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Hypoxic-ischemic (HI) can cause neonatal brain damage leading to disability. Patients with HI experience long-term neurological issues impacting quality of life. Limited clinical treatments are available despite extensive research on HI's molecular mechanisms. Genistein-3′-sodium sulfonate (GSS), a phytoestrogen, has been found to improve acute brain injury in neonatal rats caused by hypoxic-ischemia, but its potential for chronic stage neurological recovery in HI is unknown. HI neonatal rats were treated with 1 mg/kg GSS once a day for 21 days. Then, a series of behavioral experiments was performed to evaluate the learning, memory, cognition, anxiety level and depression-like behaviors of the rats. GSS treatment reduced neuronal loss, enhanced learning, memory and cognitive function while also alleviated anxiety and depression-like behaviors in HI rats during the recovery period. These findings indicated that GSS exerted enhance neurological function in HI rats during the chronic stage, prompting further research on how it works to potentially develop new therapies.

This is an essay in the field of ceramics and composites.With the increasing demand for crude oil processing technology, China′s platinum rhenium catalyst industry is also developing. Catalyst is the core material of petroleum smelting, and impregnation method is the most important preparation method of reforming catalyst. Catalytic reforming catalysts generally refer to trace rhenium catalysts. The main research direction in the future is to increase the rhenium content of the catalyst and reduce the amount of platinum in the catalyst. The characteristics of platinum-rhenium catalysts are good stability, slightly poor selectivity, long-term stable operation, and regeneration cycle that can reach 3~5 years, which have been widely used in large-scale production of semi-regenerative reforming processes. Since the late 20th century, China′s refining catalyst market has formed a′Sinopec and PetroChina′s catalyst companies, supplemented by private catalyst companies′ market structure, catalyst demand is stable at more than 10 tons per year.

With the carbon emission ratio of buildings increasing, including buildings in the carbon market, an inevitable trend of social development is created. Buildings are characterized by their strong seasonality, high simultaneity rate, and large load, which is not only the main factor leading to the peak load of a power grid but also an important resource for load control. Under the guidance of dynamic carbon emission prices and real-time electricity prices, it is worth considering how best to balance the peak response ability of buildings and the cause of the secondary peak leading to load rebound. Therefore, this paper proposes a low-carbon demand response strategy for buildings considering load rebound. First, a building load is classified based on how schedulable the load remains, and a building demand response model which considers how the load schedulability is constructed. Second, according to the different rebounds of different building loads, a load rebound optimization model based on linear attenuation was built, and the rebound parameters of various loads were designed. Then, the real-time carbon emission factor, load fluctuation reward, punishment factor, and real-time electricity price are introduced. Thus, the lowest running cost of the building load is taken as the objective function needed to construct a building demand response optimization model while also considering load rebound. Finally, an example analysis is used to verify the effectiveness of the proposed model in reducing the comprehensive operation cost of buildings. The proposed model not only effectively reduces the comprehensive operation cost of buildings, but also reduces carbon emissions and load fluctuations under the premise of satisfying the actual building load, which provides a new idea for the research of a low-carbon demand response strategy of buildings with load rebound.

Abstract The industrial sector is China's major element of energy consumption and carbon emissions, and carbon emissions reduction in industrial parks has become an important foothold in carbon reduction work. Predicting the future evolution trend of carbon emissions in industrial parks under different policy orientations is of great significance for China to achieve the carbon emissions peaking and carbon neutrality goals. Based on the current status of electricity consumption and carbon emissions in industrial parks, this paper takes a light industrial park as the research object, analyzes the source of carbon emissions in this park, combines the characteristics of resource endowment and industrial structure, and considers the factors influencing society, load curve, energy supply and carbon sink, thus constructing a system dynamics model of carbon emission evolution for this park. Different influencing factors are selected to set multiple target scenarios, and the GDP, carbon emissions, carbon emissions reduction and near‐zero carbon evaluation coefficient of light industrial parks are used as characterization indicators for simulation analysis. The analysis results revealed that the comprehensive scenario set by increasing the maximum installed capacity of renewable energy units and raising the proportions of environmental and science and technology inputs while adjusting the industrial growth rate of the park performed best. In this scenario, the economic development of the park and the process of carbon emissions reduction are considered.

This is an essay in the field of mineral processing engineering. In order to make more efficient use of the resources in cassiterite tailings, we put forward the process flow of combined operation of flotation column and flotation machine on the basis of traditional flotation process. Among them, the flotation column for roughing and scavenging operations can give full play to the advantages of large processing capacity and high enrichment ratio of the flotation column; the selection operation of flotation machine can improve the overall stability of the process. Through this experiment, it is shown that under the premise of feeding ore grade of 0.28%, cassiterite middlings with grade of 15.26% and recovery rate of 61.13% can be obtained by combined flotation of flotation column and flotation machine. After shaking table operation, the concentrate grade reaches 40.87%, which can reach the index of grade 6 tin concentrate. The test results show that this process has good adaptability to low-grade and fine-grained cassiterite ore, and can reduce the construction land of the concentrator, which has certain promotion significance.

This is an essay in the field of mineral processing engineering. This study focuses on a rare earth tailing in Sichuan province. Mineralogy research shows that the distribution minerals of rare earth metal composition in the tailing are relatively consistent and the main minerals are bastnasite and synchisite. For the reason that the low grade of rare earth in the sample, only contains 1.06%, the gravity separation process is adopted to preconcentrate the rare earth minerals in the tailing to reduce the cost of processing. The floating-magnetic process is carried out on the preconcentration product with a self-developed mixed collector and water glass as inhibitor. The combined flotation process of "one roughing-one sweeping-three cleanings" and high intensity magnetic separation could obtain a rare earth concentrate with REO grade of 53.63% and recovery of 70.54% which successfully achieves the recycling rare earth from the low-grade rare earth tailings.

This is an essay in the field of mineral processing engineering. In this paper, a rare earth and associated fluorite resource in Henan Province was studied. Based on the results of process mineralogy and small beneficiation experiments, an extended continuous test with a capacity of 60 kg/h was carried out. Through chemical analysis and AMICS microscopic identification, it was found that the content of REO and CaF2 was 1.53% and 18.22%, respectively. The target minerals for REO separation were hamartite and parisite. The process of "mixed flotation - separation flotation of REO and fluorite " was used to recover REO and fluorite from ores. On this basis, the extended continuous test was carried out, and finally obtained the rare earth concentrate with REO grade of 52.54% and recovery rate of 51.15%, and the fluorite concentrate with a CaF2 grade of 94.76% and recovery rate of 60.80%. The concentrate product index was good, and comprehensive recovery and utilization of REE associated fluorite were preliminarily realized. It is helpful to provide technical support for the production of enterprises and provide a basis for the comprehensive development and utilization of the same type of rare earth and co-associated resource deposits.

To address the negative influence caused by power randomness of distributed PV output on energy system’s economic operation, in this work, an economic operation strategy considering the uncertainty features of PV output has been designed and applied on an integrated hydrogen energy system. First, the thermal system operation model and the thermoelectric output control model are precisely built for the integrated hydrogen energy system and the hydrogen-based fuel cell, respectively. Then, referring to the PV output prediction data, the uncertainty of light intensity variation is analyzed to correct the PV output prediction curve. Finally, a cost–benefit model for the optimal economic operation of the integrated hydrogen energy system including PV, hydrogen fuel cell and cogeneration unit is designed with an objective function of achieving an optimal economic operation of the multi-energy coupling devices. The simulation tests validate that considering the influence of PV output uncertainty on hydrogen fuel cell output can make the system operation more reasonable, which ensures the economic and reliable operation of hydrogen energy systems.

Abstract Background Perturbation-based balance training on a treadmill is an emerging method of gait stability training with a characteristic task nature that has had positive and sustained effects on balance recovery strategies and fall reduction. Little is known about the effects produced by shod and barefoot walking. We aimed to investigate which is more appropriate, shod or barefoot walking, for perturbation-based balance training in older adults. Methods Fourteen healthy older adults (age: 68.29 ± 3.41 years; body height: 1.76 ± 0.10 m; body mass: 81.14 ± 14.52 kg) performed normal and trip-like perturbed walking trials, shod and barefoot, on a treadmill of the Gait Real-time Analysis Interactive Lab. The marker trajectories data were processed by Human Body Model software embedded in the Gait Offline Analysis Tool. The outcomes of stride length variability, stride time variability, step width variability, and swing time variability were computed and statistically analyzed by a two-way repeated-measures analysis of variance (ANOVA) based on gait pattern (normal gait versus perturbed recovery gait) and footwear condition (shod versus barefoot). Results Footwear condition effect (p = 0.0310) and gait pattern by footwear condition interaction effect (p = 0.0055) were only observed in swing time variability. Gait pattern effects were detected in all four outcomes of gait variability. Conclusions Swing time variability, independent of gait speed, could be a valid indicator to differentiate between footwear conditions. The lower swing time variability in perturbed recovery gait suggests that barefoot walking may be superior to shod walking for perturbation-based balance training in older adults.

The fatty acid composition in organisms can reflect the trophic level, feeding habits, and utilization of local resources. In the present study, the living resources of different functional areas (artificial reef area, shellfish area, macroalgae area) in the Zhelin Bay Marine Ranch were investigated, and fatty acid analysis was used to elucidate the trophic relations across the food web. The results showed that 22:6ω3 FA (docosahexaenoic acid, DHA), 20:5ω3 FA (eicosapentaenoic acid, EPA), 16:1ω7 FA, and 16:0 FA are fatty acid biomarkers that distinguish carnivorous, omnivorous, and herbivorous organisms. The ratios of DHA/EPA, polyunsaturated fatty acids/saturated fatty acids (PUFA/SFA), and sum of ω3 fatty acids/sum of ω6 fatty acids (Σω3/Σω6) can be used as an important basis to assess the trophic level and feeding habits of organisms. In the comparison of the food web structure of different functional areas, the DHA/EPA ratio of carnivorous organisms in the Artificial Reef area was higher than that in other functional areas due to the abundant living resources, indicating that the food web of the Artificial Reef area presents lower stability to cope with perturbations. Furthermore, MixSIAR was used to quantitatively estimate the diet composition of consumers in the Zhelin Bay Marine Ranch based on their fatty acids. The results of the present study are a valuable contribution to understanding the trophic relationships in the Zhelin Bay Marine Ranch and provide theoretical support for future planning and construction of marine ranches.

With the rapid development of electric power systems, load estimation plays an important role in system operation and planning. Usually, load estimation techniques contain traditional, time series, regression analysis-based, and machine learning-based estimation. Since the machine learning-based method can lead to better performance, in this paper, a deep learning-based load estimation algorithm using image fingerprint and attention mechanism is proposed. First, an image fingerprint construction is proposed for training data. After the data preprocessing, the training data matrix is constructed by the cyclic shift and cubic spline interpolation. Then, the linear mapping and the gray-color transformation method are proposed to form the color image fingerprint. Second, a convolutional neural network (CNN) combined with an attentionmechanism is proposed for training performance improvement. At last, an experiment is carried out to evaluate the estimation performance. Compared with the support vector machine method, CNN method and long short-term memory method, the proposed algorithm has the best load estimation performance. [ABSTRACT FROM AUTHOR]

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Marked temporal point process models (MTPPs) aim to model event sequences and event markers (associated features) in continuous time. These models have been applied to various application domains where capturing event dynamics in continuous time is beneficial, such as education systems, social networks, and recommender systems. However, current MTPPs suffer from two major limitations, i.e., inefficient representation of event dynamic's influence on marker distribution and losing fine-grained representation of historical marker distributions in the modeling. Motivated by these limitations, we propose a novel model called Marked Point Processes with Memory-Enhanced Neural Networks (MoMENt) that can capture the bidirectional interrelations between markers and event dynamics while providing fine-grained marker representations. Specifically, MoMENt is constructed of two concurrent networks: Recurrent Activity Updater (RAU) to capture model event dynamics and Memory-Enhanced Marker Updater (MEMU) to represent markers. Both RAU and MEMU components are designed to update each other at every step to model the bidirectional influence of markers and event dynamics. To obtain a fine-grained representation of maker distributions, MEMU is devised with external memories that model detailed marker-level features with latent component vectors. Our extensive experiments on six real-world user interaction datasets demonstrate that MoMENt can accurately represent users' activity dynamics, boosting time, type, and marker predictions, as well as recommendation performance up to 76.5%, 65.6%, 77.2%, and 57.7%, respectively, compared to baseline approaches. Furthermore, our case studies show the effectiveness of MoMENt in providing meaningful and fine-grained interpretations of user-system relations over time, e.g., how user choices influence their future preferences in the recommendation domain. [ABSTRACT FROM AUTHOR]

The article introduces ELAKT, a model for Attentive Knowledge Tracing (AKT) that addresses challenges related to low attention in shifting knowledge concepts and stochastic behaviors. Topics include utilizing transformer encoder and causal convolutions for comprehensive knowledge aggregation and introducing a prediction correction module to handle noise from stochastic behaviors, resulting in improved predictive performance over existing models.

The article focuses on introducing a Dual Graph Ensemble Learning method for Knowledge Tracing (DGEKT) to improve knowledge tracing by incorporating heterogeneous exercise-concept associations and interaction transitions. Topics include hypergraph modeling, directed graph modeling, and online knowledge distillation to enhance prediction accuracy and ensemble modeling for better judgment quality in knowledge tracing tasks.

We provide a strategy for minimizing losses and redistributing loads in distribution systems while emergency repairs are being made. The proposed approach takes advantage of the preexisting, network-accessible, and Power Companies' Adoption of Residential Energy Storage Batteries devices. Batteries are expected to be used increasingly often to deal with a few of the growing challenges with renewable, among them the infamous duck curve difficulty, as renewable energy sources that are widely dispersed, like photovoltaic (PV) and wind turbines, become more popular. The proposed approach may be implemented using signals in reaction to demand. To demonstrate its value, we provide a method for concurrent simulation for designing and analyzing strategies for optimizing a distribution that benefits from the synergy-connected smart grid, intelligent structures, and decentralized battery systems to reduce overall energy consumption and costs while enhancing power management. The suggested method is created and verified inside of the Smart Builds co-simulation environment. From what we can tell from simulations, energy storage devices provide interim relief for line-outage-affected distribution networks. [ABSTRACT FROM AUTHOR]

Humans’ ability to transfer knowledge through teaching is one of the essential aspects for human intelligence. A human teacher can track the knowledge of students to customize the teaching on students’ needs. With the rise of online education platforms, there is a similar need for machines to track the knowledge of students and tailor their learning experience. This is known as the Knowledge Tracing (KT) problem in the literature. Effectively solving the KT problem would unlock the potential of computer-aided education applications such as intelligent tutoring systems, curriculum learning, and learning materials’ recommendation. Moreover, from a more general viewpoint, a student may represent any kind of intelligent agents including both human and artificial agents. Thus, the potential of KT can be extended to any machine teaching application scenarios which seek for customizing the learning experience for a student agent (i.e., a machine learning model). In this paper, we provide a comprehensive survey for the KT literature. We cover a broad range of methods starting from the early attempts to the recent state-of-the-art methods using deep learning, while highlighting the theoretical aspects of models and the characteristics of benchmark datasets. Besides these, we shed light on key modelling differences between closely related methods and summarize them in an easy-to-understand format. Finally, we discuss current research gaps in the KT literature and possible future research and application directions. [ABSTRACT FROM AUTHOR]

The industrial sector is China's major element of energy consumption and carbon emissions, and carbon emissions reduction in industrial parks has become an important foothold in carbon reduction work. Predicting the future evolution trend of carbon emissions in industrial parks under different policy orientations is of great significance for China to achieve the carbon emissions peaking and carbon neutrality goals. Based on the current status of electricity consumption and carbon emissions in industrial parks, this paper takes a light industrial park as the research object, analyzes the source of carbon emissions in this park, combines the characteristics of resource endowment and industrial structure, and considers the factors influencing society, load curve, energy supply and carbon sink, thus constructing a system dynamics model of carbon emission evolution for this park. Different influencing factors are selected to set multiple target scenarios, and the GDP, carbon emissions, carbon emissions reduction and near‐zero carbon evaluation coefficient of light industrial parks are used as characterization indicators for simulation analysis. The analysis results revealed that the comprehensive scenario set by increasing the maximum installed capacity of renewable energy units and raising the proportions of environmental and science and technology inputs while adjusting the industrial growth rate of the park performed best. In this scenario, the economic development of the park and the process of carbon emissions reduction are considered. [ABSTRACT FROM AUTHOR]

The article focuses on enhancing knowledge tracing in education through fine-grained interaction modeling using a Multi-Relational Transformer (MRT-KT). It explores various forms of interactions between question-response pairs within student sequences and introduces a novel relation encoding scheme based on knowledge concepts and student performance.

In the Artificial Intelligence of Medical Things (AIoMT), Internet-Delivered Psychological Treatment (IDPT) effectively improves the quality of mental health treatments. With the advent of COVID-19, psychological tasks have become overloaded and complicated for medical professionals due to the overlap of sentimental values. The development of an AIoMT tool requires labeling of data to achieve clinical-level performance. Text data requires an appropriate set of linguistic features for vector latent representation and segmentation. Emotional biases could lead to incorrect segmentation of patient-authorized texts, and labeling emotional data is time-consuming. In this article, we propose an assistant tool for psychologists to assist them in mental health treatment and note-taking. We first extend the word and emotion lexicon and then apply a hierarchical attention method to support data labeling. The learned latent representation uses word position prediction and sentence-level attention to create a semantic framework. The augmented vector representation helps in highlighting words and classifying nine different symptoms from the text written by the patient. Our experimental results show that the emotion lexicon helps to increase the accuracy by 5% without affecting the overall results, and that the hierarchical attention method achieves an F1 score of 0.89. [ABSTRACT FROM AUTHOR]

Background: Perturbation-based balance training on a treadmill is an emerging method of gait stability training with a characteristic task nature that has had positive and sustained effects on balance recovery strategies and fall reduction. Little is known about the effects produced by shod and barefoot walking. We aimed to investigate which is more appropriate, shod or barefoot walking, for perturbation-based balance training in older adults.Methods: Fourteen healthy older adults (age: 68.29 ± 3.41 years; body height: 1.76 ± 0.10 m; body mass: 81.14 ± 14.52 kg) performed normal and trip-like perturbed walking trials, shod and barefoot, on a treadmill of the Gait Real-time Analysis Interactive Lab. The marker trajectories data were processed by Human Body Model software embedded in the Gait Offline Analysis Tool. The outcomes of stride length variability, stride time variability, step width variability, and swing time variability were computed and statistically analyzed by a two-way repeated-measures analysis of variance (ANOVA) based on gait pattern (normal gait versus perturbed recovery gait) and footwear condition (shod versus barefoot).Results: Footwear condition effect (p = 0.0310) and gait pattern by footwear condition interaction effect (p = 0.0055) were only observed in swing time variability. Gait pattern effects were detected in all four outcomes of gait variability.Conclusions: Swing time variability, independent of gait speed, could be a valid indicator to differentiate between footwear conditions. The lower swing time variability in perturbed recovery gait suggests that barefoot walking may be superior to shod walking for perturbation-based balance training in older adults. [ABSTRACT FROM AUTHOR]

To evaluate the working state of the smart grid, users need to count the status of devices collected by multiple smart meters. To avoid network congestion during data collection processes in the smart grid, an aggregation protocol is required to aggregate messages from multiple smart meters into one short message. However, since these messages may contain sensitive information of smart meters, privacy is a fundamental requirement for such a data aggregation protocol. At the same time, to avoid tampering of transmitted messages, data integrity is another important requirement during data aggregation. Currently, the privacy issue has been well addressed by using homomorphic encryption algorithms such as the Paillier cryptographic system, where multiple smart meters use the same encryption key for encrypting different messages. However, the integrity issue is much harder than the privacy issue since multiple smart meters use different private keys for signing different messages. To address the integrity issue, we propose a novel data aggregation protocol for the smart grid, called DASG. In DASG, we shall show how to aggregate signatures from multiple smart meters using the Chinese Remainder theorem and the Lagrange interpolation techniques, respectively. Since these two techniques can aggregate multiple messages into one, DASG is quite light-weight. Therefore, our newly designed data aggregation protocol for the smart grid can achieve both security and efficiency goals in the smart grid environment. Experimental results show that DASG is feasible for real world applications. [ABSTRACT FROM AUTHOR]

Mental health problems impact the quality of life of millions of people around the world. However, diagnosis of mental health disorders is a challenging problem that often relies on self-reporting by patients about their behavioral patterns and social interactions. Therefore, there is a need for new strategies for diagnosis and daily monitoring of mental health conditions. The recent introduction of body-area networks consisting of a plethora of accurate sensors embedded in smartwatches and smartphones and edge-compatible deep neural networks (DNNs) points toward a possible solution. Such wearable medical sensors (WMSs) enable continuous monitoring of physiological signals in a passive and non-invasive manner. However, disease diagnosis based on WMSs and DNNs, and their deployment on edge devices, such as smartphones, remains a challenging problem. These challenges stem from the difficulty of feature engineering and knowledge distillation from the raw sensor data, as well as the computational and memory constraints of battery-operated edge devices. To this end, we propose a framework called MHDeep that utilizes commercially availableWMSs and efficient DNN models to diagnose three important mental health disorders: schizoaffective, major depressive, and bipolar.MHDeep uses eight different categories of data obtained from sensors integrated in a smartwatch and smartphone. These categories include various physiological signals and additional information on motion patterns and environmental variables related to the wearer. MHDeep eliminates the need for manual feature engineering by directly operating on the data streams obtained fromparticipants. Because the amount of data is limited, MHDeep uses a synthetic data generation module to augment real data with synthetic data drawn from the same probability distribution. We use the synthetic dataset to pre-train the weights of the DNN models, thus imposing a prior on the weights. We use a grow-and-prune DNN synthesis approach to learn both architecture and weights during the training process. We use three different data partitions to evaluate the MHDeep models trained with data collected from 74 individuals. We conduct two types of evaluations: at the data instance level and at the patient level. MHDeep achieves an average test accuracy, across the three data partitions, of 90.4%, 87.3%, and 82.4%, respectively, for classifications between healthy and schizoaffective disorder instances, healthy and major depressive disorder instances, and healthy and bipolar disorder instances. At the patient level, MHDeep DNN models achieve an accuracy of 100%, 100%, and 90.0% for the three mental health disorders, respectively, based on inference that uses 40, 16, and 22 minutes of sensor data collection from each patient. [ABSTRACT FROM AUTHOR]

Most researchers agree that verbally gifted learners should be provided with differentiated curriculum experiences that will allow them to reach their full potential. However, research is scarce in the field. The present study examined the impact of a reading enrichment program on fourth-grade students' critical reading abilities. The program was based on the Integrated Curriculum Model (ICM). The sample consisted of forty fourth-grade verbally gifted students from a school in Dubai, who were randomly assigned to either an experimental instruction condition or a traditional instruction condition and completed pre and post-tests of language arts. A pre-and post-experimental design was used. The overall results indicated the efficacy of the differentiated enrichment program in enhancing Emirati gifted learners' critical reading abilities. The study also provides a framework for better provision and teacher training planning regarding gifted education in the UAE. [ABSTRACT FROM AUTHOR]