Search Results (12,817)

Salmonellosis remains a persistent threat to global food safety and poultry productivity, compounded by rising antimicrobial resistance. Here, we report the in silico design and immunoinformatic validation of a broad-spectrum, edible multi-epitope vaccine targeting conserved adhesion and biofilm-associated proteins (FimH, AgfA, SefA, SefD, and MrkD) of Salmonella spp. Two constructs were engineered by integrating cytotoxic (CTL) and helper (HTL) epitopes with β-defensin-3 (HBD-3) or lipopolysaccharide (LPS) adjuvants, optimized for expression in Chlorella vulgaris. Structural modeling confirmed native-like folding (z-scores −2.58 and −5.22) and high stability indices. Molecular docking and dynamics revealed that the LPS-adjuvanted construct (Construct 2) forms a highly stable complex with Toll-like receptor 3 (HADDOCK score −63.4; desolvation energy −50.2 kcal/mol), indicating potent innate immune activation. Immune simulations predicted strong IgM-to-IgG class switching and durable humoral responses, consistent with effective antigen clearance. Codon optimization achieved high adaptability for algal expression (CAI = 0.93; GC ≈ 65%), supporting scalable microalgae-based production. Compared with current parenteral vaccines, offering a low-cost, non-invasive way to curb Salmonella in poultry, this edible vaccine platform reduces dependence on antibiotics. Our approach, which combines computational vaccinology with a safe-by-design sustainable biomanufacturing perspective, outlines a One Health framework for advancing antimicrobial stewardship and food safety. Full article

With the expansion of interconnection in power systems and the extensive adoption of phasor measurement units (PMUs), the secure operation of power systems has been increasingly covered in research. In this article, a unified online framework for pre-fault and post-fault dynamic security assessment (DSA) is proposed. First, maximum mutual information (MIC) and the random subspace method (RSM) are employed to select the key variables and enhance the diversity of input data, serving as feature engineering. Then, a deep forest (DF) regressor and classifier are utilized respectively to predict security margin (SM) and security state (SS) during online pre-fault and post-fault DSA based on the selected variables. In pre-fault DSA, scenarios with high SM are identified as stable, while those with low SM are forwarded to post-fault DSA. In addition, a time self-adaptive scheme is employed to balance low response time and high prediction accuracy. This approach prevents the misclassification of unstable scenarios as stable by either outputting high-credibility predictions of unstable SS or deferring decisions on SS until the end of the decision-making period. The unified framework, tested on an IEEE 39-bus system and a practical 1648-bus system provided by the PSS/E version 35 software, demonstrates significantly improved assessment accuracy and response times. Specifically, it achieves an average response time (ART) of 2.66 cycles for the IEEE 39-bus system and 3.13 cycles for the 1648-bus system while maintaining an accuracy exceeding 98%, surpassing the performance of currently widely used deep learning models. Full article

Accurate analysis of commutation phenomena in permanent magnet DC (PMDC) motors requires simultaneous consideration of electromagnetic field distribution and armature circuit dynamics. Classical circuit-based models are unable to properly capture transient effects occurring in short-circuited coils during commutation, while purely field-based models neglect the influence of the supply circuit. In this paper, a coupled field–circuit model of a PMDC motor with an innovative magnetic circuit based on rectangular NdFeB permanent magnets is presented. The model combines a two-dimensional finite element electromagnetic analysis with a segmented armature circuit and dynamic commutator switching, allowing the electromotive force to be computed individually for each coil based on the actual magnetic field distribution. The novelty of the proposed approach lies in the integration of a non-standard rectangular permanent magnet topology with a coil-resolved field–circuit commutation model, validated on a physical motor prototype. Simulation results are compared with experimental measurements obtained from a laboratory prototype at rotational speeds of 850 and 1000 r/min. The predicted electromagnetic torque shows good agreement with measurements, with deviations below 5%, while the armature current is estimated with an error of up to approximately 20%, primarily due to model simplifications. The developed model provides direct access to transient commutation waveforms and constitutes a practical tool for the analysis and design optimization of PMDC motors operating under dynamic conditions, particularly in cost-sensitive and reliability-oriented applications. Full article

Tantalum pentoxide (Ta₂O₅) films deposited on fused silica substrates are critical components of high-power laser systems. Ion-assisted electron beam evaporation (IAD-EBE) is the mainstream technique for fabricating Ta₂O₅ films. However, it commonly requires extensive experimental efforts for deposition quality optimization, while each coating cycle is extremely time-consuming. To solve this issue, this work establishes a dataset targeting the surface roughness (Rq) and refractive index (n) of Ta₂O₅ films using atomic force microscopy, as well as ellipsometer and deposition experiments. Influence of assisting ion source beam voltage (V)/current (I) and Ar (Q₁)/O₂ (Q₂) flow rate on the n and Rq of Ta₂O₅ films are analyzed. Combining energy-field mechanism analysis with a Bayesian optimization approach (PI-BO), both deposition quality prediction and feature analysis of process parameters are achieved. The determination coefficient/mean absolute error for the prediction models of n and Rq reach 0.927/0.013 nm and 0.821/0.049 nm, respectively. Based on sensitivity analysis, the weight factors of V, I, Q₁, and Q₂ affecting n/Rq of Ta₂O₅ films are determined to be 0.616/0.274, 0.199/0.144, 0.113/0.582, and 0.072/0.000. V and Q₂ are identified as the core factors for regulating deposition quality. The optimal ranges for V and Q₂ are 600~700 V and 70~80 sccm, respectively. This study proposes a PI-BO method for predicting Rq and n of Ta₂O₅ films under small-data conditions, while determining the preferred parameter ranges and their sensitivity weight factors. These findings provide effective theoretical support and technical guidance for IAD-EBE strategy design and optimization of optical films in high-power laser systems. Full article

Background/Objectives: Hydroxyurea is currently the standard disease-modifying therapy for reducing sickle cell disease (SCD) complications; however, drug labels currently advise discontinuation of breastfeeding during hydroxyurea therapy due to limited human data on the risk of hydroxyurea exposure in breastfed neonates. Methods: A physiologically based pharmacokinetic (PBPK) model for hydroxyurea was built and verified with data from non-lactating adult patients with SCD. The model was then extended to predict hydroxyurea in nursing and in paediatric populations. Predictions were compared to the observed data. Results: The PBPK model predictions for hydroxyurea pharmacokinetics described the observed data in both adult and paediatric subjects with SCD. Observed concentration profiles were within the 5th–95th prediction intervals, and predicted PK parameters were within 2-fold of the observed values. The predicted milk-to-plasma ratio was 0.8. Neonatal exposure to hydroxyurea via breast milk as a percentage of maternal exposure increased from 0.6% at 1 day to 10% at the 4th week postpartum before declining to 5%, 3%, and 2% at 6, 9, and 12 months postpartum, respectively. Conclusions: About 56% of total milk hydroxyurea exposure is within the first 3 h of post-maternal dose. Disposal of this early milk would reduce the exposure of breastfed children. The reduction in exposure is especially pronounced around the first 1 month postpartum. Lactation PBPK models offer a physiological approach to assess real-life scenarios that are difficult to investigate in clinical studies and provide useful results for future clinical study design and clinical recommendations. This was exemplified with hydroxyurea in the current work. Full article

The construction industry is undergoing a rapid digital transformation, with Digital Twins (DTs) and Extended Reality (XR) as two emerging technologies with great potential. Despite their potential, there are several challenges regarding DT and XR use in construction projects, including implementation barriers, interoperability issues, system complexity, and a lack of standardized frameworks. This study presents a systematic literature review (SLR) of DT and XR technologies—including Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR)—in the construction industry. The study analyzes 52 peer-reviewed articles identified using the Web of Science database to explore thematic findings. Key findings highlight DT and XR applications for safety training, real-time monitoring, predictive maintenance, lifecycle management, renovation or demolition, scenario risk assessment, and education. The SLR also identifies core enabling technologies such as Building Information Modeling (BIM), Internet of Things (IoT), Big Data, and XR devices, while uncovering persistent challenges including interoperability, high implementation costs, and lack of standardization. The study highlights how integrating DTs and XR can improve construction by making it smarter, safer, and more efficient. It also suggests areas for future research to overcome current challenges and help increase the use of these technologies. The primary contribution of this study lies in deepening the understanding of DT and XR technologies by examining them through the lenses of their benefits as well as drivers for and challenges to their adoption. This enhanced understanding provides a foundation for exploring integrated DT and XR applications to advance innovation and efficiency in the construction sector. Full article

With the large-scale integration of power electronic converters, non-linear loads, and renewable energy generation, voltage and current waveform distortion in modern power systems has become increasingly severe, making harmonic resonance amplification and non-stationary distortion more prominent. Accurate and robust harmonic-level prediction and detection have become essential foundations for power quality monitoring and operational protection. However, traditional harmonic analysis methods remain highly dependent on pre-designed time–frequency transformations and manual feature extraction. They are sensitive to noise interference and operational variations, often exhibiting performance degradation under complex operating conditions. To address these challenges, a Unified Physics-Transformer-based harmonic detection scheme is proposed to accurately forecast harmonic levels in offshore wind farms (OWFs). This framework utilizes real-world wind speed data from Bozcaada, Turkey, to drive a high-fidelity electromagnetic transient simulation, constructing a benchmark dataset without reliance on generative data expansion. The proposed model features a Feature Tokenizer to project continuous physical quantities (e.g., wind speed, active power) into high-dimensional latent spaces and employs a Multi-Head Self-Attention mechanism to explicitly capture the complex, non-linear couplings between meteorological inputs and electrical states. Crucially, a Multi-Task Learning (MTL) strategy is implemented to simultaneously regress the Total Harmonic Distortion (THD) and the characteristic 5th Harmonic (H5), effectively leveraging shared representations to improve generalization. Comparative experiments with Random Forest, LSTM, and GRU systematically evaluate the predictive performance using metrics such as root mean square error (RMSE) and mean absolute percentage error (MAPE). Results demonstrate that the Physics-Transformer significantly outperforms baseline methods in prediction accuracy, robustness to operational variations, and the ability to capture transient resonance events. This study provides a data-efficient, high-precision approach for harmonic forecasting, offering valuable insights for future renewable grid integration and stability analysis. Full article

Objective: We examine whether a finite-range scalar–tensor modification of gravity can be simultaneously compatible with cosmological background data, galaxy rotation curves, and local/astrophysical consistency tests, while satisfying the luminal gravitational-wave propagation constraint ($c_T=1$) implied by GW170817 at low redshifts. Methods: We formulate the model at the level of an explicit covariant action and derive the corresponding field equations; for cosmological inferences, we adopt an effective background closure in which the late-time dark-energy density is modulated by a smooth activation function characterized by a length scale $\lambda$ and amplitude $ϵ$. We constrain this background model using Pantheon+, DESI Gaussian Baryon Acoustic Oscillations (BAOs), and a Planck acoustic-scale prior, including an explicit $\Lambda$CDM comparison. We then propagate the inferred characteristic length by fixing $\lambda$ in the weak-field Yukawa kernel used to model 175 SPARC galaxy rotation curves with standard baryonic components and a controlled spherical approximation for the scalar response. Results: The joint background fit yields ${\Omega }_m=0.293\pm 0.007$, $\lambda =7.{69}_{-1.71}^{+1.85}$$\mathrm{Mpc}$, and $H_0=72.33\pm 0.50$$\mathrm{km}{\mathrm{s}}^{-1}{\mathrm{Mpc}}^{-1}$. With $\lambda$ fixed, the baryons + scalar model describes the SPARC sample with a median reduced chi-square of ${\chi }_{\nu }^2=1.07$; for a 14-galaxy subset, this model is moderately preferred over the standard baryons + NFW halo description in the finite-sample information criteria, with a mean $\Delta$AICc outcome in favor of the baryons + scalar model (≈2.8). A Vainshtein-type screening completion with $\Lambda =1.3\times {10}^{-8}$ eV satisfies Cassini, Lunar Laser Ranging, and binary pulsar bounds while keeping the kpc scales effectively unscreened. For linear growth observables, we adopt a conservative General Relativity-like baseline (${\mu }_0=0$) and show that current $f{\sigma }_8$ data are consistent with ${\mu }_0\simeq 0$ for our best-fit background; the model predicts $S_8=0.791$, consistent with representative cosmic-shear constraints. Conclusions: Within the present scope (action-level weak-field dynamics for galaxy modeling plus an explicitly stated effective closure for background inference), the results support a mutually compatible characteristic length at the Mpc scale; however, a full perturbation-level implementation of the covariant theory remains an issue for future work, and the role of cold dark matter beyond galaxy scales is not ruled out. Full article

Objective: This study assessed the efficacy of the cervical pessary combined with progesterone to prevent preterm birth in pregnant women with short cervix and previous preterm birth. Methods: This post hoc analysis of the randomized, multicenter P5 trial examined the efficacy of the cervical pessary associated with vaginal progesterone versus progesterone alone for preventing recurrent preterm birth in 155 pregnant women with cervical length ≤30 mm and prior spontaneous preterm birth (sPPTB) (main subgroup), and in 85 women with cervical length ≤25 mm and sPPTB (higher-risk population). The primary outcome was spontaneous preterm birth (sPTB) before 34 weeks; secondary outcomes included sPTB rates before 37, 32, and 28 weeks, analyzed using Odds Ratio (OR) and Kaplan–Meier curves. A secondary objective was to identify predictive factors for sPTB recurrence in the cohort with prior preterm birth (n = 479), irrespective of treatment allocation. Results: Demographic profiles were balanced between groups. The addition of a cervical pessary to progesterone did not result in a significant reduction in sPTB before 34 weeks: to cervix ≤30 mm, OR 1.169 (95% CI 0.524–2.609; p = 0.703) and 1.167 (95% CI 0.466–2.921; p = 0.742) for ≤25 mm; similar null findings were observed across all gestational age thresholds. Kaplan–Meier survival curves demonstrated no significant differences between groups (p > 0.05). Secondary analysis (n = 479) identified principal predictors of sPTB recurrence, regardless of the cervical length: higher education (OR 2.37; 95% CI 0.99–5.63; p = 0.024), previous cervical conization (OR 4.78; 95% CI 1.08–21.19; p = 0.039) previous low birth weight < 2.5 kg (OR 2.43; 95% CI 1.22–4.85; p = 0.051), prior miscarriages (OR 1.36; 95% CI 1.10–1.69; p = 0.005), current twin pregnancy (OR 14.86; 95% CI 4.35–50.68; p < 0.001) and cervical funneling (OR 3.60; 95% CI 1.79–7.24; p < 0.001). Predictive models achieved an AUC of 0.719, with 87.0% sensitivity and 58.8% specificity. Conclusions: These findings do not support the routine use of cervical pessary combined with progesterone in women with dual risk factors. In this Brazilian population, specific clinical and obstetric characteristics—including higher education, cervical funneling, prior low birth weight delivery, previous conization, current twin gestation, and prior miscarriage—could identify women at increased risk for recurrent preterm birth. Full article

We show how a source-aware fluid closure framework for turbulent transport performs well on the confinement timescale in magnetically confined plasmas. A central result is that whether a source is resonant with the turbulence determines which fluid moments must be retained. Using a nonlinear current formulation, we show that resonance broadening—the dominant kinetic nonlinearity—cancels linear resonances and thereby justifies a quasilinear fluid closure already on the turbulence timescale. We derive a practical negative-energy criterion and identify parameter regimes satisfied by ion-temperature-gradient (ITG) modes (slab and toroidal), with parallel ion compressibility and magnetic curvature controlling the sign. The framework clarifies when velocity-space dynamics must be retained in the kinetic Fokker–Planck equation (for example, for fast-particle instabilities at frequencies about 10² higher than drift-wave frequencies). The present study provides additional support for our model by predicting transport that increases with radius and by showing—consistent with nonlinear kinetic simulations—that the diamagnetic flow dominates the Reynolds stress. Altogether, the results obtained provide a consistent, reduced-cost path to fluid closures that retain the essential kinetic physics while remaining tractable on confinement timescales. Full article

With the development of aerospace technology, hypersonic flight vehicles are evolving towards larger size, lighter weight, and higher performance. Their cross-domain maneuverability and extreme flight environment led to the rigid–flexible coupling effect and became the core bottleneck restricting performance improvement, seriously affecting flight stability and control accuracy. This paper systematically reviews the research status in the field of control for high-speed rigid–flexible coupling aircraft and conducts a review focusing on two core aspects: dynamic modeling and control strategies. In terms of modeling, the modeling framework based on the average shafting, the nondeformed aircraft fixed-coordinate system, and the transient coordinate system is summarized. In addition, the dedicated modeling methods for key issues, such as elastic mode coupling and liquid sloshing in the fuel tank, are also presented. The research progress and challenges of multi-physical field (thermal–structure–control, fluid–structure–control) coupling modeling are analyzed. In terms of control strategies, the development and application of linear control, nonlinear control (robust control, sliding mode variable structure control), and intelligent control (model predictive control, neural network control, prescribed performance control) are elaborated. Meanwhile, it is pointed out that the current research has limitations, such as insufficient characterization of multi-physical field coupling, neglect of the closed-loop coupling characteristics of elastic vibration, and lack of adaptability to special working conditions. Finally, the relevant research directions are prospected according to the priority of “near-term engineering requirements–long-term frontier exploration”, providing Refs. for the breakthrough of the rigid–flexible coupling control technology of the new-generation high-speed aircraft. Full article

Drug resistance is an important challenge in medical research and clinical practice, posing a serious threat to the effectiveness of current therapeutic strategies. Transcriptomics has played a crucial role in analyzing resistance-related genes and pathways, while the application of machine learning in high-throughput data analysis and prediction has also opened up new avenues in this field. However, existing studies mostly focus on a single drug or specific categories, and their conclusions are limited in applicability across drug categories, while studies on drugs beyond antibacterial and antitumor categories remain limited. In this study, we systematically analyzed the transcriptomic data of resistant cell lines treated with 1738 drugs spanning 82 categories and identified core genes through an integrated analysis of three classical machine learning methods. Using the antibacterial drug salinomycin as an example, we established a resistance prediction model that demonstrated high predictive accuracy, indicating the significant value of the selected core genes in prediction. Meanwhile, some of the core genes identified through the protein–protein interaction (PPI) network overlapped with those derived from machine learning analysis, further supporting the reliability of these core genes. Pathway enrichment analysis of differential genes revealed potential resistance mechanisms. This study provides a new perspective for exploring resistance mechanisms across drug categories and highlights potential directions for resistance intervention strategies and novel drug development. Full article

The rapid expansion of tunnel construction in mountainous regions faces significant challenges due to the heterogeneity of surrounding rocks caused by faults, fractures, and karst features, which strongly affect groundwater seepage. Traditional homogeneous assumptions are inadequate for accurately predicting tunnel water inflow, while current heterogeneous assumptions primarily focus on the permeability of the medium near a single tunnel. This study employs 2D numerical modeling based on the Kexuecheng Tunnel in Chongqing, China, to investigate the effects of geological heterogeneity on tunnel discharge and groundwater drawdown. A methodological advancement of this work lies in the quantification of the impact of non-permeability heterogeneity, stratigraphic continuity, and dip angles on groundwater under multi-tunnel conditions. Four stratigraphic continuities (R = 60 m, 120 m, 180 m, 240 m) and four dip angles (θ = 0°, 30°, 60°, 90°) are considered for permeability variations. Results demonstrate that heterogeneous formations produce irregular discharge and non-uniform groundwater drawdown, closely reflecting field conditions. Increased stratum continuity intensifies discharge and drawdown at smaller dip angles, while combined variations yield complex hydraulic responses. In multi-tunnel settings, reduced spacing amplifies discharge and drawdown, exacerbating groundwater impacts. Compared with homogeneous conditions, heterogeneous formations yield higher water inflow and uneven drawdown. The findings underscore the necessity of accounting for geological heterogeneity and tunnel interactions in hydrogeological evaluations and design. In addition to permeability, stratigraphic continuity and dip angles during simulation validation, especially in multi-tunnel configurations, enhance safety and reduce engineering risks. Full article

This comprehensive review examines hierarchical control principles and frameworks for grid-connected microgrids operating in environments prone to load shedding and under demand response. The particular emphasis is on South Africa’s current electricity grid issues, experiencing regular planned and unplanned outages, due to numerous factors including ageing and underspecified infrastructure, and the decommissioning of traditional power plants. The study employs a systematic literature review methodology following PRISMA guidelines, analysing 127 peer-reviewed publications from 2018–2025. The investigation reveals that conventional microgrid controls require significant adaptation to address the unique challenges brought about by scheduled power outages, including the need for predictive–proactive strategies that leverage known load-shedding schedules. The paper identifies three critical control layers of primary, secondary, and tertiary and their modifications for resilient operation in environments with frequent, planned grid disconnections alongside renewables integration, regular supply–demand balancing and dispatch requirements. Hybrid optimisation approaches combining model predictive control with artificial intelligence show good promise for managing the complex coordination of solar–storage–diesel systems in these contexts. The review highlights significant research gaps in standardised evaluation metrics for microgrid resilience in load-shedding contexts and proposes a novel framework integrating predictive grid availability data with hierarchical control structures. South African case studies demonstrate techno-economic advantages of adapted control strategies, with potential for 23–37% reduction in diesel consumption and 15–28% improvement in battery lifespan through optimal scheduling. The findings provide valuable insights for researchers, utilities, and policymakers working on energy resilience solutions in regions with unreliable grid infrastructure. Full article

Background and Objectives: Abortion on request, contraceptive access barriers, and mental health may jointly shape women’s quality of life (QoL). We examined how abortion history, structural barriers, and psychosocial factors relate to modern contraceptive use, depressive and anxiety symptoms, and QoL among women attending a Romanian tertiary center. Methods: We conducted a single-center observational study combining retrospective chart review with an online survey of 200 women aged 18–45 years. Validated instruments (Patient Health Questionnaire-9 [PHQ-9], Generalized Anxiety Disorder-7 [GAD-7], World Health Organization Five-Item Well-Being Index [WHO-5], and World Health Organization Quality of Life–BREF [WHOQOL-BREF]) and indices of access barriers, perceived stigma, and social support were used. Analyses included multivariable regression, structural equation modelling, latent class analysis, and moderation analysis. Results: Overall, 55.0% of women reported ≥1 abortion on request. Compared with those without abortion history, they were older (31.2 ± 4.9 vs. 26.8 ± 4.8 years, p < 0.001), more often had lower levels of education (51.8% vs. 33.3%, p = 0.013), and were less likely to use modern contraception at last intercourse (52.7% vs. 71.1%, p = 0.012). PHQ-9 (8.8 ± 4.0 vs. 7.3 ± 4.3) and GAD-7 (7.0 ± 3.2 vs. 5.7 ± 3.4) scores were higher (both p = 0.010), while QoL was lower (55.4 ± 8.1 vs. 59.5 ± 7.8, p < 0.001). In adjusted models, access barriers (OR per point = 1.3, 95% CI 1.1–1.6), but not abortion history, predicted non-use of modern contraception. QoL correlated strongly with PHQ-9 (r = −0.6) and WHO-5 (r = 0.5; both p < 0.001). Latent class analysis identified a “high-barrier, distressed, abortion-experienced” profile with the poorest mental health and QoL. Conclusions: Structural access barriers and current depressive and anxiety symptoms, rather than abortion history alone, were key correlates of contraceptive gaps and reduced QoL, underscoring the need for integrated reproductive and mental health care. Full article