Miracle-dz/UniFAR-FTUs huggingface dataset:
On opening 500 kV- 220 kV electromagnetic loop circuit

With the development of power transmission networks,electromagnetic loop circuits of 500 kV-220 kV appear in many places,some of which should be opened because they threaten the power system security. The insecurity, such as 220 kV-system overload caused by opening 500 kV-system,is analyzed. The security and stability of power system and the economic factors should be considered before breaking the electromagnetic loop circuit. A general procedure whether a loop circuit should be opened is proposed. The north power system of Shandong is analyzed as an example. This project is supported by State Power Corporation of China(SP11-2001- 04 -50 -10).
The role of science, engineering, and technology in the public policy process for infrastructure and natural systems

The Role of Science, Engineering, and Technology in the Public Policy Process for Infrastructure and Natural Systems. (August 2009) Timothy R.B. Taylor, B.S.; M.S., University of Kentucky Chair of Advisory Committee: Dr. David N. Ford Interactions between societal, natural, and infrastructure systems can be beneficial or harmful to society. Society benefits from natural systems by being provided with the basic necessities of life (air, water, and food). However, events such as stratospheric ozone depletion demonstrate that society ultimately can be harmed by societal impacts on natural systems. Domain knowledge is developed from observation of natural, societal, and infrastructure systems. Domain knowledge is contained within scientific knowledge and engineering knowledge. Scientific knowledge is gained through structured observation and rigorous analysis of natural and societal systems. Engineering knowledge is partially developed from scientific knowledge and is used to manipulate natural and societal systems. Technology is the application of engineering knowledge. In the past two centuries scientific and engineering knowledge have produced technologies that affect the interaction between societal and natural systems. Although scientists and engineers are in positions to advise on policies to address problems involving societal/natural system interactions, their contributions are not always fully utilized.
Halal Food Sustainability between Certification and Blockchain: A Review

Islam is the second-largest religion on a global scale. All around the world Muslims are estimated to comprise more than 1.9 billion people. Therefore, the demand for Halal commodities is expected to reach a high growth rate: thus, it is crucial to increase its global market’s reliability and traceability. Based on these grounds, the scope of this paper is to assess Halal food sustainability, examining the barriers and opportunities offered by the certification and blockchain tools. To this purpose, the authors carried out an integrative literature review, selecting 54 contributions in the Web of Science platform. Despite several limitations, such as the lack of a standardized framework or universally accepted and reliable certifying authorities, the implementation of blockchain technology has emerged as an interesting instrument to increase the trustworthiness and traceability of Halal foods. This tool could also help the development of protocols and standard procedures, ensuring hygienic and permitted products that may boost food safety and security. Besides, the enhancement of the Halal certification and the blockchain tool, even if several efforts are required in terms of innovation and cooperation by local authorities, industrial associations and leading consumers, could enhance fair trade, ethical business, green animal breeding and environmental economics, and hence sustainable development.
Chinese Named Entity Recognition in the Geoscience Domain Based on BERT

Geological reports are frequently used by geologists involved in geological surveys and scientific research to record the results and outcomes of geological surveys. With such a rich data source, a substantial amount of knowledge has yet to be mined and analyzed. This paper focuses on automatically information extraction from geological reports, namely, geological named entity recognition. Geological named entity recognition has an important role in data mining, knowledge discovery and Knowledge graph construction. Existing general named entity recognition models/tools are limited in the domain of geoscience due to the various language irregularities associated with geological text, such as informal sentence structures, several domain‐geoscience words, large character lengths and multiple combinations of independent words. We present Bidirectional encoder representations from transformers (BERT)‐(Bidirectional gated recurrent unit network) BiGRU‐ (Conditional random field) CRF, which is a deep learning‐based geological named entity recognition model that is designed specifically with these linguistic irregularities in mind. Based on the pretrained language model, an integrated deep learning model incorporating BERT, BiGRU and CRF is constructed to obtain character vectors rich in semantic information through the BERT pretrained language model to alleviate for the lack of specificity of static word vectors (e.g., word2vec) and to improve the extraction capability of complex geological entities. We demonstrate our proposed model by applying it to four test datasets, including a geoscience NER data set from regional geological reports, and by comparing its performance with those of five baseline models.
Practice Leader Role in Developing Resource Teachers: Learning and Behaviour Professional Identity through Induction.

This research paper centres on a professional inquiry across Resource Teachers: Learning and Behaviour (RTLB) clusters within New Zealand that explored the role induction played in the ongoing development of new RTLB’s professional identity. A review of the literature was timely as although the RTLB service has the RTLB Toolkit, induction practices vary across the country. There is no ‘set’ framework that all RTLB use, and evolvement of professional identity is not mentioned within the RTLB Toolkit. Through anonymous online surveys to both new RTLB to the service in the last three years, and Practice Leaders (PLs), participants shared key factors they believed supported the development of their professional identity through their induction framework. The research results highlighted six key components of an effective induction process that could foster professional identity growth. They were consistency of induction, clarity and understanding around what is professional identity, developing and maintaining trusting relationships, the importance of ako, Communities of Practice (COP) and the impact of RTLB training on professional identity.
Stability Risk Assessment of Underground Rock Pillars Using Logistic Model Trees

Pillars are important structural elements that provide temporary or permanent support in underground spaces. Unstable pillars can result in rock sloughing leading to roof collapse, and they can also cause rock burst. Hence, the prediction of underground pillar stability is important. This paper presents a novel application of Logistic Model Trees (LMT) to predict underground pillar stability. Seven parameters—pillar width, pillar height, ratio of pillar width to height, uniaxial compressive strength of rock, average pillar stress, underground depth, and Bord width—are employed to construct LMTs for rock and coal pillars. The LogitBoost algorithm is applied to train on two data sets of rock and coal pillar case histories. The two models are validated with (i) 10-fold cross-validation and with (ii) another set of new case histories. Results suggest that the accuracy of the proposed LMT is the highest among other common machine learning methods previously employed in the literature. Moreover, a sensitivity analysis indicates that the average stress, p, and the ratio of pillar width to height, r, are the most influential parameters for the proposed models.
Selective Consumption of Fish Oil at End of the Day Increases the Physiological Fatty Acid Compositions of Eicosapentaenoic Acid and Docosahexaenoic Acid in Mice

Diets with high daily fat consumption are associated with excess weight. However, the effects of fat type and consumption timing on excess weight remain unclear. We investigated the selection of a 30% (w/w) fat diet of soybean oil (SOY), lard (LARD), and fish oil (FISH) on the metabolic parameters of mice. Male C57BL/6 mice were divided into the double SOY-box (w-SOY), SOY-box/LARD-box (SOY-vs-LARD), or SOY-box/FISH-box (SOY-vs-FISH) groups and allowed to selectively consume for 8 weeks. The total energy intake was similar for all groups, but the mice selectively chose to consume LARD over SOY and SOY over FISH. Body weight in the SOY-vs-LARD group was significantly higher than that in the w-SOY and SOY-vs-FISH groups. Additionally, minimal but selective consumption of an omega-3 fatty-acid-rich FISH diet at the end of the active period increased the physiological fatty acid compositions of eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) in the SOY-vs-FISH group; their metabolic parameters were also lower than the SOY-vs-LARD group. In conclusion, selectively consuming small amounts of fish oil at the end of the day may prevent excess weight compared with LARD consumption.
Telecom Fraud Detection via Hawkes-Enhanced Sequence Model

Detecting frauds from a massive amount of user behavioral data is often regarded as finding a needle in a haystack. While tremendous efforts have been devoted to fraud detection from behavioral sequences, existing studies rarely consider behavioral targets and companions and their interactions simultaneously in a sequence model. In this paper, we suggest extracting source and target neighbor sequences from the temporal bipartite network of user behaviors, and disclose the interesting correlation mode and repetition mode hidden inside the two types of sequences as important clues for fraudsters distinguishment. We then propose a novel Hawkes-enhanced sequence model (HESM) by integrating the Hawkes process into LSTM for historical influence learning. A historical attention mechanism is also proposed to enhance the strength of the long-term historical influence in response to the repetition mode. Moreover, in order to collectively model both types of neighbor sequences for capturing the correlation mode, we propose a correlation gate to control the information flow in sequences. We conduct extensive experiments on real-world datasets and demonstrate that HESM outperforms competitive baseline methods consistently in telecom fraud detection. Particularly, the abilities of HESM in historical influence leaning and sequence correlation learning have been explored visually and intensively.
Study on surface defects of monocrystalline silicon caused by argon ion beam with different energies

In order to study the lattice damage caused by low-energy argon ions on the single crystal silicon substrate and the influence of incident energy on the substrate damage during the ion beam polishing process, the molecular dynamics (MD) was used to simulate the incidence of a single argon ion on the single crystal silicon substrate. Compare the lattice defects produced by the bombardment of single crystal silicon by ions of different incident energy. Ion beams with different incident energy were used to polish the surface of single crystal silicon, and the surface roughness after polishing was compared. Experimental and simulation results show that ion bombardment will cause lattice damage to the surface of the substrate. As the energy increases, the range of lattice damage will expand and the resulting lattice defects will be more dispersed. The use of low-energy (200~600eV) ion beams can further reduce the surface roughness of the substrate on the basis of reducing lattice damage
Assessing the Emergency Response Role of Community-Based Organizations (CBOs) Serving People with Disabilities and Older Adults in Puerto Rico Post-Hurricane María and during the COVID-19 Pandemic

In Puerto Rico, a host of factors makes the role of community-based organizations (CBOs) critically important in emergency preparedness and response (EPR) and disability-inclusive disaster risk reduction (DiDRR) addressing the needs of people with disabilities and older adults. The territory has been the site of recurring hurricanes, earthquakes, medical crises, and human-made disasters. Political, social, and economic problems unique to the archipelago have historically limited the preparedness and response capacity of governmental authorities, especially for its most at-risk populations. In a context of severe constraints on government resources, CBOs are positioned to play an outsized role in providing services for disabled and older adults before, during, and after emergencies. This study assesses the emergency preparedness and response capacity of CBOs (n = 22) for addressing the needs of people with disabilities and the elderly. Semi-structured, largely closed-ended interviews were conducted in Spanish with key informants at Puerto Rican CBOs. The interviews included questions about emergency preparedness and response training, as well as organizational capacity during COVID-19 and post-Hurricane María. This study posits that conditions in Puerto Rico place CBOs at the forefront of critical responsibilities including emergency preparedness and response, warranting assessment of their practices and resources to assist them in fulfilling their mission.
Self-Adapting Handover Parameters Optimization for SDN-Enabled UDN

Increasing the deployment density of small base stations (SBS) is a key method designed to satisfy high data traffic in 5th generation mobile network (5G). However, a large number of SBSs in such ultra-dense network (UDN) may cause ping-pong handovers (HOs), accompanied by increased delay and HO failure. In addition, because of the separation of control and data signaling in 5G, the HO procedure must be performed in both layers. In this paper, we introduce an SDN-based intelligent dynamic HO parameter optimization strategy to minimize both HO failures and ping-pong HOs together. The goal of the proposed strategy is to reduce the HO failure rate and redundant HO (i.e. ping-pong HO) while enabling user equipment (UE) to make full use of the benefits of dense deployment of BSs. Simulation results present that the method proposed in this paper effectively suppresses the ping-pong effect and keeps it at a low level in all of the investigated scenes. In addition, compared with the other algorithms, the HO failure rate is significantly reduced and the throughput of UE is greatly increased, especially in the case of high BS density. Therefore, the benefits of intensive BS deployment are retained.
Design of Cover Quality Visual Inspection System Based on Machine Vision

According to cover packaging line working environment and working condition,an aluminum lid surface inspection system based on machine vision was studied,it achieved automatically detect and remove quickly.This automatic inspection system composed of lower cover device,cover transmitting device,the light source and image acquisition system,visual processing and electrical control systems,and inferior sorting devices,etc.The aluminum cans cover passes the lower cover device into cover transmission area continuously,the transmitting device will be covered by the covers adhering to conveyor belt through vacuum.When the cover comes across the image acquisition system,fibre-optic sensor triggers the LED light source and the industry camera,then the IPC computer gets a frame of image.The imaging processing system analyzed the cover image.Finally,electrical control system sorts the inferior covers based on the visual inspection results.Experiment result demonstrates this system has superiority such as real-time and reliability,improves the efficiency of traditional cover packing system effectively.
Genetic assignment of fisheries bycatch reveals disproportionate mortality among Alaska Northern Fulmar breeding colonies

Global fisheries kill millions of seabirds annually through bycatch, but little is known about population‐level impacts, particularly in species that form metapopulations. U.S. North Pacific groundfish fisheries catch thousands of Northern Fulmars (Fulmarus glacialis rodgersii) each year, making fulmars the most frequently caught seabird in federally managed U.S. fisheries. Here, we used genetic stock identification to assign 1,536 fulmars sampled as bycatch to one of four Alaska breeding colonies and quantified the similarity of bycatch locations at sea among colonies. We found disproportionately high bycatch from the Pribilof Islands (6% of metapopulation, 23% of bycatch), and disproportionately low bycatch from Chagulak Island (34% of metapopulation, 14% of bycatch). Overlap between fisheries and colony‐specific foraging areas diverge more during the summer breeding season, leading to greater differences in bycatch susceptibility. Contemporary and historical gene flow likely contributes to low genetic differentiation among colonies (FST = 0.003–0.01), yet these values may not represent present connectivity. Our findings illustrate how genetic stock identification can link at‐sea threats to colonies and inform management to reduce bycatch from impacted colonies.
Utility of Anonymised Data in Decision Tree Derivation

: Privacy Preserving Data Publishing (PPDP) is a practice for anonymising microdata such that it can be publicly shared. Much work has been carried out on developing methods of data anonymisation, but relatively little work has been done on examining how useful anonymised data is in supporting data analysis. This paper eval-uates the utility of k -anonymised data in decision tree derivation and examines how accurate some commonly used metrics are in estimating this utility. Our results suggest that whilst classiﬁcation accuracy loss is minimal in most common scenarios, using a small selection of simple metrics when calibrating a k -Anonymisation could help signiﬁcantly improve decision tree classiﬁcation accuracy for anonymised data.
"Fake News Detector": An Automatic System for the Reliability Evaluation of Digital News

: Nowadays, information is taking on an increasingly central role in people’s lives. With the rise of internet, the amount of information has grown exponentially as the ease of publishing content of all types has increased. At the same time, however, the risks deriving from the lack of checks on the truthfulness of these have also increased. In fact, a ”Fake” information content can lead to serious reputational, economic or health damages. To overcome the problem of veriﬁcation, several studies have been carried out, but none of them appears to have been the subject of a signiﬁcant commercial implementation. In all the projects proposed so far there is a common thread, that is to act directly to the source of the news, examining, thanks to existing technologies, the possible truthfulness of the same. On the other hand, lacking a real involvement of the reader, these projects were not suitable to increase the awareness of the end user. This work implements a technological platform able to provide a reliability value of a digital news, thus measuring the level of impartiality of the author through the evaluation of a deﬁned series of parameters.
Social Capital and Vulnerability to Extreme Climate in a Semi-urban Fishing Community in Laguna de Bay, Philippines

The study looked into the risks associated with extreme climate events in the case of a semi-urban fishing community surrounding Laguna Lake in the Philippines. A survey was undertaken to determine the economic effects (loss of assets, foregone income, and changes in consumption patterns) of strong typhoons and torrential rains on fishing households. Vulnerability, estimated as the perceived probability of lower consumption after flooding or typhoons, was used to assess the economic impact on households. Household characteristics, including social capital, that may influence consumption vulnerability, were analyzed using a binary probit regression model. Social capital, a multi-dimensional concept consisting of social networks and skills possessed and used by household members to facilitate actions, was modeled using four indicators – two associational (membership in a formal organization and usefulness of informal social networks) and two behavioral (trust and cooperativeness). Regression results revealed that fishing income and household size significantly affect vulnerability. The higher the fish catch and the smaller the household, the less vulnerable is the household to strong storms and torrential rains. Social capital indicators do not significantly affect consumption vulnerability of households.
Simultaneous Integration of Continuous Mineral-Bonded Carbon Reinforcement Into Additive Manufacturing With Concrete

3D concrete printing is becoming more and more popular, not only in research but also in practice of construction. One of the main challenges however remains the integration of reinforcement. This publication focuses on the use of continuous fibers for this purpose. The special feature of the new type of carbon yarns applied is the mineral impregnation used instead of the conventional polymer one. Such reinforcement not only yields excellent mechanical properties, but also offers many advantages, specifically with respect to processing flexibility and is therefore very well suited for additive manufacturing. The concrete for 3D printing to be reinforced with such yarns must meet particular requirements, especially when the finest concrete filaments are printed. This is also a subject of the article at hand.
DEVSIM: A TCAD Semiconductor Device Simulator

DEVSIM is technology computer-aided design (TCAD) software for semiconductor device simulation. By solving the equations for electric fields and current flow, it simulates the electrical behavior of semiconductor devices, such as transistors. It can be used to model existing, fabricated devices for calibration purposes. It is also possible to explore novel device structures and exotic materials, reducing the number of costly and time consuming manufacturing iterations.
SMN promotes mitochondrial metabolic maturation during myogenesis by regulating the MYOD-miRNA axis

SMN contributes to muscle maturation by binding to the promoter regions of MYOD1 and miRNAs and regulating the expression of these molecules during myogenesis. Spinal muscular atrophy (SMA) is a congenital neuromuscular disease caused by the mutation or deletion of the survival motor neuron 1 (SMN1) gene. Although the primary cause of progressive muscle atrophy in SMA has classically been considered the degeneration of motor neurons, recent studies have indicated a skeletal muscle–specific pathological phenotype such as impaired mitochondrial function and enhanced cell death. Here, we found that the down-regulation of SMN causes mitochondrial dysfunction and subsequent cell death in in vitro models of skeletal myogenesis with both a murine C2C12 cell line and human induced pluripotent stem cells. During myogenesis, SMN binds to the upstream genomic regions of MYOD1 and microRNA (miR)-1 and miR-206. Accordingly, the loss of SMN down-regulates these miRs, whereas supplementation of the miRs recovers the mitochondrial function, cell survival, and myotube formation of SMN-deficient C2C12, indicating the SMN-miR axis is essential for myogenic metabolic maturation. In addition, the introduction of the miRs into ex vivo muscle stem cells derived from Δ7-SMA mice caused myotube formation and muscle contraction. In conclusion, our data revealed novel transcriptional roles of SMN during myogenesis, providing an alternative muscle-oriented therapeutic strategy for SMA patients. Graphical Abstract