Search Results (2,264)

The progressive degradation of surface waters should become one of the most important problems requiring an urgent solution. One of the methods developed is filtering water through loose, degraded sediments, blooms of cyanobacteria or algae, or a bed of hemp (Cannabis sativa L.) waste or hemp fibers. The conducted tests on the percolation of water samples and/or water with sediment from surface waters at sites with different ecological statuses indicate the possibility of using hemp waste for the reclamation of water reservoirs and rivers. The effect of filtration is a rapid improvement in water quality and, consequently, an improvement in the ecological status. The best result was achieved for a small freshwater reservoir with a large number of algae and loose degraded sediment. The initial turbidity value was at the limit of the device’s measurement capability, reaching 9991 NTU. After filtration through the hemp waste bed, the turbidity dropped to 42.52 NTU, a 99.57% decrease. The remaining parameters, C, TDS, and pH, were not subject to significant variability as a result of filtering. Excessive amounts of organic matter, which create a problem for surface waters, are removed. Due to the carrier (hemp waste), which is organic waste, any possible release of small amounts into the aquatic environment will not pose a threat. After applying filtration, a decision can be made on further actions regarding the water reservoir or river: Self-renewal of the reservoir or further percolation using, for example, mill gauze or cleaning the reservoir with other, non-invasive methods. After the filtering procedure, the hemp waste, enriched with organic matter and water remaining in the waste, can be used for composting or directly for soil mulching (preliminary tests have yielded positive results). A hemp waste filter effectively removes Chronomus aprilinus larvae (Chrinomidae) from water. This result indicates the possibility of removing mosquito larvae in malaria-affected areas. The use of hemp filters would reduce the amount of toxic chemicals used to reduce mosquito larvae. Improving the ecological status of surface waters by filtering contaminants with hemp waste filters can reduce the need for chemical treatment. The use of natural, biological filters enables sustainable surface water management. This is crucial in today’s rapidly increasing chemical pollution of surface waters. Full article

Background: Cefepime/enmetazobactam (FEP/META) is a novel fixed-dose β-lactam/β-lactamase inhibitor combination. The objective was to study the physicochemical stability of the approved daily dose in polypropylene syringes and elastomeric devices over a 24 or 72 h period to understand the feasibility of using FEP/META in prolonged infusions and its use for outpatient parenteral antibiotic therapy (OPAT). Methods: Solutions of FEP/META were prepared in 0.9% NaCl or 5% dextrose (D5W) and stored in syringes (6 g/1.5 g/48 mL) or silicone and polyisoprene elastomeric devices (EDs) at 6 g/1.5 g/120 mL and 6 g/1.5 g/240 mL: syringes were tested at 22–25 °C over a 24 h period, polyisoprene EDs at 2–8 °C over 72 h period, and silicone and polyisoprene EDs at 32 °C over a 24 h period. The solution was considered stable if it retained more than 90% of its initial concentration (Ci), no pH variation (±1 unit), no significant visual change, and with compliant subvisual examination. Liquid Chromatography–Electrospray Ionization–Quadrupole Time-of-Flight–Mass Spectrometry was utilized to identify intermediate degradation products. Results: At the daily dose, FEP/META retained >90% of its Ci up to 12 h in 0.9% NaCl and 24 h in D5W when stored in syringes. In silicone ED, stability was enhanced up to 24 h in D5W at all concentrations. The solution was chemically stable for 24 h when stored in polyisoprene ED in 0.9% NaCl at 2–8 °C. Conclusions: FEP/META combination showed prolonged stability with physicochemical integrity up to 12–24 h in all containers and conditions. It appears to be stable for prolonged infusions and for OPAT. Full article

To combat the critical hurdles of thermal buildup and low-temperature shutdown events in 5G-enabled smart wearables, a high-performance flexible composite film based on ellagic acid-modified single-walled carbon nanotubes (EA-SWCNTs) and aramid nanofibers (ANF) was designed and developed. The influence mechanism of the loading amount of the conductive network on the electrothermal properties of the composite material was focused on. The results show that through the π-π stacking non-covalent modification strategy, the uniform dispersion of EA-SWCNTs on the layer of ANF substrate and the construction of an ordered layered structure were successfully achieved. The prepared composite film could reach a steady-state temperature of 171 °C under a driving voltage of 3.5 V. In addition, it exhibits excellent electrothermal response characteristics and cyclic stability. It could reach the steady-state voltage within 10 s and shows no obvious performance degradation after multiple cycles. This composite film shows broad application prospects in fields such as intelligent wearable devices and flexible electronic protection. Full article

Bipolar plates are critical components in high-efficiency energy conversion devices such as electrolyzers, fuel cells, and flow batteries, and their durability directly affects the overall performance and lifespan of the system. Although graphite bipolar plates exhibit excellent electrical conductivity and corrosion resistance, their inherent brittleness and porous structure render them prone to thermal-stress-induced damage under dynamic temperature conditions. In this study, a self-designed thermal shock testing system was utilized to perform 16,000 cycles of accelerated aging tests on graphite bipolar plates, alternating between high-temperature (90 °C) and low-temperature (30 °C) water bath environments. Systematic analysis was conducted on the performance degradation behaviors under such thermal cycling conditions using multi-scale characterization techniques, including scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), electrical conductivity, contact angle, surface roughness, and corrosion current density analysis. The results demonstrate that the degradation in electrical conductivity, loss of hydrophobicity, and increased surface roughness were primarily attributed to thermal-stress-induced microcrack initiation and propagation, surface oxidation, and physical structural deterioration. Notably, the corrosion current density did not increase significantly after 16,000 thermal cycles, but slightly decreased in the later stage, indicating that the aging of graphite bipolar plates is dominated by physical fatigue damage, and the graphite matrix has excellent chemical stability. The novelty of this study lies in the construction of a thermal shock testing system under long-cycle conditions, revealing the synergistic mechanism of thermal cycle-induced performance degradation of graphite bipolar plates, which provides experimental evidence and theoretical guidance for the material selection, structural design, and protection strategies of highly durable bipolar plates. Full article

Industrial aluminum-block heating processes exhibit nonlinear dynamics, substantial time delays, and stringent requirements for fault detection and diagnosis, especially in semiconductor manufacturing and other high-precision electronic processes, where slight temperature deviations can accelerate device degradation or even cause catastrophic failures. To address these challenges, this study presents a digital twin-based intelligent heating platform for aluminum blocks with a dual-artificial-intelligence framework (dual-AI) for control and diagnosis, which is applicable to multi-port aluminum-block heating systems. The system enables real-time observation and simulation of high-temperature operational conditions via virtual-real interaction. The platform precisely regulates a nonlinear temperature control system with a prolonged time delay by integrating a conventional proportional–integral–derivative (PID) controller with a Levenberg–Marquardt-optimized backpropagation (LM-optimized BP) neural network. Simultaneously, a relay is employed to sever the connection to the heater, thereby simulating an open-circuit fault. Throughout this procedure, sensor data are gathered simultaneously, facilitating the creation of a spatiotemporal time-series dataset under both normal and fault conditions. A one-dimensional convolutional neural network (1D-CNN) is trained to attain high-accuracy fault detection and localization. PID+LM-BP achieves a response time of about 200 s in simulation. In the 100 °C to 105 °C step experiment, it reaches a settling time of 6 min with a 3 °C overshoot. Fault detection uses a 0.38 °C threshold defined based on the absolute minute-to-minute change of the 1-min mean temperature. Full article

Objective gait assessment is essential for post-stroke rehabilitation monitoring, yet optical motion capture systems remain inaccessible to most clinical settings due to cost and infrastructure constraints. This study assessed the validity of the REEV SENSE IMU for measuring spatiotemporal gait parameters in post-stroke individuals and evaluated assistive device effects on measurement accuracy. Twenty chronic post-stroke participants were enrolled, and fourteen completed the study (ten without an assistive device, four using a cane) after applying pre-defined exclusion criteria (walking speed <0.28 m/s, n = 6). Participants walked at self-selected speed while simultaneously being recorded by REEV SENSE IMUs and optical motion capture. Spatiotemporal parameters from matched heel strikes were compared using intraclass correlation coefficients (ICC), mean relative error (MRE), and Bland–Altman analysis. Temporal parameters demonstrated excellent reliability: contact time (ICC 0.96–0.99, MRE 2.77–5.45%), stride duration (ICC 0.95–0.99, MRE 2.57–2.62%), and cadence (ICC 0.98–0.99, MRE 1.80–1.93%). Spatial parameters showed greater variability, with stride length degrading substantially in slow-walking conditions (Cane group: ICC 0.76, MRE 8.60%). REEV SENSE provides reliable temporal parameter measurement comparable to commercial systems, positioning it as a practical tool for clinical gait monitoring in post-stroke rehabilitation. However, spatial parameter accuracy requires cautious interpretation in slow-walking regimes, necessitating independent validation when clinical decisions depend on precise stride length estimates. Full article

This paper proposes a resource optimized cascaded quantum surface repetition code architecture integrated with a Union Find (UF) enhanced hybrid decoder, which suppresses biased noise and improves the scalability of quantum error correction through synergistic inner outer quantum code collaboration. The hybrid architecture employs inner quantum repetition codes for local error suppression and outer rotated quantum surface codes for topological robustness, reducing auxiliary quantum qubits by 12.5% via shared stabilizers and compact lattice embedding. An optimized UF decoder employing path compression and adaptive cluster merging achieves near-linear time complexity $\mathcal{O}\left(n\alpha \right(n\left)\right)$, outperforming minimum-weight perfect matching (MWPM) decoders $\mathcal{O}\left(n^{2.5}\right)$. Under Z-biased noise $\eta =10$, simulations demonstrate a 28.2% error threshold, 2.6% higher than standard quantum surface codes, and 15% lower logical error rates via dynamic boundary expansion. At code distance $d=7$, resource savings reach 9.3% with maximum relative error below 8.5%, fulfilling fault-tolerance criteria. The UF decoder exhibits 38% threshold advantage over MWPM at low bias $\eta \approx {10}^{-3}$ and 15% less degradation at high noise $p=0.5$, enabling scalable real-time decoding. This framework bridges theoretical thresholds with practical resource constraints, offering a noise-adaptive QEC solution for near-term quantum devices including photonic quantum systems referenced in the paper’s background on repetition cat qubits. Full article

Compared to reinforcing concrete with steel bars, rebars—made of fiber-reinforced plastic—have a high potential for resource savings in the construction industry due to their corrosion resistance. For the large-volume market of reinforcement elements, efficient manufacturing processes must be developed to ensure the best possible bond behavior between concrete and rebar. In contrast to established FRP-rebars made with thermosetting materials, the use of a thermoplastic matrix enables surface profiling without severing the edge fibers as well as subsequent bending of the bar. The rebars to be produced in this study are based on the process of reactive thermoplastic pultrusion of continuously glass fiber reinforced aPA6. Their surface must enable a mechanical interlocking between the reinforcement bar and concrete. Concepts for a profiling device have been methodically developed and evaluated. The resulting concept of a double wheel embossing unit with a variable infeed and an infrared preheating section is built as a prototype, implemented in a pultrusion line, and further optimized. For a comprehensive understanding of the embossing process, reinforcement bars are manufactured, characterized, and evaluated under parameter variation according to a statistical experimental plan. The present study demonstrates the relationship between the infeed, preheating temperature, and haul-off speed with respect to the embossing depth, which is equivalent to the rib height. No degradation of the Young’s modulus was observed as a result of the profiling process. Full article

Glass-insulated terminals (GITs) are widely used in high-reliability microelectronic systems, where glass fall-offs in the sealing region may seriously degrade the reliability of the microelectronic component and further degrade the device reliability. Automatic inspection of such defects is challenging due to strong light reflection, irregular defect appearances, and limited defective samples. To address these issues, a coarse-to-fine machine-learning framework is proposed for glass fall-off detection in GIT images. By exploiting the circular-ring geometric prior of GITs, an adaptive sector partition scheme is introduced to divide the region of interest into sectors. Four categories of sector features, including color statistics, gray-level variations, reflective properties, and gradient distributions, are designed for coarse classification using a gradient boosting decision tree (GBDT). Furthermore, a sector neighbor (SN) feature vector is constructed from adjacent sectors to enhance fine classification. Experiments on real industrial GIT images show that the proposed method outperforms several representative inspection approaches, achieving an average IoU of 96.85%, an F1-score of 0.984, a pixel-level false alarm rate of 0.55%, and a pixel-level missed alarm rate of 35.62% at a practical inspection speed of 32.18 s per image. Full article

Real-time visual inference on resource-constrained hardware remains a core challenge for edge computing and embedded artificial intelligence systems. Recent deep learning architectures, particularly Vision Transformers (ViTs) and Detection Transformers (DETRs), achieve high detection accuracy but impose substantial computational and memory demands that limit their deployment on low-power edge platforms such as NVIDIA Jetson and Raspberry Pi devices. This paper presents a systematic review of model compression and optimization strategies—specifically quantization, pruning, and knowledge distillation—applied to lightweight object detection architectures for edge deployment. Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, peer-reviewed studies were analyzed from Scopus, IEEE Xplore, and ScienceDirect to examine the evolution of efficient detectors from convolutional neural networks to transformer-based models. The synthesis highlights a growing focus on real-time transformer variants, including Real-Time DETR (RT-DETR) and low-bit quantized approaches such as Q-DETR, alongside optimized YOLO-based architectures. While quantization enables substantial theoretical acceleration (e.g., up to 16× operation reduction), aggressive low-bit precision introduces accuracy degradation, particularly in transformer attention mechanisms, highlighting a critical efficiency-accuracy tradeoff. The review further shows that Quantization-Aware Training (QAT) consistently outperforms Post-Training Quantization (PTQ) in preserving performance under low-precision constraints. Finally, this review identifies critical open research challenges, emphasizing the efficiency–accuracy tradeoff and the high computational demands imposed by Transformer architectures. Future directions are proposed, including hardware-aware optimization, robustness to imbalanced datasets, and multimodal sensing integration, to ensure reliable real-time inference in practical agricultural edge computing environments. Full article

To ensure the coordinated interaction between the beam and track of large-span bridges and achieve smooth rail transition at beam joints, rail expansion regulators and beam-end expansion devices are essential at bridge ends. However, these devices are structurally fragile, making them a weak link in the seamless track system. This study selected a long-span railway bridge and its expansion devices as research objects, summarized typical in-service diseases of beam-end expansion devices (e.g., adjustable sleeper offset, sleeper skewing, and expansion device jamming), and constructed a train–track–bridge coupled model incorporating these devices. The model was used to analyze the structural performance and train operation safety under defective conditions. Based on the analysis findings, a maintenance evaluation method for the beam-end region was proposed. Criteria include adjustable sleeper offset, lateral displacement difference between adjacent beam-ends, horizontal rotation angle of adjacent beams, vertical rotation angle of beam-ends, and longitudinal expansion amount of beam-end expansion devices in order to address the corresponding issues and achieve sustainable maintenance and operation of bridge structures. Full article

Symbiotic radio (SR) has recently emerged as a promising paradigm for enabling spectrum- and energy-efficient massive connectivity in low-power Internet-of-Things (IoT) networks. By allowing passive backscatter devices (BDs) to coexist with active primary link transmissions, SR significantly improves spectrum utilization without requiring dedicated spectrum resources. However, most existing studies on multi-tag multiple-input multiple-output (MIMO) SR systems assume homogeneous traffic demands among BDs and primarily focus on rate-based performance metrics, while neglecting system-level task completion time (TCT) optimization under heterogeneous data requirements. In this paper, we investigate a joint performance optimization framework for a multi-tag MIMO symbiotic radio network. We first formulate a weighted sum-rate (WSR) maximization problem for the secondary backscatter links. The original non-convex WSR maximization problem is transformed into an equivalent weighted minimum mean square error (WMMSE) problem, and then solved by a block coordinate descent (BCD) approach, where the transmit precoding matrix, decoding filters, backscatter reflection coefficients are alternatively optimized. Second, to address the transmission delay imbalance caused by heterogeneous data sizes among BDs, we further propose a rate weight adaptive task TCT minimization scheme, which dynamically updates the rate weight of each BD to minimize the overall TCT. Simulation results demonstrate that the proposed framework significantly improves the WSR of the secondary system without degrading the primary link performance, and achieves substantial TCT reduction in multi-tag heterogeneous traffic scenarios, validating its effectiveness and robustness for MIMO symbiotic radio networks. Full article

Atherosclerosis remains a leading cause of cardiovascular mortality despite advances in pharmacological and interventional therapies. Current treatment approaches face limitations including systemic side effects, inadequate local drug delivery, and restenosis following vascular interventions. Gel-based technologies offer unique advantages through tunable mechanical properties, controlled degradation kinetics, high drug-loading capacity, and potential for stimuli-responsive therapeutic release. This review examines gel platforms across multiple scales and applications in atherosclerosis research and intervention. First, gel-based in vitro models are discussed. These include hydrogel matrices simulating plaque microenvironments, three-dimensional cellular culture platforms, and microfluidic organ-on-chip devices. These devices incorporate physiological flow to investigate disease mechanisms under controlled conditions. Second, therapeutic strategies are addressed through macroscopic gels for localized treatment. These encompass natural polymer-based, synthetic polymer-based, and composite formulations. Applications include stent coatings, adventitial injections, and catheter-delivered depots. Natural polymers often possess intrinsic biological activities including anti-inflammatory and immunomodulatory properties that may contribute to therapeutic effects. Third, nano- and microgels for systemic delivery are examined. These include polymer-based nanogels with stimuli-responsive drug release responding to oxidative stress, pH changes, and enzymatic activity characteristic of atherosclerotic lesions. Inorganic–organic composite nanogels incorporating paramagnetic contrast agents enable theranostic applications by combining therapy with imaging-guided treatment monitoring. Current challenges include manufacturing consistency, mechanical stability under physiological flow, long-term safety assessment, and regulatory pathway definition. Future opportunities are discussed in multi-functional integration, artificial intelligence-guided design, personalized formulations, and biomimetic approaches. Gel technologies demonstrate substantial potential to advance atherosclerosis management through improved spatial and temporal control over therapeutic interventions. Full article

Scientific studies have demonstrated how certain insect species can be used as bioindicators and reverse environmental degradation through their behavior and organization. Studying these species involves capturing and extracting hundreds of insects from a colony for subsequent study, analysis, and observation. This allows researchers to classify the individuals and also determine the organizational structure and behavioral patterns of the insects within colonies. The miniaturization of hardware devices for data and image acquisition, coupled with new Artificial Intelligence techniques such as Scene Graph Generation (SGG), has evolved from the detection and recognition of objects in an image to the understanding of relationships between objects and the ability to produce textual descriptions based on image content and environmental parameters. This research paper presents the design and functionality of a distributed computing architecture for image and video acquisition of bees and ants in their natural environment, in addition to a parallel computing architecture that hosts two datasets with images of real environments from which scene graphs are generated to recognize, classify, and analyze the behaviors of bees and ants while preserving and protecting these species. The experiments that were carried out are classified into two categories, namely the recognition and classification of objects in the image and the understanding of the relationships between objects and the generation of textual descriptions of the images. The results of the experiments, conducted in real-life environments, show recognition rates above 70%, classification rates above 80%, and comprehension and generation of textual descriptions with an assertive rate of 85%. Full article

Large-scale frames are increasingly used in engineering structures, particularly in aerospace structures. Among them, planar phased array satellite antennas used for global observations and target tracking have received much attention. Considering that structural deformation will degrade the coherence of antennas, a frame with inherent diagonal cables that serves to control the antennas’ static configuration is thoroughly studied. These inherent cables of planar phased arrays are pre-tensioned to preserve the structural integrity and increase the stiffness of the antenna. However, they are also used as actuators in our research; in this way, additional control devices are not needed. As a result, the antenna’s mass will decrease, and its reliability will increase. For high observation accuracy, the antennas tend to be very large. Accordingly, there is a significant deformation of space antennas when they are loaded. For this reason, a nonlinear finite element method is used to consider the structures’ geometrical nonlinearity. In order to achieve shape adjustment, the difference between active and passive cables must be carefully investigated. Furthermore, for the nonlinear structure in this paper, the active cables will deform in tandem with the structure as a whole so that the direction of the active cables’ control forces will also change during the entire control process. This paper elaborates on this problem and proposes a nonlinear optimization method considering this characteristic of the cables. Simulations of a simplified 2-bay and 18-bay satellite antenna are performed to validate the proposed method. Results of the numerical simulation demonstrate that the proposed method can successfully adjust the large-scale antenna’s static shape and achieve high precision. Full article