Miracle-dz/UniFAR-FTUs huggingface dataset:
Wind farm control ‐ Part I: A review on control system concepts and structures

Wind farm control design is a recently new area of research that has rapidly become a key enabler for the development of large wind farm projects and their safe and efﬁcient con-nection to the power grid. A comprehensive review of the intense research conducted in this area over the last 10 years is presented. Part I reviews control system concepts and structures and classiﬁes them depending on their main objective (i.e. to maximise power production or to provide grid services. The work and key ﬁndings in each paper are discussed in detail with particular emphasis on the turbine side. Additionally, the review con-tributes to the existing reviews on the area by providing an elegant classiﬁcation between model testing and control approaches. Areas where signiﬁcant work is still needed are also discussed. In Part II, a thorough review on aerodynamic wind farm models for control design purposes is provided.
Residual Deep Monocular 3D Human Pose Estimation using CVAE synthetic data

Estimating 3D human pose from a monocular RGB image is a challenging task in computer vision because annotating a large number of 3D pose ground-truth data is costly. To address the problem of lack of 3D data, many methods have been proposed. In this paper, we propose to address this issue by synthesizing data. Firstly we exploit Conditional Variational Autodecoder to generate 3D human skeletons data. In CVAE network, we acquire generous 3D pose samples through the predicted 2D pose and the existing 3D ground truth. Secondly, base the generated 3D samples, we obtain the corresponding 2D pose by projection, thus augmenting the data of 2D-3D network. Finally, we train these synthetic data on 3D pose residual estimation network. Extensive experiments show that our approach achieves state-of-the-art accuracy on standard benchmark datasets.
Digital citizenship skills among undergraduate students in Malaysia: A preliminary study

The purpose of the study is to examine level of digital citizenship skills among undergraduate students at the Sultan Idris Education University, Perak, Malaysia. The study focused on three sub-variables of digital citizenship skills; online wellbeing, online learning and online safety. The paper present report from a questionnaire conducted on 1000 students from nine different faculties. Items for each variable were constructed based on literature highlights related to digital citizenship framework and through experts’ validation. A descriptive statistic was used in determined the level of digital citizenship skills among the undergraduate students. The findings of the study showed that the level of digital citizenship skills of students is at a high level. This study expected to help stakeholders to further emphasize the study of digital citizenship and digital citizenship education in Malaysia in more detail.
What Do You Mean by Collaboration Analytics? A Conceptual Model

Using data to generate a deeper understanding of collaborative learning is not new, but automatically analyzing log data has enabled new means of identifying key indicators of effective collaboration and teamwork that can be used to predict outcomes and personalize feedback. Collaboration analytics is emerging as a new term to refer to computational methods for identifying salient aspects of collaboration from multiple group data sources for learners, educators, or other stakeholders to gain and act upon insights. Yet, it remains unclear how collaboration analytics go beyond previous work focused on modelling group interactions for the purpose of adapting instruction. This paper provides a conceptual model of collaboration analytics to help researchers and designers identify the opportunities enabled by such innovations to advance knowledge in, and provide enhanced support for, collaborative learning and teamwork. We argue that mapping from low-level data to higher-order constructs that are educationally meaningful, and that can be understood by educators and learners, is essential to assessing the validity of collaboration analytics. Through four cases, the paper illustrates the critical role of theory, task design, and human factors in the design of interfaces that inform actionable insights for improving collaboration and group learning.
Biogeochemical multi‐tag approach reveals the habitat use of a large‐scale migratory fish through a fluvio‐estuarine system

This study assesses the use of stable isotopes (δ13C and δ15N) and trace elements in gills (G), liver (L), and muscle (M) of the most commercially important migratory fish from the La Plata Basin, Prochilodus lineatus, as habitat proxies. With this purpose, fish and sediment (S) samples from the Paraná River and Río de la Plata Estuary were analyzed. Results showed differences in the stable isotopic ratios in fish tissue and sediments between the river and the estuary. In particular, δ15N values of the estuary samples were significantly lower (G = 7.38‰; L = 6.60‰; M = 8.61‰ and S = 4.36‰) than those from the river (G = 11.30–11.53‰; L = 11.03–12.35‰; M = 11.27–11.31‰, and S = 6.78–7.72‰), which could denote a shift in the isotopic baseline between these environments. Quadratic discriminant analysis effectively discriminates between sampling sites with high classification rates (L = 97.5%; G = 90%, and M = 82.5%), suggesting that combining stable isotopes and trace elements is useful to discriminate between fish populations. The liver was the most suitable tissue as a habitat indicator, using mainly δ15N, δ13C, Cr, and Hg as natural markers, and capable of discriminating between sites with a relatively high geographic resolution. Due to the spatial isotopic heterogeneity found in the La Plata Basin, these natural markers could be useful for studying other aquatic organisms' habitat use.
Traffic Change Forecast and Decision Based on Variable Structure Dynamic Bayesian Network: Traffic Decision

The rapid development of internet of things (IoT) and in-stream big data processing technology has brought new opportunities for the research of intelligent transportation systems. Traffic forecasting has always been a key issue in the smart transportation system. Aiming at the problem that a fixed model cannot adapt to multiple environments in traffic flow prediction and the problem of model updating for data flow, a traffic flow prediction method is proposed based on variable structure dynamic Bayesian network. Based on the complex event processing and event context, this method divides historical data through context clustering and supports cluster update through online clustering of event streams. For different clustered data, a search-scoring method is used to learn the corresponding Bayesian network structure, and a Bayesian network is approximated based on a Gaussian mixture model. When forecasting online, a suitable model or combination of models is selected according to the current context for prediction.
Towards a Model of Leader Character Development: Insights From Anatomy and Music Therapy

Leader character has emerged as a critical foundation for leadership. In spite of the view that leader character can be developed, there has been limited holistic attention to what it takes to develop character. Character requires conscious development, and that conscious development not only requires an understanding of what character is, but how the anatomy of character enables and inhibits character development and expression. By anatomy, we refer to the four underlying anatomical systems—physiology, affect, behavior, and cognition (PABC)—that function independently, and in an interrelated manner, to support the development of character. For illustration, we offer the practice of listening to music as a means to develop character, highlighting the links between the PABC systems and character development.
Analysis On Deep Learning Approaches For Timely Detection Of Osteoarthritis

Osteoarthritis is mainly a familiar kind of arthritis when an elastic tissue named Cartilage that softens the tops of the bones, cracks down. The Person with osteoarthritis can encompass joint pain, inflexibility, or inflammation and there is no particular examination for osteoarthritis and physicians take the amalgamation of both medical cum clinical record and X-rays imaging analysis to make a diagnosis of the state. Osteoarthritis is generally only detected following ache and bone scratch and in advance, analysis could permit for ultimate involvement to avoid cartilage worsening and bone injury. Through machinelearning algorithms, the system can be trained to automatically distinguish among people who would develop osteoarthritis and persons who would not with the detection of exact biochemical variances in the midpoint of the knee’s cartilage. The outcome of the Machine learning Techniques will give the persons who are pre-symptomatic by the occasion of the baseline imaging and also the reduction in liquid concentration. In this study, we present the analysis of various deep learning techniques for timely detection of osteoarthritis disease. Several subsets of machine learning called deep learning techniques have been in use for the timely detection of osteoarthritis disease; and therefore analysis is needed highly to choose the best as far as accuracy and reliability are concerned.
A novel framework for synthesizing nested queries in SQL from business requirements language

Different methods and systems were proposed in the past for translating Natural Language (NL) statements in to Structured Query Language (SQL) queries. Translating statements resultingin‘nested’queries havealways been a challenge and was not effectively handled. This work proposes a framework for translating requirement statementsresulting inthe construction of nested Queries. While translating nested scenarios; often thereis a need to create sub-queriesthat execute inpipeline orin parallel or both operating together.Lambda Calculus is found to be effective in representing the intermediate expressions and helps in performing the transformations that are needed in translating specific predicates into SQL, but its inflexibility in combining parallel computations is a constraint. To represent clauses that are in parallel or arein pipeline,and to perform the required transformationson theintermediate expressions involving these,more advancedprogramming constructs are needed.This work recommends the use of advanced language constructs and adoptsfunctional programming techniques for performing the required transformation at the intermediate language level.
Emotions in Accounting: A Review to Bridge the Paradigmatic Divide

This review analyzes the role of emotions in accounting research. Drawing on an analytical framework that differentiates between joy-, fear-, anger- and sadness-based emotions, we disentangle the bidirectional dynamics between emotions and accounting, and show how accounting both conditions and is conditioned by emotions. In this way, we demonstrate how an emotional turn in accounting scholarship challenges and expands our understanding of financial accounting, management accounting, and auditing. In addition, we raise awareness about the performative role of ontological and epistemological positions in accounting research on emotions. We find that scholars from different paradigmatic traditions mostly acknowledge each other's work, but often miss the opportunity to fully embrace it. By illustrating the productive insights that emerge from a stronger inter-paradigmatic engagement, we draw attention to the necessity (and excitement) of crossing the great divide. We conclude by outlining avenues of future research on emotions in accounting.
Pedagogical tool to teach electromagnetic plane wave propagation in various media environments

In the usual process of teaching students about the electromagnetic field, students often have difficulty in understanding the propagation process of invisible electromagnetic waves under different medium environments. To facilitate students understanding and learning of related knowledge, in this article, we first derive the process of solving Maxwell’s equation using finite-difference time-domain (FDTD) under different media environments for the designed experimental models to obtain the spatial distribution of the electromagnetic field. After that, we use the MATLAB language to program the FDTD solution method to verify our theoretical analysis based on the simulation results. Based on this, we develop a convenient and quick interactive mobile software tool, aiming to give students a vivid and intuitive demonstration and explanation for the propagation of electromagnetic waves in different media environments.
Fuzzy Multidimensional Assessment Approach of Travel Deprivation in Small Underdeveloped Cities: Case Study of Lhasa, China

In small, underdeveloped Chinese cities, the travel needs of economically disadvantaged residents have not been satisfactorily met for a long time, and thus, the problem of travel inequality has become increasingly serious. This study developed and applied a fuzzy multidimensional assessment approach of travel deprivation to assess the travel deprivations that arise because of this travel inequity. The resulting model includes both monetary and nonmonetary indicators, and involves multiple measurement items, dimensions, and related weights. Then, this fuzzy multidimensional assessment approach of travel deprivation is used to measure the travel deprivation in the underdeveloped, small city of Lhasa, China. The results identified both differences and similarities between different parts of the city. Among all measured dimensions, the following four dimensions cause strong travel deprivation: disposable income, travel service quality, travel time, and available transportation. Differences in the travel deprivation were identified between different parts of Lhasa, indicating multidimensional travel deprivation. Furthermore, an early warning analysis on travel deprivation and an assessment of different levels of residents’ travel deprivation in underdeveloped cities are presented. These findings provide an effective evaluation of the current situation of travel inequity in underdeveloped small cities.
Assessing the Seasonal Changes on Physicochemical Parameters and Major Nutrient Levels in Water and Sediments from Sulal River, Bureti Sub County, Kericho County, Kenya

Sulal River is a victim of nutrient pollution as it crosses different small-scale tea farms in Bureti Sub County, Kericho County. A study was conducted to evaluate different physicochemical parameters and major chemical fertilizer’s nutrients levels in water and sediments from Sulal River. In order to understand the degree of pollution in the river due to inflow of agricultural chemical fertilizer residue levels from its catchment, the samples were collected in the dry (February 2019) and wet seasons (November 2019). The ten chosen sampling sites stretched a length of 12 Km. Samples were analyzed for pH, dissolved oxygen (DO), total dissolved solids (TDS), electrical conductivity (EC), sediment and water content (WC), nitrate-nitrogen (NO3-N), phosphorous (PO3-P) and potassium (K) using standard methods. Results were analyzed using IBM SPSS 20. The average results during dry and wet seasons in water samples for pH were 6.72±0.05 and 6.38±0.21, DO 6.78±0.59 and 9.58±0.64 mg/L, EC 174±5.02 and 205±20.14 μS /cm, TDS 92±4.59 and 120.70±19.57 mg/L, Original Research Article Cherotich et al.; CSIJ, 30(1): 46-58, 2021; Article no.CSIJ. 66291 47 NO3-N 0.58±0.21 and 1.19±0.22 mg/L, PO3-P 0.00±0.00 and 0.13±0.84 mg/L and K 0.26±0.08 and 0.84±0.19 mg/L respectively. The average results during dry and wet seasons in sediment samples for pH were 6.62±0.20 and 6.75±0.17, EC, 67±6.66 and 52±10.19 μS /cm, WC 16.96±3.95 and 129.84±5.00%, NO3-N 0.70±0.35 and 0.42±0.23 mg/kg, PO3-P 0.49±0.25 and 1.14±0.40 mg/kg and K 1.53±0.45 and 2.86±0.31 mg/kg respectively. The levels of P in water during wet season and in sediments samples in both seasons were however, above the WHO (0.025 mg/L) and KEBS (0.030 mg/L) recommended limit for drinking and domestic use and WASREB (0.005 mg/L) level for the likelihood of eutrophication. Availability of excess phosphorus can accelerate eutrophication. The diversity of cancers has also been associated with drinking water containing phosphorous levels. The water from Sulal River may be used for irrigation purposes but not for drinking and domestic purposes. Significant seasonal variations were observed in all parameters analyzed except NO3-N in sediments. This indicates that rainfall had a great effect on some soil physicochemical properties.
Analysis of electrical tracking by energy absorption during surface discharge in polymeric materials

The electrical tracking that occurs in polymeric materials used as electrical insulators is an undesired phenomenon. We developed a method to quantify the amount of energy dissipated during the occurrence of surface discharges based on the standard IEC 60112. The method uses the wave form factor of the electrical voltage on a sample as the main parameter to determine the integration period of the instantaneous power. The energy as a function of time shows linear behavior, and after a certain period, the change in the slope of this curve (mean power) indicates a new stage in the surface degradation process.
Dynamic of Morphological and Physiological Parameters and Variation of Soil Characteristics during Miscanthus × giganteus Cultivation in the Diesel-Contaminated Land

Miscanthus × giganteus (M. × giganteus) is a perspective plant produced on marginal and contaminated lands with biomass used for energy or bioproducts. In the current study, M. × giganteus development was tested in the diesel-contaminated soils (ranged from 250 mg kg−1 to 5000 mg kg−1) and the growth dynamic, leaves quantity, plants total area, number of harvested stems and leaves, SPAD and NPQt parameters were evaluated. Results showed a remarkable M. × giganteus growth in a selected interval of diesel-contaminated soil with sufficient harvested biomass. The amendment of soil by biochar 1 (produced from wastewater sludge) and biochar 2 (produced from a mixture of wood waste and biohumus) improved the crop’s morphological and physiological parameters. Biochar 1 stimulated the increase of the stems’ biomass, while biochar 2 increased the leaves biomass. The plants growing in the uncontaminated soil decreased the content of NO3, pH (KCl), P2O5 and increased the content of NH4. Photosynthesis parameters showed that incorporating biochar 1 and biochar 2 to the diesel-contaminated soil prolonged the plants’ vegetation, which was more potent for biochar 1. M. × giganteus utilization united with biochar amendment can be recommended to remediate diesel-contaminated land in concentration range 250–5000 mg kg−1.
A novel common‐ground switched‐capacitor five‐level inverter with adaptive hysteresis current control for grid‐connected applications

The aim of this paper is to present a new topology of single-phase transformerless inverters, which can be tied to the local grid as a low-scaled ac module system. The proposed topology offers a common ground between the neutral point of the ac grid and the negative terminal of the dc supply, and can properly alleviate the concern of variable common mode voltage and leakage current problems. This promising feature is acquired by the aim of both the switched-capacitor and charge pumped circuit cells. In order to inject a tightly controlled ac current to the grid, an adaptive hysteresis current controller scheme is also presented, which can guarantee almost ﬁxed switching frequency operation of the involved power switches. A complete theoretical analysis, comparative study, and the relevant experimental results are also given to conﬁrm the superior performance of the proposed topology.
In Situ Triggering and Dynamically Tracking the Phase Transition in Vanadium Dioxide

As the most widely studied thermochromic material, monoclinic vanadium dioxide (VO2 (M)) shows promising applications in the energy-saving field. Solid-state transformation, especially fast annealing, plays an important role in the production of VO2 nanomaterials. On the other hand, the fast process makes it impossible to real-time monitor the phase transition in VO2. In this paper, a differential scanning calorimetry technique is proposed to in situ trigger and dynamically track the phase transition of VO2 nanoparticles, which gives a distinguished method to identify the underlying size-dependent and defect-mediated structure phase transition.
Enhancing Employee Agility Through Information Technology Competency: An Empirical Study of China

This study investigated how employees’ information technology (IT) competency is associated with employee agility via perceived task structure. Most scholar’s emphasis is on the impact of IT competency on individual work performance; however, it seems as if no attention has been given to explore the relationship between IT competency on employee’s agility through perceived task structure. This research offers and tests a new comprehensive model that links the individual’s IT competency with perceived task structure and employee agility. Data were gathered from 167 representatives who used various IT applications for daily work routines in the organization. Findings showed that IT competency is positively related to task interdependence and autonomy. Task autonomy also has significant effect on employee agility; however, task interdependence has shown insignificant effect on employee agility. Furthermore, work expertise positively moderates the relationship between IT competency and perceived task structure. Theoretical and managerial implications of study are also discussed in the last section.
Carbon Dots with Continuously Tunable Full-Color Emission and Their Application in Ratiometric pH Sensing

Two types of carbon dots (C dots) exhibiting respective excitation-independent blue emission and excitation-dependent full-color emissions have been synthesized via a mild one-pot process from chloroform and diethylamine. This new bottom-up synthetic strategy leads to highly stable crystalline C dots with tunable surface functionalities in high reproducibility. By detailed characterization and comparison of the two types of C dots, it is proved concretely that the surface functional groups, such as C═O and C═N, can efficiently introduce new energy levels for electron transitions and result in the continuously adjustable full-color emissions. A simplified energy level and electron transition diagram has been proposed to help understand how surface functional groups affect the emission properties. By taking advantage of the unique excitation-dependent full-color emissions, various new applications can be anticipated. Here, as an example, a ratiometric pH sensor using two emission wavelengths of the C dots a.
Important relationships in a multilevel world: The role of network structure in explaining closeness of relationships and access to resources in later life

We examine why some relationships are more important than others, using a multilevel statistical model and data on personal networks of Australians 50 years and older, collected via a purpose-built Facebook application. While the network data were collected automatically, participants in our study provided data on the importance of their relationships, measured by perceived closeness and access to resources. We find that the information on how network members are connected with each other (network structure) provides powerful insights into what makes a relationship important. When importance is measured by closeness of relationship, important alters are kin, and are alters who are highly connected with others or act as bridges between different groups. When importance is measured by access to resources, important alters are those who act as bridges and are in more densely-knit networks. We discuss our findings in a broader context of research into important relationships in later life, and collecting personal network data via online social networks.