Miracle-dz/UniFAR-FTUs huggingface dataset:
Transforming Energy Marketing Practices for Enhanced Solar PV Adoption

The adoption and use of solar PV systems is a complex and multifarious process influenced by personal, social, economic, technical and regulatory factors. Solar PV companies involved in the sales and interaction with the customers can play an important role in facilitating adoption. Companies’ ability to effectively market the product, disseminate information, frame value offerings and address consumers concerns can play an important role in this regard. The small size of the domestic market, an amplified competition and limited resources highlight the need to alter the way companies have been carrying out their operations. The qualitative study explores how solar PV companies can transform their marketing operations by integrating through the use of advanced digital technologies to facilitate the process.
Surface Level Estimator-An Automated Contouring Instrument

This paper introduces a dynamic electronic instrument to measure the inclination of a surface, named as Surface Level Estimator (SLE). This device is designed to reduce the human effort and time consumed for civil surveying. This paper describes the construction, working principle, programming and experimentation results of the instrument. Finally, the conclusion and scope of future is discussed at the end of the paper.
Optimizing seasonal grain intakes with non-linear programming: An application in the feed industry

In the feed sector, 95% of the input costs arise from the supply of raw materials used in feed production. The selling price is determined by competition in free market conditions. Due to the use of similar technologies and the very small share of production costs in total costs, it is unlikely that a competitive advantage will be gained through innovations in production. Between 30% and 50% of grain products are used in feed ration analysis. Cereals can only be harvested at a certain time of the year. Due to this limited time frame, feed production enterprises have to balance their financial burdens with their operational needs while making their annual stocks. The study was carried out to cover all the relevant businesses of the company, which has feed factories in four regions of Turkey. Based on the season data of the year 2020-2021, the grain purchase planning for the year 2021-2022 was tried to be optimized with non-linear programming. While creating the mathematical model, grain prices, interest rates, production needs according to production planning, sales according to sales forecasts, factory stocking capacities, licensed warehouse rental, transportation, handling and transshipment costs were taken into account. With this unique paper, in the cattle feed production sector, storage, transportation and handling costs will be minimized. Cost advantage will be provided with optimum purchase planning in the season. According to the grain pricing forecast and market data for the 2021-2022 season, model can provide a cost advantage of 0.7%. Model will also provide insight to the managers for additional storage space investments.
GWAS Reveals a Novel Candidate Gene CmoAP2/ERF in Pumpkin (Cucurbita moschata) Involved in Resistance to Powdery Mildew

Pumpkin (Cucurbita moschata Duchesne ex Poir.) is a multipurpose cash crop rich in antioxidants, minerals, and vitamins; the seeds are also a good source of quality oils. However, pumpkin is susceptible to the fungus Podosphaera xanthii, an obligate biotrophic pathogen, which usually causes powdery mildew (PM) on both sides of the leaves and reduces photosynthesis. The fruits of infected plants are often smaller than usual and unpalatable. This study identified a novel gene that involves PM resistance in pumpkins through a genome-wide association study (GWAS). The allelic variation identified in the CmoCh3G009850 gene encoding for AP2-like ethylene-responsive transcription factor (CmoAP2/ERF) was proven to be involved in PM resistance. Validation of the GWAS data revealed six single nucleotide polymorphism (SNP) variations in the CmoAP2/ERF coding sequence between the resistant (IT 274039 [PMR]) and the susceptible (IT 278592 [PMS]). A polymorphic marker (dCAPS) was developed based on the allelic diversity to differentiate these two haplotypes. Genetic analysis in the segregating population derived from PMS and PMR parents provided evidence for an incomplete dominant gene-mediated PM resistance. Further, the qRT-PCR assay validated the elevated expression of CmoAP2/ERF during PM infection in the PMR compared with PMS. These results highlighted the pivotal role of CmoAP2/ERF in conferring resistance to PM and identifies it as a valuable molecular entity for breeding resistant pumpkin cultivars.
Safety Impacts of Queue Warning in a Connected Vehicle Environment

Queue warning systems (QWSs) have been implemented to increase traffic safety by informing drivers about queued traffic ahead so that they can react in a timely manner to the queue. Existing QWSs rely on fixed traffic sensors to detect the back of a queue. It is expected that if the transmitted messages from connected vehicles (CVs) are used for this purpose, detection can be faster and more accurate. In addition, with CVs, delivery of the messages can be done with onboard units instead of dynamic message signs and provide more flexibility on how far upstream of the queue the messages are delivered. This study investigates the accuracy and benefits of the QWS on the basis of CV data. The study evaluated the safety benefits of the QWS under different market penetrations of CVs in future years. Surrogate safety measures were estimated with simulation modeling combined with the surrogate safety assessment model tool. Results from this study indicate that a relatively low market penetration—about 3% to 6%—for the congested freeway examined in this study was sufficient for an accurate and reliable estimation of the queue length. Even at 3% market penetration, the CV-based estimation of back-of-queue identification was significantly more accurate than that based on detector measurements. The results also found that CV data allowed faster detection of the bottleneck and queue formation. Further, the QWS improved the safety conditions of the network by reducing the number of rear-end conflicts. Safety effects become significant when the compliance percentage with the queue warning messages is more than 15%.
Long-Term Culture of Male Germline Stem Cells From Hamster Testes

Spermatogonial stem cells provide the foundation for spermatogenesis in male animals. We recently succeeded in culturing and genetically engineering mouse spermatogonial stem cells, but little is known regarding the culture and growth requirements of spermatogonial stem cells in other animal species. In this study, we report the successful long-term culture of spermatogonial stem cells from hamster testes. Spermatogonial stem cells were purified using an anti-ITGA6 antibody and cultured in the presence of glial cell line-derived neurotrophic factor. The cells continued to proliferate for at least 1 year. During this period, they were genetically modified using a lentivirus and underwent spermatogenesis after transplantation into the testes of immunodeficient nude mice. However, germ cells generated in the surrogate xenogeneic recipients did not differentiate beyond the spermatid stage, and these round spermatids could not produce offspring through in vitro microinsemination. These results suggest that the germ cells may not have acquired characteristics necessary for fertility in the xenogeneic microenvironment. Nevertheless, the successful establishment of culture conditions conducive for hamster spermatogonial stem cell growth and maintenance indicates that this technique can be extended to other animal species in which current genetic modification techniques are impossible or inefficient. developmental biology, gametogenesis, spermatogenesis, testis
STOCK MARKET FORECASTING BASED ON ARTIFICIAL INTELLIGENCE TECHNOLOGY

iii LIST OF FIGURES . vii CHAPTER ONE INTRODUCTION Research Background and Significance . 1 Research Questions Addressed in This Project . 6 Literature Review . 8 CHAPTER TWO ARTIFICIAL INTELLIGENCE Brief Introduction . 16 The Principle of AI . 18 CHAPTER THREE TWO TYPES OF NEURAL NETWORKS Radial Basis Function Neural Network (RBFNN) . 24 Brief Introduction . 24 Basic Principle. 25 Multilayer Feed Forward Neural Network (MLFFNN) . 27 Brief Introduction . 27 Basic Principle. 28 CHAPTER FOUR DATA SET ANALYSIS AND RESULTS Introduction of Data Set . 30 Introduction of Simulation Software . 31 Simulation Results . 31 CHAPTER FIVE CONCLUSION . 39 APPENDIX MATLAB SOURCE CODE . 41
B k Proliferation via Regulating NF-Cutting Edge: TFII-I Controls B Cell

The multifunctional transcription factor TFII-I physically and functionally interacts with Bruton’s tyrosine kinase in murine B cells. However, the downstream func-tions of TFII-I in B cells are unknown. Toward achieving this goal, we established stable posttranscriptional silencing of TFII-I in WEHI-231 immature murine B cells, which undergoes growth arrest and apoptosis either upon anti-IgM or TGF- (cid:2) signaling. In this study, we show that TFII-I promotes growth arrest of cells in a signal-depen-dent manner. Unlike control cells, B cells exhibiting loss of TFII-I function fail to undergo arrest upon signaling due to up-regulation of c-Myc expression and concomitant down-regulation of both p21 and p27. Loss of TFII-I is also associated with simultaneous increase in nuclear c- rel and decrease in p50 homodimer binding. Thus, besides controlling c- myc transcription, TFII-I controls B cell proliferation by regulating both nuclear translocation of c- rel and DNA-binding activity of p50 NF- (cid:1) B. The Journal of Immunology, 2007, 178: 2631–2635. utoreactive cells are eliminated cell which is for maintenance (1). Clonal deletion of B cells occurs primarily destroy uncontrolled which Signal transduction B Ag (Bcr) 3 clonal deletion, the cells,
Building Trust in the Cloud Environment: Towards a Consumer Cloud Trust Label

— Low consumer trust presents a significant barrier to cloud service adoption and the growth of the cloud industry. The cloud environment is generally perceived to have high levels of uncertainty and risk. Trust plays a central role in allowing consumers to overcome this risk when making adoption decisions. This paper discusses the characteristics of cloud services that form the basis for consumer trust decisions and argues that service providers need a more transparent, accessible method of communicating these characteristics to potential consumers. As such, this paper is directly relevant to conference tracks discussing consumer-oriented digital services and in particular the topic of consumer trust in digital society. Drawing on the nutrition label concept and aspects of previous computational trust models, we propose a dynamic trust label for cloud computing. The cloud trust label aims at present real time and cumulative metrics to consumers in an easily understandable format. In doing so, the label can be used to aid knowledge based trust decisions and ultimately encourage adoption of cloud services.
Development of hybrid microgrid model for frequency stabilization

Power systems of today are highly complex and highly interconnected. It generates electricity by burning fossil fuels (coal, natural gas, diesel, nuclear fuel, etc.), which produces harmful gases and particles, pollutes environment, and degrades lives. To mitigate the bad impact of burning fossil fuels and meet the increase in electrical system, demand distributed energy sources employing nonconventional energy sources like wind and solar are used. Electric power generation through the nonconventional energy sources has become more viable and cheaper than the fossil fuel–based power plants. This article explores the development of a microgrid model incorporating wind turbine generators, diesel generator, fuel cells, aqua electrolyzers, and battery energy storage systems. An optimization scheme for fixing the proportional–integral controller parameters of frequency regulation is developed for different possible combinations of wind power with other distributed energy resources in the microgrid.
Design, Implementation and Testing of a Solution to Measure Quality of Life using Mobile Devices

Measuring the Quality of Life (QoL) on patients of cancer is very important to help doctors to understand the state of the patient in their daily lives, as well as to simply increase their quality of life. The solution we propose is a mobile application to measure the quality of life on patients of cancer, through questionnaires that can be answered in the comfort of their homes. Every application has been developed in modules to be easily adaptable to various types of questionnaires, scenarios and different systems. In addition, we use a multiplatform development approach to make application available on the two major operating systems today: iOS and Android. The application is being tested in the Instituto Português de Oncologia do Porto (IPO) starting July 2017, in a group of patients focing the following pathologies: Esophageal Neoplasia and Esophagogastric Neoplasia. The patients in this specific IPO group have to answer to a questionnaire about their quality of life through our application in the first, third and sixth month of treatment. The questionnaires used in these tests are: Quality of Life QUestionnaire Esophageal 18 (QLQ-OES18), Quality of Life Questionnaire Esophagogastric 25 (QLQ-OG25) and Quality of Life Questionnaire Cancer 30 (QLQ-C30). There are no results yet, but we hope they will tell us whether the patient undergoing treatment improves their quality of life or not. Analyzing the results and finding out if they are actually able to answer the questionnaire themselves at home is also a very important result for our work. Therefore, at the end of the tests we hope that most patients will be able to have the questionnaire answered at home and that the results have improved throughout the treatment.
Strings in flat space and pp waves from N = 4 super

We explain how the string spectrum in flat space and pp-waves arises from the large-N limit, at fixed g2 YM, of U(N) N = 4 super Yang Mills. We reproduce the spectrum by summing a subset of the planar Feynman diagrams. We give a heuristic argument for why we can neglect other diagrams. We also discuss some other aspects of pp-waves and we present a matrix model associated to the DLCQ description of the maximally supersymmetric eleven dimensional pp-waves.
Segment Routing Over IPv6 Acceleration Using Intel® FPGA Programmable Acceleration Card N3000

SR leverages the source routing paradigm. A node steers a packet through a SR Policy instantiated as an ordered list of instructions called "segments" (often referred by their Segment Identifiers, or SIDs). A segment can represent any instruction, either topological (to forward packets through an identified path) or service-based (within an NFV/SDN architecture a service is represented by a Virtual Network Function (VNF) implemented by a container or a Virtual Machine (VM)). A segment can have a semantic local to an SR node or global within an SR domain. By leveraging this technology, the network no longer needs to maintain a per-flow or per-application state.
NTDROID: ANDROID MALWARE DETECTION USING NETWORK TRAFFIC FEATURES

Mobile malware samples damage the system and can lead to sensitive information leakage and huge monetary loss. Withthe increasing popularity of Android smart phones, the number of malware attacks on the Android platform has alsoincreased. In the first quarter of 2020, around 0.5 million new malware samples were detected per month. Keeping theirthreats in mind, in this paper, we propose a novel network traffic-based Android malware detection model namedNTDroid. The model is trained on network traffic files by selecting the best set of features using a mutual-informationbased feature selection algorithm. Further, the model employs machine learning classifiers to detect malicious apps. Theexperimental results demonstrate that the proposed model can efficiently distinguish between normal traffic and malicioustraffic with an accuracy of 99.99%
Clustering of Neonatal Deaths Among Families and Communities in Bangladesh Demographic and Health Survey 2017-18

1. Background Although the world has seen accelerated progress in reducing the under-five mortality rate, many children continue to die soon after their birth [1]. Globally during 2019, on average there were 17 deaths per 1000 live births during the neonatal period (first month after birth) [2]. South Asian countries experienced the highest proportion of neonatal deaths (62%) and Bangladesh is no exception [3]. Although Bangladesh has made substantial progress by lowering its neonatal death rate from 64 per live births in 1990 to 20 per 1000 live births in 2018 [4]. Each of these deaths today can be counted as a tragedy because most of them are due to preventable causes.
The consumer journey on collaborative consumption platforms : an empirical study using structure equation modeling

The consumption pattern in digital world is changing due to emerging platforms that allow individuals to share their underutilized assets so that others can access them without having any liability. These platforms based on collaborative consumption (CC) definitely provide economic gains but how consumers take this entire journey on CC platform is the focus of this research. To understand this phenomenon in detail, we first identified the key determinants of CC Platforms and then estimated the multiple relationships using partial least squares path modelling. The theoretical section included the dimension of consumer trust, social media and other relevant constructs along with technology acceptance theory, which further supported in developing the research model. In order to test the model, we conducted a detailed survey and validated the hypotheses. The outcome variables were sharing intention, user behaviour, trust, satisfaction, and intention to recommend. The explanation all the dependent variables was exceptionally good. In addition, we conducted a mediation analysis and found satisfaction as a mediator variable.
Deep Multi-Task Learning based Fingertip Detection

Human-computer interaction (HCI) is signiﬁcantly depended on hand behavior detection, e.g., hand action recognition, gesture recognition. However, ﬁngertip detection is more practical in many HCI cases, e.g., ﬁnger pointing, clicking. Although the great progress in general object detection, ﬁne-grained target detection, e.g., ﬁngertip detection is still a challenging problem for existing methods because of the limited appearance features of the ﬁngertip. In this paper, we present a simple yet effective approach for ﬁngertip detection by designing a multi-task learning based framework. More speciﬁcally, we introduce a point regression loss into the object detection framework to jointly accomplish the hand region detection and ﬁngertip location detection tasks. We then propose a new point regression loss referred to as root loss to pay more attention to small-range errors for more precise ﬁngertip localization. Our framework further achieves number-variant ﬁngertip detection without the default number of ﬁngertips. We use the benchmark EgoGesture for performance evaluation. Experimental results show the superiority of the proposed method and the ablation studies verify the effectiveness of each component. Our method achieves the state-of-the-art ﬁngertip detection accuracy while maintaining the real-time algorithm speed.
ANALYSIS OF PHISHING SUSCEPTIBILITY IN A WORKPLACE: A BIG-FIVE PERSONALITY PERSPECTIVES

Employee is frequently referred to as the weakest link in the cyber security in an organization. Differences in the employees’ personality makes it hard for any organization to design a proper mitigation strategy in order to prevent them from being a victim of phishing attack. Besides, users’ general life experience and technological experience will also influence the type of user’s personality traits while handling or interacting with the security system, which affects their susceptibility towards phishing. The objective of this paper is to examine the personality traits that influence phishing susceptibility among employees in a workplace, and to investigate the influence of employees’ experience in shaping employees’ personality and consequently their behaviour in resisting phishing attack. This study used quantitative method. A survey ( N = 252) of employees in mid-sized IT related companies in Malaysia attempted to identify individual’s characteristics that relate to phishing susceptibility and characterize the higher-risk employees that pose threats to the companies. This paper presents three notable findings. First, the results of correlation analysis emphasized the importance of employees’ technical and general experience in shaping their personality to resist phishing attack. Secondly, the results of correlation analysis show that conscientiousness and self-monitoring personality traits were positively related with employee’s secure behaviour towards phishing threats. Finally, this study concluded that extroversion personality had the strongest influence towards phishing susceptibility, followed by self-monitoring, agreeableness and conscientiousness. The findings suggest that there is an inverse influence between personality traits (independent variables) and user behaviour (dependent variable). The proposed framework is useful for research attempting to shift concern on human factors in order to help organization improving employees’ cyber security compliance.
IoT BASED SOIL MOISTURE MANAGEMENT USING CAPACITIVE SENSOR AND USER-FRIENDLY SMARTPHONE APPLICATION

The Internet of Things (IoT) based system was assembled to monitor the moisture of soils for both indoor and outdoor uses. The SKU:SEN0193 capacitive soil moisture sensor exhibited a linear response to a variation in water volume added to the soil. Microcontroller Arduino NodeMCU was used with ESP8266 Wi-Fi module to transfer the sensing data in real-time, and the soil moisture data was displayed by the Blynk application on a smartphone. When the moisture dropped under the pre-defined threshold, the user was informed via the Line application and able to remotely trigger the irrigation pump
FUZZY MODELING OF SOLAR RADIATION ON SURFACES WITH DIFFERENT EXPOSITIONS AND DECLIVITY

This paper presents a study on the application of fuzzy logic modeling of solar radiation on surfaces, proposing an intelligent expert system as an alternative to mathematical model. Data were obtained from a study conducted in the Department of Rural Engineering of UNESP, Jaboticabal SP (Brazil), from March 2002 to March 2003, in which conducted the evaluation of a mathematical model to estimate solar radiation incident. With the study of radiation levels applied to ANFIS tool (Adaptive Network-based Fuzzy Inference System) 3 membership functions and 250 epochs, obtaining an approximate fuzzy model of solar radiation as a function of a single variable, the date of year. Keyrowlds– Solar Radiation, ANFIS, Fuzzy, approximation model, irregular surfaces