Miracle-dz/UniFAR-FTUs huggingface dataset:
WIRELESS SENSOR NETWORK FOR ILLEGAL LOGGING APPLICATION: A SYSTEMATIC LITERATURE REVIEW

Wireless Sensor Networks (WSN) is a technology available for outdoor area application. The characteristic of illegal logging application is suitable to apply WSN-based application. Because the illegal logging application is implemented in a wide range area, supervised the environment forest, and consists of hundreds of sensor nodes. This paper aims to review and summarize as systematically the contribution of WSN Technology in illegal logging area research as a long-range network application especially in detection and identification method, timber tracking methods, data exchange, and transmission method. A Systematic Literature Review (SLR) were outlined in this paper as a standard methodology of predefined research strategy to solve the problems by tracing the previous research. By defining the Research Question (RQ) to guidelines the SLR process and inserting the search string in the database reputation journal, the previous research can be configured. There are 42 previous studies applied WSN to used it in the illegal logging application. The result stated that WSN has biggest contributions since 33% researcher using WSN to tracking application, 41% use the WSN as a data exchange in their system, and 48% used WSN as data transmission between sensor nodes. This paper is expected to give a contribution to the researcher who wants to build the system to tackle illegal logging since the illegal logging has been hot issues in the world.
SGD OPTIMIZER TO REDUCE COST VALUE IN DEEP LEARNING FOR CUSTOMER CHURN PREDICTION

The number of customers is an important indicator for companies to know the success of a product and service offered. In general, customers are grouped into two categories, loyal customers and disloyal customers. This disloyal customer refers to customers who have stopped using a product and service from a company or are often referred to as churn. For that, we need a system that can predict whether the customer has the potential to experience churn or not. The system is required to predict well with one of the indicators, namely a low-cost value. One way to reduce the cost value is to use the optimizer function. Researchers use deep learning algorithms to create predictive models. A total of 3333 rows and 20 columns of telecommunication customer public data are used as datasets. This study also compares several optimizer algorithms to find the lowest cost value. In addition to the cost value, the accuracy and f1 score results are also used as other considerations. Researchers also use cross-validation for the training process and validating the model created. To deal with the imbalance class dataset, the researcher uses the unweighted method on the cost function. The evaluation results show that SGD has the lowest cost value, which is 0.261. Meanwhile, the AdaGrad matrix classification shows the best value for accuracy and f1 score with 0.926 and 0.846, respectively
Molecular Detection and charchterization of stx toxins of Escherichia coli O157:H7, Escherichia coli O26:H8, and Escherichia coli O111:H8 from diarrhoeic patients in Khartoum State, Sudan

Introduction/Background: Enterohaemorrhagic Escherichia coli (EHEC) O157:H7 is the most important serotype of E. coli responsible for serious intestinal and extra-intestinal disorders worldwide, but no molecular based study for EHEC as the causative agent diarrhea was performed in the Sudan. EHEC is a subset of Shiga toxin (stx) producing E. coli (STEC) which includes well recognized human pathogens. STEC produce high level toxins of a protein family referred to as Shiga toxin and Shiga-like toxins (SLTs; alternatively known as verocytotoxins VTEC or VTs). EHEC O26:H11 and O111:H8 have emerged as the most important non-O157:H7 EHEC. These toxins are mediating intestinal disease in children and adults, and if the cases left untreated these toxins may contribution to the development of extra-intestinal sequelae e.g., the haemolytic uraemic syndrome and neurological disorders. Objective: This study was conducted for the detection and molecular characterization of Shiga like toxin (stx) of E. coli O157:H7, E. coli O26:H11, and E. coli O111:H8 in Sudanese patients with diarrhea using PCR and multiplex PCR. Materials and methods: A cross sectional laboratory-based study was carried out in Khartoum, Sudan during the period from June to October 2020. All patients with con�rmed E. coli diarrheal disease was included in this study. The isolates have been identi�ed by using the biochemical, serological, molecular identi�cation and characterization tests. SPSS IBM (software version 20.0) was used for analyses. Results: Out of 100 patients with diarrheal disease a total of 96 patients were con�rmed of having E. coli organisms. All 96 E. coli bacteria were seropositive identi�ed as members of the EHEC. Ten isolates of E. coliO157:H7, E. coli O26:H11, E. coli O111:H8 have been selected as follows; �ve, three, and two respectively.
ONTOLOGY MATCHING USING DEEP LEARNING

Ontology matching is a critical task in knowledge representation and integration, with numerous applications in various domains. Deep learning methods have shown promising results in improving the accuracy and efficiency of ontology matching. However, there is a lack of comprehensive analysis and classification of these methods in the literature. In this paper, we conducted a systematic literature review of ontology matching using deep learning methods, covering articles published between 2005 and 2022. Our analysis includes a trend analysis of the articles, a framework for classifying them, and a detailed classification of the articles based on the deep learning method used.
ZERO ORDER AND CONJUGATE IMAGES ELIMINATION FOR DIGITAL HOLOGRAMS

The zero order and the twin image severely affect the quality of reconstructions in digital holography, as a result of limited resolution and dynamic range of camera sensors. Several techniques have been proposed to minimize or to eliminate those elements. Some are purely numerical; others require experimental interventions. Amongst them are: subtraction digital holography that consists of subtracting a randomly modified recording from the original one, HRO and phase shifting methods that use controlled images for subtraction. We have investigated these methods theoretically and experimentally for both off-axis Fourier and Fresnel holography. Their performance has been assessed with a sample that is a vibrating membrane in the former case, and a stick-shaped transmission diffuser in the latter case. Experimental evidence indicates that if they are efficient for zero-order elimination on off-axis Fourier holograms, a simple digital procedure applied to a single Fresnel recording, and that combines mean value subtraction and spatial filtering in the frequency space permits remove the zero order and the twin image simultaneously.
B To Drive RANTES Production by k , JNK, and NF-a IL-15 Can Signal via IL-15R

IL-15 plays many important roles within the immune system. IL-15 signals in lymphocytes via trans presentation, where accessory cells such as macrophages and dendritic cells present IL-15 bound to IL-15R a in trans to NK cells and CD8 + memory T cells expressing IL-15/IL-2R b and common g chain ( g c ). Previously, we showed that the prophylactic delivery of IL-15 to Rag2 2 / 2 g c 2 / 2 mice (mature T, B, and NK cell negative) afforded protection against a lethal HSV-2 challenge and metastasis of B16/F10 melanoma cells. In this study, we demonstrated that in vivo delivery of an adenoviral construct optimized for the secretion of human IL-15 to Rag2 2 / 2 g c 2 / 2 mice resulted in signiﬁcant increases in spleen size and cell number, leading us to hypothesize that IL-15 signals differently in myeloid immune cells compared with lymphocytes, for which IL-15/IL-2R b and g c expression are essential. Furthermore, treatment with IL-15 induced RANTES production by Rag2 2 / 2 g c 2 / 2 bone marrow cells, but the presence of g c did not increase bone marrow cell sensitivity to IL-15. This IL-15–mediated RANTES production by Rag2 2 / 2 g c 2 / 2 bone marrow cells occurred independently of the IL-15/IL-2R b and Jak/STAT pathways and instead required IL-15R a signaling as well as activation of JNK and NF-k B. Importantly, we also showed that the trans presentation of IL-15 by IL-15R a boosts IL-15– mediated IFN-g production by NK cells
Puberty Timing and Sex-Specific Trajectories of Systolic Blood Pressure: a Prospective Cohort Study

Background: Sex differences in systolic blood pressure (SBP) emerge during adolescence but the role of puberty is not well understood. We examined sex-specific changes in SBP preceding and following puberty and examined the impact of puberty timing on SBP trajectories in females and males. Methods: Trajectories of SBP before and after puberty and by timing of puberty in females and males in a contemporary birth cohort study were analyzed. Repeated measures of height from age 5 to 20 years were used to identify puberty timing (age at peak height velocity). SBP was measured on ten occasions from 3 to 24 years (N participants, 4062; repeated SBP measures, 29 172). Analyses were performed using linear spline multilevel models based on time before and after puberty and were adjusted for parental factors and early childhood factors. Results: Mean age at peak height velocity was 11.7 years (SD, 0.8) for females and 13.6 years (SD, 0.9) for males. Males had faster rates of increase in SBP before puberty leading to 10.19 mm Hg (95% CI, 6.80–13.57) higher mean SBP at puberty which remained similar at 24 years (mean difference, 11.43 mm Hg [95% CI, 7.22–15.63]). Puberty timing was associated with small transient differences in SBP trajectories postpuberty in both sexes and small differences at 24 years in females only. Conclusions: A large proportion of the higher SBP observed in males compared with females in early adulthood is accrued before puberty. Interventions targeting puberty timing are unlikely to influence SBP in early adulthood.
Sentiment Analysis of Covid-19 Vaccine with Deep Learning

After the emergence of the Covid-19 virus, pharmaceutical companies began making vaccines against this virus. Peoples' reactions towards vaccines varies between acceptance and rejection. Information about these reactions can be found in social media which has become the largest and best source of users' opinions on a specific topic nowadays. One of the most important social media through which this data can be collected is Twitter. It is important to analyze people's opinions about these vaccines to find out the percentage of supporters and opponents of vaccines. Sentiments analysis can be used to analyze people's opinions. In this paper, we proposed a hybrid deep learning model to analyze user sentiment towards the COVID-19 vaccine. The contributions of our work are to adopt an efficient-designed model by combines Convolutional Neural Network (CNN), which has the capability to extract features, and Long Short-Term Memory (LSTM), which can monitor and study long-term dependencies between words. And provide the proposed network topology setting that contributed in producing high performance in sentiment analysis of the COVID-19 vaccine tweets. Extensive experiments have been conducted on a data set of 13,190 tweets. The results proved that the proposed model with the proposed topology setting outperformed the other machine learning models. © 2022 Little Lion Scientific.
Child maintenance and child poverty: A comparative analysis

This article uses the Luxembourg Income Study datasets from circa 2004 to analyse the contribution child maintenance makes to the reduction of child poverty. The countries compared are Canada, UK, USA, Germany, Norway, Denmark, Sweden and Finland representing countries with different child maintenance schemes. Results show that the contribution that child maintenance makes in reducing overall child poverty is minimal but it can reduce child poverty among non-widowed lone mother families if maintenance is received. Countries guaranteeing child maintenance payments by the state are more effective in reducing child poverty. This article uses the Luxembourg Income Study datasets from circa 2004 to analyse the contribution child maintenance makes to the reduction of child poverty. The countries compared are Canada, UK, USA, Germany, Norway, Denmark, Sweden and Finland representing countries with different child maintenance schemes. Results show that the contribution that child maintenance makes in reducing overall child poverty is minimal but it can reduce child poverty among non-widowed lone mother families if maintenance is received. Countries guaranteeing child maintenance payments by the state are more effective in reducing child poverty.
Convex-PL-R - Revisiting aﬀinity predictions and virtual screening using physics-informed machine learning

Virtual screening is an essential part of the modern drug design pipeline, which signiﬁcantly accelerates the discovery of new drug candidates. Structure-based virtual screening involves ligand conformational sampling, which is often followed by re-scoring of docking poses. A great variety of scoring functions have been designed for this purpose. The advent of structural and aﬃnity databases and the progress in machine-learning methods have recently boosted scoring function performance. Nonetheless, the most successful scoring functions are typically designed for speciﬁc tasks or systems. All-purpose scoring functions still perform poorly on the virtual screening tests, compared to precision with which they are able to predict co-crystal binding poses. Another limitation is the low interpretability of the heuristics being used. We analyzed scoring functions’ performance in the CASF benchmarks and discovered that the vast majority of them have a strong bias towards predicting larger binding interfaces. This motivated us to develop a physical model with additional entropic terms with the aim of penalizing such a preference. We parameterized the new model using aﬃnity and structural data, solving a classiﬁcation problem followed by regression. The new model, called Convex-PL R , demonstrated high-quality re-sults on multiple tests and a substantial improvement over its predecessor Convex-PL. Convex-PL R can be used for molecular docking together with VinaCPL, our version of AutoDock Vina, with Convex-PL integrated as a scoring function. Convex-PL R , Convex-PL, and VinaCPL are available at https://team.inria. fr/nano-d/convex-pl/.
Piecewise Pseudolikelihood for Efficient Training of Conditional Random Fields

Discriminative training of graphical models can be expensive if the variables have large cardinality, even if the graphical structure is tractable. In such cases, pseudolikelihood is an attractive alternative, because its running time is linear in the variable cardinality, but on some data its accuracy can be poor. Piece-wise training (Sutton & McCallum, 2005) can have better accuracy but does not scale as well in the variable cardinality. In this paper, we introduce piecewise pseudolikeli-hood, which retains the computational efficiency of pseudolikelihood but can have much better accuracy. On several benchmark NLP data sets, piecewise pseudolikelihood has better accuracy than standard pseudolikelihood, and in many cases nearly equivalent to maximum likelihood, with five to ten times less training time than batch CRF training.
A Faint Star-Forming System Viewed Through the Lensing Cluster Abell 2218 : First Light at z~5.6?

Published in: Astrophys. J. 560 (2001) L119-L122 citations recorded in [Science Citation Index] Abstract: We discuss the physical nature of a remarkably faint pair of Lyman alpha-emitting images discovered close to the giant cD galaxy in the lensing cluster Abell 2218 (z=0.18) during a systematic survey for highly-magnified star-forming galaxies beyond z=5. A well-constrained mass model suggests the pair arises via a gravitationally-lensed source viewed at high magnification. Keck spectroscopy confirms the lensing hypothesis and implies the unlensed source is a very faint (I~30) compact (
Integrated impact of climate change and socioeconomic development on the evolution of camel farming systems.

In Sub-Saharan countries, climate change has already been observed for several decades and is characterized by the decrease in mean rainfall with extensive periods of drought followed by short but severe rains. The dromedary camel, adapted to arid lands and low nutritive natural resources, follows the aridification of ecosystems as she/he did so when moving into Africa through the Sinai Peninsula at the beginning of the Christian era. Thus, the on-going desertification in Northern Africa increases the camel distribution area, both geographically and socially, e.g. with regard to its use by people who are not traditionally camel keepers. Elsewhere, camels are used differently, i.e. for their products (milk, agricultural work) rather than for their traditional uses (packing or riding). On the other hand, facing more contrasted crop ecosystems and an unbalanced climate, which seem to contribute to emerging diseases with complex and often unknown aetiologies, caused high unexplained deaths. These global trends would trigger more changes of
of the Association for Information Systems

The Technology Acceptance model (TAM) is one of the most influential theories in Information Systems. However, despite the model's significant contributions, the intense focus on TAM has diverted researchers’ attention away from other important research issues and has created an illusion of progress in knowledge accumulation. Furthermore, the independent attempts by several researchers to expand TAM in order to adapt it to the constantly changing IT environments has lead to a state of theoretical chaos and confusion in which it is not clear which version of the many iterations of TAM is the commonly accepted one. The present commentary discusses these concerns, speculates on the possible contributions to the current state of affairs, and makes several suggestions to alleviate the problems associated with TAM and to advance IT adoption research to the next stage.
INVESTIGATING TRUST IN MOBILE PAYMENT SERVICES IN INDIA: THE MODERATING ROLE OF GENDER

The mobile payment (m-payment) services have enabled the users to make payment using mobile devices anytime and anywhere. There is a dearth of research that establishes an integrated model for investigating the factors influencing trust in m-payment services and the impact of trust on the behavioural intention to use m-payment services in India. Present study attempts to fulfil these gaps and takes a step further to identify how gender differences affect the relationship of different factors with trust in m-payment services. Survey data was collected from 373 individuals in India and analysed using the SmartPLS 3 software. Results show a positive influence of mobility, social influence, and reputation, while a negative effect of perceived risk on trust in m-payment services. Additionally, multigroup analysis uncovered that men are more influenced by mobility and reputation, while women are more affected by social influence and perceived risk. The outcomes of present study will help m-payment service providers to better understand the factors that should receive the most attention to ensure that customers trust the m-payment service. The study also enables them to appreciate how gender differences affect trust in m-payment services.
The Value of Micro-CT in the Diagnosis of Lung Carcinoma: A Radio-Histopathological Perspective

Micro-computed tomography (micro-CT) is a relatively new imaging modality and the three-dimensional (3D) images obtained via micro-CT allow researchers to collect both quantitative and qualitative information on various types of samples. Micro-CT could potentially be used to examine human diseases and several studies have been published on this topic in the last decade. In this study, the potential uses of micro-CT in understanding and evaluating lung carcinoma and the relevant studies conducted on lung and other tumors are summarized. Currently, the resolution of benchtop laboratory micro-CT units has not reached the levels that can be obtained with light microscopy, and it is not possible to detect the histopathological features (e.g., tumor type, adenocarcinoma pattern, spread through air spaces) required for lung cancer management. However, its ability to provide 3D images in any plane of section, without disturbing the integrity of the specimen, suggests that it can be used as an auxiliary technique, especially in surgical margin examination, the evaluation of tumor invasion in the entire specimen, and calculation of primary and metastatic tumor volume. Along with future developments in micro-CT technology, it can be expected that the image resolution will gradually improve, the examination time will decrease, and the relevant software will be more user friendly. As a result of these developments, micro-CT may enter pathology laboratories as an auxiliary method in the pathological evaluation of lung tumors. However, the safety, performance, and cost effectiveness of micro-CT in the areas of possible clinical application should be investigated. If micro-CT passes all these tests, it may lead to the convergence of radiology and pathology applications performed independently in separate units today, and the birth of a new type of diagnostician who has equal knowledge of the histological and radiological features of tumors.
Research on fault current control method of DC microgrid battery energy storage system

Battery energy storage system has become an important link to maintain the stable operation of DC microgrid because of its �exible control and fast response speed. As the battery storage system generally does not have fault current control capability, when a fault occurs between the poles of the system, the battery storage system will provide a great short-circuit current to the fault point, which will cause a great danger to the system operation and equipment safety. At this stage, current-limiting reactors or fault current limiters are generally added to the ports of the energy storage system to reduce the fault current, which has the disadvantage of limited current-limiting effect or high cost and large area, so it is not suitable for wide application in DC microgrid. This paper proposes a fault current control method for DC microgrid battery storage system, which enables the battery storage system to be connected to DC microgrid through Buck DC/DC by increasing the rated voltage of the battery stack.The control mode of the battery storage system is power control mode when the DC microgrid is running on grid, and it will be changed to voltage control mode when the DC microgrid is running off-grid. In case of inter-pole fault, the Buck DC/DC of the battery storage system is controlled to operate with reduced voltage to achieve fault current limit. This scheme does not affect the normal operation of the system, and can quickly limit the peak short-circuit current without adding additional equipment, making the short-circuit current �exible and controllable. Therefore, this scheme has good fault current limiting capability and greatly reduces the technical requirements of DC microgrid for protection. Finally, this paper veri�es the feasibility of the proposed scheme and its control strategy through PSCAD/EMTDC modeling simulation.