Search Results (1,861)

The recurrent influx of pelagic Sargassum spp. along Caribbean coastlines poses a significant environmental challenge while offering potential as a resource-recovery agricultural input. However, agricultural reuse of Sargassum biomass raises concerns regarding salinity and trace-metal distribution within the soil–plant–food continuum. This study evaluated the short-term elemental response to a Sargassum-Based Liquid Biofertilizer (SBLB) produced via controlled anaerobic fermentation, using tomato (Solanum lycopersicum L.) grown under greenhouse conditions. Raw biomass, fermented biofertilizer, irrigation water, soils, vegetative tissues, and fruits were chemically characterized. Elemental concentrations were quantified by ICP–OES and ICP-MS and treatment effects were analyzed using one-way and two-way ANOVA (p < 0.05). Anaerobic fermentation resulted in lower measured concentrations of sodium, arsenic, and selected trace elements in the liquid fraction relative to raw biomass. SBLB application increased soil macronutrient availability (N, P, K, Ca, Mg), while soil trace-metal concentrations remained within international reference ranges during the experimental period. Metals of concern (As, Cd, Pb, Ni, Cr) showed no detectable short-term enrichment in soils, vegetative tissues, or fruits relative to controls. In tomato fruits, arsenic, cadmium, and lead were below the limit of quantification across all treatments. Within the experimental timeframe, SBLB application was not associated with detectable trace-element accumulation in the soil–plant system. Long-term field studies and detailed soil physicochemical characterization are required to evaluate cumulative effects under repeated applications. Full article

The anammox technology, as an efficient and energy-saving denitrification method, has been widely used in the field of wastewater treatment. Nevertheless, this process faces two key challenges in actual operation, namely the fluctuation of nitrite substrate supply and the residual nitrate, which greatly limits its promotion and application in a wider range. Although the traditional combined process of partial denitrification/anammox (PD/A) can generate nitrite substances, the coexistence of heterotrophic microorganisms and organic carbon sources in the system may have a significant inhibitory effect on the proliferation of Anammox bacteria. The sulfur-oxidizing bacteria (SOB) involved in the sulfur autotrophic denitrification process (SAD) have overlapping ecological niches with Anammox microorganisms and have stable nitrite enrichment characteristics. In view of this, sulfur-oxidizing bacteria are regarded as a potential candidate for combining with the Anammox process. However, the denitrification efficiency of this process is often restricted by the low solubility and poor bioavailability of substrates. At the same time, there are significant research gaps and data deficiencies regarding the key operating parameters for autotrophic short-range denitrification using elemental sulfur to achieve nitrite accumulation and the coupling application of this process with other wastewater treatment technologies. In view of this, this study is committed to comprehensively sorting out and evaluating the existing optimization methods of the elemental sulfur autotrophic denitrification process, while providing an in-depth analysis of its mechanism of action and environmental control factors. At the same time, this study also carried out innovative exploration on the modification process of the sulfur element from the frontier perspective of materials science and pointed out the key directions for subsequent optimization of the construction path of the elemental sulfur autotrophic denitrification system and for improving the denitrification process efficiency. In summary, this study systematically discusses the mechanism of action, practical application, and improvement scheme of PS⁰AD. Full article

This study evaluates the mechanical performance and failure characteristics of concrete reinforced with corn fibers as a sustainable natural additive. Corn fibers were incorporated at 0.25%, 0.5%, and 1.5% by weight of cement, with a control mix used for comparison. All mixtures were prepared at a constant water–cement ratio and adjusted for workability using a high-range water-reducing admixture. Results indicate that fiber dosage significantly influences strength and fracture behavior. The 0.5% fiber content yielded the best performance, improving compressive and flexural strength by approximately 36% and 24%, respectively, and promoting enhanced crack control and ductile response. In contrast, excessive fiber addition reduced performance due to fiber clustering and higher pore content. This study confirms that properly proportioned corn fibers can enhance concrete properties while encouraging sustainable construction through the reuse of agricultural waste. SEM further indicated a denser and more refined microstructure in the fiber-modified matrix. An ANOVA analysis and Tukey’s HSD post hoc test were performed to assess the influence of corn fiber content on the compressive, flexural, and tensile strengths of concrete mixtures, revealing statistically significant effects. Overall, the results highlight the potential of corn fiber reinforcement to improve the short-term mechanical performance of concrete mixes. Full article

This study introduces a portable piezoelectric thin-film microbalance platform that combines acoustic signal analysis with deep learning for point-of-care mass detection. The system employs a flexible polyvinylidene fluoride sensor, a smartphone for acoustic signal acquisition, and three deep learning models: convolutional neural network, long short-term memory network, and Transformer. Experimental findings indicate that the Transformer achieves the highest classification accuracy of 99.5%, outperforming the convolutional neural network at 96.9% and the long short-term memory network at 97.3%, attributed to its enhanced capability to capture long-range temporal dependencies. The platform facilitates real-time, label-free detection without the necessity for bulky instrumentation, providing a cost-effective and scalable solution for decentralized diagnostics. This research establishes a foundational framework for intelligent portable micro-mass sensing with significant potential applications in precision medicine, environmental monitoring, and personalized healthcare. Full article

The effective recognition of risky driving behaviors holds technical potential for supporting accident prevention and sustainable transportation. However, existing intelligent algorithms for optimizing deep learning models in this field often suffer from slow convergence and high errors. This study proposes a novel hybrid model (ICPO-XGBoost) for risky driving behavior classification. The improved crested porcupine optimizer (ICPO) was developed using logistic-tent composite mapping for population initialization, a hybrid mechanism combining refraction opposition-based learning and Cauchy mutation to avoid local optima, and an adaptive variable spiral search with inertia weight to balance global and local search. The ICPO was then employed to optimize the hyperparameters of the XGBoost classifier. The ICPO demonstrated superior optimization accuracy and convergence speed compared to benchmark algorithms. The ICPO-XGBoost model achieved accuracy, precision, recall, and F1 scores of 96.2%, 95.4%, 95.8%, and 95.6%, respectively, for classifying and identifying risky driving behaviors. Compared to various benchmark models, these results represent increases of 12.7–24.8%, 14.8–31.8%, 14.9–31.0%, and 15.0–32.4%, respectively. For specific driving behavior categories (normal driving, slow driving, short-distance tailgating, sudden acceleration/deceleration, frequent lane changing, and forced lane changing), the precision, recall, and F1 scores of the ICPO-XGBoost model fell within the ranges of 84.8–99.2%, 87.5–100.0%, and 86.2–99.2%, respectively. Compared to benchmark models, these metrics show increases of 1.5–75.8%, 5.8–68.1%, and 3.3–72.6%, respectively. Notably, the model significantly improved accuracy in identifying sudden acceleration/deceleration behaviors. The results of this model facilitate the classification and early warning of risky driving behaviors, thereby reducing the frequency of such behaviors, lowering the risk of traffic accidents, and enhancing road traffic safety. Full article

Alpine wetlands are highly sensitive to climate warming and anthropogenic disturbances such as grazing, highlighting the urgent need to identify operational indicators for monitoring soil functional changes. In this study, the Zequ National Wetland Park on the Qinghai–Tibet Plateau was selected as the study area. At the plot scale (n = 66), vegetation structure (aboveground biomass, vegetation height, and coverage), soil nutrient properties (soil organic carbon (SOC), total nitrogen (TN), total phosphorus (TP), ammonium nitrogen (NH₄⁺–N), nitrate nitrogen (NO₃⁻–N), available phosphorus (AP)), soil enzyme activities (β-glucosidase (BG), N-acetylglucosaminidase (NAG), and acid phosphatase (ACP)) were measured simultaneously. Spearman correlation analysis and redundancy analysis (RDA) were applied to examine their statistical relationships. Descriptive statistics revealed pronounced variability among plots, with aboveground biomass ranging from 115.43 to 1505.27 g·m⁻², AP from 0.75 to 70.23 mg·kg⁻¹, and BG activity from 0.25 to 14.71 μmol·g⁻¹·h⁻¹, indicating strong spatial heterogeneity in alpine wetlands. Both correlation and RDA results consistently showed that nutrient availability—particularly inorganic nitrogen and AP—was more closely associated with soil enzyme activities, whereas total nutrient contents exhibited a relatively limited ability to explain short-term variations in soil functional processes. These findings suggest that a combined indicator framework integrating nutrient availability and soil enzyme activities has strong potential for the early detection of soil quality changes and degradation in alpine wetlands, thereby providing quantitative support for sustainable wetland management and restoration assessment. Full article

To evaluate the seismic performance of single-degree-of-freedom (SDOF) systems under mainshock–aftershock (MS–AS) seismic excitation, nonlinear time-history analyses were conducted on SDOF systems with various parameter combinations, using 50 sets of real MS–AS sequences and 150 sets of artificial sequences generated by repetition, random, and attenuation methods. The results indicate that the ground motion characteristics of MS–AS sequences generated by the repetition, random, and attenuation methods differ from those of real MS–AS sequences, with the repetition and random methods tending to overestimate the peak ground motion parameters and acceleration response spectra of MS–AS sequences, and the attenuation method potentially underestimating them, while all three methods for generating MS–AS sequences are prone to overestimating the ground motion duration of MS–AS sequences. Residual deformation is influenced by relative yield strength coefficient (η), aftershock relative intensity (χ), post-yield stiffness ratio (r), natural vibration period (T) and the hysteresis model under MS–AS seismic excitation, and residual deformation exhibits a positive dependence on aftershock intensity (χ) and a negative dependence on post-yield stiffness ratio (r), while the relationship between residual deformation and relative yield strength coefficient (η) is influenced by the natural vibration period (T), showing a positive correlation in the short-period range and a negative correlation in the mid-to-long period range. A log-normal distribution can be adopted to describe the probability distribution of the ratio of residual deformation to peak elastic-plastic deformation subjected to MS–AS seismic excitation with different parameters. Finally, a probabilistic prediction model for residual deformation under MS–AS seismic excitation was proposed which can effectively predict residual deformation under MS–AS seismic excitation. Full article

The increasing risk to Vulnerable Road Users (VRUs) at urban intersections necessitates advanced safety mechanisms capable of operating effectively under diverse conditions, including adverse weather like heavy rain. While optical sensors such as cameras and LiDAR often degrade in poor visibility, Radio Frequency (RF)-based systems offer resilient, all-weather tracking. This paper presents a novel approach to enhancing VRU protection by fusing two RF modalities: radar sensors and Ultra-Wideband (UWB) technology, a strong candidate for Joint Communication and Sensing (JCS). The research, conducted as part of the VIDETEC-2 project, addresses the limitations of existing vehicle-based and infrastructure-based systems, particularly in scenarios involving occlusions and blind spots. By leveraging radar’s environmental robustness alongside UWB’s precise, cost-effective short-range communication and localization, the proposed system delivers the framework for continuous vehicle and VRU tracking. The fusion of these sensor modalities, managed through a hybrid Kalman filter approach integrating an Unscented Kalman Filter (UKF) and an Extended Kalman Filter (EKF), allows reliable VRU tracking even in challenging urban scenarios. The experimental results demonstrate a reduction in tracking uncertainty and highlight the system’s potential to serve as a more accurate and responsive safety mechanism for VRUs at intersections. This work contributes to the development of intelligent road infrastructures, laying the foundation for future advancements in urban traffic safety. Full article

Wireless Body Area Networks (WBANs) enable real-time health monitoring essential for timely clinical intervention, yet their performance is frequently hindered by sensor degradation, noise interference, and strict low-latency constraints in resource-limited environments. Conventional preprocessing approaches indiscriminately reprocess all incoming data, including uncorrupted samples, thereby increasing computational overhead, introducing latency, and potentially distorting valid physiological trends. This study introduces a unified real-time monitoring framework tailored for WBAN systems. The key contributions include: (1) an adaptively gated multi-stage preprocessing pipeline that selectively restores corrupted samples while preserving clean data, (2) an overlap-aware sliding-window mechanism enabling low-latency operation, and (3) a clinically informed risk assessment strategy for early-warning support. By avoiding unnecessary modification of intact signals, the framework maintains physiological integrity while substantially improving reconstruction and predictive reliability. Across multiple vital signs, the proposed approach achieves substantial reconstruction gains, with Mean Squared Error (MSE) reductions ranging from 53% to 67% under strong degradation conditions. An adaptive ARIMA-based forecasting layer captures short-term physiological dynamics with directional accuracies of approximately 65–70% for one-step (10 s) ahead prediction. Early-warning behavior is intentionally conservative, prioritizing false alarm suppression over aggressive alerting. Per-signal evaluation reveals high sensitivity for blood pressure signals, whereas glucose and certain high-variability modalities exhibit conservative sensitivity under modality-specific thresholds. Importantly, the aggregated multi-modal risk decision achieves strong overall system-level performance, with sensitivity and specificity of 0.89 and 0.92, respectively. Overall, the proposed framework establishes a robust, low-latency, and computationally efficient foundation for dependable physiological monitoring in WBAN environments, leveraging selective processing to optimize both resource utilization and clinical reliability. Full article

Modern wheat breeding has focused on maximizing yields under high-input systems. Although ancient wheat varieties generally show lower yields and no clear nutritional superiority, they are increasingly valued in organic and local food systems for their resilience, cultural identity, and suitability for artisanal processing. This study evaluated the physicochemical, rheological, and technological properties of stone-milled flours and semolato from ancient common, durum, and Khorasan wheat to develop artisanal bread and pasta. Ancient cultivars showed relatively high protein content, ranging from 10.9% to 15.9% (on a dry matter basis). Gluten quality was generally weak, with gluten index values below 30% in most cultivars and alveograph W values below 60 × 10⁻⁴ J, mainly in durum wheats. Among common wheat cultivars, Autonomia B and Rano Solina showed the best bread-making suitability and were selected to produce bread prototypes via the application of pre-gelatinization. Optimized fermentation and pre-gelatinization significantly improved the crumb structure, softness, and sensory acceptance. Pasta from durum cv. Senatore Cappelli and Khorasan showed good cooking and sensorial quality, with Khorasan receiving a better score for overall acceptability. This study demonstrates that appropriate processing strategies can successfully unlock the technological and sensory potential of ancient wheat varieties, supporting their use in short value chains and enhancing product differentiation. Full article

We investigate the defect-dependent electronic structure and gas-sensing potential of cubic α-CsPbI₃ using first-principles density functional theory and nonadiabatic molecular dynamics. Among the intrinsic defects, interstitials, vacancies, antisites, and switches studied, the I_Pb and Pb_I antisite defects exhibit transition energy levels near the middle of the band gap, thus functioning as deep traps. Short-term adsorption of ammonia selectively modifies the electronic structure, coordinating with Pb at Pb_I sites and Cs at I_Pb sites, significantly altering recombination pathways. Detailed analysis reveals that NH₃ reduces anharmonicity at I_Pb defects, enabling enhanced recombination at elevated temperatures, while trap-assisted recombination dominates at room temperature. Other analytes, including CH₃NH₂ and NO₂, show negligible impact on the band gap or recombination dynamics, highlighting the potential selectivity of NH₃ interactions. Ab initio nonadiabatic molecular dynamics simulations at 300 K and 600 K further demonstrate temperature-dependent modulation of carrier lifetimes, with NH₃ accelerating recombination at ambient conditions and suppressing certain pathways at higher temperatures. These findings suggest that α-CsPbI₃ can serve as a selective and sensitive ammonia sensor over a broad temperature range and offer insights for ammonia detection under industrially relevant conditions. Full article

Background: Periodontitis is a highly prevalent chronic inflammatory disease characterized by a complex host–microbe interaction and modulated by systemic regulatory pathways, including stress-related neuroendocrine and immunological mechanisms. Mind–body movement-based interventions such as yoga, tai chi, and qigong have demonstrated beneficial effects on stress and inflammation in general medicine, yet their relevance for periodontal health has not been systematically mapped. Methods: A scoping review was conducted in accordance with the Joanna Briggs Institute methodology and the PRISMA-ScR guidelines. Eligibility criteria included studies conducted in adult human participants examining mind–body movement-based interventions in relation to periodontal health. Sources of evidence comprised peer-reviewed studies identified through systematic searches in CINAHL, BIOSIS, Embase, PubMed/MEDLINE, the Cochrane Library, Web of Science, and LIVIVO. Data were charted using a standardized extraction form capturing key study characteristics and outcomes. Eligible studies reported clinical periodontal parameters and/or biological or psychosocial outcomes related to stress or inflammation. Results: Eleven studies investigating mind–body movement-based interventions and periodontal health were included. Interventions comprised yoga, pranayama, tai chi, and qigong, with study designs ranging from one randomized controlled trial to non-randomized interventional and observational studies. Most studies reported clinical periodontal parameters and/or periodontal-related biological markers, including inflammatory, oxidative, and immune markers, and several also assessed stress-related outcomes. The interventions were applied both as adjuncts to conventional periodontal therapy and as stand-alone approaches. Overall, the included studies reported short-term changes in periodontal parameters and stress-related measures that were generally directed towards associated with improvement; however, long-term periodontal outcomes were rarely assessed. Conclusions: Mind–body movement-based interventions, such as yoga and pranayama, have been examined in relation to periodontal health, with studies reporting periodontal clinical parameters, biological markers, and stress-related outcomes. The available evidence is heterogeneous and largely limited to short-term observations. Further methodologically rigorous studies with standardized outcome measures and longer follow-up periods are needed to better characterize the relationship between mind–body interventions and their potential adjunctive relevance in periodontal care, as current evidence does not allow conclusions regarding their routine adjunctive use. Full article

Background: Most of the public hospitals in Hong Kong provide a regular colorectal endoscopic submucosal dissection (ESD) service. The current retrospective study aims to review the long- and short-term outcomes of these services, so as to identify areas for improvement. Method and Results: From January 2017 to March 2020, 634 lesions in 623 patients were removed by an ESD technique in seven endoscopic units. The mean lesion size was 31 mm (SD 13 mm, range 10–95 mm), and the mean procedure time was 121 min (SD 67 min). En bloc resection and R0 resection could be achieved in 91.3% and 79.3% of the lesions, respectively. The intra-procedure perforation rate was 12.3%. The delayed bleeding rate was 2.1%, and the delayed perforation rate was 0.8%. Only 0.9% (6/634) of the procedures needed emergency surgical salvage due to complications. Most of the lesions were adenomas (564/634), and 55 of them were adenocarcinomas. The cumulative local recurrence rate was 4.0% at a mean follow-up of 34 months. In multivariate analysis, longer procedure time, submucosal fibrosis, hybrid ESD and piecemeal removal were associated with intra-procedure perforation. Risk factors for failed en bloc resection included non-granular and polypoid morphology, colonic location, longer procedure time and low centre volume. Malignant pathology without salvage surgery was the only independent risk factor for local recurrence. Conclusions: Colorectal ESD has been carried out in Hong Kong with acceptable short-and long-term outcomes despite the technique still being in the learning phase in some centres. Potential areas for improvement should include targeted training to speed up the procedure and enable better handling of difficult cases, aiming to decrease the perforation rate and local recurrence. Full article

Fishes contribute to the biological carbon pump, yet their overall role remains poorly constrained due to the difficulty of obtaining direct metabolic measurements and, consequently, is poorly understood. Electron transport system (ETS) activity is commonly used as a proxy for potential respiration, but its application requires an appropriate relationship between respiration (R, measured as oxygen consumption MO₂) and ETS activity. Here, we examined the relationship between swimming activity, oxygen consumption, and ETS activity in juvenile Sparus aurata using swimming-tunnel respirometry. Oxygen consumption increased with swimming speed following a four-parameter sigmoidal model, whereas ETS activity remained independent of short-term changes in activity. Normalizing respiration by ETS produced R/ETS ratios ranging from 0.17 to 0.71, values consistent with those reported for zooplankton and micronekton. Lower ratios correspond to minimal aerobic demand and may represent quiescent behaviour, while higher ratios reflect elevated demands associated with active movement or feeding. These ratios are suggested for the assessment of respiration rates from ETS activity during diel vertical migration in the ocean to improve estimates of respiratory flux. However, methodological issues related to ETS activity in different body regions must be solved to enable reliable measurements. Full article

Sustainable energy systems such as anaerobic digestion (AD) bioreactors exhibit complex nonlinear dynamics that complicate the monitoring of key stability indicators using conventional laboratory-based methods. As a preliminary investigation, this pilot study explores the feasibility of using machine learning-based soft sensing to estimate Total Volatile Fatty Acids (TVFA(M)) from routinely measured physicochemical parameters. Using a short-term laboratory dataset obtained from controlled CO₂ biomethanisation experiments, several regression models were benchmarked, including an attention-based deep learning architecture (TabNet), multi-architecture artificial neural networks (ANNs), gradient-boosting ensembles (CatBoost, XGBoost, LightGBM), and classical kernel-based approaches. Model performance was evaluated under a cross-validated framework to assess predictive capability and consistency across folds within the limited experimental scope. Among the tested models, TabNet achieved highly competitive performance, yielding an R² of 0.8551, an RMSE of 0.0090, and an MAE of 0.0067. To support model transparency and interpretability, Explainable Artificial Intelligence (XAI) techniques based on SHapley Additive exPlanations (SHAP) were applied, identifying pCO₂ as the dominant contributor to TVFA(M) predictions within the studied operational range. The results demonstrate the potential of explainable machine learning models as soft sensors for TVFA(M) estimation under controlled laboratory conditions. Although restricted to controlled laboratory conditions and a short observation period, this pilot study demonstrates the potential of explainable machine learning models for TVFA(M) estimation and provides a methodological benchmark for future validation using larger and more diverse datasets. Full article