Miracle-dz/UniFAR-FTUs huggingface dataset:
A Field Enhancement Integration Design Featuring Misalignment Tolerance for Wireless EV Charging Using LCL Topology

This article proposes a magnetic integration design for EV wireless power transfer (WPT) systems, where the compensation and transmitting coils overlap one on top of the other to share the ferrite layer without any decoupling consideration. The magnetic field generated by both the compensation and transmitting coils are exploited to transfer power. To this end, a compensation method is proposed to enable magnetic field enhancement without any reactive power flow between the compensation and the transmitting coils, and to achieve input zero phase angle and constant current output. In addition, an efficient finite element analysis-based coil optimization algorithm is proposed to improve the coil misalignment tolerance on the horizontal plane, in which a reversely connected inner coil is used to stabilize the system output under misalignment conditions. Analytical and simulation results confirm transmission flux density enhancement and reduced leakage field characteristics of the proposed coil design. Finally, a scaled-down WPT prototype is built and tested to verify the performance and effectiveness of the design. The proposed design achieves 91.17% efficiency and misalignment tolerance up to 200 mm in any XY-direction while maintaining the power transfer and its efficiency. The results of this work provide insights into magnetic integration design of high-order compensation topologies featuring higher compactness, less ferrite usage, magnetic field enhancement, and misalignment tolerance for WPT systems.
Multimodal Spatiotemporal Representation for Automatic Depression Level Detection

Physiological studies have shown that there are some differences in speech and facial activities between depressive and healthy individuals. Based on this fact, we propose a novel spatio-temporal attention (STA) network and a multimodal attention feature fusion (MAFF) strategy to obtain the multimodal representation of depression cues for predicting the individual depression level. Specifically, we first divide the speech amplitude spectrum/video into fixed-length segments and input these segments into the STA network, which not only integrates the spatial and temporal information through attention mechanism, but also emphasizes the audio/video frames related to depression detection. The audio/video segment-level feature is obtained from the output of the last full connection layer of the STA network. Second, this article employs the eigen evolution pooling method to summarize the changes of each dimension of the audio/video segment-level features to aggregate them into the audio/video level feature. Third, the multimodal representation with modal complementary information is generated using the MAFF and inputs into the support vector regression predictor for estimating depression severity. Experimental results on the AVEC2013 and AVEC2014 depression databases illustrate the effectiveness of our method.
A Nonconvex Archetypal Analysis for One-Class Classification Based Anomaly Detection in Cyber-Physical Systems

Data-driven anomaly detection is one of the central issues for the implementation of predictive maintenance in cyber–physical systems (CPS). The increasing nonstationary dynamics in CPS lead to complex shapes of collected process data, e.g., convex and nonconvex. Algorithms with the ability of handling nonconvex data are desired for anomaly detection tasks in CPS. Archetypal analysis selects extreme points (archetypes) to represent a dataset—mainly to improve run times, e.g., for anomaly detection. The classic archetypal analysis uses convex combinations of archetypes to represent a set of observations (data points). This leads to the performance depression of the classic archetypal analysis methods for anomaly detection tasks on nonconvex datasets. Such nonconvex sets are typical for CPS. In this article, the anomaly detection tasks are considered as one-class classification problem due to the lack of abnormal samples. A novel nonconvex archetypal one-class classification algorithm is proposed to address the challenge of nonconvex data, which combines the random projection and the AdaBoost algorithm. The major advantages of this method are its high efficiency, flexibility, and its ability to handle both convex and nonconvex datasets, i.e., it can be applied to CPS analysis tasks. The usefulness of the presented approach is evaluated for fault diagnosis tasks in CPS.
Hybrid AC/DC Network With Parallel LCC-VSC Interlinking Converters

This paper presents a unified control strategy for parallel LCC-VSCs interlinking converters (IC)s in hybrid AC/DC grids. The parallel LCC-VSCs configuration takes advantage of the low cost of the LCC system as well as the bidirectional power flow capability of VSCs and offers an excellent option for lowering system costs with high performance. Moreover, drawbacks of the LCCs such as low power quality, reactive power consumption, and commutation failure can be addressed by using the parallel LCC-VSCs configuration with proper control and coordination. The proposed unified control system is developed by modifying the LCC's V-I graph to create a droop equivalent along with droop-based control of the VSCs. As a result, it provides voltage/frequency support for the AC and voltage support for the DC sub-grids in various operating conditions. In this paper, the performance of the proposed control strategy under different operation conditions is investigated. Case study results, with a realtime simulator, are provided to verify the effectiveness of the proposed control strategy.
A Phenomenological Study of the Place of Joining Erasmus+ Program in Pre-Service English Teachers' Professional Development.

This phenomenological research aims to explore the place of the Erasmus+ program (EP) in enhancing pre-service English teachers’ (PSETs) professional development. Additionally, this study targets learning about their experiences in the EP and their recommendations on how the program could be ameliorated to better serve prospective PSET participants. The data collected through in-depth interviewing were analysed adopting an inductive approach. The findings revealed that the PSETs believed participating in the EP helped them to develop their speaking skills and broaden their extant knowledge of different cultures, yet they overwhelmingly pointed out that since some courses they took at the host universities were not directly related to the field of English language teaching, the EP did not significantly contribute to their professional development. The findings also demonstrated PSETs’ suggestions concerning how to improve the EP to alleviate the problems they confronted for other PSETs whose future plans contain participating in the program.
APPLICATION OF MACHINE LEARNING TECHNIQUES IN DATA CENTRE ENERGY MANAGEMENT

Cloud computing is one of the leading computing paradigms that offers services like Infrastructure as a Service called IaaS, Platform as a Service called PaaS, Software as a Service called SaaS to users on a pay per use model. The massive data centers that help cloud offer all the above stated services are virtualized. Virtualization enables easy management of resources. However, the massive physical servers in the data centers tend to consume enormous energy, leading to high environmental impact. So, energy conservation with optimum usage and management is one of the prominent areas of research in cloud. The major techniques to manage energy is to identify unused physical resources and put them to low power state or sleep state. But, the usage of resources depends heavily on the user requirements in an elastic environment like cloud. Hence machine learning techniques can be used to predict the usage patterns thereby identifying the physical resources required to fulfill the user demand. This paper aims to survey the avenues wherein machine learning can be applied to help energy management in a cloud data center.
DROPS: Dynamic Radio Protocol Selection for Energy-Constrained Wearable IoT Healthcare

We propose “DROPS”, a scheme which dynamically selects radio protocols in an energy-constrained wearable IoT healthcare system. We consider the use of multiple radio protocols, which are capable of transmitting a patient’s sensed physiological parameters to the server through Local Processing Units (LPUs). As the health parameters are non-stationary and temporally fluctuating, especially for critical patients, the selection of an appropriate radio protocol is essential to maintain the accuracy and timely delivery of data from the patient to the server. Additionally, the mobility of patients through various locations within the hospital mandates the selection of the best radio protocol among the multiple available ones for each location, to enable data to offload to the remote server. We use single-leader-multiple-follower Stackelberg non-cooperative game to map the strategic interactions between a patient’s LPU and the hospital’s server. “DROPS” dynamically selects the appropriate radio protocol, based on the criticality index of a patient, the reputation of the radio, the Euclidean distance between the radios and the LPU, and the load on the protocol. Results on real-life data and their large-scale emulation show that the data rate increases by almost 78% and throughput by approximately 7%, as compared to existing schemes.
Produção e germinação de sementes de Paspalum notatum : resposta à fertilização nitrogenada e manejo de cortes

The present work was developed at the INIA La Estanzuela Experimental Station, in Colonia del Sacramento, Uruguay, from November 2015 to March 2017, with the objective of evaluating the parameters related to the production and quality of seeds of the specie Paspalum notatum INIA Sepé. We aimed understanding of the components of the seed yield under different levels of nitrogen fertilization and cutting management. A completely randomized block design with four replications was used. The treatments consisted of twelve factorial combinations of nitrogen fertilization doses (0, 75, 150 and 225 kg of N.ha) and three cutting times: early cut, late cut and no cut, totaling a factorial arrangement 4x3. The standard for cutting was the fraction of the photosynthetically active radiation intercepted by the plants (50% for early cutting and 80% for late cutting). For each year of study, different locations were used within the same experimental station in order to evaluate the seed production of plants with similar age (3 years old), which were named environment 2016 and environment 2017, wherein in the environment 2016 occurred greater soil water deficit. The application of N promoted the increase in forage mass, crude protein content, nitrogen nutrition index and the number of tillers. The maximum seed yield reached was close to 1000 kg.ha, yield reached with 150 kg of N.ha in the environment 2016 and at dose 75 in the environment 2017. Such productivity was obtained by performing two harvests annually, which was possible by the use of early cutting and uncut treatments. However, with late cutting was possible to perform only one annual harvest, which resulted in lower total seed yield, but with a higher yield in only one harvest. Regarding to the seed germination, we observed that in situations of water deficit, plants with N supply compromise their seed quality in detriment of vegetative growth in the case of early harvests. On the other hand, the germination was improved when N was associated with late cut. In this way, we concluded that Paspalum notatum INIA Sepé ecotype has a good seed yield, which is positively influenced by nitrogen fertilization and early cut application.
Habitat-mediated effects of diurnal and seasonal migration strategies on juvenile salmon survival

Behavioral decisions during periods of vulnerability to predation risk, such as migrations during the juvenile life-history stage, may strongly affect the probability of survival. Habitats through which animals migrate are heterogeneous, and risk-reducing behaviors may be more important in some habitats than others. Using biotelemetry data, diurnal and seasonal riverine migration patterns of >3800 juvenile salmon across 4 species, 12 watersheds, and 5 years were quantified to evaluate possible effects of migration timing on survival from lower river reaches to coastal waters. In small, clear rivers most salmon avoided migrating during daylight hours and average survival of fish migrating at night (55%; 95% confidence limits 50–61%) was twice that of fish migrating in daylight (24%; CL 17–31%). Conversely, in the large, heavily silted Fraser River neither preference for nocturnal travel nor effects of diurnal timing on survival were observed. Early ocean survival was also influenced by the timing of ocean entry, but in opposite directions for fish from the Fraser River and smaller rivers. In the Fraser River, average survival for later migrants (69%; CL 60–77%) was nearly twice that of earlier migrants (38%; CL 33–44%), likely related to seasonal increases in river flow. In contrast, in smaller rivers, average survival for earlier migrants (70%; CL 65–74%) was 3-fold greater than survival for later migrants (19%; 95% CL 14–25%). Together, these results demonstrate that timing decisions affecting survival of juvenile salmon during their migration are likely mediated by landscape characteristics that plausibly influence the risk of predation.
Benefits of Strategically Sizing Wind-Integrated Energy Storage and Transmission

We examine the behavior of a strategic firm that invests-in and operates wind, energy storage, and transmission. The capacity of the energy storage and transmission are co-optimized with the firm's wind-supply and energy-storage offers into a centrally dispatched electricity market. We employ a bi-level stochastic optimization model. The upper level determines the capacities and offering strategies to maximize the firm's expected profits. Multiple lower-level problems represent market clearing under different operating conditions, which capture uncertainties. The resulting large-scale optimization model is solved using multi-cut Benders's decomposition. The model is applied to a case study that is based on Alberta's electricity market.
Spatial variability of soil physical properties as a result of different tillage systems

Geostatistical tools allow soil physical properties to be monitored in order to describe the pattern of spatial variability and indicates the places with higher variation. Therefore, the purpose of this study is to assess the spatial variability of physical properties of soils under direct seeding and conventional tillage, using a native forest soil profile as reference. The areas where the samples were collected belong to the Federal University of Viçosa in the municipality of Viçosa in the state of Minas Gerais (Brazil). The studied soil physical properties were density, hydraulic conductivity, total porosity and moisture. The GS+ was used in order to do spatial distribution analysis and semivariograms. The spatial dependence index was fixed by the parameters based on the relative size of the nugget effect and sill. It was considered as strong spatial dependence when ≤ 25%, moderate spatial dependence between 26% and 74% and weak spatial dependence ≥ 75%. The range achieved in this study was higher than the distances between the sample points, proving the spatial continuity of the studied parameters. The forest area presented the lowest value of moisture and the highest value of hydraulic conductivity. Direct seeding area presented the highest density value and the lowest hydraulic conductivity when compared to the other two areas. Conventional tillage area had the highest total porosity in comparison to the other areas due to greater soil inversion. The spatial dependence indexes were classified as moderate for the variables soil density, total porosity and saturated hydraulic conductivity.
Path-Guided Containment Maneuvering of Mobile Robots: Theory and Experiments

This article investigates the path-guided containment maneuvering of networked two-wheeled mobile robots with multiple virtual leaders moving along multiple parameterized paths. A two-level cooperative control architecture is proposed to achieve a containment formation. At the kinematic level, distributed guidance laws and path update laws are developed for mobile robots and virtual leaders, respectively. Containment maneuvering errors are involved in the path update laws design to enforce the coordination between mobile robots and virtual leaders. At the kinetic level, two extended state observers are utilized to estimate the unknown uncertainties existing in surge and yaw dynamics. Then, antidisturbance kinetic control laws are developed through the estimated uncertainties. By using the proposed controllers, the states of mobile robots not only converge to the convex hull spanned by multiple virtual leaders, but also keep a certain formation pattern specified by the convex combination of virtual leaders. The input-to-state stability of the closed-loop path-guided containment maneuvering system is analyzed through Lyapunov theory. The effectiveness of the presented method is verified via experiment on CSICET-MR01 mobile robots.
Suppressive effect of bacoside A on hypertrophic scar formation by downregulation of TGF-β1

Purpose: To determine the anti-scarring property of bacoside A (BA) in a rabbit model of post-burns hypertrophic scar (HS). Methods: A total of 16 healthy male rabbits were divided into four groups (4 rabbits/group). The rabbits were subjected to full-thickness burn wound by placing a 10-mm brass rod at 90 oC on their ears for 20s, and were untreated (HS group). The remaining rabbits also underwent HS process but were topically treated daily with 0.2, 0.4 or 0.6 % BA gel (HS + 0.2, 0.4, and 0.6 % BA groups) from day 8 to day 34. Results: The mean values of collagen (types I & III), hydroxyproline (precursor of collagen) and hexosamine, as well as scar index significantly (p < 0.001) decreased after 28 days of topical treatment with BA. Moreover, BA application resulted in improved histological changes with proper arrangement of collagen fibers. Significant (p < 0.001) upregulation or downregulation of transforming growth factor beta-1 (TGF-β1) and matrix metalloproteinase-1 (MMP-1) were observed in the BA-treated rabbits. Conclusion: Treatment with BA significantly lowers the scarring area of burn wounds by downregulation of TGF-β1. Thus, BA can potentially be developed into a drug for managing injury scars. Keywords: Hypertrophic scar, Bacoside A, Scar elevation index, Transforming growth factor beta-1, Collagen
Toward Efficient Data Trading in AI Enabled Reconfigurable Wireless Sensor Network Using Contract and Game Theories

Reconfigurable Wireless Sensor Network (RWSN) schedules a set of devices with reconfigurable wireless interface to accomplish different data collection plans in a cost-effective way. AI technologies are applied to optimize decision making for high-level network reconfiguration. Besides, AI based data mining tools are exploited by third parities to extract useful information underlying raw data. This leads to the emergence of AI enabled RWSN. We further study a data trading market to provide the data-centric environment for large-scale applications of AI enabled RWSN. A network operator employs the devices to gather environmental data, and sells the collected data to interested third parities as data consumers. After that, two-level optimizations are performed to ensure efficient data trading. In data collection, a contract based incentive mechanism is presented for the network operator to stimulate the devices and simultaneously achieve the contractor's goal subject to feasible constraints. In data selling, a non-cooperative game is formulated among multiple data consumers. They balance the data demand since the final data price is correlated with the total data demand. Nash equilibrium is analyzed and solved under different conditions. Finally, numerical results are provided to demonstrate the effectiveness of our scheme.
Consensus Binding Site Structures at the Atomic Level: The Adenine Ring Moiety as a Test Example.

superimposed with concerted movement of the protein atoms in contact with the rings. LPC software [4] was used to determine the protein atoms in contact with the ligand and to classify the atom contacts according to their physico-chemical properties. A search for atomic clusters was then made. Atoms were defined as belonging to a cluster if they were within a given distance of each other and came from different PDB entries. Additionally, members of a cluster must form attractive contacts with the ligand. For example, hydrophobic clusters were found above and beneath the plane of the adenine rings which include some hydrophilic atoms acting as proton donors hydrogen-bonded to the conjugated system. The set of atomic clusters so determined was considered the consensus binding-site structure for the adenine ring of ATP. Next, a program was created to search for a set of atoms from any PDB file having the spatial characteristics of the cluster set. Using the 20 proteins of our dataset , we tested the searching program to determine how many putative ATP binding sites (including the correct one) it finds. The search was also applied to other adenine-containing files in the PDB. References:
Removal of Industrial Dye Effluent (Drimarene Yellow) by Renewable Natural Resources

Dyes and pigments represent one of the problematic groups emitted into wastewaters from various industrial branches, mainly from the dye manufacturing and textile finishing which have complex aromatic structures and provide them physico-chemical, thermal and optical stability. Present investigation deals with the utilization of natural biosorbents i.e., jute stick, chitin, chitosan for the removal of toxic reactive dye providing many beneficial aspects including low cost, biodegradability, nontoxicity, and biocompatibility. Diamerene yellow is a reactive dye which lowered the pH of the solution from 7 to 3.17. Among the jute stick, chitin and chitosan; chitosan showed better results for the removal of diamerene yellow and the removal potential was decreased with incubation time. The amount of dye removed by adsorbent was increased with the increase of adsorbent dose and initial dye concentration. Chitosan and chitin showed better results at 30 °C whereas jute sticks at 40 °C. For the irradiated chitin, dye uptake was reduced with the higher dose of gamma radiation. So, the utilization of this biosorbent can reduce the use of chemicals which is a potential burden for the aquatic biodiversity.