Miracle-dz/UniFAR-FTUs huggingface dataset:
BASNEF Model Based Educational Guidelines regarding Foot Care among Type II Diabetic Patients

Background: Patients with diabetes need to take special care of their feet because poor foot health can lead to major foot complications including ulcers and amputation, which can be avoided through adequate guidance. Aim : This study aimed to appraise the effect of BASNEF model based educational guidelines regarding foot care among type II diabetic patients. Research design : A quasi-experimental research design was utilized. Setting: The study was carried out at two Outpatient Clinics (Internal Medicine and Surgery) at Benha University Hospital. Sample: Purposive sample of 110 patients separated into study and control groups, each group included 55 patients. Tools: Three tools were used; Tool I : An interviewing questionnaire comprised three parts as patients’ demographic characteristics, patients' medical history and patients’ knowledge. Tool II : Reported foot care practices. Tool III : BASNEF model to assess patients’ belief, attitude, subjective norms and enabling factors regarding foot care. Results : The present study revealed that there was a significant enhancement in mean scores of study group total knowledge, reported foot care practices and BASNEF model scores, post and after three months of educational guidelines implementation compared with control group. Additionally; there was a statistically significant correlation between the total BASNEF scores and total knowledge and total reported practices of study group post and follow up the educational guidelines. Conclusion : Implementing BASNEF model based educational guidelines enhanced the study group patients’ knowledge, reported foot care practices, beliefs, attitudes, subjective norms and enabling factors regarding foot care. Recommendations : Distribution of an educational booklet to the Internal Medicine and Surgery Outpatient clinics at Benha City Hospitals to be available to all diabetic patients.
Ultrasonic computed tomography-based internal-defect detection and location of underwater concrete piers

Concrete piers are the key load-bearing components of wading bridges. Most detection equipment and methods focus on the surface defects of underwater piers, whereas the detection methods of internal defects are very deficient. This study proposes an ultrasonic computed tomography method combining linear travel time interpolation and simultaneous iterative reconstruction techniques for detecting internal defects in underwater concrete piers. The two-dimensional median filtering and self-organizing feature map are utilized to post-process tomographic images and the confidence approach is employed to detect defects. The efficacy of the proposed method is validated through numerical simulation and underwater experiments, respectively. The Comsol Multiphysics is utilized to simulate the two-dimensional random aggregate model of the concrete and internal defects are added to validate the method’s effectiveness from a mesoscopic perspective. Then actual scale piers containing embedded defects are cast and detected. The results demonstrate that the method can identify and locate internal defects in numerical simulations and practical tests. The numerical simulation experiments results are superior to those of scale concrete specimens. In addition, underwater concrete piers with larger internal defects, square holes, and no steel reinforcements tend to have better detection results.
Painful to Discuss: The Intersection of Chronic Pain, Mental Health, and Analgesic Use among People with HIV

Objective: This retrospective chart review study aims to identify patients in an HIV clinical setting in an area of high HIV prevalence in Atlanta, Georgia, USA who have chronic pain, analgesic prescriptions, and/or mental health diagnoses. Design: People living with HIV (PLWH) are at higher risk for experiencing trauma, mental health conditions, and chronic pain than their HIV-negative counterparts. This study was designed to evaluate the intersection of these factors within an urban HIV clinic. Methods: Retrospective chart review study. Results: Of the adult patients enrolled at an HIV clinic in Atlanta, Georgia USA between 2011–2022 (n=15,970), 93.7% were prescribed analgesics, 40.5% had documented pain diagnoses, and 23.5% had documented mental health diagnoses. Additionally, 14.3% of all enrolled patients had all three factors concurrently. Conclusions: The complexity of HIV, chronic pain, mental health challenges, and analgesic use demand a patient-centered, collaborative approach including a multidisciplinary care team. Seeing persistent pain among PLWH with a trauma-informed approach to care within the lens of co-occurring mental health diagnoses will allow us to better understand, treat, and sustain patients in life-saving HIV care.
Particle swarm methods for transport and logistics optimization processes

This article describes heuristic approaches to static optimization methods, taking into account the nature of the motion of a swarm of particles representing, for example, birds, ants, bees, fireflies, bats, krill, cuckoos, cuttlefish, cockroaches, or the pollination process of flowers. In the given descriptions of the methods, the features of swarm intelligence are detailed, the optimization quality indicators are formulated, and flow charts of the computational algorithms are provided.
Changing interactions between humans and nature in Sarayaku, Ecuadorian Amazon

Increasing scarcity of natural resources is currently of serious concern for many people in the community of Sarayaku. In this thesis, a combination of participatory research, ecological modelling, GIS, and economic experiments was used to identify and examine problems regarding natural resource use in the community. The study showed that the interactions between humans and nature in the area have undergone significant changes during the last four or five centuries, related to changes in population density, settlement pattern, and technology. The community is only partly integrated in the cash economy, and most food is locally produced. Wild game and fish are important sources of dietary protein and fat. Fields and fallows make up about 4% of the area, whereas the rest is dominated by old-growth forest. Near the village fallows dominate the landscape, and the distance from settlements to old-growth forest has increased. Several major game species are severely depleted, particularly in the vicinity of the village. The problem of game depletion can partly be explained as a “tragedy of the commons”, i.e. the result of uncoordinated resource withdrawal by a large number of appropriators, in which each one behaves in a self-interested manner. However, unawareness of the impact of hunting on game populations has also been a contributing cause. The community has long had some capacity for managing resources in a coordinated way, as shown by the existence of rules for how to harvest palm leaves for thatch. During recent decades, the community has created a democratic consensus-based community organization that has developed into a local government. Increasingly, this organization is dealing with regulating natural resource use, in order to solve conflicts and improve sustainability of resource use. Oil development threatens to damage the environment and undermine the local democracy. If this can be avoided, however, improving the sustainability of resource use is an achievable goal.
Assessing the Performance and Risk-Adjusted Returns of Financial Mutual Funds

In this study, we provide a comprehensive examination of the performance of financial (specialty sector financial) mutual funds over a 23-year period, a much longer time frame than what has been analyzed in previous literature. To fully understand the performance of these mutual funds, we consider multiple factors, including risk-adjusted performance, both unconditional and conditional multifactor analysis, and market timing and selectivity. Financial mutual funds have higher risk-adjusted performance than the overall market and financial sector benchmarks. However, fund alphas are not different from zero, and managers do not exhibit market timing or security selection abilities. Our analysis not only includes the overall performance of these mutual funds, but we also delve into sub-samples before and after the 2008 financial crisis and during the recent Coronavirus pandemic.
Chapter 11: Kissimmee Basin

SUMMARY The Kissimmee Basin encompasses more than two dozen lakes in the Kissimmee Chain of Lakes (KCOL), their tributary streams and associated marshes, and the Kissimmee River and floodplain. The basin forms the headwaters of Lake Okeechobee and the Everglades; together they comprise the Kissimmee-Okeechobee-Everglades (KOE) system. In the 1960s, the Central and Southern Florida Flood Control (CS (2) conduct ecological monitoring programs for restoration evaluation; (3) develop a long-term management strategy for resolving water and other management issues in the Kissimmee Chain of Lakes; and (4) retain the existing level of flood control in the Kissimmee Basin. In addition to the Kissimmee River Restoration Project, major Kissimmee Basin initiatives designed to meet these program objectives are the Kissimmee Basin Modeling and Operations Study, the Kissimmee Basin Water Reservations, and the Kissimmee Chain of Lakes Long-Term Management Plan.
Theory of radiative transfer models applied in optical remote sensing of vegetation canopies

In this thesis the work of the author on the modelling of radiative transfer in vegetation canopies and the terrestrial atmosphere is summarized. The activities span a period of more than fifteen years of research in this field carried out at the National Aerospace Laboratory NLR. For the interpretation of optical remote sensing observations of vegetation canopies from satellites or aircraft the use of simulation models can be an important tool, as these models give insight in the relations between vegetation properties and observed remote sensing data. The models discussed here are first presented from a theoretical point of view. An attempt has been made to construct a framework in which all the discussed models, for vegetation as well as for the atmosphere, can be represented. After an introduction on basic radiometric quantities and relations (chapter 2), and a classification of radiative transfer models and solution methods (chapter 3), examples of existing models are discussed in chapter 4. In chapter 5 a new generalized theory of radiative transfer models for azimuthally isotropic media is presented, namely the (N+2)-stream theory. This theory describes radiative transfer in vegetation canopies or atmospheres to any desired numerical accuracy. In the formulation of the analytical solution of this model, which is based on eigenvector decomposition, much attention has been paid to possible numerical problems. Symmetry relations are exploited in order to reduce memory requirements and computation time, and expressions have been found for which the so-called reciprocity relations are automatically fulfilled. The numerical capabilities of this model for simulation of atmospheric radiative transfer are demonstrated in chapter 6. The theory of the vegetation canopy bidirectional reflectance models SAIL and SAILH are discussed in chapters 7 and 8. Both are four-stream models. In SAILH the so-called hot spot effect, which is related with the finite leaf size, is incorporated. After these more theoretic chapters, practical aspects of the radiative transfer models are discussed in the following chapters. In chapter 9 the application of a four-stream atmosphere model to the correction and calibration of Landsat Thematic Mapper images is demonstrated. This atmosphere model has been coupled with the SAILH model into an overall four-stream model of optical remote sensing observations from any altitude. This model is called OSCAR (optical soil-canopyatmosphere radiance) and is presented in chapter 10. Applications of radiative transfer modelling to optical remote sensing problems are illustrated in chapter 11, and chapter 12, summarizes the conclusions of this thesis.
Spatiotemporal gait characteristics during single- and dual-task walking are associated with the burden of cerebral small vessel disease

Introduction Gait impairment is a common symptom among individuals with cerebral small vessel disease (CSVD). However, performance differences between single-task walking (STW) and dual-task walking (DTW) among individuals with CSVD remain unclear. Therefore, we aimed to examine differences in gait characteristics during STW and DTW as well as the association between gait performance and neuroimaging markers. Methods We enrolled 126 older individuals with CSVD. The speed, cadence, stride length, stride time, and their dual-task cost (DTC) or variability were measured under the STW, motor-cognitive DTW (cognitive DTW), and motor-motor DTW (motor DTW) conditions. We examined neuroimaging features such as white matter hyperintensities (WMHs), lacunes, microbleeds, and total burden. Further, we analysed the association of neuroimaging markers with gait performance, including gait variability and DTC. Results Almost all spatiotemporal characteristics, as well as their DTCs or variabilities, showed significant among-group differences according to disease severity in the cognitive DTW condition; however, relatively lesser differences were observed in the STW and motor DTW conditions. The total CSVD burden score was moderately correlated with all the spatial parameters, as well as their DTCs or variabilities, in the cognitive DTW condition. Moreover, WMHs showed a correlation with speed, stride time, and cadence, as well as their DTCs, in the cognitive DTW condition. Furthermore, lacunes showed a moderate correlation with speed, stride length, and the DTC of speed, whilst microbleeds were only related to the DTC of stride length in the cognitive DTW condition. Neuroimaging biomarkers were not correlated with spatiotemporal parameters in STW and motor DTW conditions after Bonferroni correction. Moreover, the correlation coefficient between the total CSVD burden score and gait parameters was greater than those of other biomarkers. Discussion Parameters in the cognitive DTW condition are more appropriate than those in the motor DTW condition for the evaluation of gait abnormalities in patients with CSVD. Moreover, the total CSVD burden score might have better predictive utility than any single neuroimaging marker. Patients with CSVD, especially those with moderate-to-severe disease, should concentrate more on their gait patterns and reduce the load of secondary cognitive tasks whilst walking in daily life.
Weighted Multiclass Intrusion Detection System

Attackers are continuously coming up with new attack strategies since cyber security is a field that is continually changing. As a result, it’s important to update and enhance the system frequently to ensure its efficiency against fresh threats. Unauthorised entry, usage, or manipulation of a computer system or network by a person or programme is referred to as an intrusion. There are numerous ways for an incursion to happen, including using software flaws, phishing scams, or social engineering techniques. A realistic solution to handle the risks brought on by the interconnectedness and interoperability of computer systems is to use deep learning architectures to build an adaptive and resilient network intrusion detection system (IDS) to identify and categorise network attacks. Artificial neural networks (ANNs) or deep learning can help adaptive intrusion detection systems (IDS) with learning capabilities identify well-known and unique or zero-day network behavioural patterns, which can significantly reduce the risk of compromise. The NSL-KDD dataset, which represents both synthetically manufactured attack actions and real-world network communication activity, is used to show the effectiveness of the model. Model trained with this dataset to detect a wide range of attack patterns, which help in building an effective IDS.
Wind-Induced Rainfall and Surface Temperature Anomalies in the Australian Region.

An extended coupled atmosphere-ocean model simulation has been analyzed to explore the relationship between Australian rainfall and regional surface temperature anomalies. The interannual variability of seasonal rainfall in the model (which has a weak El Nino) was generally similar to that from a simulation by the atmospheric model with SSTs specified to follow a repeated annual cycle, suggesting that much of the variability is generated internally to the atmosphere. Given the expected role of winds, rotated principal component analyses of both January and July regional 800-hPa wind anomalies from the coupled model were performed. In each season, two wind patterns correlated with realistic rainfall patterns: a monsoon surge pattern brought rainfall to northern Australia in January; a northwesterly flow induced winter rainfall along a band extending to the southeast; and a pattern including northerlies along the east coast brought rainfall to eastern Australia, in both winter and summer. The wind patterns also induced significant surface temperature anomalies over both land and sea. Correlations between the wind-related rainfall indices and seasonal SSTs produced regional SST patterns with much in common to those observed. In particular, the northwesterly rainfall coincided with an eastern Indian Ocean dipole pattern. Aside from the monsoon surge, there is little long-term precursor to these wind anomalies in the model, however, and hence little predictability of the rainfall patterns via the SSTs. Clearly, substantial seasonal rainfall-SST relationships do not necessarily result from forcing of wind anomalies by SSTs, and, excepting El Nino, much of the Australian relationship may, evidently, be unforced.
Norman Wells: The Oil Center of the Northwest Territories

In 1920, a drilling team funded by Imperial Oil discovered a petroleum deposit along the shore of the Mackenzie River north of the settlement of Fort Norman. This wilderness site later became the community of Norman Wells and its growth has been directly attributable to petroleum. The current expansion of production at Norman Wells is aimed at southern Canadian markets and a pipeline is being constructed from Norman Wells to existing pipelines in northern Alberta. As the focal point of this major resource expansion, the character, size, and functions of the community are changing. These changes are transforming Norman Wells into an important regional center. Key words: Norman Wells, petroleum development
CARL: A Synergistic Framework for Causal Reinforcement Learning

Causal Reinforcement Learning (CRL) is an emerging field where two essential areas for the development of artificial intelligence are integrated. Existing works in the area have shown how causality can contribute to mitigate some of the limitations of reinforcement learning (RL), ranging from data-inefficiency, lack of interpretability, and long learning times, among others. However, how to use reinforcement learning to support causal discovery (CD) has so far been less explored. In this article, we introduce CARL, a Causality-Aware Reinforcement Learning framework for simultaneously learning and using causal models to speed-up the policy learning in online Markov decision process (MDP) settings. In a synergistic way, our method alternates between: (i) (RL for CD), where it promotes the selection of actions to obtain better causal models in fewer episodes than traditional methods of obtaining data in RL, (ii) (CD), where a score-based algorithm is used to learn causal models and (iii) (RL using CD), where the learned models are used to select actions that speed up the learning of the optimal policy by reducing the number of interactions with the environment. Experiments in simulated environments show that our method achieves better results in policy learning than traditional model-free and model-based algorithms while it is also able to learn the underlying causal models. We also demonstrate how the learned causal models can be directly transferred to a similar task of greater complexity, significantly reducing the number of episodes required to learn an optimal policy Finally, the method’s scalability to high-dimensional states, where the action-value function needs to be represented with deep neural networks, was verified.
USE OF COLUMN LEACHING TEST TO STUDY THE LEACHIBILITY OF POLYCYCLIC AROMATIC HYDROCARBONS FROM CONTAMINATED SOIL

Column leaching tests become increasingly important for assessing the risk of release of pollutants from soil into water. In this study a column leaching test was designed to provide insight into the fate of selected PAHs in soils and their leaching behavior. Two columns (columns 1 and 2) were prepared based on a standard procedure in which 5 and 20 percent of contaminated soil were used, respectively. The percolates were collected at specified L/S ratios. Results showed relatively high initial concentrations of flourene, fluoranthene and benzo(a)anthracene for column 2. Released amounts of PAH compounds were decreasing with increasing molecular size, or rather the hydrophobicity of the compound. The effect of dissolved organic carbon on PAHs leaching was also investigated in this research. Approximately 2.45% and 0.67% of the total initial amounts of studied PAHs in the soil was leached in columns 1 and 2, respectively. Results showed that high PAHs concentration in the solid phase does not necessarily imply serious groundwater contamination risk during a leaching event (e.g. rain fall) and significant amount of contaminants may stay in the soil layer for a long time and do not leach down into groundwater.
Case Study of Intervention using Mint Leaf Aroma Therapy in Bronchial Asthma Patients

Objective: This case study was conducted to present interventions and to see the effectiveness of using mint leaf aromatherapy interventions in improving ineffective breathing problems in clients with bronchial asthma. Method: The research design used was a descriptive method with a case study approach, which included an intensive study of one unit of asthma cases with ineffective breathing patterns. Enforcement of nursing diagnoses refers to the Indonesian Nursing Diagnosis Standards (IDHS). Indonesian Nursing Intervention Standards (SIKI) and Indonesian Nursing Outcomes Standards (SLKI) The focus of the intervention in this case study was giving mint leaf aromatherapy, which was carried out for 3 days in the emergency room at the Banjar City Public Hospital. Nursing evaluations are documented using the SOAPIER method. Result: Based on the results of the study conducted on Mr. S on May 28, 2023, it was found that Mr. S was 48 years old with a medical diagnosis of bronchial asthma with a nursing problem, namely an ineffective breathing pattern associated with difficulty breathing (D.0005). After the act of giving aromatherapy for 3 days to the patient, which aims to reduce shortness of breath by widening the airways and thinning the phlegm, The results obtained in the Tn.S. patient said that it was better than before; there was no coughing up phlegm, no wheezing breath sounds, BP: 140/110 MmHg, pulse: 90x/minute, temperature: 36.5°C, RR: 23x/minute, and SpO: 99%. Conclusion: The mint leaf aromatherapy intervention has proven to be useful for increasing respiration and spo within normal limits, The benefits of this therapy will be maximized if it is carried out periodically and gradually