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The primary aim of this study was to explore the impact of dietary supplementation with a postbiotic derived from Bacillus subtilis ACCC 11025 on growth performance, meat yield, meat quality, excreta bacterial populations, and excreta ammonia emissions of broiler chicks. A total of 480 day-old Arbor Acre broiler chicks, initially weighing 52.83 ± 1.38 g, were randomly allocated into 4 distinct groups. Each group was housed in 6 separate cages, each containing 20 birds. The experimental phase spanned 42 d, divided into 2 periods (d 1–21 and d 22–42). Dietary interventions were based on a basal diet, with postbiotic supplementation at levels of 0.000, 0.015, 0.030, or 0.045%. Our findings indicate that dietary supplementation with postbiotic had a positive influence on body weight gain (BWG) and feed efficiency. The most substantial improvements in BWG and feed efficiency were observed in the group of broiler chicks fed a diet containing 0.015% postbiotic. Furthermore, the inclusion of postbiotic in the diet led to an increase in the yield of breast and leg muscles, with a significant difference in meat yields observed between the control group and the group receiving 0.015% postbiotic supplementation. It's noteworthy that dietary manipulation did not exert any discernible impact on the quality of breast and leg muscle samples. Concurrently, we observed an elevation in serum albumin and total protein contents corresponding to the increasing postbiotic dosage in the diet. Additionally, dietary supplementation with postbiotic effectively controlled the emission of ammonia from excreta and reduced the abundance of Salmonella in excreta while enhancing the presence of Lactobacillus bacteria. The group receiving 0.015% postbiotic supplementation displayed the lowest levels of ammonia emission and the highest counts of Lactobacillus bacteria in excreta. In light of these results, we conclude that dietary supplementation with 0.015% postbiotic represents an efficacious strategy for increasing BWG and meat yield of broiler chicks by enhancing feed efficiency as well as mitigating ammonia emissions from excreta by modulating the composition of excreta bacterial communities. [ABSTRACT FROM AUTHOR]

The poor compatibility of carbonate-based electrolytes with lithium metal anodes results in unstable solid electrolyte interphase, leading to lithium dendrite formation, low Coulombic efficiency, and short cycle life. To address this issue, we propose a novel fluorinated electrolyte that leverages lithium bis(fluorosulfonyl)imide (LiFSI), along with fluorinated solvents. An extremely low concentration of lithium nitrate exerts a substantial impact on the Li ion solvation structure, inducing an anions-rich solvation structure, results in an inorganic-rich electrolyte interphase layer mainly composed of Li 3 N and LiF, which effectively inhibits lithium dendrite formation, enhances the interfacial stability between the electrode and electrolyte, and yields excellent cycling performance in lithium metal batteries. When coupled with a high nickel content cathode (LiNi 0.8 Co 0.1 Mn 0.1 O 2), the cells exhibit impressive cycling performance with 1000 cycles at 4 C, retaining 68.6 % capacity (with charge times under 15 min). Despite the relatively low oxidation stability of Dimethoxyethane in the electrolyte, the cell demonstrates exceptional high-voltage electrochemical performance, even up to 4.5 V, the cells do not show extensive electrolyte decomposition and structural changes, preserving 79.2 % capacity retention after 300 cycles. Using 50 µm lithium foil in the cells, remarkable capacity retention of 89.5 % is achieved after 400 cycles at 1 C. This remarkable compatibility between the anode and cathode represents a significant breakthrough in enhancing the reliability and performance of lithium metal batteries. [Display omitted] • Traces of lithium nitrate affect the solvation structure of the electrolyte leading to stable anode and cathode interfaces with the electrolyte. • The inorganic rich SEI layer enables fast Li+ diffusion and decrease the overpotential of Li deposition. • The electrolyte is resistant to high-voltage oxidation and the cell exhibits excellent performance at 4.5 V. • Li||NCM811 cells achieve an ultra-long stable cycling of 1000 cycles at 4 C rate. • A 1100 mAh pouch cell demonstrates highly reversible capacity. [ABSTRACT FROM AUTHOR]

Virtual reality (VR) is considered to be a promising therapeutic technology for the rehabilitation of upper extremities (UEs) post-stroke. Recently, we designed and then implemented a neuroscientifically grounded VR protocol for the rehabilitation of patients with stroke. The system provides unilateral and bilateral limb mirroring exercises in a fully immersive virtual environment that may stimulate and activate the mirror neuron system in the brain to help patients for their rehabilitation. Twelve patients with subacute stroke underwent the newly implemented VR treatment in addition to conventional rehabilitation for 8 consecutive weekdays. The treatment effect on brain reorganization and motor function was investigated using resting-state fMRI (rs-fMRI) and the Fugl-Meyer assessment for Upper Extremity (FM-UE), respectively. Fifteen healthy controls (HCs) also underwent rs-fMRI scanning one time. The study finally obtained usable data from 8 patients and 13 HCs. After the intervention, patients demonstrated significant improvement in their FM-UE scores (p values < 0.042). Voxel-wise functional connectivity (FC) analysis based on the rs-fMRI data found that HCs showed widespread bilateral FC patterns associated with the dominant hemispheric primary motor cortex (M1). However, the FC patterns in patients revealed intra-hemispheric association with the ipsilesional M1 seed and this association became visible in the contra-hemisphere after the intervention. Moreover, the change of FC values between the bilateral M1 was significantly correlated with the changes in FM-UE scores (p values < 0.037). We conclude that unilateral and bilateral limb mirroring exercise in an immersive virtual environment may enhance cortical reorganization and lead to improved motor function. [ABSTRACT FROM AUTHOR]

Neuregulin-1 (NRG-1), an endogenously produced polypeptide, is the ligand of cardiomyocyte ErbB receptors, with cardiovascular protective effects. In the present study, we explored whether the cardioprotective effect of NRG-1 against I/R injury is mediated by inhibiting myocardial endoplasmic reticulum (ER) stress. In vitro , NRG-1 directly inhibited the upregulation of ER stress markers such as glucose-regulated protein 78, CCAAT/enhancer binding protein homologous protein and cleaved caspase-12 induced by the ER stress inducers tunicamycin or dithiothreitol in both neonatal and adult ventricular myocytes. Attenuating ErbB signals by an ErbB inhibitor AG1478 or ErbB4 knockdown and preincubation with phosphoinositide 3-kinase inhibitors all reversed the effect of NRG-1 inhibiting ER stress in cultured neonatal rat cardiomyocytes. Concurrently, cardiomyocyte ER stress and apoptosis induced by hypoxia-reoxygenation were decreased by NRG-1 treatment in vitro . Furthermore, in an in vivo rat model of myocardium ischemia/reperfusion (I/R), intravenous NRG-1 administration significantly decreased ER stress and myocardial infarct size induced by I/R. NRG-1 could protect the heart against I/R injury by inhibiting myocardial ER stress, which might be mediated by the phosphoinositide 3-kinase/Akt signaling pathway. [ABSTRACT FROM AUTHOR]

NASICON-type electrolytes such as Li 1.5 Al 0.5 Ge 1.5 (PO 4) 3 (LAGP) potentially enable high safety and high energy for solid-state batteries. However, the poor interfacial stability with lithium metal remains a main issue. To overcome this challenge, we proposed a bilayer solid electrolyte architecture implementing a novel ultrathin solid polymer electrolyte (SPE) film in combination with LAGP to improve the interface with lithium metal. The SPE film is composed of a bis(fluorosulfonyl)imide-based ionic liquid and polyethylene oxide, which shows a remarkable ionic conductivity of 1.25 × 10−3 S cm−1 at room temperature. The application of this thin interlayer leads to an outstanding interface stability, allowing >2000 h of continuous Li stripping/plating in symmetric Li|SPE/LAGP/SPE|Li cells without any increase in polarization or indication of a short circuit. As a result, this approach enables Li|SPE/LAGP|NCM811 cells with a discharge capacity of ∼200 mAh g−1 at 0.1C and stable cycling for >400 cycles at 0.2C with a capacity retention of 83%. Additionally, the cell shows an extremely high average Coulombic efficiency of 99.96% demonstrating that the approach enables to achieve high-energy and long-term stable solid-state lithium-metal batteries. [Display omitted] • High energy lithium metal batteries. • Hybrid electrolytes enabled solid-state batteries. • Artificial interphases in high energy batteries. [ABSTRACT FROM AUTHOR]

The lithium metal anode is regarded as ideal for high-energy rechargeable batteries. Unfortunately, the uncontrolled lithium dendritic growth and the infinite volume expansion upon cycling result in low Coulombic efficiency, fast capacity decay and safety issues. Herein, a mismatch electrical-conductivity framework has been designed as a stable host to regulate lithium deposition behaviour. Due to the ionic conductivity of the lithiophilic layer and the electron conductivity of hollow carbon nanofibres (HCF), lithium metal is preferentially deposited into and encapsulated by the HCF, resulting in greatly improved electrochemical performance. The heat generation upon lithium storage and release in the Al 2 O 3 coated HCF (A–HCF) during cycling is lower compared to the plating/stripping on copper foil. The A-HCF electrodes show high Coulombic efficiency (97%) upon 500 cycles employing a conventional, alkyl carbonate-based electrolyte, demonstrating improved reversibility of Li plating/stripping. Complete cells assembled employing LiNi 0.8 Co 0.1 Mn 0.1 O 2 (NMC811)-based positive electrodes exhibit high capacity retention (94%) after 120 cycles at 1 C, delivering a high energy density (363 Wh per kg of NMC811). Even upon cycling at 5 C rate, the cells, employing less than three times excess lithium, deliver a very high capacity (133 mAh per g of NMC811) for 50 cycles. The mismatch electrical conductivity skeleton effectively regulates the lithium deposition behavior, guiding the Li metal uniform plating on the inside and along the surface of the hollow nanofibers and by this restraining dendritic Li formation as well as decreasing heat generation during cycling. [Display omitted] • Effect of a mismatch electrical conductivity framework host (A-HCF) with plentiful cavities to regulate Li deposition behavior. • Li storage mechanism and deposition morphology. • Li storage upon long-term cycling. • Heat generation upon Li storage and release by microcalorimetry. • Electrochemical performance of Li metal cells. [ABSTRACT FROM AUTHOR]

A palladium-catalyzed cycloisomerisation reaction of 1,6-enyne acetic esters have been developed. This cyclization reaction shows excellent regioselectivity and good functional group tolerance to obtain five-membered nitrogenated heterocyclic conjugated trienes in moderate to excellent yields. The resulting conjugated trienes could be facilely converted to highly substituted benzenes through Diels-Alder reactions. [ABSTRACT FROM AUTHOR]

• An entire route eco-driving model based on the DRL for EB is proposed. • A rule-based approach is developed to incorporate constraints into the DRL model, which not only accelerates the convergence process but also enhances computational efficiency of the model. • An energy consumption model for EB has been introduced to enhance the accuracy of eco-driving control and energy efficiency estimation. • A novel eco-driving strategy adaptation for EB is developed, responsive to the varying traffic flow and battery charge conditions. Electric bus (EB) has gradually become one of the main ways of transportation in cities due to the low energy consumption and low pollutant emissions. As battery endurance is easily affected by various factors such as external temperature, vehicle load, and driving habits, the anxiety for the endurance of EB has become a concern for researchers. To bridge the gap, an eco-driving method based on deep reinforcement learning (DRL) is proposed to achieve the entire route energy-saving. Firstly, the significant factors including the dynamic passenger load and air conditioner is considered for the energy consumption model of the EB. Secondly, a rule-based reinforcement learning algorithm is utilized for optimizing the driving speed and strategy, which can accelerate the convergence of the proposed model and improve the average reward of the reward function. Thirdly, by adjusting the reward function of reinforcement learning algorithm, three eco-driving modes of EB, namely efficiency priority mode, energy-efficiency balance mode and energy saving priority mode under various operational states are proposed. Finally, the results indicate that the efficiency priority mode achieves about an 8% increase in traffic efficiency and a reduction of approximately 20% in energy consumption compared to the baseline model. With the energy-efficiency balance mode, the model attains a 34.05% reduction in energy consumption with almost the same traffic efficiency. Under the energy saving priority mode, the proposed model exhibits a minor reduction in traffic efficiency within an acceptable limit but decreases energy consumption by 40.69%, achieving the optimization goals. [ABSTRACT FROM AUTHOR]

Tungsten doping has attracted great attention to improve the electrochemical performance of layered Ni-rich cathodes. However, the role of tungsten dopant remains unclear since tungsten is usually rich on surface rather than incorporated into bulk lattices in layered Ni-rich particles, therefore the improved performance is mostly ascribed to the W coating layers or preferred orientation microstructures. In this work, as low as 0.1 mol% of tungsten bulk doping is introduced into a layered Ni-rich material during the coprecipitation of precursor. As a result, the tungsten bulk-doped LiNi 0.8 Co 0.1 Mn 0.1 O 2 (NCM811-W) cathode shows significantly improved cycling stability at 1 C, with a capacity retention of 95.06 % after 200 cycles (compared to 70.83 % for the undoped NCM811 cathode). The improved performance of NCM811-W cathode is mainly attributed to strong W–O bonding within the lattices of NCM811, which can increase the reversibility of the two-phase reaction (H2–H3) of layered Ni-rich material and reduce the migration energy barrier of lithium ions. This work represents a step forward in understanding the high-valence metal ions-doping for high-performance Ni-rich cathodes. • Bulk doping is achieved in LiNi 0.8 Co 0.1 Mn 0.1 O 2 to explore the role of tungsten. • W doping can increase the reversibility of the two-phase reaction (H2–H3) • W doping can reduce the migration energy barrier for lithium ions in NCM cathodes. • The improved performance can be attributed to strong W–O bonding. [ABSTRACT FROM AUTHOR]

Traffic energy consumption estimation is significant for the sustainable transportation. However, it is difficult to directly employ macro traffic flow data to accurately estimate the traffic energy consumption due to many traffic energy consumption models need second-by-second vehicle trajectory. To solve this problem, this paper proposes a traffic energy consumption model based on the macro-micro data, which the macro data derived from the fixed-location sensors and sparse micro data derived from the Connected and Automated Vehicles (CAVs). The completed vehicle trajectories are constructed by the nonparametric kernel smoothing algorithm and variational theory. To test the performance of the proposed method, the Next Generation Simulation micro (NGSIM) dataset and Caltrans Performance Measurement System macro dataset obtained from the same road and time are used. The results indicate that the proposed method not only can reflect the characteristics of traffic kinematic waves caused by traffic congestion, but also minimize the errors generated by the macro-micro transformation. In addition, it can significantly improve the accuracy of energy consumption estimation. • Estimation of fuel consumption and emissions is closer to the real values than other methods. • Reconstructing second-by-second vehicle trajectories based on macroscopic traffic data. • Introduces CAVs trajectory data as a reference in the process of estimating the road spatio-temporal speed evolution. • Proposes a spatio-temporal consistency validation framework by using macro and micro data. • Experimental tests the effect of cell size and probe vehicle penetration on reconstruction accuracy. [ABSTRACT FROM AUTHOR]

An accurate vehicle trajectory prediction promotes understanding of the traffic environment and enables task criticality assessment in advanced driver assistance systems (ADASs) in autonomous vehicles and intelligent connected vehicles. Nevertheless, conventional prediction models are characterized by low prediction accuracy, the inability of long-term prediction, and a single-road section adaptation. To tackle these limitations, this study proposes a trajectory prediction model based on a deep encoder–decoder and a deep neural network (DNN). One modification included introducing an attention mechanism into the traditional encoder–decoder framework. Overall, 1794,1400,2100 trajectory samples from highways, intersections, and roundabouts are used to train the proposed framework and obtain optimal deep encoder–decoder architectures for different road section types. Since the experiments revealed no significant advantages of using the attention mechanism in deep encoder–decoder, the mechanism is not included in the optimal architecture. Next, to achieve higher prediction accuracy and better long-term prediction capability, different DNN structures are tested as trajectory correction networks, and the optimal DNN structure is selected. Finally, the experiments are conducted using the proposed deep encoder–decoder framework and the optimal DNN. The results show that the proposed model reaches 92.87%, 86.65%, and 89.15% average trajectory fit ratio (TFR) on a highway, intersection, and a roundabout, respectively. Therefore, the model enables accurate long-term predictions of vehicle trajectories in these road segments. The proposed model and presented results provide a basis for ADASs' trajectory prediction algorithms. [ABSTRACT FROM AUTHOR]