Miracle-dz/UniFAR-FTUs huggingface dataset:
Observation‐Driven Characterization of Soil Moisture‐Precipitation Interactions in the Central United States

Soil moisture feedbacks that initiate, enhance, or suppress convection initiation and precipitation are important components of regional hydroclimatology. However, soil moisture feedbacks and the processes through which they operate are notoriously challenging to observe and study outside of model environments. In this study, we combine a climatological assessment of event frequency‐based measurements of soil moisture‐precipitation coupling in the central United States with a process‐based analysis of the mechanisms by which wet‐ and dry‐soil feedbacks may operate in the region. We use the Thunderstorm Observation by Radar algorithm to identify the location of convection initiation, circumventing the issue of using precipitation accumulation as a proxy for convection initiation. Results show substantial spatial variability in the climatological sign and strength of soil moisture‐precipitation coupling in the central United States, including regions that exhibit signs of both wet‐ and dry‐soil feedbacks. Within the regions with the strongest feedback signals, we find consistently strong coupling between soil moisture and the partitioning of surface heat flux, and strong coupling between surface heat flux—particularly sensible heat flux—and diurnal change in planetary boundary layer height. In all three regions assessed, the process‐based metrics confirmed the potential of wet‐ and/or dry‐soil feedbacks leading to convection initiation.
Impact of Awareness Obtained from Studying Dentistry on Oral Health-Related Quality of Life at Shahid Beheshti University

Background: Today, the impact of oral health on indicators of quality of life is of great importance. As few studies have evaluated dental students who are future dentists of a society, this study was performed to assess the impact of awareness on oral health-related quality of life on first, third- and sixth-year Shahid Beheshti dental students in 2017-2018. Methods: This descriptive cross-sectional and analytical study was conducted on 220 Shahid Beheshti dental students. The required data was collected by using a sociodemographic questionnaire (including gender, academic year indicating level of awareness, and GPA) and Persian version of oral health-related quality of life questionnaire, OHIP-14. The questionnaires’ scores were compared by one-way analysis of variance (ANOVA) or Welch’s t-test. Results: The average total score of OHIP-14 questionnaire in dental students was 8.26 (SD=7.41). A significant relationship between the academic year and oral health-related quality of life was not noticed except in “Psychological Discomfort” domain where oral health-related quality of life deteriorated with an increase in the academic year (p=0.0001). Conclusion: Oral health-related quality of life in dental students did not change significantly with an increase in the academic year except in “Psychological Discomfort” domain.
Approach to HIV Prevention in Cuba for Men Who Have Sex with Other Men

: This paper describes the main interventions for the prevention of human immunodeficiency virus infection, aimed at men who have sex with men, who constitute a risk group for acquiring the disease. For its preparation, the scientific literature published in national and international sources and documents considered relevant to the subject was selected. The analysis and recommendations focused on the health sector and the group of men who have sexual relations with other men. The approaches to the human immunodeficiency virus in Cuba are considered among the high-impact prevention programs, prepared both by civil society, through subsidies from the World Fund to Fight AIDS, Tuberculosis and Malaria, as well as by technicians from the National Program for Prevention and Control of STIs-HIV/AIDS. They include personalized service packages, which contain structural, biomedical and behavioral elements for key population groups in the social contexts where they are most needed. As is well known, the risk for each individual depends on their practices and not on the group to which they belong. These offer a common base for the development and promotion of effective programs for prevention, which allow organizing efforts directed at key populations, especially men who have sex with other men, and in this way reduce the number of new infections each year.
Path generation for rendezvous of dissimilar UAVs using Particle Swarm Optimization of Dubin’s curve sets

The one dominant trend of development of small scale UAVs is toward multi-platform or multi-agent systems, where each agent is a single flying platform. Numerous intelligent algorithms for formation flight from literature focus on the positioning and relative motion of individual agents. The purpose of formation flight is numerous. It can be to refuel or repower each other, as well as to perform a mission by pooling resources. Prior to adopting close proximity, however, the agents have to be guided to rendezvous conditions such that these intelligent algorithms may be implemented. The guidance and navigation for optimal approach of multiple agents have to be examined from a point of view of arbitrary initial conditions, which are relative position and velocity vectors. This paper proposes a method to generate flyable trajectories for two agents in three dimensional Cartesian space. It is able to do this in real time by applying particle swarm optimisation procedure to a candidate set of Dubin’s curves. By simulating a series of critical test cases, it is shown that the curve generated is the optimal trajectory for rendezvous for the given initial and terminal conditions.
Singularity Analysis of 5-DOF Parallel Mechanisms 3T2R using Grassmann-Cayley Algebra

This paper deals with the singular configura- tions of symmetric 5-DOF parallel mechanisms performing three translational and two independent rotational DOFs. The screw theory approach is adopted in order to obtain the Jacobian matrices. The regularity of these matrices is examined using Grassmann geometry, i.e., Grassmann line geometry and Grassmann-Cayley algebra. More emphasis is placed on the geometric investigation of singular con- figurations by means of Grassmann-Cayley Algebra for a class of simplified designs. The results yield to algebraic expressions for the singularity conditions in terms of some superbrackets for which all singular configurations can be enumerated.
Genetic and Epigenetic Factors in Risk and Susceptibility for Childhood Asthma

Asthma is a common respiratory disease that affects people of all ages, characterized by considerable heterogeneity in age, clinical presentation, genetics, epigenetics, environmental factors, treatment response, and prognostic outcomes. Asthma affects more than 330 million people worldwide, of which 33% are children under 14 years, and 27% are adults whose first symptoms occurred in childhood. However, the genetic and epigenetic mechanisms of childhood allergic diseases and asthma are still not fully understood. Here, we conducted a biomedical narrative review of genes associated with the risk, severity, and susceptibility of childhood asthma since it differs from asthma in adults regarding their pathophysiology, development, and outcomes. We also systematized the available information on epigenetic changes associated with childhood asthma.
IDENTIFICATION OF RICE PURITY LEVEL FROM MIXED RICE VARIETIES USING DEEP LEARNING

The current study was conducted in Multan, Pakistan to investigate an automated appearance based system for purity level identification of seven common rice (Oryza sativa L.) varieties from mixed rice grain samples. Adulteration is a major hurdle that affects rice export in Pakistan that refers to the mixing of premium rice grain varieties with the low grade rice grains to be marketed at a high cost. This study was based on the dataset collected from Rice Research Institute, Kala Shah Kaku, Pakistan during 2018-2020. Three Pakistani premium rice varieties (Basmati Shaheen, Basmati Super, and Basmati Pak) were mixed with four low quality varieties (Basmati 198, Basmati 2000, Basmati 370 and Basmati 385) in weight ratios of 10%, 15%, 20%, 25% and 30%. Classification and recognition of purity level of basmati rice achieved average accuracy of 89.88% using convolutional neural network. The proposed system has the potential to be used at a commercial scale to test the purity level of exported rice.
The Essential Elements of the PNF-concept, an Educational Narrative

Introduction: The term Proprioceptive Neuromuscular Facilitation (PNF) was introduced in the 1950s by Dr. Kabat and Mrs. Knott. From the mid-1950s the PNF-concept has been in a progressive development clarifying the use of techniques, performances and clinical applications. The concept of PNF was originally developed by Dr. Kabat and Mrs. Knott for rehabilitation purposes. The International PNF Association (IPNFA) consider themselves to be the official successor of Dr. Kabat and Mrs. Knott. The PNF-concept evolved towards a comprehensive rehabilitation approach for various indications. There seems to be a misunderstanding about the PNF-concept, it is often seen as a basic stretching regime for wellbeing and general fitness. This has been criticized to avoid a confusion with the PNF-concept as a comprehensive rehabilitation approach. Purpose: Providing a background with an explanation for the essential elements of the PNF-concept, being a total and comprehensive rehabilitation approach in physiotherapy and rehabilitation. The elements from the PNF-concept facilitate treatment effects in the fields of musculoskeletal and central neurological disorders and indications with a focus on motor learning. Implications: The clarification of the essential elements from the PNF-concept in various fields of physiotherapy and physical rehabilitation will simplify how to transfer and incorporate the elements from the PNF-concept into individual treatments.
The Mediation Role of Flexibility on the Relationship Between Sustainable Supply Chain and Operational Performance: US Manufacturing Industry

This paper investigates the interaction between flexibility practices, sustainability practices, sustainability performance, and operational performance metrics of manufacturing companies. A survey of domestic automotive manufacturing companies in the United States (U.S.) was carried out with 101 responding facilities in a face-to-face interview from 101 different manufacturing plants (19 different Automotive manufacturing companies). The model-hypothesized relationships were tested through a partial least square structural equation model (SmartPLS). The paper provides insight into the positive mediating role/influence of flexible manufacturing systems practices on the relationship between sustainability, supply chain, and operational performance. Data indicate a significant correlation between flexibility and supply chain dimensions. The following research is one of few that investigates comprehensive flexibility, supply chain, and sustainability dimensions and interactions based on what is currently applied by the automotive industry in the U.S. The results indicate that flexibility practices implantation is critical for maximizing the outcomes of sustainability implementation in the US automotive facilities. The findings provide an important guide for practitioners and researchers implementing supply chain and sustainability dimensions in the manufacturing industry and the effect of flexibility dimensions implementation levels on such practices.
Multimodal medical image fusion using residual network 50 in non subsampled contourlet transform

ABSTRACT Medical image fusion technology and its collective diagnosis are becoming crucial day by day. This task confers the latest algorithm for image fusion of medical images to many diagnostic complications. Firstly, transform is employed on input source images. The result of the application of transform is the decomposition of source images into various subbands. Eminent features are extracted from these subbands by using resnet50. These features are fused by phase congruency and guided filtering fusion rules. Finally, inverse transform gives the original image. The experiment results of this algorithm are compared with different methods by taking some pairs of medical images. Subjective and objective outcomes prove that the proposed algorithm exceeds the current methods by giving optimal performance measures in the area of medical diagnosis. Thus, it is revealed that the suggested multimodal image fusion model provides elevated performance over existing models via diverse diseases using MRI-SPECT and MRI-PET.
Kernel-based adaptive K-Medoid clustering

Aiming at the weakness of K-Medoid algorithm that it is unable effectively to cluster big data set and high-dimension data, kernel-based learningmethod is introduced to the K-Medoid clustering algorithm, kernel-based adaptiveK-Medoid algorithm is proposed, the algorithm firstly maps the data from their input space to a high dimensional feature space by kernel function, then performs K-Medoid clustering in the high dimensional space. In data cluster process, data can join the optimum cluster, and the cluster result has no relation with how to select the originalk centers. Thenew algorithm can cluster largedata set and high-dimensiondata. The results ofexperiments show that kernel-based adaptive K-Medoid algorithm has better precision ratio than K-Medoid algorithm.
Determinants and Impacts of Livelihood Choice Strategies on Farm Households’ Food Security Status in North Shewa Zone Oromia, Ethiopia

Livelihoods diversification strategies are one means of meeting the overgrowing world population’s food demand. This study identified household-level determinants of livelihood diversification strategies and its impact on food security status in North Shewa, Oromia, Ethiopia. Both primary and secondary data about the 2021/22 production year were collected for this study. Primary data was collected from 400 smallholder farmers that were collected using a simple random sampling technique. Descriptive statistics and econometric models were used for data analysis. Looking into the estimated coefficients, the results indicate that livelihood strategy is significantly influenced by fourteen explanatory variables. Agro ecology, sex, family size, farm size, economic active member, training, credit access, livestock holding, education level, experience in farming, irrigation experience, media, distance from the market were significant variables that affect the household livelihood strategy status. Impact evaluation estimated result indicated that participation of farming with non-farming livelihood diversification strategies increases farm household food security status by 25% while, participation in farming with off farming and farming with both non-farming and off farming livelihood diversification strategies increases households' food security status by 43 and 37% respectively over non-diversified households at a 1% probability level. This study indicated that there is room to improve rural households’ level of food security status using more of the aforementioned socio-economic variables. Therefore, policymakers should give due emphasis to the identified variables and improve the livelihoods of rural households.
Improving Innovative Capabilities of Trucking Company: Action Research Approach

Amid intense competition and fast market dynamics, companies must keep innovating to stay competitive. Hence innovative capability is critical. Through action research, this study aims to improve the innovative capability of a trucking company in Indonesia. A series of improvement actions, divided into five stages, have been performed from December 2021 to May 2022. The level of innovative capability is measured using a maturity model. Further, the values before and after the improvements are compared. The results showed an increase in the company's innovative capability. Hopefully, these findings will provide practitioners with valuable insights and serve as exciting triggers for further research.
Quality Assurance for Freshwater Quality Monitoring - Technical Guidance Document

This technical guidance document is intended for scientists and practitioners who work with freshwaters in the field and in the laboratory but who do not have specialist knowledge of quality assurance. It provides an introduction to the key concepts and approaches that can be used in Quality Assurance and Quality Control. Without appropriate and adequate quality assurance measures at each step, from taking a water sample to storing and sharing the data, the outputs of the monitoring activities may not reflect, as fully as they should, the true quality condition of the water body. Although the main focus is on freshwaters, the general principles outlined are applicable to most types of aquatic monitoring including drinking water, wastewater, and industrial effluents.
Ethical principles, challenges and opportunities when conducting genetic counselling for schizophrenia

Ethical challenges of genetic counselling for schizophrenia include effective communication of critical scientific information in an easily understood manner by patients and relatives, and the ability to ensure communication is unencumbered by medical jargon. Levels of literacy in the target population may limit this process, making it difficult for patients to attain the desired levels of informed consent to make crucial decisions during genetic counselling. Multilingualism in target communities may further complicate such communication. This paper outlines the ethical principles, challenges and opportunities facing clinicians when conducting genetic counselling for schizophrenia and how these might be met, drawing on lessons from South African studies. The paper draws on reflections of clinician and researcher experiences gained from clinical practice or research on the genetics of schizophrenia and psychotic disorders in South Africa. The context of genetic studies in schizophrenia is used to illustrate the ethical challenges in genetic counselling for schizophrenia, both in clinical and research settings. Attention is also drawn to multicultural and multilingual populations, particularly where the preferred language lacks a well-developed scientific language of communication for some of the genetic concepts that have to be presented during the genetic counselling process. The authors describe the ethical challenges and how to address these to empower patients and relatives to make well-informed decisions despite these obstacles. Principles applied by clinicians and researchers during the genetic counselling are described. Potential solutions, including the establishment of community advisory boards to address potential ethical challenges inherent to the genetic counselling process, are also shared. Genetic counselling for schizophrenia still faces ethical challenges which require a balance of principles of beneficence, autonomy, informed consent, confidentiality and distributive justice, while striving to present accuracy in the science that guides the process. Evolution in language and cultural competency therefore needs to occur alongside scientific advances in genetic research. Key stakeholders need to partner and build capacity and expertise in genetic counselling through the provision of funding and resources. The goal of partnerships is to empower patients, relatives, clinicians and researchers to share scientific information in a manner guided by empathy while retaining scientific accuracy.
Video Sign Language Recognition using Pose Extraction and Deep Learning Models

Sign language recognition (SLR) has long been a studied subject and research field within the Computer Vision domain. Appearance-based and pose-based approaches are two ways to tackle SLR tasks. Various models from traditional to current state-of-the-art including HOG-based features, Convolutional Neural Network, Recurrent Neural Network, Transformer, and Graph Convolutional Network have been utilized to tackle the area of SLR. While classifying alphabet letters in sign language has shown high accuracy rates, recognizing words presents its set of difficulties including the large vocabulary size, the subtleties in body motions and hand orientations, and regional dialects and variations. The emergence of deep learning has created opportunities for improved word-level sign recognition, but challenges such as overfitting and limited training data remain. Techniques such as data augmentation, feature engineering, hyperparameter tuning, optimization, and ensemble methods have been used to overcome these challenges and improve the accuracy and generalization ability of ASL classification models. We explore various methods to improve the accuracy and performance in this project. From the approach, we were able to first reproduce a baseline accuracy of 43.02% on the WLASL dataset and further achieve an improvement in accuracy at 55.96%. We also extended the work to a different dataset to gain a comprehensive understanding of our work.
Integrating Smart Energy Management System with Internet of Things and Cloud Computing for Efficient Demand Side Management in Smart Grids

The increasing price of and demand for energy have prompted several organizations to develop intelligent strategies for energy tracking, control, and conservation. Demand side management is a critical strategy for averting substantial supply disruptions and improving energy efficiency. A vital part of demand side management is a smart energy management system that can aid in cutting expenditures while still satisfying energy needs; produce customers’ energy consumption patterns; and react to energy-saving algorithms and directives. The Internet of Things is an emerging technology that can be employed to effectively manage energy usage in industrial, commercial, and residential sectors in the smart environment. This paper presents a smart energy management system for smart environments that integrates the Energy Controller and IoT middleware module for efficient demand side management. Each device is connected to an energy controller, which is the inculcation of numerous sensors and actuators with an IoT object, collects the data of energy consumption from each smart device through various time-slots that are designed to optimize the energy consumption of air conditioning systems based on ambient temperature conditions and operational dynamics of buildings and then communicate it to a centralized middleware module (cloud server) for management, processing, and further analysis. Since air conditioning systems contribute more than 50% of the electricity consumption in Pakistan, for validation of the proposed system, the air conditioning units have been taken as a proof of concept. The presented approach offers several advantages over traditional controllers by leveraging real-time monitoring, advanced algorithms, and user-friendly interfaces. The evaluation process involves comparing electricity consumption before and after the installation of the SEMS. The proposed system is tested and implemented in four buildings. The results demonstrate significant energy savings ranging from 15% to 49% and highlight the significant benefits of the system. The smart energy management system offers real-time monitoring, better control over the air conditioning systems, cost savings, environmental benefits, and longer equipment life. The ultimate goal is to provide a practical solution for reducing energy consumption in buildings, which can contribute to sustainable and efficient use of energy resources and goes beyond simpler controllers to address the specific needs of energy management in buildings.