Miracle-dz/UniFAR-FTUs huggingface dataset:
The Energy Management of Multiport Energy Router in Smart Home

Although smart home has received wide attention in recent years, numerous scholars focus more on energy optimization strategy than energy dispatch hardware device (named energy router). Meanwhile, this energy router should have several features, i.e., high renewable energy utilization, energy multi-port and low volume. Thus, this paper designs a nine-port energy router regarding smart home and proposes a multimode hierarchical management strategy for this energy router. First, for the multi-port demand of wind, solar, storage and utilization, this paper presents a nine-port energy router to improve the renewable energy consumption and power supply flexibility. In addition, to reduce the volume of the energy router, a non-isolated AC/DC hybrid topology is constructed through embedding the integrated power electronic converters, which achieves the miniaturization of the energy router. In order to improve the renewable energy utilization rate, the decentralized module control is proposed for the components of energy router to provide the voltage and frequency support for system, and realizes the power sharing of distributed generations (DGs). Furthermore, the power exchange control with three-mode switching is proposed to guarantee the global energy flow balance under complex conditions. Eventually, the feasibility of the energy router is verified by the simulation and experiments.
Role of activin a hormone, betacellulin (BTC) growth factor, and Heparin binding epidermal growth factor on mice embryonic development after oral administration of glycyrrhiza glabra

Glycyrrhiza glabra is a herbal plant used for treatment of many infertility diseases. Its roots, a common herbal source, are described to soothe fetuses in pregnant mice, but there isn't enough research to know if it's safe to take during pregnancy and its effects on implantation factors. This study examines the impact of the growth factors betacellulin (BTC), heparin-binding epidermal growth factor, and activin A hormone on embryonic development after oral administration of Glycyrrhiza glabra aqueous extract to mice. In line with clinical use, an aqueous extract of Glycyrrhiza glabra roots was prepared. Pregnant mice were randomly assigned to one of four groups: Two mice groups 8 and 28 weeks old treated with water as a negative control, mice aged 8 weeks old treated with aqueous extract from one of two groups for 10 days, and mice 28 years old treated with aqueous extract for 10 days. On study groups, the parameters of certain implantation factors such serum progesterone and Activin A hormones, Heparin-binding epidermal growth factor and betacellulin (BTC) growth factor were evaluated. The four groups had substantial differences in parameters.
Intelligent Diagnosis Based on Double-Optimized Artificial Hydrocarbon Networks for Mechanical Faults of In-Wheel Motor

To avoid the potential safety hazards of electric vehicles caused by the mechanical fault deterioration of the in-wheel motor (IWM), this paper proposes an intelligent diagnosis based on double-optimized artificial hydrocarbon networks (AHNs) to identify the mechanical faults of IWM, which employs a K-means clustering and AdaBoost algorithm to solve the lower accuracy and poorer stability of traditional AHNs. Firstly, K-means clustering is used to improve the interval updating method of any adjacent AHNs molecules, and then simplify the complexity of the AHNs model. Secondly, the AdaBoost algorithm is utilized to adaptively distribute the weights for multiple weak models, then reconstitute the network structure of the AHNs. Finally, double-optimized AHNs are used to build an intelligent diagnosis system, where two cases of bearing datasets from Paderborn University and a self-made IWM test stand are processed to validate the better performance of the proposed method, especially in multiple rotating speeds and the load conditions of the IWM. The double-optimized AHNs provide a higher accuracy for identifying the mechanical faults of the IWM than the traditional AHNs, K-means-based AHNs (K-AHNs), support vector machine (SVM), and particle swarm optimization-based SVM (PSO-SVM).
Toward an Online Continual Learning Architecture for Intrusion Detection of Video Surveillance

With recent advances in deep learning technologies, many commercialized video surveillance systems have adopted Artificial Intelligence (AI)-powered video analytics technologies as a way to make our life smarter and safer. Nevertheless, there is no robust architecture with an appropriate network model for commercial services considering both high accuracy and low computational cost. Existing deep learning technologies would not be enough to model and represent the dynamics of the real-world scene, so it is difficult to satisfy all environments using a generic model. Appropriate training data from false-alarm and/or missed cases can address this limitation but is rarely available due to legal issues relating to the privacy of personal data and the unpredictability of new incoming data. In this paper, we propose a novel end-to-end hybrid video surveillance architecture for reliable object detection, consisting of front-end and back-end intelligence. For the intelligent front-end, we propose a new object detector with a Multi-scale ResBlock scheme to consider the scalability and flexibility of the system. We are also developing a new domain adaptation method to replace the generic model with each camera’s individual personal model by understanding real-time space and context information for intelligent back-end architecture. It is an iterative and continuous process in which new upcoming data and previous models are consistently engaged in a continuous improvement process. We conducted a series of experiments, including an interesting proof-of-concept tests called the Chameleon project, which demonstrated the high accuracy and versatility of the new architecture, while producing robust results that can be implemented in practice.
SYSTEMATIC REVIEW OF MEDICATION PROBIOTICS ACUTE DIARRHEA IN CHILDREN IN THE GROUP

: Aim: Oral rehydration salts, zinc, and kept taking care of are the suggested medicines for network procured intense the runs among small kids. In any case, probiotics are getting progressively famous medicines for looseness of the bowels in certain nations. We tried to assess the impact of probiotics on loose bowels dismalness what's more, death in youngsters < 5 years old. Methods: We also performed a randomized managed quantitative study to determine the effect of probiotic microorganisms on the care in children's networks extensively. In the Infant Health Epidemiology Comparison Group (CHERG) adaptation of GRADE method the details concerned a standardized table and the values of the sample were assessed. Our current research was conducted at Services Hospital, Lahore from May 2019 to April 2020. Despite reports of rehydration on hospitalizations, duration and severity, we measured the cumulative benefit of probiotic therapy. We calculated at this stage the standard percentage difference for each continuous test and performed a meta-examination for discreet findings. Results: We recognized 8 examinations for incorporation in the last information base. No investigations revealed loose bowels mortality and generally speaking the proof was low to direct quality. Probiotics decreased loose bowels term by 15.1% (96% CI: 4.9- 25.3%) and stool recurrence on the second day of treatment by 14.2% (96% CI: 0.9 – 26.4%). There was no impact on the danger of looseness of the bowel’s hospitalizations. Conclusion: Probiotics might be effective in lessening loose bowels span and stool recurrence during the run’s scene. Nonetheless, just barely any investigations have been led in low-salary nations and none utilized zinc (the flow proposal) accordingly extra exploration is expected to comprehend the impact of probiotics as subordinate treatment for loose bowels among youngsters in creating nations.
Machine Learning-based Prediction of HBV-related Hepatocellular Carcinoma and Detection of Key Candidate Biomarkers

Objective: This study aimed to classify open-access gene expression data of patients with hepatitis B virus-related hepatocellular carcinoma (HBV + HCC) and chronic HBV without HCC (HBV alone) using the XGBoost method, one of the machine learning methods, and reveal important genes that may cause HCC. Methods: This case-control study used the open-access gene expression data of patients with HBV + HCC and HBV alone. Data from 17 patients with HBV + HCC and 36 patients with HBV were included. XGBoost was constructed for the classification via 10-fold cross-validation. Accuracy, balanced accuracy, sensitivity, selectivity, positive-predictive value, and negative-predictive value performance metrics were evaluated for model performance. Results: According to the feature-selection method, 18 genes were selected, and modeling was performed with these input variables. Accuracy, balanced accuracy, sensitivity, specificity, positive-predictive value, negative-predictive value, and F1 score obtained from XGBoost model were 98.1%, 98.6%, 100%, 97.2%, 94.4%, 100%, and 97.1%, respectively. Based on the predictor importance findings acquired from XGBoost, the RNF26, FLJ10233, ACBD6, RBM12, PFAS, H3C11, and GKP5 can be employed as potential biomarkers of HBV-related HCC. Conclusions: In this study, genes that may be possible biomarkers of HBV-related HCC were determined using a machine learning-based prediction approach. After the reliability of the obtained genes are clinically verified in subsequent research, therapeutic procedures can be established based on these genes, and their usefulness in clinical practice may be documented.
Kobe University Repository : Kernel タイトル Tit le Operator analysis of physical states on magnet ized T 2 / Z ( N ) orbifolds 著者

We discuss an effective way for analyzing the system on the magnetized twisted orbifolds in operator formalism, especially in the complicated cases T /Z3, T /Z4 and T /Z6. We can obtain the exact and analytical results which can be applicable for any larger values of the quantized magnetic flux M , and show that the (non-diagonalized) kinetic terms are generated via our formalism and the number of the surviving physical states are calculable in a rigorous manner by simply following usual procedures in linear algebra in any case. Our approach is very powerful when we try to examine properties of the physical states on (complicated) magnetized orbifolds T /Z3, T /Z4, T /Z6 (and would be in other cases on higher-dimensional torus) and could be an essential tool for actual realistic model construction based on these geometries. (Note: This article is registered under preprint number: arXiv:1409.5421.) © 2014 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/3.0/). Funded by SCOAP3. * Corresponding author. E-mail addresses: t-abe@scphys.kyoto-u.ac. jp (T.-h. Abe), Fujimoto@het.phys.sci.osaka-u.ac. jp (Y. Fujimoto), kobayashi@particle.sci.hokudai.ac. jp (T. Kobayashi), takashi.miura@people.kobe-u.ac. jp (T. Miura), nishiken@kias.re. kr (K. Nishiwaki), dragon@kobe-u.ac. jp (M. Sakamoto). http://dx.doi. org/10.1016/j.nuclphysb. 2014.11. 022 0550-3213/© 2014 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/3.0/). Funded by SCOAP3. T. -h. Abe et al. / Nuclear Physics B 890 (2015) 442–480 443
Video Game Design for Learning to Learn

Abstract Over the past 20 years, the proposal that immersive media, such as video games, can be leveraged to enhance brain plasticity and learning has been put to the test. This expanding literature highlights the extraordinary power of video games as a potential medium to train brain functions, but also the remaining challenges that must be addressed in developing games that truly deliver in terms of learning objectives. Such challenges include the need to: (1) Maintain high motivation given that learning typically requires long-term training regimens, (2) Ensure that the content or skills to be learned are indeed mastered in the face of many possible distractions, and (3) Produce knowledge transfer beyond the proximal learning objectives. Game design elements that have been proposed to support these learning objectives are reviewed, along with the underlying psychological constructs that these elements rest upon. A discussion of potential pitfalls is also included, as well as possible paths forward to consistently ensure impact.
Effects of Whistling Compared with Buzzy Device During Blood Sampling on Pain and Fear in Children’s Emergency Department

ABSTRACT Blood sampling from a peripheral vein in children can be significant. Pediatric nurses should be able to use the techniques which decrease the pain and fear duration of the blood sampling. This research studied the effect of vibration bee and whistling (concentrate on breathing) blood sampling on pain and fear in the children’s emergency. In this randomized clinical trial study, 120 children ages 3–6 years referred to a pediatric ward in Najaf, Iraq, using Random Blocking Method, were divided into three groups; vibration bee, whistling, and control group. A musical vibration bee, with the cold bag attached to it, was closed for 5 minutes at approximately 5-10 cm above the blood sampling site. A final minute, the vibration was applied; then, the procedure was done. The pain severity was measured by Wong Baker Faces scale. Data were analyzed by SPSS19 software at the significant level of 95%. There was a significant difference in pain intensity between the control groups and the intervention groups (p = .000). Moreover, there was a significant difference in children’s medical fear scale between the control groups and the intervention groups (p = .000). Whistling (concentrate on breathing) may be an easily accessed, inexpensive, and effective technique to control or reduce pain and fear in young children. But, vibration bee with cold has a greater effect for reducing pain and fear. It is suggested to use vibration bee (with cold) in painful aggressive procedures in children.
Variable Selection in QSAR Studies. II. A Highly Efficient Combination of Systematic Search and Evolution

Recently two evolutionary strategies for the derivation of regression models, a genetic function approximation and the mutation/ selection algorithm MUSEUM have been described. The MUSEUM (Mutation and Selection Uncover Models) algorithm starts from a model containing randomly chosen variables. Random mutation, first by addition or elimination of only one or very few variables, afterwards by simultaneous random additions, eliminations and/or exchanges of several variables at a time, leads to new models which are evaluated by an appropriate fitness function. Only the “fittest” model is stored and used for further mutation and selection, leading to better and better models. However, the fitness of all models with up to three X variables can be determined much faster by calculation of the correlation coefficients ry.ij and ry.ijk from the partial correlation coefficients ryi, rij, ryj.j, rjk.i and ryk.ij. Using the Selwood data set (n = 31 compounds, k = 53 variables), it is demonstrated that systematic search is the best strategy for regression models with two or three X variables. The variables contained in the best three-variable models can be selected for further investigation, using the evolutionary approach. With the exception of complex models, containing six and more variables, nearly all relevant regression models are found by this combination of systematic search with the mutation/selection algorithm MUSEUM; the results are obtained in considerably shorter time than by including all variables in the calculations. In addition, systematic search is also a valuable tool for variable selection prior to stepwise regression and PLS analyses.
ENDOPHYTIC BACTERIA OF COMMON TAMARISK (TAMARIX GALLICA L.) AND ALKALI SEEPWEED (SUAEDA FRUTICOSA (L.) FORSSK.) AS POTENTIAL BIOCONTROL AND PLANT GROWTH-PROMOTING AGENTS IN ARID ENVIRONMENTS

In this study, fifty-two endophytic halotolerant bacteria from Tamarix gallica L. and Suaeda fruticosa (L.) Forssk. growing in the M'Zab valley (Southern Algeria) were isolated to test their ability to reduce salt stress in barley ( Hordeum vulgare L.), and tomato ( Solanum lycopersicum L.) seeds. Identification by Gas Chromatography (GC) and matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) confirmed the presence of strains belonging mainly to the genera Bacillus followed by Pseudomonas , Pantoea , Klebsiella and Providencia . All isolates were characterized for their antagonistic activities against some plant pathogenic bacteria and fungi. In addition, isolates were screened for their in vitro plant growth promotion (PGP) abilities, including ACC-deaminase activity, inorganic phosphate solubilization and production of indole-3-acetic acid (IAA), siderophores and lytic enzyme activity. Four isolates that expressed interesting PGP capabilities were tested for their efficacy in modulating salt stress and promoting the growth of barley and tomato seedlings in Petri dish experiments. The strain TE7 showed interesting performances under salt stress for tomato seedlings while strains SE5 and SE19 produced positive results for barley seedlings under normal conditions. The results of this study suggest that endophytic bacteria from natural plants in saline habitats present an interesting source for the isolation of salt-tolerant PGP bacteria that can be used in plant cultivation under normal and saline conditions.
The Theory of Straight Homogeneous Generalized Cylinders

Abstract : In recent years, Binford's generalized cylinders have become an important tool for imagine understanding. However, research has been hampered by a lack of analytical results for these shapes. In this paper, a definition is presented for Straight Homogeneous Generalized Cylinders, those generalized cylinders, with a straight axis and with cross-sections which have constant shape but vary in size. This class of shapes, while still quite large, has properties which make considerable analysis possible. The results begin with deriving formulae for points and surface normals for these shapes. Theorems are presented concerning the conditions under which multiple descriptions can exist for a single solid shape. Then projections and contour generators are analyzed for some subclasses of shapes. The strongest results are obtained for solids of revolution (which the authors name Right Circular SHGCs), for which a closed-form method for analyzing image contours is presented. It is seen that a picture of the contours of a solid of revolution is ambiguous, with one degree of freedom related to the angle between the line of sight and the solid's axis. (Author)
Local and Global Feature Learning With Kernel Scale-Adaptive Attention Network for VHR Remote Sensing Change Detection

Change detection is an important task of identifying changed information by comparing bitemporal images over the same geographical area. Currently, many existing methods based on U-Net and attention mechanism have greatly promoted the development of change detection techniques. However, they still suffer from two main challenges. First, faced with the diversity of ground objects and the flexibility of scale changes, vanilla attention mechanisms cripple spatial flexibility in learning object details due to the same scale convolution kernels at different convolution layers. Second, the complex background and high similarity between changed information and nonchanged information makes it difficult to fuse low-level details and high-level semantic by simple skip-connection in U-Net. To address the above issues, a local and global feature learning with kernel scale-adaptive attention network (LGSAA-Net) is proposed in this article. The proposed network makes two contributions. First, a scale-adaptive attention (SAA) module has been designed to exploit the relationships between feature maps and convolutional kernel scales. The SAA module can achieve better feature discrimination than vanilla attention mechanism. Second, a multilayer perceptron based on patches embedding has been employed by skip-connection to learn the local and global pixel association, which is helpful for achieving globally deep fusion of low-level details and high-level semantics. Finally, experiments and ablation studies are conducted on three datasets of LEVIR/WHU/GZ. Experimental results demonstrate that the proposed LGSAA-Net performs favorably against comparative current approaches and provides more accurate contour and better internal compactness for changed targets, thus verifying the effectiveness and superiority of the proposed LGSAA-Net in VHR remote sensing change detection.
Mycoremediation of Flotation Tailings with Agaricus bisporus

Due to their enzymatic and bioaccumulation faculties the use of macromycetes for the decontamination of polluted matrices seems reasonable for bioremediation. For this reason, the aim of our study was to evaluate the mycoremediation ability of Agaricus bisporus cultivated on compost mixed with flotation tailings in different quantities (1, 5, 10, 15, and 20% addition). The biomass of the fruit bodies and the content of 51 major and trace elements were determined. Cultivation of A. bisporus in compost moderately polluted with flotation tailings yielded significantly lower (the first flush) and higher (the second flush) biomass of fruit bodies, compared with the control treatment. The presence of toxic trace elements did not cause any visible adverse symptoms for A. bisporus. Increasing the addition of flotation tailings to the compost induced an elevated level of most determined elements. A significant increase in rare earth elements (both flushes) and platinum group elements (first flush only) was observed. The opposite situation was recorded for major essential elements, except for Na and Mg in A. bisporus from the second flush under the most enriched compost (20%). Nevertheless, calculated bioaccumulation factor values showed a selective accumulation capacity—limited for toxic elements (except for Ag, As, and Cd) and the effective accumulation of B, Cu, K, and Se. The obtained results confirmed that A. bisporus can be used for practical application in mycoremediation in the industry although this must be preceded by larger-scale tests. This application seems to be the most favorable for media contaminated with selected elements, whose absorption by fruiting bodies is the most efficient.
The Impact of Emerging Technologies on Workplace Privacy Case Study-Electronic Monitoring

Due to the COVID-19 epidemic pandemic, the Internet invades our life aspects and activities, the way we work or shop has changed, the educational system has changed, and the social structure and many other things in our lives have changed. In this research work, we analyze three hypotheses regarding electronic monitoring during the COVID-19 pandemic. The first hypothesis is the relationship between Electronic Monitoring and safety, the second is the relationship between Electronic Monitoring and privacy, and the third is related to acceptance of electronic monitoring in the workplace. For that, we have surveyed the COVID-19 pandemic to measure the effects of new technologies-in particular-electronic monitoring on productivity, creativity, and the effect of social networks in increasing the acceptance of electronic monitoring in the workplace. Our results show that the attitudes toward electronic monitoring have not changed over the years and a higher percentage of employees who were polled do not accept being monitored at the workplace, which means the introduction of new technologies in our life has not changed the opinion of people about electronic monitoring. © 2022. Arwa Zabian and Sami Qawasmeh. This open-access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license.