more accurate assessment of mobility-related behavioural markers across diverse mental health conditions. This research contributes to the growing field of mobile health technology by providing a robust algorithmic framework for leveraging smartphone sensing capabilities in healthcare applications
Measurement Uncertainty and Traceability in Upper Limb Rehabilitation Robotics: A Metrology-Oriented Review
No full textUpper-limb motor impairment is a major consequence of stroke and neuromuscular disorders, imposing a sustained clinical and socioeconomic burden worldwide. Quantitative assessment of limb positioning and motion accuracy is fundamental to rehabilitation, guiding therapy evaluation and robotic assistance. The evolution of upper-limb positioning systems has progressed from optical motion capture to wearable inertial measurement units (IMUs) and, more recently, to data-driven estimators integrated with rehabilitation robots. Each generation has aimed to balance spatial accuracy, portability, latency, and metrological reliability under ecological conditions. This review presents a systematic synthesis of the state of measurement uncertainty, calibration, and traceability in upper-limb rehabilitation robotics. Studies are categorised across four layers, i.e., sensing, fusion, cognitive, and metrological, according to their role in data acquisition, estimation, adaptation, and verification. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol was followed to ensure transparent identification, screening, and inclusion of relevant works. Comparative evaluation highlights how modern sensor-fusion and learning-based pipelines achieve near-optical angular accuracy while maintaining clinical usability. Persistent challenges include non-standard calibration procedures, magnetometer vulnerability, limited uncertainty propagation, and absence of unified traceability frameworks. The synthesis indicates a gradual transition toward cognitive and uncertainty-aware rehabilitation robotics in which metrology, artificial intelligence, and control co-evolve. Traceable measurement chains, explainable estimators, and energy-efficient embedded deployment emerge as essential prerequisites for regulatory and clinical translation. The review concludes that future upper-limb systems must integrate calibration transparency, quantified uncertainty, and interpretable learning to enable reproducible, patient-centred rehabilitation by 2030
A Time-Dependent Dijkstra’s Algorithm for the Shortest Path Considering Periodic Queuing Delays at Signalized Intersections
No full textIn urban road networks, queuing delays at signalized intersections often account for over half of the total travel time. The complexity of traffic signals and vehicle queuing makes traditional shortest path algorithms insufficient for real-time optimal path finding. This study proposes a Time-Dependent Dijkstra’s algorithm to address these challenges. The network topology is redesigned to model vehicle turning behaviors accurately. A periodic queuing delay parameter matrix for signalized intersections is introduced, storing traffic flow and signal phase parameters. Additionally, a time-varying weight matrix tracks the vehicle’s position in the signal cycle upon intersection arrival. Using cumulative curve theory, a periodic queuing-delay model is constructed to capture delays for vehicles arriving at different times. The algorithm updates the network weight matrix in real-time based on vehicle arrival times at intersections, enabling FIFO-consistent time-dependent shortest path computation for a given departure time. Numerical and SUMO simulations on a real-world road network in Suzhou Industrial Park (comprising 15 signalized intersections and 22 road segments) demonstrate the algorithm’s effectiveness. Results show a 25.36% reduction in travel time compared to the traditional Dijkstra’s Algorithm and a 10.46% reduction compared to an algorithm considering only signalized intersection waiting time when departure times vary. The results highlight the impact of periodic queuing delays, with the algorithm reducing travel time and improving path planning
Antarctic Microalga Chlamydomonas sp. ICE-L Cryptochrome CiCRY-DASH1 Mediates Efficient DNA Photorepair of UV-Induced Cyclobutane Pyrimidine Dimer and 6-4 Photoproducts
No full textCryptochromes (CRYs) are a conserved class of blue light and near-ultraviolet light receptors that regulate diverse processes, including photomorphogenesis in plants. In the extreme Antarctic environment, ice algae endure intense UV radiation, prolonged darkness, and low temperatures, where cryptochromes play a vital role in light sensing and stress response. In this study, we cloned the complete open reading frame (ORF) of the cryptochrome gene CiCRY-DASH1 from the Antarctic microalga Chlamydomonas sp. ICE-L. Both in vivo and in vitro DNA photorepair assays showed that CiCRY-DASH1 effectively repairs cyclobutane pyrimidine dimer (CPD) and 6-4 photoproducts (6-4PPs) induced by UV radiation. Furthermore, deletion of the N-terminal and C-terminal loop regions, combined with activity assays, revealed that the C-terminal loop region plays a crucial role in photorepair activity. These findings elucidate the adaptive photorepair mechanisms of Antarctic microalgae and establish CiCRY-DASH1 as a valuable genetic resource. Specifically, the high catalytic efficiency and evolutionary robustness of the engineered variants position it as a promising marine bioactive agent for photoprotective therapeutics and a strategic target for constructing microbial chassis to enable sustainable drug biomanufacturing
Changes in the Amino Acid Composition of Bee-Collected Pollen During 15 Months of Storage in Fresh-Frozen and Dried Forms
No full textBee pollen (BP) is a nutritionally valuable natural product whose biological activity is strongly influenced by its amino acid profile. This study evaluated qualitative and quantitative changes in free amino acids in Lithuanian BP subjected to freezing (−20 °C and −80 °C) or low-temperature drying and stored for 15 months. Seventeen amino acids, including all nine essential amino acids, were identified using UHPLC-ESI-MS/MS, accounting for 47–48% of the total amino acid content (TAAC). Arginine, proline, and aspartic acid were the predominant free amino acids. Both frozen and dried samples showed a statistically significant decrease in TAAC after nine months of storage (p < 0.05), resulting in a 1.5–1.7-fold reduction after prolonged storage. Frozen storage at −20 °C and −80 °C better preserved free amino acids, particularly alanine, glutamic acid, and proline, whereas dried BP stored at room temperature exhibited accelerated degradation. Sulfur-containing amino acids, especially cysteine and methionine, were highly unstable under all storage conditions. These results provide practical guidance for selecting storage strategies that minimize amino acid losses and help maintain the nutritional quality of bee pollen during long-term storage
Bio-RegNet: A Meta-Homeostatic Bayesian Neural Network Framework Integrating Treg-Inspired Immunoregulation and Autophagic Optimization for Adaptive Community Detection and Stable Intelligence
No full textContemporary neural and generative architectures are deficient in self-preservation mechanisms and sustainable stability. In uncertain or noisy situations, they frequently demonstrate oscillatory learning, overconfidence, and structural deterioration, indicating a lack of biological regulatory principles in artificial systems. We present Bio-RegNet, a meta-homeostatic Bayesian neural network architecture that integrates T-regulatory-cell-inspired immunoregulation with autophagic structural optimization. The model integrates three synergistic subsystems: the Bayesian Effector Network (BEN) for uncertainty-aware inference, the Regulatory Immune Network (RIN) for Lyapunov-based inhibitory control, and the Autophagic Optimization Engine (AOE) for energy-efficient regeneration, thereby establishing a closed energy–entropy loop that attains adaptive equilibrium among cognition, regulation, and metabolism. This triadic feedback achieves meta-homeostasis, transforming learning into a process of ongoing self-stabilization instead of static optimization. Bio-RegNet routinely outperforms state-of-the-art dynamic GNNs across twelve neuronal, molecular, and macro-scale benchmarks, enhancing calibration and energy efficiency by over 20% and expediting recovery from perturbations by 14%. Its domain-invariant equilibrium facilitates seamless transfer between biological and manufactured systems, exemplifying a fundamental notion of bio-inspired, self-sustaining intelligence—connecting generative AI and biomimetic design for sustainable, living computation. Bio-RegNet consistently outperforms the strongest baseline HGNN-ODE, improving ARI from 0.77 to 0.81 and NMI from 0.84 to 0.87, while increasing equilibrium coherence κ from 0.86 to 0.93
Factors Influencing Changing Consumption Patterns in Emerging Urban Markets: A Study of Youth Intentions Toward Luxury and General Secondhand Fashion Products
No full textThis study examines the socio-economic and behavioral factors influencing sustainable consumption through secondhand clothing purchases among young consumers in Hanoi, Vietnam. By addressing the changing consumption patterns, this research contributes to understanding how youth behavior supports the transition toward sustainability in emerging urban markets. This research integrates the Theory of Planned Behavior (TPB) with additional constructs such as perceived economic benefits, environmental concern, perceived risk, shopping experience, and gender differences to provide an integrated socio-economic framework. Data were collected through a structured questionnaire administered to university students and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Results indicate that perceived economic benefits and subjective norms are the strongest predictors of purchase intention across both general and luxury secondhand fashion segments, emphasizing affordability and social acceptance. Environmental concern and attitude also positively influence general secondhand purchase intentions, while perceived behavioral control notably impacts luxury secondhand purchases. Contrary to prior studies, perceived risk was found to be insignificant, and male consumers exhibited a higher engagement rate than females in this context. These findings underscore the complex interplay of economic, social, and environmental dimensions shaping sustainable fashion consumption among youth. This study suggests targeted marketing and policy strategies to promote sustainable consumption and supports the expansion of circular economy practices in emerging urban markets. Limitations related to sample scope and self-reported data warrant further research to generalize the findings and explore additional moderating variables