Miracle-dz/UniFAR-FTUs huggingface dataset:
Keypoint-Based Planar Bimanual Shaping of Deformable Linear Objects under Environmental Constraints with Hierarchical Action Framework

This paper addresses the problem of contact-based manipulation of deformable linear objects (DLOs) towards desired shapes with a dual-arm robotic system. To alleviate the burden of high-dimensional continuous state-action spaces, we model DLOs as kinematic multibody systems via our proposed keypoint encoding network. This novel encoding is trained on a synthetic labeled image dataset without requiring any manual annotations and can be directly transferred to real manipulation scenarios. Our goal-conditioned policy efficiently rearranges the configuration of the DLO based on the keypoints. The proposed hierarchical action framework tackles the manipulation problem in a coarse-to-fine manner (with high-level task planning and low-level motion control) by leveraging two action primitives. The identification of deformation properties is bypassed since the algorithm replans its motion after each bimanual execution. The conducted experimental results reveal that our method achieves high performance in state representation and shaping manipulation of the DLO under environmental constraints.
Static magnetic fields and nerve function

Hypothetical mechanisms by which magnetic fields may influence nerve function were investigated. The motivation for this interdisciplinary research was to devise a non-invasive method by which an anaesthetic effect may be produced, without the use of drugs or injections. If achievable, such a method would eliminate the risks to patient welfare posed by traditional anaesthetic drugs and could minimise the cost of surgery. Two hypothetical methods of non-invasive anaesthesia were evaluated, each intended to prevent the propagation of action potentials along nerve fibres. The first method attempted to hyperpolarise a nerve with the use of a rotating magnetic field. The second method hypothesised that the rate of diffusion of sodium ions during the depolarisation of a nerve fibre could be reduced by a magnetic field aligned with the axis of the nerve. These methods were analysed by computer simulation of the electrical properties of a nerve fibre, in vivo experimentation and by experiments with ion exchange membranes, using applied magnetic fields in the range of 0.1-0.4 tesla. Both hypothetical techniques of non-invasive anaesthesia were concluded to be unfeasible. A further benefit of this research is to clarify some of the potential risks to human health that may be caused by exposure to static magnetic fields.
A Survey on Stock Market Price Prediction System using Machine Learning Techniques

Abstract: Prediction of stock prices is one of the most researched topics and gathers interest from academia and the industry alike. In the finance world stock trading is one of the most important activities. Stock market prediction is an act of trying to determine the future value of a stock other financial instrument traded on a financial exchange. This paper explains the prediction of a stock using Machine Learning. The technical and fundamental or the time series analysis is used by the most of the stockbrokers while making the stock predictions. The programming language is used to predict the stock market using machine learning is Python. In this paper we propose a Machine Learning (ML) approach that will be trained from the available stocks data and gain intelligence and then uses the acquired knowledge for an accurate prediction. The paper focuses on the use of Linear Regression, Moving Average, K-Nearest Neighbours, Auto ARIMA, Prophet, and LSTM based Machine learning techniques to predict stock values. Factors considered are open, close, low, high and volume. The models are evaluated using standard strategic indicators: RMSE and MAPE. The low values of these two indicators show that the models are efficient in predicting stock closing price. We conducted comprehensive evaluations on frequently used machine learning models and conclude that our proposed solution outperforms due to the comprehensive feature engineering that we built. The system achieves overall high accuracy for stock market price prediction. This work contributes to the stock analysis research community both in the financial and technical domains. Keywords: Stock Market, Machine Learning, Prediction, LSTM, Python, Analysis, Linear Regression, Feature Engineering.
A Case against CSR Initiatives

There is fuzzy information in the existing body of knowledge regarding what CSR is. CSR initiatives require extensive resources and investment from the organizations and their shareholders. But, interest in implementing CSR initiatives in the contemporary business world is ever-growing. Business leaders cherish CSR initiatives, and consumers also expect business organizations to do more for the greater good of societies and communities. Then again, shareholders may not appreciate the idea of investing wealth and resources in CSR initiatives if CSR initiatives fail to be a proven tool or strategy to enhance financial or organizational performance. The purpose of this article is to investigate the scholarly arguments against CSR initiatives in operations so that business organizations and shareholders can make informed decisions if they should invest resources and wealth in CSR initiatives. In other words, the article explores why business organizations or shareholders should not employ CSR initiatives in their operations for the sustainability and profitability of the organizations. Based on the systematic literature review, the study found that CSR initiatives did not always negatively impact financial or organizational performance. CSR initiatives and financial or organizational performance also had a neutral, mixed, or favorable relationship. The literature review results imply that organizations with CSR initiatives have a greater chance of attaining enhanced financial or organizational performance than those without CSR initiatives. It recommends further research on what percentage of operating costs or marketing costs organizations should invest in CSR initiatives to generate break-even or enhanced financial or organizational performance.
Efficacy of Music Therapy on Adolescents with Depressive Symptoms

Adolescents in the USA are experiencing increasing rates of depressive symptoms each year, resulting in a variety of emotional, social, neurological, and behavioral problems. Current traditional treatments are limited in their ability to reduce symptoms of depression. In the past few decades, music therapy has emerged as a viable treatment for depression and its comorbidities across various age groups and demographics. Music therapy is a unique form of clinical and evidence-based treatment characterized by a relationship between the music therapist and patient to address the patients’ needs through various methods such as song-writing, listening, and/or singing. This literature review analyzes a wide range of qualitative and quantitative sources studying various cohorts and contexts to examine the efficacy of music therapy for treating adolescent depression in comparison to traditional treatments. We found that most sources concluded that music therapy is more effective than traditional treatments and can be used as a supplement to such treatments. Music therapy has multiple positive socio-emotional, behavioral, and neurological effects, particularly for adolescents with depressive symptoms. Furthermore, many studies noted improvements in the comorbidities and related disorders of depression. In the future, studies should be conducted with standardized trials focusing specifically on adolescents and music therapy treatments to refine therapeutic applications. In conclusion, we make the case that music therapy should be made more accessible to adolescents as a treatment option.
Trends for Readmission and Mortality After Heart Failure Hospitalisation in Malaysia, 2007 to 2016

Background and objectives: Data on population-level outcomes after heart failure (HF) hospitalisation in Asia is sparse. This study aimed to estimate readmission and mortality after hospitalisation among HF patients and examine temporal variation by sex and ethnicity. Methods: Data for 105,399 patients who had incident HF hospitalisations from 2007 to 2016 were identified from a national discharge database and linked to death registration records. The outcomes assessed here were 30-day readmission, in-hospital, 30-day and one-year all-cause mortality. Results: Eighteen percent of patients (n = 16786) were readmitted within 30 days. Mortality rates were 5.3% (95% confidence interval (CI) 5.1–5.4%), 11.2% (11.0–11.4%) and 33.1% (32.9–33.4%) for in-hospital, 30-day and 1-year mortality after the index admission. Age, sex and ethnicity-adjusted 30-day readmissions increased by 2% per calendar year while in-hospital and 30-day mortality declined by 7% and 4% per year respectively. One-year mortality rates remained constant during the study period. Men were at higher risk of 30-day readmission (adjusted rate ratio (RR) 1.16, 1.13–1.20) and one-year mortality (RR 1.17, 1.15–1.19) than women. Ethnic differences in outcomes were evident. Readmission rates were equally high in Chinese and Indians relative to Malays whereas Others, which mainly comprised Indigenous groups, fared worst for in-hospital and 30-day mortality with RR 1.84 (1.64–2.07) and 1.3 (1.21–1.41) relative to Malays. Conclusions: Short-term survival was improving across sex and ethnic groups but prognosis at one year after incident HF hospitalisation remained poor. The steady increase in 30-day readmission rates deserves further investigation.
Prediction of Electronic and Optical Properties of Boron Selenide BSe (2H) monolayer based on First-Principles

Abstract. In this study, we examined some properties of 2D monolayer of Boron selenide BSe (2H) such as structural, electronic and optical properties. The BSe (2H) monolayer has an indirect bandgap of 2.62eV from Γ to M points. We explored from density of states (DOS), in valance band close to fermi level 4p state of selenium (Se) atom is hybridized with 2p state of B atom, but close to lower part of conduction band 2p state of boron (B) atom is ascendant over the 4p state of selenium atom. We have also calculated optical parameter like imaginary and real component of dielectric function, refractive index, absorption coefficient from random phase approximation method(RPA).
"Effects of Drug Addiction and Abuse on Academic Performance of Students Within the Age of 14-30 Years"

mental stress relief, curiosity, build-up self-esteem & ability to increase performance, sense of euphoria. Most of the drug addicts think that peer pressure and frustration are major cause of drug addiction. Due to drug addiction many physiological consequences have been faced by the student’s likely headache, memory loss, gastritis, highest was insomnia (32.87%), bad tempered and instability. Drug addiction effected on academic performance resulted severe grade decades, drop out, low concentration span (41.26%), discontinuation of academic career, lack of interest in extra curriculum activities and strained relationship with other students. So the research concludes and recommends that parents and public enlighten should be carried out periodically on the dangers of drug addiction. While the regulatory bodies be strengthened to carry out their functions.
TOWN-CRYING FOR KNOWLEDGE DISSEMINATION ON CLIMATE CHANGE TO NON-LITERATE RURAL PEOPLE IN NIGERIA

This paper interrogated the level of climate change education among rural dwellers in Nigeria. To achieve this objective, the paper relied on extensive document review/content analysis for its information as well as the symbolic interactionist perspective as its theoretical framework. A purely qualitative approach was adopted to analyze information gathered from secondary sources. Based on the information gathered and the results arrived at, the paper submits that a significant number of the rural population in Nigeria have very little or no knowledge of climate change and its associated effects. Subsequently, the paper recommendation the use of using town-criers and town-hall engagement sessions to communicate the causes, trends and progress on climate change issues to rural people will extend the frontiers of knowledge in this regard and ensure that more people in the rural areas, especially non-literate ones, become increasingly aware of climate change and the threats it poses to them.
Energy Optimization With Multi-Sleeping Control in 5G Heterogeneous Networks Using Reinforcement Learning

The massive deployment of small cells in 5G networks represents an alternative to meet the ever increasing mobile data traffic and to provide very-high throughout by bringing the users closer to the Base Stations (BSs). This large increase in the number of network elements demands a significant increase in the energy consumption and carbon footprint followed by complex interference management. In order to address these challenges, we consider multi-level Sleep Mode (SM) where BS components with similar activation/deactivation times can be put to sleep. The deeper and higher energy efficient the SM is, the longer it will take the BS to activate, which might impose degradation in the Quality of Service (QoS). While this adds operational flexibility to the BS, it brings complex management to the operator. In this paper, we consider a heterogeneous network architecture where small cells can switch to different SM levels to save energy and reduce dropping rate. We propose a reinforcement learning algorithm for small cells that adapts their activities subject to service delay constraint. In this regard, the algorithm intelligently learns from the environment based on the co-channel interference, the cell buffer size and the expected cell throughput in order to decide the best SM policy. Numerical values show that important energy savings can be obtained with an acceptable dropping rate. Moreover, we show that while offloading users to the macro cell can significantly reduce their delay, dropping rate and the cluster energy consumption, it comes at a cost of decreasing the network energy efficiency up to 5 times compared with the case of no offload.
Professional teacher's development in enlarged learning contexts. The transition from skills to agency Lo sviluppo professionale docente nei contesti d'apprendimento allargati. La transizione dalle competenze all'agentività

The policies on education and training systems seem to point today at a new defininition of framework to promote a New Welfare of active citizenship, that we could call “Learnfare”. In pre-primary education this is expressed as a neces sity to integrate enlarged learning contexts, in order to build an integrated sys tem of education. So, what implications this prospective produce on the teacher’s practice? Moreover, if we think at the roles traditionally associated to the pre-primary teacher’s professional profile, what does it mean this kind of new framework? Today, professional teacher’s development requires a concep tual change: it cannot longer be interpreted in terms of basic and strategic skills learning, but must include reflexive and transformative competences buit on the interaction within a context that includes the parental and intergenerational re lationships. Through the implicit and/or latent resources arising from these wider contexts, a teacher can activate a capability process on both his personal and professional training. Furthermore, the approach proposed about profes sional teacher’s development could enact new pedagogical practices and theo retical pathways, in order to establish a new conceptual framework in the space of capability learnfare through which to address future policies. Le politiche sui sistemi d’istruzione e formazione sembrano puntare oggi alla ridefinizione della prospettiva di learnfare per promuovere un New Welfare di cittadinanza attiva. Nell’educazione prescolastica questo si traduce nell’esigen za di integrare i contesti d’apprendimento allargati per costruire un sistema for mativo integrato. Ma quali implicazioni produce questo sulla pratica dell’inseg nante? Ma soprattutto, cosa comporta un rinnovamento di questo genere nelle funzioni tradizionalmente associate al profilo professionale? Oggi lo sviluppo professionale richiede un cambiamento concettuale: esso non puo piu essere interpretato nei termini dell’apprendimento di competenze basiche e strate giche, ma deve necessariamente comprendere la costruzione di competenze riflessive e trasformative sollecitate dall’interazione con i contesti parentali, inter generazionali e sociali allargati. Attraverso le risorse implicite e/o latenti sca turenti dai contesti allargati l’insegnante attiva un processo di capacitazione sulla propria formazione personale e professionale insieme. L’approccio dello sviluppo professionale proposto diventa cosi capace di creare nuove direzionalita pedagogiche ed educative, in modo da stabilire un nuovo quadro concettuale di learnfare delle capacitazioni entro cui orientare le politiche future.
Single-phase series active power filter with transformer-coupled matrix converter

This study presents a series active power filter based on a single-phase matrix converter. Back-to-back voltage-source converters with nested control loops and passive filters are normally used for this application. A matrix converter, as proposed in this study, allows a simpler implementation both in terms of hardware (no grid filter and no DC link) and of control. Moreover, a novel zero-voltage compensation technique is introduced in order to reduce the distortion around the input voltage zero crossing. Simulations and experiments are used to validate the considered control methods, confirming the feasibility of the proposed architecture.
Methodologies and Applications of Artificial Intelligence in Systems Engineering

This paper presents an overview of the methodologies and applications of artificially intelligent systems (AIS) in different engineering disciplines with the objective of unifying the basic information and outlining the main features. These are knowledge-based systems (KBS), artificial neural networks (ANN), and fuzzy logic and systems (FLS). To illustrate the concepts, merits, and demerits, a typical application is given from each methodology. The relationship between ANN and FLS is emphasized. Two recent developments are finally presented: one is intelligent and autonomous systems (IAS) with particular emphasis on intelligent vehicle and highway systems, and the other is the very large scale integration (VLSI) systems design, verification, and testing.
Bilingual Color Learning Application as Alternative Color Learning for Preschool Student

Color recognition skills for early childhood are very important as a basis for starting other learning, by recognizing colors a child will get the benefits of creative development, development of sensory abilities and other positive benefits. Based on the results of observations that had been done in the kindergarten school that locate in Tangerang, Indonesia, it was found that most children were still often confused or had difficulty distinguishing colors through conventional teaching. In this research, a color recognition application was built in two languages, which aims to help students to recognize color faster. Testing will be carried out using the pretest and posttest methods with the "paired sample t test" method to measure the level of student development before and after using the application, user acceptance testing will also be carried out using UAT to measure the level of user acceptance of the application that had been made. From these evaluation, it was found that the results of the development were quite good with the t-stat results of 8.69 and the user acceptance test results of 86.72%.
"Cognition and Omega-3 Fatty Acids: A Narrative Review of the Literature"

Today we know quite well that a healthy lifestyle that includes physical activity, healthy eating or moderating toxic habits such as alcohol and tobacco are associated with a lower risk of suffering from some diseases such as cardiovascular diseases, some types of neoplasms, and neurodegenerative diseases in general, and particularly cognitive impairment or dementia. The dietary factor is perhaps the most complex, and much research is aimed at elucidating which foods are associated with this beneficial effect and why. To this respect, one of the most implicated actors in this positive effect has shown to be polyunsaturated fatty acids, especially those belonging to the omega-3 group. Fatty acids are biomolecules consisting of a linear hydrocarbon chain of variable length, with a carboxyl group (-COOH) at one end and a methyl group (-CH3) at the other. The carbon atoms in the chain are joined by single or double covalent bonds. The absence of double bonds defines the acid as saturated, while the presence of one double bond in the chain defines it as monounsaturated acid, and the presence of multiple double bonds as polyunsaturated. Polyunsaturated fatty acids are known by their acronym PUFAs (Poly Unsaturated Fatty Acids). Omega-3 fatty acids (ω-3), together with omega-6 fatty acids (ω-6), make up the group of so-called essential fatty acids, which owe their name to the fact that they are essential for the body since the body is not capable of producing them on its own and must acquire them from foods that contain them. Whether a fatty acid is referred to as omega-3 or omega-6 is established by the location of the first double bond from the methyl-terminal end. In omega-3s, the double bond is at carbon 3 [C3-C4] and can also be identified as n-3. In omega-6, the double bond is at carbon 6 (C6-C7) and is also known as n-6.
Investigating Pre-Service Mathematics Teachers’ Self-Assessment Process

Teaching practice requires a variety of thinking skills, the most important of which is reflective thinking, which is defined as a self-assessment ability that enables an individual to overcome the uncertainties he encounters by questioning his experiences through critical and conscious research. This study aims to examine pre-service mathematics teachers' self-assessment processes related to their classroom practices and professional development. For this purpose, a self-assessment form composed of openended questions was prepared, in order to allow pre-service teachers reflect on their teaching practices. The form was filled by eight pre-service teachers immediately after their in-class practice sessions through an eight–week training program. Items included in the self-assessment forms were analyzed under three categories, based on the classification proposed by Schön (1987): i) Reflectionon-action, ii) Reflection-in-action, iii) Reflection-for-action. The analysis revealed that the pre-service teachers often engaged in self-assessments in the reflection-on-action and reflection-in-action categories, but mostly steered clear of the reflection-for-action category.
Entwurf von Mehrgrößen-Ausgangsreglern auf Störverhalten mittels verallgemeinerter Fourierreihen/ Design of multivariable controllers for disturbance behaviour via generalized Fourier series

In [1; 2; 3] D. Franke proposed a new technique for the description and design of linear control systems, it is based on the expansion of the system variables in generalized Fourier series. This leads to an algebraic system description, which is used for the design of controllers for disturbance behaviour. Suitable disturbance step responses of the closed loop are prescribed. By balancing the Fourier coefficients one obtains linear equations for the controller parameters.