Miracle-dz/UniFAR-FTUs huggingface dataset:
Improved Functional Outcome After Peripheral Nerve Stimulation of the Impaired Forelimb Post-stroke

Lack of blood flow to the brain, i.e., ischemic stroke, results in loss of nerve cells and therefore loss of function in the effected brain regions. There is no effective treatment to improve lost function except restoring blood flow within the first several hours. Rehabilitation strategies are widely used with limited success. The purpose of this study was to examine the effect of electrical stimulation on the impaired upper extremity to improve functional recovery after stroke. We developed a rodent model using an electrode cuff implant onto a single peripheral nerve (median nerve) of the paretic forelimb and applied daily electrical stimulation. The skilled forelimb reaching test was used to evaluate functional outcome after stroke and electrical stimulation. Anterograde axonal tracing from layer V pyramidal neurons with biotinylated dextran amine was done to evaluate the formation of new neuronal connections from the contralesional cortex to the deafferented spinal cord. Rats receiving electrical stimulation on the median nerve showed significant improvement in the skilled forelimb reaching test in comparison with stroke only and stroke with sham stimulation. Rats that received electrical stimulation also exhibited significant improvement in the latency to initiate adhesive removal from the impaired forelimb, indicating better sensory recovery. Furthermore, axonal tracing analysis showed a significant higher midline fiber crossing index in the cervical spinal cord of rats receiving electrical stimulation. Our results indicate that direct peripheral nerve stimulation leads to improved sensorimotor recovery in the stroke-impaired forelimb, and may be a useful approach to improve post-stroke deficits in human patients.
Review: Influences of Semiconductor Metal Oxide Properties on Gas Sensing Characteristics

Semiconductor metal oxides (SMOxs) are widely used in gas sensors due to their excellent sensing properties, abundance, and ease of manufacture. The best examples of these sensing materials are SnO2 and TiO2 that have wide band gap and offer unique set of functional properties; the most important of which are electrical conductivity and high surface reactivity. There has been a constant development of SMOx sensor materials in the literature that has been accompanied by the improvement of their gas-sensitive properties for the gas detection. This review is dedicated to compiling of these efforts in order to mark the achievements in this area. The main material-specific aspects that strongly affect the gas sensing properties and can be controlled by the synthesis method are morphology/nanostructuring and dopants to vary crystallographic structure of MOx sensing material.
The Relationship Between Nutrition and Atherosclerosis

Atherosclerosis is the basic pathological process of many diseases, such as coronary atherosclerosis and stroke. Nutrients can affect the occurrence and development of atherosclerosis. At present, in nutrition science, the research on atherosclerosis focuses on which nutrients play an important role in its prevention strategy, and what are the possible mechanisms of its action. In the current study, the process of atherosclerosis can be affected by adjusting the proportion of nutrients in the diet. In this review, we pay attention to the effects of phytosterols, omega-3-polyunsaturated fatty acids, polyphenol, vitamin, and other nutrients on atherosclerosis, pay attention to their current epidemiological status, current nutritional research results, and prevention or a possible mechanism to reduce the risk of development of atherosclerosis. So that more personalized nutritional approaches may be more effective in terms of nutritional intervention responses to atherosclerosis.
How Traditional Industries Use Capabilities and Routines to Tap Users for Product Innovation

Overview: While customer-centered innovation has thus far focused on best practices for user-producer collaboration and organizing users to obtain relevant inputs, the internal organization that enables firms to integrate user knowledge into product innovation outputs is less well understood. We analyzed five case studies to derive the innovation routines from firms in traditional industries that employ user knowledge to improve existing products or develop new products. The routines are linked to organizational capabilities relevant for incorporating user knowledge into innovative outcomes.
Delineation of cities based on scaling properties of urban patterns: a comparison of three methods

ABSTRACT Identifying urban boundaries involves analysing both the functional and the morphological aspects of urban systems. In this paper, we adopt a purely morphological approach and compare three methods for the morphological delineation of cities. Each method avoids using any predefined quantified threshold (size, distance, built density, etc.) to detect crucial discontinuities in space. The first method identifies Natural Cities by clustering points. The other two are the fractal-based MorphoLim method and the Hierarchical Percolation; both involve transforming the data using a step-by-step dilation process. The three methods are critically compared and illustrated by applications to theoretical urban patterns. We further apply each method to the urban agglomeration of Brussels, the Belgian capital, using different data (building footprints, building centroids and street nodes) and considering two study areas, namely the former province of Brabant and the entire country of Belgium. The results show that it is impossible to draw an unambiguous morphological boundary for an urban agglomeration. Consequently, it is crucial to relate the data used, the size of the study area and the method chosen to the objectives of the delineation.
Branch-and-Reduction Algorithm for Indefinite Quadratic Programming Problem

This paper presents a rectangular branch-and-reduction algorithm for globally solving indefinite quadratic programming problem (IQPP), which has a wide application in engineering design and optimization. In this algorithm, first of all, we convert the IQPP into an equivalent bilinear optimization problem (EBOP). Next, a novel linearizing technique is presented for deriving the linear relaxation programs problem (LRPP) of the EBOP, which can be used to obtain the lower bound of the global optimal value to the EBOP. To obtain a global optimal solution of the EBOP, the main computational task of the proposed algorithm involves the solutions of a sequence of LRPP. Moreover, the global convergent property of the algorithm is proved, and numerical experiments demonstrate the higher computational performance of the algorithm.
Designing of a Simulation Tool for the Performance Analysis of Hybrid Data Center Networks (DCNs)

Data center (DC) technology changes the mode of computing. Traditional DCs consist of a single layer and only have Ethernet connections among switches. Those old-fashioned DCs cannot fulfill the high resource demand compared with today’s DCs. The architectural design of the DCs is getting substantial importance and acting as the backbone of the network because of its essential feature of supporting and maintaining the rapidly increasing Internet-based applications which include search engines (e.g., Google and Yandex) and social networking applications (e.g., YouTube, Twitter, and Facebook). Every application has its parameters, like latency and blocking in the DC network. Every data center network (DCN) has its specialized architecture. It has a specific arrangement of layers and switches, which increase or decrease the DC network’s efficiency. We develop a simulation tool that comprises two different DC architectures: basic tree architecture and c-Through architecture. Using this simulation, we analyze the traffic behavior and the performance of the simulated DCN. Our main purpose is to focus on mean waiting time, load, and blocking with respect to the traffic within the DCN.
Identification of Molecular Regulatory Features and Markers for Acute Type A Aortic Dissection

Background Acute type A aortic dissection (ATAAD) is one of the most lethal cardiovascular diseases, and its molecular mechanism remains unclear. Methods Differentially expressed genes (DEGs) between ATAAD and control were detected by limma R package in GSE52093, GSE153434, GSE98770, and GSE84827, respectively. The coexpression network of DEGs was identified by the WGCNA package. Enrichment analysis was performed for module genes that were positively correlated with ATAAD using clusterProfiler R package. In addition, differentially methylated markers between aortic dissection and control were identified by ChAMP package. After comparing with ATAAD-related genes, a protein-protein interaction (PPI) network was established based on the STRING database. The genes with the highest connectivity were identified as hub genes. Finally, differential immune cell infiltration between ATAAD and control was identified by ssGSEA. Results From GSE52093 and GSE153434, 268 module genes were obtained with consistent direction of differential expression and high correlation with ATAAD. They were significantly enriched in T cell activation, HIF-1 signaling pathway, and cell cycle. In addition, 2060 differentially methylated markers were obtained from GSE84827. Among them, 77 methylation markers were ATAAD-related DEGs. Using the PPI network, we identified MYC, ITGA2, RND3, BCL2, and PHLPP2 as hub genes. Finally, we identified significantly differentially infiltrated immune cells in ATAAD. Conclusion The hub genes we identified may be regulated by methylation and participate in the development of ATAAD through immune inflammation and oxidative stress response. The findings may provide new insights into the molecular mechanisms and therapeutic targets for ATAAD.
A Tetravalent Nanocluster

The bottom-up assembly of nanoparticles into diverse ordered solids is a challenge because it requires nanoparticles, which are often quasi-spherical, to have interaction anisotropy akin to atoms and molecules. Typically, anisotropy has been introduced by changing the shape of the inorganic nanoparticle core. Here, we 10 present the design, self-assembly, optical properties and total structural determination of Ag29(BDT)12(TPP)4, an atomically precise tetravalent nanocluster (NC) (BDT: 1,3-benzenedithiols; TPP: triphenylphosphine). It features four unique tetrahedrally symmetrical binding surface sites facilitated by the supramolecular assembly of 12 BDT—wide footprint bidentate thiols—in the ligand shell. When each of these sites was selectively functionalized by a single phosphine ligand, particle stability, synthetic yield and the propensity to self-assemble into 20 macroscopic crystals increased. The solid crystallized NCs have a substantially narrowed optical bandgap compared to that of the solution state, suggesting strong inter-particle electronic coupling occurs in the solid state.
Analysis and Design of the Battery Initial Energy Level with Task Scheduling for Energy-Harvesting Embedded Systems

When the energy-harvesting embedded system (EHES) is running, its available energy (harvesting energy and battery storage energy) seems to be sufficient overall. However, in the process of EHES task execution, an energy shortage may occur in the busy period such that system tasks cannot be scheduled. We call this issue the energy deception (ED) of the EHES. Aiming to address the ED issue, we design an appropriate initial energy level of the battery. In this paper, we propose three algorithms to judge the feasibility of the task set and calculate the appropriate initial energy level of the battery. The holistic energy evaluation (HEE) algorithm makes a preliminary judgment of the task set feasibility according to available energy and consumption energy. A worst-case response time-based initial energy level of the battery (WCRT-IELB) algorithm and an accurate cycle-initial energy level of the battery (AC-IELB) algorithm can calculate the proper initial battery capacity. We use the YARTISS tool to simulate the above three algorithms. We conducted 250 experiments on As Late As Possible (ALAP) and As Soon As Possible (ASAP) scheduling with the maximum battery capacities of 50, 100, 200, 300, and 400. The experimental results show that setting a reasonable initial energy level of the battery can effectively improve the feasibility of the task set. Among the 250 task sets, the HEE algorithm filtered 2.8% of them as infeasible task sets. When the battery capacity is set to 400, the WCRT-BIEL algorithm increases the success rates of the ALAP and ASAP by 17.2% and 26.8%, respectively. The AC-BIEL algorithm increases the success rates of the ALAP and ASAP by 18% and 26.8%, respectively.
Time-Frequency Analysis and Target Recognition of HRRP Based on CN-LSGAN, STFT, and CNN

Aiming at the problem of radar target recognition of High-Resolution Range Profile (HRRP) under low signal-to-noise ratio conditions, a recognition method based on the Constrained Naive Least-Squares Generative Adversarial Network (CN-LSGAN), Short-time Fourier Transform (STFT), and Convolutional Neural Network (CNN) is proposed. Combining the Least-Squares Generative Adversarial Network (LSGAN) with the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP), the CN-LSGAN is presented and applied to the HRRP denoise. The frequency domain and phase features of HRRP are gained by STFT in order to facilitate feature learning and also match the input data format of the CNN. These experimental results show that the CN-LSGAN has better data augmentation performance and can effectively avoid the model collapse compared to the generative adversarial network (GAN) and LSGAN. Also, the method has better recognition performance than the one-dimensional CNN method and the Long Short-Term Memory (LSTM) network method.
A Spatial-Temporal Self-Attention Network (STSAN) for Location Prediction

With the popularity of location-based social networks, location prediction has become an important task and has gained significant attention in recent years. However, how to use massive trajectory data and spatial-temporal context information effectively to mine the user’s mobility pattern and predict the users’ next location is still unresolved. In this paper, we propose a novel network named STSAN (spatial-temporal self-attention network), which can integrate spatial-temporal information with the self-attention for location prediction. In STSAN, we design a trajectory attention module to learn users’ dynamic trajectory representation, which includes three modules: location attention, which captures the location sequential transitions with self-attention; spatial attention, which captures user’s preference for geographic location; and temporal attention, which captures the user temporal activity preference. Finally, extensive experiments on four real-world check-ins datasets are designed to verify the effectiveness of our proposed method. Experimental results show that spatial-temporal information can effectively improve the performance of the model. Our method STSAN gains about 39.8% Acc@1 and 4.4% APR improvements against the strongest baseline on New York City dataset.
Rebate Strategy Selection and Channel Coordination of Competing Two-Echelon Supply Chains

Rebate has long been a crucial tool that has attracted researchers from a diverse range of fields including marketing and supply chain management. When a manufacturer uses a retailer for reaching end customers, the rebate strategy undertakes an additional dimension. Here we showwhether the two rebate strategies, manufacturer rebate and channel rebate, can be the optimal choice for the manufacturer and the retailer. And we aim at full coordination with rebate. Game theory is exploited to identify the equilibrium rebate decisions, which are fully characterized with two rebate strategies considering rebate sensitivity. Furthermore, we demonstrate how the decisions depend on parameters, such as market size, rebate redemption rate, and competition intensity in monopoly and duopoly supply chain systems. Our work also coordinates the supply chain with two coordination policies and examines if they can achieve full coordination. Counterintuitive findings suggest that the channel rebate with sensitivity and discrimination is not effective and the manufacturer rebate is the unique optimal option. Besides, the coordination can be realized with a centralized rebate in monopoly setting when the manufacturer forgoes her own interest. *en full coordination can be achieved in duopoly setting with a new coordination policy, rebate combination, given the redemption rate for the channel rebate is lower compared with the manufacturer rebate. Managerial insights are suggested that offering rebates with discrimination can have significant inventory and coordination policy implications and can lead to a double win under a well-controlled redemption rate.
Additive Tridiagonal Codes over F4

In this paper, we introduce a additive Tridiagonal and Double-Tridiagonal codes over $\mathbb{F}_4$ and then we study the properties of the code. Also, we find the number of additive Tridiagonal codes over $\mathbb{F}_4. $ Finally, we study the applications of Double-Tridiagonal codes to secret sharing scheme based on matrix projection.
Load Frequency Control for Power System using Generalized Extended State Observer

This study investigates load frequency control based generalized extended state observer (GESO) for interconnected power systems subject to multi-kind of the power plant. First, the mathematical model of the interconnected power system is proposed based on the dynamic model of thermal power plant with reheat turbine and hydropower plant. Second, the GESO is designed to estimate the system states and disturbances. In addition, the problem of unmeasurable system states in the interconnected power network due to lack of sensor has been solved by using the proposed load frequency control based GESO. The numerical experiments are carried out by using MATLAB/ SIMULINK simulation. The simulation results point out that the proposed control approach has the capacity to handle the uncertainties and disturbances in the interconnected power system with better transient performances in comparison with the existing control approach. The relevant dynamic models have already been used for the simulation of the physical constraints of the governor dead band (GDB) and generation rate constraint (GRC) effect in the power plants. It is evident that the robustness of the suggested controller in terms of stability and effectiveness of the system. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium provided the original work is properly cited.
High-Order Sliding Mode Control for Networked Control System with Dynamic Noncooperative Game Scheduling

Specific to the NCSs where sensor signals can be processed centrally, a collaborative design scheme of dynamic game scheduling and advanced control theory was proposed in the present study. Firstly, by using the Jordan standard state space equation of the research object, the three elements of state noncooperative game were built, and the existence and uniqueness of Nash equilibrium solution were verified. In addition, the iterative equation of the scheduling matrix was derived by complying with the designed utility function. Secondly, refer to the number of restricted states the order of sliding mode was determined. And based on it, the corresponding sliding surface was designed. Subsequently, the quadratic optimization theory was adopted to regulate the control value following the implementation of the scheduling strategy to ensure that the control quality was further enhanced in the limited network service. Lastly, a TrueTime simulation example is established to verify the effectiveness of the proposed scheme.
Basin of Attraction Analysis of New Memristor-Based Fractional-Order Chaotic System

Memristor is the fourth basic electronic element discovered in addition to resistor, capacitor, and inductor. It is a nonlinear gadget with memory features which can be used for realizing chaotic, memory, neural network, and other similar circuits and systems. In this paper, a novel memristor-based fractional-order chaotic system is presented, and this chaotic system is taken as an example to analyze its dynamic characteristics. First, we used Adomian algorithm to solve the proposed fractional-order chaotic system and yield a chaotic phase diagram. Then, we examined the Lyapunov exponent spectrum, bifurcation, SE complexity, and basin of attraction of this system. We used the resulting Lyapunov exponent to describe the state of the basin of attraction of this fractional-order chaotic system. As the local minimum point of Lyapunov exponential function is the stable point in phase space, when this stable point in phase space comes into the lowest region of the basin of attraction, the solution of the chaotic system is yielded. In the analysis, we yielded the solution of the system equation with the same method used to solve the local minimum of Lyapunov exponential function. Our system analysis also revealed the multistability of this system.
Cardiac Disorder Classification by Electrocardiogram Sensing Using Deep Neural Network

Cardiac disease is the leading cause of death worldwide. Cardiovascular diseases can be prevented if an effective diagnostic is made at the initial stages. The ECG test is referred to as the diagnostic assistant tool for screening of cardiac disorder. The research purposes of a cardiac disorder detection system from 12-lead-based ECG Images. The healthcare institutes used various ECG equipment that present results in nonuniform formats of ECG images. The research study proposes a generalized methodology to process all formats of ECG. Single Shoot Detection (SSD) MobileNet v2-based Deep Neural Network architecture was used to detect cardiovascular disease detection. The study focused on detecting the four major cardiac abnormalities (i.e., myocardial infarction, abnormal heartbeat, previous history of MI, and normal class) with 98% accuracy results were calculated. The work is relatively rare based on their dataset; a collection of 11,148 standard 12-lead-based ECG images used in this study were manually collected from health care institutes and annotated by the domain experts. The study achieved high accuracy results to differentiate and detect four major cardiac abnormalities. Several cardiologists manually verified the proposed system’s accuracy result and recommended that the proposed system can be used to screen for a cardiac disorder.
KB Construction and Hypothesis Generation Using SAMSON

This document describes SAMSON, Raytheon BBN and partners’ system used in the KB Construction and Hypothesis Generation tasks of the 2019 TAC SM-KBP competition. For KB Construction, SAMSON performs entity linking and disambiguation using vector embeddings of the KB. For Hypothesis Generation, SAMSON uses two diﬀerent methods of relaxed querying. The ﬁrst method constructs spaces of hierarchical clusters over various classes in the KB. We then translate both the KB and the query to cluster-level semantics and use a Prolog meta-interpreter to run the query. The second method uses local graph search to ﬁnd KB nodes similar to those requested in the query.