Search Results (50,660)

Additive manufacturing also known as three-dimensional printing (3D printing), provided the ability to produce precise three-dimensional structures, representing a rapidly growing field in Orthopaedics. Its clinical value has been attributed to the ability to create complex three dimensional objects with relative ease and at low cost. However, the available evidence regarding its applications in trauma was heterogeneous. This narrative review aimed to analyze the clinical applications of 3D printing in traumatology. Additionally, the research gaps that emerged in our literature search were underscored. Four application domains were selected based on their prevalence in the screened literature and relative level of clinical implementation within orthopaedic traumatology, including (1) 3D-printed anatomical models, (2) patient-specific surgical guides (PSSGs), (3) 3D-printed implants, and (4) temporary 3D-printed external fixation devices. 3D-printed anatomical models were found to help in reducing operative time, estimated blood loss, and the intraoperative radiation exposure. The use of PSSGs was shown to improve intraoperative accuracy and to provide a basis for consistent, accurate, and reproducible outcomes. However, their implementation was hindered by preparation time, the need for stable anatomical landmarks, and reduced accuracy due to potential soft-tissue injury and swelling. In contrast, 3D-printed implants and external fixation devices constituted promising but less extensively studied applications of 3D printing in trauma. The production of customized implants and external fixators, as suggested by the studies available, was deemed feasible, with comparable mechanical properties and significantly lower cost. Larger multicenter studies are required to support and validate these findings. Overall, based on the available evidence, 3D-printed anatomical models and patient-specific surgical guides demonstrate the highest level of clinical applicability, primarily in preoperative planning and intraoperative guidance. Full article

Fifth-generation (5G) networks enable railway digitalization but face signal degradation challenges in high-mobility environments. While the existing literature attributes degradation primarily to Doppler frequency shifts, this study presents empirical evidence challenging this paradigm. Analysis of 13.7 million 5G New Radio measurements across 370 km of Estonian railway reveals that visible cell density, not velocity, dominates signal quality degradation. Nine geographic hotspots exhibit 5.4–18.0 dB degradation at moderate velocities (54–66 km/h, mean 60.2 km/h) with zero high-speed measurements, excluding the Doppler effect as the reason behind service quality degradation. Cell complexity demonstrates a 3.25× stronger correlation with degradation (r = −0.390) than velocity (r = −0.120), consistent with automatic frequency control tracking instability under high cell ID churn rates (40–115 visible cells per location), though direct confirmation of this mechanism requires access to internal modem frequency-lock state data. Frequency band analysis shows that 700 MHz is optimal at 98.1% of locations, with a 19 dB advantage over 3.5 GHz. Degradation mechanism decomposition reveals within-cell effects (60%, 7.85 dB) and handover boundary effects (40%, 2–6 dB). The findings challenge velocity-centric optimization paradigms and recommend network planning focused on cell overlap reduction rather than Doppler compensation enhancement. Practical recommendations include 700 MHz prioritization, handover parameter optimization, and geographic targeting of identified hotspots for European railway 5G deployment. Full article

Over the past two decades, organic semiconductors have attracted significant research interest due to their advantageous features, including low-cost fabrication, lightweight properties, and portability, for photonic device applications. In this study, nickel sulfide doped with cobalt $(\mathrm{N}\mathrm{i}\mathrm{S}/\mathrm{C}\mathrm{o})$ nanocomposites were successfully synthesized via a wet-chemical processing technique and used as a dopant in the active layer of thin-film organic solar cells (TFOSCs). The poly(3-hexylthiophene) (P3HT) and [6,6]-phenyl-C61-butyric acid methyl ester (PC61BM) blend was used as the active layer in this investigation. The devices were fabricated with $\mathrm{N}\mathrm{i}\mathrm{S}/\mathrm{C}\mathrm{o}$ nanocomposites at 1 wt%, 2 wt%, and 3 wt% in the active layer to determine the optimal dopant concentration. However, the experimental evidence clearly showed that the solar cell’s performance depends on the concentration of the $\mathrm{N}\mathrm{i}\mathrm{S}/\mathrm{C}\mathrm{o}$ nanocomposites. As a result, the highest power conversion efficiency (PCE) recorded in this experimental work was 6.11% at a 1% doping concentration, compared with 2.48% for the pristine reference device under AM 1.5G illumination (100 mW/cm²) in ambient conditions. The optical and electrical properties of the active layers are found to be strongly influenced by the inclusion of $\mathrm{N}\mathrm{i}\mathrm{S}/\mathrm{C}\mathrm{o}$ nanocomposites in the medium. However, the device doped with 1 wt% $\mathrm{N}\mathrm{i}\mathrm{S}/\mathrm{C}\mathrm{o}$ nanocomposite exhibits the highest absorption intensity, consistent with the better performance observed in this study, which can be attributed to the localized surface plasmon resonance (LSPR) effect. The optical and morphological characteristics of the synthesized $\mathrm{N}\mathrm{i}\mathrm{S}/\mathrm{C}\mathrm{o}$ nanocomposites were comprehensively analyzed using high-resolution transmission electron microscopy (HRTEM), high-resolution scanning electron microscopy (HRSEM), and additional complementary techniques. Full article

The Midong Block is currently a primary target for coalbed methane (CBM) exploration and development in Xinjiang. However, fracturing operations in this region generally exhibit low flowback rates, which escalate the risk of reservoir damage and ultimately suppress daily gas production. To elucidate the impact of various geological and engineering factors on flowback efficiency and permeability damage, as well as their underlying mechanisms, this study conducted fracturing fluid flowback simulation experiments. The pulse-decay permeability measurement and weighing methods were employed to quantify the variations in flowback rates and permeability damage intensities under different conditions. Experimental results indicated that the permeability damage rate in the Xishanyao Formation coal samples ranged from 3.12% to 92.86% after flowback, with 92% of the samples exhibiting a flowback rate of less than 10%. This significant impairment was primarily attributed to the synergistic effects of stress-induced fracture closure, clay mineral hydration swelling, and coal fines migration. Specifically, elevated confining pressures and prolonged soaking times exacerbated reservoir damage. A low flowback pressure differential intensified the water locking effect, hindering fluid recovery. Notably, the flowback velocity displayed a U-shaped velocity sensitivity profile. In the low-temperature regime, damage characteristics fluctuated, controlled by competitive thermal–hydro–mechanical (THM) coupling mechanisms. Full article

Drone ad-hoc networks are inherently vulnerable to performance-degradation attacks such as jamming, packet disruption, and routing interference due to dynamic topology changes and unstable wireless channels. In such environments, conventional threshold-based detection schemes often fail to identify threats in their early stages because individual performance metrics remain within normal ranges despite emerging abnormal temporal patterns. To address this limitation, this study proposes an LSTM-based early threat detection method that learns the temporal dynamics of network performance indicators, including packet delivery ratio (PDR), connection reliability (CR), and delay. By modeling inter-metric correlations and evolving degradation trends, the proposed approach enables probabilistic inference of abnormal state transitions prior to explicit threshold violations. The proposed method is validated through simulation experiments conducted in a drone ad-hoc network environment under jamming attack scenarios, and its performance is compared with that of conventional threshold-based schemes. The results show that while the threshold-based approach first detected the attack at t = 65 s when predefined metric boundaries were exceeded, the proposed LSTM-based detector identified the attack at t = 45 s with an estimated attack probability of 0.63, achieving approximately 20 s earlier detection. This improvement is attributed to the LSTM’s capability to capture subtle temporal dependencies, directional trends, and cross-metric interactions that precede abrupt metric degradation. Furthermore, the LSTM output probabilities exhibited monotonic growth during the attack period and gradual decay during recovery, indicating robust tracking of network state transitions rather than isolated event detection. These results demonstrate that the proposed method not only enhances early threat awareness but also contributes to resilience-oriented operation by enabling proactive mitigation in drone ad-hoc networks. This study provides quantitative evidence that sequence learning over performance metrics can overcome the structural limitations of threshold-based detection and enable effective early threat detection in drone ad-hoc network environments. Full article

Forest recreation sites provide accessible settings for everyday leisure while accommodating multiple, and often competing, uses, making zoning both a central planning challenge and solution. This study advances micro-zoning as a novel, site-scale extension of established recreation zoning concepts, examining how zoning principles can be operationalized within intensively used forest recreation areas. Data were collected from 302 visitors using a structured questionnaire on visit patterns, valued forest attributes, disturbances, and socio-demographic characteristics. Descriptive statistics and tests of association were used to identify needs, disturbances, and recurring combinations of use. The results show that these forests function as everyday recreation spaces for diverse group visits, with high importance placed on peacefulness, shade, cleanliness, natural scenery, and basic infrastructure, alongside frequent reports of disturbance from music, crowding, and litter. Building on these patterns, the study develops a micro-zoning framework that delineates three interpretive planning micro-areas: Drive-in Forest Recreation, representing high-intensity, infrastructure-oriented social use; Low-Intensity Recreation, a moderate-use, low-noise nature-oriented area prioritizing separation from disturbance; and Active Recreation Use, comprising movement-focused routes for walking, running, and cycling. The study illustrates how visitor survey data can guide evidence-based micro-zoning and adapt zoning frameworks to the fine spatial grain of intensively used forest recreation sites. Full article

Magnetoelectric (ME) materials that exhibit simultaneous coupling between electric polarization and magnetization have attracted significant attention due to their potential technological applications in the emerging generation of multifunctional devices. In this research, Ba_0.85Ca_0.15Ti_0.92Zr_0.08O₃-CoFe₂O₄:x (x = 0.1, 0.2, 0.3% mol) composites were synthesized using solid-state and sol–gel combustion chemical methods to elucidate their ME coupling at ultra-low concentrations of the magnetic phase. Rietveld refinement and Raman spectroscopy results confirm a shift in the morphotropic phase boundary (MPB), evidenced by an increase in the tetragonal phase relative to the orthorhombic structure. High stability of the P4mm and Amm2 symmetries is reached at 1300 °C without diffusion of Fe and Co into the octahedral site. At this temperature, the CoFe₂O₄ spinel structure remains stable without secondary phases. The orthorhombic phase fraction decreases from 55% to 37% as the magnetic phase fraction increases, driven by stress and constraint rather than ionic interactions alone. The Curie temperature decreases from 99 to 90 °C, attributed to the grain-size reduction effect rather than structural disorder. The dielectric permittivity (ε_r) reaches an absolute value of 5070 and progressively decreases with increasing magnetic saturation. An increase in compressive residual stress is observed, which ensures the mechanical stability of the electroceramics. Magnetoelectric (ME) coupling, evaluated through measurements of electric polarization as a function of the magnetic field, shows an increase from 3.8 to 4.9 μC/cm² under a magnetic field of 50 Oe. The composites with x = 0.2 and 0.3 mol% exhibit potential for applications in fast-switching magnetoelectric devices and magnetic field sensors. Full article

This paper proposes a novel method for measuring music similarity. Existing music similarity measurements have often been used for music appreciation, but this paper proposes a method for measuring the similarity between music samples which are used for music production. Conventional music recommendation approaches often rely on either metadata-based similarity or audio-based feature similarity in isolation, which limits their effectiveness in sample-based recommendation scenarios where both compositional context and acoustic characteristics are important. To address this limitation, the proposed framework combines a hypergraph-based information similarity module with a feature-based similarity module learned using Siamese networks and triplet loss. In the information-based module, metadata attributes such as beats per minute (BPM), genre, chord, key, and instrument are modeled as vertices in a hypergraph, and Random Walk–Word2Vec embeddings are learned to capture structural relationships between music samples and their attributes. In parallel, the feature-based module employs vertex-specific Siamese networks trained on instrument and key classification tasks to learn perceptual similarity directly from audio signals. The two modules are trained independently and jointly utilized at the recommendation stage to provide attribute-specific similarity results for a given query sample. Results show that the proposed system achieves high Precision@k across multiple attributes and forms stable similarity structures in the embedding space, even without relying on user interaction data. These results reflect embedding consistency evaluated over the entire dataset where training and retrieval are performed on the same sample pool, rather than generalization to unseen samples. These results demonstrate that the proposed hybrid framework effectively captures both structural and perceptual similarity among music samples and is well suited for sample-based music recommendation in music production environments. Full article

Millenarianism originated from apocalyptic literature in Judaism, emphasizing that the “Messiah” would establish a “millennial kingdom” on earth ruled by the Jews. This ideology became a theoretical weapon for Jews to resist imperial tyranny during classical antiquity and was later embraced by early Christian theology. By the early modern period, with the intense unfolding of the Reformation and social upheavals, the theory of the “millennial kingdom” re-emerged as a mainstream topic in Christian theology. Regarding the nature of the “millennial kingdom” and how it would be realized, early modern Christian factions split into two interpretive camps. One emphasized the spiritual attributes of the “millennial kingdom”, while the other stressed its material aspects, advocating the violent establishment of a political entity on earth ruled by Christians. These two distinct interpretive models ultimately converged on the issue of colonial expansion, transforming millenarianism into a theoretical tool to justify overseas expansion. Full article

Arcanobacterium haemolyticum is a facultative anaerobic, Gram-positive bacillus that has garnered attention due to its role in human infections, particularly among adolescents and young adults. Traditionally associated with pharyngitis, this organism is increasingly recognized for its involvement in systemic infections, including bacteremia, central nervous system abscesses, and Lemierre’s syndrome. The pathogenicity of A. haemolyticum is attributed to its production of hemolysins and neuraminidase, facilitating tissue invasion and immune evasion. Clinically, infections often present with sore throat, fever, and a characteristic scarlatiniform rash, which can lead to their misdiagnosis as streptococcal pharyngitis. Severe manifestations, though rare, have been documented, particularly in immunocompromised individuals. Diagnosis is challenging due to the organism’s slow growth and potential misidentification as diphtheroids in cultures. Accurate identification necessitates specific culture conditions and biochemical testing. Treatment typically involves beta-lactam antibiotics; however, the emergence of resistance patterns necessitate susceptibility testing to guide therapy. This review aims to consolidate current knowledge on A. haemolyticum, emphasizing its clinical presentations, diagnostic challenges, and management strategies, thereby enhancing recognition and treatment of infections caused by this emerging pathogen. Full article

To address the challenge of detecting weak targets with small radar cross-sections (RCS), this work explores an integrated framework that leverages cross-level multimodal fusion of radar echoes. This method considers the target’s motion properties via Doppler spectrum and phase sequences (direct physical level), and introduces the Gramian Angular Field (GAF) to map the echo amplitude sequence into two-dimensional (2D) structured images, thereby revealing the dynamic evolution characteristics of echo energy (abstract representation level). This approach integrates direct physical attributes and abstract system evolution features within a unified representation. To accommodate the structural differences among modalities, a heterogeneous branch processing network is designed: the Transformer is employed to capture long-range dependencies in one-dimensional (1D) sequences, while ResNet18 is used to extract spatial texture features from two-dimensional images. A self-attention mechanism is further introduced to achieve adaptive fusion of the multimodal data. Experimental results based on the IPIX dataset suggest that this cross-level strategy provides improved detection performance across various scenarios, as observed in complex marine environments. Full article

Background/Objectives: The aim of the current study was to investigate the mechanistic hepatoprotective efficacy of selenium (SE) and chitosan-coated selenium nanoparticles (CS-SENPs) using a rat model induced by lipopolysaccharide (LPS). Methods: CS-SENP was prepared and characterized for particle size, polydispersity index (PDI), zeta potential, transmission electron microscope (TEM), and Fourier transform infrared spectroscopy (FTIR). Male albino rats (n = 40) were divided into four groups: control, LPS, SE, and CS-SENP. SE and CS-SENPs (5 mg/kg orally for 14 days) were given before LPS injection. Tissue architecture was assessed using histopathological analysis. HSP-47 and STEAP-3 protein expression levels were measured using ELISA, and oxidative stress markers were quantitatively evaluated. The expression of HO-1, TLR-4, STAT-3, TRAF-6, and IL-17A was measured using immunohistochemical analysis. Furthermore, HSP-90 expression was evaluated by immunofluorescence labeling. Results: CS-SENP characterization revealed uniform (PDI = 0.125 ± 0.04) nanoparticle size (108.54 ± 2.24 nm), with high zeta potential (+63.92 ± 6.287 mV), attributed to the CS layer, which was confirmed by FTIR and TEM as an electron-lucent halo enveloping the individual SENP cores. CS-SENPs significantly reduced lipid peroxidation (MDA) and restored glutathione (GSH) more effectively than SE. CS-SENPs improved redox (upregulated HO-1) and iron balance (downregulated STEAP-3), and also increased the anti-inflammatory effect (suppressed TLR-4, IL-17A, TRAF-6, and STAT-3). CS-SENPs showed superior antifibrotic efficacy (suppresses stress proteins, HSP-47 and HSP-90). Rats treated with CS-SENPs had nearly normal liver structure. Conclusions: The results concluded that CS-SENPs had superior and multi-targeted hepatoprotection against LPS-induced liver damage. Full article

As widely adopted privacy models in privacy-preserving data publishing (PPDP), k-anonymity and ℓ-diversity have been extensively studied by researchers to enable the release of useful information while preserving data privacy. However, existing methods suffer from several limitations. They often rely on single-dimensional privacy models and lack unified metrics for accurately quantifying privacy leakages. Many approaches overlook the impact of semantic similarity and adversarial prior and posterior beliefs among sensitive attributes and frequently employ suboptimal similarity measures that fail to account for the heterogeneous nature of quasi-identifiers, thereby degrading both privacy protection and data utility. To address these challenges, this paper proposes CAMDP, a unified clustering-based anonymization method for privacy-preserving data publishing with multidimensional privacy quantification. CAMDP constructs equivalence classes that satisfy k-anonymity while simultaneously enhancing sensitive attribute diversity, reducing semantic similarity, and limiting divergence between prior and posterior adversarial beliefs. A unified multidimensional metric is introduced to jointly quantify privacy leakage and information loss, guiding the anonymization process. Additionally, a similarity-aware distance metric tailored to mixed-type quasi-identifiers is employed to reduce information loss. Experimental results on three benchmark datasets, Adult, Careplans, and Airline, demonstrate that CAMDP consistently outperforms state-of-the-art methods. Across all tested configurations, CAMDP achieves the lowest average privacy leakage (0.1235, 0.0795, and 0.1855, respectively), lower average information loss (0.626, 0.636, and 0.60, respectively), and the lowest average intra-cluster dissimilarity (0.586, 0.635, and 0.573, respectively), while maintaining competitive execution time across the three datasets. Full article

Deep neural networks (DNNs) have demonstrated remarkable capability in accurately predicting equilibrium combustion products and thermodynamic properties of diesel combustion. However, the lack of awareness of uncertainty and interpretability has limited their scientific credibility and practical application. In this work, an enhanced DNN framework with uncertainty quantification and explainability is developed. The model achieves high accuracy across all outputs, with R² values exceeding 0.99 for major thermodynamic variables. In this model, Monte Carlo dropout sampling is used to estimate epistemic uncertainty, and prediction confidence intervals are analyzed across all species and thermodynamic outputs, revealing strong correlations for major components. Model explainability is further explored using Shapley additive explanations (SHAP), which attribute the influence of equivalence ratio, temperature, and pressure on each predicted species and combustion characteristics. The combined uncertainty quantification and explainability framework not only enhances confidence in DNN combustion models but also provides physical insight into the relationships between input conditions and equilibrium thermochemistry that are learned by the DNN. Full article

A system’s architectural design plays a vital role in its quality, since quality attributes are system-wide and impacted by the architecture. The evaluation of a software architecture aims to assess its suitability for the purpose of the system, making this practice a core component in the software quality assessment toolkit. Experience shows that evaluating an architecture is not straightforward, and key practical guidance for evaluation progression is a major challenge in real-world cases. This article presents a software architecture evaluation guiding and progression assessment mechanism based on the identification of five essential elements: Architecture Description, Quality Attributes, Business Goals, Architecture Decisions, and Evaluation Adoption. To describe them, this work proposes an extension of the SEMAT Kernel, where each evaluation “essential” is represented as an Alpha with a set of States that depict the (healthy) progression of architecture evaluations. The practicality and usefulness of the approach is assessed with two case studies derived from two previously executed real-world architecture evaluations. The results suggest that when using this conceptualization and description to guide and assess architecture reviews in legacy systems under classic development and maintenance approaches, architects and stakeholders can better understand how to guide and audit the progression of an architecture review; have a ground for reporting results; and regain focus on the evaluation in some scenarios. A key directly derived future research direction is to evaluate the suitability of the proposal in agile-based development contexts. The authors expect that a wider use of this principled definition of the key elements for software architecture evaluations will provide practical and concrete guidance to evaluators, allowing stakeholders to assess specific evaluation efforts, and to eventually improve teaching and learning of the evaluation practice. Full article