Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessment of Solar Home Systems (SHS) for Isolated Rural Communities in Vanuatu Using Project Lifecycle/Sustainability Framework

viii Chapter 1: Introduction and Framework for Assessment 1 Chapter 2: Solar Home System (SHS) 4 2.1. SHS electrical components 4 2.1. 1. Solar Panel 4 2.1. 2. Charge Controller 5 2.1. 3. Battery 6 2.1. 4. Loads 6 2.1. 5. Balance of system components 7 2.2. SHS Design 7 2.2.1: Solar Resource 7 2.2.2: Solar panel sizing 9 2.2. 3: Battery sizing 11 2.2. 4: Estimating local solar insolation and importance of panel orientation 13 2.2. 5: SHS Design worksheet 16 Chapter 3: Project Assessment Methodology: Life Cycle-Sustainability Matrix 17 3.1 Project life cycle stages 17 3.2 Sustainability factors 19 3.3 Matrix Framework 21 Chapter 4: Assessment Case Studies 27 4.1 Case study 1: Japan International Cooperation Agency (JICA) funded SHS rural electrification pilot project on Efate Island 27 4.1. 1 JICA internal project evaluation 28 4.1. 2 Assessment application 29 4.1. 3 JICA project site observed issues 35 4.2 Case study 2: Various donor funded solar system at local community institutions. 35
A New Approach to Data Analysis Using Machine Learning for Cybersecurity

The internet has become an indispensable tool for organizations, permeating every facet of their operations. Virtually all companies leverage Internet services for diverse purposes, including the digital storage of data in databases and cloud platforms. Furthermore, the rising demand for software and applications has led to a widespread shift toward computer-based activities within the corporate landscape. However, this digital transformation has exposed the information technology (IT) infrastructures of these organizations to a heightened risk of cyber-attacks, endangering sensitive data. Consequently, organizations must identify and address vulnerabilities within their systems, with a primary focus on scrutinizing customer-facing websites and applications. This work aims to tackle this pressing issue by employing data analysis tools, such as Power BI, to assess vulnerabilities within a client’s application or website. Through a rigorous analysis of data, valuable insights and information will be provided, which are necessary to formulate effective remedial measures against potential attacks. Ultimately, the central goal of this research is to demonstrate that clients can establish a secure environment, shielding their digital assets from potential attackers.
Human Activity and Correlated Posture Monitoring Using Earlobe-Worn Wearable Sensor System and Deep Learning Algorithm

An approach for monitoring human activities and correlated postures using an earlobe-worn wearable sensor and a deep learning algorithm is proposed. The herein-used miniaturized wearable is called TRACE and is to be mounted on an earlobe, for which smaller movements are expected compared with common locations for wearable devices. This work adopts both biological [heart rate (HR)] and physical [acceleration (ACC)] signals that are simultaneously recorded in real time. The recorded raw data are sensor-fused, adequately labeled, processed, and trained by a deep learning algorithm. Using the data, several deep learning models based on different deep learning algorithms are developed and compared. Among our experimental conditions, the best classification accuracy is achieved when the biological and physical sensor signals are fused and a long-short term memory (LSTM) algorithm is used. The proposed approach successfully classified human activities and postures with high accuracy reaching $>$ 95% for two patterns of daily activities. More specifically, the proposed method achieved 97.83%, 97.31%, and 98.19% accuracy for the deep neural network (DNN), convolutional neural network (CNN), and LSTM models, respectively.
Determinants of Improved Groundnut Varieties Adoption among Farmers in Northwest Nigeria

: Given their proven, financial and environmental benefits, such as increased production and income for farmers, the adoption of improved groundnut varieties among farmers in sub-Saharan Africa remains small. Groundnut is Nigeria's primary export commodity, rendering Nigeria the fifth largest producer in the world, accounting for 50% of Africa's needs and 30% of global needs. To date, however, minimal studies have been undertaken to analyze the degree to which new groundnut varieties have been embraced, in particular in sub-Saharan Africa. The research, therefore, looked at the determinants of the adoption of improved groundnut varieties among farmers in Northwest Nigeria through the technical skills of extension officers, mobile extension networks, and extension methods. Primary survey results were collected from 339 farmers from four northwestern states in Nigeria using purposeful and random sampling techniques. Data is evaluated using descriptive statistics and multiple regression approaches. The findings indicate that the adoption by farmers of improved groundnut varieties is significantly related to the technical skills of extension agents, the usage of radio as an extension tool, and group and individual extension methods. The group extension approach provides the most significant contribution to the adoption of improved groundnut varieties. It is therefore essential to provide regular training of extension agents on suitable extension teaching methods, which will include ample awareness of the characteristics of the different strategies and the ability to familiarize themselves with the characteristics of the respondents, thereby promoting the selection of correct methods for
Proper Names Translation in “The Secret Garden” Novel by Frances Hodgson Burnett

In literary works such as novels, language plays a vital role in telling stories. Novels that will be distributed to readers from various countries will usually be translated first, and it is the job of the translator to maintain several important elements such as proper names so that they remain under the original meaning of the text. This way, readers can fully grasp the essence of the story in the translated version. The main aim of this study is to discover the different kinds of proper names and translation techniques used in Frances Hodgson Burnett's novel, The Secret Garden. To achieve this, the research incorporates Newmark's theory. The data was collected through observation and documentation, with notes being taken during the process. As a result, the study identified three distinct types of proper names in The Secret Garden novel, such as people’s names, geographical terms, and names of objects. Furthermore, during the research, seven different translation procedures were discovered. These procedures include transference, naturalization, couplets, expansion, shift or transposition, cultural equivalent, and literal translation. Out of all the translation procedures studied, the transference procedure was the one most used. It allowed the translator to retain the original names as much as possible while translating the novel into another language.
2D LiDAR-Based Human Pose Tracking for a Mobile Robot

: Human pose tracking is a practical feature for service robots, which allows the robot to predict the user’s trajectory and behavior and thus provide appropriate assistance for them. In this paper, we propose a human pose tracking method based on a knee-high 2D LiDAR mounted on the mobile robot. Inspired by human gait, a motion intention zoning, and a walking gait model are proposed to adapt to various motion patterns and achieve accurate orientation estimation. We propose a Kalman Filter-based human pose tracker that considers the leg occlusion problem and the data association of legs. We evaluate the proposed method’s performance in various complex scenarios and demonstrate robustness to leg occlusion. We released our implementation as open-source code ∗ .
A Long-Term Funds Predictor Based on Deep Learning

: Numerous neural network models have been created to predict the rise or fall of stocks since deep learning has gained popularity, and many of them have performed quite well. However, since the share market is hugely influenced by various policy changes or unexpected news, it is challenging for investors to use such short-term predictions as a guide. In this paper, a suitable long-term predictor for the funds market is proposed and tested using different kinds of neural network models, including the Long Short-Term Memory (LSTM) model with different layers, the Gated Recurrent Units (GRU) model with different layers, and the combination model of LSTM and GRU. These models were evaluated on two funds datasets with various stock market technical indicators added. Since the fund is a long-term investment, we attempted to predict the range of change in the future 20 trading days. The experimental results demonstrated that the single GRU model performed best, reached an accuracy of 92.14% to correctly predict the direction of rise or fall, and the accuracy of predicting the specific change also hit 85.35%.
Exploring Segnet Architectures for iGPU Embedded Devices

: Image segmentation is an important topic in computer vision which encompasses a variety of techniques to divide image into multiple areas or sub-regions in order to extract meaningful information. Artiﬁcial Neural Networks (ANNs), biologically inspired algorithms, are nowadays widely used to perform such tasks and popular models are usually based on encoder-decoder architectures. Segnet was one of the ﬁrst proposed model of this kind in the literature and, despite its efﬁciency, it has several drawbacks for embedded systems especially due to the huge amount of arithmetic operations and memory used in the original version. However, its simple sequential based architecture offers interesting properties for optimization and real-time analysis. In this paper, we deeply investigate how to tune and adapt original Segnet architecture to allow efﬁcient run-time execution on embedded targets equipped with an iGPU. We propose our own implementation design which is experimented and validated on iGPU embedded devices for two state of the art datasets from Unmanned Aerial Vehicle (UAV) applications.
Global ionospheric total electron contents (TECs) during the last two solar minimum periods

The last solar minimum period was anomalously extended and low in EUV irradiance compared with previous solar minima. It can readily be expected that the thermosphere and the ionosphere must be correspondingly affected by this low solar activity. While there have been unanimous reports on the thermospheric changes, being cooler and lower in its density as expected, the ionospheric responses to low solar activity in previous studies were not consistent with each other, probably due to the limited ionospheric observations used for them. In this study, we utilized the measurements of total electron content (TEC) from TOPEX and JASON‐1 satellites during the periods of 1992 to 2010, which includes both the last two solar minimum periods, in order to investigate how the ionosphere responded to the extremely low solar activity during the last solar minimum compared with previous solar minimum. Although the global daily mean TECs show negligible differences between the two solar minimum periods, the global TEC maps reveal that there are significant systematic differences ranging from about −30% to +50% depending on local time, latitude, and season. The systematic variations of the ionospheric responses seem to mainly result from the relative effects of reduced solar EUV production and reduced recombination rate due to thermospheric changes during the last solar minimum period.
Knowledge Graphs Extracted from Medical Appointment Transcriptions: Results Generating Triples Relying on LLMs

: Knowledge Graphs (KGs) represent computer-interpretable interactions between real-world entities. This can be valuable for representing medical data semantically. We address the challenge of automatically transforming transcripted medical conversations (clinical dialogues) into RDF triples to structure clinical information. In this article, we design and develop a software tool that simplifies clinical documentation. Our solution explores advanced techniques, such as the Fine-tuned GPT-NeoX 20B model, to extract and summarize crucial information from clinical dialogues. We designed the solution’s architecture, supported by technologies such as Docker and MongoDB, to be durable and scalable. We achieve accurate medical entity detection from Portuguese-language textual data and identify semantic relationships in interactions between doctors and patients. By applying advanced Natural Language Processing techniques and Large Language Models (LLMs), our results improve the accuracy and relevance of RDF triples generated from clinical textual data.
Extracting photogrammetric ground control from lidar DEMs for change detection

Research has shown the importance of measuring topography for surface change detection and that the use of remote sensing methods is ideal for this application. A prerequisite for measuring change is a historical data‐set that covers the time period of interest. Many remote methods of topographic data collection such as lidar are relatively recent developments and therefore cannot provide records longer than about a decade. Alternatively, aerial photography has been in common use since the early 20th century and archives upwards of 50 years are therefore not uncommon. While photogrammetry is dependent on well distributed, high quality ground control points (GCPs), such data has been successfully applied retroactively. Therefore, contemporary lidar data, which requires little ground‐truth data, should be an ideal source of GCPs for controlling historical aerial photographs. This study aims to evaluate the success with which GCPs can be extracted from a high‐resolution lidar data‐set for controlling aerial photography for digital elevation model (DEM) production. Using a data‐set collected in Upper Wharfedale, northern England, GCPs were measured both by means of a lidar data‐set and by using traditional field‐based methods. The results showed that while the use of lidar‐derived ground control produced a DEM of inferior quality, increasing the number of GCPs used in the model produced results comparable to the GPS‐controlled DEM. These results are significant especially for surface change detection research in remote areas where high quality ground control is difficult to secure.
Long Term Two-Phase Flow Analysis of the Deep Low Permeability Rock at the Bruce DGR Site

Abnormal pressures have been measured in the deep boreholes at the Bruce site, southern Ontario, where a deep geologic repository for low and intermediate level radioactive waste disposal has been proposed. The pressure regime in the stratigraphic units exhibits either higher than hydrostatic pressure (over-pressured) or lower than hydrostatic pressure (under-pressured) are considered to be abnormal. At the Bruce site, the Ordovician sediments are under-pressured while the underlying Cambrian sandstone and the overlying Guelph carbonate are over-pressured. Hypotheses have been documented in literature to explain the phenomenon of abnormal pressures. These hypotheses include osmosis, glacial loading and deglaciation unloading, exhumation of overlying sediments, crustal flexure and the presence of an immiscible gas phase. Previous work on the Bruce site has shown that the under-pressures in the Ordovician limestone and shales could not be explained by glaciation and deglaciation or by saturated analyses. The presence of a gas phase in the Ordovician formations has been determined to be a reasonable cause of the under-pressure developed in the Ordovician shales and limestones at the Bruce site. Support for the presence of a gas phase includes solution concentrations of methane, concentrations of environmental isotopes related to methane and estimates of water and gas saturations from laboratory core analyses. The primary contribution of this thesis is the sensitivity analyses performed on the hydrogeologic parameters with respect to a one dimensional two-phase flow model. First, a one dimensional two-phase air and water flow model was adopted and reconstructed to simulate the long-term evolution of the groundwater regimes at the DGR site. Then the hydrogeologic parameters which impact the presence of under-pressure in the groundwater are investigated. Data required to quantify the properties of geologic media and groundwater are adopted directly from borehole testing and laboratory testing results. The permeable boundaries of the domain are assumed to be water saturated and pres-
Valuing European Option Under Double 3/2-Volatility Jump-Diffusion Model With Stochastic Interest Rate and Stochastic Intensity Under Approximative Fractional Brownian Motion

In this study, we propose a more comprehensive and realistic option pricing model based on approximative fractional Brownian motion, building upon recent advancements in this area. Specifically, we utilize the double 3/2-volatility Jump-Diffusion model, which incorporates approximative fractional Brownian motion with 3/2-volatility, stochastic interest rate, and stochastic intensity. To account for the stochastic interest rate, we employ a two-factor Vasicek model. Notably, our model accommodates negative interest rates. Consequently, we develop a multi-factor model with a stochastic interest rate structure for pricing European options and derive a closed-form pricing formula with an analytical solution by applying some algebraic calculations and Lie symmetries. In order to demonstrate the superiority of our proposed model over other classical approaches, we present numerical results that showcase the value of a European call option. This comparative analysis underscores the advantages of our model in comparison to traditional models.
Histidine Triad Nucleotide-Binding Protein 1 Improves Critical Limb Ischemia by Regulating Mitochondrial Homeostasis

Critical limb ischemia (CLI) is a common complication of diabetes mellitus that typically occurs in the later stages of the disease. Vascularization is indeed an important physiological process involving the formation of new blood vessels from existing ones. It occurs in response to various normal and pathophysiological conditions, and one of its critical roles is to compensate for inadequate oxygen supply, which is often seen in situations like chronic limb ischemia (CLI). Histidine triad nucleotide-binding protein 1 (Hint1) is a member of the Hint family that has been shown to attenuate cardiac hypertrophy, but its role in vascularization still needs to be clarified. In this study, we investigated the role of Hint1 in CLI. We found that Hint1 is significantly reduced in the muscle tissue of STZ-induced diabetic mice and high-glucose (HG)-treated endothelial cells (ECs). Hint1 deletion impaired blood flow recovery and vascularization, whereas Hint1 overexpression promoted these processes. In addition, our in vitro study showed that Hint1 deficiency aggravated mitochondrial dysfunction in ECs, as evidenced by impaired mitochondrial respiration, decreased mitochondrial membrane potential, and increased reactive oxygen species. Our findings suggest that Hint1 deficiency impairs blood perfusion by damaging mitochondrial function and that Hint1 may represent a potential therapeutic target for treating CLI.
The Effect of Auction Fee, Administration Fees of State Receivables Management and Management of State-Owned Assets Against Actualization of State Non-Tax Revenue on Kantor Pelayanan Kekayaan Negara dan Lelang Medan Period 2019-2021

This study examined the effects of auction fees, state receivables management costs, and state property management costs on the realization of non-tax state revenue at the State Asset Service Office and Medan Auction from 2019 to 2021. The Medan KPKNL Performance Achievement Report, which contains the Budget Report and Realization of PNBP for the 2019–2021 year and has a total population of 36, is the population in this research. Due to the large population in this study being relatively small (limited), the sample was chosen using the saturation or census sampling approach. To characterize each variable in this study, this form of research employs a descriptive quantitative technique utilizing secondary data. The Statistical Product and Service Solution (SPSS) Version 25 will process the data. The data analysis method used in this study employs multiple linear regression analysis tests, a coefficient of determination test, a partial hypothesis test (t-test), and a simultaneous test (F test) with a significance level of 0.05. Each variable is stated to have a positive and significant effect on Non-Tax State Revenue based on the results of data analysis tests that have partially been conducted on Auction Fees, Administrative costs for managing state receivables, and Management of state property, and the consequences of research simultaneously stated that all independent variables have an effect positive and significant to PNBP simultaneously with the test results of the coefficient of determination showing in 72.1%.
Intelligent Analysis of Vehicle Accidents to Detect Road Safety: A Systematic Literature Review

Current road accident research focuses mainly on the key role and importance of Artificial Intelligence (AI) in road accident analysis and prevention. After reviewing the literature, this study found that AI had a wide range of potential applications. It can analyse traffic data more accurately and quickly through advanced machine learning and deep learning technologies, and identify accident risks and dangerous driving behaviours, thereby helping to predict and avoid accidents. Furthermore, the research provides insight into the different types of traffic accidents and the severity of injuries they cause, highlighting the importance of understanding these differences to improve road safety and help inform decision-making. The paper has attempted to develop a comprehensive and diverse road crash impact model by exploring some of the elements that influence road crashes, including human factors, vehicle factors and road environment factors. Finally, this paper identifies some innovations and future research directions for this study, including addressing imbalances and quality issues in collecting and processing data, improving the interpretability and transparency of injury severity expressions, and adopting a more comprehensive approach to analysing road crashes. These innovations will promote greater theoretical and practical progress in road crash research to improve road safety and reduce the damage caused by accidents.
Technical Change, Factor Bias and Input Adjustments: Panel Data Evidence from Irrigated Rice Production in Southern Palawan, Philippines

Approximately 90 percent of the world's production and consumption of rice occurs in Asia, and regional demand is projected to increase by 70 percent over the next 30 years. Meeting this demand requires an expansion of the total area under irrigation in the coming decades. In this article, the authors observe the factors of labor, fertilizer and pesticides in relation to a profit-maximizing combination of inputs for rice production, the reaction of these factors to technological improvements such as irrigation and machinery, and their implications on the environment, particularly upland forest resources.