Miracle-dz/UniFAR-FTUs huggingface dataset:
PD PATTERN RECOGNITION METHOD USING BOX DIMENSION AND INFORMATION DIMENSION AS DISCRIMINATING FEATURES IN GIS

Five kinds of typical artificial defect models in Gas Insulated Substation(GIS) and PD detection system were developed. PD sample data caused by typical artificial defects in GIS were got by PD detection system in laboratory. Utilizing these sample data, GIS PD gray intensity images were constructed. Minimum box-counting method which is used to estimate image fractal dimension was presented. Fractal features of GIS PD gray intensity images such as box dimension and information dimension are extracted. GIS PD pattern recognition scheme was put forward. In this scheme, PD fractal dimensions were used as inputs, Radial Basis Function(RBF) neural network was used as classifier. Good recognition result is obtained using the scheme.
Stand-level windthrow patterns and long-term dynamics of surviving trees in natural secondary stands after a stand-replacing windthrow event

We examined typhoon windthrow patterns in stand-scale permanent plots within three natural forests and the long-term dynamics of surviving trees in two of these plots. The aim was to verify whether pre-disturbance composition and structure affected windthrow damage and stand recovery. The stands, which were in Hokkaido, the northernmost main island of Japan, differed in pre-disturbance composition (coniferous, mixed and broadleaved stands) and structure, and the basal area losses by the windthrow event were 81–93 per cent. Tree mortality was significantly related to tree size (diameter at breast height) and species. The windthrow patterns differed among the three stands; and, windthrow severity was affected by the pre-disturbance composition and structure. The mortality of trees that survived the windthrow event was ~60 per cent in the two plots. The surviving trees comprised a majority of the canopy layer in the secondary stands. The relative basal area of surviving trees decreased rapidly with increasing tree density in the stand initiation stage but differed between plots. Consequently, the pre-disturbance composition and structure influenced windthrow severity, stand recovery and secondary succession in the recovered stands.
An innovative experimental teaching method of hardware-software co-design-Taking a hardware accelerator of neural network using FPGA

In order to cultivate students' software and hardware collaborative design thinking and system construction ability, this paper proposes an innovative experimental teaching method of software and hardware collaborative design to better develop students'sys-tem view. We create a student-centered learning environment and adopt a mixed teaching method whereby experiment courses are conducted in a “traditional + discussion + scientific research-driven” manner. The course will focus on the design of the “Convolutional Neural Network for Handwritten Digit Recognition”, and run through curriculums that involve from software to hardware, algorithms to systems, and design to verification. We explore teaching methods that revolve around principles of using experiments to derive theories, and using theories to guide experiments. Besides, we establish an online laboratory that can provide an experiment platform (MOOE) for “PC+ARM+FPGA”, which realizes the seamless integration of physical operations, semi-physical emulators, and virtual models. Through investigation and research, most students who have completed this course have formed a good knowledge of computer systems and the necessary comprehensive development ability.
Relevance of Context for the Temporal Completion of Call Detail Record

Call Detail Records (CDRs) are an important source of information in the study of different aspects of human mobility. However, their utility is often limited by spatio-temporal sparsity. In this paper, we first evaluate the effectiveness of CDRs in measuring relevant mobility features. We then investigate whether the information of user's instantaneous whereabouts provided by CDRs enables us to estimate positions over longer time spans. Our results confirm that CDRs ensure a good estimation of radii of gyration and important locations, yet they lose some location information. Most importantly, we show that temporal completion of CDRs is straightforward and efficient: thanks to the fact that they remain fairly static before and after mobile communication activities, the majority of users' locations over time can be accurately inferred from CDRs. Finally, we observe the importance of user's context, i.e., of the size of the current network cell, on the quality of the CDR temporal completion.
Fundamentals of forecasting explosive gas accumulation formation in working areas of coal mines

This article presents a methodological approach to solving the problem of predicting local methane accumulations in order to prevent occupational injuries resulting from explosions of methane-air mixtures. On the basis of the analysis of accidents that occurred in coal mines and industrial injuries of miners (mine workers) it is established that the leading role belongs to explosions of methane-air mixtures. The method/methodology considered in the article is based on a comprehensive analysis, first of all, of both mining and geological and other influencing factors and conditions of operating mines of the Pechersk coal basin, as well as aerogas-dynamic processes of mass transfer of air flows in mine workings. The assumption about influence of mining equipment on the process of formation of methane accumulation sites is made. The algorithm of consecutive actions of managerial engineering and technical personnel to reduce industrial injuries is proposed. The developed algorithm implies the use of applied software for modelling aerogas-dynamic processes in the mining excavation operating region. FlowVision and ANSYS Fluent application software packages were selected to determine the methane accumulation locations based on the analysis of existing application programmes.
Destructive Phytophthora on orchids: current knowledge and future perspectives

Anton de Bary first coined the genus, Phytophthora, which means “plant destroyer”, viewing its devastating nature on potatoes. Globally plants have faced enormous threat from Phytophthora since its occurrence. In fact, a century ago, Phytophthorapalmivora was first reported on Dendrobium maccarthiae in Sri Lanka. Since then, members of beautiful flowering crops of the family Orchidaceae facing the destructive threat of Phytophthora. Several Phytophthora species have been recorded to infect orchids with economic loss worldwide. To date, orchids are attacked by 12 species of Phytophthora. Five Phytophthora species (P. palmivora, P. nicotianae, P. cactorum, P. multivesiculata, P. meadii) are the major pathogenic Oomycetous Chromista” rather than true fungi frequently occurred on Orchidaceae. Phytophthora palmivora (having ~32 orchid host genera in 15 countries), Phytophthora nicotianae (having ~15 orchid host genera in 16 countries), Phytophthora cactorum (having ~43 orchid host genera in 6 countries), Phytophthora multivesiculata (having 2 orchid host genera in 5 countries) and Phytophthora capsici (having 2 orchid host genera in all Vanilla growing countries) are potential destroyers of Orchidaceae. Most of them are water loving Oomycetes cause disease in moist environments (> 80% RH) at 16–28°C. In artificially constructed orchidaria, anthropogenic factors are mostly contributed to the dissemination Phytophthora diseases in addition to many other factors. Water management, clean cultivation, and agro-chemicals are the major options for effective management of orchid Phytophthora, as the eco-friendly management options like development of resistant hybrids/cultivars, biological disease management, transgenic approaches, RNAi technology remained in the infant stage. In this review, we intended to highlight the insight of Phytophthora diseases associated with the orchid disease with reference to the historical aspect of the diseases, symptoms and signs, the pathogens, taxonomy, geographic distribution, host range within the Orchidaceae, pathogen identification, molecular diagnostics, mating types and races, management options and strategies and future perspectives.
Direct Calculation Method of Fringing Field Reluctance in MEC Analysis for An Axisymmetric Solenoid Actuator

In this paper, a method is introduced to directly calculate the reluctances of fringing fields in analyzing a magnetic equivalent circuit model for an axisymmetric solenoid actuator including radial air gaps as well as axial air gaps. The introduced method was motivated by the possibility to calculate the cross section area depending on the radial position of an infinitesimal element when the infinitesimal element is defined to be integrated in the flux flow direction in a circular-arc straight-line model of an axisymmetric model. In this method, the reluctance of fringing field near an air gap can be calculated directly. For an objective model, the MEC analysis was performed using the introduced method and an iterative method. In the iterative method, the nonlinearity of the ferromagnetic material parts is resolved. In order to validate the introduced method, MEC analysis and FEA results are compared. And for validating the method in a view of computation also, an optimized design satisfying pre-defined constraints was searched using MEC analysis, and then the results of the MEC analysis using the introduced method and FEA results are compared for the searched design and the initial design.
A Deep Learning Method for Change Detection in Synthetic Aperture Radar Images

With the rapid development of various technologies of satellite sensor, synthetic aperture radar (SAR) image has been an import source of data in the application of change detection. In this paper, a novel method based on a convolutional neural network (CNN) for SAR image change detection is proposed. The main idea of our method is to generate the classification results directly from the original two SAR images through a CNN without any preprocessing operations, which also eliminate the process of generating the difference image (DI), thus reducing the influence of the DI on the final classification result. In CNN, the spatial characteristics of the raw image can be extracted and captured by automatic learning and the results with stronger robustness can be obtained. The basic idea of the proposed method includes three steps: it first produces false labels through unsupervised spatial fuzzy clustering. Then we train the CNN through proper samples that are selected from the samples with false labels. Finally, the final detection results are obtained by the trained convolutional network. Although training the convolutional network is a supervised learning fashion, the whole process of the algorithm is an unsupervised process without priori knowledge. The theoretical analysis and experimental results demonstrate the validity, robustness, and potential of our algorithm in simulated and real data sets. In addition, we try to apply our algorithm to the change detection of heterogeneous images, which also achieves satisfactory results.
Identifying Fake Digital Information Using Machine Learning Algorithms: Performance Analysis and Recommendation System

This work focuses on the detection of fake digital information using various machine learning and deep learning algorithms to prevent its spread through Internet of Things (IoT) devices and systems. The research highlights the significance of detecting and preventing false or misleading information in critical areas such as healthcare, public safety, and emergency response. The study compares the performance of several supervised machine learning algorithms and identifies logistic regression as the most accurate (98.03%). The empirical analysis used data from The Indian Express, PolitiFact, and Kaggle and leveraged natural language processing (NLP) to prepare, clean, and model the data. To detect fraudulent posts, the study employed random forest, a supervised machine learning algorithm, which achieved an impressive accuracy rate of 99.71% on a Kaggle dataset. The research also developed a model for detecting false reporting related to COVID-19, utilizing the support vector machine technique, which achieved an accuracy rate of 78.69%. The presented work also determined the authenticity of images through convolutional neural networks (CNNs). Lastly, a content-based recommendation system was developed to enhance people’s security and confidence.
Optimizing Indonesian Education Through National Standards: Implications for Engineering and Computer Science

This study examines the National Education Standards (NES) in the context of improving the quality of education in Indonesia. The research employs a quantitative approach, specifically path analysis, to test the relationship between various components of the Indonesian NES using eight key variables: content, process, graduate competencies, educators and educational staff, facilities and infrastructure, management, financing, and educational assessment. The population for this study consists of educational institutions across Indonesia, including primary and secondary levels. Data will be collected through accreditation documents, gathered from the National Accreditation Agency. The research findings indicate that management standards have a positive influence on evaluation, process, and content standards, underscoring the importance of effective management in promoting continuous improvement and aligning educational practices with desired outcomes. The standards for educational personnel were found to negatively impact evaluation standards, while infrastructure and facilities standards positively affected evaluation but negatively influenced process and content standards. Financing standards showed no significant influence on outcome standards. In enhancing graduate competency standards, content standards negatively impacted, while process and evaluation standards contributed positively. The study findings suggest that optimizing educational quality necessitates improving management standards, engaging teachers in professional development, ensuring efficient learning practices, and maintaining rigorous evaluation processes. When applied to engineering and computer science education, these findings can foster an environment of continuous learning and innovation, promote professional development for educators, highlight the need for well-equipped facilities, and emphasize the importance of aligning instructional practices with curricular goals, thereby preparing graduates to meet the challenges of the 21st century.
Land-atmosphere feedbacks: Precipitation recycling in the NAMS region

It has long been thought that in the semi-arid North American Monsoon System (NAMS) region, the moisture sources for monsoon precipitation are predominantly of oceanic origin. In this study we show that precipitation recycling is also a significant source of moisture for monsoon precipitation and it plays an important role during long monsoons. Here we present the analysis of precipitation recycling over the NAMS using Dynamic Recycling Model (Dominguez et al. 2006), which enables us to calculate precipitation recycling at the daily timescale. The input data for the model is obtained from the North American Regional Reanalysis (NARR) data which was developed as an improvement upon the earlier Global Reanalysis, and has focused significant efforts on improved hydrologic modeling (Mesinger et al. 2006). Furthermore, the Level II ecoregions of North America are the geographical units of analysis (See Figure 1). This classification implicitly takes into account the complex feedback mechanisms between the vegetated surface and the overlying atmosphere. One of the innovative features of this analysis is the delineation of the source and sink regions of recycled precipitation within the NAMS domain.
An Energy-Efficient Cluster Formation Based on Optimal Node Distribution in Full Capacity Multi-Hop LoRa Networks

Our most recent findings on multi-hop LoRa networks imply that earlier developed multi-hop routing methods, such as our previously suggested Extended-DRESG scheme, had overlooked some essential information. Although the researchers employed uniform node distributions for their multi-hop models, a full capacity LoRa network may be treated as a Gaussian distribution. Extended-DRESG performs better with a Gaussian distribution than with a uniform distribution of nodes. In contrast to optimal ring-hop combinations created earlier, this observation led to the suggested algorithm for optimal ring positioning and ring-hop combinations in Extended-DRESG. The Extended-DRESG cluster developing technique, which may produce much superior performance in terms of energy efficiency when uniform node distribution is encountered, is further suggested in light of the observations made in this research. Future multi-hop models can benefit from the current observations in two ways: (i) a better understanding of the packet forwarding structure, and (ii) a good cluster formation technique when clusters are established based on their distances from the gateway.
A Use of Medicinal Plant with Transfersome as a Vesicular Carrier that Increase the Permeation

The goal of the study was to create Transfersomes, a unique vesicular carrier with improved anti-Inflammatory effects by lowering the gastrointestinal toxicity linked to oral administration. Phospholipids, extract from medicinal plants, and transfersomes made by Thin Film Hydration Techniques employing surfactants are some of the components of medicinal plants that are used in varying proportions. Particle size, entrapment efficiency, zeta potential, in vitro drug release, and transmission electron microscopy were assessed for the prepared Transfersomes. Using Fourier transform infrared spectroscopy [FT-IR], the compatibility between the medicinal plant extract and the excipients was assessed, and they were found to be compatible. Vesicle size was observed in the batch Transfersomes that were optimized. The best batch formulations of Transfersomes were mixed with polymer and tested for drug content, pH, spreadability, and in vitro penetration against commercial products. Compared to branded formulation, the deformed vesicle formed formulation performs better when combined with edge activator. Transfersome formulations containing medicinal plant extract have been found to offer a higher permeation rate than commercial formulations, based on parameters such as extract of medicinal plant release that were analyzed.
Chapter 6 Reproducing Productivism in Spanish Agricultural Systems

The purpose of this chapter is to shed light on some outstanding patterns of change observed in Spanish agriculture over the last decades, and to discuss the ways in which the productivist rationale is reproduced in them. We start by providing an overall picture of the structural transformations of agriculture revealed by the national statistics, calling attention to the increasing importance of a “hard core” of farms progressing under a rationale of growth and modernization. Later, drawing on the literature, we comment some meaningful trajectories of farm change observed in three selected Mediterranean farming systems, namely horticulture (as an example of a long-consolidated intensive agriculture), vine growing (an orientation which has undergone stunning changes in Spain in recent times), and small ruminant production (an extensive farming system with a high conservation value). The three farming systems are advancing, to a greater or a lesser degree, along intensification, concentration, and specialization pathways. However, the introduction of new elements in the most expansionary farm strategies (such as the participation in quality schemes) will provide interesting elements of discussion of the adaptable nature of productivism and its capacity to accommodate to external opportunities and constrains.
Curating a historical source corpus of 20th century patient organization periodicals

Acting out Disease: How Patient Organizations Shaped Modern Medicine (ActDisease) explores the history of patient organizations in 20th century Europe. By combining traditional historiographic methods with text mining techniques, the project aims to shed light on how patient organizations co-constructed concepts of and management of disease. Part of the project is to digitize print sources and build a digital corpus for historical text mining. The corpus consists of periodical publications from selected British, French, German and Swedish patient organizations, a type of material that poses a number of challenges in scan quality, layout, and lack of consistency. This paper discusses the technical process of building the ActDisease corpus from digitizing patient organization periodicals to OCR post-processing. It touches upon the methodological questions and challenges of curating a corpus of fragmented and heterogeneous historical source material tailored to a specific project.
Effects of the inclusion of physical activity in secondary education academic classes on educational indicators and health markers: rationale and methods of the ACTIVE CLASS study

Background Increasing physical activity (PA) levels and reducing sedentary behaviors in children and adolescents is a need, especially in schools. Active breaks and physically active learning are examples of two emerging methodologies that have been shown to be effective in increasing PA levels and additionally produce improvements in children’s educational markers. However, the evidence in adolescents is very limited. This paper presents the design, measurements, and interventions implemented in the ACTIVE CLASS study, whose objectives are: (i) evaluate the effects of two interventions on PA levels, sedentary time, health-related physical fitness academic indicators, cognition, and markers of psychological health among secondary education students; (ii) evaluate teachers’ and students’ experiences about the implementation of these the two school-based PA intervention. Methods A randomized controlled study is conducted with a total of 292 students aged 12–14 years old from six schools (7th and 8th grade) in Spain (three in Cadiz and three in Caceres). One school from each study provinces is randomly assigned to either the active break intervention group, the physically active learning intervention group, or the control group. The interventions have a duration of 16 weeks. Nine main measurement categories are assessed: PA and sedentary time, health-related physical fitness, academic indicators, cognition, psychological health, motivational variables, dietary patterns, sociodemographic characteristics, as well as qualitative information through semi-structured individual interviews and focus groups. Three independent measurements of evaluation are distinguished: pre-intervention, post-intervention (week 16) and retention measurement (4 weeks after the intervention). For quantitative variables, descriptive, correlational, regression and repeated measures ANOVA will be applied. Discussion To the best of our knowledge, the ACTIVE CLASS study is the first of its kind in Spain to evaluate the effects of incorporating active breaks and physically active learning in secondary education. In addition, this project provides important information on the effects of two school-based PA intervention arms on educational variables and health markers in adolescents. This will provide valuable and innovative training to the educational community, enabling them to implement teaching methodologies that have the potential to enhance academic performance and improve the quality of life for their students. Clinical trial registration clinicaltrials.gov, NCT05891054.