Search Results (8,774)

This study presents a novel method for analyzing per- and polyfluoroalkyl substances (PFAS) in dried blood spots (DBS), employing a hybrid solvent-based and matrix-matched calibration curve, minimal sample volume, and high-throughput preparation. This process produces a concentrated, purified sample, which is analyzed using liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). As part of method validation, key performance metrics such as accuracy, precision, sensitivity, carryover, specificity, interferences, and maximum dilution capability were assessed. The method quantifies 39 PFAS compounds across various classes, achieving limits of detection in the low ng L⁻¹ range, with most analytes having a limit of quantitation below the first calibrator (50 ng L⁻¹). The total method accuracy exceeded 81%, surpassing the performance goal of ≥70%, while method imprecision was below 28%, outperforming the goal of ≤30%. Specificity, as measured by a normalized percent matrix bias, was below 11%, with limited and minimal matrix effects observed. Interference from bile acids was determined to be insignificant at biologically relevant concentrations. Over the past three years, this method has been successfully utilized in biomonitoring studies of Michigan residents, demonstrating its robustness and efficiency. Full article

To address the limitations of conventional polymer hydrogels in terms of sustainability and functionality, green soy protein (SP)-based hydrogels (SPHs) demonstrate significant potential. As an abundant, renewable plant protein, SP provides an ideal molecular platform for constructing high-performance, multifunctional hydrogels. This review systematically consolidates recent progress in SPHs. Firstly, the molecular fundamentals and gelation mechanisms of soy protein are analyzed in depth. Subsequently, key construction strategies, including physical, chemical, and enzymatic crosslinking, as well as composite/hybrid approaches, are comprehensively reviewed with respect to their mechanisms, advantages, and limitations. Following this, innovative applications of SPHs in biomedical, food and nutrition, environmental/agricultural, and smart material fields are highlighted. Finally, the current challenges facing research in mechanical properties, structure–property relationships, and scalable production are identified. Future directions include developing novel green crosslinking systems, deepening multi-scale structural control, and advancing smart integrated design. This review aims to provide researchers with a systematic knowledge framework spanning from “molecular understanding” to “functional customization,” thereby propelling soy protein hydrogels toward higher toughness, intelligence, and sustainability. Full article

Monoclonal antibodies (mAbs) are widely used as therapeutic molecules for the treatment of serious diseases, primarily cancer. The market for mAbs is one of the fastest-growing segments of the biopharmaceuticals industry. Purification is a crucial stage in the production of mAbs. While staphylococcal protein A (SpA) affinity chromatography remains the gold standard in industrial mAb purification, its limitations—low alkaline stability and insufficient binding capacity of SpA, as well as the need for harsh acidic elution conditions—have driven extensive efforts for novel progressive affinity ligands. This review focuses on the development and performance of bacterial protein-based affinity resins for the purification of class G immunoglobulins (IgGs), including conventional proteins A and G, the promising protein L, and the more recently discovered protein M (from M. genitalium), each offering unique specificities for different antibody fragments and species. Hybrid ligands combining domains from multiple bacterial proteins are also discussed, along with next-generation synthetic alternatives such as affibodies, affimers, nanobodies, etc., as well as peptide-based or mixed-mode ligands. The key finding is that the reliable and time-tested resins like those based on protein A continue to dominate the market, although future trends also point toward smaller, more stable, and cost-effective synthetic ligands for specific applications. Full article

Aimed at addressing the issue of frequent short-duration ICE start–stop events in P2-configuration plug-in hybrid electric vehicles (PHEVs) employing conventional instantaneous optimization-based Equivalent Consumption Minimization Strategy (ECMS)—and the resulting deterioration of vehicle smoothness, NVH performance, fuel economy, and emission performance—this paper proposes a novel energy management strategy, designated ECMS-ISS, which integrates instantaneous optimization with an engine unnecessary start suppression algorithm. A multilayer perceptron (MLP) neural network is first constructed as an online identifier to recognize high-frequency intervals of frequent start–stop events in real time. A dedicated penalty function is then embedded within these identified intervals, with the penalty intensity adaptively adjusted according to the accumulated count of short-duration start–stop events, enabling zoned and targeted intervention without affecting engine torque output during normal operating intervals. Simulation results under NEDC and WLTC driving cycles demonstrate that, compared with the conventional A-ECMS, ECMS-ISS reduces engine start–stop events by 35.48% and 32.31%, respectively, and reduces comprehensive fuel consumption by 2.13% and 5.40%, while significantly decreasing CO, NOx, and HC emissions. Compared with RB-EMS, ECMS-ISS also exhibits superior fuel economy and emission reductions, with the final SOC maintained within a reasonable range throughout. The proposed strategy demonstrates distinct advantages in reconciling multiple objectives, including start–stop rationality, fuel economy, emission performance, and battery health, thereby providing a practical and adaptive solution to the frequent engine start–stop problem in P2-configuration PHEVs. Full article

Mobile networks have become targets of sophisticated cyber attacks. Critical vulnerabilities persist in the General Packet Radio Service Tunneling Protocol (GTP). Signature-based Intrusion Detection Systems (IDS) are inadequate against zero day exploits and novel attack patterns, necessitating more adaptive approaches. We propose GTP-AEGIS (Adaptive Ensemble with Gated Input Selection), a hybrid IDS that integrates signature-based detection with a CatBoost gradient boosting classifier via a Selective Heterogeneous Ensemble (SHE) framework. An Input-Dependent Confidence Gate (IDCG) applies a per-sample priority rule over CatBoost, a NearestCentroid Rule Engine (NCRE), and a signature pathway. A real Suricata engine detects attacks with 100% precision but only 69.2% binary recall when run standalone; within the ensemble, the signature role is played by an idealized Signature-Detection Surrogate (SDS), so the reported ensemble gains are upper bounds. On the evaluated GTP-U dataset, GTP-AEGIS reaches accuracy above 90% with 10% of the training data and raises recall for the rare invalid-TEID class from 48.9% to 64.4%; this improvement comes from the signature pathway rather than the NCRE, and the aggregate accuracy gain is not statistically significant after correction for multiple comparisons. All accuracies are obtained under a packet-level split, which a group-aware comparison shows to be optimistic by approximately 19 percentage points. The model flags 81 to 100% of packets from unseen attack families as non-normal, although this does not constitute unknown-class recognition. We report the limits of signature-only detection and of packet-level evaluation alongside the gains. Full article

Precision agriculture increasingly requires intelligent systems capable of integrating multimodal sensing with transparent decision support to enable timely and reliable crop management. This study proposes a hybrid intelligent IoT framework integrating environmental monitoring, wearable plant physiological sensing, AI-based pest-monitoring, machine-learning-based prediction of crop physiological stress, and explainable fuzzy rule-based decision support into a unified architecture for crop stress assessment. A novel Physiological Stress Index (PSI) was developed by combining vapor pressure deficit, relative humidity, Delta-T, leaf capacitance, and relative irradiance to provide an interpretable indicator of crop physiological stress. The proposed framework was experimentally validated under real field conditions using a commercial environmental monitoring station, wearable leaf sensors, AI-enabled pest-monitoring devices, and cloud-based analytics. Correlation analysis confirmed strong relationships between PSI and the principal environmental variables (VPD: r = 0.980, Delta-T: r = 0.990, RH: r = −0.961), demonstrating the internal consistency and sensitivity of the proposed index. At the 15 min forecasting horizon, Linear Regression and Gradient Boosting demonstrated virtually identical performance: Gradient Boosting achieved a marginally lower RMSE and higher R² (RMSE = 0.0273; R² = 0.9810), whereas Linear Regression achieved a slightly lower MAE (MAE = 0.0186). At the 1 h forecasting horizon, Gradient Boosting achieved the strongest performance (R² = 0.9034), indicating increasing relevance of nonlinear modelling at longer prediction horizons. The proposed framework demonstrates the feasibility of combining multimodal sensing, machine learning, explainable artificial intelligence, and edge-enabled IoT technologies to support proactive, transparent, and intelligent precision agriculture. Full article

The scalability of multiprocessor networks on-chip is restricted by the performance of on-chip interconnects. Optical networks-on-chip (ONoCs) offer ultra-high bandwidth and energy-efficient solutions for these bottlenecks. Although 2D ONoCs have several advantages, scalability is a challenge because of limited chip area; hence, recently, research has been focused on 3D integration. This paper presents a novel 3D ONoC architecture based on two newly designed routers, the Horizontal Dimension Order Routing Aware Router for intra-layer communication and the Vertical Optical Router for inter-layer connectivity. To evaluate the performance of the routers and the proposed ONoC, comprehensive mathematical models are developed for insertion loss, crosstalk noise, and signal-to-noise ratio (SNR) measurements. The results show that the proposed design reduces average insertion loss by 31.28% and improves worst-case SNR by 9.23% compared with existing 3D ONoC designs. It also outperforms traditional designs in terms of Bit Error Rate (BER). The hybrid switching approach is used for XYZ dimension order routing, enabling high-speed data transmission and avoiding the need for buffering and its latency. Furthermore, the results show that vertical chip expansion improves SNR and BER over horizontal expansion, taking a step toward reliable, high-performance all-optical computing. Full article

Background and Objectives: The hybrid assistive limb (HAL) is a wearable robotic device used for rehabilitation that assists the voluntary movements of the user by detecting muscle action potentials and driving actuators positioned next to the hip and knee joints. Although upper-limb HAL training has been studied for brachial plexus injury (BPI), its electrophysiological influence remains unclear. The purpose of this study was to assess the electrophysiological influences of upper-limb HAL-assisted biofeedback (BF) training during elbow flexion rehabilitation in patients with BPI. Methods: Five patients with BPI (average age, 37.2 years) were enrolled after undergoing elbow flexor reconstruction through intercostal nerve-to-musculocutaneous nerve transfer. All participants received outpatient elbow flexion training with the upper-limb HAL at frequencies ranging from once weekly to once monthly. All patients started upper-limb HAL training when re-innervation was observed, and a biceps brachii muscle strength of grade 1 was achieved. Muscle activity was measured using surface electromyography in five patients during upper-limb HAL training, when the biceps brachii muscle strength was graded as Medical Research Council grades 1 and 2, to compare activity with and without HAL. Results: In five patients, electromyographic activity of the biceps brachii during elbow flexion reached 74.9 ± 22.7% of maximal contraction while using the HAL device, compared with 60.3 ± 16.7% without HAL assistance, indicating significantly greater muscle activation during HAL-assisted movement. Conclusions: Robotic BF training for elbow flexion with the upper-limb HAL may serve as a high-quality electromyographic rehabilitation approach for patients recovering from BPI. Full article

Earthquakes remain a critical threat to global infrastructure. Recent catastrophic events, such as the 2023 Kahramanmaraş earthquakes in Türkiye and Syria, underscore the vital necessity of rapid, accurate post-disaster building damage evaluations. Structural collapse under seismic loading leads to substantial loss of life and severe economic disruption, particularly in regions dominated by aging building stocks that predate modern seismic design codes. To address the limitations of conventional manual inspections, this study introduces a comprehensive artificial intelligence (AI) framework designed to automate and enhance post-earthquake structural assessments. Leveraging a heterogeneous dataset from the 2021 Haiti earthquake, which includes both categorical building attributes and post-disaster imagery, the proposed approach employs rigorous data preprocessing and exploratory analysis to identify key vulnerability indicators and resolve data inconsistencies. Independent predictive pipelines were developed utilizing state-of-the-art machine learning algorithms for tabular data and deep learning architectures for image analysis. Subsequently, a novel hybrid meta-classifier was implemented to fuse these distinct modalities. By integrating spatial and structural context with direct visual evidence of damage, the hybrid model is successful in estimating structural damage severity. Among all evaluated approaches, this multimodal framework significantly improved predictive reliability. The hybrid model achieved a classification accuracy of 89%, consistently outperforming isolated tabular and image-based models. These findings highlight the efficacy of multimodal data fusion in disaster analytics and suggest that AI-driven hybrid architectures can serve as robust, scalable decision support tools for structural engineers and emergency response agencies. Full article

Moruga Hill Rice (MHR) is an African rice (Oryza glaberrima Steud.) brought to Trinidad by formerly enslaved African Americans and has been grown for many generations in Trinidad at subsistence and commercial scale. Despite its historical and agricultural significance, genomic resources specific to MHR remain unexplored, and its genetic composition, evolutionary history, and potential agronomic traits have not been characterized. This current study presents the first draft genome assembly of the MHR genome using a hybrid sequencing approach. The MHR genome size was found to be ~372.9 Mb with 56,073 predicted genes. Variant analysis revealed a total of 3,318,242 variants, of which 2,440,476 were SNPs, and 877,766 were InDels. Several candidate genes encoding proteins with orthology to previously characterized biotic resistance and abiotic stress-responsive genes in rice were identified. Potential gene families identified prompt further investigation of their roles in MHR drought and salt stress responses. Phylogenomic analysis of O. glaberrima landraces suggests that MHR shares close genetic affinity with the IRGC−104595 Malian landrace, consistent with historical records. This assembly thus expands the African rice genomic repository, providing a foundation to understand the genetic architecture underlying key phenotypic traits and identifying potential novel gene sources in MHR for rice improvement in the Caribbean region. Full article

Hybrid CNN–Transformer (HCT) synthesis for automated neurodevelopmental diagnostics is an effective approach to constructing intelligent detection systems that are not merely oriented toward feature classification but primarily toward solving spatiotemporal pattern recognition problems in motor disorder assessment. In neurodevelopmental diagnostics, existing automated methods rely on fixed, single-model architectures that process spatial or temporal motion features independently, failing to adapt to the heterogeneous motor irregularities characteristic of developmental coordination disorder and degrading detection sensitivity and generalization across diverse patient populations. There is therefore a pressing need for models capable of simultaneously capturing intra-frame spatial coordination patterns and inter-frame temporal movement dependencies against interrelated diagnostic criteria including accuracy, sensitivity, and motor irregularity specificity. To address this challenge, this paper proposes HCT, a novel framework that integrates ResNet-based spatial feature extraction from optical flow maps and pose estimation skeletons with multi-head self-attention Transformer encoding for modeling long-range temporal dependencies across multi-frame motion sequences. Unlike conventional single-stream approaches, where spatial and temporal processing remain confined to independent architectures, HCT decouples spatiotemporal feature learning through a cross-modal fusion pipeline, constructing a unified discriminative architecture that captures motor coordination dependencies between motion imaging inputs and multiple diagnostic criteria simultaneously. The convolutional encoder generates diverse joint displacement features, which are consolidated through cross-modal attention fusion into a robust, unified embedding with enhanced generalization and resilience to inter-individual motor variability. Integration within neurodevelopmental assessment frameworks facilitates reliable developmental coordination disorder classification, motor irregularity prediction, and interpretable diagnostic decision support, advancing the accuracy, flexibility, and clinical validity of intelligent motor disorder diagnostic systems. Full article

Campylobacter jejuni and Campylobacter coli are major foodborne pathogens whose accurate species-level discrimination is important for outbreak investigations as well as rapidly assessing putative antimicrobial resistance profiles and the pathogenic potential of the bacterium. To facilitate species differentiation, a novel assay that integrates the nucleic acid-sensing capability of the oligo-Alpha with the multiplexing capacity of the AlphaPlex bead chemistries was developed. This wash-free system (designated as oligo-Plex) enables the detection and differentiation of C. jejuni and C. coli within a single reaction and can be completed in approximately 75 min. It works by using custom oligonucleotides modified for bead attachment, which hybridize sequentially along Campylobacter’s glyA gene and ultimately bridge the donor and acceptor beads. Improvements in assay stringency were made by increasing incubation temperatures, thus allowing the resolution of target from non-target. Comparisons of FITC–europium and DIG–terbium labeling systems revealed superior performance by the FITC–europium pair and suggested that helical positioning and steric accessibility likely influence donor–acceptor efficiency. Maximized signal separation was seen when using terbium for the detection of C. coli and europium for the detection of C. jejuni. Testing was performed in a Tris-based buffer and milk to confirm matrix tolerance, with potential areas for further optimization identified. The oligo-Plex presented here establishes a streamlined, adaptable platform suitable for high-throughput screening of multiple nucleic acid analytes that is readily extendable to a diverse array of pathogens through appropriate oligo selection. Full article

Background/Objectives: Although lung cancer is the leading cause of cancer-related deaths in the United States, rates of screening have remained persistently low nationwide. This study sought to identify barriers, facilitators, and support strategies necessary for implementing a novel community health navigator workflow to improve lung cancer screening uptake in both rural and urban settings. Methods: Semi-structured interviews were conducted with primary care providers (n = 5), community scientists (n = 7), community health navigators (n = 4), and radiology staff (n = 2). Interview transcripts were analyzed using a rapid qualitative analysis approach. Three authors coded based on the Consolidated Framework for Implementation Research (CFIR) and the Expert Recommendations for Implementing Change (ERIC) frameworks using a hybrid deductive–inductive approach. Results: Participants highlighted several primary barriers: access to knowledge and information (e.g., knowledge of eligibility, knowledge of insurance coverage), IT infrastructure (e.g., quality of pack-year data), relative priority (e.g., need to discuss other conditions), and patient needs and resources (e.g., time off work, transportation, difficulty scheduling). Key facilitators for screening were again IT infrastructure (e.g., automated electronic health record alerts) as well as relational connections (e.g., trust between patients and providers). To address provider-level barriers, participants recommended educational meetings, using clinical champions, and providing feedback on current lung cancer screening rates. To address patient-level barriers, participants recommended health education tools, providing transportation vouchers, hosting weekend lung cancer screening clinics, and assisting with scheduling. Conclusions: A community navigator approach to lung cancer screening should address key barriers to implementation on both the patient and provider level, including knowledge, prioritization, and patient access. Full article

Drug discovery is still an expensive and time-consuming process where finding the right drug associations is important for therapeutic development. In this paper, a new system is proposed for drug design called PharmaGraphFormer (PGF). It consists of five stages: (i) Data acquisition and preprocessing (DAP), (ii) Feature extraction and feature fusion (FEF), (iii) Molecular representation (MR), (iv) Multi-task prediction, and (v) Explainable artificial intelligence (XAI). This study employs a hybrid graph neural network (GNN)-transformer architecture that combines structural and sequence-based representations. Through DAP, several processes are executed, including the imputation or removal of missing values, outlier rejection, and class balancing. Next, through FEF1, features are extracted to represent the input data efficiently. Initially, compound-protein features are generated to document the interactions and relationships between chemical compounds and their corresponding target proteins. Secondly, drug characterizations are computed to encapsulate the physical, chemical, and structural attributes of each drug. After that, MR is performed using a graph-based molecule representation. Then, a novel model integrating GNNs and graph transformers, termed GNN-T, is proposed. Initially, GNNs represent the most promising deep learning models adept at processing non-Euclidean data. The Graph Transformer layer enhances atom representations by consolidating the representations of adjacent atoms through an attention mechanism. Finally, XAI is applied to explain the internal mechanisms of AI systems, rendering them comprehensible and interpretable. Across five independent runs, the proposed model achieved an accuracy of $0.963\pm 0.002$, a precision of $0.971\pm 0.002$, a recall of $0.958\pm 0.003$, an F1-score of $0.964\pm 0.002$, and a ROC-AUC of $0.993\pm 0.001$. These results demonstrate an outstanding performance when compared with all other models and emphasize that the proposed model is reliable in solving the problems of prioritizing compounds in line with the latest developments in AI-powered virtual screening and drug–target interaction modeling. Full article

To address the challenges of structural deformation and limited load-bearing capacity in morphing wings, this paper proposes a novel rigid–flexible composite morphing wing based on a foldable membrane–skeleton structure with a high folding ratio. Inspired by the deployment mechanics of biological wings and the cooperative support principle of multi-bar mechanisms, an optimization model was established to resolve hinge interference in the skeletal design. Through geometric reconstruction of the skeleton, the design achieves compact stowage in the folded state and maximizes wing area in the deployed configuration. Furthermore, an integrated design model for the membrane–skeleton interface was established based on rigid–flexible hybrid connection principles, followed by an analysis of the wing’s static structural characteristics via finite element simulation. A prototype was fabricated to experimentally validate its morphing functionality and out-of-plane load-bearing performance. Results demonstrate that the mechanism attains an effective folding ratio of approximately 7.19. Additionally, the influence of membrane prestress on the overall structural load capacity was systematically investigated. Full article