Search Results (7,183)

Background: Clinical pharmacists play a crucial role in identifying and resolving drug-related problems (DRPs) in hospitals. However, there is limited empirical evidence concerning their effect on service organizations in smaller regional facilities in Saudi Arabia. Methods: A retrospective serial cross-sectional study was conducted using three years of administrative data (2023–2025) from two public hospitals in the Al-Baha region. A total of 1812 different database records were identified after full-scale data cleaning, validation, and deduplication. The statistical framework included chi-square test of independence, pairwise two-proportions Z-test, three multivariate binary logistic regression models, Cramer’s V association analysis, and Ward’s hierarchical cluster modeling. Results: Active intervention rates significantly increased from 48.9% in 2023 to 72.4% in 2024 (z = −8.58, p < 0.001), stabilizing at 69.6% throughout 2025 (p < 0.001 versus the 2023 baseline), because 2023 data came from a different source system than 2024 to 2025 data, this pattern is reported descriptively rather as a programmatic integration. The most frequent clinical actions addressed therapy optimization (28.4%), inappropriate dosage regimens (23.6%), and missing drug orders (18.8%). Most entries recorded were for adult intensive care units (ICUs) (74.1%), and most involved patients aged 65 years or older (61.1% of records). ICU setting was independently associated with the intervention being classified as therapy optimization (adjusted odds ratio [aOR] 2.64, p < 0.001), but it was an inverse predictor of dosage-error tracking (OR 0.30, p < 0.001). Conclusions: This is among the first serial cross-sectional studies to build an evidence-based framework describing clinical pharmacy practices in the Al-Baha region, showing pharmacists’ roles and prioritizing advanced clinical training programs, reinforcing the need for critical analysis of health cluster policies across peripheral Saudi networks. Full article

Background: Necrotizing fasciitis complicating diabetic foot infection requires rapid source control and is associated with substantial morbidity. A topical desiccating agent (TDA) may assist debridement by dehydrating necrotic tissue and potentially disrupting biofilm, but comparative clinical evidence is limited. Methods: We conducted a retrospective, single-center cohort study of 87 patients who underwent emergency surgery for diabetic foot-related necrotizing fasciitis between July 2023 and February 2026. Nineteen patients received a single application of TDA at the index operation in addition to standard care, and 68 received standard care without TDA. Treatment allocation was non-random and occurred in routine clinical practice. The primary outcome was the Laboratory Risk Indicator for Necrotizing Fasciitis (LRINEC) score at Day 7. The primary analysis used linear regression adjusted for baseline LRINEC score and baseline septic status, with heteroskedasticity-consistent type 3 (HC3) standard errors. LRINEC was treated as a laboratory-based surrogate rather than as a validated treatment-response measure. C-reactive protein (CRP), white blood cell count, and glucose at Day 0 and Day 7 were summarized descriptively. Secondary clinical outcomes were 30-day major amputation and all-cause mortality. Results: The mean LRINEC score decreased from 8.58 to 2.95 in the TDA group and from 7.51 to 5.51 in the control group. The mean change was −5.63 in the TDA group and −2.00 in the control group, an unadjusted between-group difference of −3.63 points (95% confidence interval [CI], −4.83 to −2.43; p < 0.001). Baseline sepsis was more frequent in the TDA group than in the control group (89.5% vs. 51.5%). After adjustment for baseline LRINEC score and septic status, TDA exposure was associated with a 3.01-point lower Day-7 LRINEC score (95% CI, −4.00 to −2.02; p < 0.001). At Day 7, 18 of 19 TDA-treated patients (94.7%) and 34 of 68 controls (50.0%) had an LRINEC score below 6; this threshold analysis was exploratory. CRP, white blood cell count, and glucose decreased descriptively in both groups, with numerically larger reductions in the TDA group. No major amputations or deaths occurred within 30 days in either group. No TDA-related adverse event was documented in the retrospective clinical records. Conclusions: Adjunctive TDA use was associated with a lower Day-7 LRINEC score and a greater reduction from baseline. Because treatment was not randomized, baseline severity differed between groups, LRINEC is not a validated longitudinal treatment-response measure, and assessment of clinical outcomes was limited, the findings should be interpreted as exploratory associations and confirmed in prospective multicenter studies. Full article

Selecting a sensing architecture for knee osteoarthritis (OA) screening requires balancing biomechanical information, system complexity, and reproducibility. We audited a public Korean multimodal gait dataset and performed a leakage-resistant internal evaluation. The release contained 180 participants (90 normal, 90 knee OA) measured using a smart insole, instrumented gait mat, and inertial measurement units (IMUs); all 1080 JavaScript Object Notation (JSON) files were checked for structural, value, provenance, and duplication errors. The primary benchmark was a fixed class-balanced L2 logistic regression model using nine gait mat variables, evaluated with subject-level repeated stratified five-fold cross-validation and 10,000 outcome-stratified bootstrap resamples. The audit identified 14 source-path metadata errors and one opposing-label duplicate smart insole payload, but no parsing, schema, range, or cross-partition subject errors. The gait mat model achieved an area under the receiver operating characteristic curve (AUROC) of 0.924 (95% confidence interval [CI], 0.879–0.962), balanced accuracy 0.883 (0.833–0.928), sensitivity 0.856, specificity 0.911, and Brier score 0.102. Adding smart insole and/or IMU features did not improve AUROC. Provider-model reproduction was descriptive because the public Validation partition informed model selection. In this release, the compact gait mat feature set provided the most favorable observed balance of discrimination, interpretability, and sensing complexity; external prospective evaluation is required before clinical use. Full article

Background: Neonates evaluated for infection undergo rapid physiologic transition, making isolated measurements difficult to interpret. Because a positive culture field does not by itself establish clinical sepsis, we explored whether physiologic and laboratory trajectories differed by recorded culture status during the first three postnatal days. Methods: This single-center prospective observational longitudinal study included 65 neonates with linked measurements on days 1, 2, and 3. The exposure was a binary culture field in the source database; specimen source, organism identity, and pathogen-versus-contaminant adjudication were unavailable. Group-specific medians were summarized, and gestational-age-adjusted linear models with patient-clustered robust standard errors tested culture status, time, and culture-status-by-time effects. Results: The cohort included 26 culture-positive and 39 culture-negative neonates. On day 1, the culture-positive group had lower median systolic blood pressure (48.5 vs. 57.0 mmHg), diastolic blood pressure (24.5 vs. 33.0 mmHg), and MAP (31.0 vs. 41.0 mmHg), more negative base excess (−7.65 vs. −3.60 mmol/L), and lower platelet counts (219 vs. 282 × 10⁹/L). Diastolic pressure rose in both groups and converged by day 3; the adjusted culture-status-by-time interaction was significant (p = 0.012). MAP increased over time, but its interaction was not significant (p = 0.244). Culture-positive status was associated with a lower day-1 platelet count (adjusted beta, −73.0 × 10⁹/L; 95% CI, −128.1 to −17.8; p = 0.010), without evidence of a differential platelet trajectory (interaction p = 0.623). CRP was retained as a descriptive outcome only because of lower-limit censoring. Conclusions: Recorded culture status was associated with selected early hemodynamic, metabolic, and hematologic differences in this small exploratory cohort. These associations cannot be considered sepsis-specific and require confirmation after clinical culture adjudication, standardized timestamping and blood-pressure measurement, inclusion of fatal cases, and adjustment for illness severity and treatment exposures. Full article

Electrochemical parameter identification is crucial for evaluating the electrochemical processes in proton exchange membrane water electrolysis (PEMWE). Conventional characterization techniques-including polarization-curve fitting, electrochemical impedance spectroscopy (EIS), cyclic voltammetry (CV), and current interruption (CI)-face significant limitations for rapid diagnostics under high-current dynamic operation, arising from constraints in instrument current rating, measurement time, zero-current control, and noise amplification in numerical differentiation. In this study, we present a simple current step (CS) method to accurately identify key electrochemical parameters and perform overpotential breakdown by using a simplified equivalent circuit model with a current source. To address the numerical instability in derivative calculation caused by sampling noise during voltage transient analysis, a physics-informed neural network (PINN) is introduced to enhance signal smoothness while guaranteeing physical consist ency. Compared with standard characterization, the proposed CS-PINN method demonstrates high accuracy, with an error of less than 2% in overpotential breakdown, less than 5.3% in ohmic resistance, and 2.8% in the Tafel slope (at 5 A/cm²). These results confirm that the CS-PINN method provides a fast, accurate, and equipment-friendly route for rapid electrochemical parameter identification in PEMWE. Full article

The diverse application scenarios envisioned for sixth generation (6G) are characterized by the deep integration of sensing and ubiquitous connectivity, which imposes unprecedentedly stringent security requirements. However, due to the open nature of wireless channels, mobile communications systems always face severe information security threats such as falsification, spoofing, interception, and repudiation. Cryptography-based symmetric and asymmetric encryption techniques remain mainstream solutions for information protection. Symmetric encryption is efficient and secure for legitimate users but suffers from key-distribution difficulties over open wireless channels, whereas asymmetric encryption resolves this problem but faces increasing risks from quantum computing due to its reliance on structured mathematical hardness assumptions. In response to these limitations, physical layer security (PLS) has gained a great deal of research attention as a powerful security component that leverages the features of varying wireless channels. Existing PLS schemes can be broadly classified into two categories. The first one takes advantage of the legitimate link’s opportunistic channel-quality superiority over or different spatial-domain directions from the attacking link, which still faces many practical implementation difficulties. The second category is termed physical-layer key generation (PLKG). It extracts the unique features of the legitimate link’s channel variation, which is often reciprocal, as the source of secret key generation and therefore can naturally implement secure key distribution tasks. This advantage no doubt injects new vigor to symmetric encryption as a stronger protection approach. Following this trend, we in this paper concentrate on the PLKG techniques. Specifically, we present a comprehensive overview on existing PLKG schemes, discussing diverse secret key generation and reconciliation methods. We further investigate the model-driven and deep-learning-based approaches tailored for the scenario with imperfect channel reciprocity between the sender and receiver. After comprehensively reviewing major existing schemes, we further discuss a recently proposed PLKG design based on codeword reconstruction, which makes use of the strong error-correcting capability of the forward-error-correction (FEC) codes to effectively implement secure and consistent secret key generation between the legitimate sender and receiver. Finally, we share our opinions on the unsolved challenges and potential research directions dedicated to PLKG toward meeting the security requirements of 6G. Full article

Financial media risk is jointly driven by multisource content, including news reports, corporate announcements, social media posts, short videos, and livestreams, while content authenticity, propagation velocity, asset relevance, and trading infrastructure conditions can simultaneously influence short-term market fluctuations. To address the limitations of existing methods, including their reliance on either textual information or market sequences, insufficient source verification, and inadequate alignment of asynchronous multimodal signals, FMRP-Net is proposed for artificial intelligence-driven sensing. Event semantics, risk categories, and asset association information are first extracted through a pretrained financial language model and cross-modal consistency analysis. A dual-layer hardware reliability perception module is then employed to integrate sensing evidence from cameras, microphones, terminal inertial signals, server temperature, power consumption, network traffic, and transmission latency. Heterogeneous temporal propagation graphs, cross-frequency alignment, and bidirectional propagation–market coupling are further incorporated to jointly predict market direction, volatility, risk level, and propagation trends. Experimental results demonstrate that FMRP-Net achieved an Accuracy of $0.832$, a Macro-F1 of $0.824$, a ROC-AUC of $0.891$, an MCC of $0.665$, and a PR-AUC of $0.883$ for market direction prediction over future horizons of 5, 15, 30, and 60 min, indicating a balanced performance in terms of Precision and Recall. For volatility prediction, MAE, RMSE, and MAPE values of $0.0178$, $0.0271$, and $12.46\%$ were obtained, respectively, together with an $R^2$ of $0.812$. In the ablation study, the media risk Macro-F1 and source reliability AUC reached $0.842$ and $0.929$, respectively, while the propagation-scale prediction error was reduced to $0.109$ and the average early-warning lead time reached $10.6$ min. These results demonstrate that the integration of multimedia semantics, hardware sensing evidence, and propagation structures can effectively improve the accuracy, stability, and interpretability of financial market prediction and risk early warning. Full article

Cutterhead driving torque is the primary load indicator of earth-pressure-balance shield machines, yet its dependence on strongly coupled multi-source operating parameters limits the reliability of empirical formulations. This study proposes IALA-edRVFL-FIS-Reg, a fuzzy ensemble deep random vector functional link regression model optimized by an improved artificial lemming algorithm (IALA). The base learner maps continuous operating parameters into fuzzy-state features through a Gaussian-membership Sugeno inference layer, propagates the concatenated raw and fuzzified inputs through stacked randomized hidden layers with direct input links, and obtains layer-wise output weights by regularized closed-form least squares before ensembling, thereby combining fuzzy-state representation with deep random feature mapping without gradient back-propagation. Distinct from the standard ALA, IALA introduces three explicitly defined mechanisms: an error-feedback exploration–exploitation transition factor normalized by the initial-population loss, which replaces the fixed energy factor; an adaptive step size coupling sigmoid error-gating with cosine annealing to preserve jumping capability while refining local search; and a stagnation-counter-triggered directional-disturbance jump for escaping local optima. Using 48,646 valid tunnelling records from 301 rings of Beijing Metro Line 22 and 65 raw and mechanism-based engineered features, the model attains R² = 0.9555, RMSE = 382.52 kN·m, MAE = 302.95 kN·m and MAPE = 9.18%, outperforming eleven benchmarks on a ring-disjoint holdout, previously unseen rings of the same section, IALA yields an R² gain of 0.0104 over ALA. Full article

This study develops an analytical model for a mid-deep U-shaped borehole heat exchanger (UBHE) based on the segmented finite line source method, integrating geothermal gradient, five-layer geological stratification, and groundwater seepage within a unified framework. The injection, horizontal, and extraction sections are represented by independent local coordinates and coupled through the position- and time-dependent unit-length heat-transfer rate $q_l\left(s_n,t\right)$. Validation against the benchmark results of Bao et al. yields a mean absolute error of 0.87 °C and a mean relative error of 1.6%. Numerical-independence tests identify a 1 h time step and 100/50 m vertical/horizontal segment lengths as the adopted settings, and the iterative residual reaches 10⁻⁴ °C within eight iterations for the representative case. In a homogeneous, no-seepage limiting case, the model agrees with the classical finite line-source solution with an MAE of 0.012 °C and a maximum relative-error magnitude of 0.41%. The extraction-well fluid-temperature peak occurs at 600–800 m depth, whereas the local heat-transfer direction changes near 1600 m. For the investigated 2500–650–2500 m geometry, the highest cycle-averaged outlet temperature is obtained at an insulation length of 1600 m. Sensitivity analyses further quantify the effects of seepage velocity, equivalent thermal conductivity, geothermal gradient, and the validation insulation-length assumption. Full article

Binary code similarity detection (BCSD) ranks candidates but does not quantify the reliability of an already selected Top-1 match. We study this post-retrieval problem in a known-source, closed-set protocol: the target Top-1 is frozen before same-source cross-compilation views are queried, so auxiliary evidence audits cannot replace it. A frozen 34-variable map feeds a low-capacity logistic model with project-grouped cross-fitting, Platt calibration, and training-side threshold selection. On 413 families from 16 projects, cross-view evidence improved discrimination over target score/margin features. GCC-O0 was a dominant-anchor regime: Full showed no statistically resolved ROC-AUC gain over Primary-anchor, whereas Clang-O0 benefited from complementary non-primary evidence. On 240 project-identity-disjoint families from 55 projects, the design-locked structural branch accepted 75/240 GCC and 99/240 Clang candidates (31.3%/41.3% coverage) with no observed family-level errors. Correspondence mismatch reduced discrimination toward chance. Corrected TF-IDF remained supportive because correction followed label access. The contribution of this paper is a versioned candidate-preserving audit interface with explicit evidence and deployment boundaries, but not a universal retrieval improvement or distribution-free guarantee. Full article

Wearable electronic systems require compact and sustainable power sources together with reliable motion-sensing functions. This study presents a gear-driven plantar energy harvester that integrates biomechanical energy conversion with self-sensing locomotion recognition. The device converts low-frequency vertical foot loading into rotary motion through a wedge–lever transmission and amplifies the rotational speed using a multistage gear train with a total transmission ratio of 12. A one-way bearing enables directional power transmission during loading and prevents reverse rotation during recovery. The generated voltage serves both as the electrical output and as the sensing signal for locomotion recognition. Human-subject experiments were conducted under six locomotion modes: walking at 1, 2 and 3 m/s; running; ascending; and descending. Voltage signals were sampled at 2000 Hz and segmented into overlapping sequences. A CNN–LSTM model was used to extract local waveform features and temporal dependencies from the nonstationary signals. The model achieved an overall recognition accuracy of 98.8%, with most errors occurring between ascending and descending. The results demonstrate that a single plantar device can simultaneously harvest biomechanical energy and provide motion-related information, offering a compact solution for integrated energy harvesting and self-sensing in wearable systems. Full article

Water leakage in water supply pipeline networks leads to water resource loss, drinking water contamination, and ground subsidence. The existing acoustic leak detection techniques are constrained by low signal-to-noise ratios of leakage signals, difficulties in wave velocity estimation, and rapid signal attenuation, often resulting in localization errors of several meters. This study proposes a “wavenumber–frequency spectrum filtering and phase analysis” method for leak source localization, achieving precise positioning of underground water pipeline leaks through surface-mounted sensor array sampling. Based on the numerical simulation results, this research reveals the wavenumber–frequency spectrum characteristics of leakage noise on the ground surface, analyzes the impact of the sampling strategies on the wavenumber extraction, and ultimately constructs a localization model suitable for near-field array sampling. A mean localization error of 0.0374 m was achieved in a controlled experiment under single-layer sandy soil conditions, demonstrating the feasibility and accuracy of the proposed method under these tested conditions and providing a promising basis for precise leak localization in water supply networks that warrants further field validation. Full article

The transition toward urban-scale digital twin and smart city management requires survey-grade 3D basemaps, yet conventional documentation remains time-consuming and prone to inaccuracies. While Terrestrial Laser Scanning (TLS) offers rapid 3D acquisition, capturing complex, GNSS-denied multi-story interiors frequently causes cumulative registration errors and isolated indoor–outdoor data silos. To address this, this study proposes a comprehensive typology-agnostic framework for developing high-fidelity digital twin basemaps. Treating the building as a unified spatial network, the methodology systematically mitigates error propagation through strategic linkage planning, rigid shell-first registration, continuous vertical core anchoring (via stairwells), and adaptive multi-source data fusion. Implemented on an adaptively reused heritage building, the developed basemap achieved an absolute georeferencing accuracy of 30.0 mm (RMSE) against an independent total station control network, alongside a mean relative error of 3.11 mm. Comparative analysis against legacy 2D CAD floor plans revealed simplified geometric representations and categorical dimensional deviations of up to 29.8 cm. Demonstrating its practical utility, the point cloud-centric geometric hub avoids forced geometric idealization, successfully supporting direct immersive visualization, architectural slicing (floor plans, sections, elevations), and multi-LOD algorithmic planar segmentation. This spatially constrained acquisition strategy bypasses legacy limitations, delivering a mathematically verified 3D reality capture essential for smart facility management, heritage conservation, and downstream semantic intelligence. Full article

To address the insufficient flexibility in power systems caused by renewable energy output uncertainty during extreme weather events, a coordinated source–network–load–storage (SNLS) dispatch method that combines a flexibility supply–demand balance approach with a partitioned grid framework is proposed to achieve the effective enhancement of operational resilience. Firstly, a convolution method is employed to aggregate net load forecast error distributions, and expected flexibility demand metrics are introduced to construct a probabilistic model of compound weather impacts, thereby improving flexibility requirement quantification. Secondly, uncertainties arising from extreme meteorological conditions are considered, and an integrated economic dispatch model for the partitioned grid is established based on chance-constrained reserves and regulation capability envelopes, in order to co-optimize generation costs, demand response, and expected flexibility insufficiency penalties. Then, inter-zone power exchange and spatiotemporal unit commitment dynamics are introduced to optimally redistribute spatial generation surpluses and load deficits, so that a system-wide flexibility supply–demand balance is enabled. Finally, simulations are conducted on the real-world Guangdong 500 kV transmission network under typhoon, heatwave, and rainstorm scenarios, and the results demonstrate the effectiveness of the proposed method in eliminating flexibility deficits, reducing total dispatch costs, and capturing distinct weather-adaptive operational patterns. Full article

Quantum key distribution (QKD) employs quantum states to generate shared cryptographic keys. An attacker interacting with the modeled non-orthogonal quantum signals can affect the monitored statistics, and hence they can be detected under the specified protocol assumptions, but this trait does not inherently authenticate the classical channel, and it does not prevent implementation side channels. In this work, we introduce ModPhase-8 (QUEST), a proposed QKD modulation and adaptive-receiver architecture evaluated through analytical modeling and simulation. Instead of using only a few quantum signal types, our system uses eight carefully designed signal variations created by adjusting the phase between two very short light pulses. The eight phase states are organized into four phase bases, each containing two antipodal states that encode one binary raw-key value. The enlarged signal set diversifies the physical representation of the key bit and changes the state-discrimination problem faced by an eavesdropper, but it does not increase the raw-key payload beyond one bit per successfully sifted signal. On the receiving side, the system adaptively switches between two measurement techniques based on the prevailing channel conditions. This adaptive detection mechanism enhances reliability and helps maintain low error rates even when the communication channel is affected by noise. We provide an analytical security assessment under the stated collective-attack, source, channel, receiver, and trusted-device assumptions, supplemented by attack-specific analyses of intercept–resend, beam-splitting, source-side multi-photon leakage, and selected implementation-related vulnerabilities. Simulation studies were conducted to examine the physical-layer and post-processing behavior of the proposed protocol under explicitly stated channel, receiver, detector, and finite-sample values. Under the adopted simulation model, ModPhase-8 maintains low error rates in the low- and moderate-noise operating regimes and exhibits favorable receiver-level robustness across the investigated channel conditions. The reported rate values are model-based performance estimates rather than rigorously certified secret-key lower bounds. In particular, Qiskit simulation does not establish a composable security proof or an optimal bound on Eve’s information for the exact eight-state time-bin ensemble. A protocol-specific numerical security analysis incorporating the homodyne–heterodyne measurement operators, post-selection, reconciliation efficiency, finite-size effects, and Eve’s Holevo information remains necessary before definitive rate comparisons can be made. ModPhase-8 should therefore be interpreted as a practically motivated receiver and modulation framework whose security-rate performance remains subject to further protocol-specific analysis. Full article