Search Results (909,211)

Recent progress in microfluidic technologies has led to the development of compact and highly efficient electrochemical platforms, including lab-on-a-chip (LoC) systems, that integrate multiple testing functions into a single, portable device. Combined with smartphone-based electrochemical devices, these systems enable rapid and accurate on-site detection of food contaminants, including pesticides, heavy metals, pathogens, and chemical additives at farms, markets, and processing facilities, significantly reducing the need for traditional laboratories. Smartphones improve the performance of these platforms by providing computational power, wireless connectivity, and high-resolution imaging, making them ideal for in-field food safety testing with minimal sample and reagent requirements. At the core of these systems are electrochemical biosensors, which convert specific biochemical reactions into electrical signals, ensuring highly sensitive and selective detection. Advanced nanomaterials and integration with Internet of Things (IoT) technologies have further improved performance, delivering cost-effective, user-friendly food monitoring solutions that meet regulatory safety and quality standards. Analytical techniques such as voltammetry, amperometry, and impedance spectroscopy increase accuracy even in complex food samples. Moreover, low-cost engineering, artificial intelligence (AI), and nanotechnology enhance the sensitivity, affordability, and data analysis capabilities of smartphone-integrated electrochemical devices, facilitating their deployment for on-site monitoring of food and agricultural contaminants. This review explains how these technologies address global food safety challenges through rapid, reliable, and portable detection, supporting food quality, sustainability, and public health. Full article

Heterotetramerization of Kv1.1 and Kv1.2 α-subunits expands the functional diversity of voltage-gated potassium Kv1 channels in the central nervous system (CNS), thus necessitating the study of the properties of these heterochannels, including their interactions with ligands. We report on the expression, electrophysiological, and ligand-binding properties of human heterochannels Kv(1.1-1.2)₂ formed by dimeric concatemers Kv1.1-Kv1.2 fused with fluorescent protein mKate2 in Neuro-2a cells. Kv(1.1-1.2)₂ is a low-voltage-activated, highly active, non-inactivating channel with a fast activation rate. Its activation rate and half-maximum activation voltage are similar to that of the Kv1.1 channel, but differ from that of Kv1.2. This suggests that the membrane expression of Kv(1.1-1.2)₂ may functionally compensate for the absence of membrane presentation of homotetrameric Kv1.1 channels in CNS. Hongotoxin 1 fused with fluorescent protein GFP (HgTx-G) is shown to be a pore-blocking ligand of Kv(1.1-1.2)₂ with a dissociation constant of 100 pM. Using confocal microscopy and competitive binding assay, HgTx-G and cells expressing Kv(1.1-1.2)₂, the apparent dissociation constants of the complexes between Kv(1.1-1.2)₂ and peptides Ce1, Ce4, hongotoxin 1, MeKTx11-1, agitoxin 2, charybdotoxin, and scyllatoxin were evaluated to be 14, 33, 40, 250, 800, and >>3300 pM, respectively. Heterotetramerization of α-subunits has a different effect on the affinity of ligands compared to those for Kv1.1 and Kv1.2 channels. Full article

This study deals with P-type iterative learning control (ILC) techniques for switched impulsive systems governed by composite fractional derivatives. The systems considered incorporate non-instantaneous impulses and an initial state offset, with the objective of accurately tracking time-varying reference trajectories over a finite time interval using a finite number of iterations. By implementing a P-type learning law integrated with an initial iteration mechanism, we derive sufficient conditions that guarantee the convergence of the tracking error. The effectiveness and robustness of the proposed control concepts are validated through a comprehensive illustrative example. Full article

Field workers require expeditious and pertinent access to information to execute their duties, frequently in arduous environments. Conventional document search interfaces are ill-suited to these contexts, while fully automated approaches often lack the capacity to adapt to the variability of situations. This article explores a hybrid approach based on the use of specialized small language models (SLMs), combining natural language interaction, context awareness (static and dynamic), and structured command generation. The objective of this study is to demonstrate the feasibility of providing contextualized assistance for mobile agents using an intelligent conversational agent, while ensuring that reasonable resource consumption is maintained. The present case study pertains to the supervision of illumination systems on a university campus by technical agents. The static and the dynamic contexts are integrated into the user command to generate a prompt that queries a previously fine-tuned SLM. The methodology employed, the construction of five datasets for the purposes of evaluation, and the refinement of selected SLMs are presented herein. The findings indicate that models of smaller scale demonstrate the capacity to comprehend natural language queries and generate responses that can be effectively utilized by a tangible system. This work opens prospects for intelligent, resource-efficient, and contextualized assistance in industrial or constrained environments. Full article

Meta-DETR is a few-shot target detection algorithm that combines meta-learning and transformer architecture to solve the problem of data sample scarcity. This algorithm uses deformable attention to focus feature learning process more accurately on the target and its surroundings. However, the number of sampling points in the deformable attention is fixed, which limits the effective information involved in feature extraction, resulting in insufficient feature extraction of the target and affecting detection performance. To solve this problem, a Meta-DETR few-shot target detection algorithm based on adaptive sampling deformable attention is proposed. Firstly, the cosine similarity between feature points is calculated by query features that are integrated with support features. Secondly, the number of related features of each feature point is counted by the similarity threshold. Thirdly, the final number of sampling points of the feature map are calculated by using the idea of maximum inter-class variance to achieve adaptive sampling. Finally, adaptive sampling deformable attention is integrated into Meta-DETR to achieve few-shot target detection. From the attention activation map, it can be seen that the deformable attention based on adaptive sampling pays more attention to the target itself. Compared with Meta-DETR, the proposed algorithm improves the detection accuracy of novel classes by 0.9%, 0.7%, 1.4%, and 2.1%, respectively, for shots 1, 2, 3, and 10 in partition 1 on the PASCAL VOC dataset; 3.5%, 0.1%, 5.5%, and 5.7%, respectively, for shots 2, 3, 5, and 10 in partition 2; and 1.9%, 1.0%, 2.1%, and 0.1%, respectively, for shots 2, 3, 5, and 10 in partition 3. Compared with MPF-Net, CRK-Net, and FSCE, the proposed algorithm achieves the best performance and can effectively realize detection under few-shot conditions. In addition, experiments on a self-made infrared dataset further validate the effectiveness of the algorithm proposed in this paper. Full article

This paper proposes optimal sliding mode fault-tolerant control for multiple robotic manipulators in the presence of external disturbances and actuator faults. First, a quantitative prescribed performance control (QPPC) strategy is constructed, which relaxes the constraints on initial conditions while strictly restricting the trajectory within a preset range. Second, based on QPPC, adaptive gain integral terminal sliding mode control (AGITSMC) is designed to enhance the anti-interference capability of robotic manipulators in complex environments. Third, a critic-only neural network optimal dynamic programming (CNNODP) strategy is proposed to learn the optimal value function and control policy. This strategy fits nonlinearities solely through critic networks and uses residuals and historical samples from reinforcement learning to drive neural network updates, achieving optimal control with lower computational costs. Finally, the boundedness and stability of the system are proven via the Lyapunov stability theorem. Compared with existing sliding mode control methods, the proposed method reduces the maximum position error by up to 25% and the peak control torque by up to 16.5%, effectively improving the dynamic response accuracy and energy efficiency of the system. Full article

Indoor positioning systems based on received signal strength (RSS) achieve indoor positioning by leveraging the position-related features inherent in spatial RSS fingerprint images. Their positioning accuracy and robustness are directly influenced by the quality of fingerprint features. However, the inherent spatial low-resolution characteristic of spatial RSS fingerprint images makes it challenging to effectively extract subtle fingerprint features. To address this issue, this paper proposes an RSS-based indoor positioning method that combines enhanced spatial frequency fingerprint representation with fusion learning. First, bicubic interpolation is applied to improve image resolution and reveal finer spatial details. Then, a 2D fast Fourier transform (2D FFT) converts the enhanced spatial images into frequency domain representations to supplement spectral features. These spatial and frequency fingerprints are used as dual-modality inputs for a parallel convolutional neural network (CNN) model with efficient multi-scale attention (EMA) modules. The model extracts modality-specific features and fuses them to generate enriched representations. Each modality—spatial, frequency, and fused—is passed through a dedicated fully connected network to predict 3D coordinates. A coordinate optimization strategy is introduced to select the two most reliable outputs for each axis (x, y, z), and their average is used as the final estimate. Experiments on seven public datasets show that the proposed method significantly improves positioning accuracy, reducing the mean positioning error by up to 47.1% and root mean square error (RMSE) by up to 54.4% compared with traditional and advanced time–frequency methods. Full article

Introduction: The management of peri-operative pain significantly impacts the post-operative recovery following kidney transplant. For decades, regional blocks have been utilized for post-operative pain management following abdominal surgery. The data on the routine use of regional blocks peri-operatively during kidney transplants are limited. We aim to review our current clinical practice of peri-operative use of regional blocks during kidney transplants and management of peri-operative pain up to 24 h. Methods: A consecutive series of 100 patients who underwent kidney transplant was reviewed. All demographic data including patient’s age, gender, race, and body mass index were collected. Pre-transplant co-morbidities were summarized for all patients and included the American Society of Anesthesiologists (ASA) score. Patients were divided into two groups based on whether they received a transversus abdominis plane (TAP) block. Group A consisted of patients who received an ultrasound-guided TAP block, while Group B included patients who did not receive any form of TAP block. The intra-operative and post-operative use of analgesia was recorded for up to 24 h post kidney transplant. All peri-operative complications were reviewed. The chi-square test and Fisher’s exact test was used to compare symptoms (nausea, vomiting, and pruritus) between the two groups. Similarly, the use of analgesia was also compared. Results: A total of 100 patients were identified and equally distributed between the two groups [Group A = 50 (TAP block), Group B = 50 (non-TAP block)]. There was a statistically significant reduction in the use of intraoperative fentanyl (p = 0.04) in Group A. There was no difference in the post-operative use of hydromorphone (p = 0.665), oxycodone (p = 0.75), and acetaminophen (p = 0.64) up to 24 h after the kidney transplant procedure. There was no difference between post-operative nausea (p = 0.766), vomiting (p = 0.436), and pruritus. There were no complications recorded secondary to the use of regional blocks in Group A. Conclusions: The use of regional anesthesia in kidney transplant recipients is a safe approach without complications. The study concluded that regional blocks decrease the use of intra-operative opioids. However, there was no difference in the use of post-operative requirements for analgesia or side effects up to 24 h after kidney transplant. Full article

Human milk contains a wide variety of proteins, possessing antimicrobial and immunomodulatory activities, which are essential for normal infant growth. Over the past few years, the widespread interest in milk’s nutritional quality and its association with infant health care has led to scientific research on the composition of different milk types. In this study, the similarity between protein content in human and donkey milk during the ninth-month lactation period is estimated. Our results demonstrate that donkey milk may be a valid substitute for cow’s milk to supplement the nutrition of allergic children. Full article

The topography of the flood path significantly influences the hydraulic characteristics of flood events, necessitating in-depth analysis to better understand the continuous dynamics during dam failure scenarios. These analyses are useful for the hydraulic evaluation of infrastructures downstream of a dam site. This study examined the effects of four distinct converging configurations of guide-banks on the propagation of unsteady flow in a rectangular channel. The configurations studied included trapezoidal and crescent side contractions, as well as trapezoidal and crescent barriers located at the channel’s center, each with varying lengths and widths. Numerical simulations using computational fluid dynamics (CFD) simulation were validated against experimental data from the literature. The results reveal that the flow experienced a depth increase upon encountering converging geometries, leading to the formation of a hydraulic jump and the subsequent upstream progression of the resulting wave. The width of the obstacles and contractions had a marked influence on the flow profile. Increased channel contraction led to a more pronounced initial water elevation rise when the flood flow encountered the topography, resulting in a deeper reflected wave that propagated upstream at less time. The reflected wave increased the water elevations up to 0.64, 0.72, and 0.80 times the initial reservoir level (0.25 m), respectively, for cases with 33%, 50%, and 66% contraction ratios to the channel width (0.3 m). For the same cases at a certain time of t = 5.0 s, the reflected wave reached 1.1 m downstream, 0.5 m downstream, and 0.1 m upstream of the initial dam location. Waves generated by the trapezoidal configuration affected the upstream in less time than those formed by the crescent contraction. The length of the transitions or their placement (middle of/across the channel) did not significantly affect the flow profile upstream; however, within the converging zone, longer configurations resulted in a wider increased water elevation. Overall, the intensity of the hydraulic response can be related to one factor in all cases, namely, the convergence intensity of the flow lines as they entered the contractions. Full article

Fossils are portals to the past, providing researchers with vital information about the evolution of life on Earth throughout the geological eras. The present study synthesizes the recent trends in fossil research, emphasizing the most common techniques found in the specialized literature over the past 20 years. The bibliographic survey revealed that destructive methods continue to play a significant role in scientific production related to this topic, particularly in studies on 3D morphologies, diagenesis, nutritional ecology, dating, elucidating dietary or habitat preferences, or understanding the physiology of extinct species. However, noninvasive tools, such as Raman spectroscopy, are rapidly rising, particularly when integrated with imaging techniques. As such, fossil research continues to advance even beyond the borders of our planet, exploring extraterrestrial samples in a quest to unlock the universal mystery of life. At the same time, the advent of advanced AI methods—particularly model chatbots that rival the capabilities of experienced scientists—has facilitated and enhanced data interpretation and classification. As fossil research evolves, upcoming technological advancements in spatial resolution, penetration depth, and detection sensitivity will integrate state-of-the-art spectroscopic tools. This will undoubtedly take fossil research to new heights, generating breakthroughs that optimize analysis while preserving invaluable specimens. Overall, the present study offers a holistic overview of analytical techniques through meta-analysis and bibliometric mapping, including a critical assessment of commonly used methods and offering a glimpse into the integration of machine learning and AI tools in fossil research. Full article

Mosses are key players in semi-arid ecosystems; however, the functional roles of mosses on hydrologic buffering and water quality have hardly been assessed. In the present study, the water storage, saturation dynamics, and ion release experiment of a set of four moss species (Hypnum lacunosum, Homalothecium lutescens, Dicranum scoparium, and Tortella tortuosa) was performed by a more simplified immersion and drainage procedure with water chemistry analyses. All species reached a sorption equilibrium between 10 and 20 min, with pleurocarpous taxa retaining 20–35% more water than acrocarpous species and possessing water-holding capacities (WHCs) between 300% and 700% of dry weight. Species-specific differences in water chemistry (pH, EC, and TDS) were observed: Tortella tortuosa presented the greatest ionic flux, and Hypnum lacunosum presented little variation in pH and electrical conductivity. These findings imply that the mosses operate as micro-scale buffers regulating both water quantity and water quality, and thereby the soil stability, infiltration, and drought resilience. The combined hydrological and biogeochemical view offers a novel understanding of bryophyte ecohydrology and highlights the significance of mosses in the practice of watershed management and climate-change mitigation. Full article

This research focused on determining the technical and economic feasibility of the design of a solar-powered electric vehicle charging station (EVCS) in Cairo, Egypt. Using HOMER Grid, hybrid system configurations are assessed technically and economically to reduce costs and ensure reliability. These systems incorporate photovoltaic (PV) systems, lithium-ion battery energy storage systems (ESS), and diesel generators. A comprehensive analysis was conducted in Cairo, Egypt, focusing on small vehicle charging needs in both grid-connected and generator-supported scenarios. In this study, a 468 kW PV array integrated with 29 units of 1 kWh lithium-ion batteries and supported by time-of-use (TOU) tariffs, were used to optimize energy utilization. This study demonstrated the feasibility of the system in a case of eight chargers of 150 kW each and forty chargers of 48 kW. Conclusions suggest that the PV + ESS has the lowest pure power costs and reduced carbon emissions compared to traditional network-dependent solutions. The optimal configuration of USD 10.23 million over 25 years, with lifelong savings, results in annual savings of tool billing of around USD 409,326. This study concludes that a solar-powered EVC in Egypt is both technically and economically attractive, especially in the light of increasing energy costs. Full article

Background: Aortic stenosis (AS) is a prevalent acquired heart valve disease with increasing incidence, particularly among older adults. Gender-specific differences in AS presentation, comorbidities, and outcomes remain underexplored, necessitating further investigation to optimize personalized treatment strategies. Objective: To evaluate the clinical and demographic characteristics, comorbidities, and survival outcomes of patients with AS, stratified by gender and aortic valve morphology. Methods: A retrospective analysis of 145,454 echocardiographic examinations (2009–2018) at the Federal State Budgetary Institution “V.A. Almazov National Medical Research Centre” identified 84,851 patients meeting the inclusion criteria (Vmax ≥ 2.0 m/s, age ≥ 18 years). Patients were stratified by gender and valve morphology (bicuspid aortic valve [BAV] vs. tricuspid aortic valve [TAV]). Survival was assessed in 475 pts with AS over a 16-year period (2009–2025) using Kaplan–Meier analysis. Statistical comparisons utilized STATISTICA v. 10.0, with p-values derived from P-tests. Results: Of the cohort, 4998 men and 6322 women had AS. Men with AS were older (median 64 vs. 57 years, p < 0.0001) and had higher systolic blood pressure (140 vs. 130 mmHg, p < 0.0001) than men without AS. Women with AS were also older (median 70 vs. 58 years, p < 0.0001) with higher systolic (140 vs. 130 mmHg, p < 0.0001) and diastolic blood pressure (80 vs. 80 mmHg, p < 0.0001). Men with AS had higher rates of hyperlipidemia (HLP) (26.3% vs. 10.3%, p < 0.0001), while women with AS had increased coronary artery disease (CAD) (35.7% vs. 26.4%, p < 0.0001), diabetes mellitus (DM) (13.4% vs. 10.2%, p < 0.0001), and obesity (10.9% vs. 10.2%, p = 0.06). Chronic heart failure (CHF) was more frequently reported in patients with AS, regardless of gender, compared to patients without AS (in men 53.4% vs. 41.8%, p < 0.0001; in women 54.5% vs. 37.5%, p < 0.0001). BAV was associated with higher AS prevalence (54.5% in men, 66.4% in women). Survival analysis revealed higher mortality. Over the 16-year follow-up period, the mortality rate was 21.7%. Conclusions: Mortality in a representative AS cohort reached 21.7%, underscoring the progressive nature of the disease and its long-term impact. Survival was negatively affected by age over 68.5 years, as well as the presence of aortic regurgitation (AR), increased peak aortic jet velocity, and enlarged maximum aortic diameter. Aortic valve replacement demonstrates an insignificant effect on patient survival rates. Beta-blocker therapy in patients with varying degrees of aortic AS severity has not only demonstrated its safety but has also shown a positive effect on reducing mortality (improving survival). In contrast, the combination of angiotensin II receptor blockers (ARBs) with calcium channel blockers (CCBs) is quite dangerous for patients with AS and reduces their survival. Aortic valve replacement demonstrates an insignificant effect on patient survival rates. In contrast, the absence of fibrinolytic therapy and anticoagulant treatment is associated with an improved prognosis. Conversely, the administration of antiarrhythmic agents and statins is correlated with enhanced survival outcomes, potentially attributable to their influence on coexisting comorbidities. Further research is required to delineate their precise mechanisms and contributions. These results emphasize the importance of early identification, comprehensive risk assessment, and individualized management strategies in improving outcomes for patients with AS. Full article

Crystallization is one of the approximately twenty unit operations and is considered to be among the most important due to the large number of chemical compounds it produces, as well as due to the enormous quantities of these substances being manufactured around the world. This article aims to present a mathematical model for the shortcut design of a cooling crystallization unit consisting of the crystallizer and auxiliary equipment, such as an evaporator with its preheater and condenser, a heat pump that acts as the cooling system of the crystallizer, and a crystallizer pressure regulator modeled as an expansion valve. The model estimates an extensive series of variables, including mass and volume flow rates of the streams, heat duties of each piece of equipment, sizing variables such as heat transfer areas of heat exchangers and volumes of the vessels, and product flow rates for each specific feed. It embraces equations for the calculation of a series of stream properties, such as density, specific heat capacity, and latent heat of vaporization. For the sizing of the crystallizer, which is the main equipment of the unit, both flow rates and crystallization kinetics are taken into account. The latter is estimated by experimental data taken in a laboratory crystallizer and includes the crystal’s growth rate as a function of residence time. Full article