Search Results (1,280)

In this paper, we propose a multilevel stochastic model for the energy consumption of public proof-of-work blockchains. The main novelty is the proposal of a closed form for the expected energy consumption in one proof of work mining round. In the case of homogeneous per-hash efficiency, this proposition shows that the expected spending is $e_0/p$ depending only on the protocol difficulty and not on the distribution of the hash power among the miners. The proposal connects three levels of analysis: a local model of mining at the node level, a semi-global model of competitive block discovery and propagation, and a global stochastic model of workload, computational capacity, network connectivity and power consumption. This leads to the above closed form energy result. The mining process is approximated locally by exponential waiting times of Bernoulli hash trials. This extends to the semi-global model where the competition among miners and the delay in the propagation lead to the wasted computation. The global layer is modeled as a set of stochastic differential equations which models the interaction between workload dynamics, capacity constraints and communication overheads. The core analysis does not need Bayesian or Markov decision components but these are recommended for modeling estimation and adaptive control. We start with preliminary simulations on the VIBES platform and find qualitative properties of the full model: the total energy cost scales roughly linearly with the size of the network, the average energy per node decreases with increasing network size, the propagation latency is the primary source of wasted computation due to stale blocks and nodes tend to operate in a capacity-depleted regime with the workload-induced degradation being substantially higher than the recovery rate. The results give a structural analysis of how the design of the protocol and the network conditions affect the energy consumption and emphasize the importance of quantitatively calibrating with empirical data from Bitcoin. Full article

Accurate recognition of partially occluded faces remains challenging in unconstrained and real-time environments, especially under masks, partial occlusions, pose variation, and illumination changes. This study presents a controlled comparison of three hybrid multi-face recognition pipelines for robust occluded face recognition. For fair evaluation, all pipelines use the same SCRFD face detector, preprocessing protocol, Linear SVM classifier, and 60% unknown rejection threshold, while varying only the feature extractor: ResNet29, ConvNeXt, and ResNet100 with ArcFace embeddings. To reduce data leakage, models are trained only on normal, non-occluded faces and tested on unseen partially occluded faces. Evaluation is performed on a custom dataset and the public Real-World Occluded Faces dataset, alongside three existing paper methods with publicly available code tested under the same experimental protocol. The SCRFD with ArcFace ResNet100 and Linear SVM pipeline achieved the best results compared to existing papers and our other pipelines, reaching 97.475% real-time accuracy for five faces and over 99% confusion-matrix-based accuracy on the custom dataset. On the ROF dataset, it also achieved closed-set accuracies of 98.66% for sunglasses and 97.92% for masks, with threshold-based accuracies of 96.35% for the sunglass test and 95.14% for the mask test. Furthermore, it obtained EER values below 0.007 and AUC values above 99%. In real-time testing, it achieved 29.25 FPS with 34.18 ms/frame latency on a GPU-enabled laptop and approximately 5 FPS with 273.4 ms/frame latency on a Raspberry Pi 4. Full article

Selective androgen receptor modulators (SARMs) are commonly described as tissue-selective anabolic agents, yet the extent to which this selectivity is reflected at the level of receptor-binding energetics remains uncertain. This study evaluated the receptor interaction profiles of the steroidal SARM YK11 and the nonsteroidal SARM ostarine across five steroid hormone nuclear receptors. Flexible molecular docking was performed with AutoDock 4.2 against the androgen (AR), estrogen (ER), progesterone (PR), glucocorticoid (GR), and mineralocorticoid (MR) receptors, using testosterone, estradiol, progesterone, cortisol, and aldosterone as endogenous reference ligands. Binding free energy, docking-derived inhibition constants, intermolecular interaction energies, conformational sampling, and two-dimensional interaction maps were analyzed. Ostarine showed favorable binding across all receptor systems, with binding energies ranging from −10.42 to −12.05 kcal/mol and no pronounced energetic preference for the androgen receptor. YK11 displayed stronger predicted binding, particularly toward the glucocorticoid, progesterone, and androgen receptors, with a docking energy trend of GR > PR > AR > MR > ER. Interaction analysis revealed conserved polar anchoring residues across receptor pockets, together with scaffold-specific contacts that may explain cross-receptor compatibility. These findings indicate that, within the AutoDock 4.2 flexible docking framework applied in this study, docking-derived binding energies primarily describe thermodynamic compatibility with nuclear receptor ligand-binding domains and should not be interpreted as direct predictors of functional SARM tissue selectivity. The observed discordance between predicted receptor affinity and the established tissue-selective pharmacology of ostarine highlights the need for caution when using single-method docking workflows to infer selectivity among closely related steroid hormone receptors. The novelty of this study lies in demonstrating, using a defined AutoDock 4.2-based comparative protocol, that receptor-binding energetics alone do not recapitulate the functional tissue-selective behavior attributed to SARMs. Full article

Real-time roadway surveillance now leans hard on automated detection. How a model trained in one geographic context actually behaves on another, though, is still underexplored for Southeast Asian deployments. We answer that question for Thai roadway closed-circuit television with a cross-domain validation framework. A YOLOv11n (Ultralytics v8.2.0; Ultralytics, Los Angeles, CA, USA) detector trained with focal loss feeds a confidence-aggregation step that turns per-detection scores into a per-frame accident score, and we put four aggregation operators head-to-head. Reliability comes from DeLong variance estimation paired with non-parametric bootstrap on 1245 Thai frames that carry 23 positive accident events. Under maximum-class aggregation the proposed configuration reaches a frame-level AUROC of 0.959 ± 0.020 across three random seeds. Under top-K aggregation it reaches 0.965 ± 0.018. Per-seed DeLong 95 percent intervals exclude chance performance throughout. We also evaluate three baseline configurations: YOLOv5su comes in at 0.738, YOLOv8n at 0.868, and a Chiang Mai-tuned YOLOv11n variant at 0.918. The architectural progression seen on standard benchmarks therefore carries cleanly into the cross-domain setting. The same Chiang Mai-tuned variant reached an in-domain mAP50 of 0.952 yet only 0.918 cross-region AUROC on a separate Thai region, which is a quiet but clear signal that geographic proximity within a country does not on its own remove distributional shift. Bounding-box localisation appears as a secondary diagnostic because the operational target here is frame-level alerting rather than pixel-precise annotation. Edge deployment optimisation falls outside the present scope. What the work leaves behind is a reproducible baseline and a statistical protocol that follow-up Southeast Asian roadway-safety research can build on. Full article

Industrial systems increasingly rely on MQTT-based message streaming to enable automated, data-driven production processes at the network edge. While semantic models such as the SSN/SOSA ontology enable machine-interpretable descriptions of observations and actuations, an explicit model of message transport is rarely considered. Consequently, MQTT-based communication remains opaque, particularly regarding information processing, hindering the semantic analysis of application-specific topic structures and the behavior of transport protocols. To close this gap, this work introduces the revised MQTT4SSN ontology as a key contribution, extending existing semantic models with protocol-aware representations of MQTT entities, control packets, and transport-level interactions. MQTT4SSN enables end-to-end semantic traceability, from sensor observations and actuator controls to the underlying message transmission within distributed systems. Building on this contribution, the MQTT2RDF integration framework incorporates MQTT4SSN as its core to capture live MQTT traffic and represent both payload meaning and transport-level provenance within an RDF knowledge graph. This work presents a novel approach for representing edge computing and information processing over MQTT, addressing two key challenges. First, the framework supports semantic interpretation of topic hierarchies and provides configurable mappings between MQTT topics, payload structures, and observation or actuation semantics. This approach facilitates the setup of edge computing systems and enables context-aware subscription management and structured data formatting, thereby improving interoperability between heterogeneous deployments. Second, transport-level provenance analytics provide a semantic basis for query-based detection, classification support, and diagnostic analysis of malformed or incomplete MQTT communication. The approach provides explainable, traceable information processing through transport provenance, which is essential for safety-critical industrial environments. The contributions are validated through an industrial use case from a production environment, demonstrating its applicability for system monitoring, troubleshooting, and semantic analytics of MQTT-based infrastructures. Full article

Arthrogenic muscle inhibition (AMI), neurophysiological suppression of voluntary quadriceps activation triggered by joint effusion and inflammation, is consistently initiated within hours of any form of knee surgery. If not actively counteracted during the first two postoperative weeks, AMI may drive a cascade of neuromuscular, morphological, and biomechanical deficits that can persist for years, substantially increasing the risk of post-traumatic osteoarthritis, reinjury, and long-term functional disability. Emerging evidence indicates that preoperative patient-related factors, including baseline quadriceps strength, age, body mass index, and physical fitness, further modulate the rehabilitation response and should be considered in planning early postoperative protocols. This narrative review, which was not designed as a systematic review or meta-analysis and therefore does not include formal quality assessment or pooled statistical analysis, evaluates evidence for seven early-phase (0–2 weeks postoperative) knee muscle activation interventions: neuromuscular electrical stimulation (NMES), isometric quadriceps exercise, blood flow restriction (BFR) training, electromyographic (EMG) biofeedback, open and closed kinetic chain (OKC/CKC) exercise, cryotherapy, and continuous passive motion (CPM). Findings are synthesized against six clinically relevant dimensions, safety in the 0–2 week window, home-based usability, capacity to overcome AMI, requirement for volitional effort, objective monitoring capability, and progressive resistance, to characterize a consistent pattern: no single existing modality simultaneously meets all combined requirements for home deployment, volitional engagement, objective monitoring, and progressive resistance from postoperative day one. This collective unmet need provides direction for future device development and clinical research. Full article

Objectives: This study evaluates the protocol-level image quality and quantitative diffusion metrics of a clinically implemented deep-learning–reconstructed readout-segmented echo-planar imaging protocol with water-excitation spectral fat saturation (DL-rs-EPI with WEXfs) compared with conventional rs-EPI using spectral attenuated inversion recovery (SPAIR) at 3 T. Methods: Overall, 80 patients underwent breast magnetic resonance imaging (MRI) with both conventional rs-EPI with SPAIR and DL-rs-EPI with WEXfs protocols (b-values: 0, 800, and 1200 s/mm²). ROI-based relative image-quality metrics, including signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and lesion contrast, were assessed at b = 800 and b = 1200 s/mm²; apparent diffusion coefficient (ADC) values were calculated using multi-b-value data. Fat suppression, background diffusion signal, lesion conspicuity, and artifact severity were qualitatively evaluated. A temperature-controlled diffusion phantom (CaliberMRI) was scanned; ADC values were compared with reference values at 24 °C. Results: DL-rs-EPI with WEXfs demonstrated higher ROI-based relative SNR estimates (b800: 5.79 vs. 5.28; b1200: 5.41 vs. 4.94; p < 0.001) and CNR estimates (b800: 3.35 vs. 3.12, p = 0.024; b1200: 3.67 vs. 3.37, p = 0.001), with unchanged lesion contrast. Tumor ADC values were comparable between protocols, whereas normal fibroglandular tissue ADC values were slightly higher, and ADC contrast increased with DL-rs-EPI with WEXfs. Phantom ADC values from both protocols closely matched reference values at 24 °C, without significant differences. DL-rs-EPI with WEXfs demonstrated more homogeneous fat suppression and reduced background diffusion signal, with comparable lesion conspicuity and artifact severity. Conclusions: The combined DL-rs-EPI with WEXfs protocol demonstrated improved qualitative and relative quantitative image quality while preserving tumor ADC measurements. As a protocol-level evaluation, these composite improvements support its clinical feasibility for high-quality breast DWI without implying the isolated effect of DL reconstruction alone. Full article

Public concrete datasets often contain duplicate records, coupled variables, and cross-source distribution shifts, which may lead to overly optimistic model evaluation. Based on a deduplicated UCI high-performance concrete dataset (1005 samples), this study develops a leakage-controlled data-driven workflow with applicability-domain assessment. TabPFN, SHAP, and NSGA-II are used for compressive strength prediction, model-response attribution, and scenario-based candidate mix screening, respectively. Model evaluation follows a unified data split, inner training-set cross-validation, and an independent test-set protocol. In addition, 502 non-overlapping records from the Mendeley PCC dataset are used as an external benchmark to examine cross-source transferability and sensitivity to distribution shift. The results show that TabPFN achieves the highest R² and the lowest RMSE, MAE, and MAPE on the internal UCI test set, with values of 0.953, 3.744 MPa, 2.265 MPa, and 7.580%, respectively; however, its advantage over strong baselines such as CatBoost is limited. On the external Mendeley PCC dataset, TabPFN remains competitive, with R², RMSE, and MAE values of 0.490, 15.175 MPa, and 11.457 MPa, respectively, but its performance is close to that of random forest, XGBoost, and CatBoost. The 5NN applicability-domain stratification shows that external samples located within the 95% 5NN applicability domain achieve improved performance (R² = 0.634 and RMSE = 12.367 MPa), suggesting that external prediction errors are associated with the distance from the source-domain distribution. SHAP results indicate that cement, ground granulated blast-furnace slag, curing age, and water are the main attribution variables in the model output; their response directions should be interpreted as statistical attributions rather than material causal mechanisms. The Pareto candidate mixes generated by NSGA-II satisfy basic engineering constraints. Nevertheless, because the external benchmark reveals sensitivity to cross-source distribution shift, the resulting mix proportions should be treated as pre-experimental screening candidates rather than engineering-validated low-GWP concrete mix proportions. Full article

Background/Objectives: Postoperative monitoring of microvascular free flaps is critical for early detection of vascular complications and flap survival. Nurses play a central role in this process; however, qualitative evidence on their experiences and challenges remains limited. This study explored nurses’ experiences in free tissue flap care to identify clinical practices, challenges, and improvement needs. Methods: A phenomenological qualitative design was used. Data were collected through semi-structured interviews with nine nurses experienced in free tissue flap care, recruited via purposive and snowball sampling. Interviews were conducted online and lasted 30–45 min. Data were analyzed using content analysis with MAXQDA 2025. Inter-researcher reliability was 97%. Results: The findings were categorized into four main themes and seventeen subthemes: (1) clinical monitoring and evaluation in the care process, (2) challenges and difficulties, (3) emotional and professional reflections, and (4) suggestions for improving care. Nurses reported that flap care requires intensive monitoring, rapid decision-making, and close collaboration with physicians, especially within the first 24–48 h. Monitoring was largely based on observation and experience due to the lack of standardized protocols. Major challenges included high workload, frequent assessments, and donor site management. Emotional burden, stress, and responsibility were also prominent. Conclusions: Free flap care is a complex and demanding process for nurses. The lack of standardized monitoring tools and protocols is a key gap. Developing structured tools, improving training, and strengthening multidisciplinary collaboration may enhance patient safety and care quality. Full article

Background/Objectives: Evidence on the role of muscle mechanical (myotonic) properties in athletic performance remains limited in young adult and sub-elite populations, particularly in American football, and sex-specific patterns of association are not well understood. This study aimed to investigate the associations between lower extremity myotonic properties and performance outcomes (strength and balance) in American football athletes, with a specific focus on sex-related differences and candidate predictors. Methods: A cross-sectional design was implemented involving 35 American football athletes (17 female, 18 male). Lower extremity muscle tone, stiffness, and elasticity were assessed using MyotonPRO. Strength parameters (lower limb, handgrip, back, and shoulder internal rotation) and balance performance (static and dynamic under eyes-open and eyes-closed conditions) were evaluated using standardized measurement protocols. Pearson correlation analysis was conducted to examine bivariate associations, followed by Least Absolute Shrinkage and Selection Operator (LASSO) regression to determine candidate predictors while addressing multicollinearity. Results: Male athletes exhibited significantly greater height, body mass, and BMI (p < 0.001), alongside elevated myotonic values compared to females. Correlation analyses indicated distinct sex-specific association patterns between myotonic properties and performance metrics. LASSO regression revealed a distinct sex-specific divergence in strength prediction: female strength was predominantly driven by proximal musculature (quadriceps and hamstring elasticity/stiffness), whereas male strength was anchored by distal musculature (gastrocnemius tone/stiffness). Furthermore, rigorous penalization shrunk nearly all balance coefficients to zero in both sexes, indicating that resting myotonic properties do not independently predict dynamic or static postural control. Conclusions: While lower extremity myotonic properties are candidate predictors of multi-regional strength via sex-specific proximal and distal strategies, they do not independently predict balance performance, suggesting postural control relies primarily on active motor recruitment rather than passive resting mechanics. Given the cross-sectional design of this study, causal inferences cannot be drawn, and these findings should be interpreted accordingly. The observed sex-specific differences may support consideration of individualized, sex-informed training strategies in American football athletes. Full article

Intrusion detection systems (IDSs) are commonly evaluated through aggregate classification metrics, although operational workflows require detected flows to be interpreted, prioritized, and transformed into actionable evidence. This study proposes a detection-to-triage framework for multiclass intrusion detection using a CSE-CIC-IDS2018-derived experimental subset containing 213,463 records across one benign class and fourteen attack classes. The framework combines supervised multiclass classification, SHAP-style post hoc explanation, class-specific false positive analysis, and a Suspicious Flow Priority Score (SFPS) for analyst-oriented suspicious flow ranking. The practical role of SFPS is to reorder suspicious flows by combining model confidence, explanation strength, predefined attack severity, and validation-based false positive control, thereby producing a transparent triage list rather than a probability-only alert queue. Three detection backbones were evaluated under a shared preprocessing protocol: Random Forest, XGBoost, and a lightweight multilayer perceptron baseline. To assess stability, experiments were repeated across five random seeds. XGBoost achieved the strongest mean performance across most aggregate indicators, with an accuracy of 0.9494 ± 0.0011, a macro F1-score of 0.8366 ± 0.0193, a weighted F1-score of 0.9494 ± 0.0011, and a Matthews Correlation Coefficient of 0.9429 ± 0.0012. Random Forest produced closely comparable results, while the lightweight MLP remained lower on aggregate and macro-level indicators. False positive analysis showed that the alert burden was concentrated in selected classes and differed across models, confirming that aggregate performance alone is insufficient for assessing IDS usefulness. SHAP-style analysis identified stable flow-level contributors to XGBoost discrimination, while SFPS substantially changed the post-detection ordering of suspicious flows compared with probability-only ranking. The study does not claim universal state-of-the-art superiority, causal explanation, or deployment validation; instead, it demonstrates how multiclass IDS outputs can be extended into explainable, false positive-aware, and triage-oriented rankings for analyst review. Full article

Greenhouse microclimate regulation is challenging due to nonlinear coupling among temperature, humidity, soil moisture, and light intensity, which limits the effectiveness of conventional threshold-based and PID control strategies under time-varying environmental disturbances. This paper presents a forecast-augmented ensemble control framework that combines Random Forest, Gradient Boosting, and Support Vector Machine classifiers with one-hour-ahead weather forecasts for closed-loop greenhouse microclimate regulation. The proposed system was deployed and validated in a working greenhouse cultivating cucumber (cv. ‘Madora F1’) over 28 consecutive days. Sensor measurements and forecast inputs were processed through a unified preprocessing pipeline, while control actions were generated through majority voting and executed on Raspberry Pi 4B edge hardware with a worst-case inference latency below 18 ms. The proposed framework achieved a temperature RMSE of 0.83 °C during field deployment. For reference, RMSE values of 3.21 °C and 1.94 °C were obtained for the threshold-based and PID baseline controllers, respectively, under the adopted disturbance-consistent evaluation protocol. Compliance rates reached 96.4% for temperature, 94.1% for relative humidity, and 97.2% for soil moisture across 40,320 resampled observation intervals (60 s analysis grid) derived from the original 10 s acquisition stream. Integration of short-term weather forecasts enabled anticipatory irrigation management, reducing irrigation pump operation by 18% without compromising soil-moisture compliance and yielding an estimated annual energy saving of 158 kWh per greenhouse zone. Unlike prediction-oriented greenhouse artificial-intelligence studies, the proposed approach implements a deployable forecast-augmented closed-loop control architecture validated under continuous real-world greenhouse operation. Full article

Background: Cardiac telemetry monitoring represents an important aspect of in-hospital patient safety in both telemetry and critical care settings. Despite technological advancements, telemetry effectiveness may be diminished due to systemic failures including operational processes, instructional policies, and human factors. Alarm fatigue, recognized by the Joint Commission as a leading contributor to serious patient harm, lies at the forefront of these failures. Objective: This narrative review utilized and synthesized sources indexed through PubMed, PubMed Central, MEDLINE, Web of Science, Google Scholar, Directory of Open Access Journals (DOAJ), and Scopus to illustrate the factors involved in hospital related monitoring failures. We purport that alarm fatigue and telemetry monitoring failures are the result of complex systemic failures comprising technological and human failures. Through this narrative, we propose an evidence-based framework known as the Safer Telemetry Architecture (STA) to pinpoint redundancies and promote closed-loop communication regarding alarm management. Conclusions: Monitored in-hospital environments represent a key area of preventable morbidity and mortality due to systemic design flaws. Our STA framework addresses such flaws via improvements in nurse-driven protocols, alarm routing, mandatory coverage standards for backup, and increased performance auditing. Systemic improvements via such a framework may represent an important institutional strategy for hospitals with cardiac monitoring, but requires further prospective validation. Managing redundancies in alerts and sounds, improving backup and nursing telemetry protocols, and promoting closed or continuous loops targeting alarm response times and telemetry utilization are key to effectively improving patient safety. Full article

Metanephrine (MN) and normetanephrine (NMN) are critical biomarkers for neuroendocrine tumors (pheochromocytoma and paraganglioma). Following our previous development of a molecularly imprinted solid-phase extraction (MISPE) sorbent for urine analysis, this study evaluated MISPE coupled with HILIC-MS/MS for determining metanephrines in human plasma. Unlike conventional phases, the novel polymer selectively binds analytes as in situ-generated anions via quaternary alkylammonium groups in hydroxide form, ensuring accurate extraction from just 25 µL of plasma. Validated per U.S. FDA guidelines, the assay showed good intra- and interday precision (CV < 10.8%), accuracy (bias < −10.6%) and excellent linearity (R² > 0.99) across pathological ranges (184.3–877.8 ng/L for MN; 174.8–923.0 ng/L for NMN), with low relative standard errors (<6.9%). Excellent selectivity was demonstrated in the presence of structurally close analogs (catecholamines, DOPA and its derivatives). Compared with commercial WCX, the sorbent yielded cleaner extracts, significantly reducing the phospholipid interference. Although lower limits of quantification (92.2 ng/L MN; 87.4 ng/L NMN) slightly exceeded healthy upper thresholds, the method has potential for use in specific clinical scenarios with pronounced biomarker elevations: diagnosis of pheochromocytoma/paraganglioma, monitoring post-treatment metanephrine decline, and tracking tumor-induced hypertensive crises in emergencies. This accessible protocol forms a solid foundation for advanced diagnostics. Full article

Multimodal large language models can read architectural images and design instructions but they still struggle to turn architectural rules into editable, executable models in professional modeling environments. To address this limitation, this paper presents MLLMto3D, an MCP-driven closed-loop framework that connects multimodal reasoning with Rhino-based modeling, feedback, and revision. The framework consists of five phases: visual parsing, JSON-based intent serialization, code synthesis, MCP-driven Rhino execution and feedback, and verification with bounded repair. Its core mechanism is JSON-based intent serialization, which converts image-derived architectural information into machine-readable modeling parameters under a predefined JSON schema. The schema separates geometric and compositional constraints, including height, bay rhythm, facade zones, and alignment rules, from design variables such as materials, openings, and ornament. Building on this mechanism, Skills modules externalize facade typology knowledge and safe Rhino scripting patterns, providing callable professional constraints for code synthesis to reduce design-intent deviation and API hallucination. The framework is evaluated through an experimental design case study on a site in Shanghai’s Hengfu Historic District, where the generation of new façades is informed by a nearby heritage architectural reference. The results show that MLLMto3D can generate a parametrically adjustable Rhino model while preserving the main compositional constraints, thereby advancing AI-assisted architectural 3D generation toward a controllable, verifiable, and iterative modeling process. Full article