Search Results (6,836)

Background: Slow transit constipation (STC) represents a refractory gastrointestinal disorder with limited therapeutic options. Deglycosylated azithromycin (Deg-AZM) is a small molecule Transgelin agonist effective against STC, which has been approved for 2024 clinical trials. Objectives: This study comprehensively evaluated the cardiac safety (hERG inhibition), acute cardiovascular–respiratory effects, and single/repeated-dose toxicity of Deg-AZM in Beagle dogs to de-risk clinical translation. Methods: Using automated patch-clamp (hERG-HEK293 cells; 0.1–1000 μM), telemetric monitoring in Beagles (3/8/24 mg/kg; Latin square design), and GLP-compliant toxicity studies (single-dose: 150–300 mg/kg; 28-day: 5–50 mg/kg/day), we assessed functional, biochemical, histopathological, and toxicokinetic parameters. Results: Deg-AZM showed negligible hERG inhibition (maximum 21.3% at 1000 μM). Transient PR prolongation (24 mg/kg; resolved by 4 h) and respiratory rate reduction (8–24 mg/kg; resolved by 2 h) occurred at supratherapeutic doses. Single-dose toxicity revealed one mortality at 300 mg/kg (acute cardiac ischemia), while 28-day studies identified fully reversible myocardial vacuolation at 50 mg/kg. Toxicokinetics demonstrated dose-proportional exposure (AUC and C_max) and low accumulation (accumulation factors ≤ 1.5). No hematological, coagulation, or hepatic toxicity was observed. Conclusions: With absent hERG liability and manageable transient physiological effects, Deg-AZM exhibited a favorable preclinical safety profile supporting its clinical development for STC. Full article

Neuropathic pain presents a persistent therapeutic challenge, arising from diverse etiologies such as trigeminal neuralgia, postherpetic neuralgia, post-amputation pain, painful polyneuropathy, peripheral nerve injury pain, and painful radiculopathy. Given the limitations and side effects associated with pharmacologic treatments, interest in interventional therapies has surged. Herein, ultrasound guidance provides real-time, radiation-free visualization that enhances procedural accuracy and safety. This narrative review synthesizes current evidence on ultrasound-guided techniques—including nerve blocks, pulsed radiofrequency, hydrodissection, and peripheral nerve stimulation—in the management of neuropathic pain. These minimally invasive approaches demonstrate potential in providing significant and durable pain relief, enhancing functional outcomes, and reducing reliance on systemic medications. Notably, much of the existing literature comprises small-scale or observational studies and larger randomized controlled trials are therefore essential to confirm efficacy, define optimal treatment parameters, and inform clinical guidelines for broader adoption. Full article

This study evaluated a novel methodology for assessing food safety vulnerabilities in shopping malls by integrating Hazard Analysis and Critical Control Points (HACCP), Threat Assessment and Critical Points (TACCP), and Failure Mode and Effects Analysis (FMEA). Inspections were conducted in nine shopping centers across Poland, the Czech Republic, Slovakia, and Spain to identify the risk of intentional/unintentional contamination with chemical, biological, radiological, and nuclear agents. The assessment considered key operational areas, including food delivery, transportation, staff security, back-office access, product handling, and inspection protocols. Risk levels were quantified using FMEA parameters. The findings revealed an overall high to average risk score with the most critical vulnerabilities linked to back-office access, unauthorized personnel entry, and susceptibility to fraudulent inspections. Observations also highlighted infrastructural shortcomings, insufficient monitoring, and procedural gaps that could facilitate contamination. The proposed methodology offers a structured, quantitative framework for identifying and prioritizing food safety hazards in public environments. Implementing targeted countermeasures—such as enhanced surveillance, strict access control, staff training, and dedicated food handling protocols—can substantially reduce risks, thereby strengthening public health protection and operational resilience. This approach may serve as a promising framework for integrating food defense and safety assessments for food defense in high-density commercial facilities. Full article

High-voltage transmission lines are the backbone of modern power systems, facilitating the delivery of electricity from diverse generation sources, including conventional power plants and renewable energy systems, to consumers. As the electricity demand grows, the expansion of transmission infrastructure becomes essential to connecting new consumers with power suppliers. However, traditional transmission lines require significant right-of-way, posing challenges related to land use and environmental impact, as well as limited loadability. To address this issue, compact unconventional High Surge Impedance Loading (HSIL) transmission lines offer a viable solution by reducing right-of-way requirements while enhancing line natural power, mainly leading to less voltage drop. Before the implementation of the new unconventional HSIL lines, it is crucial to assess key parameters, such as magnetic field distribution under the lines, to ensure compliance with environmental and safety standards. This paper presents a numerical analysis of the magnetic field characteristics of compact unconventional HSIL transmission lines with different subconductor configurations. The results show that the proposed HSIL designs can reduce the magnetic field at ground level by up to 71.74% compared to a conventional 500 kV line near the center, as well as by up to 74% at the right-of-way edge, while maintaining magnetic field levels well below the limits set by ICNIRP and state-specific regulations. This study evaluates the magnetic field distribution within the right-of-way, providing insights into the electromagnetic performance and potential implications for transmission line design. Full article

Heat source parameters are critical input variables in welding thermal analysis, directly and significantly affecting the accuracy of the temperature field distribution, welding distortion, and residual stress prediction. This is particularly important in safety-critical welded structures, where high-precision heat source parameter identification is essential for ensuring the thermal simulation accuracy and mechanical performance reliability. Traditional parameter identification methods based on finite element simulations or experiments have limitations in adapting to complex working conditions and variable environments. To address this, this paper proposes the Heat Source Parameter Identification Network (HSPINet) model based on a residual convolutional neural network (ResNet) architecture with an attention mechanism capable of extracting key features from the weld morphology of T-joint structures, while accounting for the influence of process parameters and joint dimensions to achieve efficient and accurate identification of heat source parameters. This study not only enhances the intelligence level of heat source parameter identification but also provides a practical, intelligent tool for welding simulation and thermal field evaluation in complex industrial applications, demonstrating significant theoretical value and broad applicability in laser processing and manufacturing scenarios. Full article

The combustion of liquid fuels that have leaked into inert porous media, such as sand, is a critical issue for industrial safety and fire risk assessment. Despite its importance, the complex influence of porous media on the combustion process, particularly the governing mechanisms of flame morphology and heat release, remains poorly understood, hindering accurate hazard prediction. This study addresses this gap by systematically investigating the combustion characteristics of 92# gasoline on quartz sand substrates with thicknesses ranging from 0 to 4 cm. Through a series of controlled laboratory experiments, key parameters including mass loss rate, heat release rate (HRR), and flame morphology were quantified. The findings reveal that, unlike the classical three-stage combustion of pool fires, the presence of porous media introduces a “slow burning period,” resulting in a unique four-stage combustion mode. The sand layer significantly suppresses combustion intensity, with the dimensionless heat release rate (Q*) being proportional to the dimensionless layer thickness (d*) raised to the power of −2.54. Crucially, flame height was found to be governed not by the HRR, but by a competition between the capillary effect (driving upward fuel transport) and the thermal effect (insulation and heat absorption). Based on this mechanism, a novel flame height prediction model was developed, which showed excellent agreement with 23 experimental datasets (R² = 0.92, average relative error 1.72%). This study elucidates the core physical mechanisms governing liquid fuel combustion in porous media. The proposed model provides a robust theoretical foundation for predicting fire development and assessing the risks associated with leaked fuel fires, offering a valuable tool for safety engineering and emergency response. Full article

Thursday Island, a remote administrative hub in Australia’s Torres Strait, exemplifies the socio-technical challenges of transitioning to sustainable energy amid diesel dependence and the intermittency of renewables. As Australia pursues Net Zero by 2050, innovative storage solutions are pivotal for enabling green innovation in isolated microgrids. This study evaluates Vanadium Redox Flow Batteries (VRFBs) and Lithium-Ion batteries as key enabling technologies, using a stochastic Monte Carlo simulation to assess their economic viability through Levelized Cost of Storage (LCOS), incorporating uncertainties in capital costs, operations, and performance over 20 years. Employing a stochastic Monte Carlo simulation with 10,000 iterations, this study provides a probabilistic assessment of LCOS, incorporating uncertainties in key parameters such as CAPEX, OPEX, efficiency, and discount rates, offering a novel, data-driven framework for evaluating storage viability in remote microgrids. Results indicate VRFBs’ superiority with a mean LCOS of 168.30 AUD/MWh versus 173.50 AUD/MWh for Lithium-Ion, driven by scalability, durability, and safety—attributes that address socio-economic barriers like high operational costs and environmental risks in tropical, off-grid settings. By framing VRFBs as an innovative green solution, this analysis highlights opportunities for new business models in remote energy sectors, such as reduced fossil fuel reliance (3.6 million litres diesel annually) and enhanced community resilience against energy poverty. It also underscores challenges, including capital uncertainties and policy needs for innovation uptake. This empirical case study contributes to the sustainable energy transition discourse, offering insights for policymakers on overcoming resistance to decarbonization in geographically constrained contexts, aligning with green innovation goals for systemic sustainability. Full article

Energy policies and economies of scale have promoted the expansion of renewable energy sources, leading to the displacement of conventional generation units and a consequent reduction in system inertia. Low inertia amplifies frequency deviations in response to generation–load imbalances, increasing the risk of load shedding and service interruptions. To address this issue, this paper proposes a coordinated control strategy based on neuro-fuzzy networks, applied to a hybrid energy storage system (HESS) composed of batteries and supercapacitors. The controller is designed to simultaneously emulate virtual inertia and implement virtual droop control, thereby improving frequency stability and reducing reliance on spinning reserve. Additionally, a state-of-charge (SOC) management layer is integrated to prevent battery operation in critical zones, mitigating degradation and extending battery lifespan. The neuro-fuzzy controller dynamically coordinates the power exchange both among the energy storage technologies (batteries and supercapacitors) and between the HESS and the conventional generation unit, enabling a smooth and efficient transition in response to power imbalances. The proposed strategy was validated through simulations in MATLAB R2022b using a two-area power system model with parameters sourced from the literature and validated references. System performance was evaluated using standard frequency response metrics, including performance indicators (ITSE, ISE, ITAE and IAE) and the frequency nadir, demonstrating the effectiveness of the approach in enhancing frequency regulation and ensuring the operational safety of the energy storage system. Full article

Yerba mate (Ilex paraguariensis) is widely consumed in South America and is valued for its bioactive compounds, such as polyphenols and methylxanthines. However, during traditional processing, mainly in the fire-based scorch and drying steps, polycyclic aromatic hydrocarbons (PAHs) and anthraquinone (AQ), substances with carcinogenic potential, may be formed. This study aimed to develop and validate an analytical method based on the balls-in-tube matrix solid-phase dispersion technique (BiT-MSPD) and analysis by gas chromatography with mass spectrometry (GC-MS) for the simultaneous determination of 16 priority PAHs and AQ in yerba mate. Parameters such as sorbent type, solvent, sample-to-sorbent ratio, and extraction time were optimized. The method showed good linearity (r² > 0.99), detection limits between 1.8 and 3.6 µg·kg⁻¹, recoveries ranging from 70 to 120%, and acceptable precision (RSD ≤ 20%). The method was applied to 31 yerba mate samples, including 20 commercial samples and 11 collected at different stages of processing. Most commercial samples showed detectable levels of PAHs, with some exceeding the limits established by the European Union. AQ was detected in 40% of the samples, with some values above the permitted limit of 20 µg·kg⁻¹. The results confirm that scorch (sapeco) and drying contribute to contaminant formation, highlighting the need to modernize industrial processing practices. The proposed method proved to be effective, rapid, and sustainable, representing a promising tool for the quality control and food safety monitoring of yerba mate. Full article

Infrared imaging technology demonstrates significant advantages in aviation safety monitoring due to its exceptional all-weather operational capability and anti-interference characteristics, particularly in scenarios requiring real-time detection of aerial objects such as airport airspace management. However, traditional infrared target detection algorithms face critical challenges in complex sky backgrounds, including low signal-to-noise ratio (SNR), small target dimensions, and strong background clutter, leading to insufficient detection accuracy and reliability. To address these issues, this paper proposes the AFK-YOLO model based on the YOLO11 framework: it integrates an ADown downsampling module, which utilizes a dual-branch strategy combining average pooling and max pooling to effectively minimize feature information loss during spatial resolution reduction; introduces the KernelWarehouse dynamic convolution approach, which adopts kernel partitioning and a contrastive attention-based cross-layer shared kernel repository to address the challenge of linear parameter growth in conventional dynamic convolution methods; and establishes a feature decoupling pyramid network (FDPN) that replaces static feature pyramids with a dynamic multi-scale fusion architecture, utilizing parallel multi-granularity convolutions and an EMA attention mechanism to achieve adaptive feature enhancement. Experiments demonstrate that the AFK-YOLO model achieves 78.6% mAP on a self-constructed aerial infrared dataset—a 2.4 percentage point improvement over the baseline YOLO11—while meeting real-time requirements for aviation safety monitoring (416.7 FPS), reducing parameters by 6.9%, and compressing weight size by 21.8%. The results demonstrate the effectiveness of dynamic optimization methods in improving the accuracy and robustness of infrared target detection under complex aerial environments, thereby providing reliable technical support for the prevention of mid-air collisions. Full article

The integration of large language models (LLMs) into structural engineering workflows presents both a transformative opportunity and a critical challenge. While LLMs enable intuitive, natural language interactions with complex data, their limited arithmetic reasoning, contextual fragility, and lack of verifiability constrain their application in safety-critical domains. This study introduces a novel automation pipeline that couples generative AI with finite element modelling through the Model Context Protocol (MCP)—a modular, context-aware architecture that complements language interpretation with structural computation. By interfacing GPT-4 with OpenSeesPy via MCP (JSON schemas, API interfaces, communication standards), the system allows engineers to specify and evaluate 3D frame structures using conversational prompts, while ensuring computational fidelity and code compliance. Across four case studies, the GPT+MCP framework demonstrated predictive accuracy for key structural parameters, with deviations under 1.5% compared to reference solutions produced using conventional finite element analysis workflows. In contrast, unconstrained LLM use produces deviations exceeding 400%. The architecture supports reproducibility, traceability, and rapid analysis cycles (6–12 s), enabling real-time feedback for both design and education. This work establishes a reproducible framework for trustworthy AI-assisted analysis in engineering, offering a scalable foundation for future developments in optimisation and regulatory automation. Full article

Rock mechanical parameters are essential to design, stability analysis, and safety construction in rock underground engineering. Inverse analysis is an effective tool for determining the mechanical properties of rock masses in deep underground engineering. Given that conventional methods cannot accurately solve such problems, proxy models are widely used. This study proposes a novel inverse analysis framework integrating the CatBoost algorithm and Simplicial Homology Global Optimization (SHGO) to overcome limitations of conventional methods. CatBoost efficiently constructs a proxy model, replacing time-consuming numerical simulations. SHGO then searches for optimal rock parameters using this proxy. The method was validated in the D2 laboratory of the second phase project of the Jinping Underground Laboratory (CJPL–II) in China and applied to invert surrounding rock parameters using field displacement monitoring data and numerical simulations. Investigations examined inversion accuracy under varying excavation steps, numbers of monitoring points, and wider parameter ranges. Results show inverted parameters converge towards true values as excavation steps and monitoring points increase. Crucially, even within the most extensive parameter range, relative errors between inversion results and true values remain below 20%. This integrated CatBoost–SHGO framework provides a feasible, scientific, and promising approach for determining rock mechanical parameters. Full article

Background and objectives: Antibiotic resistance in Cutibacterium acnes is undermining topical macrolides and clindamycin, prompting renewed interest in zinc oxide nanoparticles (ZnO-NPs) as non-antibiotic alternatives. We aimed to (i) determine the antimicrobial and anti-inflammatory performance of topical ZnO-NP formulations across in vitro, animal and early human models; (ii) identify physicochemical parameters that modulate potency and tolerance; and (iii) delineate translational gaps and priority design elements for randomised trials. Methods: We systematically searched PubMed, Scopus and Web of Science until 1 June 2025 for in vitro, animal and human studies that evaluated ≤100 nm ZnO-NPs applied topically to C. acnes cultures, extracting data on bacterial load, lesion counts, biophysical skin parameters and acute toxicity. Eight eligible investigations (five in vitro, two animal, one exploratory human) analysed particles 20–50 nm in diameter carrying mildly anionic zeta potentials. Results: Hyaluronic acid-coated ZnO-NPs achieved a sixteen-fold higher selective kill ratio over Staphylococcus epidermidis at 32 µg mL¹, while centrifugally spun polyvinyl alcohol dressings reduced C. acnes burden by 3.1 log₁₀ on porcine skin within 24 h, and plant-derived nanogels generated inhibition zones that were 11% wider than benzoyl-peroxide’s 5%. In human subjects, twice-daily 0.5% hyaluronic–ZnO nanogel cut inflammatory-lesion counts by 58% at week four and lowered transepidermal water loss without erythema. Preclinical safety was reassuring, zero mortality among animals at 100 µg mL¹ and no irritation among patients, although high-dose sunscreen-grade ZnO (20 nm) delayed rat wound closure by 38%, highlighting dose-dependent differences. Conclusions: Collectively, the evidence indicates that nanoscale reformulation markedly augments zinc’s antibacterial and anti-inflammatory performance while maintaining favourable acute tolerance, supporting progression to rigorously designed, adequately powered randomised trials that will benchmark ZnO-NPs against benzoyl peroxide and retinoids, optimise dosing for efficacy versus phototoxicity, and establish long-term dermatological safety. Full article

Systemic scleroderma (SSc) is an autoimmune disease characterized by excessive collagen production and progressive fibrosis. As the disease advances, vascular injury leads to fibrosis of the skin and internal organs, among which interstitial lung disease (ILD) carries the worst prognosis. Recent advances in biomarkers, imaging techniques, and innovative therapies offer hope for improving outcomes and quality of life in patients with SSc and ILD. To evaluate the usefulness of disease biomarkers and the efficacy and safety of immunomodulatory therapies in SSc-associated ILD (SSc-ILD), a literature review was conducted using the PubMed database for studies published mainly over the last 5 years. After applying inclusion criteria, 53 clinical studies were analyzed. Treating SSc-ILD remains challenging, with therapeutic strategies aiming to suppress inflammation and limit fibrosis progression. Clinical studies have demonstrated moderate to good efficacy of immunosuppressants such as cyclophosphamide (CYC) and mycophenolate mofetil (MMF), showing improvements in lung function parameters, such as forced vital capacity (FVC), and slowing disease progression. Additionally, biological agents such as nintedanib and tocilizumab have shown promising results—nintedanib in reducing the annual rate of FVC decline and tocilizumab in decreasing inflammatory biomarkers and stabilizing pulmonary function. However, despite these therapeutic advances, many studies had small sample sizes, heterogeneous patient populations, and varying inclusion criteria. Given the challenges in diagnostics and the critical need to evaluate the efficacy alongside the safety of immunomodulatory and anti-fibrotic therapies in systemic sclerosis-associated interstitial lung disease (SSc-ILD), there remains a strong demand for large, well-designed, multicenter trials with clearly defined patient cohorts to reliably assess the long-term outcomes of agents such as tocilizumab and nintedanib. Full article

Accurate estimation of the State-of-Health (SOH) of lithium-ion batteries is crucial for ensuring the safety and longevity of electric vehicles and energy storage systems. However, conventional LSTM models often fail to capture the nonlinear degradation dynamics and long-term dependencies of battery aging. This study proposes a Fractional-Derivative Enhanced LSTM (F-LSTM), which incorporates fractional parameters α and Δt into the cell state update to model multi-scale memory effects. Experiments conducted on the CALCE LiCoO₂ dataset and the Tongji University NCA dataset demonstrate that, compared with the standard LSTM, the proposed F-LSTM reduces RMSE and MAE by more than 40% while maintaining robust performance across different chemistries, temperatures, and dynamic conditions. These results highlight the potential of integrating fractional calculus with deep learning to achieve accurate SOH prediction and support intelligent battery management. Full article