Miracle-dz/UniFAR-FTUs huggingface dataset:
ZERO-FORCING BEAMFORMING ENERGY EFFICIENCY OPTIMIZATION FOR THE SECURITY CONTROL OF WIRELESS POWER TRANSFER SYSTEM

—This paper proposes a zero-forcing beamforming design for the energy eﬃciency optimization of the magnetic resonance based wireless power transfer system with multiple transmitter coils, which aims to secure energy transfer control. A scheme based on beamforming technology is proposed to prevent unauthorized users from accessing the system, which builds a beamforming model consisting of multiple transmitter coils, a target receiver, and a non-target receiver to simulate the actual system. Then to optimize the proposed system’s energy eﬃciency while constraining the target receiver’s energy, spectral eﬃciency, and transmitter’s power, the proposed beamforming model is constructed as an optimization problem. To solve this non-convex nonlinear fractional programming problem, the Dinkelbach algorithm is used for fractional conversion, and then the zero-forcing constraints are equivalently replaced. Finally, two solutions of the nonlinear solution and closed-form solution are derived. The simulation results show that the energy eﬃciency optimization strategies of zero-forcing beamforming with the two derived solutions can satisfy the design requirements.
Factors Associated with Contraceptive Discontinuation among Women (15-49 Years) in Uganda

Contraceptive discontinuation is the dilemma behind full utilization of family planning services, leading to an unnecessary increase in fertility rates, unwanted pregnancies, and induced abortions. The objective of this work is to investigate factors associated with contraceptive discontinuation among women aged 15-49 years in Uganda. The study is based on secondary data set from Performance and Monitoring Accountability 2020 (PMA 2020) for PMA2016 Uganda Round 4, with a selection of a sample of 4047 women. The binary logistic regression is used at the multivariate analysis level. The overall findings reveal that 275 women (6.8%) discontinued the use of contraceptives. The logistic regression revealed that the younger women (25-29, 30-35) had higher odds of discontinuing (OR = 3.7, p=0.020), (OR = 4.6, p=0.016), compared to those of (15-19) age group respectively. Women who were formerly married had higher odds of discontinuing (OR = 4.2, p=0.007), compared to the odds of currently married women. More so, women who have used any method recently had higher odds of discontinuing (OR = 160.9, p=007), than women who used long-lasting injectable. Lastly, Women concerned about their health had higher odds of contraceptive discontinuation (OR = 1.923, p=0.044), than women who had no health concerns. Contraceptive discontinuation is associated with age categories, marital status method of contraceptives and health concerns, which are vital in the proper use and continuation of contraceptives. The study recommends that interventions that encourage young people to use contraceptives be prioritized and supported, as they will fill the gap created by an unmet need for family planning, promotion of partner involvement and awareness need to be taken into account as many different types of contraceptives cannot be used in secrecy. While adopting and applying the mentioned recommendations, emphasis should be put on women most especially between 25-35 years.
PARTIAL ELECTRICAL EQUIVALENT CIRCUITS AND FINITE DIFFERENCE METHODS COUPLING; APPLICATION TO EDDY CURRENTS CALCULATION FOR CONDUCTIVE AND MAGNETIC THIN PLATES

This paper presents a new integro-differential coupling between partial equivalent electrical circuits (PEEC) and finite difference method (FDM) taking into account the magnetization effect. This coupling is intended for thin plates having simultaneously significant conductive and magnetic properties in the presence of exciting coils of complex topologies. These cases exist in eddy current nondestructive testing (ECNDT), eddy current separation, induction or levitation melting devices, and more other applications. The choice of FDM, is in relation with rectangular surfaces generated by numerical meshes leading to mathematical integrations of magnetic and electrical quantities with independent variables, unlike more complicated forms of surfaces generated by finite element method (FEM) or others. Fully successful analytical expressions have been realized and implemented in overall coupling process. The PEEC method is mainly used to calculate the magnetic field applied to the nodes of the plate from different inclined polygonal coils. The results of magnetic field and eddy current distributions on thin plates are presented, and parts of them are compared with those realized by Flux 3D software.
Animation Therapy for Improving Social Interaction in Children with Autism Spectrum Disorder

Introduction: Animation is about creativity, movement, and imagination. It offers the opportunity to bring an object, drawing or person to life. The creative process of animation has been adapted to provide a clinically effective framework to complement evidence-based therapeutic practice. Using simple techniques, this approach has been designed to facilitate motivation with difficult to reach clients. With the flexibility to work with verbal, non-verbal materials animation enables the service user to express thoughts and feelings using visual, tactile, and auditory techniques. Aim: To identify high functioning autism children using the High-Functioning Autism Spectrum Screening Questionnaire (ASSQ). To review the effect of animation therapy for improving social skills, communication, and self-confidence in an experimental group of youngsters with an autism spectrum disorder. Method: The study was done on 40 subjects, 20 in the control group and 20 in the experimental group. The screening for top functioning autism clients was done using the High-Functioning Autism Spectrum Screening Questionnaire (ASSQ). The pre-test and post-test were done using the Gilliam Autism Rating Scale (GARS-2). The scores of both groups were compared to find out the effectiveness. Result: Statistical analysis shows there is a statistically important distinction in the social interaction of youngsters. Conclusion: This study concludes that animation medical care is simpler in up social interaction in kids with syndrome Autism Spectrum Disorder.
Particle formation methods and their products

A step of the present invention is such that the active ingredient and the additive co-precipitation is brought into contact with the active ingredients and additives to prepare a target solution is dissolved in both media, and the target solution, the anti-solvent fluid, using the SEDS The additive is generally a protective additive, in particular the taste and / or odor masking agent with. The present invention also provides a fine particle ball formulation prepared by the above method, the ball formulation has a finite gradient in the relative additive concentration, and the concentration of the rich surface from which the active component of the particle-rich core additive components It increases toward radially outwardly.
A Review on Multimodal Brain Image Fusion using Deep Learning for Alzheimer's disease

: Alzheimer's disease (AD) may be a progressive encephalopathy and therefore the commonest explanation for dementia in later life. It causes cognitive deterioration, eventually leading to inability to hold out activities of lifestyle. Alzheimer’s disease is an incurable, progressive neurological encephalopathy. Early diagnosis of Alzheimer’s disease can help with proper treatment and stop brain tissue damage. Several statistical and machine learning models are exploited by researchers for Alzheimer’s disease diagnosis. Detection of Alzheimer’s disease is exacting thanks to the similarity in Alzheimer’s disease resonance Imaging (MRI) data and standard healthy MRI data of older people. Recently, advanced deep learning techniques have successfully demonstrated human-level performance in numerous fields including medical image analysis. convolutional neural network for Alzheimer’s disease diagnosis using brain MRI data analysis. Experiments on the Alzheimer’s Disease specify that the proposed image fusion method achieves better overall performance than unimodal and feature fusion methods, and that it performance state-of-the-art methods for AD diagnosis.
Acute effects of two difference volume of resistance exercises on cardiac hormones, angiotensin- II and lipolysis markers in active young male

Objective: The natriuretic peptides (NPs) are endogenous cardiac hormones that have been entered in current guidelines for the diagnosis of heart problems. Resistance exercise (RE) has a beneficial effect on the NPs secretion, but there is limited research on what type of volume training of RE is appropriate. Therefore, the purpose of this study was to compare the pro-ANP, NT-pro-BNP, ANG-II, NEFA and Glycerol response during the RE with different volumes. Methods: Fifteen active men volunteers (age, 26.1 ± 0.9; years; weight, 74.4±1.9 kg; height, 176.3±1.3 cm) after determination of their strength (10-Repetition Maximum) performed two resistance exercise trials with a one-week interval. subjects performed similar resistance protocols while only set frequencies were differing (three-set or five-set for ten movements). Blood samples were taken in three times including, reference point, 3 and 30 minutes after exercise. Data were analysed using two-way repeated measures of ANOVA. Results: Finding revealed that resistance exercise high-volume has a significant effect on increase NT-pro-BNP (p=0.041) but the increment in ANP and decrement ANG-II was not meaningful. Additionally, lipolysis concentrations as a result of resistance exercise (increased in glycerol and decreased in NEFA) were significant without meaningful between protocols. Conclusion: Significant augment in NT-pro-BNP due to resistance high-volume (five-set) and synchronized overdrive neuro hormonal with left-ventricular dysfunction demonstrate that resistance exercise high-volume might be related to changes in pressure inside the heart that can contribute to heart problems. Acute effects of two difference volume of resistance exercises on cardiac hormones, angiotensinII and lipolysis markers in active young male. Nikseresht Ali1, Nikseresht Amjad 2, Ahmadizad Sajad 2 1Faculty of sport sciences, Razi University of Kermanshah, Iran 2Faculty of Sport Sciences and health. Shahid-Beheshti University of Tehran, Iran Accepted on July 28, 2021
Toxicity tests using flurbiprofen, naproxen, propranolol and carbamazepine on Lepidium sativum, Daphnia magna, and Aliivibrio fischeri

The residues of active pharmaceutical ingredients present risk to the environment and can cause health problems. In this study, the toxicities of flurbiprofen, naproxen Na, propranolol HCl, and carbamazepine, all of which belong to the most frequently used analgesic, beta-blocker, and antiepileptic drugs, were investigated. Toxicity tests were conducted using a watercress (Lepidium sativum), a freshwater invertebrate (Daphnia magna), and a luminescent bacterium (Aliivibrio fischeri). The various biological test methods used in this study were compared in terms of the sensitivity of these species to the drugs tested. When the three toxicity-test results were analyzed, different sensitivities were determined in the synthetic pharmaceutical wastewater samples that had different characteristics. In general, D. magna was the most sensitive among the test organisms, A. fischeri was the second most sensitive, and L. sativum was the least sensitive. The results showed that all four micropollutants were capable of causing toxicity in luminescent bacteria, a freshwater invertebrate and in watercress with a 50% effective concentration of ~1.9 to 50 mg/L. In particular, “flurbiprofen” had a more toxic effect than the substances in all the toxicity tests.
The Role of Banking Crisis in the Effect of Income Diversity on Risk of Banking Industry in Iran

Today, with the globalization of the economy and the increased competition between the banks, the profits of traditional banking activities have diminished, and their risk has been on rise. Thus, banks should adopt more efficient risk management methods. In the last three decades, Iranian economy has faced many fluctuations in macroeconomic areas, e.g. the banking industry. The main goal of the current study is to examine the role of banking crisis in the effect of income diversity on risk of banking industry using panel econometric method )EGLS (. For this purpose, 8 accepted banks in the Tehran Stock Exchange were evaluated as a research statistical population during (2005-2018). The results show that an increase in the share of Non-interest income (income diversity) in the time of banking crisis does not have a significant effect on the banking stability. Another finding of the study suggests that there is a negative significant relationship between the index of concentration during the crisis and the stability of the banking industry. Also, there is a positive significant relationship between the size of the bank, the loan ratio, and the capital adequacy index during the crisis with banking risk.
Inclusion of uncertainty with different types of fuzzy numbers into DEMATEL

Nowadays the multi-criteria decision making is very complicated due to uncertainty, vagueness, limited sources, knowledge and time. The Decision-making Trial and Evaluation Laboratory (DEMATEL) method is a widely used multi-criteria decision-making method to analyze the structure of a complex system. It is useful in analysing the cause and effect relationships between the components of the system. Fuzzy sets can be used to include uncertainty in multi-criteria decision making. Linguistic assessments of decision makers can be translated into fuzzy numbers. In this study, fuzzy numbers, intuitionistic fuzzy numbers and neutrosophic fuzzy numbers were used for the decision makers evaluations in the DEMATEL method. The aim of this study was to evaluate how different types of fuzzy numbers affect the final results. An application of risk in construction projects was selected from the literature, where seven experts used a linguistic scale to evaluate different criteria. The results showed that there are only slight differences between the weights of the criteria with regard to the type of fuzzy numbers.
The perception of Pathumthani residents toward its environmental quality, suburban area of Thailand

The urbanization process is often concentrated mostly in urban areas, resulting into urban development sprawl. This has effects on the lifestyles and activities of urban people, which in turn significantly affects the health of the city. The quality of the urban environment plays an important role in public health with respect to urban issues ranging from quality of utilities and services to quality of life. This study aims to study the perception of Pathumthani residents toward its environmental quality through spatial cluster analysis. A total of 1,000 sets of data collected from the interview survey among residents or commuters traveling through Pathumthani province was used for this study. The residents' response towards environmental factors was examined through the classification of their different opinions among built environment and health aspects. The statistical analysis which was performed in this study was cluster analysis to demonstrate its relationship. With the level of satisfaction on environment aspect and health status in indicating number of congenital diseases, the result of this study found that condition of living environment (through Likert scale) affects the urban health with statistical significance of (P <0.05). Therefore, provincial health policy should focus more on developing a healthy city in consistence with economic and social development while putting adequate mechanisms for environmental surveillance monitoring at the community level. The result of study can confirm the usefulness of this unconventional approach by asking residents or commuters about their satisfaction on built environment which can represent as an evidence-based planning approach by linking local people attitudes and translating them into creating liveable and better urban environmental quality. To have a good understanding of local people preferences, the recommendation to be given to the capacity of communities can be focused for improving people's quality of life by providing better accessibility, high quality of infrastructures and services. Finally, a set of features of satisfied built environment can help to support the continued growth of the city in term of basic need and sufficiency provision of facility and utility system.
Sentiment Analysis of Twitter Data Using Machine Learning Algorithm

—Sentiment analysis is a technique used for mining data, viewing, evaluating, or analyzing a sentence to predict its emotion using Natural Language Processing. It is done to gain an impartial understanding of the writer’s viewpoint towards any particular topic. In this paper, sentiment analysis of political leader’s timeline tweets which they have tweeted during the election campaign and also include to find out sentiment of the tweets in which these political leaders are tagged. This paper proposes a quicker method of sentiment analysis for a broad population based on the use of multiple classifiers to categorise data opinions as positive, negative, or neutral. Valence Aware Dictionary and Sentiment Reasoner were used for faster and accurate labelling of tweets. Support Vector Machine obtained the highest accuracy out of all the remaining classifiers.
The Mind as a Market of Minds

There is substantial evidence from neuroscience and psychology that what we perceive as a unified mind is actually a vast collection of relatively autonomous minds. A decentralized brain appeals from an evolutionary perspective too, as it requires less interconnected wiring than a fully centralized brain, can more easily evolve through replication and can more easily adjust to damage. The main downside is potentially suboptimal learning relative to a truly unified brain. This paper suggests that the downside is limited. Indeed, under some conditions decentralized learning can match the optimal unified solution. These conditions liken the decentralized mind to a competitive capital market and its neural components to wealth-seeking gamblers. The very possibility lends support to the Thousand Brains theory of Hawkins (2021), by showing how the Thousand might behave like a rational One.
FinBERT—A Deep Learning Approach to Extracting Textual Information

In this paper, we develop FinBERT, a state-of-the-art deep learning algorithm that incorporates the contextual relations between words in the finance domain. First, using a researcher-labeled analyst report sample, we document that FinBERT significantly outperforms the Loughran and McDonald (LM) dictionary, the naïve Bayes, and Word2Vec in sentiment classification, primarily because of its ability to uncover sentiment in sentences that other algorithms mislabel as neutral. Next, we show that other approaches underestimate the textual informativeness of earnings conference calls by at least 32% compared with FinBERT. Our results also indicate that FinBERT’s greater accuracy is especially relevant when empirical tests may suffer from low power, such as with small samples. Last, textual sentiments summarized by FinBERT can better predict future earnings than the LM dictionary, especially after 2011, consistent with firms’ strategic disclosures reducing the information content of textual sentiments measured with LM dictionary. Our results have implications for academic researchers, investment professionals, and financial market regulators who want to extract insights from financial texts.
Central Bank Digital Currency: A Corporate Finance Perspective

We build models with an interest-bearing central bank digital currency (CBDC) to investigate the impacts of issuing CBDC on banking and the macroeconomy. When CBDC is the only asset, a higher interest rate on CBDC does not necessarily lead to financial disintermediation. It could promote bank lending and firm investment because CBDC and bank deposits are complements. The interest rate on reserves and the required reserve ratio can affect bank lending and investment when the reserve constraint binds. In our extensions, cash and interest-bearing CBDC can coexist, where the coexistence may require the central bank to adjust either the CBDC interest rate or the interest rate on reserves. Our results suggest that the design of CBDC and banking matters for understanding how CBDC affects the macroeconomy.
Pairing for Greenhorn: Survey and Future Perspective

Pairing is the most powerful tool in cryptography that maps two points on the elliptic curve to the group over the finite field. Mostly cryptographers consider pairing as a black box and use it for implementing pairing-based cryptographic protocols. This paper aims to give the overview of pairing as simple as possible for greenhorn and those who are working and wish to work in the pairing. The paper gives the concrete background of pairing and recommends an appropriate pairing among different choices for constructing pairing-based cryptographic protocols. We also analyze the bandwidth and computational efficiency of pairing and submitting those pairing suitable for implementing a cryptographic protocol for lightweight devices. Additionally, we discuss the extension of bilinear pairing to tri-linear and multilinear pairing and discuss a few assumptions to check their feasibility to implement multilinear pairing.
Enel: Context-Aware Dynamic Scaling of Distributed Dataflow Jobs using Graph Propagation

Distributed dataflow systems like Spark and Flink enable the use of clusters for scalable data analytics. While runtime prediction models can be used to initially select appropriate cluster resources given target runtimes, the actual runtime performance of dataflow jobs depends on several factors and varies over time. Yet, in many situations, dynamic scaling can be used to meet formulated runtime targets despite significant performance variance.This paper presents Enel, a novel dynamic scaling approach that uses message propagation on an attributed graph to model dataflow jobs and, thus, allows for deriving effective rescaling decisions. For this, Enel incorporates descriptive properties that capture the respective execution context, considers statistics from individual dataflow tasks, and propagates predictions through the job graph to eventually find an optimized new scale-out. Our evaluation of Enel with four iterative Spark jobs shows that our approach is able to identify effective rescaling actions, reacting for instance to node failures, and can be reused across different execution contexts.
Improving callsign recognition with air-surveillance data in air-traffic communication

Automatic Speech Recognition (ASR) can be used as the assistance of speech communication between pilots and air-traffic controllers. Its application can significantly reduce the complexity of the task and increase the reliability of transmitted information. Evidently, high accuracy predictions are needed to minimize the risk of errors. Especially, high accuracy is required in recognition of key information, such as commands and callsigns, used to navigate pilots. Our results prove that the surveillance data containing callsigns can help to considerably improve the recognition of a callsign in an utterance when the weights of probable callsign n-grams are reduced per utterance. In this paper, we investigate two approaches: (1) G-boosting, when callsigns weights are adjusted at language model level (G) and followed by the dynamic decoder with an on-the-fly composition, and (2) lattice rescoring when callsign information is introduced on top of lattices generated using a conventional decoder. Boosting callsign n-grams with the combination of two methods allowed us to gain 28.4% of absolute improvement in callsign recognition accuracy and up to 74.2% of relative improvement in WER of callsign recognition.