Miracle-dz/UniFAR-FTUs huggingface dataset:
Practices of De-Motivators in Adopting Agile Software Development Methods at Large Scale Development Teams From Management Perspective

During the last few decades, agile methodologies have become the most widely used software development methods. Agile methodologies provide great customer satisfaction and fast product delivery at the low cost. The low cost and customer satisfaction make agile methods highly attractive. Agile methods were designed and used, in its early days, for small-scale projects. There are many challenges in applying agile methods for large-scale projects as determined by some previous studies. The objective of this research is to identify the practices of De-motivators in adopting agile projects at a large scale from management point of view by employing a systematic literature review (SLR). In the review process, a total of 72 practices were discovered. De-motivator factors have been identified and ranked, through the analytical hierarchical process, through SLR in our previous study (Abrar et al., 2020). Analytical hierarchical processing (AHP) is a well-known method used for multi-criteria decisions implemented by Satty (2023). In this study AHP is used to prioritize de-motivators and their categories based on their relative importance. The prioritized de-motivators and their categories provide a strong background to the software project manager and practitioners for adopting Agile Software Development in large-scale projects.
Social cognition and emotional rehabilitation in participants with schizofrenia

Introduction People with schizophrenia have deficits in social cognition, emotion and social perception, as well as attributional style. The purpose of this study was to evaluate the efficacy of a multicomponent social cognition training program, e-Motional Training® (ET), in people with schizophrenia and to compare its efficacy with people who did not receive it. Therefore, a single-blind RCT was conducted in participants with a diagnosis of schizophrenia. Methods A randomized, single-blind, clinical trial was conducted with 50 stable outparticipants with schizophrenia (registry number CHUC_2019_109). All participants (control and intervention) were treated with pharmacotherapy, case management and were on Individual Placement and Support methodology for competitive employment. The intervention group was treated with ET, an online program designed for social cognition rehabilitation. Pre and post assessment was performed using different battery of tests. General mixed models with subject identification and repeated measures over time were used. Results Different pre and post measurements were performed in the two groups. No significant differences were found in sociodemographic characteristics between the control and intervention groups. Improvements were obtained in the intervention group in the Ekman test (p = 0.009), mainly enhanced by the improvement shown in three emotions: fear, sadness and disgust (p = 0.041, p = 0.021 and p = 0.038 respectively). Conclusion ET is a promising online training tool for social cognition deficits in schizophrenia, in particular, for the improvement of emotions. Clinical Trial Registration: https://beta.clinicaltrials. gov, NCT05866328.
Occurrence, Ecological and Health Risks of Organic Pollution of the Waters from a Tropical Lake System

The monitoring of the organic pollution level and the ecological and health risks of the waters from the Déganobo lake system were the subject of this study. In its implementation, the water samples were collected monthly in this aquatic ecosystem over a year (from August 2021 to July 2022). The organic pollution level of these waters was assessed using their Chemical Oxygen Demand, determined in accordance with NF T 90-101 standard. The ecological and health risks were assessed based on the SEQ-Eau V2 water quality guidelines. Their COD seasonal mean values were between 220,000±51,872 and 361,333±48,273 mg/L O2. So, their organic pollution was relatively very significant, with the corollary of the zero of its seasonal transparencies and of its seasonal dissolved oxygen contents. The seasonal dynamics of organic matter in these waters was essentially linked to the hydroclimate (rainfall and ambient temperature) on the watershed of this lake system. The low meteorite waters inputs in dry seasons favoured the increase of their COD by the important punctual wastes inputs of various origins (domestic, agricultural, industrial, hospital, etc.); in opposite of the important meteorite waters inputs in rainy seasons causing their renewal. The consequences of their important COD values are severe ecological and health risks, as highlighted by SEQ-Eau V2 water quality guidelines. So, this lake system is under the strong anthropogenic pressures. This study could serve as complementary information on its pollution level and contribute to decision-making for its rehabilitation, its protection within the framework of its sustainable development.
Optimal capacity threshold for reversible watermarking using score function

Histogram shifting is an important technique of reversible watermarking, which can embed large payloads into digital images with low distortion. The technique must determine two threshold values to achieve the lowest possible distortion. Appropriate threshold values might be found by iterative methods, but it is computationally inefficient when the payloads are high and varied. In this paper, we show that the optimal threshold values lie on a straight line and occur at the boundary of the payload-satisfying region. Moreover, we propose a high performance algorithm to approximate the optimal threshold values. Under the same image quality, experimental results indicate that the proposed scheme could get closer threshold values to the optimal threshold values, compared to previous work. Therefore, it requires a smaller number of iterations to obtain the desirable threshold values.
Evaluation of Prevalence and Severity of Anemia among Non-pregnant women of Reproductive Age in Garmian Province, Iraqi Kurdistan Region

Anemia is a major significant public health issue that affects both pregnant and non-pregnant women throughout the world. This study aimed to determine the prevalence and severity of anemia, as well as its classification, based on MCV values in non-pregnant women residing in the Garmian province in the east of Iraq. Blood samples were collected from non-pregnant women between the ages of 16 and 49 years. Anemia is classified as mild, moderate and severe, and as microcytic, normocytic and macrocytic depending on the level of Hb or mean corpuscular volume (MCV) respectively. In this study sociodemographic and hematological characteristics were also evaluated. The results showed that 56% of the study samples were anemic, and of the total anemic patients, 54 (35%), 86 (55%) and 16 (10%) had mild, moderate, and severe anemia respectively. Also, of the total anemic patients, 102 (65%) had microcytic type of anemia, 51 (33%) had normocytic anemia and only 3 (2%) had macrocytic anemia. The majority of anemic women (77.6%) had normal RBC counts but low Hct, MCH and MCHC values of 76.3%, 79.5% and 81.4%, respectively. The results showed also that there was no significant difference between the severity of anemia and age groups, occupation, blood pressure, bleeding, and haemorrhoids. However, diabetics showed a significant difference in anemia severity with <0.0004 P-value. The results showed that anemia had become a major public health issue in the study area.
The Role and Associated Factors of Liquid-Based Cytology of Bronchoalveolar Lavage Fluid in Lung Cancer Diagnosis: A Prospective Study

Background Liquid-based cytology (LBC) has shown advantages over conventional smears (CS), but previous applications in bronchoalveolar lavage (BAL) fluid have produced inconsistent results. This study compared LBC and CS for diagnosing lung cancer using BAL fluid. Methodology A prospective study was conducted on 92 patients suspected of having lung cancer. All patients underwent bronchoscopy and had a final diagnosis confirmed by histopathology of lesions tissue through biopsy. The study aimed to assess the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the two cytological methods, in a pair-wise fashion. In addition, the study evaluated the correlation of factors, such as the volume of fluid used in LBC and bronchoscopy lesion morphology, with the sensitivity of LBC. Results The study involved 78 participants who were diagnosed with lung cancer. The sensitivity, specificity, PPV, and NPV of LBC were 25.7%, 100%, 100%, and 19.4%, respectively, whereas those of CS were 15.4%, 85.7%, 85.7%, and 15.4%, respectively. Although the sensitivity of LBC was higher than that of CS, the difference was not statistically significant (p=0.096, McNemar test). Furthermore, the median fluid volume performed during LBC in patients with positive results was significantly higher than in those with negative results (p=0.001, Mann-Whitney U test). Conclusions The application of LBC to BAL fluid has demonstrated similar and potentially superior diagnostic accuracy compared to CS in detecting lung cancer. It is recommended that further investigation be undertaken to examine the relationship between the volume of fluid utilized during the LBC process and its diagnostic accuracy to enhance its sensitivity.
The Impact of Data on Structure-Based Binding Affinity Predictions Using Deep Neural Networks

Artificial intelligence (AI) has gained significant traction in the field of drug discovery, with deep learning (DL) algorithms playing a crucial role in predicting protein–ligand binding affinities. Despite advancements in neural network architectures, system representation, and training techniques, the performance of DL affinity prediction has reached a plateau, prompting the question of whether it is truly solved or if the current performance is overly optimistic and reliant on biased, easily predictable data. Like other DL-related problems, this issue seems to stem from the training and test sets used when building the models. In this work, we investigate the impact of several parameters related to the input data on the performance of neural network affinity prediction models. Notably, we identify the size of the binding pocket as a critical factor influencing the performance of our statistical models; furthermore, it is more important to train a model with as much data as possible than to restrict the training to only high-quality datasets. Finally, we also confirm the bias in the typically used current test sets. Therefore, several types of evaluation and benchmarking are required to understand models’ decision-making processes and accurately compare the performance of models.
A Review of Machine Learning Methods Applicable to Quality Issues

Disruptive technology, especially machine learning (ML), is changing the paradigm in many fields, including quality. Advancements in data science, increasing processing powers of computers, and the availability of massive datasets, have made machine learning a useful tool to solve the problem at scale. In this work, a systematic review of literature has been conducted to analyze the type of industry and quality problems that can be detected with ML. ML applications in industries such as service, manufacturing, food, software/IT, and healthcare to detect quality issues and detect fraud in healthcare and health insurance have been presented. The paper has also summarized the common themes in applying ML in detecting quality problems and discussed the advantages and disadvantages of various ML algorithms in detecting quality issues and anomalies, including fraud, in various industries.
Water Resources Suitability Model by Using GIS (Case Study: Borujerd Rangeland, Sarab Sefid)

Water resources suitability is one of the most important factors to sustainability utilization of natural potential by rangeland grazing suitability. To determine this factor in short time, low cost and high accuracy are most challenges of experts and ranchers. Using suitable technique for this subject can be sustainable utilization comprising from rangeland ecosystems according to its degradation and heavy grazing. This research was conducted in Boroujerd Sarab Sefid rangeland, Lorestan province, Iran from 2011 to 2012. Some factors such as slope, height and direction maps provided by using satellite images, basic information, analog and digitized maps such as; topographic and DEM (Digital Elevation Model). Also measurements of field were conducted by excursion and interview with herders to determine places of water resources such as permanent and temporary springs and permanent and temporary rivers too. Water resources area's map was prepared by using Arc GIS9.3 software with integration of digitized information and field data. At the end water resources suitability were determined by using three sub models such as quantity, quality and distance from water resources. Results show that all of 16 plant vegetation types were dropped in І and П classes of water resources suitability which didn"t have any limitation according to quantity and quality and distance of animal husbandry and livestock. As a final conclusion this research shows that using RS and GIS could be useful to water resources suitability of rangeland ecosystems with low cost and high accuracy and speed, if consider standards and criteria of using GIS and RS.
GPU-Accelerated Deep Learning-Based Correlation Attack on Tor Networks

The Tor network, renowned for its provision of online privacy and anonymity, faces the constant threat of correlation attacks that aim to compromise user identities. For almost two decades, these correlation attacks were based on statistical methods. However, in recent years, deep learning-based correlation attacks have been introduced to make them more accurate. Nevertheless, in addition to being accurate, fast correlation attacks on Tor are crucial for assessing the real-world viability of such attacks because reduced correlation time aids in estimating its practical implications. Moreover, a reduction in correlation time also helps improve efficiency and ensures practical relevance of the attack. The existing state-of-the-art implementation of a correlation attack on Tor suffers from slow performance and large memory requirements. For instance, training the model required 133 GB of memory, and correlating 10,000 flows takes about 976 seconds. In this paper, we present a novel GPU-based correlation strategy and a fast traffic flow loading technique to reduce time complexity by $7.12\times $ compared to existing methods. Our computational approach, reliant on PyCUDA, facilitates the parallelization of operations used in the attack, thereby enabling efficient execution through the utilization of GPU architecture. Leveraging these two approaches, we introduced an improved correlation attack, which shows high accuracy and fast performance compared to state-of-the-art methods. Moreover, we address resource limitation issues by reducing memory consumption by 47.37% during the training phase, which allows the model to be trained with much fewer resources.
The effect of financial performance on the profit growth of banks listed on the Indonesia Stock Exchange

This study aims to provide empirical evidence showing how financial performance affects the profit growth of public sector banks. The sample comprises companies in the banking industry listed on the Indonesia Stock Exchange from 2018–2021. The research considers quantitative data from annual reports produced from 2018–2021 using purposive sampling. This research evaluated financial performance as measured by two proxies: CFROA was found to not affect bank profit growth, while ROE did have an effect. This research is expected to contribute to the development of new proxies for measuring banking financial performance. This research will also help banks focus on maintaining financial ratios according to regulations, such as net profit margin, operating costs and operating income, and non-performing loans. Furthermore, profit growth, as a factor that increases credibility, needs to be considered to maintain banking viability and customer and investor trust.
Literature Review of Coal Waste Utilization

Fly ash and bottom ash has been one of the problems of energy, textile and other product companies that have involved coal for a long time. Apart from its small selling value, this waste also has a bad impact on the environment. The coal demand that continues to increase every year makes the production of ash waste uncontrollable. This literature review aims to provide an analysis and classification of alternatives to coal waste utilization that can be carried out in Indonesia so that most references are taken from Indonesia. This review uses Oliveira's systematic literature review methodology to analyze references systematically and comprehensively. Based on the results of the analysis of 23 references with 17 references coming from Indonesia, there are 4 types of classifications that can be distinguished, namely treated waste, the method used, the results of the research and the type of utilization. The coal waste treatment studied is often used for 3 aspects, namely mixed construction materials, adsorption materials and planting media. However, most of the references still carry out testing of coal waste processing through laboratories and the mix required for the utilization of this coal waste varies so that further research is still needed.
Chrome(VI) ion biosorption modelling in a fixed bed column on Dioscorea rotundata hull

: This work aimed to evaluate the yam peel in a bed column packaged as a chromium(VI) ion adsorbent in an aqueous solution. Yam peel was used as adsorbent, prior washing, drying, size reduction, and selection. The experimental work consisted in determining the effect of bed depth, particle size, and temperature, keeping inlet flow = 0.75 cm 3 ∙s –1 , pH = 2 and initial concentration of 100 mg∙dm –3 . The Adsorption Scanning Electron Microscopy (SEM) and Energy Dispersive X-ray (EDS) analysis on yam ( Dioscorea rotundata ) peel showed a heterogeneous, porous structure, with functional groups characteristic in lignocellulosic materials. It was analysed regarding the influence of temperature, bed height, and adsorbent particle size on the removal efficiency; it was found that the decrease of particle size and the increase of the bed height favour the elimination of the metallic ion, with removal rates between 92.4 and 98.3%. The bed maximum adsorption capacity was 61.75 mg∙g –1 , and break time of 360 min. The break curve’s adjustment to the Thomas, Yoon–Nelson, Dose–Response and Adams–Bohart models was evaluated, concluding that the Yoon–Nelson and Dose–Response models best described the behaviour of the break curve with a coefficient of determination ( R 2 ) of 0.95 and 0.96, respectively. The results show that the bio-adsorbent studied can be used to eliminate Cr(VI) in a continuous system
Bronze Age human–landscape interactions in the southern Transural steppe, Russia – Evidence from high-resolution palaeobotanical studies

The Transural steppe is a cultural contact zone between areas east and west of the Ural Mountains. Mobile pastoralism is the traditional way of life in the steppe, while sedentary cultures constitute an exception, probably as a result of climatic variations. A change of lifestyle together with other innovations is documented at the turn of the 3rd to the 2nd millennia BC and often believed to have been accompanied by a shift to agro-pastoralism. To examine the ecology and economy in the Bronze Age steppe, we employed a combination of methods. As proxy-data, plant macro-remains from archaeological excavations of Sintashta fortified settlements and pollen from off-site archives were used for a high-resolution palaeoenvironmental reconstruction. Statistical comparisons of past and present pollen spectra show no significant differences in vegetation distribution. This allowed us to map the recent vegetation units by multispectral satellite imagery and to use them for modelling. Models further incorporate steppe productivity, carrying capacity and population figures to estimate herd sizes. Even if the climate was suitable for agriculture, evidence is missing from all botanical records. The economic mainstay was animal husbandry. Models consider autonomous activity zones of at least 4 km radius surrounding each Sintashta settlement where grazing resources could easily sustain the estimated population and their livestock. The river is seen as the determining factor to settle in this region as it provided constant access to water and valuable natural grazing areas. During dry years and winter, the productive meadow steppes functioned as reserve pastures.
Determining the impacts of hydrological drought on endangered Nooksack dace (Rhinichthys cataractae) at the population and individual level : Implications for minimum environmental flow requirements

ii Table Of Contents . iii List Of Tables . v List Of Figures . vi Acknowledgements . viii Dedication . x Chapter 1: General Introduction . 1 1.1 Background . 1 1.2 Nooksack Dace Conservation And Management . 4 1.2.1 Conservation Context . 4 1.2.2 Habitat Requirements And Threats . 5 1.3 Thesis Objectives And Structure . 6 Chapter 2: Drought And The Role Of Refugia In An Endangered Riffle-Dwelling Freshwater Fish . 9 2.1 Introduction . 9 2.2 Methods. 11 2.2.1 Field Survey: Flow Effects On Nooksack Dace Populations And Habitat Use . 12 2.2.1.1 Site Selection . 12 2.2.1.2 Sampling Methods . 13 2.2.1.3 Data Analysis . 13 2.2.2 Experimental Manipulation: Flow Effects On Nooksack Dace Growth Rate . 14 2.2.2.1 Experimental Design . 14 2.2.2.2 Sampling Methods . 15 2.2.2.3 Data Analysis . 16 2.3 Results . 17 2.3. 1 Field Survey: Flow Effects On Nooksack Dace Populations And Habitat Use . 17 2.3. 2 Experimental Manipulation: Flow Effects On Nooksack Dace Growth Rate . 18 2.4 Discussion . 19 2.5 Conclusion . 24