Miracle-dz/UniFAR-FTUs huggingface dataset:
Determining The Factors Influencing Indigenous Youth Become A Successful Entrepreneurs

Eleventh Malaysia Plan, 2016-2020, focused more on macroeconomic policies, create more jobs and highly skilled human resources. The national development agenda also includes indigenous youth in Malaysia. Poverty was identified as the main challenge faced by the government in developing the indigenous community youth. To solve this problem, the government through the current economic plan is to focus attention on efforts to produce more entrepreneurs among indigenous youth in Malaysia. However, the number of indigenous youth entrepreneurs still low and most of the indigenous are still below the poverty line. Therefore, this study aims to determine the factors that influence indigenous youth to become successful entrepreneurs. This qualitative study participant were experts involved and directly related to indigenous entrepreneurship in Malaysia. Participants in this study were selected to use purposive sampling. The instrument of this study was the interview protocol, where the researcher provided only the main question, and the follow-up question was based on the interview participant's answer. The semi-structured interview data were analyzed using thematic analysis, by demarcating interview data, coding, finding themes, revising themes, defining and naming themes and generating reports. The study found that there are five main factors that influence indigenous youth to become successful entrepreneurs, among them are the demographic factors, suitable entrepreneurship and characteristics of entrepreneurs suitable for indigenous youth, transferable skills and policy and financial assistance to expand indigenous businesses. Therefore, it is expected that this study will provide guidance to the parties concerned to increase the number of successful entrepreneurs among indigenous youth in Malaysia.
ON ROUTING PROBLEM WITH STARTING POINT OPTIMIZATION1

One problem focused on engineering applications is considered. It is assumed that sequential visits to megacities have been implemented. After all visits have been made, it is required to return to the starting point (a more complex dependence on the starting point is also considered). But the last requirement is not strict: some weakening of the return condition is acceptable. Under these assumptions, it is required to optimize the choice of starting point, route, and specific trajectory. The well-known dynamic programming (DP) is used for the solution. But when using DP, significant difficulties arise associated with the dependence of the terminal component of the criterion on the starting point. Starting point enumeration is required. We consider the possibility of reducing the enumeration associated with applied variants of universal (relative to the starting point) dynamic programming. Of course, this approach requires some transformation of the problem.
Blockchain Technology and Trust in Supply Chain Management: A Literature Review and Research Agenda

Applying blockchain technology for information sharing in supply chain is driven by many factors, but developing trust is one of the most proposed. However, trust is a multidimensional, intangible concept without an agreed-upon definition. Whereas some argue that trust is the main driver of blockchain technology, others have found a negative relationship. This study focuses on how applying blockchain in supply chain management can influence trust and proposes a corresponding research agenda. Trust and blockchain technology discussions are scattered throughout the literature. Thus, a systematic literature review was performed based on a conceptual trust framework. This study discovered a gap in linking trust theories to blockchain technology applications especially in supply chain management, and provided insights into trust’s reciprocal nature. Current literature strongly expects trust as a consequence for blockchain adoption if considered in the technology. Simultaneously, trust in supply chain partners is strongly expected as an antecedent to blockchain because it requires openness in information sharing. Thus, propositions and agenda for future research are suggested. The research is limited to literature findings due to the immaturity and low scalability of blockchain technology adoption; however, the most reviewed articles in less than two years old, increase the results’ accuracy .
The cyclone separator application on physicochemical characterization of coconut shell-liquid smoke grade C

The further processing of coconut shell pyrolysis into liquid smoke has been developed in recent years. However, the common technology by directly condensing the smoke has many impurity compounds which decreases the yield of liquid smoke produced. Hence, in this study the cyclone separator was applied to increase the quality of physicochemical content in coconut shell-liquid smoke (CS-LS) grade C. The physical parameters analyzed were yield, pH, density and color. The chemical parameters was analyzed by GC-MS. The result showed that CS-LS processing with cyclone separator was able to increase the total yield into 3.33%, with better color, pH and density compared to the CS-LS produced using common method (direct condensation). The application of cyclone separator was also able to increase the phenolic compounds (97%), alcoholic compounds (92%), cycloalkene compounds (91%) and also decreased the benzoic acid, carbonyl and ester up to 100% compared with common method for CS-LS grade C production.
Kinetic studies of marine psychrotolerant microorganisms capable of degrading diesel in the presence of heavy metals

The presence of heavy metals in Antarctica is an emerging issue as human influence becomes more discernible over the years. The study of pollution in Antarctica can help people to understand the real influence of human activities on the environmental pollution from polar regions. Bioremediation of petroleum hydrocarbons in the polar environment where toxic metals co-existed involves selecting strictly autochthonous Antarctic strains with dual catabolic competence and tolerance to toxic metals. In this study, diesel degradation was observed in the presence of 1 ppm of eight selected heavy metals; Ag, Al, Cd, Co, Cr, Hg, Ni and Zn. Bacterial growth was inhibited in increasing order of Zn> Cr> Cd> Al> Ni> Hg> Co> Ag. Bacterial growth was the highest in Zn at OD600 0.556 (P>0.05) and lowest in Ag at OD600 0.151 (P<0.05). Diesel degradation was inhibited in the order of Co> Ni> Cd> Ag> Zn> Al> Cr> Hg, which was analysed using gravimetry analysis. Degradation was the highest in Hg at 52.23% (P>0.05) and lowest in Co at 22.76% (P<0.05). This work serves as a pilot study in gathering data to analyse and gather more data for inhibition concentration of heavy metals for the Antarctic marine bacteria.
Implicit Change Leadership, Affective Commitment to Change, and the Mediating Role of Organizational Trust

ARTICLE DETAILS ABSTRACT History Revised format: November 2020 Available Online: December 2020 This paper aims to know the implicit change leadership schemas, the commitment of the employees towards change, and their relationship with the trust culture of academic institutions. Quantitative approach with surveys of 300 employees of academic institutions has been used in this study. SPSS software has been used to examine the relationship between variables and constructs. Findings reveal that organizational trust has a mediating effect on implicit change leadership and affective commitment to change. Moreover, a positive relationship was found between trust culture and change leadership and change leadership with a commitment to change. Furthermore, the results suggest that change leaders must take into account the trusting culture perspective of the organization to motivate the employees to make them accept change.
A Comparative Analysis on Feature Extraction and Classification of EEG Signal for Brain-Computer Interface Applications

Classification of EEG signal for Brain-Computer Interface (BCI) applications consists of three stages: Pre-processing; Feature extraction and Classification . There are different methods implemented in these stages found in existing literature. However, the performance of the methods has been measured on different datasets which made the results incomparable to each other. To address this problem, in this paper, different combination of feature extraction and classification methods has been implemented to classify a well known dataset (dataset 2A, BCI Competition IV) so that a comparative analysis can be made based on identical platform to find out the best combination of methods. In the pre-processing step, the EEG data was band-pass filtered to remove the artifacts and Common Spatial Pattern (CSP) was applied to increase the discriminativity of the data. Two types of features: Time Domain Parameters (TDP) and Adaptive Auto-Regressive (AAR) parameters were extracted from the pre-processed EEG signal. The features were classified using two types of classifiers: Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM). A comparative analysis has been conducted to identify the best combination of feature and classifier. The analysis reveals that, TDP features classified using LDA classifier provides best performance and hence demands application in real time BCI system.
The Improvement of Machine Translation Quality with Help of Structural Analysis and Formal Methods-Based Text Processing

: This article considers the issues of enhancing the quality of machine translation from one language into another one by structuring linguistic patterns and using identification methods for the situations that cannot be processed by the suggested approach and are subject to individual processing. According to the BLEU score metrics, the described approach allows to increase the quality of machine translation on average by 0.1 and reduce postprocessing time due to the identification of idioms and words with context-dependent meanings by translation. The experiment data base of the study was built upon online available pairs of texts that cover the events of FIFA World Cup 2018 and well-known idioms.
Biosystems Diversity

The challenge of searching for patterns of species turnover dynamics in communities of living organisms is directly related to solving problems of stability and functioning of ecosystems. Avian communities are an essential structural and functional component of terrestrial and aquatic ecosystems which are highly diverse and play an important role in a wide range of ecosystem functions. The issue of changes in the dynamics of amphibiotic landscape complexes, where terrestrial and aquatic ecosystems conjugate, is practically not solved. In this connection, a study was carried out within a landscape system, which presents terrestrial and aquatic ecosystems that were in different degrees of anthropogenic transformation. The dynamics of bird communities was considered in the context of recent global climate change. The investigation was conducted in the landscapes of the south and south-east of Ukraine in the nesting seasons 1988–2018. Within the landscape system associated with the Molochny estuary, the ten most important types of ecosystems
A New Model for Permeability Estimation In Carbonate Reservoirs By Using NMR T2 Distribution and Lsboost Ensemble Technique

Permeability is arguably the most critical property for evaluating flow in the reservoir. It is also one of the challenging parameters which must be measured in the field. Nuclear Magnetic Resonance (NMR) logging across the borehole is among the popular techniques, which it is utilized to determine permeability across the reservoir. However, available correlations in literature for estimating permeability from NMR data do not usually provide acceptable accuracy in the carbonate rocks. Therefore, a new model is proposed to estimate permeability by establishing a relationship between core derived permeability and extracted features from the T2 distribution curve of NMR data with the ensemble LSBoost algorithm. The feature extraction process is performed using peak analysis on T2 distribution curves which it leads to 5 relevant parameters, including T2lm, TCMR, prominence, peak amplitude and width. The proposed model is validated by comparing the proposed method’s correlation coefficient against Timur-Coates and SDR equation estimation accuracy. The results show that our model generally provides better prediction accuracies in comparison with the empirical equation-based derived permeabilities.
Institutional Corporate Social Responsibility and Organizational Performance: The Moderating effect of Transformational and Transactional Leadership

This study aims to explore the relationship between transformational leadership (TFL), transactional leadership (TSL) styles, and corporate social responsibility (CSR) practices. The study explains the role of leadership styles between CSR and organizational performance (OP) to investigate the influence of leadership styles over CSR practices this study is the first of its kind in the Asian perspective and Pakistan in particular. Self-administered questionnaires are used to conduct this large-scale field study, data is collected from major cities of Pakistan. Questionnaires are filled by executive managers and administrative staff. Purposive sampling technique is adopted to approach the respondents. Obtained results prove that TFL is more closely interlinked to CSR practices and this relationship becomes robust with the involvement of stakeholder-oriented marketing. Moreover, findings suggest that TSL increases while the TFL decreases the relationship between CSR practices and OP.
THE PSYCHOLOGY OF PERSONALITY NARRATIVE IDENTITY: FORMATION MECHANISM

The article discusses interpretations of identity from a postmodern perspective. A three-level model of personality is used to represent the methodological framework for analyzing identity. From a postmodern perspective, personal identity is defined as a socio-cultural representation. Narrative identity is formed through dialogic interaction, which results in the integration and internalization of life experience.
Fabrication of Aluminium Nitride by Electrophoretic Deposition

The electrophoretic deposition (EPD) technique has been developed to prepare AlN ceramics and the topic of this paper was the investigation of the AlN ceramic suspensions for electrophoretic deposition. The only addition of triethylamine didn’t promote the hydrolysis of AlN powder in 95% ethanol. The absorption of the polyacrylic acid (PAA) resulted in an increased Zeta potential, indicating that PAA is a very effective dispersant for the AlN and Y2O3 powders. Furthermore, by adding a combination of PAA and triethylamine, an stable AlN suspension could be obtained which was necessary for realising a high packing density in the electrophoretically deposited green bodies. The conductivity measurements indicated that suspension conductivity was lower than liquid conductivity. The lower conductivities of the suspensions could be explained by the fact that in concentrated suspensions the free ionic species are hindered in their movement by the particles. The conductivity of the centrifugate was higher than that of the suspension. It can be concluded that during the EPD the current in the suspension is mainly carried by the free ionic species. The transport of positively charged ionic species to the upper electrode (cathode) was slower than their consumption by the electrode reaction, an increased concentration overvoltage caused a significant increase in total electrical resistance due to the higher potential drop at the cathode. Finally, the EPD was usually stopped according to the increase in potential drop.
Dwarfs of the fortress: A new cryptic species of dwarf gecko of the genus Cnemaspis Strauch, 1887 (Squamata, Gekkonidae) from Rajgad fort in the northern Western Ghats of Maharashtra, India

A new species of the genus Cnemaspis Strauch, 1887 is described from the Rajgad fort in the northern Western Ghats of Maharashtra, India. The new species, belonging to the flaviventralis clade, is one of the smallest known Indian Cnemaspis and can be distinguished from other congeners by its genetic distinctiveness and few key morphological characters. The new species can be diagnosed from all other Indian congeners by its small body size (SVL < 27 mm), the absence of conical and spine-like tubercles on flank; heterogeneous dorsal pholidosis; presence of only femoral pores and no precloacal pores in males; weakly keeled scales on the ventral surface of neck, pectoral, abdominal region and limbs; granular scales on the tail with whorls of slightly enlarged, strongly keeled tubercles; and the absence of enlarged median subcaudal scales. The new species is currently known to inhabit the man-made historical structures from a single locality in Rajgad, Maharashtra, where it is presumed to be uncommon.
Radar Sensor Modelling Using Deep Generative Networks For Verification of Autonomous Driving Master’s thesis in Complex Adaptive Systems

The current advancements within the field of autonomous vehicles call for the development of reliable and robust testing methods of these systems. While empirical testing is necessary, virtual verification through simulations has the advantage of being safe, fast and cost effective. However, to recreate a realistic driving scenario in a simulation environment, correct models of the sensors used by self-driving systems are needed. Some of the sensors used by these systems are the lidar and the radar sensor. The purpose of this thesis is to construct a model of a radar sensor, based on data from both a lidar and a radar sensor. To model a radar sensor, deep generative networks such as conditional variational autoencoders (CVAE) and conditional generative adversarial networks (CGAN) are utilised. The networks are trained on data from a radar sensor, while data from a lidar is used as conditional input. The conditional input contains an environmental description from which the radar model can create its output. Both radar and lidar data are converted into suitable formats before training the models. Both types of generative networks show potential to perform accurate radar modelling. The CVAE is able to learn general behaviour of the radar sensor, while it has difficulty adapting to specific scenarios. The CGAN, on the other hand, shows sensitivity to the conditional input, but lacks the generic properties captured by the CVAE. For both of the network results, it is apparent that errors in the data have an adverse effect on the model performance. Future improvements that can be made for the radar modelling are to include more features in the data that is given to the networks, to create a network that combines a VAE and a GAN, and to improve on the evaluation methods for the network output.
Adenocarcinoma of the colon presenting as bone metastases of unknown primary- A case report

The liver and lung are the usual sites of distant metastasis from colorectal tumours. Bone metastasis in patients with colorectal carcinoma is considered extremely rare. It often signifies the terminal phase of colon carcinoma with a median survival of less than ten months. High index of suspicion is required, and the possibility or risk of osseous metastases arising from primary lesion in the colon should be borne in mind when assessing bony lesions that present initially as metastasis of unknown origin. We present a case of adenocarcinoma of the colon which remained undiagnosed until patient reported with a pathological fracture of the humerus and paraparesis
Feasibility Study of Hydropower Plant at Lubuak

The feasibility study is one of the principal documents in building a hydropower plant consisting of technical, economic, and financial aspects. Contained technical studies on civil, mechanical, and electrical. This requires data on hydrologic, geology, land contours, river discharge, water catchment areas, and so on. Economic and financial studies include cost and financial parameters such as; BEP, IRR, NVP, BCR, and others. The installed capacity of a hydropower plant is given in optimization based on the Flow Duration Curve (FDC) and the Capacity Factor (CF) used the Newton Interpolation Method. The results showed that the installed power capacity was 11.99 MW. The water discharge was 31.603 m/secs and the effective head was 37.5 meters. Annual income is around IDR 103.026 billion. Finally, HPP Lubuak Gadang is technically, economically, and financially feasible, so it is feasible to carry out the next process. Keywords— Economic, Capacity Factor, Flow Duration Curve, Hydropower Plant
Combined Detection and Segmentation of Archeological Structures from LiDAR Data Using a Deep Learning Approach

Until recently, archeological prospection using LiDAR data was based mainly on expert-based and time-consuming visual analyses. Currently, deep learning convolutional neural networks (deep CNN) are showing potential for automatic detection of objects in many fields of application, including cultural heritage. However, these computer-vision based algorithms remain strongly restricted by the large number of samples required to train models and the need to define target classes before using the models. Moreover, the methods used to date for archaeological prospection are limited to detecting objects and cannot (semi-)automatically characterize the structures of interest. In this study, we assess the contribution of deep learning methods for detecting and characterizing archeological structures by performing object segmentation using a deep CNN approach with transfer learning. The approach was applied to a terrain visualization image derived from airborne LiDAR data within a 200 km2 area in Brittany, France. Our study reveals that the approach can accurately (semi-)automatically detect, delineate, and characterize topographic anomalies, and thus provides an effective tool to inventory many archaeological structures. These results provide new perspectives for large-scale archaeological mapping.
Analyzing the Sentiments with Neural Network

: In the modern era, ubiquitous means of communications are used. Thus, allowing various platforms where people can exchange their thoughts and opinions. This leads to the situation where the task of sentiment analysis comes into play which is the task of natural language processing. Many approaches such as Maximum Entropy, classifier of Naïve Bayes were used for the purpose of analyzing the sentiments of the users which helps the internet surfers to interpret whether a particular thing is liked or disliked by others. The paper proposes a system with back-propagation neural network (BPNN) which not only analyzes simple reviews but also the opinions that contain emoticons and negation. The neural network was implemented with various datasets and with several different activation functions such as ReLu (Rectified Linear Unit), sigmoid and hyperbolic tangent. The results depict that sigmoid activation function performed better than other.
CS442 Module 8: Object-Oriented Programming

In this module, we discuss Object-Oriented Programming (OOP), a paradigm that has enjoyed considerable popularity in recent decades. Proponents of OOP cite its potential for information-hiding and code-reuse; opponents argue that the hierarchical type systems imposed by object-oriented languages are not always an accurate reflection of reality, and can lead to compromised and unintuitive type hierarchies.