Miracle-dz/UniFAR-FTUs huggingface dataset:
Influence of socio-demographic factors and housing characteristics on satisfaction with privacy in gated communities in Accra (Ghana)

Abstract Gated Communities (GCs) are rapidly popularizing and becoming a dominant form of housing in Global South Cities. Using binary logistic regression analysis, this article examines how socio-demographic and housing characteristics predict residents’ satisfaction with privacy in Devtraco and Manet (GCs) in Accra, Ghana. Ceteris paribus, residents of Devtraco had lower odds of being satisfied with privacy compared to those in Manet (OR = 0.23; p < 0.05). Level of education, age, home renovations were positively associated with satisfaction with privacy. Respondents with post-secondary education had higher odds of being satisfied with privacy compared to those with secondary education or less (OR= 10.56; p < 0.01). Respondents who changed their interior doors reported higher satisfaction with privacy in their homes compared to those that did not change their interior doors (OR = 4.94; p < 0.05). Complaints against housing features and security service charges decrease residents’ satisfaction with privacy. Thus, the findings might inform decision making for different housing actors.
Complex Network Study of Solar Magnetograms

In this paper, we study solar magnetic activity by means of a complex network approach. A complex network was built based on information on the space and time evolution of sunspots provided by image recognition algorithms on solar magnetograms taken during the complete 23rd solar cycle. Both directed and undirected networks were built, and various measures such as degree distributions, clustering coefficient, average shortest path, various centrality measures, and Gini coefficients calculated for all them. We find that certain measures are correlated with solar activity and others are anticorrelated, while several measures are essentially constant along the solar cycle. Thus, we show that complex network analysis can yield useful information on the evolution of solar activity and reveal universal features valid at any stage of the solar cycle; the implications of this research for the prediction of solar maxima are discussed as well.
Improved Bidirectional GAN-Based Approach for Network Intrusion Detection Using One-Class Classifier

Existing generative adversarial networks (GANs), primarily used for creating fake image samples from natural images, demand a strong dependence (i.e., the training strategy of the generators and the discriminators require to be in sync) for the generators to produce as realistic fake samples that can “fool” the discriminators. We argue that this strong dependency required for GAN training on images does not necessarily work for GAN models for network intrusion detection tasks. This is because the network intrusion inputs have a simpler feature structure such as relatively low-dimension, discrete feature values, and smaller input size compared to the existing GAN-based anomaly detection tasks proposed on images. To address this issue, we propose a new Bidirectional GAN (Bi-GAN) model that is better equipped for network intrusion detection with reduced overheads involved in excessive training. In our proposed method, the training iteration of the generator (and accordingly the encoder) is increased separate from the training of the discriminator until it satisfies the condition associated with the cross-entropy loss. Our empirical results show that this proposed training strategy greatly improves the performance of both the generator and the discriminator even in the presence of imbalanced classes. In addition, our model offers a new construct of a one-class classifier using the trained encoder–discriminator. The one-class classifier detects anomalous network traffic based on binary classification results instead of calculating expensive and complex anomaly scores (or thresholds). Our experimental result illustrates that our proposed method is highly effective to be used in network intrusion detection tasks and outperforms other similar generative methods on two datasets: NSL-KDD and CIC-DDoS2019 datasets.
Inter‐temporal mutual‐fund management

Traditionally, mutual funds are mostly managed via an ad hoc approach, namely a terminal‐only optimization. Due to the intricate mathematical complexity of a continuum of constraints imposed, effects of the inter‐temporal reward for the managers are essentially neglected in the previous literature. For instance, the inter‐temporal optimal investment problem from the fund manager's viewpoint, who earns proportional management fees continuously (a golden rule in practice), has been outstanding for long. This article completely resolves this challenging question especially under generic running and terminal utilities, via the Dynamic Programming Principle which leads to a nonconventional, highly nonlinear HJB equation. We develop an original mathematical analysis to establish the unique existence of the classical solution of the primal problem. Further numerical calibrations and simulations for both the portfolio weight and the value functions illustrate the robustness of the optimal portfolio towards the manager's risk attitude, which allows different managers with various risk characteristics to sell essentially the same investment vehicle. Simulation studies also indicate that the policy of charging a substantial terminal‐only management fee can be replaced by another one with only a negligible amount over the interim period, which substantially reduces the total management fee paid by the clients without lowering the manager's satisfaction at all; this last observation echoes the magic of the alchemy of finance.
Current State and Perspective for Artificial Turf as Sport Environment : Focusing on Third-generation Artificial Turf as Football Playing Surface

The third generation of artificial turf which has a longer fiber (60-65 mm in length) than those of the first and second generations, and a 40mm layer of rubber and sand infill instead of pure sand, was developed in 1998. Surface temperature on the artificial turf peaked around 65°C by daytime in the summertime, and at the same time the surface temperature of the natural turf was around 40°C. The albedo seems to be one of the main factors in estimating the surface temperatures of outdoor sport surfaces. If the materials and the vertical structure of surfaces remain the same, we might comparatively estimate the surface temperature by using the albedo. Granulated rubber is commonly used as the rubber infill and provides the right level of shock absorbency and deformability. It was the same level on the natural turf and the artificial turf for the force reduction, which was effective in estimating the impact of the sports surfaces. There was no difference in the overall risk of injury on the third-generation artificial turf compared with natural turf. Most of the artificial turfs were sand-based and used rubber (SBR) infills that were derived from recycled used car tires. The concentrations of leaching heavy metals increased with an increase in the acidity of the acid solutions as acid rain. The concentrations of zinc (Zn) at less than 4.0 of pH, exceeded the Japanese effluent standard. The concentrations of Zn decreased with the aging of the SBR infills. The concentrations of Zn leached from SBR coated with polyurethane, ethylene-propylene rubber , Eco-fill, and cedar tree bark as an alternative of SBR were less than the Zn effluent standard in Japan. 1) Biwako Seikei Sport College, 1204 Kitahira, Otsu-shi, 520-0503 Japan
Multiphase flow in pipelines: An analysis of the influence of empirical correlations on mechanistic models

Mechanistic models represent process behaviour through sets of equations based upon fundamental conservation laws and scientific knowledge of the process. In order to compensate for the lack of sufficient knowledge of the physical phenomena, empirical correlations are used to estimate certain model parameters. Empirical correlations in mechanistic models make them data-sensitive, to the extent that they become data-driven. The current industry practice for model predictions is to check the data fit of a selection of mechanistic models, for the purpose of choosing the most accurate model. This study proposes a method to use a set of alternative empirical correlations in a mechanistic model with the view of optimising or simplifying the model. This suggests that the current industry practice needs to be changed to first test the data fit of a set of empirical correlations in each model and only then select the most accurate mechanistic model. To achieve mechanistic model optimisation a two stage modelling approach is taken. During the first stage, training, a history table of optimal correlation sets is created. For the second stage, prediction, two methods are proposed: 1. A history matching algorithm based on sensitivity analysis. This is an algorithm based on the minimisation of an objective function, with the view of identifying an optimal correlation set to be used for prediction. 2. Data mining. Sorting and grouping techniques are used to identify regions where a certain empirical correlation set is optimal. Mechanistic model simplification is useful for situations where it is difficult to find empirical correlations for certain model parameters, or the empirical correlations have a computationally-intensive component. A method based on sensitivity analysis is proposed to keep constant empirical parameters with a low influence on the model output. Based on the observation that each empirical correlation depends on a possibly different set of input parameters, the structure of the calculation algorithm used by the model may change each time a different correlation is used. Essentially, one cannot “unplug” an empirical correlation out of a mechanistic model and replace it with an alternative correlation, without first analysing whether the calculation algorithm has to be modified. This study proposes a method to analyse the changes in the calculation algorithm, when a new empirical correlation is used. The theoretical concepts developed in this investigation were applied to the Zhang et al. (2000) mechanistic slug flow model, widely used in the oil and gas industry for the prediction of pressure gradient and liquid holdup in two-phase pipe flow. In order to achieve model optimisation, a set of empirical correlations
Effect of Instructional Supportive Guidelines on Alleviating Endometriosis Related pain Symptoms

: Endometriosis is a complex disorder characterized by pain and infertility that, if not treated properly, can compromise the patient’s quality of life and health significantly. The purpose of the study was to investigate the effect of instructional supportive guidelines on alleviating endometriosis-related pain symptoms. Design: A quasi-experimental research design (non–equivalent group design) was utilized. Sample: A convenience sample of 100 women diagnosed with endometriosis. Settings: The study was conducted at Obstetrics and Gynecology outpatient clinics affiliated with Menoufia University Hospital and Shebin El-Kom Teaching Hospital. Instruments: A structured interviewing questionnaire, numerical rating scale, and self-care practices' specific effect. Results: There was a highly statistically significant difference in total knowledge scores regarding the endometriosis and the instructional supportive guidelines after implementing the guidelines between the study and the control groups. Also, there was a highly statistically significant decrease in the intensity of endometriosis-related pain in the study group compared to the control group. There was a highly statistically difference in the score of women's self-care practices for alleviating endometriosis-related symptoms after implementation of the guidelines compared to their self-care practices before it. Conclusion: The instructional supportive guidelines resulted in a highly statistically significant increase in the total knowledge scores, a decrease in the intensity of endometriosis-related pain symptoms. Also, there was a highly statistically significant difference in the score of woman's self-care practices' in the study group compared to the control group. Recommendations: providing health education programs for women with endometriosis to improve their quality of life and increase their awareness regarding the guidelines to decrease the endometriosis-related pain symptoms is necessary. Also, performing regular follow up to detect any health problems early is recommended.
Effects of an Applied Workshop on Teacher Self-Efficacy and Intent to Teach Hydraulics

Agricultural mechanics is an important component of school-based agricultural education programs, yet many university agricultural education programs lack the necessary facilities, personnel, and available credit hours to adequately prepare preservice teachers in basic agricultural technologies, much less more advanced technologies. This study investigated the effects of a one-day applied hydraulics workshop on teachers; ( n = 22) self-efficacy and intent to teach hydraulics. Prior to the workshop, a majority of teachers had a negative perception of their understanding of hydraulics concepts and their ability to effectively teach hydraulics. Upon completion of the workshop, a majority of the respondents had positive perceptions of their understanding of hydraulic concepts and their ability to effectively teach hydraulics. Teachers' participation in the workshop resulted in a statistically significant (p < .;001) overall increase in self-perceived understanding and ability to teach hydrauliss; the Cohen's d of 1.65 indicated workshop participation had a large effect on teachers' perceptions. In addition, the percentage of teachers indicating they planned to teach hydraulics in one or more courses increased from 36.4% prior to the workshop to 87.3% after the workshop.
Diagnostics in software-defined infrastructure on the SAVI testbed

Diagnostics in Software-Defined Infrastructure on the SAVI testbed Joseph Elias Kamel Wahba Masters Of Applied Science Graduate Department of Electrical and Computer Engineering University of Toronto 2016 With the increasing complexity of data center networks, the operations, management and diagnosis of data centers is a critical problem. The need of a self-managing, selfconfiguring, and self-optimizing network is crucial which is known as Autonomic Networking concept. In this thesis, we propose an autonomic Diagnostics framework in Software-Defined Infrastructure running in the SAVI testbed. We have analyzed and discussed various design requirements and open challenges in literature. We have leveraged Graph Mining and Machine Learning techniques in our approach in order to detect different kinds of anomalies. We have studied different algorithm performance under different settings for detecting anomalies. We have tested our framework on several use cases: Standalone Webserver, Standalone Database, Webserver-Database workload pattern, bandwidth throttling between a pair of VMs, denial-of-service attack on a Webserver and Spark Job failure. Our framework was able to detect the aforementioned anomalies
Response to: Metacognition in functional cognitive disorder: contradictory or convergent experimental results?

Dementia Research Centre, University College London, 8-11 Queen Square, London, UK Wellcome Centre for Human Neuroimaging, University College London, London, UK Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK UCL Institute of Child Health, Great Ormond Street, London, UK Department of Experimental Psychology, University College London, London, UK National Hospital for Neurology and Neurosurgery, London, UK South West London and St George’s Mental Health NHS Trust, London, UK Division of Psychiatry, University College London, London, UK Max Planck University College London Centre for Computational Psychiatry and Ageing Research, London, UK
Meta-Analysis on the Effectiveness of Using the Drama Approach in Social Studies Course

The aim of this study is to explore the effect size of the drama approach used in the social studies course on academic achievement through the meta-analysis method in the Turkish sample. The academic studies included in this study were searched at the following databases, namely, National Thesis Centre of the Council of Higher Education (YÖK), ULAKBİM TR Index, Google Scholar, and Scopus by using the keywords “creative drama”, “drama”, and “dramatization”. As a result of the database search, the meta-analysis was carried out with 31 academic studies, including 26 master’s theses, 2 doctoral dissertations, and 3 academic papers, all conducted on the effect of drama approach on academic achievement in social studies course. The data related to the studies included in the meta-analysis were analyzed by entering them into the CMA (Comprehensive Meta-Analysis) and MetaWin software programs. Due to the fact that the studies included in our meta-analysis were heterogeneous to one another, effect sizes were calculated according to the random effects model. In addition to this, grade levels, the weeks of application, and the number of samples were identified and analyzed as moderator variables. The results showed that the drama approach used in the social studies course had a positive and significant impact on students’ academic achievement. Nevertheless, the moderator analyses indicated that the grade level, the week of application, and the number of samples led to no change in the effect size on academic achievement. No publication bias was found in the studies included in the analysis. Based on the results of the study, we have made a number of recommendations for future research to be conducted on the drama approach for teaching purposes in the social studies course.
Respects of human rights and perception of quality of care, the users’ point of view comparing mental health and other health facilities in a region of Italy

Abstract This work is part of a research project that aims to measure organisational well-being, human rights respect and quality of care in mental health services in Sardinia, Italy, country that has replaced long-stay psychiatric hospitals with community mental health services. Previous contributions have seen Italian health professionals and users as the most satisfied and optimistic about the quality of the mental health care provided and the respect they offer for service users’ rights. Our aim is to confirm these findings by comparing experiences of users of mental health services with those of other care services in the same region. Our findings indicate that mental health services users show higher level of satisfaction for care and higher perception of users’ human rights respect compared to non-mental health facilities users. They also have greater satisfaction with organisational aspects of services and they are more convinced that the health professionals rights are respected. In contrast, they are less satisfied with the resources available for care centres than other users and require more professional psychosocial support. We want to allow future comparisons to other regions on quality assessment through the perception of users and worker on respect for standards and human rights.
Machine Learning for Aerial Image Labeling

Information extracted from aerial photographs has found applications in a wide range of areas including urban planning, crop and forest management, disaster relief, and climate modeling. At present, much of the extraction is still performed by human experts, making the process slow, costly, and error prone. The goal of this thesis is to develop methods for automatically extracting the locations of objects such as roads, buildings, and trees directly from aerial images. We investigate the use of machine learning methods trained on aligned aerial images and possibly outdated maps for labeling the pixels of an aerial image with semantic labels. We show how deep neural networks implemented on modern GPUs can be used to efficiently learn highly discriminative image features. We then introduce new loss functions for training neural networks that are partially robust to incomplete and poorly registered target maps. Finally, we propose two ways of improving the predictions of our system by introducing structure into the outputs of the neural networks. We evaluate our system on the largest and most-challenging road and building detection datasets considered in the literature and show that it works reliably under a wide variety of conditions. Furthermore, we are releasing the first large-scale road and building detection datasets to the public in order to facilitate future comparisons with other methods.
Land Evaluation for Alternate Land Use Planning of Sugarcane Growing Soils of Chittoor District in Andhra Pradesh

Seventeen typical pedons representing major sugarcane growing soils of Chittoor district were studied and evaluated by using qualitative and quantitative methods. The qualitative method employed were USDA land capability classification and land suitability classification, while the quantitative evaluation method includes Riquier’s parametric approach. The land evaluation methods indicated that information on production potential of soils provide a basis for comparison among different soils. The soils of study area were classified into land capability classes IV, V and VI. However, the land suitability evaluation suggested that these sugarcane growing soils were moderately suitable (S2) (P2, P5, P6, P13 and P16) and moderate to marginally suitable (S2-S3) (P1, P3, P4, P7, P8, P9, P10, P11, P12, P14, P15 and P17) for growing sugarcane crop. Riquier’s parametric approach was found to be good indicator for identification of production potential of sugarcane growing soils. The land evaluation study revealed that, characteristics and suitability of these soils for sugarcane crop were highly variable, hence their management must be site specific. Soil management strategies based on crop residue retention, nutrient recycling, minimum tillage, crop rotation, cover crops and inter cropping not only helps to conserve soil and water more effectively in sugarcane growing soils but also increase organic matter content and improve fertilizer use efficiency. Adoption of suggested ameliorative measures and land use not only improves the yields but also sustains the soil productivity.
Neurobiological Links between Stress, Brain Injury, and Disease

Stress, which refers to a combination of physiological, neuroendocrine, behavioral, and emotional responses to novel or threatening stimuli, is essentially a defensive adaptation under physiological conditions. However, strong and long-lasting stress can lead to psychological and pathological damage. Growing evidence suggests that patients suffering from mild and moderate brain injuries and diseases often show severe neurological dysfunction and experience severe and persistent stressful events or environmental stimuli, whether in the acute, subacute, or recovery stage. Previous studies have shown that stress has a remarkable influence on key brain regions and brain diseases. The mechanisms through which stress affects the brain are diverse, including activation of endoplasmic reticulum stress (ERS), apoptosis, oxidative stress, and excitatory/inhibitory neuron imbalance, and may lead to behavioral and cognitive deficits. The impact of stress on brain diseases is complex and involves impediment of recovery, aggravation of cognitive impairment, and neurodegeneration. This review summarizes various stress models and their applications and then discusses the effects and mechanisms of stress on key brain regions—including the hippocampus, hypothalamus, amygdala, and prefrontal cortex—and in brain injuries and diseases—including Alzheimer's disease, stroke, traumatic brain injury, and epilepsy. Lastly, this review highlights psychological interventions and potential therapeutic targets for patients with brain injuries and diseases who experience severe and persistent stressful events.