Search Results (1,518)

Background/Objectives: Temporomandibular joint (TMJ) mobility disorders encompass opposing clinical problems, ranging from hypermobility, subluxation, and recurrent dislocation to restricted mandibular mobility caused by disc displacement without reduction (DDwoR). Dextrose-based injections have been investigated in both settings, although their therapeutic objectives may differ. This scoping review mapped the clinical indications, injection protocols, outcomes, safety findings, and evidence gaps. Methods: PubMed/MEDLINE, Europe PMC, and BASE were searched from inception through 14 June 2026. Study selection and data charting were conducted independently by two reviewers, and primary studies underwent design-specific JBI appraisal. Results: Eighteen reports were included: 14 reports describing 13 primary clinical studies (two reports were companion publications from the same randomized pilot cohort) and four systematic reviews used for evidence mapping. Among the primary studies, twelve addressed excessive TMJ mobility, and one evaluated DDwoR with limited mouth opening. In hypermobility and recurrent dislocation, within-group reductions in pain, excessive mouth opening, and recurrence were commonly reported, but superiority over placebo or active comparators was inconsistent. In DDwoR, 5% dextrose was associated with improvements in pain, mouth opening, and chewing function, although the evidence was limited by the small sample. No serious adverse events were reported. Conclusions: Substantial heterogeneity precluded quantitative synthesis and prevented identification of an optimal protocol. Indication-specific, adequately powered randomized trials with standardized outcomes and longer follow-up are required. Full article

Robotic piano playing is a challenging benchmark for biomimetic dexterous manipulation, requiring precise timing, coordinated multi-finger motion, and stable key contact. This study proposes a robotic piano-playing framework based on a two-stage reinforcement learning curriculum. Musical Instrument Digital Interface (MIDI) data are converted into target-key and fingering grids to provide future musical goals for policy learning in a parallel MJLab simulation environment. A Soft Actor–Critic (SAC) agent takes a 2106-dimensional observation vector, including joint states, previous actions, musical phase, future key targets, fingering assignments, and piano-key states, and outputs a 21-dimensional continuous action vector for wrist, finger, and global hand-positioning control. Stage 1 weakens physical regularization to facilitate key-pressing acquisition, whereas Stage 2 strengthens power, velocity, acceleration, collision, posture, and finger-speed constraints to improve the regularity of policy outputs and readiness for real-world deployment. Simulation experiments on 30 s right-hand excerpts from Für Elise, Canon, and Beethoven’s Symphony No. 5 achieve frame-wise key-state F1 scores above 0.99 on the first two excerpts and approximately 0.945 on Beethoven. Real-world deployment uses open-loop playback of policy-generated high-level trajectories with low-level joint-position feedback and achieves F1 scores of 0.95, 0.91, and 0.83, respectively, while reproducing representative piano techniques such as chords, octaves, mixed black-and-white-key patterns, overlapping finger actions, and rapid sequential movements. These physical results demonstrate the feasibility of the proposed sim-to-real pipeline for complete 30 s executions; they are not intended as a statistical repeatability study. The results further show that biomimetic robotic hands can learn complex piano-playing skills from MIDI-based task objectives without relying on human motion demonstration trajectories. Full article

Periprosthetic joint infection is among the most serious complications of joint replacement, and the implant surface, where infection begins, is a rational prevention target. Antibacterial titanium surfaces act by releasing an agent, by contact killing, or by resisting adhesion. Yet the field cannot readily tell which method performs best, because efficacy is evaluated with methods that differ at every level and are rarely comparable: the same surface can read as highly effective under one assay and inactive under another, so the test rather than the material decides the verdict. This review asks where comparability is lost and what would restore it, for permanent load-bearing arthroplasty implants. We dissect the gap across in vitro assays, durability testing, surface characterization, in vivo models, clinical infection definitions, and regulatory expectations and ask whether the antibacterial–osseointegration trade-off is assessed coherently. We then propose a ten-item, stage-stratified minimum-reporting checklist and test it retrospectively on thirteen open-access studies. No study reported every item required at its stage; none related antibacterial dose to a stated cytotoxicity threshold; and studies fell short at different tiers, so their reported efficacies are not commensurable. The checklist is offered as a testable starting point for multi-stakeholder consensus, not a finished standard. Full article

Utilising the open-source design of the Federica Prosthetic Hand, we introduce a redesigned print-in-place version that minimises assembly requirements without sacrificing functionality. The original Federica hand has a complicated, time-consuming assembly process. The print-in-place design removes the process of assembling the hand, making it easier for new users to use the design. The approach results in a 21% reduction in the bill of materials and a 21% reduction in weight. Though the 8 N force of the original Federica hand was not reached in this trial, the measured results show that under the same test conditions, the print-in-place hand can match the performance of the original. In a single-specimen analysis, the print-in-place hand produced a higher peak force in four of eight orientations. The redesigned prosthetic hand includes functional joints fully integrated into its structured design with a locking mechanism, eliminating the need for several metal bolts, and a revised dorsal finger contour, reducing the requirement for additional support structures during printing. As with the original hand, the print-in-place prosthetic is open source for anyone to access. Full article

The growing volume of unstructured clinical and genetic text calls for automated processing methods that can meaningfully support clinical decision-making. Most existing work, however, treats the analysis of patient clinical descriptions and the interpretation of genetic reports as unrelated tasks, disregarding the sequence in which real diagnostic decisions unfold. We study two stage-specific families of classification tasks corresponding to the two points at which such decisions are taken—pre-genetic triage from clinical narratives, and post-genetic interpretation of completed genetic reports—and evaluate each family under a single leakage-controlled protocol using TF-IDF text representation with classical machine learning. All experiments use a corpus of 546 records with confirmed provenance, partitioned by a group-constrained master split (349 train/88 validation/109 test, seed 42) in which all 181 text-similarity candidate pairs are confined to a single partition. Task subsets contain 299 and 326 documents at the first stage and 375 and 461 at the second. Text representation relied on Word TF-IDF, Character TF-IDF and combined Word + Character TF-IDF with Logistic Regression and Linear SVM; every configuration was selected on validation Macro F1 alone, with the test partition opened only after selection. RuBERT-tiny2, XLM-RoBERTa base and PubMedBERT were fine-tuned as transformer baselines over three seeds each. Under this protocol, the first stage reached Macro F1 = 0.6876 (Accuracy = 0.8308, ROC-AUC = 0.8500) and the second-stage Macro F1 = 0.8225 (Accuracy = 0.8571, ROC-AUC = 0.9377) for the binary classification of diagnostic status. A controlled comparison holding the documents, split, vectorizer, classifier and hyperparameters fixed and varying only the label version increased Accuracy from 0.5738 to 0.8361 (exact McNemar p = 0.000145), whereas Macro F1 increased numerically from 0.5165 to 0.5966 but not significantly (paired bootstrap p = 0.354; 95% CI of the difference [−0.091, 0.229]). Two Russian and multilingual encoders collapsed to the majority class (Macro F1 = 0.4583), while an English biomedical encoder scored higher than the classical comparator (0.7705 ± 0.0119 against 0.6876) without reaching significance (paired bootstrap p = 0.463). Thus, the controlled experiment supports improved overall correctness, mainly associated with the majority class, but does not establish improved balanced class-wise performance at this sample size. In the second-stage leakage analysis, removing the diagnostic conclusion reduced Macro F1 from 0.8225 to 0.6478 (paired bootstrap p = 0.001), masking the exact label-generating rules reduced it to 0.7455 (p = 0.021), and combined masking reduced it to 0.6389 (p < 0.001). The unmasked Stage 2 score therefore depends materially on explicit report cues and should not be interpreted as evidence of independent diagnostic reasoning. The contribution of this work is a stage-oriented formulation of clinical genetic text classification in which the target variable of each task is aligned with the information available at the corresponding point of the diagnostic pathway, evaluated under a reproducible leakage-controlled protocol with bootstrap confidence intervals and paired significance testing. A unified single-model baseline on the joint target reached Macro F1 = 0.5238, below either stage-specific model on its own target. Because the labels are rule-derived rather than independently expert-validated, the corpus is small, and no external validation was performed, these results characterize what is achievable on this corpus rather than demonstrating clinical readiness. Full article

Low-frequency, irregular water waves with continuously changing propagation directions are difficult to harvest efficiently using conventional power generation devices. This work proposes a gyroscope-pendulum-coupled multilayer triboelectric nanogenerator (GP-TENG), in which a multi-axis gyroscope mechanism, an inertial pendulum, and a helical-structured power generation module are integrated inside a spherical floating body. The gyroscope joints enable the pendulum to respond to waves arriving from any horizontal direction, while the heave and tilting motions of the floating body jointly drive periodic contact and separation of the multilayer triboelectric materials. Motor-driven platform and water tank experiments were conducted to investigate the effects of the number of generating layers, excitation frequency, translational stroke, swing amplitude, and external resistance on the output performance. In the controlled translational tests, the maximum root-mean-square open-circuit voltage, short-circuit current, and transferred charge reached 98.6 V, 2.3 μA, and 242 nC, respectively, and a maximum output power of 16.3 μW was obtained at a load of 81 MΩ. In the water tank, the GP-TENG showed a stable response near 1.42 Hz, with maximum output power of 3.45 μW at a 60 MΩ load. The generator successfully charged the capacitor, lit up LEDs, and powered a commercial temperature and humidity sensor. These results indicate that the GP-TENG provides a compact and low-cost approach for omnidirectional low-frequency wave energy harvesting and a distributed power supply for low-power marine electronic devices. Full article

Retrieval-augmented generation has emerged as a core technological paradigm for addressing the bottlenecks of hallucinations and knowledge lag in large language models. However, many existing reviews focus on a single technical branch or vertical application scenario, making only scattered references to hardware, evaluation methods, and cross-industry empirical evidence, and lacking a systematic, end-to-end integration. This paper conducts research based on a total of 115 papers, comprising foundational literature from 1998–2019 and core RAG literature from 2020–2026, systematically cataloging end-to-end technologies and supporting solutions, establishing a quantitative hardware comparison table and comparing 11 categories of open-source and commercial APIs, constructing a two-tier, four-level standardized evaluation framework, and compiling empirical evidence and implementation challenges across eight industries from 2024 to 2026. Based on this, the paper identifies four major structural contradictions—the retrieval–creation trade-off, the geometric–semantic misalignment as a symmetry problem between representation space and semantic structure, the autonomy–reliability paradox, and evaluation blind spots—as a unified analytical framework for the five major technological strands. Finally, this paper proposes four research directions for practical implementation—differentiable joint optimization, hybrid geometric space learning, interpretable causal reasoning, and multidimensional diagnostic evaluation—providing a systematic reference for both theoretical research on RAG and its deployment in the private sector. Full article

Background: Multi-head self-attention is central to Transformer-based time-series forecasting, yet its head-level information-selection behavior lacks a unified information-theoretic characterization. How much information a single head selects, how inter-head redundancy should be measured, and under what conditions a head can be removed without degrading predictions remain open questions. Methods: We treat each attention head as a discrete auxiliary selection channel whose conditional distribution is the attention weight vector. This yields a closed-form information identity and an entropy-dependent upper bound on selection information: $I(X;J_t^h)\le logL-\mathbb{E}\left[H\left({\alpha }_t^h\right)\right]$. We introduce total correlation—the Kullback–Leibler divergence between the joint head distribution and the product of its marginals—as a distributionally principled redundancy measure and relate head-removal sensitivity to conditional task information under population log-loss. Importantly, the selection-information bound characterizes input-dependent positional selection induced by attention weights, rather than the task information carried by the continuous value-weighted head output. Results: Synthetic experiments confirm the entropy-regularized optimality of softmax attention, the selection-information bound, and the redundancy decomposition under controlled conditions. Time-series forecasting experiments across nine benchmark datasets reveal that the head count achieving the lowest observed mean MSE varies across datasets, and that redundancy–sensitivity relationships are dataset- and head-count-dependent, though none remains statistically significant after multiple-comparison correction. Conclusions: The framework provides a principled diagnostic tool for analyzing selection behavior, inter-head dependence, and head-removal sensitivity in multi-head self-attention. It is a diagnostic framework rather than a new forecasting architecture or a standalone pruning algorithm. Pairwise redundancy carries diagnostic signal but is not, by itself, a complete predictor of head-removal sensitivity. Full article

Robotic joint motor drive systems must combine torque density and dynamic response with low mechanical impedance, safe interaction, and thermal robustness. This review treats gear ratio as a system-level design coordinate realized jointly by the motor, transmission, thermal path, sensing, and control. It synthesizes how ratio selection changes torque–speed capability, reflected inertia, losses, thermal duty, reducer nonidealities, backdrivability, and control bandwidth. The proposed spectrum uses nominal ratio as its primary coordinate while treating reducer topology, application domain, integration level, and compliance as distinct, overlapping descriptors. Mechanism-level conclusions are based on peer-reviewed studies; manufacturer specifications, open-source structures, and model-based engineering examples are identified and interpreted within narrower evidence boundaries. Representative robotic-joint cases connect these mechanisms to application demands, and an iterative framework translates the synthesis into checks on the task envelope, motor–reducer matching, thermal feasibility, transmission nonlinearity, sensing, and control. Relative to gearbox-centered reviews and task-specific motor–transmission optimization studies, this review provides a cross-domain decision map rather than a product ranking or universal predictive model. Full article

Background: Transport costs in industrial waste management can account for up to 60% of total expenditures, yet existing optimization models often rely on simplified distance metrics and treat facility location and routing separately. This paper addresses these gaps. Methods: A simulation model is developed integrating real OpenStreetMap road networks, differentiated environmental risk coefficients by waste hazard class, and joint optimization of the number, location, and capacity of sorting stations and recycling plants. A nearest-available-facility heuristic is applied for routing. The model is implemented as an agent-based simulation in AnyLogic and validated on real data from the Republic of Tatarstan. Results: The optimized configuration (five sorting stations and two new recycling plants) increased the recycling rate from 29.8% to 69.1%, reduced waste sent to storage from 59.3% to 21.5%, and achieved a positive net present value. Transport costs became the dominant cost item (47% of total costs). Conclusions: The model provides a practical decision-support tool for transport planners and logisticians, enabling an assessment of infrastructure decisions on transport work, mileage, and emissions. Integrating real road networks and environmental risk coefficients significantly improves the accuracy of logistics optimization in waste management systems. Full article

Fluids whose viscosity and surface tension depend on deformation history are poorly described by integer-order models, and fractional differential equations have been adopted for them across rheology and applied mathematics. Work on this subject remains scattered, and no synthesis has established which operators are in use, how the equations are solved, or what remains undone. This review supplies that synthesis and states the open problems that follow. Under the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 protocol, 682 records from Scopus, ScienceDirect, and SpringerLink were reduced to 23 eligible articles by deduplication of 61 records, title and abstract screening of 533, and full-text assessment of the 81 retrievable reports. A supplementary search on the term “fractional viscoelastic”, run as a sensitivity analysis, added five more, giving a final corpus of 28 studies from 1986 to 2026, classified by operator, model, solution method, fluid, property, and validation status. The springpot-based Fractional Maxwell Model and the Caputo-derivative dominate, no study applies the Atangana–Baleanu–Caputo or Caputo–Fabrizio operator constitutively, and joint modeling of the two properties is confined to three studies of one lubricating oil. Five open problems are stated, each with its supporting evidence and the direction that would resolve it. Full article

Digital transformation can expand manufacturing firms’ capacity for interorganizational collaboration, but digitalization does not automatically translate into coupled open innovation. Drawing on dynamic capability theory and social comparison theory, this study examines the association between digital transformation and coupled open innovation, evaluates absorptive, adaptive, and innovative capabilities as parallel capability-related pathways, and investigates the external executive pay gap as a boundary condition. The analysis uses 12,791 firm-year observations from 2199 Chinese A-share listed manufacturing firms during 2011–2022. Digital transformation is measured from annual-report disclosures, while coupled open innovation is operationalized as joint patent applications with external co-applicants. Firm and year fixed-effects regressions with firm-clustered standard errors are complemented by Poisson pseudo-maximum-likelihood and negative-binomial count models and firm-cluster bootstrap mediation analyses. Digital transformation is positively associated with coupled open innovation across the linear and count specifications. In the simultaneous parallel-mediator model, the indirect associations through the absorptive-, adaptive-, and innovative-capability proxies are statistically distinguishable from zero, although the innovative-capability association is substantively small. The linear interaction specification indicates a stronger Digital–COI association at higher observed levels of the external executive pay gap, while the exploratory conditional-indirect analysis suggests selective moderation through the innovative-capability pathway rather than a uniform pattern across all three capability pathways. The results clarify how digital resources, organizational capabilities, and executive compensation context relate to formal collaborative innovation and the organizational and economic sustainability of manufacturing firms. Full article

Concrete-faced rockfill dams (CFRDs) constructed in narrow and steep valleys are strongly influenced by the mechanical interaction between the dam body and abutment bedrock. Under long-term construction and reservoir impoundment, time-dependent frictional slip along the dam–abutment interface may alter deformation transfer within the dam system. This study investigated the Dashixia extra-high CFRD through large-scale contact rheological tests and three-dimensional finite element analysis. A contact rheological model was established from interface tests and incorporated into a full-scale numerical model considering valley topography, staged construction, and reservoir impoundment. The influence of contact rheology on dam deformation, face-slab response, and peripheral-joint behavior was evaluated. The results show that contact rheology has little effect on global dam settlement but significantly increases horizontal displacement and redistributes local deformation near the abutments. Under the normal reservoir water level, the maximum upstream displacement, downstream displacement, and settlement increase by 0.7, 3.4, and 1.5 cm, respectively. Meanwhile, the maximum peripheral-joint settlement increases from 43.7 to 69.8 mm, and the maximum tensile opening increases from 8.7 to 12.8 mm. For the Dashixia CFRD, inclusion of dam–abutment contact rheology increases the predicted maximum peripheral-joint settlement and tensile opening by 59.7% and 47.1%, respectively, highlighting the greater sensitivity of local joint deformation compared with global dam settlement. Full article

Interlocking permeable pavements (IPPs) are increasingly adopted within sustainable urban drainage systems to reduce runoff, improve water quality, and strengthen climate-resilient urban infrastructure. However, clogging remains the principal constraint on their long-term hydraulic performance and wider implementation. This review synthesises current evidence on clogging mechanisms, hydraulic decline, and lifecycle mitigation strategies for permeable interlocking concrete pavements (PICPs), concrete grid pavements (CGPs), and plastic grid pavers (PGPs). The literature is dominated by PICP studies, with CGP and PGP underrepresented, restricting typology-specific assessment. Sediment accumulation within joints, grid openings, bedding layers, and near-surface interfaces is consistently identified as the primary clogging mechanism, while traffic, rainfall-runoff loading, biological processes, pollutant retention, and sediment inputs from adjacent impervious surfaces further influence hydraulic deterioration. The findings indicate that hydraulic performance is influenced not only by pavement age but also by interactions among pavement design, filler or joint material, drainage configuration, construction quality, sediment exposure, monitoring, and maintenance. Effective mitigation therefore requires lifecycle management, encompassing source control, pretreatment, appropriate material selection, construction quality assurance, routine hydraulic monitoring, and timely preventive and restorative maintenance. Future research should prioritise standardised clogging assessment protocols, improved laboratory–field integration, targeted investigation of CGP and PGP, biological and pollutant-linked clogging processes, climate-driven rainfall extremes, and decision-support. Full article

Unmanned aerial vehicle (UAV)-based multispectral imaging enables flexible, non-destructive crop monitoring. Although UAV imagery offers much higher spatial resolution than satellite platforms, its effective spatial detail at typical operational flight altitudes can still be insufficient for plant-level interpretation and fine canopy structure, which can reduce vegetation-index reliability. Most super-resolution (SR) research targets RGB or satellite imagery and emphasizes perceptual or pixel-wise quality, leaving the spectral fidelity of reconstructed UAV multispectral imagery under-examined. This study benchmarked an SR evaluation framework for UAV-based five-band crop imagery (Blue, Green, Red, Red-edge, and near-infrared) using the open-source AI Hub cabbage dataset, with low-resolution inputs generated by controlled downsampling at ×2, ×3, and ×4. Nine methods (bicubic, SRCNN, EDSR, RCAN, SwinIR-based, ESRGAN-based, HAT-based, DAT-based, and DRCT-based SR) were compared under joint five-channel and band-wise reconstruction on 2170 test scenes using image-quality, spectral-angle, vegetation-index (NDVI, GNDVI, NDRE), band-wise, and efficiency metrics. EDSR and RCAN gave the most balanced performance. At ×4, band-wise reconstruction was strongest for per-band spatial fidelity, where EDSR reduced RMSE by 12.6%, and RCAN lowered near-infrared RMSE by about 21% relative to bicubic, whereas joint reconstruction with its spectral-angle and vegetation-index losses best preserved spectral relationships (spectral angle and vegetation-index errors). Learning-based gains were clearest at ×4. The recently proposed HAT-based, DAT-based, and DRCT-based attention models achieved the strongest pixel-wise RMSE and PSNR but did not surpass EDSR or RCAN on spectral angle or vegetation-index preservation under the equalized training budget. These results indicate that UAV multispectral SR should be assessed by spatial fidelity together with spectral consistency and agricultural index reliability. Full article