Search Results (19,646)

This paper investigates whether transcendent worldviews—those oriented toward a beyond—decline while immanent worldviews—those oriented toward this world—increase. We draw on an inventory spanning positions from theism and deism to naturalism and existentialism, administered seven times in West Germany (1982–2023) and six times in East Germany (1992–2023). In West Germany, existentialist worldviews ranked first, followed by naturalist, theist, and deist ones. While existentialist worldviews remained stable, transcendent worldviews declined and immanent ones grew, producing a substantial and growing advantage for immanent over transcendent orientations. In East Germany, existentialist and naturalist worldviews were markedly dominant, well above transcendent ones throughout the observation period. Both remained stable, while transcendent worldviews increased only minimally, leaving the gap largely intact. To test whether these period effects persist under controls, we employ OLS regressions with robust standard errors, accounting for cohort, age, church attendance and belonging, community size, parenthood, work engagement, education, and gender. In West Germany, transcendent worldviews declined and immanent ones increased non-monotonically. In East Germany, the pattern reversed: transcendent worldviews increased and immanent ones decreased non-monotonically. While mean levels do not differ significantly between the two regions, the direction and structure of effects do. The discussion addresses why transcendent worldviews are better explained than immanent ones, and what accounts for the divergent trajectories between East and West Germany. Full article

Introduction: Compassion is defined as the emotional response that arises when an individual perceives another’s suffering and is motivated to alleviate it. Purpose: To explore levels of self-compassion among nurses working in pediatric hospitals and examine their associations with nurses’ characteristics. Materials and Methods: This cross-sectional study included a convenience sample of 208 nurses from a public pediatric hospital. Data were collected through interviews using the Neff Self-Compassion Scale (SCS) which includes the following subscales: Self-Kindness, Common Humanity, Mindfulness, Self-Judgment, Isolation, and Over-Identification. The Greek-validated version of the instrument was used with acceptable internal consistency in the present sample (Cronbach’s alpha = 0.849). Data analysis included descriptive statistics and inferential tests (non-parametric comparisons and multiple linear regression), with statistical significance defined as p < 0.05. Results: The mean total Self-Compassion score was 83.24 ± 12.6 (range: 26–130). Regarding family-related factors, total Self-Compassion (p = 0.029), Common Humanity (p = 0.033), and Over-Identification (p = 0.041) were associated with the number of children. In relation to age, Self-Kindness (p = 0.033), Isolation (p = 0.005), and Over-Identification (p = 0.005) showed significant associations. Professional factors were also relevant, as Isolation was associated with total years of nursing experience (p = 0.032) and choice of nursing as a profession (p = 0.004), while Over-Identification was associated with years of experience in pediatric settings (p = 0.004) and choice of nursing as a profession (p = 0.049). Additionally, marital status was associated with Over-Identification (p = 0.045). Conclusions: Demographic and professional characteristics appear to influence the expression of Self-compassion. Healthcare organizations should implement targeted training programs to individualize professional development. Future research should explore work-related and personal factors influencing self-compassion to improve care quality and outcomes. Full article

Wheat is a major staple crop, and storage mold growth poses a severe threat to grain safety and quality stability. Natural mold development in stored wheat exhibits subtle, localized, and highly heterogeneous characteristics. Existing unimodal methods and global fusion approaches generally suffer from insufficient local feature sensitivity, hindering fine-grained mold severity grading. To address this limitation, we propose a Mask-Guided Fine-Grained Fusion Network, a weakly supervised framework based on local RGB–HSI fusion. This framework employs a dynamic parallel A/B experimental design to construct time-matched proxy labels via weakly supervised learning. A standardized preprocessing pipeline including single-kernel extraction, foreground segmentation, and cross-modal registration is established to resolve RGB–HSI spatial misalignment, ensuring physical-level spatial consistency of multimodal features. The model incorporates a Foreground-Aware Spectral Recalibration (FASR) module to suppress background noise, a Mask-Guided Dilated Cross-modal Local Attention (MDCLA) mechanism to establish fine-grained local mappings between RGB visual phenotypes and hyperspectral responses, and a sample-level adaptive fusion strategy to dynamically weight features by modal reliability, enhancing representation of complex samples across all mold stages. Experiments show that the Mask-Guided Fine-Grained Fusion Network achieves 0.9689 classification accuracy, 0.9698 Macro-F1 score, and 0.0593 Mean Absolute Error (MAE), significantly outperforming state-of-the-art unimodal deep models and global attention fusion baselines. This work provides a proof-of-principle framework for fine-grained non-destructive mold risk assessment in stored wheat. Full article

Background: Unhealthy alcohol use may lead to arrhythmia and cardiomyopathy, but its impact on sudden death is not well understood. Objective: To investigate the association of unhealthy alcohol use with sudden death. Methods: We conducted a case-control study in Wake County, a large (~1 million inhabitants), diverse county in North Carolina. We screened and adjudicated victims of sudden, unexpected, out-of-hospital deaths in adults aged 18–64 years reported by emergency medical services between 2013 and 2015. We randomly selected sex- and age-matched control patients from a university health system from the same county and time period. Characteristics of sudden death victims and controls were ascertained via standardized chart reviews. Unhealthy alcohol use was identified via chart review and was defined as any evidence of excessive alcohol use, such as it being stated in the social history or medical history, alcohol abuse being listed as a possible contributor to death, or alcohol-related diagnoses. We used logistic regression to estimate odds ratios (ORs) for the association of unhealthy alcohol use and sudden death, adjusting for age, sex, race, and other psychiatric diagnoses, including depression, anxiety, schizophrenia, bipolar disorder, and substance use disorders other than tobacco and alcohol. We also calculated the E-value to estimate the impact of any unmeasured confounders. Results: We identified 399 sudden death victims, of whom 374 (94%) had alcohol use data available. Among these 374 included victims, 256 (68%) were male, and 239 (62%) were White, with a median age at death of 55 years (IQR 48, 60). The demographic characteristics of the 1114 matched controls were similar to those of sudden death victims. Unhealthy alcohol use was present in 115 (31%) sudden death victims and 27 (2%) controls. In analyses adjusted for demographics only, unhealthy alcohol use was associated with a higher incidence of sudden death, with an OR of 17.5 (95% CI 11.4, 27.8). When further adjusted for other psychiatric diagnoses, the OR was 11.2 (95% CI 7.1, 18.0). The calculated E-value was 21.8, meaning an unmeasured confounder would need to be associated with both unhealthy alcohol use and sudden death by 21.8-fold to explain away the observed OR. Conclusions: Unhealthy alcohol use was strongly associated with higher sudden death risk in working-age adults. Our calculated E-value indicates it is unlikely that any unmeasured confounders alone would account for the observed association. Our findings suggest that interventions to reduce unhealthy alcohol use may be an effective strategy to prevent sudden death in working-age adults. Full article

Background/Objectives: Discharging patients after hospitalization for an acute exacerbation of chronic obstructive pulmonary disease (COPD) is a critical transition in care associated with a high risk of early readmission. This survey aimed to describe physician-reported discharge practices following COPD exacerbations, identify perceived gaps and organizational barriers, explore attitudes toward structured COPD discharge summaries, and use a SWOT analysis as an interpretative framework. Methods: In this cross-sectional observational survey, 100 physicians involved in COPD care were recruited from the official mailing list of the Respiratory Society of Serbia, which represents approximately 71% of the Society’s members. The survey assessed discharge procedures, multidisciplinary practices, patient education, comorbidity management, perceived causes of readmission, and barriers to structured discharge summaries. Data were analyzed descriptively and complemented with a structured SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis. Results: Most respondents worked in tertiary care settings and were involved in managing patients hospitalized for COPD exacerbations. Although 24% of physicians routinely used structured discharge summaries, 45% reported never using them. The most frequently perceived contributors to 30-day readmissions were active smoking (90%), poor treatment adherence (81%), comorbidities (77%), and incorrect inhaler technique (72%). Major barriers to implementing structured discharge summaries included the lack of standardized templates, time constraints, poor coordination across healthcare levels, and technical limitations. Willingness to implement structured discharge tools was high (mean score 8.86/10). SWOT analysis identified strong professional support for discharge standardization alongside organizational and system-level barriers to implementation. Conclusions: This exploratory survey identified important gaps between recommended and routine COPD discharge practices and highlighted organizational barriers to implementation. The findings may inform future evaluation and development of structured discharge tools in this healthcare setting. Full article

IoT devices operate as integrated systems spanning hardware, firmware/software layers, and communication layers. In operational settings, many faults and performance degradations are emergent: they arise from cross-layer interactions, workload changes, and telemetry artifacts, rather than a single physics-of-failure mechanism. These realities make traditional supervised fault classification difficult because labeled fault data are rarely available during deployment, and the fault surface is unknown and a priori. This paper presents a practitioner-oriented, label-free fault detection and diagnosis (FDD) pattern based on Dynamic Time Warping (DTW) for rapid implementation in production IoT telemetry. The method represents a device as a sequence of overlapping episodes and organizes telemetry into interpretable layers (hardware sensors, communication health proxies, and software/firmware-derived KPIs). A reference library of regular episodes is built from an assumed-healthy training window; new episodes are scored using constrained DTW distances against this library, while retaining per-layer and per-channel contributions for attribution. We show that production performance depends strongly on operational parameterization, including episode length, DTW constraints, robust threshold learning, and temporal validation. Within a verified-healthy evaluation window, the tuned configuration achieves an AUROC of 0.97 for the temporally structured faults DTW is suited to (bias, drift, and interaction faults, with spikes detected at an AUROC of 0.93), detecting 100% of injected faults, with a mean delay under 25 min. We further show that constant-value (stuck-at) and missing-data (dropout) faults fall outside DTW’s shape-matching scope (AUROC about 0.66) and are better served by complementary variance- and missingness-based detectors, a consequence of DTW’s shape-matching scope rather than a parameter choice. This work contributes a system-level methodological framework for deploying DTW as an IoT fault-detection-and-diagnosis capability: an episode-and-layer architecture aligned with hardware, communication, and software/firmware ownership; a label-free reference library requiring only assumed-healthy data; per-layer and per-channel attribution for cross-domain triage; and a reproducible operational tuning procedure. Together, these deliver a fast-to-deploy, scalable, and accurate first-line detector for label-scarce IoT systems. Full article

Bipolar junction transistor (BJT)-based 2D temperature-sensor arrays are factory-calibrated to ±0.1 °C, but post-deployment thermal and mechanical stresses drift their per-sensor gain–offset parameters by an order of magnitude, and in-lab recalibration is impractical. We present RASC (Region-Aware Self-Calibration), a five-stage algorithm that decomposes the global ill-posed problem into local cluster-level problems, runs robust alternating estimation (trimmed-mean field reconstruction + Huber iteratively reweighted least squares (IRLS)) inside each cluster, and reconciles overlapping estimates by linear consensus on the cluster-overlap graph with provable exponential convergence. On 7632 frames from a deployed 16 × 16 array exhibiting ≈5× factory-spec non-uniformity, RASC cuts the locally non-smooth fixed-pattern residual by 71 ± 5% (10-fold cross-validation (CV)), reducing this residual to a level comparable to the ±0.1 °C factory specification (as assessed by local-smoothness residual metrics, not independent absolute-temperature validation) while perturbing the calibrated field by only 0.041 °C RMSE; reduction concentrates at the edges (78% vs. 55% interior). In simulations on 8 × 8 to 32 × 32 arrays, RASC matches an oracle centralised extended Kalman filter (EKF) within 0.10 °C with ≈4× lower bandwidth. The real-data evaluation is a single-deployment proof of concept on one array and one host PCB; broader, longitudinal validation remains future work. Full article

Gaussian networks are degree-four symmetric interconnection networks defined over residue classes of Gaussian integers. Earlier work showed that, when the generator $\alpha =a+bi$ satisfies $gcd(a,b)=1$, the real and imaginary dimensions directly form two edge-disjoint Hamiltonian cycles. A later construction extended the result to the non-coprime case $gcd(a,b)=d>1$, but its proof relied on long node-sequence tables and separate odd/even cases for d. This paper presents a unified closed-form construction that covers both $d=1$ and $d>1$, and both odd and even d, without separate case tables. In the rectangular representation with d rows and $r=(a^2+b^2)/d$ columns, the construction uses a constant-time local switch rule, meaning constant time per individual switch, for each $q=1,2,\dots ,d-1$ at column $a_q=q-1$. Each switch removes two horizontal edges and inserts two vertical edges. The switched horizontal structure forms the first Hamiltonian cycle, while its edge-complement in the Gaussian network forms the second Hamiltonian cycle. Thus, the full edge set is partitioned into two edge-disjoint Hamiltonian cycles. The construction requires $O\left(d\right)$ switch-generation time and $O\left(N\right)$ time to list the two cycles, where $N=a^2+b^2$. Exhaustive validation for all $1\le a\le b\le 100$, excluding only the degenerate $N=2$ network, and large-scale validation up to $N=3,250,000$ confirm implementation correctness and demonstrate practical scalability. Full article

The work described in this paper continues basic research aimed at improving clustering algorithms such as K-Means and Random Swap through careful seeding and genetic concepts. This paper, in particular, develops a variation in the Hartigan–Wong (HW) algorithm, which, although computationally more expensive, is recognized as a better solution than K-Means. The new algorithm is named Recombinator Hartigan–Wong (Rec-HW). Rec-HW first builds a population of candidate solutions, each tailored to the minimization of the Sum-of-Squared-Errors (SSE) objective function cost. Candidate solutions are then systematically recombined by exploiting the standard behaviour of HW, which performs crossover and mutation operations. Recombinations, as experimentally confirmed, reduce the number of iterations required by basic HW and tend to favour the emergence of a solution close to the optimal one. The paper describes the design of Rec-HW, whose current implementation depends on parallel Java. Good clustering performance is demonstrated by using both benchmark and real-world datasets. Full article

Introduction: Vaccination against COVID-19 has been crucial in controlling the pandemic. While side effects are typically mild, rare neurological complications have been reported. This is a case series of ten patients who reported of persistent fasciculations after COVID-19 vaccination. Methods: We describe the clinical presentation and diagnostic work-up of ten patients with new-onset fasciculations in temporal proximity to COVID-19 vaccination. Patients with prior SARS-CoV-2 infection or known alternative causes of fasciculations were excluded. Routine clinical data, including neurological examination, laboratory results, and electrophysiology (electromyography and nerve conduction studies), were analyzed. Results: Ten patients (5 male, 5 female; mean age 42.4 years) reported fasciculations beginning within 6 h to 13 days post-vaccination and persisting for 2–12 months at the time of presentation. Fasciculations were accompanied by additional symptoms such as paresthesia and fatigue. Laboratory results were mostly unremarkable; two patients had positive myositis antibodies without clinical correlates. Electrophysiology was unremarkable in six patients, while fasciculation potentials were detected in four patients. Nine were diagnosed with probable benign fasciculation syndrome (BFS), and one met diagnostic criteria for amyotrophic lateral sclerosis (ALS). Discussion: In this small, retrospective case series, most cases of post-vaccination fasciculations were benign and compatible with BFS. Whether BFS onset was causally linked to vaccination or due to a nocebo effect remains unclear. One patient was diagnosed with ALS, though a causal link remains speculative given the study’s limitations and rarity of similar reports. Larger, prospective studies are needed to validate these observations and explore underlying pathophysiological mechanisms. Full article

The rapid growth in the use of carbon fiber-reinforced polymers (CFRPs) and fused-deposition-modeled (FDM) polylactic acid (PLA) has generated substantial non-biodegradable and thermoplastic waste streams, creating urgent needs for scalable recycling and valorization strategies. This study develops and evaluates an integrated route that chemically recovers carbon fibers (CFs) from CFRP waste and converts them into high-performance reinforcements for recycled PLA matrices. CFRP fragments were pre-swollen in acetic acid (120 °C, 1 h), then depolymerized by means of oxidation with 1 M KMnO₄ (100 °C, 2 h), washed, dried (100 °C, 24 h), and size-reduced by means of cryogenic milling. Recycled CFs (treated) and untreated CFRP fragments were blended with 3D-printing PLA waste at 10, 20 and 30 wt.% via melt mixing (175 °C, 5 min, 70 rpm) and molded into ASTM D638 dog-bone specimens. Materials were characterized via XRD, FTIR, Raman, SEM and mechanical testing. XRD and Raman confirmed retention of the graphitic backbone after treatment; FTIR and Raman revealed oxygen-containing surface functionalization consistent with oxidation, while SEM showed effective removal of epoxy and improved fiber surface cleanliness. Compared with neat PLA (tensile strength 45.4 MPa; modulus 2.6 GPa; elongation 6.3%), composites reinforced with chemically recycled CFs exhibited marked mechanical enhancement: at 30 wt.% treated CF, the tensile strength increased to 102.6 MPa (+126%), elastic modulus to 11.7 GPa (+350%), and toughness to 250.3 MPa, while ductility decreased to 2.9%. Equivalent composites with untreated CFRP exhibited smaller gains (30 wt.%: tensile 87.3 MPa; modulus 10.3 GPa), highlighting the benefit of epoxy removal and surface activation for fiber–matrix adhesion. The proposed chemical recycling pathway is operationally simple and cost-effective, produces reusable CFs with preserved graphitic structure and enhanced surface chemistry, and enables the fabrication of high-performance, waste-derived PLA composites suitable for structural and engineering applications. This work demonstrates a viable waste-to-value approach that advances circularity for both CFRP and 3D-printing polymer waste streams. Full article

Low-frequency sound insulation remains a major challenge for conventional passive materials, as improved attenuation is usually achieved at the expense of increased thickness and mass. In this work, a smooth fixed third-order ring-curve fractal-maze acoustic metamaterial is proposed for compact low-frequency sound insulation, and a physics-guided long short-term memory–physics prediction surrogate network (LSTM–PPS-Net) tandem framework is developed for its full-spectrum inverse design. Different from conventional Hilbert-type, right-angled, or sharply folded labyrinthine structures, the proposed topology uses recursively arranged curved channels to extend the effective acoustic propagation path and enhance phase accumulation within a limited space. Based on this mechanism, four physically meaningful parameters, namely slit width d, characteristic radius R₃, wall thickness t_w, and inter-column spacing l_E, are selected to construct a low-dimensional design space. A COMSOL–MATLAB automated finite-element method (FEM) workflow is established to generate 1000 valid transmission-loss (TL) spectra over 100–1700 Hz with a 5 Hz interval. For forward prediction, PPS-Net is developed by integrating geometry encoding, frequency-conditioned spectral decoding, and peak-weighted learning. The proposed PPS-Net achieves the best prediction accuracy among the tested models, with a mean absolute error (MAE) of 0.75 dB, a root mean square error (RMSE) of 1.88 dB, and a coefficient of determination (R²) of 0.96, outperforming multi-layer perceptron (MLP), convolutional neural network (CNN) and Transformer models under the same dataset and training protocol. For inverse design, the LSTM encoder extracts frequency-ordered spectral features from the target TL curve, while the frozen PPS-Net decoder provides differentiable acoustic-response feedback, thereby addressing the non-unique mapping from acoustic response to structural parameters. Furthermore, a compactness-oriented optimization strategy is introduced to balance spectral consistency, peak alignment, bandwidth preservation, and occupied-area reduction. In two representative cases, the optimized designs reduce the occupied area by approximately 21% in both representative cases, while maintaining the target attenuation characteristics after FEM verification. These results demonstrate that the proposed framework provides an efficient and physically interpretable route for the full-spectrum inverse design and compact optimization of low-frequency acoustic metamaterials. Full article

Introduction: Pedestrians lying on the road—collapsed through medical emergency, intoxication, or displacement following a prior collision—represent a disproportionately lethal and underaddressed category in road traffic safety. Forensic database analyses derived from Japan’s national police records document a fatality rate of 33.0% for collisions involving pedestrians lying on the road, more than double the rate for upright pedestrian collisions. Standard Advanced Driver-Assistance Systems (ADAS) yield a True Positive Rate (TPR) of only 21.4% for detecting pedestrians lying on the road under night conditions—a classification gap of 73.3 percentage points. Methods: In simulation trials, we evaluated the Advanced Falling Object Detection System (AFODS—where “falling object” denotes the low-profile human form at road level, distinguishing the prone pedestrian from the upright postures addressed by conventional ADAS) on a composite dataset of 3200 annotated fall events and 12,000 negative samples (training/validation), with 320 independent controlled simulation trials used for performance evaluation, spanning real-world, forensic-reconstruction, and Total Human Body Model for Safety (THUMS)-validated synthetic scenarios. No physical prototype has been evaluated; all performance data are derived from simulation, and 37.5% of positive samples are synthetically generated. These simulation conditions represent a first feasibility demonstration pending real-world hardware validation. This paper introduces three original contributions absent from prior work: a three-stage quantitative injury-risk model, a formal ISO 26262 Hazard Analysis and Risk Assessment (HARA), and a medicolegal SHAP interpretability framework. The injury-risk model translated detection latency via impact velocity to Head Injury Criterion (HIC) and estimated fatal injury probability (AIS ≥ 5); these model outputs should be interpreted as exploratory estimates pending ATD validation. Reporting follows principles consistent with the TRIPOD statement. Results: Under clear daytime conditions, AFODS demonstrated a TPR of 98.2% (95% CI: 97.4–98.8%) in simulation, decreasing to 95.6% under night dry-road conditions and 89.4% under night rain. The system achieved an AUC of 0.981 and a mean end-to-end latency of 46.5 ms, representing a 76.8 percentage-point improvement in simulation over the monocular RGB baseline (p < 0.001). The injury-risk model projects a reduction in estimated fatal head injury probability from 66.2% (Monte Carlo mean) (no detection, 50 km/h full-speed impact) to 0.7% under AFODS worst-case night/rain conditions, and to ≈0% under clear daytime simulation conditions. Conclusions: A 73.3 percentage-point classification gap places pedestrians lying on the road outside the effective detection envelope of current ADAS, compounded by the systematic exclusion of non-upright postures from regulatory test protocols and benchmark datasets. AFODS supports proof-of-concept feasibility under simulation conditions. Three translational steps are required: prototype validation on real-world hardware using instrumented Anthropomorphic Test Devices (ATDs); prone-posture biomechanical injury modelling using HIC and BrIC criteria; and regulatory extension of pedestrian AEB test standards to non-upright scenarios. Full article

UV-C light is a promising non-thermal technology for microbial inactivation on food surfaces; however, its efficacy may be compromised by the spatial structure of microbial colonies. The present work investigated the influence of Listeria monocytogenes colony size on UV-C treatment effectiveness using agar-based model systems. Petri dishes were inoculated at defined concentrations and incubated to generate colonies of varying sizes, which were subsequently exposed to a UV-C dose of 0.12 J/cm². Colony growth was monitored over 48 h using an image-based analysis workflow implemented in MATLAB, combined with individual colony tracking. A neural network model was developed to predict the probability of growth cessation based on colony diameter, and quantitative PCR combined with bead-beating was used to estimate cell counts per colony. UV-C treatment applied immediately after inoculation achieved high inactivation efficacy, consistent with minimal cell aggregation. As colony size increased, treatment effectiveness declined markedly. Bootstrap analysis of the neural-network predictions identified a minimum mean growth cessation probability at a colony diameter of approximately 0.862 mm. At this diameter, the predicted probability was 56.8%, with a pointwise 95% bootstrap interval of 50.3–62.8%, corresponding to approximately ${10}^{6.14}$ (viable + non-viable) cells per colony. These findings demonstrate that colony spatial structure substantially limits UV-C efficacy and underscore the importance of early-stage intervention in food surface UV-C decontamination protocols. Full article

Molecular dynamics simulations were conducted to investigate the thermal stability and structural evolution of Li-Mg alloys subjected to thermal cycling between 100 K and 400 K. Alloy compositions containing 0, 5, 10, and 20 at.% Mg were analyzed using a modified embedded-atom method interatomic potential. Structural characterization was performed through radial distribution functions, Polyhedral Template Matching (PTM), and mean squared displacement (MSD) calculations. The results showed that heating promoted the temporary formation of HCP, FCC, and other local atomic environments, indicating partial loss of crystalline ordering even below the melting temperature of Li. Nevertheless, the BCC structure remained dominant for all compositions, and the structural changes were reversible during cooling. Increasing Mg concentration improved the thermal stability of the alloys by reducing the formation of non-BCC atomic structures and decreasing atomic mobility during thermal cycling. In particular, the 20 at.% Mg alloy preserved more than 90% of the BCC population throughout the simulations. In addition, the energy variations between cycles remained very small, indicating stable thermodynamic behavior during heating and cooling. These findings provide atomistic insight into the temperature-dependent behavior of Li-Mg alloys that may be useful in works related to lithium-metal battery applications. Full article