PtAu-Carbon Nanotube/Glassy Carbon Electrode Composite based Electrochemical Sensor for High-Precision Detection of Tetracycline.

With enrichment of tetracycline (TC) in ecosystems, its accurate detection has become a major concern. Noble-metal nano-particles have attracted great interest as potential materials for sensing applications because of their remarkable electrical properties and adaptability. Herein, a novel electro-chemical detection technique based on carbon nano-tubes (CNTs) as the support material is developed to detect TC with high precision.

Correlated spin-wave generation and domain-wall oscillation in a topologically textured magnetic film.

Spin waves, or magnons, are essential for next-generation energy-efficient spintronics and magnonics. Yet, visualizing spin-wave dynamics at nanoscale and microwave frequencies remains a formidable challenge due to the lack of spin-sensitive, time-resolved microscopy. Here we report a breakthrough in imaging dipole-exchange spin waves in a ferromagnetic film owing to the development of laser-free ultrafast Lorentz electron microscopy, which is equipped with a microwave-mediated electron pulser for high spatiotemporal resolution.

Privacy-preserving approach for IoT networks using statistical learning with optimization algorithm on high-dimensional big data environment.

In the present digital scenario, the explosion of Internet of Things (IoT) devices makes massive volumes of high-dimensional data, presenting significant data and privacy security challenges. As IoT networks enlarge, certifying sensitive data privacy while still employing data analytics authority is vital. In the period of big data, statistical learning has seen fast progressions in methodological practical and innovation applications.

Dual-task-related gait patterns as possible marker of precocious and subclinical cognitive alterations in Parkinson disease.

Subtle gait and cognitive dysfunction are common in Parkinson's disease (PD), even before most evident clinical manifestations. Such alterations can be assumed as hypothetical phenotypical and prognostic/progression markers. To compare spatiotemporal gait parameters in PD patients with three cognitive status: cognitively intact (PD-noCI), with subjective cognitive impairment (PD-SCI) and with mild cognitive impairment (PD-MCI) in order to detect subclinical gait differences.

Sustainability metrics targeted optimization and electric discharge process modelling by neural networks.

Aluminium and its alloys, especially Al6061, have gathered significant interest among researchers due to its less density, great durability, and high strength. Due to their lightweight properties, the precise machining of these alloys can become expensive through conventional machining operations for intricate products. Therefore, non-traditional machining such as electric discharge machining (EDM) can potentially be opted for the cutting of Al6061.

The transcriptional response of cortical neurons to concussion reveals divergent fates after injury.

Traumatic brain injury (TBI) is a risk factor for neurodegeneration, however little is known about how this kind of injury alters neuron subtypes. In this study, we follow neuronal populations over time after a single mild TBI (mTBI) to assess long ranging consequences of injury at the level of single, transcriptionally defined neuronal classes. We find that the stress-responsive Activating Transcription Factor 3 (ATF3) defines a population of cortical neurons after mTBI.

Exploring the Internal Environmental Changes of Muscle Cells and Apoptotic Phase of Mitochondria in Dry-Cured Loin Using Electrical Stimulation: Promoting the Precise Regulation of Loin Ham Quality.

Traditional dry-curing methods have a long cycle time and low efficiency, resulting in the inconsistent quality of dry-cured ham. By applying electrical stimulation (ES) technology in the dry-curing process, it was found that ES affected mitochondrial apoptosis by modulating the intracellular environment of muscle cells, which, in turn, enhanced the quality of dry-cured pork loin. Specifically, ES accelerated glycogen and ATP depletion, which led to a rapid decline in pH.

Recursive hierarchical parametric identification of Wiener-Hammerstein systems based on initial value optimization.

In this paper, a novel recursive hierarchical parametric identification method based on initial value optimization is proposed for Wiener-Hammerstein systems subject to stochastic measurement noise. By transforming the traditional Wiener-Hammerstein system model into a generalized form, the system model parameters are uniquely expressed for estimation. To avoid cross-coupling between estimating block-oriented model parameters, a hierarchical identification algorithm is presented by dividing the parameter vector into two subvectors containing the coupled and uncoupled terms for estimation, respectively.

Cobalt regulation biocathode with sulfate-reducing bacteria for enhancing the reduction of antimony and the removal of sulfate in a microbial electrolysis cell simultaneously.

Antimony (Sb) contamination in water resources poses a critical environmental and health challenge globally. Sulfate reducing bacteria (SRB) are employed to reduce SO to S for removing Sb in a microbial electrolysis cell (MEC). Yet, the reduction efficiency of reducing SO and Sb(Ⅴ) through SRB remains relatively low, and the underlying mechanism remains elusive.

Bioimpedance Measurements of Fibrotic and Acutely Injured Lung Tissues.

In injured and diseased tissues, changes in molecular and cellular compositions, as well as tissue architecture, lead to alterations in both physiological and physical characteristics. Notably, the electrical properties of tissues, which can be characterized as bioelectrical impedance (bioimpedance), are closely linked to the health and pathological conditions of the tissues. This highlights the significant role of quantitatively characterizing these electrical properties in improving the accuracy and speed of diagnosis and prognosis.

A multi-domain feature fusion epilepsy seizure detection method based on spike matching and PLV functional networks.

The identification of spikes, as a typical characteristic wave of epilepsy, is crucial for diagnosing and locating the epileptogenic region. The traditional seizure detection methods lack spike features and have low sample richness. This paper proposes a seizure detection method with spike-based phase locking value (PLV) functional brain networks and multi-domain fused features.

Enhancing brain tumor surgery precision with multimodal connectome imaging: Structural and functional connectivity in language-dominant areas.

Objectives: Language is a critical aspect of human cognition and function, and its preservation is a priority for neurosurgical interventions in the left frontal operculum. However, identification of language areas can be inconsistent, even with electrical mapping. The use of multimodal structural and functional neuroimaging in conjunction with intraoperative neuromonitoring may augment cortical language area identification to guide the resection of left frontal opercular lesions.

Quantifying the sources and health risks of groundwater nitrate via dual NO isotopes and Monte Carlo simulations in a developed planting-breeding area.

Nitrate (NO) pollution in groundwater is a worldwide environmental issue, particularly in developed planting-breeding areas where there is a substantial presence of nitrogen-related sources. Here, we explored the key sources and potential health risks of NO in a typical planting-breeding area in the North China Plain based on dual stable isotopes and Monte Carlo simulations. The analysis results revealed that the NO concentration ranged from 0.

A solar-powered electrocoagulation process with a novel CNT/silver nanowire coated basalt fabric cathode for effective oil/water separation: From fundamentals to application.

Electrocoagulation (EC) has proven its high efficiency and environmental sustainability for treating several types of wastewaters. However, the primary drawbacks of the conventional EC process are the suitable electrode materials and the relatively high cost due to the requirement for electric energy. To overcome these practical challenges, this study investigated effective oil/water separation by a solar-powered electrocoagulation (SPEC) process using a novel highly conductive basalt fabric (BF) cathode.

Class-Wise Combination of Mixture-Based Data Augmentation for Class Imbalance Learning of Focal Liver Lesions in Abdominal CT Images.

In this paper, we propose a method to address the class imbalance learning in the classification of focal liver lesions (FLLs) from abdominal CT images. Class imbalance is a significant challenge in medical image analysis, making it difficult for machine learning models to learn to classify them accurately. To overcome this, we propose a class-wise combination of mixture-based data augmentation (CCDA) method that uses two mixture-based data augmentation techniques, MixUp and AugMix.

Cryogenic in-memory computing using magnetic topological insulators.

Machine learning algorithms have proven to be effective for essential quantum computation tasks such as quantum error correction and quantum control. Efficient hardware implementation of these algorithms at cryogenic temperatures is essential. Here we utilize magnetic topological insulators as memristors (termed magnetic topological memristors) and introduce a cryogenic in-memory computing scheme based on the coexistence of a chiral edge state and a topological surface state.

Behavioral and neurophysiological effects of electrical stunning on zebrafish larvae.

Two methods dominate the way that zebrafish larvae are euthanized after experimental procedures: anesthetic overdose and rapid cooling. Although MS-222 is easy to apply, this anesthetic takes about a minute to act and fish show aversive reactions and interindividual differences, limiting its reliability. Rapid cooling kills larvae after several hours and is not listed as an approved method in the relevant European Union directive.

Tilting deformation analysis and instability prediction of arch-locked-segment landslides induced by rainfall.

The failure of locked-segment landslides is associated with the destruction of locked segments that exhibit an energy accumulation effect. Thus, understanding their failure mode and instability mechanism for landslide hazard prevention and control is critical. In this paper, multiple instruments, such as tilt sensors, pore water pressure gauges, moisture sensors, matrix suction sensors, resistance strain gauges, miniature earth pressure sensors, a three-dimensional (3D) laser scanner, and a camera, were used to conduct the physical model tests on the rainfall-induced arch locked-segment landslide to analyze the resulting tilting deformation and evolution mechanism.

High-Precision prediction of curling trajectory multivariate time series using the novel CasLSTM approach.

As a multivariate time series, the prediction of curling trajectories is crucial for athletes to devise game strategies. However, the wide prediction range and complex data correlations present significant challenges to this task. This paper puts forward an innovative deep learning approach, CasLSTM, by introducing integrated inter-layer memory, and establishes an encoder-predictor curling trajectory forecasting model accordingly.

State switch control of magnetically suspended flywheel energy storage system in uninterrupted power supply system.

The magnetically suspended flywheel energy storage system (MS-FESS) is an energy storage equipment that accomplishes the bidirectional transfer between electric energy and kinetic energy, and it is widely used as the power conversion unit in the uninterrupted power supply (UPS) system. First, the structure of the FESS-UPS system is introduced, and the working principles at different working states are described. Furthermore, the control strategy of the FESS-UPS is developed, and the switch oscillation of the FESS-UPS system between the charging and discharging states is analyzed.

High-performance triboelectric nanogenerators based on Ag-doped ZnO loaded electrospun PVDF nanofiber mats for energy harvesting and healthcare monitoring.

This study investigates the potential of zinc oxide (ZnO) and Ag-doped zinc oxide (Ag-ZnO) nanoparticles (NPs) (1, 3 and 5 wt%) electrospun into poly(vinylidene fluoride) (PVDF) based triboelectric nanogenerators (TENGs) to harness electrical energy from ambient mechanical vibrations. ZnO and Ag-ZnO NPs were developed using a co-precipitation method. 3 wt% Ag-ZnO doping was optimized to exhibit a higher β-crystalline phase in PVDF (PAZ3).

Ocean wave-driven covalent organic framework/ZnO heterostructure composites for piezocatalytic uranium extraction from seawater.

Piezoelectric catalysis possesses the potential to convert ocean wave energy into and holds broad prospects for extracting uranium from seawater. Herein, the Z-type ZnO@COF heterostructure composite with excellent piezoelectric properties was synthesized through in situ growth of covalent organic frameworks (COFs) on the surface of ZnO and used for efficient uranium extraction. The designed COFs shell enables ZnO with stability, abundant active sites and high-speed electron transport channels.

Evaluation of auditory alerting systems for safe electric scooter operations.

It is well understood that a significant shift away from fossil fuel based transportation is necessary to limit the impacts of the climate crisis. Electric micromobility modes, such as electric scooters and electric bikes, have the potential to offer a lower-emission alternative to journeys made with internal combustion engine vehicles, and such modes of transport are becoming increasingly commonplace on our streets. Although offering advantages such as reduced air pollution and greater personal mobility, the widespread approval and uptake of electric micromobility is not without its challenges.