Search Results (2,039)

Foodborne pathogens remain a significant public health concern, necessitating the development of rapid, sensitive, and reliable detection methods for various food matrices. Traditional biosensors, while effective in many contexts, often face limitations related to complex sample environments, signal interpretation, and on-site usability. The integration of artificial intelligence (AI) into biosensing platforms offers a transformative approach to address these challenges. This review critically examines recent advancements in AI-assisted biosensors for detecting foodborne pathogens in various food samples, including meat, dairy products, fresh produce, and ready-to-eat foods. Emphasis is placed on the application of machine learning and deep learning to improve biosensor accuracy, reduce detection time, and automate data interpretation. AI models have demonstrated capabilities in enhancing sensitivity, minimizing false results, and enabling real-time, on-site analysis through innovative interfaces. Additionally, the review highlights the types of biosensing mechanisms employed, such as electrochemical, optical, and piezoelectric, and how AI optimizes their performance. While these developments show promising outcomes, challenges remain in terms of data quality, algorithm transparency, and regulatory acceptance. The future integration of standardized datasets, explainable AI models, and robust validation protocols will be essential to fully harness the potential of AI-enhanced biosensors for next-generation food safety monitoring. Full article

In the context of sustainable forest resource development, balancing ecological conservation with rational utilization is essential to achieving forest multifunctionality. Longyuwan National Forest Park, located in Luanchuan County, Henan Province, serves as a transitional zone between rural mountainous ecosystems and nearby urban settlements. Increasingly, this area faces urbanization pressures such as tourism expansion, infrastructure development, and intensified land use, which may threaten ecological stability. This study aims to evaluate the ecological sensitivity of the park and optimize its spatial functional zoning. Using the Analytic Hierarchy Process (AHP), we followed four key steps: constructing the hierarchical model, generating the pairwise judgment matrices, computing the weights and conducting the consistency check, and determining the final weights. A hierarchical evaluation framework was constructed using the AHP, incorporating twelve ecological indicators across geomorphological, hydrological, atmospheric, biological, and anthropogenic dimensions. Spatial analysis tools in ArcGIS 10.2, including reclassification and weighted overlay, were employed for single-factor and integrated sensitivity assessments. The results indicated that land-use type, elevation, and water-body distribution were the most influential indicators. Ecological sensitivity across the park was categorized into five levels: extremely high (0.02%), high (11.99%), moderate (73.53%), low (14.19%), and extremely low (0.28%). Based on these findings, four functional zones were delineated: ecological conservation (50.99%), core landscape (22.86%), general recreation (23.94%), and management and service (2.21%). This research provides spatially explicit insights into forest management under anthropogenic stress, offering theoretical support for the sustainable governance of forest–urban interface landscapes. Full article

With the rapid development of transportation infrastructure, bridges increasingly face prominent issues of dynamic response and fatigue damage induced by vehicle–bridge interaction (VBI). To effectively suppress the coupled vibrations and enhance both vehicle ride comfort and bridge service life, this paper proposes an active inerter-spring-damper (ISD) suspension system based on Particle Swarm Optimization (PSO) algorithm and Linear Quadratic Regulator (LQR) control. By establishing a VBI model considering general boundary conditions and employing the modal superposition method to solve the system response, an LQR controller is designed for multi-objective optimization targeting the vehicle body acceleration, suspension dynamic travel, and tire dynamic load. To further improve control performance, the PSO algorithm is utilized to globally optimize the LQR weighting matrices. Numerical simulation results demonstrate that, compared to passive suspension and unoptimized LQR active suspension, the PSO-LQR control strategy significantly reduces vertical body acceleration and tire dynamic load, while also improving the convergence and stability of the suspension dynamic travel. This research provides a new insight into the control method for VBI systems, possessing both theoretical and practical engineering application value. Full article

Background/Objectives:Hydrogels are highly hydrated, biocompatible polymer networks increasingly investigated as drug-delivery systems (DDS) for analgesics. Their ability to modulate local release, prolong drug residence time, and reduce systemic toxicity positions them as promising platforms in perioperative, chronic, and localized pain settings. This scoping review aimed to systematically map clinical applications, efficacy, and safety of hydrogel-based DDS for analgesics, while also documenting non-DDS uses where the matrix itself contributes to pain modulation through physical mechanisms. Methods: Following PRISMA-ScR guidance, PubMed, Embase, and Cochrane databases were searched without publication date restrictions. Only peer-reviewed clinical studies were included; preclinical studies and non-journal literature were excluded. Screening and selection were performed in duplicate. Data extracted included drug class, hydrogel technology, clinical setting, outcomes, and safety. Protocol was registered with Open Science Framework. Results: A total of 26 clinical studies evaluating hydrogel formulations as DDS for analgesics were included. Most were randomized controlled trials, spanning 1996–2024. Local anesthetics were the most frequent drug class, followed by opioids, corticosteroids, Non-Steroidal Anti-Inflammatory Drugs (NSAIDs), and neuromodulators. Application sites were predominantly topical/transdermal and perioperative/incisional. Across the DDS cohort, most of the studies reported improved analgesic outcomes, including reduced pain scores and lower rescue medication use; neutral or unclear results were rare. Safety reporting was limited, but tolerability was generally favorable. Additionally, 38 non-DDS studies demonstrated pain reduction through hydrogel-mediated cooling, lubrication, or barrier effects, particularly in burns, ocular surface disorders, and discogenic pain. Conclusions: Hydrogel-based DDS for analgesics show consistent clinical signals of benefit across diverse contexts, aligning with their mechanistic rationale. While current evidence supports their role as effective, well-tolerated platforms, translational gaps remain, particularly for hybrid nanotechnology systems and standardized safety reporting. Non-DDS applications confirm the intrinsic analgesic potential of hydrogel matrices, underscoring their relevance in multimodal pain management strategies. Full article

Transmembrane proteins (TMPs) constitute approximately 30% of the mammalian proteome and are critical targets in biomedical research due to their involvement in signaling, transport, and drug interactions. However, their unique structural characteristics pose significant challenges for conventional multiple sequence alignment (MSA) methods, which are typically optimized for soluble proteins. In this paper, we propose TMP-M2Align, a novel topology-aware multiobjective algorithm specifically designed for the multiple alignment of TMPs. The method simultaneously optimizes two complementary objectives: (i) a topology-aware Sum-of-Pairs (SPs) score that integrates region-specific substitution matrices and gap penalties, and (ii) an Aligned Regions (ARs) score that rewards consistent alignment of functional and topological domains. By combining these objectives, TMP-M2Align generates Pareto front approximations of alignment solutions, enabling researchers to select trade-offs that best suit their biological questions. We evaluated TMP-M2Align on BAliBASE Reference Set 7 and on complete datasets of human G protein-coupled receptors (GPCRs) from classes A, B1, and C. Experimental results demonstrate that TMP-M2Align consistently outperforms both traditional alignment tools and specialized TM-specific methods in terms of SPs and Total Column metrics. Moreover, qualitative topological analyses confirm that TMP-M2Align preserves the integrity of transmembrane helices and loop boundaries more effectively than competing approaches. These findings highlight the effectiveness of integrating topology-aware scoring with multiobjective optimization for achieving accurate and biologically meaningful alignments of TMPs. Full article

In the realm of image classification, the capsule network is a network topology that packs the extracted features into many capsules, performs sophisticated capsule screening using a dynamic routing mechanism, and finally recognizes that each capsule corresponds to a category feature. Compared with previous network topologies, the capsule network has more sophisticated operations, uses a large number of parameter matrices and vectors to express picture attributes, and has more powerful image classification capabilities. However, in the practical application field, the capsule network has always been constrained by the quantity of calculation produced by the complicated structure. In the face of basic datasets, it is prone to over-fitting and poor generalization and often cannot satisfy the high computational overhead when facing complex datasets. Based on the aforesaid problems, this research proposes a novel enhanced capsule network topology. The upgraded network boosts the feature extraction ability of the network by incorporating a multi-scale feature extraction module based on proprietary star structure convolution into the standard capsule network. At the same time, additional structural portions of the capsule network are changed, and a variety of optimization approaches such as dense connection, attention mechanism, and low-rank matrix operation are combined. Image classification studies are carried out on different datasets, and the novel structure suggested in this paper has good classification performance on CIFAR-10, CIFAR-100, and CUB datasets. At the same time, we also achieved 98.21% and 95.38% classification accuracy on two complicated datasets of skin cancer ISIC derived and Forged Face EXP. Full article

The proliferation of renewable energy sources and distributed generation systems interfaced to the grid by power electronics systems is forcing us to better understand the issues arising due to the quality of electrical signals generated through these devices. Understanding and monitoring these harmonics is crucial to ensure the smooth and seamless operation of these networks, as well as to protect and manage the renewable energy sources-based power system. In this paper, we propose an advanced method of dynamic modal decomposition, called Higher-Order Dynamic Mode Decomposition (HODMD), one of the recently proposed data-driven methods used to estimate the frequency/amplitude and phase with high resolution, to identify the harmonic spectrum in power systems dominated by renewable energy generation. In the proposed method, several time-shifted copies of the measured signals are integrated to create the initial data matrices. A hard thresholding technique based on singular value decomposition is applied to eliminate ambiguities in the measured signal. The proposed method is validated and compared to Synchrosqueezing Transform based on Short-Time Fourier Transform (SST-STFT) and the Concentration of Frequency and Time via Short-Time Fourier Transform (ConceFT-STFT) using synthetic signals and real measurements, demonstrating its practical effectiveness in identifying harmonics in emerging power networks. Finally, the effectiveness of the proposed methodology is analyzed on the energy storage-based laboratory-scale microgrid setup using an Opal-RT-based real-time simulator. Full article

The persistent conflict between strength and electrical conductivity in copper-based materials presents a fundamental limitation for next-generation high-performance applications. Graphene, with its unique two-dimensional architecture and exceptional intrinsic characteristics, has become a promising reinforcement phase for copper matrices. This comprehensive review synthesizes recent advancements in graphene–copper composites (CGCs), focusing particularly on structural design innovations and scalable manufacturing approaches such as powder metallurgy, molecular-level mixing, electrochemical deposition, and chemical vapor deposition. The analysis examines pathways for optimizing key properties—including mechanical strength, thermal conduction, and electrical performance—while investigating the fundamental reinforcement mechanisms and charge/heat transport phenomena. Special consideration is given to how graphene morphology, concentration, structural quality, interfacial chemistry, and processing conditions collectively determine composite behavior. Significant emphasis is placed on interface engineering strategies, graphene alignment, consolidation control, and defect management to minimize electron and phonon scattering while improving stress transfer efficiency. The review concludes by proposing research directions to resolve the strength–conductivity paradox and broaden practical implementation domains, thereby offering both methodological frameworks and theoretical foundations to support the industrial adoption of high-performance CGCs. Full article

Organoid technology has emerged as a revolutionary tool in cancer research, offering physiologically accurate, three-dimensional models that preserve the histoarchitecture, genetic stability, and phenotypic complexity of primary tumors. These self-organizing structures, derived from adult stem cells, induced pluripotent stem cells, or patient tumor biopsies, recapitulate critical aspects of tumor heterogeneity, clonal evolution, and microenvironmental interactions. Organoids serve as powerful systems for modeling tumor progression, assessing drug sensitivity and resistance, and guiding precision oncology strategies. Recent innovations have extended organoid capabilities beyond static culture systems. Integration with microfluidic organoid-on-chip platforms, high-throughput CRISPR-based functional genomics, and AI-driven phenotypic analytics has enhanced mechanistic insight and translational relevance. Co-culture systems incorporating immune, stromal, and endothelial components now permit dynamic modeling of tumor–host interactions, immunotherapeutic responses, and metastatic behavior. Comparative analyses with conventional platforms, 2D monolayers, spheroids, and patient-derived xenografts emphasize the superior fidelity and clinical potential of organoids. Despite these advances, several challenges remain, such as protocol variability, incomplete recapitulation of systemic physiology, and limitations in scalability, standardization, and regulatory alignment. Addressing these gaps with unified workflows, synthetic matrices, vascularized and innervated co-cultures, and GMP-compliant manufacturing will be crucial for clinical integration. Proactive engagement with regulatory frameworks and ethical guidelines will be pivotal to ensuring safe, responsible, and equitable clinical translation. With the convergence of bioengineering, multi-omics, and computational modeling, organoids are poised to become indispensable tools in next-generation oncology, driving mechanistic discovery, predictive diagnostics, and personalized therapy optimization. Full article

Natural Deep Eutectic Solvents (NADES) were synthesized from food-grade components and evaluated as green extractants for the simultaneous recovery and liquid chromatography coupled to quadrupole-Orbitrap mass spectrometry (LC–Q-Orbitrap-MS) analysis of biopesticide residues in a complex matrix like honey. Conventional solid–liquid extraction (SLE) was applied, initially using choline chloride-2,3-butanediol (1:4, molar ratio) as the NADES extractant solvent, before systematically evaluating other NADES formulations. Extraction parameters, such as time (10 min, 20 min, and 30 min), technique (rotary mixing vs. sonication), and NADES composition, namely lactic acid–glucose–water (LGH, 5:1:9, molar ratio), lactic acid–glycerol–water (LGLH, 1:1:3, molar ratio), urea–glycerol–water (UGLH, 1:1:2, molar ratio), and choline chloride–2,3-butanediol (ChClBt, 1:4, molar ratio), were systematically optimized. Rotating agitation for 10 min yielded the highest overall recoveries and was therefore selected as the optimal extraction time. Rotary shaking was chosen over sonication due to its superior performance across both simple and complex matrices. Among the NADES tested, UGLH proved to be the most effective composition for the honey matrix. The analytical method was validated for the honey matrix. Linearity showed excellent performance across the tested concentration range, with R² values above 0.95 for all analytes. Matrix effects were within ±20% for nearly half of the compounds, while a few exhibited moderate matrix enhancement. Recoveries ranged from 50.1% to 120.5% at 500 µg/kg and 1000 µg/kg, demonstrating acceptable extraction performance. Intra-day and inter-day precision showed relative standard deviations (RSDs) below 20% for most analytes. Limits of quantification (LOQs) were established at 500 µg/kg for eight compounds based on recovery and precision criteria. These results confirm the suitability of the proposed NADES-based method for sensitive and reliable analysis of biopesticide residues in honey. When compared to conventional extraction methods, the proposed NADES-based protocol proved to be a greener alternative, achieving the highest AGREEprep score due to its use of non-toxic solvents, lower waste generation, and overall sustainability. Full article

The design of informatively rich input signals is essential for accurate system identification, yet classical Fisher-information-based methods are inherently local and often inadequate in the presence of significant model uncertainty and non-linearity. This paper develops a Bayesian approach that uses the mutual information (MI) between observations and parameters as the utility function. To address the computational intractability of the MI, we maximize a tractable MI lower bound. The method is then applied to the design of an input signal for the identification of quasi-linear stochastic dynamical systems. Evaluating the MI lower bound requires the inversion of large covariance matrices whose dimensions scale with the number of data points N. To overcome this problem, an algorithm that reduces the dimension of the matrices to be inverted by a factor of N is developed, making the approach feasible for long experiments. The proposed Bayesian method is compared with the average D-optimal design method, a semi-Bayesian approach, and its advantages are demonstrated. The effectiveness of the proposed method is further illustrated through four examples, including atomic sensor models, where input signals that generate a large amount of MI are especially important for reducing the estimation error. Full article

The uneven expansion of renewable energy generation in different regions highlights the necessity of accurately assessing the available transfer capability (ATC) in power systems. This paper proposes a distributed probabilistic inter-regional ATC assessment framework. First, a spatiotemporally correlated wind power output model is established using wind speed forecast data and correlation matrices, enhancing the accuracy of wind power forecasting. Second, a two-stage probabilistic ATC assessment optimization model is proposed. The first stage minimizes both generation cost and risk-related costs by incorporating conditional value-at-risk (CVaR), while the second stage maximizes the power transaction amount. Thirdly, a privacy-preserving two-level iterative alternating direction method of multipliers (I-ADMM) algorithm is designed to solve this mixed-integer optimization problem, requiring only the exchange of boundary voltage phase angles between regions. Case studies are performed on the 12-bus, the IEEE 39-bus and the IEEE 118-bus systems to validate the proposed framework. Hence, the proposed framework enables more reliable and risk-aware intraday ATC evaluation for inter-regional power transactions. Moreover, the impacts of risk parameters and wind farm output correlations on ATC and generation cost are further investigated. Full article

To design novel imaging devices, we use masks coded with numerical sequences. These masks work in conjunction with varifocal systems that implement zero-throw tunable magnification. Some masks control field depth, even when the size of the pupil aperture remains fixed. Pairs of vortex masks are used to implement tunable phase radial profiles, like axicons and lenses. The autocorrelation properties of the Barker sequences are applied to the generation of narrow passband windows on the OTF. For this application, we apply Barker matrices in rectangular coordinates. A similar procedure, but now in polar coordinates, is useful for sensing in-plane rotations. We implement geometrical transformations by using zero-throw, tunable, anamorphic magnifications. Full article

Spatial reasoning ability plays a critical role in predicting academic outcomes, particularly in STEM (science, technology, engineering, and mathematics) education. According to the Cattell–Horn–Carroll (CHC) theory of human intelligence, spatial reasoning is a general ability including various specific attributes. However, most spatial assessments focus on testing one specific spatial attribute or a limited set (e.g., visualization, rotation, etc.), rather than general spatial ability. To address this limitation, we created a mixed spatial test that includes mental rotation, object assembly, and isometric perception subtests to evaluate both general spatial ability and specific attributes. To understand the complex relationship between general spatial ability and mastery of specific attributes, we used a higher-order linear logistic model (HO-LLM), which is designed to simultaneously estimate high-order ability and sub-attributes. Additionally, this study compares four spatial ability classification frameworks using each to construct Q-matrices that define the relationships between test items and spatial reasoning attributes within the HO-LLM framework. Our findings indicate that HO-LLMs improve model fit and show distinct patterns of attribute mastery, highlighting which spatial attributes contribute most to general spatial ability. The results suggest that higher-order LLMs can offer a deeper and more interpretable assessment of spatial ability and support tailored training by identifying areas of strength and weakness in individual learners. Full article

As native bovine whey (WHEY) poses environmental concerns as a high-water-content by-product, this trial aimed at assessing the effectiveness of a thermal–mechanical microparticulation coupled with a fermentative process to concentrate it into a high-protein soft dairy cream. Compared to native whey, in microparticulated (MPW) and fermented (FMPW) matrices, there was a significant increase in proteins (from 0.7 to 8.8%) and lipids (from 0.3 to 1.3%), and a more brilliant yellowness colour. A factorial discriminant analysis (FDA) showed that FMPW had a higher content of saturated fatty acid (SFA) and some specific polyunsaturated fatty acid (PUFA) n-6, and also identified C14:0, C18:1, C18:1 t-11, C18:2 n-6, and C18:3 n-6 as informative biomarkers of microparticulation and fermentative treatments. The SDS-PAGE indicated no effects on the protein profile but indicated its rearrangement into high molecular weight aggregates. Z-sizer and transmission electron microscopy analyses confirmed a different supramolecular structure corresponding to a higher variability and greater incidence of very large molecular aggregates, suggesting that MPW could be accounted as a colloidal matrix that may have similar ball-bearing lubrication properties. Microparticulation of whey could facilitate its circularity into the dairy supply chain through its re-generation from a waste into a high-value fat replacer for dairy-based food production. Full article