Search Results (69,208)

Identity-based cryptography introduced by Shamir (Crypto’84) has seen many advances through the years. In the context of post-quantum identity-based schemes, most of the efficient designs are based on lattices. In this work, we propose an identity-based identification (IBI) scheme and an identity-based signature (IBS) scheme based on codes. Our design combines the hash-and-sign signature scheme, Wave, with a Stern-like signature scheme, BGKM-SIG1, instantiated over a ternary field using the large-weight Syndrome Decoding Problem (SDP). Our scheme significantly outperforms existing code-based identity-based signature constructions. Full article

Background/Objectives: Due to the significant overlap in symptoms between COVID-19 and other respiratory infections, a multiplex PCR-based platform was developed to simultaneously detect 22 respiratory pathogens. Target sequences were retrieved from the GenBank database and aligned using Clustal Omega 2.1 to identify conserved regions prioritized for primer design. Primers were designed using Primer Express^® 3.0.1 and evaluated in Primer Explorer to ensure specificity and minimize secondary structures. A multiplex strategy organized primers into three groups, each labeled with distinct fluorophores (FAM, VIC, or NED), allowing for detection by conventional PCR or capillary electrophoresis (CE). Methods: After reverse transcription for RNA targets, amplification was performed in a single-tube reaction. A total of 340 clinical samples—nasopharyngeal and saliva swabs—were collected from patients, during the COVID-19 pandemic period. The automated analysis of electropherograms enabled precise pathogen identification. Results: Of the samples analyzed, 57.1% tested negative for all pathogens. SARS-CoV-2 was the most frequently detected pathogen (29%), followed by enterovirus (6.5%). Positive results were detected in both nasopharyngeal and saliva swabs, with SARS-CoV-2 predominating in saliva samples. Conclusion: This single-tube multiplex PCR-CE assay represents a cost-effective and robust approach for comprehensive respiratory pathogen detection. It enables rapid and simultaneous diagnosis, facilitating targeted treatment strategies and improved patient outcomes. Full article

Contamination of food with heavy metals is an important factor leading to serious health concerns. Rapid identification of these heavy metals is of utmost priority. There are several methods to identify traces of heavy metals in food. Conventional methods for the detection of heavy metal residues have their limitations in terms of cost, analysis time, and complexity. In the last decade, voltammetric analysis has emerged as the most prominent electrochemical determination method for heavy metals. Voltammetry is a reliable, cost-effective, and rapid determination method. This review provides a detailed primer on recent advances in the development and application of graphene-based electrochemical sensors for heavy metal monitoring over the last decade. We critically examine aspects of graphene modification (fabrication process, stability, cost, reproducibility) and analytical properties (sensitivity, selectivity, rapid detection, lower detection, and matrix effects) of these sensors. Furthermore, to our knowledge, meta-analyses were performed for the first time for all investigated parameters, categorized based on graphene materials and heavy metal types. We also examined the pass–fail criteria according to the WHO drinking water guidelines. In addition, the effects of heavy metal toxicity on human health and the environment are discussed. Finally, the contribution of heavy metal contamination to the seventeen Sustainable Development Goals (SDGs) stated by the United Nations in 2015 is discussed in detail. The results confirm the significant impact of heavy metal contamination across twelve SDGs. This review critically examines the existing knowledge in this field and highlights significant research gaps and future opportunities. It is intended as a resource for researchers working on graphene-based electrochemical sensors for the detection of heavy metals in food safety, with the ultimate goal of improving consumer health protection. Full article

In this paper a new state model is introduced, an adaptative state model in a binary temporal representation (ASMBRT) as well as its application in constructing an algorithmic trading system. The presented model uses the binary temporal representation, which allows for a precise analysis of exchange rates without losing any informative value of the data. The basis of the model is the trajectory analysis for the ensuing changes in price quotations and dependencies between the duration of each change. The main advantage of the model is to eliminate the threshold analysis, used in existing state models. This solution allows for a more accurate identification of investor behavior patterns, which translates into a reduction of investment risk. In order to verify obtained results in practice, the paper presents a concept of creating an algorithmic trading system and an analysis of its financial effectiveness for the exchange rate most popular among investors, namely EUR/USD. Full article

Cancer is a major global health issue, with rising incidence rates highlighting the urgent need for more effective treatments. Despite advances in cancer therapy, challenges such as adverse effects and limitations of existing treatments remain. Immunotherapy, which harnesses the body’s immune system to target cancer cells, offers promising solutions. Gamma delta (γδ) T cells are noteworthy due to their potent ability to kill various cancer cells without needing conventional antigen presentation. Recent studies have focused on the role of γδ T cells in α-galactosylceramide (α-GalCer)-mediated immunity, opening new possibilities for cancer immunotherapy. We engineered humanized T cell receptor (HuTCR)-T1 γδ mice by replacing mouse sequences with human counterparts. This study investigates the cytotoxic activity of humanized γδ T cells against several human cancer cell lines (A431, HT-29, K562, and Daudi) in vitro, aiming to elucidate mechanisms underlying their anticancer efficacy. Human cancer cells were co-cultured with humanized γδ T cells, with and without α-GalCer, for 24 h. The humanized γδ T cells showed enhanced cytotoxicity across all tested cancer cell lines compared to wild-type γδ T cells. Additionally, γδ T cells from HuTCR-T1 mice exhibited higher levels of anticancer cytokines (IFN-γ, TNF-α, and IL-17) and Granzyme B, indicating their potential as potent mediators of anticancer immune responses. Blocking γδ T cells’ cytotoxicity confirmed their γδ-mediated function. These findings represent a significant step in preclinical development of γδ T cell-based cancer immunotherapies, providing insights into their mechanisms of action, optimization of therapeutic strategies, and identification of predictive biomarkers for clinical application. Full article

This article presents a comprehensive assessment of mudflow risk in the Talgar River basin through the application of Multi-Criteria Decision Analysis (MCDA) methods and numerical modeling using the Rapid Mass Movement Simulation (RAMMS) environment. The first part of the study involves a spatial assessment of mudflow hazard and susceptibility using GIS technologies and MCDA. The key condition for evaluating mudflow hazard is the identification of factors influencing the formation of mudflows. The susceptibility assessment was based on viewing the area as an object of spatial and functional analysis, enabling determination of its susceptibility to mudflow impacts across geomorphological zones: initiation, transformation, and accumulation. Relevant criteria were selected for analysis, each assigned weights based on expert judgment and the Analytic Hierarchy Process (AHP). The results include maps of potential mudflow hazard and susceptibility, showing areas of hazard occurrence and risk impact zones within the Talgar River basin. According to the mudflow hazard map, more than 50% of the basin area is classified as having a moderate hazard level, while 28.4% is subject to high hazard, and only 1.8% falls under the very high hazard category. The remaining areas are categorized as very low (4.1%) and low (14.7%) hazard zones. In terms of susceptibility to mudflows, 40.1% of the territory is exposed to a high level of susceptibility, 35.6% to a moderate level, and 5.5% to a very high level. The remaining areas are classified as very low (1.8%) and low (15.6%) susceptibility zones. The predictive performance was evaluated through Receiver Operating Characteristic (ROC) curves, and the Area Under the Curve (AUC) value of the mudflow hazard assessment is 0.86, which indicates good adaptability and relatively high accuracy, while the AUC value for assessing the susceptibility of the territory is 0.71, which means that the accuracy of assessing the susceptibility of territories to mudflows is within the acceptable level of model accuracy. To refine the spatial risk assessment, mudflow modeling was conducted under three scenarios of glacial-moraine lake outburst using the RAMMS model. For each scenario, key flow parameters—height and velocity—were identified, forming the basis for classification of zones by impact intensity. The integration of MCDA and RAMMS results produced a final mudflow risk map reflecting both the likelihood of occurrence and the extent of potential damage. The presented approach demonstrates the effectiveness of combining GIS analysis, MCDA, and physically-based modeling for comprehensive natural hazard assessment and can be applied to other mountainous regions with high mudflow activity. Full article

The southeastern Yunnan region in the southwestern Nanpanjiang Basin is one of the most important manganese enrichment zones in China. Manganese mineralization is mainly confined to marine mud–sand–carbonate interbeds of the Middle Triassic Ladinian Falang Formation (T₂f), which contains several medium to large deposits such as Dounan, Baixian, and Yanzijiao. However, the geological processes that control manganese mineralization in this region remain insufficiently understood. Understanding the tectonic evolution of the basin is therefore essential to unravel the mechanisms of Middle Triassic metallogenesis. This study investigates how rift-related tectonic activity influences manganese ore formation. This study integrates global gravity and magnetic field models (WGM2012, EMAG2v3), audio-frequency magnetotelluric (AMT) profiles, and regional geological data to investigate ore-controlling structures. A distinct gravity low–magnetic high belt is delineated along the basin axis, indicating lithospheric thinning and enhanced mantle-derived heat flow. Structural interpretation reveals a rift system with a checkerboard pattern formed by intersecting NE-trending major faults and NW-trending secondary faults. Four hydrothermal plume centers are identified at these fault intersections. AMT profiles show that manganese ore bodies correspond to stable low-resistivity zones, suggesting fluid-rich, hydrothermally altered horizons. These findings demonstrate a strong spatial coupling between hydrothermal activity and mineralization. This study provides the first identification of the internal rift architecture within the Nanpanjiang Basin. The basin-scale rift–graben system exerts first-order control on sedimentation and manganese metallogenesis, supporting a trinity model of tectonic control, hydrothermal fluid transport, and sedimentary enrichment. These insights not only improve our understanding of rift-related manganese formation in southeastern Yunnan but also offer a methodological framework applicable to similar rift basins worldwide. Full article

This study presented a comprehensive investigation into the performance of object detection models tailored for edge devices, particularly in the context of Unmanned Aerial Vehicle swarms. Object detection plays a pivotal role in enhancing autonomous navigation, situational awareness, and target tracking capabilities within UAV swarms, where computing resources are constrained by the onboard low-cost computers. Initially, a thorough review of the existing literature was conducted to identify state-of-the-art object detection models suitable for deployment on edge devices. These models are evaluated based on their speed, accuracy, and efficiency, with a focus on real-time inference capabilities crucial for Unmanned Aerial Vehicle applications. Following the literature review, selected models undergo empirical validation through custom training using the Vision Meets Drone dataset, a widely recognized dataset for Unmanned Aerial Vehicle-based object detection tasks. Performance metrics such as mean average precision, inference speed, and resource utilization were measured and compared across different models. Lastly, the study extended its analysis beyond traditional object detection to explore the efficacy of instance segmentation and proposed an optimization to an object tracking technique within the context of unmanned Aerial Vehicles. Instance segmentation offers finer-grained object delineation, enabling more precise target or landmark identification and tracking, albeit at higher resource usage and higher inference time. Full article

Rock bolts are crucial components in the subterranean support systems in underground mines that provide adequate structural reinforcement to the rock mass to prevent unforeseen hazards like rockfalls. This makes frequent assessments of such bolts critical for maintaining rock mass stability and minimising risks in underground mining operations. Where manual surveying of rock bolts is challenging due to the low-light conditions in the underground mines and the time-intensive nature of the process, automated detection of rock bolts serves as a plausible solution. To that end, this study focuses on the automatic identification of rock bolts within medium- to large-scale 3D point clouds obtained from underground mines using mobile laser scanners. Existing techniques for automated rock bolt identification primarily rely on feature engineering and traditional machine learning approaches. However, such techniques lack robustness as these point clouds present several challenges due to data noise, varying environments, and complex surrounding structures. Moreover, the target rock bolts are extremely small objects within large-scale point clouds and are often partially obscured due to the application of reinforcement shotcrete. Addressing these challenges, this paper proposes an approach termed DeepBolt, which employs a novel two-stage deep learning architecture specifically designed for handling severe class imbalance for the automatic and efficient identification of rock bolts in complex 3D point clouds. The proposed method surpasses state-of-the-art semantic segmentation models by up to 42.5% in Intersection over Union (IoU) for rock bolt points. Additionally, it outperforms existing rock bolt identification techniques, achieving a 96.41% precision and 96.96% recall in classifying rock bolts, demonstrating its robustness and effectiveness in complex underground environments. Full article

Fritillaria unibracteata is a rare and endangered medicinal plant in the Liliaceae family, whose bulbs have been used in traditional Chinese traditional medicine for over 2000 years. The mevalonate (MVA) pathway is involved in the growth, development, response to environmental stress, and active ingredient production of plants; however, the functional characterization of MVA-pathway genes in the Liliaceae family remains poorly documented. In this study, an Acetyl-CoA C-acetyltransferase gene (FuAACT) was first cloned from F. unibracteata. It exhibited structural features of the thiolase family and showed the highest sequence identity with the Dioscorea cayenensis homolog. The K_m, V_max, and K_cat of the recombinant FuAACT were determined to be 3.035 ± 0.215 μM, 0.128 ± 0.0058 μmol/(min·mg), and 1.275 ± 0.0575 min⁻¹, respectively. The optimal catalytic conditions for FuAACT were ascertained to be 30 °C and pH 8.9. It was stable below 50 °C. His361 was confirmed to be a key amino acid residue to enzymatic catalysis by site-directed mutagenesis. Subsequent subcellular localization experiments demonstrated that FuAACT was localized in chloroplasts and cytoplasm. FuAACT-overexpressing transgenic Arabidopsis thaliana plants showed higher drought tolerance than wild-type plants. This phenotypic difference was corroborated by significant differences in seed germination rate, lateral root number, plant height, and leaf number (p < 0.05). Furthermore, the FuAACT transgenic plants resulted in the formation of a more developed fibrous root system. These results indicated that the FuAACT gene revealed substantial biological activity in vitro and in vivo, hopefully providing the basis for its further research and application in liliaceous ornamental and medicinal plants. Full article

Acinetobacter baumannii is one of the most challenging nosocomial pathogens associated with a variety of hospital infections, such as ventilator-associated pneumonia, wound and urinary tract infections, meningitis, and sepsis, primarily in patients treated in critical care settings. Its classification as a high-priority pathogen is due to the emergence of multidrug-resistant strains in healthcare environments and its tendency to spread clonally. A. baumannii belongs to the Acinetobacter calcoaceticus–Acinetobacter baumannii (Acb) complex, a group of genotypically and phenotypically similar species. Differentiating between the species is important because of their distinct clinical significance. However, conventional phenotypic methods, both manual and automated, often fail to provide accurate species-level identification. This review aims to summarize current phenotypic and genotypic methods for the identification of species within the Acb complex, evaluating their strengths and limitations to offer guidance for their appropriate application in diagnostic settings and epidemiological investigations. Full article

Simulated data created in silico using a previously reported method were sampled by bootstrapping to generate data sets for training multiple copies of an ensemble learner (i.e., a machine learning (ML) method). The posterior probabilities of class membership obtained by applying the ensemble of ML models to previously unseen validation data were fitted to a beta distribution. The shape parameters for the fitted distribution were used to calculate the subjective opinion of sample membership into one of two mutually exclusive classes. The subjective opinion consists of belief, disbelief and uncertainty masses. A subjective opinion for each validation sample allows identification of high-uncertainty predictions. The projected probabilities of the validation opinions were used to calculate log-likelihood ratio scores and generate receiver operating characteristic (ROC) curves from which an opinion-supported decision can be made. Three very different ML models, linear discriminant analysis (LDA), random forest (RF), and support vector machines (SVM) were applied to the two-state classification problem in the analysis of forensic fire debris samples. For each ML method, a set of 100 ML models was trained on data sets bootstrapped from 60,000 in silico samples. The impact of training data set size on opinion uncertainty and ROC area under the curve (AUC) were studied. The median uncertainty for the validation data was smallest for LDA ML and largest for the SVM ML. The median uncertainty continually decreased as the size of the training data set increased for all ML.The AUC for ROC curves based on projected probabilities was largest for the RF model and smallest for the LDA method. The ROC AUC was statistically unchanged for LDA at training data sets exceeding 200 samples; however, the AUC increased with increasing sample size for the RF and SVM methods. The SVM method, the slowest to train, was limited to a maximum of 20,000 training samples. All three ML methods showed increasing performance when the validation data was limited to higher ignitable liquid contributions. An ensemble of 100 RF ML models, each trained on 60,000 in silico samples, performed the best with a median uncertainty of 1.39 × ${10}^{-2}$ and ROC AUC of 0.849 for all validation samples. Full article

The compound effects of extreme climate change and intensive urban development have led to more frequent urban inundation, highlighting the urgent need for the fine-scale evaluation of stormwater drainage system performance in high-density urban built-up areas. A typical basin, located in Shenzhen, was selected, and a pipe–river coupled SWMM was developed and calibrated via a genetic algorithm to simulate the storm drainage system. Design storm scenario analyses revealed that regional inundation occurred in the central area of the basin and the enclosed culvert sections of the midstream river, even under a 0.5-year recurrence period, while the downstream open river channels maintained a substantial drainage capacity under a 200-year rainfall event. To systematically identify bottleneck zones, two novel metrics, namely, the node cumulative inundation volume and the conduit cumulative inundation length, were proposed to quantify the local inundation severity and spatial interactions across the drainage network. Two critical bottleneck zones were selected, and strategic improvement via the cross-sectional expansion of pipes and river culverts significantly enhanced the drainage efficiency. This study provides a practical case study and transferable technical framework for integrating hydraulic modeling, spatial analytics, and targeted infrastructure upgrades to enhance the resilience of drainage systems in high-density urban environments, offering an actionable framework for sustainable urban stormwater drainage system management. Full article

Today, ‘social sustainability’ is a key feature of many organisations’ environmental, social, and governance strategies, as well as underpinning sustainable development goals. The term refers to the implementation of targets such as reduced societal inequalities, the promotion of social well-being, and the practice of positive community relations. Building a meaningful, accountable, and quantifiable evidence-base from which to translate these high-level concepts into tangible and achievable goals is, however, challenging. The complexities of measuring social capital—often described as a building block of social sustainability—have been documented. The challenge lies in measuring the person, group, or collective in interaction with the context under investigation, whether that be a climate goal, an institution, or a national policy. Social identity theory is a social psychological approach that articulates the processes through which an individual internalises the values, norms, and behaviours of their contexts. Levels of social identification—a concept capturing the state of internalisation—have been shown to be predictive of outcomes as diverse as communication and cognition, trust and citizenship, leadership and compliance, and health and well-being. Applying this perspective to the articulation and measurement of social sustainability provides an opportunity to build an empirical approach with which to reliably translate this high-level concept into achievable outcomes. Full article

Downy mildew caused by Plasmopara viticola is an important disease in grape production, particularly in the highly susceptible, widely cultivated Vitis vinifera L. Breeding for disease resistance is an effective solution, and V. vinifera intraspecific crosses can yield progeny with both disease resistance and high quality. To assess the potential of intraspecific recurrent selection in V. vinifera (IRSV) in improving grapevine resistance to downy mildew and to analyze the pattern of disease resistance inheritance, the disease-resistant variety Ecolly was selected as one of the parents and crossed with Cabernet Sauvignon, Marselan, and Dunkelfelder, respectively, creating three reciprocal combinations, resulting in 1657 hybrid F1 progenies. The primary results are as follows: (1) significant differences in disease resistance among grape varieties and, significant differences in disease resistance between different vintages of the same variety were found; (2) the leaf downy mildew resistance levels of F1 progeny of different hybrid combinations conformed to a skewed normal distribution and showed some maternal dominance; (3) the degree of leaf bulbous elevation was negatively correlated with the level of leaf downy mildew resistance, and the correlation coefficient with the level of field resistance was higher; (4) five progenies with higher levels of both field and in vitro disease resistance were obtained. Intraspecific hybridization can improve the disease resistance of offspring through super-parent genetic effects, and Ecolly can be used as breeding material for recurrent hybridization to obtain highly resistant varieties. Full article