Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhancing Organizational Performance through Social Media use and Knowledge Management

Researchers acknowledge the importance of knowledge, innovation, social capital, and social media for organizations. Based on practical and academic importance of these variables, hypotheses were developed and relationships have been tested among knowledge management, innovation and performance for various contexts. Furthermore, hypotheses have also been drawn to test the relationships amongst knowledge management, social capital, and social media use. However, limited research examined relationships amongst all these constructs in the same research framework, and this research attempted to cover that gap. Thus, this research pays original contribution in the form of testing an extended model regarding the impact of social media use on companies’ performance involving the investigation of serial mediating effect of social capital, knowledge management, and innovation. Pakistan’s home appliance industry markets around 2500 electronic products and has 95% share domestically. Further, innovation and knowledge sharing are critical success factors for home appliance industry Therefore, this study was conducted for this industry and used survey method which resulted in 150 responses from the managers. The study finds that commercial social media’s use by companies impacts knowledge management through social capital; and a serial mediating impact of these constructs exists amongst social media use and organization performance. There are extremely limited studies for electronic companies in Pakistan given the concerned variables. Therefore they are helpful for the managers concerned as these findings suggest effective use of social media by electronic companies in particular to create better organizational knowledge and improve company performance.campaigns motivate citizens to pay for safe drinking water
Automatic Parking Path Planning and Tracking Control Research for Intelligent Vehicles

As a key technology for intelligent vehicles, automatic parking is becoming increasingly popular in the area of research. Automatic parking technology is available for safe and quick parking operations without a driver, and improving the driving comfort while greatly reducing the probability of parking accidents. An automatic parking path planning and tracking control method is proposed in this paper to resolve the following issues presented in the existing automatic parking systems, that is, low degree of automation in vehicle control; lack of conformity between segmented path planning and real vehicle motion models; and low success rates of parking due to poor path tracking. To this end, this paper innovatively proposes preview correction which can be applied to parking path planning, and detects the curvature outliers in the parking path through the preview algorithm. In addition, it is also available for correction in advance to optimize the reasonable parking path. Meanwhile, the dual sliding mode variable structure control algorithm is used to formulate path tracking control strategies to improve the path tracking control effect and the vehicle control automation. Based on the above algorithm, an automatic parking system was developed and the real vehicle test was completed, thus exploring a highly intelligent automatic parking technology roadmap. This paper provides two key aspects of system solutions for an automatic parking system, i.e., parking path planning and path tracking control.
Use of Sacubitril/Valsartan in a Chronic Heart Failure Patient with Reduced Ejection Fraction Accompany Dilated Cardiomyopathy and Myocardial Indensification: A Case Report

Rationale: Dilated cardiomyopathy (DCM) is a progressive cardiac disease characterized by ventricular dilation and contractile dysfunction and is the third most common cause of heart failure and the most common cause of heart transplantation. Heart failure (HF) and atrial fibrillation (AF) often coexist and share a mutually beneficial relationship. The presence of atrial fibrillation increases the tendency for heart failure, which can worsen its severity and increase the risk of stroke. DCM、AF and HF are causal to each other in pathophysiological view. However, how these pathogens translates upon acute decompensation heart failure(ADHF) is unknown. Control acute attack of chronic heart failure is the first step of treatment.Case summary: Herein, we described a 68-year-old man with acute decompensated heart failure (ADHF) with severe DCM and atrial fibrillation who was treated with Sacubitril/Valsartan and had a reduced ejection fraction. The patient’s echocardiography features had a significant improvement under taking Sacubitril/Valsartan. Sacubitril/Valsartan may act as an intermediary that balancing of the good and the bad neuroendocrine response.Discussion: DCM is a major cardiomyopathy and a major cause of heart transplantation. DCM’s clinical prognosis is poor, additionally, along with myocardial indensification, early diagnosis and treatment is helpful to prolong the patients' life. The emergence of Sacubitril/Valsartan represents the advent of a new strategy for treating HFrEF. Its beneficial effects are related in part, at least, to an improvement of echocardiographic features and positive modulation of the neuroendocrine response to HF.
An intelligent self-sustained RAN slicing framework for diverse service provisioning in 5G-beyond and 6G networks

Network slicing is a key technology to support the concurrent provisioning of heterogeneous Quality of Service (QoS) in the 5th Generation (5G)-beyond and the 6th Generation (6G) networks. However, effective slicing of Radio Access Network (RAN) is very challenging due to the diverse QoS requirements and dynamic conditions in the 6G networks. In this paper, we propose a self-sustained RAN slicing framework, which integrates the selfmanagement of network resources with multiple granularities, the self-optimization of slicing control performance, and self-learning together to achieve an adaptive control strategy under unforeseen network conditions. The proposed RAN slicing framework is hierarchically structured, which decomposes the RAN slicing control into three levels, i.e., network-level slicing, next generation NodeB (gNodeB)-level slicing, and packet scheduling level slicing. At the network level, network resources are assigned to each gNodeB at a large timescale with coarse resource granularity. At the gNodeB-level, each gNodeB adjusts the configuration of each slice in the cell at the large timescale. At the packet scheduling level, each gNodeB allocates radio resource allocation among users in each network slice at a small timescale. Furthermore, we utilize the transfer learning approach to enable the transition from a model-based control to an autonomic and self-learning RAN slicing control. With the proposed RAN slicing framework, the QoS performance of emerging services is expected to be dramatically enhanced.
Art-Integration in Computational Thinking as an Unplugged Pedagogical Approach at A Rural Sarawak Primary School

Purpose – Promoting Computational Thinking (CT) in education has become an interest among scholars to develop CT skills to promote critical thinking and problem-solving skills among young people. However, it remains a challenge for Malaysian educators to teach CT at school, especially in rural primary schools. The purpose of this study is to explore the use of visual art in learning the problem-solving process and as an unplugged approach to involve learners in computational thinking. Methodology – This study employed qualitative exploratory method to understand the use of visual art as a pedagogical tool for young learners to manifest CT. Twenty-two Primary 4 and Primary 5 students, aged 10 to 11 years old, were chosen as the participants of the study. The participants are from an indigenous community of Sarawak Penan, who used to be nomadic. We used on-site observation to collect qualitative data. The content analysis method was also used to examine classroom activities and participants’ task outcomes. Findings – Through the art-making experience and unplugged approach, the participants were able to Illustrate their ability to grasp essential concepts of computational thinking – abstraction, decomposition, and algorithms. The CT activities conducted were simple, manageable, and easy to understand. The findings have shown that implementing the art-integration approach in computational thinking suited the needs of the young novice rural learners. The approach was found to be accessible for the local teachers, as it eases the burden of copious preparation and implementation. The approach enabled the teachers to overcome common comprehension problems when relating new foreign concepts to young learners staying in remote rural regions. During the drawing activity, researchers have observed that male students performed better in drawing and abstraction skills, while female students performed better in recognising patterns and colours. International Journal of Academic Research in Business and Social Sciences Vol. 1 1 , No. 14, Contemporary Business and Humanities Landscape Towards Sustainability. 2021, E-ISSN: 2222-6990 © 2021 HRMARS 23 Significance – The findings will help remote rural teachers to reflect on attempts to teach and improve CT instruction using non-computerised materials and incorporate art into other subjects. Our work contributes to the pedagogical strategies that link foreign concepts to indigenous ways of knowing, through the introduction of an unplugged approach and art-integration into the presentation of new learning content. Our work has illustrated how important it is for remote rural teachers to find the right balance between explanation and demonstration of visual worked examples.
REVIEW ON ROLE OF METAL SALT IN THE RE-MINERALIZATION OF DENTAL CARIES

Dental caries is a most common disease that affects large number of people. It is a worldwide public health problem, affecting numerous urban and rural communities. White-spot lesions (WSL) are the earliest macroscopic evidence of enamel caries. Typically, the enamel surface layer stays intact during subsurface demineralization, but, without treatment, will eventually collapse into a full cavity. Near-neutral pH of saliva is endowed with a natural buffering capacity. Natural demineralization of tooth at an early stage is reversed by saliva, which contains calcium ions, phosphate ions, buffering agents, fluoride, and other substances. The strategy for aided remineralisation is to have ions directly delivered to where and when they are needed the most. Several mechanisms are available for aided remineralisation. The most well known is the delivery of topical and systemic fluoride, which has been proven to be a highly effective measure for prevention of caries. However limitations and risks associated with the use of fluoride as a remineralising agent fuelled the need to develop newer non-toxic techniques that deliver calcium and phosphate ions directly into subsurface lesion and/or boost remineralising properties of saliva. Encouraging microscopic changes in the demineralized enamel surface following the treatment with different concentration of metal salts, involving zinc, strontium, magnesium, in addition to stannous fluoride, many metal salts are in use for the treatment of dental caries. Microscopic examination was conducted on teeth samples before and after pH-cycling procedure and following the treatment with the selected metal salt under polarized microscope (100X). The best obvious remineralisation was found following treatment with stannous fluoride, followed by zinc chloride. The stannous fluoride was the best in the remineralisation of initial caries, results for other metals seem to be promising if used for prevention of dental caries. Mineralization of teeth is determined by major inorganic elements as calcium, phosphorous in addition to other elements that occur in traces within tooth structure (Thylstrup and Fejerskov, 1996 Murry, 1996 Peter, 2004). Fluoride now a day is widely used either systemically or topically to increase resistance of teeth surface, it has been well documented as a major contributing factor in the decline of the incidence and severity of dental caries in many countries (Murry, 1996 Curzon,1999). So far fluoride has been considered to play an almost unique role in the prevention of dental caries however there is no reason to dismiss the role of other elements as being potential cariostatic agents. Different concentrations of different metal ions as zinc and copper have been tested for better effect on remineralisation in vitro (Dedhiya, et al., 1974 Fang et al., 1980). However controversy is present in the literature concerning their effectiveness in prevention of dental caries when used topically at effective concentrations (Torredo et al., 2004 Elzbieta et al., 2008 Curzon, 1983).
Investigating the Sensitivity of Particle Size Distribution on Part Geometry in Additive Manufacturing

Selective laser melting process is a powder bed fusion additive manufacturing process that finds applications in aerospace and medical industries for its ability to produce complex geometry parts. As the raw material used is in powder form, particle size distribution (PSD) is a significant characteristic that influences the build quality in turn affecting the functionality and aesthetics aspects of the end product. This paper investigates the effect of PSD on deformation for 316L stainless steel powder, where three coupled in-house simulation tools based on Discrete Element Method (DEM), Computational Fluid Dynamics (CFD), and Structural Mechanics are employed. DEM is used for simulating the powder distribution based on the different particle size distribution of the powder. The CFD is used as a virtual test bed to determine thermal parameters such as density, heat capacity and thermal conductivity of the powder bed viewed as a continuum. The values found as a stochastic function of the powder distribution is used to test the sensitivity of the melted zone and distortion using Structural Mechanics. Results showed significant influence of particle size distribution on the packing density, surface height, surface roughness, the stress state and displacement of the melted zone. The results will serve as a catalyst in developing geometry assurance strategies to minimize the effect of particle size distribution on the geometric quality of the printed part.
Design of a novel fuel cell-Fenton system: a smart approach to zero energy depollution

A model azo dye pollutant, Acid Orange 7 (AO7), was removed efficiently from an aqueous medium by a smart eco-friendly Fuel Cell-Fenton (FC-Fenton) system without any external power supply. In this approach, AO7 was degraded by an electro-Fenton process at a designed cathode (Carbon Felt (CF)/porous Carbon (pC)) supplied by direct clean electrical energy from abiotic glucose oxidation at a CF/gold anode (CF@Au). The highly active cathode was fabricated by an attractive route combining Atomic Layer Deposition (ALD) of ZnO on commercial carbon felts (CFs) followed by subsequent solvothermal conversion of the metal oxide to a metal organic framework (here ZIF-8). The as-prepared composite material was further calcined at high temperature under a controlled atmosphere. A pC-based support with high specific surface area and nitrogen as a dopant was thus obtained, enhancing both conductivity and electrocatalytic properties toward H2O2 production from oxygen reduction. Degradation kinetics of AO7 (0.1 mM initial concentration) at the CF@pC cathode was monitored by UV-vis spectrophotometry and High-Performance Liquid Chromatography (HPLC) to prove the efficiency of the composite material for the degradation of such a bio-refractory model molecule. Benefitting from the H2O2 production rate (9.2 mg L−1 h−1) by the pC layer, AO7 (35.0 mg L−1) was degraded by the electro-Fenton process in acidic medium (pH = 3) with removal efficiency reaching 90% in 10 h. The durability of the system was extended for more than 2 months with an average power output of 170 mW m−2, confirming this abiotic FC-Fenton system as a promising, green, future technology for both environmental and energy-related areas, including membrane-coupled reactor systems.
Optimising e-commerce customer satisfaction with machine learning

Customer insighs is the key to the success of e-commerce. Therefore, factors affecting customer satisfaction leading to product purchase and re-purchase should be studied extensively. This study intends to identify the key drivers that influence the satisfaction and the model which can predict the likelihood of customer satisfaction. The outcome would provide insights to prioritise factors that are significant, as well as to provide advice to a wide range of sellers. Four classification machine learning algorithms decision tree, random forest, artificial neural network and support vector machine are evaluated to classify customer satisfaction based on a 3-year historical data from an e-commerce retailer. There were a few challenges with the dataset, such as imbalanced, skewed and missing. Data pre-processing was conducted, and different techniques were evaluated. Of the algorithms evaluated, the best result is achieved by Random Forest with the highest accuracy and reasonable processing time. Meeting the estimated delivery date and the number of days taken to deliver an order is found to be the top two important factors affecting customer satisfaction.
Snippet generation using textbook corpus - an NLP approach based on BERT

In today’s technology-driven world, most millennials are tech-savvy. They have neither the time nor the interest in reading textbooks, newspapers or journals. They would like to immediately get instant answers and clarifications for all their doubts and questions. On many occasions, we are unable to find the exact word or meaning which we are searching for. So, if we have a clear, concise summary of a piece of literature, and we could understand what it contains with just a glimpse, we would be able to save a lot of time. This paper dwells about utilizing Natural Language Processing (NLP) to summarize a given text/textbook/paper. The state-of-the-art technology in this field has been demonstrated by Google’s Bidirectional Encoder Representations from Transformers (BERT), one of the latest developments in NLP. BERT is believed to understand English better than other models because of its underlying bidirectional architecture. The present proposal is to use BERT as a sentence similarity extractor. By applying the TextRank algorithm, the sentences holding the most important information are extracted. This comes under the domain of extractive summarization. Abstractive summarization is much talked about, but since Google BERT is not built for generating text, we are utilizing it in a different way to achieve the requirement. This paper intends to discuss the use of BERT for the gen-next kids which will save time and initiate further interest for researchers in developing new programs continuously in the future.
Ageing Tests of Samples of Glass-Epoxy Core Rods in Composite Insulators Subjected to High Direct Current (DC) Voltage in a Thermal Chamber

In this article, we presented the results of the tests performed on three sets of samples of glass-reinforced epoxy (GRE) core rods used in alternating current (AC) composite insulators with silicone rubber housing. The objective of this examination was to test the aging resistance of the rod material when exposed to direct current (DC) high voltage. We hypothesized that the long-term effects of the electrostatic field on the GRE core rod material would lead to a gradual degradation of its mechanical properties caused by ionic current flow. Further, we hypothesized that reducing the mechanical strength of the GRE core rod would lead to the breakage of the insulator. The first group of samples was used for reference. The samples from the second group were subjected to a temperature of about 50 °C for 6000 h. The third group of samples were aged by temperature and DC high voltage for the same time. The samples were examined using the 3-point bending test, micro-hardness measurement and microscopic analysis. No recordable degradation effects were found. Long-term temperature impact and, above all, the combined action of temperature and DC high voltage did not reduce the mechanical parameters or change the microstructure of the GRE material.
The Challenge of Career Guidance and Counseling during the Covid-19 Pandemic

The Covid-19 pandemic has also opened up opportunities on how career guidance services build roles in the wider community. Whereas in the world of work, globally everything has stopped, projects have been postponed, and workplaces have been closed indefinitely. This journal is a literature review that discusses career guidance and counseling services that are very important in an effort to help victims of layoffs that have occurred as well as workers in general. This problem can be solved through the career guidance function as a treatment to help individuals overcome career-related problems. The essence of the function of career guidance as a stimulus is the ability to anticipate all future possibilities and achieve career maturity. In the future, workers mindsets need to be changed through career guidance services in the form of knowledge assistance on how to manage finances. Through providing career guidance services as a form of treatment and stimulus, individuals will have self-motivation to rise from adversity and get new jobs that match their interests and expertise, find another source of income and to build passive income.
PURE AND BINARY ADSORPTION OF METHANE AND NITROGEN ON SILICALITE

Separation processes comprise a large portion of the activity in the chemical and petrochemical industries. For the chemical, petroleum refining, and materials processing industries as a group, separation processes are considered to be critical. Almost all the applications of chemical industries involves mixtures, so innovation in separation technology not only enhances productivity and global competitiveness of U.S. industries, but is also critical for achieving the industrial energy and waste reduction goals. Traditionally, air separation to produce nitrogen and oxygen and to separate nitrogen from methane was practiced by cryogenic distillation, which involved expensive high pressure units and large requirement of energy. The separation of nitrogen from methane is becoming increasingly important for upgrading LGF (Landfill gas), coal gas, and natural gas. Natural gases contain significant amounts of nitrogen. From the environmental perspective, Methane is the most important non-CO2 greenhouse gas responsible for global warming with more than 10 % of total
Microscale Enhancement of Heat and Mass Transfer for Hydrogen Energy Storage

The document summarized the technical progress associated with OSU’s involvement in the Hydrogen Storage Engineering Center of Excellence. OSU focused on the development of microscale enhancement technologies for improving heat and mass transfer in automotive hydrogen storage systems. OSU’s key contributions included the development of an extremely compact microchannel combustion system for discharging hydrogen storage systems and a thermal management system for adsorption based hydrogen storage using microchannel cooling (the Modular Adsorption Tank Insert or MATI).