Miracle-dz/UniFAR-FTUs huggingface dataset:
The construction and integration of grid model based on IEC61970/61968

CIM model is extended and adjusted based on IEC61970/61968 standards to adapt to the transmission and distribution business.The concept of CIM is introduced,and several network structures in power system and two corresponding topology abstract methods are described.Combined with the business characteristics of GIS,PMS,and DMS,this paper makes the difference analysis of different system network resources models,points out the integration direction and data range,and makes unified abstract and expression on substation device and transmission and distribution equipment.The specific exchange mode and mechanism are illustrated by case and data,which supplies technical ways for implementing network data integration.
Design and performance analysis of an ultra‐high‐speed 5‐2 compressor

A novel ultra‐high‐speed 5‐2 compressor is presented and discussed in this paper. The proposed structure is developed by modifying the truth table focusing on the carry rippling problem. By reducing the number of the states in the truth table and the complexity of the structure, the total delay is reduced while the power and active area are also improved. The proposed compressor is compared with the best recently reported ones where the designed architecture shows 9%–80% improvement in power delay product (PDP). For a better demonstration of the advantages, the proposed structure is placed in the body of a 16 × 16‐bit multiplier and is compared with the other multipliers consisting of the other 5‐2 compressor architectures. The results indicate an improvement equal to 13%. It must also be mentioned that the gate‐level delay calculation is emphasized in this paper, where a precise estimation method is utilized considering the capacitances at the middle‐stage nodes. Based on the calculations, the designed circuit has the minimum gate‐level delay compared to the other related works due to its minimal intermediate connections. For simulating the proposed structure, a standard CMOS 0.18‐μm process along with HSPICE software is used, where the results show a delay of 266.5 ps with a power consumption of 164 μw.
Analysis of Circulating Current for Direct Parallel Grid-connected Inverters

It is desirable to have a directly connected structure for grid-connected inverter because of compact size and low cost. The circulating current will be generated when grid-connected inverters are in direct parallel operation. So the mechanism of circulating current is analyzed in detail for grid-connected inverters in direct parallel operation,and the state average model of the parallel grid-connected inverters is constructed in rotating coordinates. Based on this model,interaction between the parallel grid-connected inverters is analyzed,and the influence on circulating current when it is adopted zero vector with space vector pulse width modulation (SVPWM) is also analyzed. The circulating current can be effectively inhibited by controlling the operation time for different zero vectors in each PWM cycle. The parallel grid-connected inverters are tested under different circumstances. The experimental results show that the control strategy can effectively inhibit circulating current,so the feasibility and correctness for the control strategy are verified.
Operational Strategy for Low-Carbon Supply Chain under Asymmetric Information of Fairness Concerns

Carbon emission reduction has become a common hot topic around the world. Although the previous literature has proven that the asymmetric information and fairness concerns would influence the operational strategy for low-carbon supply chain, it hardly touched the asymmetric information of fairness concerns, which contradicted practical observations and experimental evidence. Incorporating the asymmetric information of fairness concerns, this paper investigates a low-carbon supply chain consisting of a manufacturer and a retailer with discrete types including selfish S-type and fairness-concerned F-type. The manufacturer can observe and thereby know the behavioral type of the retailer in the scenario of symmetric information, while it cannot in the scenario of asymmetric information. In the approach of game theory, the optimal carbon emission reducing strategy and pricing strategy in the symmetric scenario and asymmetric scenario are achieved successively. By comparing the above two scenarios, the impacts stemming from the asymmetric information of fairness concerns at the individual level and systematic level are analyzed, respectively. A case study is offered before concluding some implications for the supply chain management. The findings include the following: Firstly, the asymmetric information of fairness concerns enhances the carbon emission reduction significantly. Although the fairness concerns alone decrease the carbon emission reduction, the asymmetric information increases with the dominating power. Secondly, the asymmetric information of fairness concerns raises the wholesale price and retail price dramatically. Although the impact of either fairness concerns or asymmetric information randomly changes with the behavioral type and information structure, their interactive impacts are stable and change smoothly. Thirdly, the asymmetric information of fairness concerns promotes a fairer profit distribution, while either fairness concerns or asymmetric information alone hardly changes the overall profit of the low-carbon supply chain.
Limited effect of wood ash application on soil quality as indicated by a multisite assessment of soil organic matter attributes

In Canada, the combustion of forest biomass for bioenergy production has been increasing with an associated increase in residual wood ash. Wood ash is typically landfilled as waste but there is growing interest in applying wood ash to the soils of commercial forests. Ideally, wood ash supplies nutrients that may have been removed through biomass harvesting, increases soil pH, which improves nutrient availability, and potentially improves site productivity, but there is also potential for detrimental effects, such as toxicity, that impair soil functions. The objective of this study was to investigate the effects of wood ash application on soil organic matter attributes at eight experimental sites across Canada that are examining the effects of wood ash application on site fertility, productivity, and soil biodiversity. Wood ash application had an effect on total carbon (TC) and total nitrogen, microbial biomass carbon (MBC), hot water extractable carbon (HWEC), mineralizable C, sand size C, and HWEC and MBC normalized to TC, but changes were typically restricted to single sites or differed in their direction, that is, positive or negative. Based on the limited and inconsistent effects of ash on established indictors of soil quality measured in this study, there does not appear to be any advantageous or detrimental effects of adding wood ash to forest soil quality.
Preoperative CT-guided Fiducial Marker Placement for Surgical Localization of Pulmonary Nodules.

Purpose To assess the technical success and complication rates of CT-guided fiducial marker placement for the localization of pulmonary nodules and to assess the surgical localization failure rate. Materials and Methods This was a single-center, retrospective analysis of consecutive patients who underwent CT-guided fiducial marker placement procedures between 2014 and 2020. End points included the technical success of the fiducial marker placement, procedural complications, and the surgical localization failure rate. A two-sample t test and a Fisher exact test were used to compare continuous and categorical variables, respectively. Multivariate logistic regression was used to identify independent risk factors for complications. Results A total of 198 preoperative CT-guided fiducial marker placement procedures were performed in 190 patients (mean age, 64 years ± 12 [standard deviation]; 121 women) to localize 205 nodules (mean size, 10 mm ± 4; mean distance to the pleura, 10 mm ± 9). The technical success rate was 98.5% (195 of 198). There were no major complications. A total of 202 nodules were resected during 193 procedures performed 5 days ± 13 after the fiducial marker placement (range, 0-123 days). Surgical localization failure occurred in one patient (0.5%). Of the resected nodules, 146 were lung cancers, 26 nodules were metastases, two were carcinoid tumors, and 28 were benign. Conclusion The CT-guided fiducial marker placement of pulmonary nodules was safe, effective, and resulted in a low surgical localization failure rate.Keywords: CT, Percutaneous, Thorax, Lung.
A Rigid Morphing Mechanism Enabled Earthworm-like Crawling Robot

Inspired by natural earthworms' locomotion mechanism, this paper investigates how the earthworm's muscle works and presents the approach to mimic segmental muscle by employing rigid elements-based morphing structures. Specifically, the proposed earthworm-like robot employs a class of 2D rigid elements and their array to achieve programmable bidirectional 3D deformation, making the formed mechanism precisely controllable and work effectively, thus facilitating the robot's peristaltic locomotion more efficient. To comprehensively investigate the morphing structure and it formed earthworm-like robot, the kinematics, mechanics, deformation-dependent locomotion framework with its adapted model, as well as the factors that affect the optimal velocity are developed and presented. Extensive simulations and experiments on the proposed robot are performed. The results verify the effectiveness of the morphing mechanism and it enabled earthworm-like robot, and the consistency between the proposed locomotion model and the practical tests. The results also prove that regardless of the condition of the contact surface, the optimal phase shift angle can be achieved when each segment approximately contract and relax once in one wavelength period. Our developed prototype achieves a speed of 4.57~9.23 body lengths per minute, which is very competitive compared with most developed earthworm-like robots.
Deep Cascade Residual Networks (DCRNs): Optimizing an Encoder–Decoder Convolutional Neural Network for Low-Dose CT Imaging

To suppress noise and artifacts caused by the reduced radiation exposure in low-dose computed tomography, several deep learning (DL)-based image restoration methods have been proposed over the past few years. Many of these popular DL-based methods adopt an encoder–decoder framework, for instance, the residual encoder–decoder convolutional neural network. However, this popular framework may suffer from information loss for continual downsampling operations. In this article, deep cascaded residual networks (DCRNs) are proposed to optimize the popular encoder–decoder network. First, cross up- and downsampling operations as well as attention extraction are substitutes for the strict “downsampling and then upping” principle. What is more, four hybrid loss functions, namely, mean absolute error, edge loss, perceptual loss and adversarial loss, are engaged to achieve better visual effects and suppress noise. The experiments are conducted on three individual clinical CT datasets: dental CT data collected with a scanner manufactured by Zhongke Tianyue Company (ZTC), data from the American Association of Physicists in Medicine (AAPM) Challenge, and data collected with a commercial CT scanner from United Imaging Healthcare (UIH). The experimental results indicate the effective noise reduction and detail preservation capabilities of the proposed methods under different radiation dose-reduction strategies.
Customized Screen-Printed Electrodes Based on Ag-Nanoseeds for Enhanced Electroanalytical Response towards Cd(II), Pb(II) and As(V) in Aqueous Samples

Electrochemical analysis based on screen-printed electrodes (SPEs) represents a great alternative to conventional analytical methods such as ICP-MS or LC-MS due to their portability, sensitivity, selectivity, and cost-effectiveness. In addition, the functionalization of SPEs with nanomaterials has been reported to provide an enhanced analytical performance. In this regard, silver nanoparticles (AgNPs) were synthesized and appropriately characterized, showing spherical silver nanoseeds (Ag-NS) with a diameter of 12.20 ± 0.04 nm. Using the drop-casting methodology, the synthesized AgNPs were used to modify screen-printed carbon nanofiber electrodes (SPCNFEs). Ag-NS deposition onto the electrode surface was confirmed by scanning electron microscopy (SEM). Furthermore, the analytical response of the modified electrodes (Ag-NS-SPCNFE) was evaluated for the determination of trace Pb(II), Cd(II), and As(V) using differential pulse anodic stripping voltammetry (DPASV), obtaining detection limits of 3.3, 3.7, and 2.6 µg L−1, for Pb(II), Cd(II) and As(V), respectively. Finally, Ag-NS-SPCNFE was tested towards the determination of As(V) in a spiked tap water sample, showing a good agreement with concentrations determined by ICP-MS.
Intelligent Ultrareliable and Low-Latency Communications: Flexibility and Adaptation

As one of the key communication scenarios, ultrareliable low-latency communication (uRLLC) has become an important pillar to promote the vigorous development of intelligent mobile communications. In the practical scenarios, uRLLC services have strict and diverse Quality-of-Service (QoS) requirements. However, the existing networks are difficult to meet the various delay and reliability requirements of uRLLC services. Moreover, the improvement of performance should not ignore the shortage of resources. A flexible and on-demand network solution is quite necessary, which could provide customized services according to the specific requirements and maximize the utilization efficiency of network resources. In this article, we propose an intelligent and flexible network solution (IFNS) based on the stochastic network calculus (SNC) model. Three key technologies are considered in the IFNS, that are flexible transmission time interval scheduling, flexible packet duplication transmission, and rate-adaptive reliable transmission. While providing customized services for users with various requirements, it realizes the balance between system energy efficiency and spectral efficiency and improves the resource utilization efficiency of the network. Based on the basic domain knowledge and the past experience, we propose the knowledge-assistance meta actor–critic (K-MAC) algorithm to solve the complex optimization problem caused by SNC modeling. Finally, simulation results show that the performance of the IFNS is improved 23.15%, the K-MAC algorithm has good convergence performance and reduces the complexity up to 89.4% compared with common learning algorithms.
Reputation Management in Times of the Pandemic - Empirical Analysis of Selected Health Care Providers Operating in the Slovak Market

The issue of corporate reputation management is one of the key areas in the process of building trust between the supply and demand side of the market. The services sector, which, as a rule, works predominantly with intangible products, is particularly vulnerable to reputational damage. The presented study aims to examine the impact of the COVID-19 pandemic on the overall level of reputation of eleven Slovak teaching hospitals. The data sources used in the form of publicly available databases cover both the traditional factors of the brick-and-mortar market, as well as the factors related to the virtual Internet environment. The results of the analysis point to the fact that while the traditional level of reputation of the entities reflected changes in the market only to a relatively limited extent, the level of the online reputation of the analysed entities decreased significantly due to the pandemic. This finding underscores the fact about the fragility of reputation in the Internet environment. By comparing individual reputational factors in the pre-pandemic period with the situation during the peak of the third wave of the pandemic, the study offers a relatively compact knowledge base for formulating basic transformations for comprehensive research into the impact of a pandemic on corporate competitiveness.
Physical and Mechanical Properties of Bulk-Fill, Conventional, and Flowable Resin Composites Stored Dry and Wet

Surface degradation, margin, and bulk fracture are common reasons that necessitate replacement of resin composite restorations. The purpose of this study was to determine filler weight (FW), fracture toughness (FT), Vickers hardness (VHN), sorption/solubility (S/S), and colour change (ΔE) of four resin composites in dry and wet conditions. Four resin composites of shade A2 were investigated: Aura bulk-fill (AB) (SDI), Tetric Evoceram (TE) (Ivoclar), G-ænial Universal Flo (GUF) (GC), and GC Kalore (GCK) (GC). For FT, VHN, and ΔE, the specimens were prepared, divided into 2 groups, and stored dry or immersed in distilled water. The specimens were subdivided into three subgroups and stored for 1, 7, and 60 days and then subjected to the relevant tests. Six fractured remnants were weighed for each material to measure FW%. To test S/S, ISO 4049 was used. The data were analysed using ANOVA and Tukey's test. There was an inverse correlation between FW and FT. A significantly higher FT was found for GUF. There were no significant differences between conditions in materials except for AB. The highest VHN was found for GCK and AB. After 1 and 7 days, a significant difference was observed in S/S between all materials with the highest values for GUF. There was a correlation between sorption and solubility. The material, the media, and aging have an influence on the properties of resin composites. It is important to emphasise that each material should be used for a specific clinical need based on their properties.
An Empirical Analysis of Requirements for Data Scientists Using Online Job Postings

A data scientist is a relatively new job title and is not yet fully defined or understood. Little research has been conducted on data scientists and there is still incongruity among its practitioners. Yet, the job market for data scientists is already active with high demand. Many companies have created their own definition of a data scientist based on their own needs. The purpose of this research is to explore the definition of a data scientist by examining how it is accepted in these industries and businesses. A content analysis of 1,240 job ads from various companies recruiting data scientists was conducted to identify what types of knowledge and skills were generally demanded. As a result, we found that data scientists were expected to be highly experienced professionals with advanced degrees. The main requisite areas of profession were statistics, modeling, machine learning, and analysis.
Knowledge Graph Quality Management: A Comprehensive Survey

As a powerful expression of human knowledge in a structural form, knowledge graph (KG) has drawn great attention from both the academia and the industry and a large number of construction and application technologies have been proposed. Large-scale knowledge graphs such as DBpedia, YAGO and Wikidata are published and widely used in various tasks. However, most of them are far from perfect and have many quality issues. For example, they may contain inaccurate or outdated entries and do not cover enough facts, which limits their credibility and further utility. Data quality has a long research history in the field of traditional relational data and recently attracts more knowledge graph experts. In this paper, we provide a systematic and comprehensive review of the quality management on knowledge graphs, covering overall research topics about not only quality issues, dimentions and metrics, but also quality management processes from quality assessment and error detection, to error correction and KG completion. We categorize existing works in terms of target goals and used methods for better understanding. In the end, we discuss some key issues and possible directions on knowledge graph quality management for further research.
A Generalized Regression Neural Network Model for Predicting the Curing Characteristics of Carbon Black-Filled Rubber Blends

In this study, a new generalized regression neural network model for predicting the curing characteristics of rubber blends with different contents of carbon black filler cured at various temperatures is proposed for the first time The carbon black contents in the rubber blend and cure temperature were used as input parameters, while the minimum and maximum elastic torque, scorch time, and optimal cure time, obtained from the analysis of 11 rheological cure curves registered at 10 various temperatures, were considered as output parameters of the model. A special pre-processing procedure of the experimental input and target data and the training algorithm is described. Less than 55% of the experimental data were used to significantly reduce the total number of input and target data points needed for training the model. Satisfactory agreement between the predicted and experimental data, with a maximum error in the prediction not exceeding 5%, was found. It is concluded that the generalized regression neural network is a powerful tool for intelligently modelling the curing process of rubber blends even in the case of a small dataset, and it can find a wide range of practical applications in the rubber industry.
Dynamic Shake-Table Testing and Analytical Investigation of Self-Centering Steel Plate Shear Walls

AbstractRecent research has shown that self-centering steel plate shear walls (SC-SPSWs) offer an enhanced seismic performance over conventional steel plate shear walls by providing an additional self-centering capability using steel frames detailed with posttensioned (PT) beam-to-column rocking connections. As with other previously proposed self-centering frames detailed with similar beam-to-column connections, this detailing facilitates the incorporation of replaceable energy dissipation components, as part of the lateral force resisting system (LFRS). In doing so, by design, the gravity frame components of the LFRS are also protected from damage during an earthquake. To investigate the dynamic seismic response of this proposed structural system, one-third scaled SC-SPSW specimens were subjected to ground motions during a series of dynamic shake-table tests. The experimental investigation results presented in this paper are the first shake-table tests conducted on SC-SPSWs. This test program was compose.
Streaming Graph Embeddings via Incremental Neighborhood Sketching

Graph embeddings have become a key paradigm to learn node representations and facilitate downstream graph analysis tasks. Many real-world scenarios such as online social networks and communication networks involve streaming graphs, where edges connecting nodes are continuously received in a streaming manner, making the underlying graph structures evolve over time. Such a streaming graph raises great challenges for graph embedding techniques not only in capturing the structural dynamics of the graph, but also in efficiently accommodating high-speed edge streams. Against this background, we propose SGSketch, a highly-efficient streaming graph embedding technique via incremental neighborhood sketching. SGSketch cannot only generate high-quality node embeddings from a streaming graph by gradually forgetting outdated streaming edges, but also efficiently update the generated node embeddings via an incremental embedding updating mechanism. Our extensive evaluation compares SGSketch against a sizable collection of state-of-the-art techniques using both synthetic and real-world streaming graphs. The results show that SGSketch achieves superior performance on different graph analysis tasks, showing 31.9% and 21.9% improvement on average over the best-performing static and dynamic graph embedding baselines, respectively. Moreover, SGSketch is significantly more efficient in both embedding learning and incremental embedding updating processes, showing 54x-1813x and 118x-1955x speedup over the baseline techniques, respectively.