bluuebunny/crossref_metadata_2025 huggingface dataset:
EFFECTS OF SQUATTING SPEED AND DEPTH ON LOWER EXTREMITY KINEMATICS, KINETICS AND ENERGETICS
Authors: CHAN CHOW-KHUEN , AZAH HAMZAID NUR , YEOW CHEN-HUA , GOH SIM-KUAN , TING HUA-NONG , SALMAH KARMAN
Published: May 2022

Squatting has received considerable attention in sports and is commonly utilized in daily activities. Knowledge of the squatting biomechanics in terms of its speed and depth may enhance exercise selection when targeting for sport-specific performance improvement and injury avoidance. Nonetheless, these perspectives have not been consistently reported. Hence, this preliminary study intends to quantify the kinematics, kinetics, and energetics in squat with different depths and speeds among healthy young adults with different physical activity levels; i.e., between active and sedentary groups. Twenty participants were administered to squat at varying depths (deep, normal, and half) and speeds (fast, normal, and slow). Motion-capture system and force plates were employed to acquire motion trajectories and ground reaction force. Joint moment was obtained via inverse dynamics, while power was derived as a product of moment and angular velocity. Higher speeds and deeper squats greatly influence higher joint moments and powers at the hip ([Formula: see text]) and knee ([Formula: see text]) than ankle, signifying these joints as the prime movers with knee as the predominant contributor. These preliminary findings show that the knee-strategy and hip-strategy were employed in compensating speed and depth manipulations during squatting. In certain contexts, appreciating these findings may provide clinically relevant implications, from the performance and injury avoidance viewpoint, which will ameliorate the physical activity level of practitioners.
MODELING OF STOCK RETURNS IN CONTINUOUS VIS-À-VIS DISCRETE TIME IS EQUIVALENT, RESPECTIVELY, TO THE CONDITIONING OF STOCK RETURNS ON A RANDOM WALK PROCESS FOR TRADE IMBALANCES VIS-À-VIS A RANDOM WALK PROCESS FOR EVOLUTION OF INFORMATION
Authors: OBRIMAH OGHENOVO A. , WONG WING-KEUNG
Published: May 2022

Let [Formula: see text], [Formula: see text], [Formula: see text] and [Formula: see text] denote, respectively, the current stock price, the future stock price that is conditioned on information, the minimum stock market tick size and the realized future stock price. Formal theoretical proofs in this study show modeling of stock returns in continuous time induces stock returns that have parameterization as gambles over lotteries. Stock returns have parameterization as gambles because in the presence of fairness of formation of [Formula: see text], regardless arrival of liquidity and speculative trades feasibly induces [Formula: see text]. Evolution of ‘(conditional) trade imbalances’ as random walks is shown to be a necessary and sufficient condition for parameterization of stock returns as gambles over lotteries. Suppose, on the contrary, a resort to modeling of stock returns in discrete time. The formal theory arrives at two dichotomous sufficiency conditions, which predict directionality and sizes of price changes, and facilitate evolution of stock returns as random walks. In presence of the two dichotomous conditions, fairness of formation of [Formula: see text] necessarily induces, regardless of arrival of liquidity and speculative trades, [Formula: see text]. Risk is parameterized by [Formula: see text], because all else constant, an inversion of the perturbing conditionally positive trade imbalance induces [Formula: see text]. Whereas then, [Formula: see text] has parameterization as ‘materialization of risk’, always, it is [Formula: see text] that is statistic for risk; risk, as such is well parameterized, that is, does not coincide with its materialization (note that whereas volatility is statistic for risk, it is not a statistic for materialization of risk; a statistic for risk necessarily is robust to non-materialization of risk). Given modeling in continuous time does not facilitate either of the two sufficiency conditions, always, risk has parameterization as the probability that [Formula: see text]. Since risk, as such coincides qualitatively with its materialization, it is not well parameterized. Given study findings parameterize general equilibrium, formal theoretical predictions have characterization as axiomatic statements, as opposed to propositional (parameter-dependent) statements.
Explaining Provincial Variation in Implementation of China’s Clean Water Policies
Authors: Xu Fang
Published: May 2022

This research analyzes the local implementation of China’s central clean water policies over the recent decades. Based on a series of panel data analyses on Chinese provinces between 2004 and 2015, this study empirically examines the impact of decentralization and interest groups on water policy implementation, leading to three main findings. First, fiscal decentralization has a significant positive effect on policy output as measured using per-capita provincial expenditures on industrial wastewater. But it has no significant influence on policy outcome as measured by per-capita emissions of chemical oxygen demand. Second, while increasing environmental decentralization at the provincial level tends to increase provincial expenditures, wastewater discharge also increases. Third, increased industrial contributions to the provincial economy are associated with increased environmental spending from the province, yet foreign trade and environmental petitions do not have the expected impact. The results suggest that China’s decentralization reforms appear to have increased provincial policy responses without improving the actual environmental outcomes. Experiences from authoritarian China may provide lessons for other countries.
Random \( \Theta (\log n) \) -CNFs are Hard for Cutting Planes
Authors: Fleming Noah , Pankratov Denis , Pitassi Toniann , Robere Robert
Published: May 2022

The random
k
-SAT model is one of the most important and well-studied distributions over
k
-SAT instances. It is closely connected to statistical physics and is a benchmark for satisfiability algorithms. We show that when

\( k = \Theta (\log n) \)

, any Cutting Planes refutation for random
k
-SAT requires exponential length in the regime where the number of clauses guarantees that the formula is unsatisfiable with high probability.
FLEE: A Hierarchical Federated Learning Framework for Distributed Deep Neural Network over Cloud, Edge, and End Device
Authors: Zhong Zhengyi , Bao Weidong , Wang Ji , Zhu Xiaomin , Zhang Xiongtao
Published: May 2022

With the development of smart devices, the computing capabilities of portable end devices such as mobile phones have been greatly enhanced. Meanwhile, traditional cloud computing faces great challenges caused by privacy-leakage and time-delay problems, there is a trend to push models down to edges and end devices. However, due to the limitation of computing resource, it is difficult for end devices to complete complex computing tasks alone. Therefore, this article divides the model into two parts and deploys them on multiple end devices and edges, respectively. Meanwhile, an early exit is set to reduce computing resource overhead, forming a hierarchical distributed architecture. In order to enable the distributed model to continuously evolve by using new data generated by end devices, we comprehensively consider various data distributions on end devices and edges, proposing a hierarchical federated learning framework
FLEE
, which can realize dynamical updates of models without redeploying them. Through image and sentence classification experiments, we verify that it can improve model performances under all kinds of data distributions, and prove that compared with other frameworks, the models trained by
FLEE
consume less global computing resource in the inference stage.
A Text-based Deep Reinforcement Learning Framework Using Self-supervised Graph Representation for Interactive Recommendation
Authors: Wang Chaoyang , Guo Zhiqiang , Li Jianjun , Li Guohui , Pan Peng
Published: May 2022

Due to its nature of learning from dynamic interactions and planning for long-run performance, Reinforcement Learning (RL) has attracted much attention in Interactive Recommender Systems (IRSs). However, most of the existing RL-based IRSs usually face large discrete action space problem, which severely limits their efficiency. Moreover, data sparsity is another problem that most IRSs are confronted with. The utilization of recommendation-related textual knowledge can tackle this problem to some extent, but existing RL-based recommendation methods either neglect to combine textual information or are not suitable for incorporating it. To address these two problems, in this article, we propose a
T
ext-based deep
R
einforcement learning framework using self-supervised
G
raph representation for
I
nteractive
R
ecommendation (TRGIR). Specifically, we leverage textual information to map items and users into a same feature space by a self-supervised embedding method based on the graph convolutional network, which greatly alleviates data sparsity problem. Moreover, we design an effective method to construct an action candidate set, which reduces the scale of the action space directly. Two types of representative reinforcement learning algorithms have been applied to implement TRGIR. Since the action space of IRS is discrete, it is natural to implement TRGIR with Deep Q-learning Network (DQN). In the TRGIR implementation with Deep Deterministic Policy Gradient (DDPG), denoted as TRGIR-DDPG, we design a policy vector, which can represent user’s preferences, to generate discrete actions from the candidate set. Through extensive experiments on three public datasets, we demonstrate that TRGIR-DDPG achieves state-of-the-art performance over several baselines in a time-efficient manner.
Rethinking Model-Based Gaze Estimation
Authors: Kaur Harsimran , Jindal Swati , Manduchi Roberto
Published: May 2022

Over the past several years, a number of data-driven gaze tracking algorithms have been proposed, which have been shown to outperform classic model-based methods in terms of gaze direction accuracy. These algorithms leverage the recent development of sophisticated CNN architectures, as well as the availability of large gaze datasets captured under various conditions. One shortcoming of black-box, end-to-end methods, though, is that any unexpected behaviors are difficult to explain. In addition, there is always the risk that a system trained with a certain dataset may not perform well when tested on data from a different source (the "domain gap" problem.) In this work, we propose a novel method to embed eye geometry information in an end-to-end gaze estimation network by means of a "geometric layer". Our experimental results show that our system outperforms other state-of-the-art methods in cross-dataset evaluation, while producing competitive performance over within dataset tests. In addition, the proposed system is able to extrapolate gaze angles outside the range of those considered in the training data.
Pupillary Light Reflex Correction for Robust Pupillometry in Virtual Reality
Authors: Eckert Marie , Robotham Thomas , Habets Emanuël A. P. , Rummukainen Olli S.
Published: May 2022

Virtual reality (VR) headsets with an integrated eye tracker enable the measurement of pupil size fluctuations correlated with cognition during a VR experience. We present a method to correct for the light-induced pupil size changes, otherwise masking the more subtle cognitively-driven effects, such as cognitive load and emotional state. We explore multiple calibration sequences to find individual mapping functions relating the luminance to pupil dilation that can be employed in real-time during a VR experience. The resulting mapping functions are evaluated in a VR-based n-back task and in free exploration of a six-degrees-of-freedom VR scene. Our results show estimating luminance from a weighted average of the fixation area and the background yields the best performance. Calibration sequence composed of either solid gray or realistic scene brightness levels shown for 6 s in a pseudo-random order proved most robust.
Remarn: A Reconfigurable Multi-threaded Multi-core Accelerator for Recurrent Neural Networks
Authors: Que Zhiqiang , Nakahara Hiroki , Fan Hongxiang , Li He , Meng Jiuxi , Tsoi Kuen Hung , Niu Xinyu , Nurvitadhi Eriko , Luk Wayne
Published: May 2022

This work introduces Remarn, a reconfigurable multi-threaded multi-core accelerator supporting both spatial and temporal co-execution of Recurrent Neural Network (RNN) inferences. It increases processing capabilities and quality of service of cloud-based neural processing units (NPUs) by improving their hardware utilization and by reducing design latency, with two innovations. First, a custom coarse-grained multi-threaded RNN/Long Short-Term Memory (LSTM) hardware architecture, switching tasks among threads when RNN computational engines meet data hazards. Second, the partitioning of this hardware architecture into multiple full-fledged sub-accelerator cores, enabling spatially co-execution of multiple RNN/LSTM inferences. These innovations improve the exploitation of the available parallelism to increase runtime hardware utilization and boost design throughput. Evaluation results show that a dual-threaded quad-core Remarn NPU achieves 2.91 times higher performance while only occupying 5.0% more area than a single-threaded one on a Stratix 10 FPGA. When compared with a Tesla V100 GPU implementation, our design achieves 6.5 times better performance and 15.6 times higher power efficiency, showing that our approach contributes to high performance and energy-efficient FPGA-based multi-RNN inference designs for datacenters.
NB-IoT Coverage and Sensor Node Connectivity in Dense Urban Environments: An Empirical Study
Authors: Yau Cheuk-Wang , Jewsakul Sukanya , Luk Man-Ho , Lee Angela P. Y. , Chan Yun-Hin , Ngai Edith C. H. , Pong Philip W. T. , Lui King-Shan , Liu Jiangchuan
Published: May 2022

Wireless sensor networks have enabled smart infrastructures and novel applications. With the recent roll-out of Narrowband IoT (NB-IoT) cellular radio technology, wireless sensors can be widely deployed for data collection in cities around the world. However, empirical evidence regarding the coverage and connectivity of NB-IoT in dense urban areas is limited. This article presents an empirical study that focuses on evaluating the coverage and connectivity of NB-IoT in a dense urban environment. We have designed an NB-IoT sensor node and deployed over 100 of them in high-rise apartment buildings in Hong Kong. These sensor nodes utilize a commercial NB-IoT network to collect high-resolution water flow data for machine learning model training and provide timely feedback to users. We collect and analyze the empirical NB-IoT signal measurements from the sensor nodes deployed in various challenging outdoor and indoor environments for over three months. These empirical measurements reveal correlations between NB-IoT connectivity and sensor installation environments. We also observe that inter-cell interference, as a result of coverage by multiple neighboring NB-IoT cells in a dense urban environment, is a source of connectivity degradation. We discuss potential issues that IoT application designers and system integrators might encounter in practical NB-IoT devices deployment, and we propose a transmission decision algorithm based on signal measurements for mitigating energy wasted due to transmission failures. Finally, we demonstrate the results and the benefits of using high-resolution water flow data collected by our purpose-built NB-IoT sensor nodes for studying the patterns of domestic water consumption in Hong Kong.
CausalSE: Understanding Varied Spatial Effects with Missing Data Towards Adding New Bike-sharing Stations
Authors: Wang Qianru , Guo Bin , Cheng Lu , Yu Zhiwen , Liu Huan
Published: May 2022

To meet the growing bike-sharing demands and make people’s travel convenient, the companies need to add new stations at locations where demands exceed supply. Before making reliable decisions on adding new stations, it is required to understand the spatial effects of new stations on the station network. In this paper, we study the deployment of the new station by estimating its varied causal effects on the demands of nearby stations, e.g.,
how does adding a new station (treatment) causally influence the demands (outcome) of nearby stations?
When working with observational data, we should control hidden confounders, which cause spurious relations between treatments and outcomes. However, previous studies use historical data of the individual unit (e.g., the station’s historical demands) to approximate its hidden confounders, which cannot deal with the lack of historical data for new stations. And the conventional methods overlook the differences between units, which cannot be applied to our problem. To overcome the challenges, we propose a novel model (CausalSE) to estimate the varied effects of new stations on nearby stations, which uses the shared knowledge (i.e., similar traveling patterns among stations) to approximate hidden confounders. Experimental results on real-world datasets show that CausalSE outperforms 6 state-of-the-art methods.
Exploiting the Balance Between Conductivity and Adsorption Capacity/Redox Electrocatalytic Ability In MIL-Based Porous Crystalline Materials for the Electrochemical Response
Authors: Pham Tuyet Nhung , Pham Xuan Nui , Thi Nguyen Hoa , Pham Thanh , Tran Quang Huy , Le Anh-Tuan
Published: May 2022

MIL-53(Fe), MIL-101(Cr), and MIL-53(Al) were successfully prepared and selected as promising modifying materials on electrode surface. With the difference in porous textural parameters and metal nodes, the physical characteristics, electrochemical behaviors, and performances towards chloramphenicol (CAP) detecting at each modified electrode were systematically evaluated through cyclic voltammetry (CV) and differential pulse voltammetry (DPV) measurements. Results pointed out that both MIL-53(Fe)/SPE and MIL-101(Cr)/SPE exhibited excellent electrochemical performance through the enhancement of the EASA value, electrocatalytic ability, adsorption capacity (Γ), diffusion ability, and interaction with the CAP molecules, promising to be great materials in fabricating electrode. In which, MIL-101(Cr)/SPE with a huge BET, large pore volume, and good redox electrocatalytic ability of Cr3+ metal nodes significantly enhanced electrochemical response of CAP, despite it was still limited by poor adsorption capacity and diffusion due to the strong water-molecule interaction force of the Cr3+ centers and steric effect of the cramped microporous system. While, MIL-53(Fe) with a much smaller specific surface area and pore volume, it still showed good electrocatalytic activity of Fe3+ ions, along with high interact-ability and large adsorption capacity with CAP through hydrogen bonding and weak interaction force with water. In contrast, MIL-53(Al)/SPE showed poor electrochemical performance due to weak electron conductivity and the lack of electrocatalytic active sites. Obviously, in addition to conductivity, the merits of high adsorption capacity and excellent electrocatalytic activity of unsaturated metal centers need to be maximumly taken advantage of. A perfect balance in terms of the conductivity and adsorption capacity, as well as the electrocatalytic ability in MIL materials still needs to be further preferred in electrochemical sensors.
Reversible Hydrogen and Pd Hydride Reference Electrodes with Electrochemically Supplied H2 for High Temperature and Pressure Electrochemistry
Authors: Leuaa Pradipkumar , Chatzichristodoulou Christodoulos
Published: May 2022

A reversible hydrogen electrode (RHE) and a Palladium hydride (PdH) reference electrode are proposed with a convenient design for use at high temperatures and pressures (HTP). A constant electrochemically driven supply of H2 on the RHE or PdH surface ensures preservation of the H2 equilibrium potential and mixed (α + β)-phase in the PdH layer, respectively, thereby ensuring stable potentials at HTP conditions for prolonged periods of time. Furthermore, the potential of the PdH reference electrode is calibrated as a function of temperature and pressure with respect to the RHE. In contrast to earlier calibration attempts, we find that the potential of the PdH electrode in the mixed (α + β)-phase is independent of temperature and pressure, with a value of ∼55 mV vs the RHE at 25 °C, 1 bar, 45 wt% KOH. When the PdH is charged with enough hydrogen to assume the H-rich β-phase only, its potential follows that of the RHE, but shows a sluggish equilibration upon changing pressure or temperature, due to the slow diffusion of H in β-PdH.
Electro–Oxidation of Nitroxide Radicals: Adsorption–Mediated Charge Transfer Probed Using SERS and Potentiometry
Authors: Shaheen Nora A. , Dean William , Penley Drace , Kersten Bethany , Rintamaki Jacob , Vukmirovic Miomir B. , Gurkan Burcu E. , Akolkar Rohan
Published: May 2022

Organic compounds containing nitroxide radicals such as 4–hydroxy–2,2,6,6–tetramethylpiperidine–1–oxyl (4–hydroxy–TEMPO) are redox–active and are of interest for potential applications in redox flow batteries. The mechanisms governing charge–transfer reactions of such compounds are not well understood. Specifically, the anodic charge transfer coefficient (α
a) corresponding to the electro–oxidation of 4–hydroxy–TEMPO in an aqueous medium is ∼0.9, i.e., α
a deviates considerably from the expected value (0.5) for a symmetric single–step one–electron transfer redox reaction. In a previous publication (J. Electrochem. Soc., 2020, 167, 143505), we have proposed a reaction mechanism to explain such asymmetric behavior by invoking adsorption–desorption processes. In the proposed mechanism, reversible oxidation of 4–hydroxy–TEMPO leads to the adsorption of the oxidation product, which then undergoes slow rate–limiting desorption from the electrode surface. In the present contribution, supporting evidence is provided for this mechanism. In situ surface–enhanced Raman spectroscopy combined with density functional theory simulations are employed to confirm the presence of surface–adsorbed species at a Au electrode during electro–oxidation of 4–hydroxy–TEMPO. Furthermore, we employ chronopotentiometry to track the gradual re–equilibration of the electrode–electrolyte interface following the electro–oxidation of 4–hydroxy–TEMPO. Analysis of the chronopotentiometry data further suggests the presence of adsorbed species, which were previously proposed and are now confirmed by direct spectroscopic evidence.
In Situ Formed Edge-Rich Ni3S2-NiOOH Heterojunctions for Oxygen Evolution Reaction
Authors: Yan Qing , Liu Zheng , Bai Xiaojing , Zhang Xuan , Gao Ruiqin , Yuan Weiyong , Chen Zhengfei , Li Zhoupeng , Li Yiju
Published: May 2022

Developing highly active, earth-abundant, and durable electrocatalysts is desired but challenging for oxygen evolution reaction (OER). In this work, we design an electrocatalyst of the edge-rich nickel sulfide arrays on the nickel foam (Ni3S2 NSs-NF) by a facile yet efficient wet-chemical method. Benefiting from the three-dimensional nanostructure with numerous active edges, the prepared Ni3S2 NSs-NF exhibits superior OER performance in alkaline conditions. An in-depth study reveals that the real active sites toward OER are the in situ formed heterogenous Ni3S2-NiOOH. Density functional theory (DFT) calculations indicate the density of state (DOS) of the Ni3S2-NiOOH heterojunction near the Fermi level is enhanced, contributing to higher electronic conductivity. As a result, the Ni3S2 NSs-NF with abundant Ni3S2-NiOOH heterojunctions exhibits an efficient electrochemical activity toward OER in alkaline conditions. The Ni3S2 NSs-NF electrode shows an overpotential of 244 mV at 10 mA cm−2 with a Tafel slope of 75 mV dec−1 and possesses ultrastable performance even at 100 mA cm−2.
Activated Carbon Mediated Hydrothermally Synthesized CuO Thin Films for Electrochemical Supercapacitors
Authors: Bhat T. S. , Teli A. M. , Beknalkar S. A. , Mane S. M. , Tibile P. D. , Patil P. S. , Kim H. J. , Shin J. C.
Published: May 2022

In this paper, we used a simple and fast hydrothermal approach to create copper oxide (CuO) nanostructured thin film electrodes for supercapacitor applications. Nanostructures and material conductivity are critical in ion transport, and short ion and electron diffusion paths give more active sites for electrochemical processes. Activated carbon enhanced the conductivity of the CuO sample (AC). The specific capacitance obtained from CV curve is 333.3 F g−1 at 20 mV s−1 while from GCD curve is 277 F g−1 at 1 mA cm−2. The CuOAC1 electrode preserves more than 66% of capacitance when operated at a high scan rate of 100 mV s−1. Furthermore, the sample performs well, with an energy density of 9.63 Wh kg−1 and a power density of 509.8 W kg−1 at 1 mA cm−2 current density in 1 M acq. KOH electrolyte. The CuOAC1 sample had the lowest series (∼Rs = 0.9 Ω) and charge transfer resistance (∼Rs = 4.0 Ω). Also, the symmetric coin cell was assembled which exhibited areal capacitance of 16.7 mF cm−2 with 2.3 μWh cm−2 energy density at 500 μW cm−2 power density measured at 2 mA cm−2 current density. This paper describes a new platform for increasing the conductivity of pseudocapacitive electrode materials, resulting in a high-performance supercapacitor for future portable electronic devices.
Review—Temperature Dependence of Luminescence Intensity and Decay Time in Cr3+-Activated Oxide and Fluoride Phosphors
Authors: Adachi Sadao
Published: May 2022

Studying temperature dependence of light emission intensities in luminescent materials is not only of scientific interest but also technological importance. It is well known that Mn4+-activated “fluoride” phosphors sometimes show an anomalous thermal quenching (TQ) behavior. This behavior is an increase in the integrated photoluminescence (PL) intensity I
PL with increasing temperature T, called negative TQ, and is understood to be due to the electric dipole (parity) forbidden transitions of 2
E

g
→ 4
A
2g
gained by coupling with the odd-parity lattice vibrations, ν
3, ν
4, and ν
6. The present article discusses the temperature dependence of the integrated PL intensity for the Cr3+-activated “oxide” and “fluoride” phosphors with an emphasis on negative TQ phenomenon. The effects of the 4
A
2g
→ 4
T
2g
excitation transitions in conjunction with those of the normal (positive) TQ are considered for developing a new analysis model of I
PL(T) data. Our new analysis model shows a good agreement with the experimental I
PL(T) data. Discussion is also given on the temperature dependence of luminescence decay time τ(T), demonstrating a strong correlation between I
PL(T) and τ(T) except for negative TQ occurring only in I
PL(T).
FTIR and Electrical Properties of Cr Substituted Li0.5Fe2.5−xO4 Synthesized Using Sol-Gel Auto Combustion
Authors: Patil Swati D. , Mane Sagar. M. , Kharade Pravin M. , Thombare Jagannath V. , Gaikwad Rajendra S. , Tirpude Manish P. , Shin Jae Cheol , Dhasade Shankar S. , Kim Hyo Jin
Published: May 2022

The highly desirable properties of lithium ferrite and various metal ion substituted lithium ferrite make it a useful material for many electronic and magnetic applications. This research used a simple sol-gel auto-combustion method for the synthesis of Cr substituted Li0.5Fe2.5O4 ferrite. Here, in the B-site, Fe3+ ions were swapped by non-magnetic Cr3+ ions at different concentrations. All the samples of Cr substituted Li0.5Fe2.5O4 were confirmed through X-ray diffraction patterns to have a single-phase spinel-type structure with cubic symmetry, which was thereafter confirmed by its refinement through Rietveld. An increase in the substitution of Cr3+ instead of Fe3+ ions in the spinel ferrite system results in an increased nanocrystalline size. The average crystallite size decreased from 30 nm to 10 nm with an increase in Cr3+ content. Analogous particle size was evaluated through transmission electron microscopy (TEM) analysis. Fourier transform infrared spectroscopy (FTIR) provides an overview of the structural distortion of the lithium ferrite lattice with the substitution of Cr3+ ions. The Semiconducting (NTCR) nature of the Cr substituted Li0.5Fe2.5O4 nanoparticles was confirmed through the temperature-dependent DC resistivity. The evaluated trend of the dielectric properties (ε′, ε″, and tan δ) strengthens the facts observed in the electrical conduction mechanism.
Effects of Genistein and Exercise Training on Brain Damage Induced by a High-Fat High-Sucrose Diet in Female C57BL/6 Mice
Authors: Li Rongzi , Ding Xiaowen , Geetha Thangiah , Fadamiro Moni , St Aubin Chaheyla R. , Shim Minsub , Al-Nakkash Layla , Broderick Tom L. , Babu Jeganathan Ramesh
Published: May 2022

In recent decades, a shift in the nutritional landscape to the Western-style diet has led to an unprecedented rise in the prevalence of obesity and neurodegenerative diseases. Consumption of a healthy diet and engaging in regular physical activity represents safe and affordable approaches known to mitigate the adverse consequences of the Western diet. We examined whether genistein treatment, exercise training, and a combination treatment (genistein and exercise training) mitigated the effects of a Western diet-induced by high-fat, high-sugar (HFHS) in brain of female mice. HFHS increased the amyloid-beta (Aβ) load and phosphorylation of tau, apoptosis, and decreased brain-derived neurotrophic factor (BDNF) levels. Exercise training and genistein each afforded modest protection on Aβ accumulation and apoptosis, and both increased BDNF. The greatest neuroprotective effect occurred with combination treatment. BDNF and all markers of Aβ accumulation, phosphorylation of tau, and apoptosis were improved with combined treatment. In a separate series of experiments, PC12 cells were exposed to high glucose (HG) and palmitate (PA) to determine cell viability with genistein as well as in the presence of tamoxifen, an estrogen receptor antagonist, to assess a mechanism of action of genistein on cell apoptosis. Genistein prevented the neurotoxic effects of HG and PA in PC12 cells and tamoxifen blocked the beneficial effects of genistein on apoptosis. Our results indicate the beneficial effects of genistein and exercise training on HFHS-induced brain damage. The benefits of genistein may occur via estrogen receptor-mediated pathways.
ncRNA-Mediated High Expression of LPCAT1 Correlates with Poor Prognosis and Tumor Immune Infiltration of Liver Hepatocellular Carcinoma
Authors: Sun Qiu , Liu Xudong , Peng Qunlong , Hu Lei , Jiang Xiaochun
Published: May 2022

Purpose. To investigate the expression of LPCAT1 in liver hepatocellular carcinoma (LIHC) and its relationship with prognosis and immune infiltration and predict its upstream nonencoding RNAs (ncRNAs). Method. In this study, expression analysis and survival analysis for LPCAT1 in pan cancers were first performed by using The Cancer Genome Atlas (TCGA) data, which suggested that LPCAT1 might be a potential LIHC oncogene. Then, ncRNAs contributing to the overexpression of LPCAT1 were explored in starBase by a combination of expression analysis, correlation analysis, and survival analysis. Immune cell infiltration of LPCAT1 in LIHC was finally investigated via Tumor Immune Estimation Resource (TIMER). Result. SNHG3 was observed to be the most promising upstream lncRNA for the hsa-miR-139-5p/LPCAT1 axis in LIHC. In addition, the LPCAT1 level was significantly positively associated with tumor immune cell infiltration, biomarkers of immune cells, and immune checkpoint expression in LIHC. Conclusion. To summarize, the upregulation of LPCAT1 mediated by ncRNAs is associated with poor prognosis, immune infiltration, and immune checkpoint expression in LIHC.
Experimental Study on Dynamic Response and Damage Evolution of Coal under Shocks by Multiple High-Pressure Air Blasting
Authors: Yan Shaoyang , Yang Xiaolin , Chu Huaibao , Wang Jinxing , Wang Chang , Yu Mengfei
Published: May 2022

To explore the dynamic response and damage evolution of coal under multiple high-pressure air blasting (HPAB), simulated coal specimens were used in the HPAB experiments, and the variation laws of stress field, vibration field, damage field, and cumulative fracture failure process in specimens were analyzed from a macro point of view. A scanning electron microscope (SEM) and mercury intrusion porosimetry (MIP) were used to observe the pore distribution near the blasthole of the specimen and analyze the variation law of pore structure parameters under multiple HPAB from the microscopic point of view. The test results show that (1) the stress wave generated by HPAB has a great impact on the near zone. After multiple HPAB, the damage value at the place 50 mm away from the blasthole increases by 3.91 times compared with the one shock from HPAB, and the strain peak and vibration velocity are reduced by 17.86% and 63.05%, respectively. With the increase of distance, the internal damages of the specimens in the middle and far zones are mainly driven by the stress wave and the high-pressure air, and the strain peak, vibration velocity, and damage degree gradually decrease. (2) With the increase of shock times in HPAB tests, the stress wave attenuation index decreases at first and then increases, and the damages degree of the middle and far zones increase slowly in the first few shocks and then increase rapidly. The site coefficient (

k

) shows an overall decreasing trend, whereas the attenuation coefficient (

α

) tends to increase. (3) The multiple HPABs have a great impact on the pore structure of the specimens. Compared with unshocked specimen, the cumulative mercury injection and pore volume increased by 152.04% and 135.05%, respectively. The number of connecting pores with large pore diameter is significantly increased. The multiple HPAB can effectively improve the pore and fracture structure in the specimens and form a relatively developed fracture network channel. The study results have certain guiding value for solving practical engineering problems of low extraction efficiency in low permeability coal seams.
A Literature Review of Digital Literacy over Two Decades
Authors: Peng Danhua , Yu Zhonggen
Published: May 2022

The COVID-19 pandemic has forced online learning to be a “new normal” during the past three years, which highly emphasizes students’ improved digital literacy. This study aims to present a literature review of students’ digital literacy. Grounded on about twenty journal articles and other related publications from the Web of Science Core Collection, this paper focused on the definition of digital literacy; the factors affecting students’ digital literacy (age, gender, family socioeconomic status, and parent’s education level); the relationship between students’ digital literacy and their self-control, technostress, and engagement; and the three approaches to gauge the level of students’ digital literacy. The study also provided some advice for educators and policymakers. Finally, the limitations and implications were presented.
Application of Business Intelligence Based on the Deep Neural Network in Credit Scoring
Authors: Feng Wei , Chen Ming
Published: May 2022

In order to solve the economic globalization and the intensification of financial market volatility, this study puts forward the judgment of credit risk of loan enterprises, so as to effectively improve the ability of commercial banks to control credit risk. At the same time, neural network technology is applied to solve nonlinear data modeling. Experiments and evaluation are carried out by using the artificial neural network (ANN) evaluation model. The experimental evaluation model data obtained from the experimental evaluation model show that the prediction effect is good, which proves the effectiveness of this method. According to the data of China Banking Regulatory Commission, at the end of the third quarter of 2016, the balance of nonperforming loans of Chinese commercial banks was 1493.9 billion yuan. By the end of the third quarter of 2021, the balance of nonperforming loans of Chinese commercial banks had reached 28153.5 trillion yuan, a month on month increase of about 42.7 billion yuan. At the end of the third quarter of 2021, the domestic and foreign currency assets of Chinese banking financial institutions were 339.4 trillion yuan, a year-on-year increase of 7.7%. This shows that the credit risk remains high, but it is still at a controllable level. Commercial banks are the center of the modern economic system. Their credit risk is very important to the stability and safe development of national economy. It is also the key factor for its own business sustainability and the protection of the interests of depositors. Therefore, effectively preventing and reducing credit risk is a key field in the financial risk management of commercial banks.
Shape Reconstruction of Eccentric Defect in Cylindrical Component by Modified Born Approximation Method
Authors: Zheng Gangfeng , Li Ze , Liu Songfeng , Dong Hao , Wu Bin , He Cunfu
Published: May 2022

In this paper, a cylindrical aluminum sample with an eccentric circular hole was prepared, and ultrasonic measurements were carried out by experimental means. The measurement area was limited to a plane edge perpendicular to the axis of the cylindrical component. The measured waveform data were input into the formula of approximate correction method, and the section image was obtained by using a modified Born approximation (MBA) method. Then, the three-dimensional (3D) shape of the defects in the aluminum sample was reconstructed by superimposing the cross-sectional images. Results showed that the defect reconstruction effect of the two-dimensional section and the 3D defect reconstruction effect were significantly improved by the MBA method.
The Comparative Efficacy of Multiple Acupuncture for Alzheimer’s Disease: A Bayesian Network Meta-Analysis
Authors: Yin Zihan , Li Xiang , Wang Linjia , Sun Mingsheng , Zhao Ling , Liang Fanrong
Published: May 2022

Background. Alzheimer’s disease (AD) is a progressive neurodegenerative disease. Numerous cases have illustrated that the acupuncture method could improve AD patients’ cognitive function and daily living ability. However, the optimal acupuncture treatments remain controversial. Therefore, we aimed to conduct a systematic review to compare the efficacy of multiple acupuncture therapies for AD and identify the optimal acupuncture intervention for delaying AD progression. Methods. To select potentially concerned randomized controlled trials (RCTs), we searched four English databases, four Chinese databases, and additional sources from 1 May 2021. Two independent reviewers conducted study screening, data extraction, and methodological quality assessment. The primary outcome was global cognitive function improvement. Pairwise and Bayesian network meta-analyses were performed using STATA v15.0 and ADDIS v1.16.8. The Grading of Recommendations Assessment, Development, and Evaluation (GRADE) tool was used to assess the quality of evidence. Results. This study included 34 RCTs with 2,071 participants. Regarding global cognitive function improvement, the pairwise meta-analysis confirmed that electronic acupuncture (EA) plus conventional medicine (CM) and manual acupuncture (MA) plus CM were statistically significantly different from CM, and EA plus CM was ranked as the best combination in the network meta-analysis. In terms of response rate, MA outperformed CM statistically significantly; warm acupuncture (WA) was ranked as the best in the network meta-analysis. Regarding activity of daily living improvement, EA plus CM, MA plus CM, and fire acupuncture plus CM, MA, and scalp acupuncture were statistically significantly different from CM, and EA plus CM was ranked as the best combination in the network meta-analysis. However, the evidences were ranked as low to critically low. Conclusions. Acupuncture, as a monotherapy or an adjuvant therapy, may have a beneficial effect on efficacy for AD. EA plus CM may be the optimal acupuncture therapy for AD and should be administered to AD patients. It may aid and support patient, operative, and societal decision-making. Due to the dearth of high-quality evidence, additional high-quality studies should be conducted to ensure these findings in the future. This study is registered with PROSPERO (CRD42021252305).
Identification and Laboratory Fungicides Screening of the Pathogenic Fungus of Stem Spot of Pitaya (Hylocereus spp.) Stems
Authors: Luo Hui , Guo Kun , Shang Xueying , Peng Lei , Peng Zhijun , Jin Jilin , Wang Bin , Zhang Xingwu
Published: May 2022

In this study, the pathogenic fungus of stem spot disease of pitaya (Hylocereus spp.) stems was identified by isolation and purification, pathogenicity test, morphological characteristics, and analysis of rDNA-ITS sequences. The results turned out that the rDNA-ITS sequences of the H1 strain showed 100% of identity with Botryosphaeria dothidea, indicating that the pathogenic fungus of stem spot disease of pitaya stems was Botryosphaeria dothidea. Meanwhile, the H1 strain was then used as a reference strain to screen some commercial fungicides. The bioassay test results indicated that prochloraz had an obvious inhibitory effect on Botryosphaeria dothidea with the EC50 value of 0.0798 μg/mL. Our study could provide a theoretical basis for the effective control method of stem spot disease of pitaya.
Development of a Bounded Two-Stage Data Envelopment Analysis Model in the Intuitionistic Fuzzy Environment
Authors: Mohammadi Ardakani Shabnam , Babaei Meybodi Hamid , Sayyadi Tooranloo Hossein
Published: May 2022

Data Envelopment Analysis is a powerful tool for evaluating the efficiency of decision-making units for the purpose of ranking, comparing, and differentiating efficient and inefficient units. Classical Data Envelopment Analysis methods operate by measuring the efficiency of each DMU compared to similar units without considering their internal workings and structures, which make them unsuitable for cases where DMUs are multistaged processes with intermediate products or when inputs and outputs are ambiguous or nonconfigurable. In problems that involve uncertainty, intuitionistic fuzzy sets can offer a better representation and interpretation of information than classic sets. In this paper, the noncooperative network data envelopment analysis model of Liang et al. (2008), which is based on Stackelberg game theory and efficiency decomposition, is expanded using the concepts of best and worst relative returns Data Envelopment Analysis model of Azizi et al. (2013) into an interval efficiency estimation model with α-β cuts for two-stage DMUs with trapezoidal intuitionistic fuzzy data. Furthermore, the method of Yue (2011) is used to rank these DMUs in terms of their intuitionistic fuzzy interval efficiency. A numerical example is also provided to illustrate the application of the proposed bounded two-stage intuitionistic Data Envelopment Analysis model.
Two-Stream Retentive Long Short-Term Memory Network for Dense Action Anticipation
Authors: Zhao Fengda , Zhao Jiuhan , Li Xianshan , Zhang Yinghui , Guo Dingding , Chen Wenbai
Published: May 2022

Analyzing and understanding human actions in long-range videos has promising applications, such as video surveillance, automatic driving, and efficient human-computer interaction. Most researches focus on short-range videos that predict a single action in an ongoing video or forecast an action several seconds earlier before it occurs. In this work, a novel method is proposed to forecast a series of actions and their durations after observing a partial video. This method extracts features from both frame sequences and label sequences. A retentive memory module is introduced to richly extract features at salient time steps and pivotal channels. Extensive experiments are conducted on the Breakfast data set and 50 Salads data set. Compared to the state-of-the-art methods, the method achieves comparable performance in most cases.
[Retracted] Treatment of Pharma Effluent using Anaerobic Packed Bed Reactor
Authors: Vijayan D. S. , Mohan A. , Nivetha C. , Sivakumar Vidhyalakshmi , Devarajan Parthiban , Paulmakesh A. , Arvindan S.
Published: May 2022

The treatment of pharmaceutical effluent using an appropriate technology has become so important. Anaerobic packed bed reactor is an efficient method for pharmaceutical effluent treatment because of the high organic content present in it. In this study, a heavy‐polluted pharma effluent is treated using an anaerobic packed bed reactor. The performance of the anaerobic reactor was identified with respect to chemical oxygen demand (COD) removal, methane yield, and gas production. The results showed that COD was reduced from 73% to 60% for an organic loading rate (OLR) of 0.6036–1.7487 kg COD m−3·d−1. As the OLR increases, the removal efficiency of COD decreases gradually to around 52% for an OLR of 2.34 kg COD m−3·d−1.
Electro‐Magnetohydrodynamic Fractional‐Order Fluid Flow with New Similarity Transformations
Authors: Al-Duais Fuad S. , Asiri Saeed Ahmed , Saeed Anwar , Majdoubi Jihen , Alam Mohammad Mahtab , Gul Taza
Published: May 2022

The fractional‐order differential equations that exist in the field of science and engineering have been studied in this paper. The 2D fluid flow problems are recommended for the classical‐ and fractional‐order analysis. It has been found that the nonlinear fractional‐order problems are more realistic than the classical models to describe the proposed flow problems since the behavior of the stress of the model problem is not linear. The similarity variable in this study has been used in fractional form to transform the modeled governing equations from partial PDEs. The acquired equations are the nonlinear ordinary differential equations (ODEs) in fractional form. The electromagnetic field has also been imposed into the fluid motion to calculate the embedded constraints in the case of classical and noninteger orders. The nonlinear problems are attempted using the FDE‐12 technique. The important physical phenomena including Nusselt number and skin friction are also calculated in case of the integer‐ and noninteger‐order problems. The electric and magnetic fields are examined and discussed in the case of fractional form and classical form.
Dynamic Allocation Method of Economic Information Integrated Data Based on Deep Learning Algorithm
Authors: Cao Zhitao
Published: May 2022

In view of the low efficiency of traditional data fusion algorithms in wireless sensor networks and the difficulty in processing high-dimensional data, a new algorithm CNNMDA, based on the deep learning model is proposed to realize data fusion. Firstly, the algorithm trains the constructed feature extraction model CNNM at the sink node; then each terminal node extracts the original data features through CNNM and finally sends the fused data to the sink node, so as to reduce the data transmission amount and prolong the network life. Simulation experiments show that compared with similar fusion algorithms, the CNNMDA can greatly reduce network energy consumption under the same data amount, and effectively improve the efficiency and accuracy of data fusion. In order to solve the problem that parameter synchronization takes too long in synchronous parallel, a dynamic training data allocation algorithm in multimachine synchronous parallel is proposed. Based on the computing efficiency of compute nodes, the amount of sample data to be processed by nodes will be dynamically adjusted after each iteration. This mechanism not only enables the model to be synchronized and parallel but also reduces the time of waiting for gradient updates. Finally, a comparative experiment is carried out on the Tianhe-2 supercomputer, and the experimental results show that the proposed optimization mechanism achieves the expected effect.