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Abstract The BESIII experiment has been operating since 2009 and has received several upgrades, to study the $$\tau $$ τ -charm physics utilizing the BEPCII accelerator. The BEPCII is a double-ring multi-bunch collider with a luminosity of $$1 \times 10^{33} \, {\text {cm}}^{-2} \, {\text {s}}^{-1}$$ 1 × 10 33 cm - 2 s - 1 optimized at a center-of-mass (cms) energy of $$2 \times 1.89$$ 2 × 1.89 GeV. The rich physics topics, long lifetime and the large volume of data present challenges to offline data processing. This paper describes the design and implementation of the offline software system to meet the requirements and challenges of BESIII, including offline software architecture, geometry management, database management, event data model and tag-based analysis.

Multi-channel neural electrodes as a crucial means are of great significance for information exchange between the brain and computers. Herein, we present a 16-channel Si-based active neural probe system that achieves a monolithic integration between the electrodes and circuits in a single probe, making it a standalone integrated electrophysiology recording system. The ASIC prepared on a base (2×2mm2) is a 16-channel analog frontend (AFE) for neural recording, and each channel has a low-noise amplifier (LNA), a bandpass filter (BPF), a buffer and a current bias circuit. The 258 neural signal recording electrodes (22×24μm2) are densely packed on a 50 μm thick, 100 μm wide, and 3 mm long shank. The ASIC of neural probe, internal interconnecting wires are all implemented in commercial SMIC 0.18 μm CMOS technology. The neural probe system achieves a 3.6 μVrms input-referred noise (IRN) in a bandwidth of 1.1Hz-10 kHz, 70.8 μW power consumption, 0.0785 mm2 area per channel, as well as an AFE gain of 58.1 dB Furthermore, the impedances of the Au electrodes can be obtained as 0.5–2.1 MΩ at a frequency of 1 kHz. The functionality of a 16-channel silicon-based neural probe is validated in an in-vivo experiment on lab rats.

In light of the growing urgency surrounding energy and environmental concerns, this paper presents a two-layer iterative energy dispatch strategy tailored for a multi-energy-flow virtual power plant (VPP) operating within the distribution power grid. The proposed strategy unfolds in two key phases. First, it establishes an energy dispatch framework designed specifically for the multi-energy-flow VPP within the distribution power grid. Subsequently, it introduces an improved ant colony algorithm aimed at optimizing the output power of each VPP. In addition, the paper presents an optimization method for substation energy dispatch. This method uses a delay-aware consensus algorithm with the substation dispatch cost increment rate as the consensus variable, taking into account the communication delay between VPPs. Integrating a proportional–derivative (PD) control mechanism enhances the convergence speed of the delay-aware consensus algorithm and enables real-time energy dispatch of the multi-energy-flow VPP. The paper presents its conclusions by validating the efficacy of the proposed approach through simulation, thereby addressing the challenges and adapting to the shifting energy and environmental landscape.

Vitamin E is a fat-soluble vitamin with various physiological functions including antioxidant activity. However, its fat solubility limits its application in food systems where water-soluble systems are the mainstream, and reduces its bioavailability. The solubility of vitamin E can be changed by using food delivery systems such as liposomes, nanoparticles, emulsions, microcapsules, and cyclodextrin inclusion complexes. Choosing an appropriate delivery system and screening and modifying the raw materials of the system can effectively improve the stability and bioavailability of vitamin E. This manuscript reviews the research and application progress of vitamin E embedding methods, loading characteristics and product functions in different food-grade delivery systems, and provides useful guidance for the developing delivery systems with well performance and improving the stability and bioavailability of vitamin E.

Demand response plays an important role in improving the balance of power generation and consumption between the distribution grid and photovoltaic (PV) microgrids. However, due to the uncertainty and volatility of PV output, as well as the different operation goals of PV microgrids, a conventional single-tier optimization approach is infeasible to realize the coordinated interaction between the distribution grid and PV microgrids. To address these challenges, we propose a second-order cone and improved consensus algorithm-based hybrid bilevel optimization algorithm for the interaction between the distribution grid and PV microgrids. First, we construct price-based and incentive-based differentiated demand response models to deal with various supply and demand dynamics of the distribution grid and PV microgrids. Building upon this foundation, we construct a hybrid bilevel optimization model. In the lower level, distributed optimization is adopted, and an improved consensus algorithm is used to optimize power output of PV microgrids to maximize the revenue based on output power of upper-level generator sets. In the upper level, centralized optimization is adopted, and second-order cone programming is employed to minimize the grid loss in the distribution grid based on the power output of lower-level PV microgrids. Hybrid bilevel optimization is iterated until the convergence condition is satisfied. Simulation results verify the proposed algorithm for achieving a coordinated interaction between the distribution grid and PV microgrids.

Abstract Flexible wearable sweat sensors allow continuous, real-time, noninvasive detection of sweat analytes, provide insight into human physiology at the molecular level, and have received significant attention for their promising applications in personalized health monitoring. Electrochemical sensors are the best choice for wearable sweat sensors due to their high performance, low cost, miniaturization, and wide applicability. Recent developments in soft microfluidics, multiplexed biosensing, energy harvesting devices, and materials have advanced the compatibility of wearable electrochemical sweat-sensing platforms. In this review, we summarize the potential of sweat for medical detection and methods for sweat stimulation and collection. This paper provides an overview of the components of wearable sweat sensors and recent developments in materials and power supply technologies and highlights some typical sensing platforms for different types of analytes. Finally, the paper ends with a discussion of the challenges and a view of the prospective development of this exciting field.

Surface electromyogram (sEMG)-based gesture recognition has emerged as a promising avenue for developing intelligent prostheses for upper limb amputees. However, the temporal variations in sEMG have rendered recognition models less efficient than anticipated. By using cross-session calibration and increasing the amount of training data, it is possible to reduce these variations. The impact of varying the amount of calibration and training data on gesture recognition performance for amputees is still unknown. To assess these effects, we present four datasets for the evaluation of calibration data and examine the impact of the amount of training data on benchmark performance. Two amputees who had undergone amputations years prior were recruited, and seven sessions of data were collected for analysis from each of them. Ninapro DB6, a publicly available database containing data from ten healthy subjects across ten sessions, was also included in this study. The experimental results show that the calibration data improved the average accuracy by 3.03%, 6.16%, and 9.73% for the two subjects and Ninapro DB6, respectively, compared to the baseline results. Moreover, it was discovered that increasing the number of training sessions was more effective in improving accuracy than increasing the number of trials. Three potential strategies are proposed in light of these findings to enhance cross-session models further. We consider these findings to be of the utmost importance for the commercialization of intelligent prostheses, as they demonstrate the criticality of gathering calibration and cross-session training data, while also offering effective strategies to maximize the utilization of the entire dataset.

ObjectiveTo investigate the effect of current resistance on therapeutic outcomes, and the mechanism of current conduction treatment in a rat model of temporal lobe epilepsy (TLE).MethodsRats were randomly divided into four groups: normal control, epileptic group, low-resistance conduction (LRC) and high-resistance conduction (HRC) group. The content of glutamate (Glu) and gamma-amino butyric acid (GABA) in the hippocampus was determined using a neurotransmitter analyzer. mRNA and protein expression of interleukin 1β (IL-1β) /IL-1 receptor 1(IL-1R1) and high mobility group protein B1 (HMGB-1)/toll-like receptor-4 (TLR-4) in hippocampal neurons were tested. Video electroencephalogram monitoring was used to record seizures and EEG discharges. Cognitive function in the rats was tested using the Morris water maze.ResultsGlu/GABA ratio in the epileptic control and HRC groups was significant differences from LRC group. The levels of HMGB1/TLR4 and IL-1β/IL-1R1 in the LRC group and normal control group were significantly lower than those in epileptic control group (p

Kidney beans (KBs), as a traditional edible legume, are an important food crop of high nutritional and economic value worldwide. KBs contain a full range of amino acids and a high proportion of essential amino acids, and are rich in carbohydrates as well as vitamins and minerals. However, KBs contain a variety of non-nutritional factors that impede the digestion and absorption of nutrients, disrupt normal metabolism and produce allergic reactions, which severely limit the exploitation of KBs and related products. Suppressing or removing the activity of non-nutritional factors through different processing methods can effectively improve the application value of KBs and expand the market prospect of their products. The aim of this review was to systematically summarize the main types of non-nutritional factors in KBs and their mechanisms of action, and to elucidate the effects of different food processing techniques on non-nutritional factors. The databases utilized for the research included Web of Science, PubMed, ScienceDirect and Scopus. We considered all original indexed studies written in English and published between 2012 and 2023. We also look forward to the future research direction of producing KB products with low non-nutritional factors, which will provide theoretical basis and foundation for the development of safer and healthier KB products.

Due to the frailty of elderly individuals’ physical condition, falling can lead to severe bodily injuries. Effective fall detection can significantly reduce the occurrence of such incidents. However, current fall detection methods heavily rely on visual and multi-sensor devices, which incur higher costs and complex wearable designs, limiting their wide-ranging applicability. In this paper, we propose a fall detection method based on nursing aids integrated with multi-array flexible tactile sensors. We design a kind of multi-array capacitive tactile sensor and arrange the distribution of tactile sensors on the foot based on plantar force analysis and measure tactile sequences from the sole of the foot to develop a dataset. Then we construct a fall detection model based on a graph convolution neural network and long-short term memory network (GCN-LSTM), where the GCN module and LSTM module separately extract spatial and temporal features from the tactile sequences, achieving detection on tactile data of foot and walking states for specific time series in the future. Experiments are carried out with the fall detection model, the Mean Squared Error (MSE) of the predicted tactile data of the foot at the next time step is 0.0716, with the fall detection accuracy of 96.36%. What is more, the model can achieve fall detection on 5-time steps with 0.2-s intervals in the future with high confidence results. It exhibits outstanding performance, surpassing other baseline algorithms. Besides, we conduct experiments on different ground types and ground morphologies for fall detection, and the model showcases robust generalization capabilities.

In this study, arsenic trioxide (ATO) was encapsulated in liposomes via copper acetate (Cu(OAc)2) gradients and high entrapment efficiency of over 80% was obtained. The average particle size and the zeta-potential of the liposomes were detected to be 115.1 ± 29.1 nm and −21.97 ± 0.6 mV, respectively. The TEM images showed rod-like precipitates in the inner aqueous phase, which was supposed be due to the formation of insoluble ATOCu complex. The in vitro drug release of ATOCu liposomes exhibited a sustained release over 72 h, and the release rates decreased with the increase of the pH of release media. Pharmacokinetic and tissue distribution studies of ATO liposomes showed significantly reduced plasma clearance rate, increased AUC0–12h and T1/2, and improved tumor distribution of As compared to iv administration of ATO solution. The anti-tumor effect of ATO loaded liposomes to S180 tumor-bearing mice was significantly improved with a tumor inhibition rate of 61.2%, meanwhile the toxicity of encapsulated ATO was greatly decreased. In conclusion, ATO can be effectively encapsulated into liposomes by remote loading method via Cu(OAc)2 gradients; the co-administration of ATO and Cu(II) via liposomal formulation may find wide applications in the treatment of various tumors.

A miniature Fourier transform spectrometer is proposed using a thin-film lithium niobate electro-optical modulator instead of the conventional modulator made by titanium diffusion in lithium niobate. The modulator was fabricated by a contact lithography process, and its voltage-length and optical waveguide loss were 2.26 V·cm and 1.01 dB/cm, respectively. Based on the wavelength dispersion of the half-wave voltage of the fabricated modulator, the emission spectrum of the input signal was retrieved by Fourier transform processing of the interferogram, and the analysis of the experimental data of monochromatic light shows that the proposed miniaturized FTS can effectively identify the input signal wavelength.

For high-energy π0 mesons, the angle between the two final-state photons decreases with the increase in the energy of the π0, which enhances the probability of overlapping electromagnetic showers. The performance of the cluster splitting algorithm in the EMC reconstruction is crucial for the mass resolution measurement of π0 with high energy. The cluster splitting algorithm is based on the theoretical lateral distribution of the electromagnetic showers. A simple implementation of the lateral distribution can be described as a (multi-)exponential function. In a realistic electromagnetic calorimeter, considering the granularity of the detector, the measured energy in a cell is actually the integral of the theoretical energy deposition, which deviates from the exponential function. Based on the simulation of the barrel EMC in the P¯ANDA experiment, a cluster splitting algorithm with a new lateral energy development function is developed. The energy resolution of overlapping showers with high energy has been improved.

Abstract Flexible tactile sensors have broad applications in human physiological monitoring, robotic operation and human–machine interaction. However, the research of wearable and flexible tactile sensors with high sensitivity, wide sensing range and ability to detect three-dimensional (3D) force is still very challenging. Herein, a flexible tactile electronic skin sensor based on carbon nanotubes (CNTs)/polydimethylsiloxane (PDMS) nanocomposites is presented for 3D contact force detection. The 3D forces were acquired from combination of four specially designed cells in a sensing element. Contributed from the double-sided rough porous structure and specific surface morphology of nanocomposites, the piezoresistive sensor possesses high sensitivity of 12.1 kPa−1 within the range of 600 Pa and 0.68 kPa−1 in the regime exceeding 1 kPa for normal pressure, as well as 59.9 N−1 in the scope of 2.3 N−1 in the region of

This paper proposes a compact bioelectronics sensing platform, including a multi-channel electrode, intracranial electroencephalogram (iEEG) recorder, adjustable galvanometer, and shunt-current conduction circuit pathway. The developed implantable electrode made of polyurethane-insulated stainless-steel materials is capable of recording iEEG signals and shunt-current conduction. The electrochemical impedance of the conduction, ground/reference, and working electrode were characterized in phosphate buffer saline solution, revealing in vitro results of 517.2 Ω@1 kHz (length of 0.1 mm, diameter of 0.8 mm), 1.374 kΩ@1 kHz (length of 0.3 mm, diameter of 0.1 mm), and 3.188 kΩ@1 kHz (length of 0.1 mm, diameter of 0.1 mm), respectively. On-bench measurement of the system revealed that the input noise of the system is less than 2 μVrms, the signal frequency bandwidth range is 1 Hz~10 kHz, and the shunt-current detection range is 0.1~3000 μA with an accuracy of above 99.985%. The electrode was implanted in the CA1 region of the right hippocampus of rats for the in vivo experiments. Kainic acid (KA)-induced seizures were detected through iEEG monitoring, and the induced shunt-current was successfully measured and conducted out of the brain through the designed circuit-body path, which verifies the potential of current conduction for the treatment of epilepsy.

The inter-country input–output table is appropriate for presenting sophisticated inter-industry dependencies from a global perspective. Using the above table one can perceive the amount of production resources that sectors obtain from their upstream ones, as well as the number of productive capacities that sectors provide for their downstream ones. In other words, competition/collaboration occurs when sectors share the same providers/consumers because all sectors’ products and services outputted to downstream ones are limited. Thus, inter-industry competition for inputs from upstream sectors, or collaboration on outputs to downstream sectors, may be quantified with input–output matrix transformation. In this paper, a novel analytical framework of inter-industry collaborative relations is established based on the bipartite graph theory and the resource allocation process. The Collaborative Opportunity Index and Collaborative Threat index are designed to quantitatively measure the industrial influence hidden in the topological structure of the global value chain (GVC) network. Scenario simulations are carried out to forecast the potential and trends of international capacity cooperation within Asian, European, and African nations related to the Belt and Road Initiative, respectively.

Most of the reported hand gesture recognition algorithms require high computational resources, i.e., fast MCU frequency and significant memory, which are highly inapplicable to the cost-effectiveness of consumer electronics products. This paper proposes a hand gesture recognition algorithm running on an interactive wristband, with computational resource requirements as low as Flash < 5 KB, RAM < 1 KB. Firstly, we calculated the three-axis linear acceleration by fusing accelerometer and gyroscope data with a complementary filter. Then, by recording the order of acceleration vectors crossing axes in the world coordinate frame, we defined a new feature code named axis-crossing code. Finally, we set templates for eight hand gestures to recognize new samples. We compared this algorithm’s performance with the widely used dynamic time warping (DTW) algorithm and recurrent neural network (BiLSTM and GRU). The results show that the accuracies of the proposed algorithm and RNNs are higher than DTW and that the time cost of the proposed algorithm is much less than those of DTW and RNNs. The average recognition accuracy is 99.8% on the collected dataset and 97.1% in the actual user-independent case. In general, the proposed algorithm is suitable and competitive in consumer electronics. This work has been volume-produced and patent-granted.

C-reactive protein (CRP) plays an important role in inflammation detection and disease monitoring. The optical biosensor is a highly sensitive and easy detection tool. The microfluidic self-driving optical sensors were fabricated with transparent glass material and used for the enhanced surface plasmon resonance (SPR) optical detection of the model protein CRP using Au nanoparticles (AuNPs) and a sandwich immune reaction. The 3D design of the chip was devised to improve the optical coupling efficiency and enable integration with a microfluidic control and rapid detection. The array of pre-fixed antibody modified by Au nanoparticles was used to achieve rapid antigen capture and improve the optical sensitivity. The Au nanoparticle amplification approach was introduced for the SPR detection of a target protein. CRP was used as a model target protein as part of a sandwich assay. The use of Au NP measurements to detect the target signal is a threefold improvement compared to single SPR detection methods.

A tactile sensor is an indispensable component for electronic skin, mimicking the sensing function of organism skin. Various sensing materials and microstructures have been adopted in the fabrication of tactile sensors. Herein, we propose a highly sensitive flexible tactile sensor composed of nanocomposites with pyramid and irregularly rough microstructures and implement a comparison of piezoresistive properties of nanocomposites with varying weight proportions of multi-wall nanotubes and carbon black particles. In addition to the simple and low-cost fabrication method, the tactile sensor can reach high sensitivity of 3.2 kPa−1 in the range of

In this work, a mini monitoring system integrated with a microfabricated metal oxide array sensor and a micro packed gas chromatographic (GC) column was developed for monitoring environmental gases. The microfabricated packed GC column with a 1.6 m length was used to separate the environmental gas, and the metal oxide semiconductor (MOS) array sensor, fabricated with nano-sized SnO-SnO2 sensitive materials, was able to effectively detect each component separated by GC column. The results demonstrate that the monitoring system can detect environmental gas with high precision.

The sulfite-reducing bacterium Bilophila wadsworthia, a common human intestinal pathobiont, is unique in its ability to metabolize a wide variety of sulfonates to generate sulfite as a terminal electron acceptor (TEA). The resulting formation of H2S is implicated in inflammation and colon cancer. L-cysteate, an oxidation product of L-cysteine, is among the sulfonates metabolized by B. wadsworthia, although the enzymes involved remain unknown. Here we report a pathway for L-cysteate dissimilation in B. wadsworthia RZATAU, involving isomerization of L-cysteate to D-cysteate by a cysteate racemase (BwCuyB), followed by cleavage into pyruvate, ammonia and sulfite by a D-cysteate sulfo-lyase (BwCuyA). The strong selectivity of BwCuyA for D-cysteate over L-cysteate was rationalized by protein structural modeling. A homolog of BwCuyA in the marine bacterium Silicibacter pomeroyi (SpCuyA) was previously reported to be a L-cysteate sulfo-lyase, but our experiments confirm that SpCuyA too displays a strong selectivity for D-cysteate. Growth of B. wadsworthia with cysteate as the electron acceptor is accompanied by production of H2S and induction of BwCuyA. Close homologs of BwCuyA and BwCuyB are present in diverse bacteria, including many sulfate- and sulfite-reducing bacteria, suggesting their involvement in cysteate degradation in different biological environments. [ABSTRACT FROM AUTHOR]