Search Results (5,587)

Background/Objectives: Clozapine remains the gold standard for treatment-resistant schizophrenia. However, its narrow therapeutic window and risk of severe side effects require close monitoring of both clozapine and its primary metabolite, norclozapine. Existing therapeutic drug monitoring (TDM) methods are limited by delays, high costs, and operational complexity. This study introduces three rapid point-of-care (POC) assays utilizing a miniature mass spectrometer (Mini-MS) to quantify clozapine and norclozapine in plasma, whole blood, and dried blood spots (DBSs), facilitating applications across diverse clinical settings. Methods: The analytical performance of the assay was evaluated for sensitivity, specificity, reproducibility, and correlation with reference methods. Clinical samples from two hospitals were analysed and validated against conventional liquid chromatography tandem mass spectrometry (LC-MS/MS) reference standards at New South Wales Health Pathology (NSWHP) and Tsinghua University laboratories. Results: The Mini-MS assay accurately quantified both analytes within therapeutic ranges across all matrices. Inter-assay coefficients of variation ranged from 7.9 to 14.1% for clozapine and from 1.6 to 14.6% for norclozapine. Accuracy fell between 85 and 117% in plasma and blood extracts. Strong linearity was demonstrated (R² = 0.98–0.99) over the concentration range of 10–1000 ng/mL. Results from the Mini-MS analysis showed excellent correlations with LC-MS/MS results (r = 0.998). Conclusions: In this proof-of-concept study, the Mini-MS-based POC assays enable rapid, reliable quantification of clozapine and norclozapine, with performance comparable to conventional laboratory methods. This platform supports real-time TDM, facilitating timely dose adjustments, adherence monitoring, and ultimately improving patient outcomes. Full article

The continuous expansion of impervious surfaces replacing the vegetation cover and surface water areas increases urban heating. Such heating leads to downward heat transfer and latent heat flux from the surface to subsurface aquifers. This study used Landsat optical and thermal satellite data for land use/land cover (LULC), land surface temperature (LST), and anthropogenic heat flux (H_as) change mapping in Bangalore City, India. The in situ sensor-based land surface temperature (LST) and groundwater temperature (GWT) measurements were used to validate the study outcome. A minor difference was observed between the satellite data and the in situ LST due to the differential data acquisition time. The built-up area increased from 7.61% to 28.78% from 1999 to 2017 at the cost of the green cover and the extent of waterbodies. Therefore, LST change was higher in green cover areas (~6 °C LST) than in urban areas (>3 °C). The anthropogenic heat fluxes increased significantly (above 65 W/m²) during the study period. The in situ GWT was strongly correlated with the H_as (R² = 0.83) and LST (R² = 0.78). The study highlights the nature of urban expansion in Bangalore City, India, and its impact on LST, H_as, and GWT. The observed changes in land use practices with urban heat indicators at 30 m scale can be used for sustainable land use planning to improve the thermal comfort of the city, preserving the urban ecosystems. The high collinearity between satellite-data-derived LST, H_as, and GWT can be used for periodic monitoring at seasonal and annual scales using the Landsat data, which can be important inputs for land use planners and policymakers. Full article

Recent innovations in thermoplastic extrusion 3D printing have promoted the development of functional materials, such as conductive composites, which lead the way to the creation of sensors embedded directly into printed structures. To this aim, this paper presents a feasibility study on the use of a commercial conductive PLA filament for the realization of a 3D-printed temperature sensor integrated into a thermoplastic structure. To this end, a series of experiments were conducted on 3D-printed samples to analyse the correlation between electrical resistance and temperatures. The results obtained show a clear and reproducible relationship between the two quantities, from which a useful function was derived to estimate the temperature from the resistance measurement. This study confirms the potential of conductive PLA as a low-cost and customisable solution for thermal monitoring and represents a step forward towards the integration of functional sensors through additive manufacturing. Full article

Data-driven prioritization of photovoltaic (PV), battery, and converter technologies is crucial for achieving sustainability, efficiency, and cost-effectiveness in the increasingly complex domain of hybrid renewable energy systems (HRES). Conducting an in-depth and systematic ranking of these components for solar-based HRESs necessitates a comprehensive multi-criteria decision-making (MCDM) framework. This study develops as the most recent and integrated approach available in the literature. To ensure balanced and objective weighting, five quantitative weighting techniques, Entropy, Standard Deviation, CRITIC, MEREC, and CILOS, were aggregated through the Bonferroni operator, thereby minimizing subjective bias while preserving robustness. The final ranking was executed using the measurement of alternatives and ranking according to compromise solution method (MARCOS). Subsequently, comparative validation was conducted across eight additional MCDM methods, supplemented by correlation and sensitivity analysis to evaluate the consistency and reliability of the obtained results. The results revealed that thin-film PV modules (0.7108), hybrid supercapacitor batteries (0.6990), and modular converters (1.1812) emerged as the top-performing technologies, reflecting optimal trade-offs among technical, economic, and environmental performance criteria. Correlation analysis (ρ > 0.9 across nine MCDM methods) confirmed the stability of the rankings. The results establish a reproducible decision-support framework for designing sustainable hybrid systems. These technologies demonstrated superior thermal stability, cycling endurance, and system scalability, respectively, thus laying a foundation for more sustainable and resilient hybrid energy system deployments. The proposed framework provides a reproducible, transparent, and resilient decision-support tool designed to assist engineers, researchers, and policy-makers in developing reliable low-carbon components for the realization of future carbon-neutral energy infrastructures. Full article

Background: This narrative review examines the role of artificial intelligence (AI) in bowel sound analysis for the diagnosis and management of inflammatory bowel disease (IBD). Inflammatory bowel disease (IBD), encompassing Crohn’s disease and ulcerative colitis, presents a significant clinical burden due to its unpredictable course, variable symptomatology, and reliance on invasive procedures for diagnosis and disease monitoring. Despite advances in imaging and biomarkers, tools such as colonoscopy and fecal calprotectin remain costly, uncomfortable, and impractical for frequent or real-time assessment. Meanwhile, bowel sounds—an overlooked physiologic signal—reflect underlying gastrointestinal motility and inflammation but have historically lacked objective quantification. With recent advances in artificial intelligence (AI) and acoustic signal processing, there is growing interest in leveraging bowel sound analysis as a novel, non-invasive biomarker for detecting IBD, monitoring disease activity, and predicting disease flares. This approach holds the promise of continuous, low-cost, and patient-friendly monitoring, which could transform IBD management. Objectives: This narrative review assesses the clinical utility, methodological rigor, and potential future integration of artificial intelligence (AI)-driven bowel sound analysis in inflammatory bowel disease (IBD), with a focus on its potential as a non-invasive biomarker for disease activity, flare prediction, and differential diagnosis. Methods: This manuscript reviews the potential of AI-powered bowel sound analysis as a non-invasive tool for diagnosing, monitoring, and managing inflammatory bowel disease (IBD), including Crohn’s disease and ulcerative colitis. Traditional diagnostic methods, such as colonoscopy and biomarkers, are often invasive, costly, and impractical for real-time monitoring. The manuscript explores bowel sounds, which reflect gastrointestinal motility and inflammation, as an alternative biomarker by utilizing AI techniques like convolutional neural networks (CNNs), transformers, and gradient boosting. We analyze data on acoustic signal acquisition (e.g., smart T-shirts, smartphones), signal processing methodologies (e.g., MFCCs, spectrograms, empirical mode decomposition), and validation metrics (e.g., accuracy, F1 scores, AUC). Studies were assessed for clinical relevance, methodological rigor, and translational potential. Results: Across studies enrolling 16–100 participants, AI models achieved diagnostic accuracies of 88–96%, with AUCs ≥ 0.83 and F1 scores ranging from 0.71 to 0.85 for differentiating IBD from healthy controls and IBS. Transformer-based approaches (e.g., HuBERT, Wav2Vec 2.0) consistently outperformed CNNs and tabular models, yielding F1 scores of 80–85%, while gradient boosting on wearable multi-microphone recordings demonstrated robustness to background noise. Distinct acoustic signatures were identified, including prolonged sound-to-sound intervals in Crohn’s disease (mean 1232 ms vs. 511 ms in IBS) and high-pitched tinkling in stricturing phenotypes. Despite promising performance, current models remain below established biomarkers such as fecal calprotectin (~90% sensitivity for active disease), and generalizability is limited by small, heterogeneous cohorts and the absence of prospective validation. Conclusions: AI-powered bowel sound analysis represents a promising, non-invasive tool for IBD monitoring. However, widespread clinical integration requires standardized data acquisition protocols, large multi-center datasets with clinical correlates, explainable AI frameworks, and ethical data governance. Future directions include wearable-enabled remote monitoring platforms and multi-modal decision support systems integrating bowel sounds with biomarker and symptom data. This manuscript emphasizes the need for large-scale, multi-center studies, the development of explainable AI frameworks, and the integration of these tools within clinical workflows. Future directions include remote monitoring using wearables and multi-modal systems that combine bowel sounds with biomarkers and patient symptoms, aiming to transform IBD care into a more personalized and proactive model. Full article

Male reproductive health is increasingly threatened by endocrine-disrupting chemicals (EDCs), which interfere with hormonal homeostasis and reproductive physiology. Rising rates of male infertility have been linked to greater exposure to pollutants such as heavy metals, phthalates, pesticides, and bisphenol A. These compounds act through multiple mechanisms, including oxidative stress, apoptosis, receptor-mediated disruption of estrogenic and androgenic signaling, alterations in the hypothalamic–pituitary–gonadal (HPG) axis, and heritable epigenetic changes. Such disruptions impair key outcomes like sperm concentration, motility, morphology, DNA integrity, and steroidogenesis. Evidence from animal studies and human epidemiology consistently demonstrates these harmful effects, with biomarkers of EDC exposure correlating with reduced semen quality, hormonal imbalances, and infertility. Beyond individual health, infertility linked to EDCs carries significant social and economic costs. This review evaluates regulatory frameworks, highlights methodological challenges in risk assessment, and synthesizes mechanistic and clinical evidence. Particular attention is given to unresolved issues such as non-monotonic dose responses, mixture effects, low-dose exposures, and transgenerational impacts. Future priorities include refining biomonitoring, addressing mixture risks, and strengthening international regulation. By integrating mechanistic, clinical, and policy insights, this review underscores the urgent need for strategies to mitigate EDC-related threats to male reproductive health. Full article

In the lower and middle sections of the Yangtze River Basin Region (YRBR) in China, challenges posed by climate change and delayed harvesting of preceding crops have hindered the timely sowing of wheat, leading to an increasing prevalence of late-sown wheat fields. This trend has emerged as a significant impediment to achieving high and stable production of wheat in this area. During the growing seasons of 2022–2023 and 2023–2024, an unmanned aerial vehicle (UAV)-based multispectral camera was used to monitor different wheat materials at various growth stages under normal sowing treatment (M1) and late sowing with increased plant density (M2). By assessing yield loss, the wheat tolerance to late sowing was quantified and categorized. The correlation between the differential vegetation indices (D-VIs) and late sowing resistance was examined. The findings revealed that the J2-Logistic model demonstrated optimal classification performance. The precision values of stable type, intermediate type, and sensitive type were 0.92, 0.61, and 1.00, respectively. The recall values were 0.61, 0.92, and 1.00. The mean average precision (mAP) of the model was 0.92. This study proposes a high-throughput and low-cost evaluation method for wheat tolerance to late sowing, which can provide a rapid predictive tool for screening suitable varieties for late sowing and facilitating late-sown wheat breeding. Full article

Low salinity-enhanced oil recovery has gained universal recognition regarding its ability to provide an environmentally friendly and low-cost method of improved oil recovery. Research findings so far based on experimentation and simulation suggest that the success of the scheme stems considerably from double layer expansion and wettability enhancement, among others. However, while the double layer expansion and wettability effects have robust theoretical foundations that can be sought within the Mean Field Poisson–Boltzmann theory, there is hardly any published research work that has tackled this task. In this paper, we fill the knowledge gap by using the MFPB theory to calculate electric double layer (EDL) parameters as functions of salinity and to successfully correlate theoretical findings to literature-based experimental observations. Additionally, we have, for the first time integrated the concept of free energy of formation of the EDL in LSWFOR research, given its intimate relationship to EDL parameters. The theoretical findings are, therefore, indicators that theoretical foundations also provide reliable and alternative means of understanding and predicting the success of LSWFOR. Full article

Metal components that undergo ultrasonic cleaning are often stored in rinse water before drying; during this dwell period, surface corrosion can nucleate and grow. Here, we investigate how two easily monitored water-quality parameters—chloride ion concentration (Cl⁻) and chemical oxygen demand (COD), a proxy for residual organic species—govern the initiation and propagation of corrosion on low-carbon steel. After ultrasonic cleaning in five representative cleaning solutions, test coupons were immersed for up to 72 h in the corresponding rinse water and the extent of corrosion was quantified by optical profilometry and mass loss. The surface area covered by corrosion scaled linearly with [Cl⁻] (0–150 mg L⁻¹) and COD (5–120 mg L⁻¹), with correlation coefficients of 0.92 and 0.88, respectively. When both parameters exceeded threshold values of 50 mg L⁻¹ (Cl⁻) and 30 mg L⁻¹ (COD), the corrosion rate doubled relative to the control. A two-step mitigation strategy—ion-exchange pretreatment followed by activated-carbon polishing—reduced Cl⁻ and COD below the thresholds and suppressed corrosion formation by >70%. These findings provide a simple water-quality guideline and a low-cost process retrofit for manufacturers that store steel parts in high-humidity environments. Full article

Satellite remote sensing has emerged as a reliable and cost-effective approach for monitoring inland water quality, offering spatial and temporal advantages over traditional in situ methods. Lake Victoria, the largest tropical lake and a critical freshwater resource for East Africa, faces increasing eutrophication driven by nutrient inflows from agriculture, urbanization, and industrial activities. This study assessed the spatiotemporal dynamics of water quality along Uganda’s Lake Victoria coast by integrating field measurements (2014–2024) with Landsat 8/9 imagery. Chlorophyll-a, a proxy for algal blooms, and Secchi disk depth, an indicator of water clarity, were selected as key parameters. Cloud-free satellite images were processed using the Dark Object Subtraction method, and spectral reflectance values were correlated with field data. Linear regression models from single bands and band ratios showed strong performance, with adjusted R² values of up to 0.88. When tested on unseen data, the models achieved R² values above 0.70, confirming robust predictive ability. Results revealed high algal concentrations for nearshore and clearer offshore waters. These models provide an efficient framework for monitoring eutrophication, guiding restoration priorities, and supporting sustainable water management in Lake Victoria. Full article

This study proposes a method for designing spatial light modulator (SLM) projection phases in digital lasers using a simulated annealing (SA) approach combined with an initialized pre-designed phase to generate structured laser beams. SLM projection phases are optimized within the SA framework using a cost function based on the correlation between the corresponding laser field patterns and the target field. Numerical simulations demonstrate both the effectiveness of the proposed phase design method and its improvement in generating three geometric beams—quadrangular pyramid, triangular pyramid, and multi-ring fields—particularly with regard to enhanced edge sharpness. The resulting structured beams, especially those with simple geometric shapes, are suitable for microfabrication applications such as photolithography and photopolymerization. The proposed SA iteration framework is not limited to the L-shaped resonator used in this study and can be extended to digital laser cavities with higher numerical apertures, enabling the generation of more complex structured light fields. Full article

Understanding the relationship between vegetation transpiration and root-zone soil moisture is essential for assessing eco-hydrological processes under global change. However, past studies often looked at only one side, and traditional field observations have the limitations of high cost and poor spatial–temporal continuity. Using daily GLDAS Catchment data from 2004 to 2023, this study investigates the spatiotemporal patterns and interactions between vegetation transpiration and root-zone soil moisture in Fujian Province. The results show that transpiration decreased before 2016 and increased thereafter temporally, with an overall spatial decline. In contrast, the root-zone soil moisture increased before 2016 and then decreased temporally, showing overall spatial growth with significant heterogeneity. A strong negative correlation was found between vegetation transpiration and root-zone soil moisture, particularly in summer and autumn. Among them, vegetation transpiration strongly influenced soil moisture, with increases (or decreases) in transpiration corresponding to decreases (or increases) in soil moisture. Moreover, transpiration changes preceded those in soil moisture, and a significant resonance relationship with a 1- to 2-year cycle was identified. These findings offer insights into the vegetation–soil moisture dynamics in humid subtropical regions, supporting eco-hydrological management under climate change. Full article

Large language models (LLMs) judge three pairs of architectural design proposals which have been independently surveyed by opinion polls: department store buildings, sports stadia, and viaducts. A tailored prompt instructs the LLM to use specific emotional and geometrical criteria for separate evaluations of image pairs. Those independent evaluations agree with each other. In addition, a streamlined evaluation using a single descriptor “friendliness” yields the same results while offering a rapid screening measure. In all cases, the LLM consistently selects the more human-centric design, and the results align closely with independently conducted public opinion poll surveys. This agreement is significant in improving designs based upon human-centered principles. AI helps to illustrate the correlational effect: living geometry → positive-valence emotions → public preference. The AI-based model therefore provides empirical evidence for a deep biological link between geometric structure and human emotion that warrants further investigation. The convergence of AI judgments, neuroscience, and public sentiment highlights the diagnostic power of criteria-driven evaluations. With intelligent prompt engineering, LLM technology offers objective, reproducible architectural assessments capable of supporting design approval and policy decisions. A low-cost tool for pre-occupancy evaluation unifies scientific evidence with public preference and can inform urban planning to promote a more human-centered built environment. Full article

The aim of this study was to analyze international differences in online consumer behavior. The analysis was carried out on a sample of 763 participants from the countries of Spain, France, Poland and Russia. Online consumer behavior was examined from the perspective of seven dimensions: shipping-related concerns and preferences, price sensitivity and perceived cost advantage, quality perception, security concerns, time-related benefits, availability and quality of information, and shopping service satisfaction. Data were verified using Average inter-item correlation, the Shapiro–Wilk test and Levene Statistic. Subsequently, Welch’s ANOVA and one-way ANOVA and the Games–Howell and Tukey HSD post hoc tests were applied. Statistically significant differences were fully identified in all examined dimensions. The largest differences were recorded in price sensitivity, shipping-related concerns and security concerns. The effect measurements, in addition to ANOVA and post hoc tests, confirm the significance of these differences. National context, shaped by culture, institutional trust and digital infrastructure, continues to influence online consumer behavior. The strategies that the businesses should adopt should focus on approaches that are tailor-made for a specific market. This means that adapting pricing models, strengthening trust (e.g., through secure payments and strengthening safe return policies), and adapting delivery options to local preferences can lead to improved customer satisfaction in cross-border e-commerce. Full article

Biomass-derived hard carbon (BHC) has attracted considerable attention as a sustainable and cost-effective anode material for sodium-ion batteries (SIBs), owing to its natural abundance, environmental friendliness, and promising electrochemical performance. This review provides a detailed overview of recent progress in the synthesis, structural design, and performance optimization of BHC materials. It encompasses key fabrication routes, such as high-temperature pyrolysis, hydrothermal pretreatment, chemical and physical activation, heteroatom doping, and templating techniques, that have been employed to control pore architecture, defect density, and interlayer spacing. Among these strategies, activation-assisted pyrolysis and heteroatom doping have shown the most significant improvements in sodium (Na) storage capacity and long-term cycling stability. The review further explores the correlations between microstructure and electrochemical behavior, outlines the main challenges limiting large-scale application, and proposes future research directions toward scalable production and integration of BHC anodes in practical SIB systems. Overall, these advancements highlight the strong potential of BHC as a next-generation anode for grid-level and renewable energy storage technologies. Full article