Search Results (15,971)

Background and Objectives: The regeneration of the pulp–dentin complex represents an alternative to conventional root canal treatment, aiming to preserve tooth biology and function. Cell-free regenerative endodontic therapy (CF-RET) exploits endogenous stem cells from the periapical region without ex vivo cell manipulation. Despite growing interest, the biological mechanisms, clinical indications, and predictability of CF-RET remain not clearly defined. This structured narrative review aimed to update a previous review by analyzing recent human studies on CF-RET. Materials and Methods: This review was conducted using the PRISMA 2020 guidelines to guide transparent reporting of the literature search and study selection process and was registered in PROSPERO (CRD420251075131). In vitro and in vivo human studies published between January 2017 and December 2024 investigating CF-RET were included, while studies involving cell transplantation, non-human models, case reports, and reviews were excluded. Study selection, data extraction, and quality assessment using the QuADS tool were performed, and the evidence was synthesized using a qualitative narrative approach. Results: Sixty-four studies were included. In vitro studies reported favorable effects of growth factors, exosomes, and biomimetic scaffolds on stem cell viability, migration, proliferation, odontogenic differentiation, and angiogenesis, while neurogenic differentiation was less consistently investigated. Scaffold composition, microstructure, and rheological properties were also considered. In vivo studies mainly focused on immature teeth with incomplete root development and demonstrated positive clinical and radiographic outcomes, including root development and canal diameter reduction. Conclusions: The current evidence supports the biological potential of CF-RET as a regenerative approach; however, substantial heterogeneity, the limited number of clinical studies and the absence of standardized protocols preclude definitive conclusions, highlighting the need for further well-designed translational and clinical investigations considering clinical applicability. Full article

The widespread connectivity of the Industrial Internet of Things (IIoT) improves the efficiency and functionality of connected devices. However, it also raises serious concerns about cybersecurity threats. Implementing an effective intrusion detection system (IDS) for IIoT is challenging due to heterogeneous data, high feature dimensionality, class imbalance, and the risk of data leakage during evaluation. This paper presents a leakage-safe hybrid intrusion detection framework that combines text-based and numerical network flow features in an IIoT environment. Each network flow is converted into a short text description and encoded using a frozen Large Language Model (LLM) called the Bidirectional Encoder Representations from Transformers (BERT) model to obtain fixed semantic embeddings, while numerical traffic features are standardized in parallel. To improve class separation, class prototypes are computed in Principal Component Analysis (PCA) space, and cosine similarity scores for these prototypes are added to the feature set. Class imbalance is handled only in the training data using the Synthetic Minority Over-sampling Technique (SMOTE). A Random Forest (RF) is used to select the top features, followed by a Histogram-based Gradient Boosting (HGB) classifier for final prediction. The proposed framework is evaluated on the Edge-IIoTset and ToN_IoT datasets and achieves promising results. Empirically, the framework attains 98.19% accuracy on Edge-IIoTset and 99.15% accuracy on ToN_IoT, indicating robust, leakage-safe performance. Full article

We evaluated the contributions of ten intrinsic and extrinsic factors readily available from website data to individual home sale prices for three major U.S. cities using a P-spline generalized additive model (GAM). We identified the relative significance of each factor by evaluating the change in the adjusted $R^2$ value resulting from its removal from the model. We combined this with information from correlation matrices to identify the added predictive value of a factor. For these three cities, the tests revealed that living area and location (latitude, longitude) had the strongest impact on explained variance, and each factor independently added predictive value. Relative impacts of the other factors were city-dependent. We utilized this information to develop an improved GAM with superior concurvity values. The improved GAM required the use of linear orthogonalization of factors combined with smoothing functions based on tensor products of correlated factors. Full article

The integration of renewable energy sources, including photovoltaic (PV) and fuel cell (FC) systems, into AC grids has attracted immense research interest in recent times. Furthermore, incorporating these renewable sources of energy into medium-voltage grids is garnering increased attention because of the obvious benefits of medium-voltage integration at elevated power levels. Photovoltaic applications entail the arrangement of solar panels capable of outputting voltages up to 1.5 kV; nonetheless, fuel cells display restricted output voltage, with a maximum market range of 400 to 700 V. Hence, the efficient integration of renewable energy sources into low-voltage or medium-voltage grids demands the utilization of a step-up direct current (DC–DC) inverter and a converter for connection to the alternating current (AC) grid, in which an efficient step-up converter is critical for the medium-voltage grid. Therefore, this study presents a three-phase buck-boost split-source inverter (BSSI) that resolves the constrained output voltage of the fuel cells. This study focuses on modifying the configuration of a conventional three-phase split-source inverter (SSI) circuit by adding a few components while maintaining the inverter’s modulation. This novel circuit design enables the reduction in voltage strains on the inverter switch components and improves DC-link use in relation to a traditional SSI configuration. For an 800 bus, maximal voltage stress on the primary inverter switches is lowered when compared with the standard SSI that delivers entire DC-bus voltage to switches. A rectifier-based model is employed to simulate the behavior of a renewable energy source. Combining these advantages with the conventional modulation of the inverter offers a more effective design. The buck-boost split-source inverter (BSSI) was analyzed using three distinct modulation techniques: the sinusoidal pulse-width modulation scheme (SPWM), the third-harmonic injected pulse-width modulation (THPWM) scheme, and space vector modulation (SVM). The proposed analysis was validated through MATLAB-SIMULINK and practical outcomes on a 5.0 kW model. The practical and SIMULINK data were found to be closely aligned with the analysis. The circuit developed in this study also ensures efficient DC-to-AC conversion, specifically with regard to low-voltage sources, like fuel cells or photovoltaic (PV) systems. Full article

In this work, we use an Echo State Network (ESN) model, which is essentially a recurrent neural network (RNN) operating according to the reservoir computing (RC) paradigm, to classify individual ECG heartbeats using the MIT-BIH arrhythmia database. The aim is to evaluate the performance of ESN in a challenging task that involves classification of complex, unprocessed one-dimensional signals, distributed into five classes. Moreover, we investigate the performance of the ESN in the presence of (i) noise in the dynamics of the internal variables of the hidden (reservoir) layer and (ii) random variability in the activation functions of the hidden layer cells (neurons). The overall accuracy of the best-performing ESN, without noise and variability, exceeded 96% with per-class accuracies ranging from 90.2% to 99.1%, which is higher than previous studies using CNNs and more complex machine learning approaches. The top-performing ESN required only 40 min of training on a CPU (Intel i5-1235U@1.3 GHz) HP laptop. Notably, an alternative ESN configuration that matched the accuracy of a prior CNN-based study (93.4%) required only 6 min of training, whereas a CNN would typically require an estimated training time of 2–3 days. Surprisingly, ESN performance proved to be very robust when Gaussian noise was added to the dynamics of the reservoir hidden variables, even for high noise amplitudes. Moreover, the success rates remained essentially the same when random variability was imposed in the activation functions of the hidden layer cells. The stability of ESN performance under noisy conditions and random variability in the hidden layer (reservoir) cells demonstrates the potential of analog hardware implementations of ESNs to be robust in time-series classification tasks. Full article

Alzheimer’s disease (AD) is the most common cause of neurocognitive disorder, and the integration of cognitive assessment with biological markers remains essential for clinical characterization. The Clock Drawing Test (CDT) is a brief and widely used screening tool assessing visuospatial and executive functions, which may reflect underlying neurodegenerative processes. This study investigated the diagnostic performance of the CDT and its association with cerebrospinal fluid (CSF) biomarkers within the A/T/(N) research framework. Ninety-seven patients with mild or major neurocognitive disorder were classified as AD or non-AD according to CSF amyloid-β, phosphorylated tau, and total tau profiles, and compared with 36 healthy participants. All subjects underwent a comprehensive neuropsychological evaluation, including the CDT scored using the quantitative–qualitative method proposed by Rouleau et al. Group comparisons, ROC analyses, and regression models adjusted for age, sex, and education were performed. CDT scores effectively distinguished patients from healthy participants, showing large effect sizes, and modestly differentiated AD from non-AD profiles, particularly on the Hands subscale. Diagnostic accuracy was fair, with adjusted AUC values ranging from 0.65 to 0.75. Lower CDT performance was significantly associated with higher CSF total tau levels, while associations with amyloid-β and phosphorylated tau were not robust after correction. These findings suggest that the CDT is sensitive to cognitive impairment severity and shows limited but meaningful relationships with neurodegenerative biomarkers, supporting its role as a practical complementary tool alongside biological assessment. Full article

Titanium carbide has developed into an exceptional reinforcement contender in Aluminium Matrix Composites (AMCs) because of its greater characteristics such as elevated hardness, elevated elastic modulus, low heat conductivity, and constancy at moderately elevated temperatures. Furthermore, it is consequently selected as the reinforcing segment in AMCs because of its good thermodynamic and wettability stability inside the aluminium melt pool. In this work, titanium carbide powder was mixed to distinguish AlSi₁₀Mg strengthening by the additive manufacturing (AM) process in the category of powder bed identified as Powder Bed Fusion (PBF). The objective of the study was to have homogeneously mixed powders for processing on the reinforcement of AlSi₁₀Mg with TiC. Different characterisation procedures were carried out, such as scanning electron microscope energy dispersive X-ray spectroscopy (SEM-EDS), pycnometry, and thermogravimetric analysis (TGA). The advancement of powder density from 2.65 to 2.72 g/cm³ and surface area from 0.02 to 0.14 m²/g was accomplished. The modelling findings concurred that the addition of Ti and C increases the density of the alloy, with Ti contributing more to AlSi than C. It was deduced that with Ti and C added to the system, the bulk modulus increases, with Al₆Si₈TiC having the largest value of 80.34 GPa. Full article

Most machine learning (ML)-based facility location studies utilize uniform grid partitioning, often overlooking spatial heterogeneity. This limitation can compromise the validity and practical applicability of the resulting site selections. In response to this issue, this paper uses fire stations as the research subject and proposes a location prediction method that considers the heterogeneous characteristics within cities. Firstly, the Gaussian Mixture Model (GMM) is adopted based on the Point of Interest (POI) data to determine the clustering centres of the study area. Secondly, a Voronoi diagram is constructed to divide the study area reasonably. Then, a comprehensive feature matrix is constructed by integrating multi-source spatial data and five machine learning models: Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost) and Logistic Regression (LR). These are then used for training and evaluation. Finally, the GBDT model with the best performance in terms of both the F1 score and the AUC value was selected to predict the location of fire stations in Chengguan District, Lanzhou City. The results demonstrate the GBDT model’s effectiveness in identifying the rationale behind existing fire station locations and predicting potential new locations. It predicts 12 suitable locations for new fire stations, and the suitability of these predicted locations is validated by comparing them with the existing fire station locations, 8 of which are in the same block as existing fire stations in Chengguan District. Adding micro fire stations at four new predicted locations would improve response efficiency. The results of the feature importance analysis show that road accessibility is the primary factor affecting fire station location selection. This study’s proposed method effectively enhances the reasonableness of fire station site selection and provides a basis for planning fire stations in new urban areas in the future. Full article

To explore the potential of phosphogypsum for resource utilization in asphalt pavements, this study evaluated its feasibility as a warm-mix asphalt (WMA) additive and investigated its influence on the rheological properties of asphalt binder. Phosphogypsum warm-mix asphalt was prepared by incorporating varying dosages of phosphogypsum warm-mix additive (PGWA) into both base asphalt and styrene–butadiene–styrene (SBS)-modified asphalt. The high-, medium-, and low-temperature performance of phosphogypsum warm-mix asphalt was evaluated using rheological tests. The results revealed that the complex modulus of PGWA-added base asphalt was higher than that of the base asphalt, with only minor changes in phase angle. The incorporation of the SBS modifier significantly enhanced the stiffness and elasticity of the asphalt binder. Compared with the control asphalt, PGWA-added asphalt exhibited lower creep strain and accumulated strain, higher creep recovery rates, and smaller non-recoverable compliance under the same stress level, indicating an improved resistance to high-temperature permanent deformation. PGWA increased the cumulative damage capacity and extended the fatigue life of the asphalt binder. Although the PGWA slightly reduced the low-temperature performance, the SBS modifier effectively compensated for this drawback. The Burgers model accurately captured the low-temperature rheological behavior of PGWA-added asphalt. Overall, PGWA-added asphalt demonstrated excellent rheological performance and high application potential, offering a promising pathway for the resource utilization of phosphogypsum and the development of sustainable, eco-friendly pavement materials. Full article

Background/Objectives: Emergency department length of stay (ED LOS) is a key indicator reflecting emergency department crowding, patient safety, and healthcare resource efficiency. Among injured patients, ED LOS may be prolonged depending on injury severity and disposition pathways (admission and inter-hospital transfer). This nationwide study using the Korean National Emergency Department Information System (NEDIS) aimed to (1) describe the distribution and determinants of ED LOS among injured patients and (2) quantify the mediating effects of disposition (admission and transfer) on the association between injury severity measured by the International Classification of Diseases-based Injury Severity Score (ICISS) and ED LOS. Methods: We analyzed NEDIS injury-related ED visit records collected from the date of IRB approval through 12 January 2026. We conducted a retrospective observational study using NEDIS data. Of 1,048,575 injury-related ED visits, 1,035,484 visits with valid ED LOS and eligible records were included after excluding missing key variables and implausible time values. ED LOS was calculated in minutes using arrival and departure timestamps. Injury severity was assessed using ICISS (primary: based on 15 diagnoses; sensitivity: based on 20 diagnoses). Determinants of ED LOS were evaluated using gamma regression with a log link. Disposition was categorized as discharge, admission, and inter-hospital transfer; admission and transfer were modeled as binary mediators. Causal mediation analyses estimated the average causal mediation effect (ACME), average direct effect (ADE), total effect, and proportion mediated. Multiple sensitivity analyses (outlier handling, missing-data approaches, alternative log-linear modeling, and EMS arrival subgroup analyses) assessed robustness. Results: The median ED LOS was 150 min (IQR 90–260). ED LOS differed substantially by disposition: 120 min for discharged patients, 420 min for admitted patients, and 360 min for transferred patients. Overall, 17.9% of visits had an ED LOS ≥ 6 h, and prolonged stays were concentrated among admitted (≥6 h: 55.0%) and transferred (≥6 h: 45.0%) patients. In gamma regression, a 0.05 decrease in ICISS (greater severity) was associated with longer ED LOSs in the unadjusted model (Ratio 1.34) and remained significant in the fully adjusted model (Ratio 1.12, 95% CI 1.11–1.13). Admission and transfer were strong determinants of ED LOS in the final model (ratios of 2.35 and 2.05, respectively). In mediation analyses, admission mediated 36.8% of the severity–ED LOS association (ACME 0.085; ADE 0.146), and transfer mediated 14.3% (ACME 0.033; ADE 0.198). Findings were consistent across sensitivity analyses. Conclusions: In this nationwide cohort of injured patients, ED LOS showed a right-skewed distribution, with prolonged stays concentrated in admission and transfer pathways. Injury severity (ICISS) was independently associated with longer ED LOS, and a substantial proportion of this association was mediated through admission and transfer. Reducing ED LOS among severely injured patients likely requires not only streamlining diagnostic and treatment processes but also system-level interventions targeting output-stage bottlenecks, including inpatient bed operations/boarding management and transfer coordination. Full article

Recently, the progress of foundation models such as CLIP and SAM has shown the great potential of zero-shot anomaly detection tasks. However, existing methods usually rely on general descriptions such as “abnormal”, and the semantic coverage is insufficient, making it difficult to express fine-grained anomaly semantics. In addition, CLIP primarily performs global-level alignment, and it is difficult to accurately locate minor defects, while the segmentation quality of SAM is highly dependent on prompt constraints. In order to solve these problems, we proposed DCS, a unified framework that integrates Grounding DINO, CLIP and SAM through three key innovations. First of all, we introduced FinePrompt for adaptive learning, which significantly enhanced the modeling ability of exception semantics by building a fine-grained exception description library and adopting learnable text embeddings. Secondly, we have designed an Adaptive Dual-path Cross-modal Interaction (ADCI) module to achieve more effective cross-modal information exchange through dual-path fusion. Finally, we proposed a Box-Point Prompt Combiner (BPPC), which combines box prior information provided by DINO with the point prompt generated by CLIP, so as to guide SAM to generate finer and more complete segmentation results. A large number of experiments have proved the effectiveness of our method. On the MVTec-AD and VisA datasets, DCS has achieved the most state-of-the-art zero-shot anomaly detection results. Full article

Background: Emergency department (ED) overcrowding strains clinicians and potentially compromises urgent care quality. Unscheduled return visits (URVs), also known as readmissions, contribute to this cycle, motivating tools that identify high-risk patients at discharge. Methods: This study performed a retrospective study using ED electronic health records (EHRs) from Kaohsiung Veterans General Hospital from January 2018 to December 2022 (n = 184,653). The model integrates structured variables, such as vital signs, medication and laboratory counts, and ICD-10–based comorbidity measures, with unstructured physician notes. Key physiologic measurements were transformed into binary form using clinical reference intervals, and random under-sampling addressed class imbalance. A multimodal, CNN was proposed and evaluated with an 8:2 train–test split and 10-fold Monte Carlo cross-validation. Results: The proposed model achieved a sensitivity of 0.717 (CI: [0.695, 0.738]), accuracy of 0.846 (CI: [0.842, 0.850]), and AUROC of 0.853. Binary transformation improved recall and AUROC relative to the original numeric representations. SHAP analysis showed that unstructured features dominated prediction, while structured variables added complementary value. In a small-scale pilot evaluation using the SHAP-enabled interface, participating physicians reported the system helped surface high-risk cohorts and reduced cognitive workload by consolidating relevant patient information for rapid cross-checking. Conclusions: An interpretable CNN-based clinical decision support system can predict ED revisit risk from multimodal EHR data and demonstrates practical usability in a real-world clinical setting, supporting targeted discharge planning and follow-up as a near-term approach to mitigate overcrowding. Full article

Impact wrenches are widely used in construction and automotive industries, yet they generate harmful vibrations that pose health risks to operators and reduce tool usability. This paper develops a practical, low-order bond-graph model of impact-wrench dynamics that captures interactions among the motor, hammer, anvil, and hand/arm constraints, and validates it against measurements during bolt tightening into a steel plate. Predictions match measured RMS accelerations and spectral modes up to 200 Hz with a maximum relative RMS error of 11%. The analysis attributes dominant vibration sources to rotational and translational impacts between the hammer and anvil; notably, the translational (z-axis) impact contributes substantially to felt vibration while not being required for bolt tightening. The model provides physical insight into vibration origins and supports actionable design decisions, such as reducing the linear (z-axis) impact and adding rotational damping or control, consistent with standardized testing practice. Full article

Background/Objectives: Oligosaccharides, such as fructooligosaccharides (FOS), have long been used to promote human health due to their beneficial effects on the intestinal environment and their anti-inflammatory properties. Recent advances in manufacturing technologies have enabled the production of novel oligosaccharides derived from rare sugars. These compounds may exert unique health benefits; however, their physiological functions remain largely unexplored. Because sleep is a conserved, lifespan-linked physiological phenotype governed by metabolic and stress-response pathways that oligosaccharides can influence, we evaluated sleep alongside lifespan to capture systemic functional effects. Methods: Using the model organism Drosophila melanogaster, we investigated the effects of 4-galactosylkojibiose (4-GK), a promising new oligosaccharide, on sleep and lifespan. We also conducted RNA sequencing following 4-GK or FOS application. Results: Our results demonstrated that both 4-GK and FOS extended lifespan, with 4-GK showing comparable or numerically greater efficacy than FOS. While the addition of 4-GK or FOS to sucrose diet did not affect overall sleep or activity levels, 4-GK alone without sucrose decreased sleep compared to sucrose alone. Furthermore, RNA-seq analysis revealed upregulation of gene groups associated with longevity in both the 4-GK and FOS treatment groups, particularly genes encoding heat shock proteins. Conclusions: 4-GK promotes longevity in D. melanogaster and activates stress-response programs, suggesting a health-promoting role comparable to FOS. Sleep effects were diet-context dependent, unchanged when added to sucrose but reduced when given without sucrose. These findings suggest a novel potential role for 4-GK in promoting longevity. Full article

Flying Ad Hoc Networks (FANETs) formed by cooperative Unmanned Aerial Vehicles (UAVs) require formally proven secure and resource-efficient authentication because open wireless channels allow active adversaries to inject commands, replay traffic, and impersonate nodes. Conventional certificate-based mechanisms impose key management overhead and remain vulnerable under device capture, while existing lightweight and Physical Unclonable Function (PUF)-assisted proposals commonly assume stable connectivity, lack formal adversarial verification, or are evaluated only through simulation. This paper presents a lightweight PUF-assisted authentication protocol designed for dynamic multi-hop FANET operation. The scheme provides mutual UAV–Ground Station (GS) authentication and session key establishment and further enables secure UAV–UAV communication using an off-path ticket mechanism that eliminates continuous infrastructure dependence. The protocol is constructed through verification-driven refinement and formally analysed under the Dolev–Yao model, establishing authentication and session key secrecy and resistance to replay and impersonation attacks. Implementation-oriented latency measurements on Raspberry-Pi-class embedded platforms demonstrate that cryptographic processing time can be further reduced with hardware improvements, while the overall end-to-end delay is still largely determined by channel conditions and connection behaviour. Comparative evaluation shows reduced communication cost and broader security coverage relative to existing UAV authentication schemes, indicating practical deployability in large-scale FANET environments. Full article