Search Results (92)

Antimicrobial peptides (AMPs) provide a robust alternative to conventional antibiotics, combating escalating microbial resistance through their diverse functions and broad pathogen-targeting abilities. While current deep learning technologies enhance AMP generation, they face challenges in developing multifunctional AMPs due to intricate amino acid interdependencies and limited consideration of diverse functional activities. To overcome this challenge, we introduce a novel de novo multifunctional AMP design framework that enhances a Feedback Generative Adversarial Network (FBGAN) by integrating a global quantitative AMP activity regression module and a multifunctional-attribute integrated prediction module. This integrated approach not only facilitates the automated generation of potential AMP candidates, but also optimizes the network’s ability to assess their multifunctionality. Initially, by integrating an effective pre-trained regression and classification model with feedback-loop mechanisms, our model can not only identify potential valid AMP candidates, but also optimizes computational predictions of Minimum Inhibitory Concentration (MIC) values. Subsequently, we employ a combinatorial predictor to simultaneously identify and predict five multifunctional AMP bioactivities, enabling the generation of multifunctional AMPs. The experimental results demonstrate the efficiency of generating AMPs with multiple enhanced antimicrobial properties, indicating that our work can provide a valuable reference for combating multi-drug-resistant infections. Full article

Expansive soils, characterized by the presence of surface and subsurface cracks, over-consolidation, and swell-shrink properties, present significant challenges to slope stability in geotechnical engineering. Despite extensive research, preventing geohazards associated with expansive soils remains unresolved. This study investigates shallow sliding failures in slopes of highly expansive soils induced by rainfall, using model tests to explore deformation and mechanical behavior under cyclic wetting and drying conditions, focusing on the interaction between soil properties and environmental factors. Model tests were conducted in a wedge-shaped box filled with Nanyang expansive clay from Henan, China, which is classified as high-plasticity clay (CH) according to the Unified Soil Classification System (USCS). The soil was compacted in four layers to maintain a 1:2 slope ratio (i.e., 1 vertical to 2 horizontal), which reflects typical expansive soil slope configurations observed in the field. Monitoring devices, including moisture sensors, pressure transducers, and displacement sensors, recorded changes in soil moisture, stress, and deformation. A static treatment phase allowed natural crack development to simulate real-world conditions. Key findings revealed that shear failure propagated along pre-existing cracks and weak structural discontinuities, supporting the progressive failure theory in shallow sliding. Cracks significantly influenced water infiltration, creating localized stress concentrations and deformation. Atmospheric conditions and wet-dry cycles were crucial, as increased moisture content reduced soil suction and weakened the slope’s strength. These results enhance understanding of expansive soil slope failure mechanisms and provide a theoretical foundation for developing improved stabilization techniques. Full article

Herbicide phytotoxicity represented a critical constraint on crop safety in soybean–corn intercropping systems, where early detection of herbicide stress is essential for implementing timely mitigation strategies to preserve yield potential. Current methodologies lack rapid, non-invasive approaches for early-stage prediction of herbicide-induced stress. To develop and validate a spectral-feature-based prediction model for herbicide concentration classification, we conducted a controlled experiment exposing three-leaf-stage vegetable soybean (Glycine max L.) seedlings to aqueous solutions containing three concentrations of nicosulfuron herbicide (0.5, 1, and 2 mL/L) alongside a water control. Hyperspectral imaging of randomly selected seedling leaves was systematically performed at 1, 3, 5, and 7 days post-treatment. We developed predictive models for herbicide phytotoxicity through advanced machine learning and deep learning frameworks. Key findings revealed that the ResNet-18 deep learning model achieved exceptional classification performance when analyzing the 386–1004 nm spectral range at day 7 post-treatment: 100% accuracy in binary classification (herbicide-treated vs. water control), 93.02% accuracy in three-class differentiation (water control, low/high concentration), and 86.53% accuracy in four-class discrimination across specific concentration gradients (0, 0.5, 1, 2 mL/L). Spectral analysis identified significant reflectance alterations between 518 and 690 nm through normalized reflectance and first-derivative transformations. Subsequent model optimization using this diagnostic spectral subrange maintained 100% binary classification accuracy while achieving 94.12% and 82.11% accuracy for three- and four-class recognition tasks, respectively. This investigation demonstrated the synergistic potential of hyperspectral imaging and deep learning for early herbicide stress detection in vegetable soybeans. Our findings established a novel methodological framework for pre-symptomatic stress diagnostics while demonstrating the technical feasibility of employing targeted spectral regions (518–690 nm) in field-ready real-time crop surveillance systems. Furthermore, these innovations offer significant potential for advancing precision agriculture in intercropping systems, specifically through refined herbicide application protocols and yield preservation via early-stage phytotoxicity mitigation. Full article

Ailanthus altissima (AA) is an invasive tree species rapidly spreading worldwide, colonizing both urban and agricultural or forestry environments. This three-year study aimed to assess its effects on the growth and yield traits of the Koroneiki olive cultivar under co-cultivation in pots, combined with two irrigation regimes, full and deficit irrigation (60% of full). Within each irrigation regime, olive trees were grown either in the presence or absence (control) of AA. The trial evaluated several parameters, including vegetative growth, yield traits, and oil quality characteristics. Co-cultivation with AA had no significant impact on tree growth after three years, though it significantly reduced oil content per fruit. Antioxidant capacity of the oil improved under deficit irrigation, while AA presence did not significantly affect it, except for an increase in o-diphenol concentration. Neither the fatty acid profile nor squalene levels were significantly influenced by either treatment. Fruit weight and color were primarily affected by deficit irrigation. During storage, olive oil quality declined significantly, with pre-harvest treatments (presence or absence of AA and full or deficit irrigation regime) playing a critical role in modulating several quality parameters. In conclusion, the presence of AA near olive trees did not substantially affect the key quality indices of the olive oil, which remained within the criteria for classification as extra virgin. Full article

Extreme rainfall events are significant manifestations of climate change, causing substantial impacts on urban infrastructure and public life. This study takes the extreme rainfall event in Beijing in 2023 as the background and utilizes data from Sina Weibo. Based on large language models and prompt engineering, disaster information is extracted, and a multi-factor coupled disaster multi-sentiment classification model, Bert-BiLSTM, is designed. A disaster analysis framework focusing on three dimensions of theme, location and sentiment is constructed. The results indicate that during the pre-disaster stage, themes are concentrated on warnings and prevention, shifting to specific events and rescue actions during the disaster, and post-disaster, they express gratitude to rescue personnel and highlight social cohesion. In terms of spatial location, the disaster shows significant clustering, predominantly occurring in Mentougou and Fangshan. There is a clear difference in emotional expression between official media and the public; official media primarily focuses on neutral reporting and fact dissemination, while public sentiment is even richer. At the same time, there are also variations in sentiment expressions across different affected regions. This study provides new perspectives and methods for analyzing extreme rainfall events on social media by revealing the evolution of disaster themes, the spatial distribution of disasters, and the temporal and spatial changes in sentiment. These insights can support risk assessment, resource allocation, and public opinion guidance in disaster emergency management, thereby enhancing the precision and effectiveness of disaster response strategies. Full article

The Guangdong-Hong Kong-Macao Greater Bay Area (GBA) is one of the most urbanized and industrialized coastal regions in China, where intense human activities contribute to substantial terrestrial sediment discharge into the adjacent marine environment. However, complex hydrodynamic conditions and high spatiotemporal variability pose challenges for accurate suspended sediment concentration (SSC) retrieval. Developing water quality retrieval models based on different classifications of water bodies could enhance the accuracy of SSC inversion in coastal waters. Therefore, this study classified the coastal waters of the GBA into clear and turbid zones based on Hue angle α, and established retrieval models for SSC using a single-scattering approximation model for clear zones and a secondary-scattering approximation model for turbid zones based on radiative transfer processes. Model validation with in-situ data shows a coefficient of determination (R²) of 0.73, a root mean square error (RMSE) of 8.30, and a mean absolute percentage error (MAPE) of 42.00%. Spatial analysis further reveals higher SSC in the waters around Qi’ao Island in the Pearl River Estuary (PRE) and along the coastline of Guanghai Bay, identifying these two areas as priorities for attention. This study aims to offer valuable insights for SSC management in the coastal waters of the GBA. Full article

As mining depths increase, the highly metamorphosed anthracite in Southwest China progressively develops into a complex dynamic disaster influenced by both in situ stress and gas pressure. By utilizing characteristic indicators of mining-induced stress and gas dynamic emissions, a grading evaluation method for coal and gas dynamic disasters (CGDDs) based on fuzzy mathematics l theory is proposed and validated at the No. 1 Well of the Yuwang Coal Mine. The results indicate that the acceleration of microseismic wave velocity and the increase in the wave velocity anomaly coefficient are indicative of a more pronounced stress concentration. The working face exhibits distinct gradations of stress concentrations, categorized as weak, moderate, and strong. Moreover, the increase in microseismic wave velocity and the anomaly coefficient further confirm the intensity of the stress concentrations. Gas dynamic emissions show a clear correlation with the drill cuttings gas desorption indicator ($K_1$ value) and drill cuttings volume ($S$ value). Characteristic indicators A, B, and D are suitable for assessing the risk of CGDDs in the working face. For the application of individual indicators for classifying the CGDD risk at different distances from the crosscut (128 m, 247.5 m, 299.4 m, and 435 m) in the 1010201-working face, contradictory classification results were observed. However, the classification results derived from the fuzzy mathematics method were consistent with the findings of field investigations. As the working face advanced through the pre-concentrated stress zone, significant changes were observed in both the source wave velocity and wave velocity anomaly coefficient. Concurrently, gas emissions displayed a distinct pattern of fluctuation characterized by increases and decreases. The consistency between the periodic weighting of the working face, the gas emission, the drill cuttings gas desorption indicator, and the stress field inversion result further validates the classification outcomes. These research results can provide theoretical support for the monitoring of CGDDs. Full article

Stroke affects approximately 12 million individuals annually, necessitating swift diagnosis to avert fatal outcomes. Current hospital imaging protocols often delay treatment, underscoring the need for portable diagnostic solutions. We have investigated silver nanostars (AgNS) incubated with human plasma, deposited on a simple aluminum foil substrate, and utilizing Surface-Enhanced Raman Spectroscopy (SERS) combined with machine learning (ML) to provide a proof-of-concept for rapid differentiation of stroke types. These are the seminal steps for the development of low-cost pre-hospital diagnostics at point-of-care, with potential for improving patient outcomes. The proposed SERS assay aims to classify plasma from stroke patients, differentiating hemorrhagic from ischemic stroke. Silver nanostars were incubated with plasma and spiked with glial fibrillary acidic protein (GFAP), a biomarker elevated in hemorrhagic stroke. SERS spectra were analyzed using ML to distinguish between hemorrhagic and ischemic stroke, mimicked by different concentrations of GFAP. Key innovations include optimized AgNS–plasma incubates formation, controlled plasma-to-AgNS ratios, and a low-cost aluminum foil substrate, enabling results within 15 min. Differential analysis revealed stroke-specific protein profiles, while ML improved classification accuracy through ensemble modeling and feature engineering. The integrated ML model achieved rapid and precise stroke predictions within seconds, demonstrating the assay’s potential for immediate clinical decision-making. Full article

Functional near-infrared spectroscopy (fNIRS) is employed as a non-invasive method to monitor functional brain activation by capturing changes in the concentrations of oxygenated hemoglobin (HbO) and deoxygenated hemoglobin (HbR). Various machine learning classification techniques have been utilized to distinguish cognitive states. However, conventional machine learning methods, although simpler to implement, undergo a complex pre-processing phase before network training and demonstrate reduced accuracy due to inadequate data preprocessing. Additionally, previous research in cognitive load assessment using fNIRS has predominantly focused on differentiating between two levels of mental workload. These studies mainly aim to classify low and high levels of cognitive load or distinguish between easy and difficult tasks. To address these limitations associated with conventional methods, this paper conducts a comprehensive exploration of the impact of Long Short-Term Memory (LSTM) layers on the effectiveness of Convolutional Neural Networks (CNNs) within deep learning models. This is to address the issues related to spatial feature overfitting and the lack of temporal dependencies in CNNs discussed in the previous studies. By integrating LSTM layers, the model can capture temporal dependencies in the fNIRS data, allowing for a more comprehensive understanding of cognitive states. The primary objective is to assess how incorporating LSTM layers enhances the performance of CNNs. The experimental results presented in this paper demonstrate that the integration of LSTM layers with convolutional layers results in an increase in the accuracy of deep learning models from 97.40% to 97.92%. Full article

The article reviews autogiros, concentrating on their flight history, development, application, flight principle, components, and advantages over other aircraft. Firstly, the history of autogiros is presented, focusing on breakthrough inventions and clarifying their significance for overall rotorcraft development. Then, contemporary scientific research on the autogiro is reviewed in detail, and the available research gap is determined. The flight principle and technical fundamentals of autogiros are also briefly discussed, and a comparison between autogiros, helicopters, and fixed-wing aircraft is performed. Autogiros’ applications for civil, military, and mixed purposes are pointed out and schematically presented. The main part of the article comprises an overview of the different components and systems in the structure of the reviewed aircraft, including the main rotor, propeller, engine, cockpit, and others. Additionally, a comprehensive classification mostly concerning contemporary and homologated autogiros is described and schematically presented. Experimental and compound gyroplane designs are also examined and marked in the classification. The aircraft are categorized depending on the main structure type, mast availability, number of seats, number of rotors and rotor blades, rotor and mast position, propeller and tail type and position, pre-rotator type, and power source. The idea of different autogiro variants presented in the classification is enhanced with visual examples. This work is an addition to the efforts of promoting autogiros and research on them. It offers complete information regarding the aircraft and could serve as a kind of starting point for engineers in the design process of such types of flying machines. Full article

Background: Hip fractures are prevalent among the elderly and impose a significant burden on healthcare systems due to the associated high morbidity and costs. The increasing use of intramedullary nails for hip fracture fixation has inadvertently introduced risks; these implants can alter bone elasticity and create stress concentrations, leading to peri-implant fractures. The aim of this study is to investigate the outcomes of peri-implant hip fractures, evaluate the potential causes of such fractures, determine the type of treatment provided, assess the outcomes of said treatments, and establish possible improvement strategies. Methods: We conducted a retrospective observational study on 33 patients with peri-implant hip fractures (PIFs) who underwent surgical management at Río Hortega University Hospital from 2010 to 2022. The collected data included demographics, initial fracture characteristics, the peri-implant fracture classification, implant details, surgical outcomes, functional scores, and complications. Functional capacity was evaluated using the Parker Mobility Score (PMS). Results: The cohort (91% female, mean age 87.6 years) included 34 peri-implant fractures. The mean time from the initial fracture to the PIF was 47.2 months (nine patients developed PIFs within 2 months). Most fractures (76%) were managed with implant removal and the insertion of a long intramedullary nail, with cement augmentation in 31% of cases. The mean surgical time was 102 min, and the average hospital stay was 9.6 days. Postoperative complications occurred in 27%, with a perioperative mortality rate of 9%. Functional capacity showed a significant decline, with an average PMS loss of 4.16 points. Mortality at one year post-PIF was 36%, rising to 83% at five years. Radiographic consolidation was observed in 72% of cases at an average of 6.04 months, though 24% of patients died before consolidation. Statistically significant correlations were found for PMS pre-index fracture (PMS1: r = 0.354, p < 0.05), pre-PIF (PMS2: r = 0.647, p < 0.001), and post-PIF (PMS3: r = 0.604, p < 0.001). Conclusions: Peri-implant hip fractures present complex challenges due to their surgical difficulty and impact on patient mobility and survival. Successful management requires individualized treatment based on fracture type, implant positioning, and patient factors. These findings underscore the need for preventive measures, particularly in implant choice and techniques like overlapping and interlocking constructs, to minimize the secondary fracture risk. Full article

Research into the adverse health effects of air pollution exposure has repeatedly considered smaller particles, to the point where particle number concentration might be a more relevant metric than mass concentration. Here, we highlight some historical research which developed metrics for air pollution severity based on particle number concentration. Because this work was published in a national journal and prior to the internet and open access, this historical research is not easy to find, and it was more through the history of the aerosol research community in Ireland that this work is now being presented. Multiple online searches for published research papers on “particle number concentrations” and “air pollution severity” were undertaken. Even when specific searches were undertaken using the author names and publication year, these featured papers were not found on any internet search. O’Dea and O’Connor proposed that air pollution severity could be classified based on particle number concentration of condensation nuclei, with ‘little’ air pollution <50 × 10³ particles per cm³, ‘mean’ 50–70 × 10³ particles per cm³, ‘strong’ 70–100 × 10³ particles per cm³, and ‘very strong’ >100 × 10³ particles per cm³. Applying their assumptions on density and mean particle size, equated to mass concentrations for a mean of 6 µgm⁻³, strong at 8.5 µgm⁻³, and very strong >10 µgm⁻³. These are consistent with the current WHO guideline values for PM_2.5. Additionally, we highlight the 1955 work by Burke and Nolan on the retention of inhaled particles, where ~40% of the inhaled number concentration is retained in the respiratory system. This is also consistent with the more recently published work on particle retention. In summary, the proposed categories of pollution severity, based on number concentrations, could form a basis for the development of future guidelines. This paper highlights that sometimes research has already been published, but it is difficult to find. We challenge researchers to find publications from their own countries which pre-date the WWW to inform current and future research. Additionally, there is scope for a repository for such information on historical publications. We have presented historical research on aerosol number concentrations, classifications of air pollution severity, and particle retention, which present lessons for current researchers. Full article

Tin-containing tailing is classified as a solid waste, but it possesses valuable resources such as tin and iron. Tin-containing tailing exhibits a fine distribution and compact symbiosis of cassiterite- and iron-containing minerals. Therefore, it is difficult to recover and separate cassiterite- and iron-containing minerals using traditional mineral processing methods. The study proposed a novel technology involving pre-concentration, reduction roasting, and magnetic separation for the treatment of tin-containing tailings with a tin grade of 0.14% and an iron grade of 12.79%. The classification pre-concentration method was achieved using a combination of shaking tables, suspension vibration cone separators, and high-gradient magnetic separation with a magnetic field strength of 1.4 T. The discarded tailings ratio reached 73.56%. The gravity pre-enriched concentrates and magnetic pre-enriched concentrates underwent reduction roasting to facilitate the conversion of hematite and goethite into magnetite, respectively. The optimal conditions for reduction roasting of the gravity pre-enriched concentrate were a 10% lignite dosage, a roasting temperature of 650 °C, and a holding time of 80 min. The optimal conditions for reduction roasting of the magnetic pre-enriched concentrate were a 8% lignite dosage, a roasting temperature of 750 °C, and a holding time of 100 min. The reduction roasted products were treated using magnetic separation with a magnetic field strength of 0.16 T. Finally, a tin-rich middling with a tin grade of 2.93% and a recovery ratio of 70.88%, as well as an iron concentrate with an iron grade of 61.95% and a recovery ratio of 68.08% were obtained. The study achieved efficient recoveries of tin and iron from tin tailings, thereby presenting a novel approach for the utilization of resources in the tailing. Full article

In recent years, there has been a surge in interest concerning emerging contaminants, also known as contaminants of emerging concern (CECs), due to their presence in environmental matrices. Despite lacking regulation, these chemicals pose potential health and environmental safety risks. Disodium EDTA, a widely utilized chelating agent, has raised concerns regarding its environmental impact. The present work aimed to verify the presence of Disodium EDTA at the exit of eight wastewater treatment plants discharging into some losing streams flowing within a large alluvial aquifer. Conducted in the Province of Parma (Northern Italy), the research employs a multidisciplinary approach, incorporating geological, hydrogeological, chemical, and microbial community analyses. Following a territorial analysis to assess industries in the region, through the use of ATECO codes (a classification system for economic activities), the study investigated the concentration of Disodium EDTA in effluents from eight diverse wastewater treatment plants, noting that all discharges originate from an activated sludge treatment plant, released into surface water courses feeding the alluvial aquifer. Results revealed detectable levels of Disodium EDTA in all samples, indicating its persistence post-treatment. Concentrations ranged from 80 to 980 µg/L, highlighting the need for further research on its environmental fate and potential mitigation strategies. Additionally, the microbial communities naturally occurring in shallow groundwater were analyzed from a hydrogeological perspective. The widespread presence of a bacterial community predominantly composed of aerobic bacteria further confirmed that the studied aquifer is diffusely unconfined or semi-confined and/or diffusely fed by surface water sources. Furthermore, the presence of fecal bacteria served as a marker of diffuse leakage from sewage networks, which contain pre-treated wastewater. Although concentrations of Disodium EDTA above the instrumental quantification limit have not been found in groundwater to date, this research highlights the significant vulnerability of aquifers to Disodium EDTA. It reveals the critical link between surface waters, which receive treated wastewaters impacted by Disodium EDTA, and groundwater, emphasizing how this connection can expose aquifers to potential contamination. At this stage of the research, dilution of wastewaters in surface- and groundwater, as well as hydrodynamic dispersion within the alluvial aquifer, seem to be the main factors influencing the decrease in Disodium EDTA concentration in the subsurface below the actual quantification limit. Consequently, there is a pressing need to enhance methodologies to lower the instrumental quantification limit within aqueous matrices. In a broader context, urgent measures are needed to address the risk of diffuse transport of CECs contaminants like Disodium EDTA and safeguard the integrity of surface and groundwater resources, which are essential for sustaining ecosystems and human health. Full article

In environmental studies, rhodamine dyes are commonly used to trace water movements and pollutant dispersion. Remote sensing techniques offer a promising approach to detecting rhodamine and estimating its concentration, enhancing our understanding of water dynamics. However, research is needed to address more complex environments, particularly optically shallow waters, where bottom reflectance can significantly influence the spectral response of the rhodamine. Therefore, this study proposes a novel approach: transferring pre-trained classifiers to develop a generalizable method across different environmental conditions without the need for in situ calibration. Various samples incorporating distilled and seawater on light and dark backgrounds were analyzed. Spectral analysis identified critical detection regions (400–500 nm and 550–650 nm) for estimating rhodamine concentration. Significant spectral variations were observed between light and dark backgrounds, highlighting the necessity for precise background characterization in shallow waters. Enhanced by the Sequential Feature Selector, classification models achieved robust accuracy (>90%) in distinguishing rhodamine concentrations, particularly effective under controlled laboratory conditions. While band transfer was successful (>80%), the transfer of pre-trained models posed a challenge. Strategies such as combining diverse sample sets and applying the first derivative prevent overfitting and improved model generalizability, surpassing 85% accuracy across three of the four scenarios. Therefore, the methodology provides us with a generalizable classifier that can be used across various scenarios without requiring recalibration. Future research aims to expand dataset variability and enhance model applicability across diverse environmental conditions, thereby advancing remote sensing capabilities in water dynamics, environmental monitoring and pollution control. Full article