Search Results (4,092)

Coplanar capacitance detection exhibits remarkable advantages in the detection of road texture in asphalt layers, including high sensitivity and minimal environmental constraints. However, the inherent performance contradiction between signal strength and penetration depth of traditional interdigitated coplanar capacitance sensors (ICCSs) has restricted their widespread application in road texture detection. To address this issue, a hybrid approach combining response surface methodology (RSM) and non-dominated sorting genetic algorithm II (NSGA-II) is developed to optimize the structural parameters that influence the signal strength and penetration depth of a novel ICCS. Initially, a central-composite design (CCD) based on RSM is employed to establish statistical models for the two key sensing performances of ICCSs, namely signal strength and penetration depth. Subsequently, Analysis of Variance (ANOVA) and three-dimensional (3D) response surface plots are utilized to investigate the significant effects of various structural parameters (electrode length, width, and inter-finger gap) on the two sensing performances. Furthermore, NSGA-II is applied to search for global optimal solutions using the established statistical models, thereby achieving multi-performance optimization of the ICCS. Finally, the fabricated ICCS is used to detect the surface texture of asphalt mixture specimens with different gradations, and the results are compared with those obtained by laser point cloud detection. The results indicate that both statistical models are highly significant, with the coefficient of determination (R-squared) exceeding 0.95. All individual structural parameters have a significant impact on the two sensing performances. Based on the optimization by the RSM-NSGA-II hybrid method, the predicted optimal parameters are verified, showing a relative error of less than 5% from the simulation results. Additionally, the detection results of the ICCS are consistent with the laser point-cloud data, demonstrating its feasibility for pavement texture detection. Full article

As an advanced non-contact dynamic sealing technology, compliant foil seals offer notable advantages, including a simple structure, light weight, ease of installation, and strong self-adaptability. However, in most designs, the foil stiffness is uniform, resulting in a substantial increase in gas leakage under high inlet pressures. Considering the distinct pressure conditions in compliant foil seals, a variable foil thickness model (VTM) along the axial direction is designed to match the pressure gradient from the inlet to the outlet. By aligning the foil thickness variation with the pressure gradient, the foil deformation is more uniformly distributed axially, thereby maintaining low leakage under high-pressure differentials. In this study, the gas film thickness equation and the Reynolds equation for the compliant foil seal are established and solved using the finite difference method combined with a point-wise iterative approach. First, the static characteristics of a traditional uniform-stiffness compliant foil seal under different rotational speeds and inlet pressures are analyzed. The results indicate that leakage increases substantially under high inlet pressure. The performance of the VTM under different operating conditions—including rotational speed and inlet pressure—is investigated. The results show that an appropriately designed VTM can maintain very low leakage under high-parameter conditions, albeit with some sacrifice in gas film pressure and an increase in viscous friction. Furthermore, surface micro-textures are integrated with the VTM. The study finds that the two approaches exhibit complementary effects: micro-textures enhance the dynamic pressure effect of VTM, while the variable-thickness design maintains extremely low leakage. The combined model demonstrates excellent performance across different speeds and inlet pressures. For instance, at a rotational speed of 30,000 r/min, the gas leakage is reduced by 50.04%, and the maximum gas film pressure is increased by 70%. Full article

In answering the following two important questions about forgetting or forgetfulness—Can a “happy forgetting” be possible? Can forgetfulness, including (non-Western) Daoist-Zhuangzian forgetfulness, be a virtue?—this article takes a cross-cultural approach, using both Western thought and Asian materials, especially the Zhuangzi, to seek viable responses. The aim is to widen our horizons to include different Western and non-Western traditions of forgetfulness and assimilate them into our contemporary discourse on human forgetting and its ethical dimensions. Full article

Food products containing plant ingredients are particularly vulnerable to economically motivated adulteration (EMA), which poses risks to consumer trust and regulatory compliance. While traditional methods—such as microscopy, chemical profiling or targeted PCR—struggle to detect adulterants in processed food products or complex mixes, DNA metabarcoding offers a non-targeted, high-throughput alternative. This study presents a nanopore sequencing-based technique that is easy to implement, cost-effective and sufficiently sensitive to detect substitutions, with a focus on spices and herbal teas as model matrices. The method was evaluated using eight single-species reference samples and five commercial multi-ingredient products. It reliably detected undeclared contaminants (e.g., mint in oregano) and species substitutions. Compared to single-barcode approaches, the combination of ITS2 + matK + trnH-psbA markers achieved higher sensitivity. The proposed workflow requires minimal infrastructure and a 2–4-day turnaround time. However, factors such as DNA degradation in highly processed foods, database gaps, and biological diversity limited detection in some cases. These findings demonstrate the workflow’s potential as a first-line screening tool for food authenticity testing, aligning with requirements such as EU regulation 1169/2011 on food labelling or the FDA’s Economically Motivated Adulteration (EMA) program. Future work should validate the method against regulatory thresholds and expand testing to a broader variety of species and matrices. Full article

During HIV infection, the immune system initiates an immune response to control the viremia. In this in vitro study, we investigated the immunomodulatory and anti-HIV potential of a traditional medicine formulation, Product Nkabinde (PN). A freeze-dried extract of PN was used to determine cytotoxicity in MT4 cells. Non-cytotoxic doses were used to evaluate the immunomodulatory and anti-HIV effects of PN using neutralization, prophylactic and treatment approaches. Post-treatment, cytokine quantification and p24 detection were performed. In the neutralization strategy, PN restored IL-1α (p = 0.0389) and IL-10 (p = 0.0443) to levels of uninfected cells. It also reduced HIV-induced elevations in IL-8 (p = 0.035), IP-10 (p = 0.0886), and MCP-1 (p = 0.0733) compared to HIV-infected cells. In the prophylactic approach, HIV infection upregulated IL-1α (p = 0.676), which was suppressed by PN. In the treatment strategy, PN reversed the elevated levels of IL-1α (p = 0.049). IL-10 was fully restored by PN to levels of uninfected cells. In all strategies, PN induced a significant and dose-dependent decrease in HIV replication. These findings demonstrate that PN exerts potent immunomodulatory effects all strategies used by reversing HIV-induced cytokine dysregulation, thereby inducing significant anti-HIV effects. Full article

Stock markets are complex adaptive systems whose feature dependencies evolve over time, and identifying which signals genuinely drive returns is fundamentally a problem of information. Traditional feature selection methods and linear models rely on statistical correlations and overlook causality, which weakens their predictive performance in non-linear, evolving markets. This paper develops an information-theoretic approach to causal feature selection based on Markov Blanket discovery. We apply the Iterative Parent–Child-based search of Markov Blanket (IPCMB), whose conditional-independence tests are conditional mutual information measures, to recover the Markov Blanket of next-month returns—the minimal feature set that carries all Shannon information about the target. We then pair it with a Classification and Regression Tree (CART), whose impurity-based splitting admits an information-gain interpretation, to predict the direction of Chinese A-share returns non-linearly. Using data on 2760 stocks, IPCMB selects 13 causal features from 72 candidates, and CART forecasts whether the next month’s return is positive. The empirical results show an accuracy of 58.0%, 7.7 percentage points above the all-features CART benchmark, and a 13-month cumulative return 35.88% higher than that of the CSI 300 index. The findings indicate that selecting features according to their causal information content and combining them with interpretable tree-based prediction can support more reliable investment decisions in emerging markets. Full article

Emerging contaminants (ECs) in cosmetics are introduced through various pathways, including illegal adulteration, raw material impurities, and packaging migration. Characterized by chronic exposure, potential toxicity, and an elusive nature, these contaminants pose a continuous and serious threat to public health. Currently, the regulatory control of ECs in the cosmetics industry is still evolving. Traditional targeted analysis, which relies heavily on predefined reference lists and standards, suffers from narrow coverage and fails to meet modern safety requirements for the comprehensive screening and early warning of ECs. To address this challenge, liquid chromatography–high-resolution mass spectrometry (LC-HRMS), combined with non-targeted screening (NTS) strategies and molecular networking (MN), serves as a powerful analytical tool. By operating in a full-scan mode without predefined targets, this technology comprehensively captures thousands of chemical features in cosmetic samples, enabling the prioritization and tentative annotation of concealed risk substances for subsequent structural confirmation. Furthermore, the spectral similarity clustering capability of MN facilitates the identification of homologous derivatives and structural analogs from a single known compound, significantly enhancing the efficiency and systematic discovery of ECs. From a regulatory perspective, this integrated approach provides crucial technical support for authorities to accurately assess risks and ensure consumer safety. Ultimately, it promotes a paradigm shift in cosmetic safety management, from a passive response to known hazards to the proactive prevention and control of emerging risks. Full article

This review examines the evolving role of muscle biopsy in the diagnosis of neuromuscular disorders in the era of modern genomic medicine. Historically the cornerstone of myopathy diagnosis, muscle biopsy enabled structural, histochemical, and ultrastructural characterization of muscle diseases. However, the introduction of next-generation sequencing and other genomic technologies has shifted the diagnostic paradigm, with genetic testing now serving as the preferred first-line approach for many hereditary myopathies due to its non-invasive nature and high diagnostic yield. However, muscle biopsy remains indispensable in the evaluation of inflammatory, toxic, metabolic, mitochondrial, and certain rare acquired myopathies. Biopsy is also valuable when genetic testing is inconclusive, particularly for interpreting variants of uncertain significance, through histopathological, immunohistochemical, and biochemical analyses. In certain disorders, diagnosis may rely primarily on biopsy findings. Emerging technologies, including RNA sequencing, transcriptomics, proteomics, spatial transcriptomics, and artificial intelligence-assisted pathology, are expanding the diagnostic value of muscle tissue beyond traditional morphological assessment. Rather than being replaced by genomic medicine, muscle biopsy is evolving into a complementary component of an integrated diagnostic strategy that combines clinical, pathological, and molecular data to improve diagnostic accuracy and guide precision medicine in neuromuscular disorders. Full article

Based on survey data from 579 farmer households in major non-timber forest product (NTFP) regions of Zhejiang Province, this study comprehensively employs binary logit/ordered probit and machine learning methods for cross-validation. It systematically examines the effects of multiple factors, including perceived property rights security, technical training, village rules and regulations, ecological awareness, and economic incentives, on forest farmers’ adoption of green production technologies. The cross-validation between econometric and machine learning approaches enhances the reliability of the findings. Results show that perceived property rights security is robustly and positively associated with green production behavior, while village rules and regulations and ecological awareness emerge as the two most critical driving factors. These associations exhibit significant NTFP-type heterogeneity: the impacts of technical training and forestry subsidies vary in direction depending on the crop cultivated, rendering traditional one-size-fits-all policies ineffective. This study highlights the crucial role of informal institutions and environmental awareness in the green transition, offering empirical evidence for designing differentiated training programs, optimizing penalty gradients, and implementing targeted subsidy policies. Full article

Online flatness detection of aircraft rudder surfaces serves as a pivotal core procedure for ensuring the manufacturing precision, aerodynamic performance and operational safety of aeronautical components. Traditional plane fitting-based detection approaches are constrained by low detection efficiency, susceptibility to local optimal solutions, weak anti-noise robustness and limited automation capability, which fail to satisfy the micron-level high-precision online detection requirements for curved composite rudder surfaces in batch manufacturing scenarios. To address the aforementioned technical bottlenecks, this study proposes a bidirectionally coupled PSO-SA hybrid optimization algorithm for non-convex minimum zone flatness evaluation of curved rudder surfaces, which overcomes the unidirectional open-loop iteration limitation inherent in conventional serial PSO-SA composite frameworks. Two targeted algorithmic improvements are elaborated in this work: a residual-adaptive nonlinear inertia weight strategy, which dynamically balances global exploration and local exploitation capabilities based on the fluctuation characteristics of free-form surface measurement residuals; and a measurement noise-modified Metropolis acceptance criterion, which substantially enhances the algorithm’s anti-interference performance against on-machine trigger sampling noise. Integrating with the trigger-type on-machine detection hardware of computer numerical control (CNC) machine tools, an integrated online detection system is established to realize the full-process functions of point cloud data acquisition, error compensation, intelligent plane fitting and flatness error evaluation. Meanwhile, the complete technical workflow involving measurement path planning, probe calibration and algorithm iterative solution is systematically illustrated. Comparative simulation experiments implemented on the MATLAB platform demonstrate that the proposed algorithm exhibits superior performance in convergence speed, fitting accuracy and optimization stability over five mainstream algorithms, including standard particle swarm optimization (PSO), standard simulated annealing (SA), comprehensive learning PSO (CLPSO), adaptive cooling SA and conventional serial PSO-SA. On-machine physical measurement experiments are conducted on 24 aircraft rudder workpieces covering aluminum alloy skins and assembled riveted components. After multi-dimensional systematic calibration, the overall detection error of the developed system is controlled within 1 μm. The experimental results indicate that the average flatness error calculated by the proposed bidirectionally coupled PSO-SA algorithm is 29.7 μm, which is 30.1% and 38.5% lower than that of standard PSO and standard SA, respectively, fully complying with the aviation flatness tolerance specification of 0.1–0.3 mm. Moreover, the full detection cycle for a single workpiece is only 2.1 min, achieving a 34.4% reduction in detection time compared with standard PSO and effectively improving the efficiency of online in-process inspection. One-way analysis of variance (ANOVA) combined with Tukey’s posthoc test further verifies that the accuracy superiority of the proposed algorithm is statistically significant. This research provides a targeted theoretical basis and complete engineering implementation scheme for intelligent flatness detection of aerospace curved thin-walled parts, and offers a valuable technical reference for form and position error evaluation of irregular industrial components under noisy measurement conditions. Full article

Background: Digital behavioral phenotyping of autism spectrum disorder (ASD) offers a promising approach for developing more scalable diagnostic frameworks across diverse global contexts. Machine learning (ML) models show promise for ASD diagnosis using behavioral videos, but critical questions remain regarding whether models trained on data from one country work in another, and how the background of the raters affects the accuracy. Our work addresses these questions by testing whether ML models can accurately diagnose ASD across different populations and rater groups. Methods: This work evaluates the performance of a supervised ML framework for binary classification of ASD versus non-ASD [speech, language and communication disorders (SLC) + neurotypical (NT)] in a cohort of 227 children in Bangladesh. We first assessed the cross-domain model transferability of a clinical-instrument-trained logistic regression model (LR-9) on behavioral ratings that were based on videos of Bangladeshi children interacting with caregivers and toys at two major child development centers in Dhaka, Bangladesh. We then trained five diverse classifiers (Logistic Regression, Random Forest, XGBoost, SVM, and RuleFit) on the full annotated Bangladeshi dataset. Using SHAP-based consensus elbow feature selection, we identified a compact set of features that maintained the performance. Finally, we developed ensemble models to improve predictive stability. Results: The LR-9 model, originally trained on U.S. clinical instrument data, was evaluated on video-based behavioral ratings from 214 Bangladeshi children. When tested on Bangladeshi clinician ratings, the LR-9 model achieved a sensitivity of 86.1% (95% CI: [0.78–0.93]) and AUC of 0.79 (95% CI: [0.73–0.86]). The distinction across rater groups was between trained raters (clinicians and students) and crowd workers, who showed lower sensitivity 28.5% (95% CI: [0.21, 0.39]). When tested on the aggregated ratings from all groups, the model achieved an AUC of 0.78 (95% CI: [0.72–0.84]). Inter-rater reliability followed the same pattern: individual agreement was fair (Krippendorff’s α = 0.26), but the multi-rater consensus was reliable (ICC(1,k) = 0.84), with Bangladeshi clinicians showing the highest agreement (α = 0.34) and crowd workers the lowest (α = 0.20). We then trained new models directly on the Bangladeshi ratings. All model types achieved similar AUC values (0.86–0.89), with overlapping confidence intervals. Using just 8–11 key behaviors kept the similar performance while cutting the features by 66–75%. Combining ensembles gave similar results (e.g., Bayesian averaging: AUC 0.88 [0.78, 0.95]) but with more stable predictions. Conclusion: This study provides evidence that mobile video-based ASD diagnosis can achieve comparable performance (AUC: 0.89 [0.76, 0.96]) to models trained on clinical instrument data. This work contributes to the development of broader adaptable autism detection tools, bypassing the dependence on traditional clinical instrument data. Full article

Antimicrobial resistance (AMR) among pathogens involved in intra-abdominal infections (IAIs) represents a critical and escalating clinical challenge. The interconnected nature of antimicrobial resistance, spanning human medicine, veterinary practice, agricultural use and environmental reservoirs, has required coordinated international responses based on the ‘One Health’ principle. This study presents an update on efforts underway worldwide to develop new antibiotics, novel combined antimicrobial agents, and alternatives to classic therapies for IAIs. New antibiotics or compounds with antibacterial activity are currently in various stages of clinical trials, including several fluoroquinolones, beta-lactamase inhibitors, and polymyxin analogues. To reduce the risk of bacterial resistance, various additions to antimicrobial treatments are being explored, such as nanoparticles (NPs), antimicrobial peptides (AMPs), bacteriophages, the CRISPR/Cas system, and probiotics. Each modality offers distinct mechanisms that circumvent established resistance pathways, including multi-target membrane disruption, sequence-specific gene editing, and microbiome restoration. Current preclinical and clinical evidence is synthesized, and key translational barriers, including delivery challenges, safety concerns, regulatory complexity, and the need for IAI-specific pharmacokinetic data are critically examined. In conclusion, the convergence of novel antibiotic agents and non-traditional antimicrobial strategies reviewed herein provides the foundation for a new paradigm in the management of drug-resistant IAIs. The transition from a monotherapy-centric approach to an integrated, multi-modal treatment framework, guided by rapid diagnostics and informed by antimicrobial stewardship, will be essential to preserve therapeutic efficacy against AMR threats of the coming decades. Full article

Precise step length estimation (SLE) is a key necessity for not only navigation systems design but also gait health monitoring in neurological conditions. Among existing solutions, non-invasive inertial sensor-based approaches operating without dedicated infrastructure are more cost-effective. Many such methods, however, rely on bodily affixed inertial sensors rather than freely held smartphone sensors. Traditional signal processing approaches, on the other hand, offer varying accuracy across diverse gait patterns due to user parameter calibration. This study, thus, proposes a regression-based SLE framework employing eight regression algorithms: linear regression (LR), k-nearest neighbors, support vector machine, decision tree, elastic network, random forest, histogram-based gradient boosting (HGB) regressor, and artificial neural network (ANN). Their extensive and rigorous evaluations across varied window sizes, using a dataset collected in normal and fast walking modes with two device positions (hand-held and trouser-pocket) during three evaluation scenarios, demonstrate the HGB regressor’s outstanding performance, achieving the lowest mean absolute error (MAE) below 1 cm across four different contexts under leave-one-out cross-validation-based evaluation and three in the seen test evaluations. Moreover, the findings report the ANN’s exceptional generalization capacity over other models and the previous method IRT-SD-SLE in unseen test evaluations, with an MAE not exceeding $6.3$ cm. The extensive evaluations of training and testing times reveal the highest computational efficiency for LR, moderate efficiency for the HGB regressor, and the highest training cost for the ANN, indicating a clear trade-off between MAE and computational expense. Additionally, this study includes an insightful discussion on the performance results, including the trade-offs between accuracy and efficiency. Full article

Recruitment and admission of highly qualified applicants to Doctor of Medicine (MD) programs are vital components of many academic institutions. We aimed to develop a structured curriculum to facilitate consistent and mission-aligned review of applicants within our MD admissions program. Across the 2023–2024 admissions cycle, we utilized Kern’s Six-Step Approach to Curriculum Development to design and implement a simulation-based curriculum to enable mission-aligned applicant evaluation, beginning with the interview process. The training targeted faculty interviewers and admissions committee members responsible for reviewing a diverse applicant pool that includes many non-traditional candidates. Our training was completed by 32 interprofessional medical school faculty, of whom 28 submitted both pre- and post-training rubrics, and 24 completed a retrospective post-then-pre survey. On a 4-point Likert scale, comfort applying competency-based interviewing (CBI) within the mission-aligned review framework increased from 3.0 to 3.8 (p < 0.0001, 95% CI 0.44–0.97), and ease of use for the new rubric was rated 3.4 compared to 3.0 for the prior rubric (p = 0.0215). Concordance with ideal scoring improved from 68% to 73% (p = 0.0287), corresponding to a mean increase from 6.11 to 6.54 of 9 competencies correctly assessed. Identification of weak competencies increased from 14% to 27% (p = 0.0287). Faculty rated the overall quality of the training as 3.7/4 (SD 0.5). These findings, based on a small sample from a single institution, suggest that simulation-based faculty development may improve interviewer comfort, rubric usability, and the process measure of scoring consistency in a simulated MD admissions setting. Full article

Although new AI systems have been developed based on the integration of information from multiple sources under conditions of uncertainty, classical probabilistic models are unable to provide structured, interpretable, and reliable representations in the presence of contextual and order effects. Specifically, the fundamental principles of Kolmogorov’s assumptions underlying the modeling overlook certain common violations in real-world decision-making processes, such as non-commutativity, contextual dependence among agents, and interaction effects between information sources characterized by experiential heterogeneity. A Positive Operator-Valued Measure (POVM) formalism defined on a Hilbert space of latent states forms the basis of this article’s structured epistemic representation framework to support the reliability and interpretability of the black box in AI. The resulting model generalizes the classical epistemic quadruplet: Probability, Plausibility, Credibility, and Possibility within a single geometric framework in which three essential non-classical effect mechanisms emerge—(i) the non-commutativity of information acquisition, (ii) the contextuality arising from incompatible observational frameworks, and (iii) the interference interactions between information acquisition channels. We propose the concept of a quantum-inspired fusion operator (QI-Happenability), which introduces symmetric and antisymmetric feedback interaction terms based on the estimation of ordered residuals. An analysis of the proposed framework is then performed using real data, validating the model on the Efron et al. diabetes regression dataset (N = 442; ten standardized physiological predictors; continuous disease progression target), available in scikit-learn, which showed a 17.4% reduction in mean absolute error (MAE) compared to traditional models and significant improvements over polynomial machine learning baselines, as well as ensemble machine learning methods, within a rigorous cross-validation protocol. Contextual analysis indicates that 68% of cases violate classical bounds (CHSH inequality, p < 0.001), empirically confirming the non-classical structured representation in multi-source data. The results confirm the proposed approach as a simpler, more interpretable, and more reliable alternative to black-box models: this work demonstrates how the use of structured epistemic representations in reasoning under uncertainty preserves formal interpretability while retaining useful semantic information. By linking quantum cognition and applied AI, this work could help lay the groundwork for a new generation of interpretable and reliable decision-making systems. Full article