Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessing the Psychological Impact of the Pandemic COVID -19 in Uninfected High-Risk Population

Purpose To assess the impact of the COVID-19 pandemic on the psyche of uninfected people with chronic diseases in the Elduim community, White Nile State, Sudan, during the COVID -19 pandemic. Methods We used a generalized anxiety disorder scale (GAD -7) and a patient health questionnaire (PHQ-9) for psychological assessment. The study included two hundred thirty-four participants; all participants with a chronic disease but not infected with COVID -19 were between 24 and 65 years of age. Residents of the study area were randomly selected. Descriptive statistics and a t-test were used for associations with a p-value of 0.05 or less. Results This study found that anxiety rated by GAD 7 was either mild (18, 7.7%), moderate (98, 41.9%), or severe (41, 17.5%) among participants. PHQ 9-rated depression showed 22 (9.4%) mild depression, most of them in participants aged 36–44 years. Participants with kidney disease showed major depression 11 (42.31%). Factors that significantly affected anxiety scores were age 24–35 years (P =0.002), university graduates (P < 0.000), married (P < 0.000), those with diabetes and hypertension (P =0.041), and urban residents (P < 0.023). Those who had secondary education were married and smoked were significantly more likely to have major depression than those with another educational status (p < 0.05). Conclusion COVID 19 pandemic had a significant impact on the psyche of uninfected people with chronic diseases in Sudan, and significant associated factors were identified. Unique interventions are strongly recommended to reduce the psychological impact of the COVID 19 pandemic.
Does economic complexity influence the efficacy of foreign direct investment? An empirical inquiry

This paper aims to determine whether economic complexity influences the efficacy of inward foreign direct investment (FDI). To this end, in the context of a theoretical framework, we estimate a model that relates economic growth to the flow as well as the stock of FDI while taking into account the role of complexity of the economy (measured by the Economic Complexity Index). This index captures the capabilities that are embodied in the productive structure of the economy. Our empirical work shows that economic complexity influences the growth effect of FDI on the host countries. We find countries that rank relatively high in economic complexity benefit from FDI while countries that rank very low may be adversely affected.
Viscoelastic Particle Focusing and Separation in a Spiral Channel

As one type of non-Newtonian fluid, viscoelastic fluids exhibit unique properties that contribute to particle lateral migration in confined microfluidic channels, leading to opportunities for particle manipulation and separation. In this paper, particle focusing in viscoelastic flow is studied in a wide range of polyethylene glycol (PEO) concentrations in aqueous solutions. Polystyrene beads with diameters from 3 to 20 μm are tested, and the variation of particle focusing position is explained by the coeffects of inertial flow, viscoelastic flow, and Dean flow. We showed that particle focusing position can be predicted by analyzing the force balance in the microchannel, and that particle separation resolution can be improved in viscoelastic flows.
Driving Style Assessment System for Agricultural Tractors: Design and Experimental Validation

The diffusion of electronics and sensors in agricultural vehicles is enabling a revolution in the field, leading—among the rest—to the introduction of advanced driving-assistance systems (ADAS). From this perspective, the three key performance indicators (KPI) in a tractor are indeed the driving safety, fuel consumption, and operator comfort. Such indexes describe the way the driver interacts with the vehicle, the environment, and other vehicles, respectively. Therefore, such information would be particularly valuable if promptly provided to the driver, e.g., on a dashboard visualizer, so as to adapt the driving style accordingly. Within this context, we propose an algorithmic solution for the on-line estimation of such KPIs. More specifically, by using an off-the-shelf smart-sensor equipped with an Electronic Control Unit (ECU), the chassis accelerations are first processed to extract physics-inspired features and then used to assess the safety and comfort levels; similarly, the speed profile is used to evaluate the economicity of the driving style. The developed method is based upon a cheap setup, and thus it is industrially amenable for its simplicity and robustness. A sensitivity analysis to establish the best sensor placement is finally carried out, together with an extensive experimental campaign considering offroad, urban, and circuit paths.
Evaluation of the Performance of a ZnO-Nanoparticle-Coated Hydrocolloid Patch in Wound Healing

Hydrocolloid dressings are an important method for accelerating wound healing. A combination of a hydrocolloid and nanoparticles (NPs), such as gold (Au), improves the wound healing rate, but Au-NPs are expensive and unable to block ultraviolet (UV) light. Herein, we combined zinc oxide nanoparticles (ZnO-NPs) with hydrocolloids for a less expensive and more effective UV-blocking treatment of wounds. Using Sprague–Dawley rat models, we showed that, during 10-day treatment, a hydrocolloid patch covered with ZnO-NPs (ZnO-NPs-HC) macroscopically and microscopically stimulated the wound healing rate and improved wound healing in the inflammation phase as shown by reducing of pro-inflammatory cytokines (CD68, IL-8, TNF-α, MCP-1, IL-6, IL-1β, and M1) up to 50%. The results from the in vitro models (RAW264.7 cells) also supported these in vivo results: ZnO-NPs-HCs improved wound healing in the inflammation phase by expressing a similar level of pro-inflammatory mediators (TNF-α and IL-6) as the negative control group. ZnO-NPs-HCs also encouraged the proliferation phase of the healing process, which was displayed by increasing expression of fibroblast biomarkers (α-SMA, TGF-β3, vimentin, collagen, and M2) up to 60%. This study provides a comprehensive analysis of wound healing by measuring the biomarkers in each phase and suggests a cheaper method for wound dressing.
Bone mineral density and vitamin D levels in parkinson’s disease: A retrospective controlled study

Objective: This study aimed to compare bone mineral density and vitamin D levels in patients with Parkinson’s Disease (PD) and healthy controls. Methods: Eighty-six patients aged 60-85, diagnosed with PD and receiving regular medical treatment for the disease were screened. Data for 34 participants (19 women and 15 men) meeting the inclusion criteria were entered into the retrospective analysis. The data for 31 healthy ageand sex-matched participants (18 women and 13 men) were also included. Bone mineral density (BMD) values and vitamin D levels of each participant were recorded. Results: BMD values for the femoral neck and total hip in the PD group were statistically significantly lower than healthy controls. In addition, T-scores for the femoral neck, total hip, and trochanteric area, and Z-scores for the total hip and trochanteric area were also lower in the patients with PD than in the healthy controls. The two groups did not have significant differences regarding lumbar spinal BMD measurements and Tand Z-score values or vitamin D levels. Discussion and Conclusion: PD’s total hip and femoral neck BMD measurements are low. Further multicenter studies involving larger patient populations are now needed to understand the incidence and mechanisms of osteoporosis in PD.
Partnership-defined quality approach to companionship during labour and birth in East New Britain, Papua New Guinea: A mixed-methods study

Companionship during labour and birth is a critical component of quality maternal and newborn care, resulting in improved care experiences and better birth outcomes. Little is known about the preferences and experiences of companionship in Papua New Guinea (PNG), and how it can be implemented in a culturally appropriate way. The aim of this study was to describe perspectives and experiences of women, their partners and health providers regarding labour and birth companionship, identify enablers and barriers and develop a framework for implementing this intervention in PNG health facilities. A mixed methods study was conducted with five facilities in East New Britain, PNG. Data included 5 facility audits, 30 labour observations and 29 in-depth interviews with women who had recently given birth, partners and maternity care providers. A conceptual framework was developed drawing on existing quality care implementation frameworks. Women and partners wanted companions to be present, whilst health providers had mixed views. Participants described benefits of companionship including encouragement and physical support for women, better communication and advocacy, improved labour outcomes and assistance with workforce issues. Adequate privacy and space constraints were highlighted as key barriers to address. Of the women observed, only 30% of women had a companion present during labour, and 10% had a companion at birth. A conceptual framework was used to highlight the interconnected inputs required at community, facility and provincial health system levels to improve the quality of care. Key elements to address included attitudes towards companionship, the need for education and training and restrictive hospital policies. Supporting women to have their companion of choice present during labour and birth is critical to improving women’s experiences of care and improving the quality of maternal and newborn care. In order to provide companionship during labour and birth in PNG, a complex, intersecting, multi-faceted approach is required.
Predicting Canopy Chlorophyll Content in Sugarcane Crops Using Machine Learning Algorithms and Spectral Vegetation Indices Derived from UAV Multispectral Imagery

The use of satellite-based Remote Sensing (RS) is a well-developed field of research. RS techniques have been successfully utilized to evaluate the chlorophyll content for the monitoring of sugarcane crops. This research provides a new framework for inferring the chlorophyll content in sugarcane crops at the canopy level using unmanned aerial vehicles (UAVs) and spectral vegetation indices processed with multiple machine learning algorithms. Studies were conducted in a sugarcane field located in Sugarcane Research Institute (SRI, Uda Walawe, Sri Lanka), with various fertilizer applications over the entire growing season from 2020 to 2021. An UAV with multispectral camera was used to collect the aerial images to generate the vegetation indices. Ground measurements of leaf chlorophyll were used as indications for fertilizer status in the sugarcane field. Different machine learning (ML) algorithms were used ground-truthing data of chlorophyll content and spectral vegetation indices to forecast sugarcane chlorophyll content. Several machine learning algorithms such as MLR, RF, DT, SVR, XGB, KNN and ANN were applied in two ways: before feature selection (BFS) by training the algorithms with all twenty-four (24) vegetation indices with five (05) spectral bands and after feature selection (AFS) by training algorithms with fifteen (15) vegetation indices. All the algorithms with both BFS and AFS methods were compared with an estimated coefficient of determination (R2) and root mean square error (RMSE). Spectral indices such as RVI and DVI were shown to be the most reliable indices for estimating chlorophyll content in sugarcane fields, with coefficients of determination (R2) of 0.94 and 0.93, respectively. XGB model shows the highest validation score (R2) and lowest RMSE in both methods of BFS (0.96 and 0.14) and AFS (0.98 and 0.78), respectively. However, KNN and SVR algorithms show the lowest validation accuracy than other models. According to the results, the AFS validation score is higher than BFS in MLR, SVR, XGB and KNN. Even though, validation score of the ANN model is decreased in AFS. The findings demonstrated that the use of multispectral UAV could be utilized to estimate chlorophyll content and measure crop health status over a larger sugarcane field. This methodology will aid in real-time crop nutrition management in sugarcane plantations by reducing the need for conventional measurement of sugarcane chlorophyll content.
Distress, Eustress, and Continuance Intentions for Distance Learners

ABSTRACT While there has been considerable research related to distance learning, there is surprisingly little research into the role stress plays in distance learning. Therefore, in this paper, we investigated two types of stress: distress and eustress. Using a combination of stress theory and the job and demand resources theory, we developed a research model to predict distance learning satisfaction and continuance intentions. Results from 525 full-time students engaging in distance learning indicate general support for our research model. Our findings clearly indicate that 1) both distress and eustress are associated with distance learning continuance intentions, 2) these impacts are mediated by distance learning satisfaction, and 3) distress has a stronger effect than eustress. Our findings can assist advisors and faculty in helping students understand whether distance learning is a good idea for them.
Short-Term Continuous Positive Air Pressure Treatment: Effects on Quality of Life and Sleep in Patients with Obstructive Sleep Apnea

Background and Objectives: The aim of this study was to evaluate short-term continuous positive air pressure (CPAP) treatment for health-related quality of life (HRQL) in patients with obstructive sleep apnea. Materials and Methods: Our subjects were 18–65 years old, diagnosed with moderate-to-severe obstructive sleep apnea and treated with CPAP between January 2020 and June 2021 in Hospital of Lithuanian University of Health Sciences Kaunas clinics. All the patients completed the Epworth Sleepiness Scale (ESS), the 36-Item Short Form Health Survey (SF-36), the and Pittsburgh Sleep Quality Index (PSQI) before and after 3 months of treatment. Polysomnography was also repeated. Statistical analyses were performed using SPSS 27.0 software. The value of p < 0.05 was considered as statistically significant. Results: The active-treatment group comprised 17 subjects with a mean age of 51.9 ± 8.9 years. The total SF-36 questionnaire score improved from 499.8 ± 122.3 to 589.6 ± 124.7 (p = 0.012). The SF-36 role limitations due to emotional problems (p = 0.021), energy (fatigue) (p = 0.035), and general health (p = 0.042) domains score significantly improved after CPAP treatment for 3 months. The PSQI mean score at baseline was 12.6 ± 2.9 and in the post-treatment group, it was −5.5 ± 2.3 (p = 0.001). The ESS also changed significantly from a pretreatment mean score of 10.9 ± 5.7 to −5.3 ± 3.2 (p = 0.002) after 3 months. Conclusions: Improvement in HRQL is seen even after a short treatment period with CPAP. Questionnaires are a good tool to evaluate CPAP treatment efficacy.
Comparative Antioxidant, Anti-Acetylcholinesterase and Anti-α-Glucosidase Activities of Mediterranean Salvia Species

Salvia species have a cosmopolitan distribution and comprise several well-known plants valuable for pharmaceutical and food industries due to their recognized medicinal, food flavouring, and preservative properties. The present study aimed to evaluate and compare the biological activities of seven wild-growing Salvia species from the Mediterranean area (S. fruticosa, S. glutinosa, S. nemorosa, S. officinalis, S. pratensis, S. sclarea, S. verticillata). All studied ethanolic leaf extracts exhibited significant DPPH and NO radical scavenging ability, lipid peroxidation inhibition, and reducing power, as well as moderate iron-chelating properties. Together with S. officinalis and S. fruticosa, S. verticillata showed anti-acetylcholinesterase activity, while S. glutinosa was also found to possess the ability to inhibit α-glucosidase. Total flavonoid (0.37–0.90%), phenolic acid (3.55–12.44%), tannin (1.22–2.60%), and anthocyanin contents (0.03–0.08%) were determined in Salvia leaves. Rosmarinic acid was the predominant hydroxycinnamic acid in all studied sage plants, ranging from 9400 to 38,800 μg/g. The correlation study showed a strong relationship between biological activities and contents of total phenolic acids, total tannins, and rosmarinic acid, indicating their significant contribution to the efficiency of tested Salvia species. Our results highlighted Mediterranean sage plants as rich sources of potent antioxidant, neuroprotective, and hypoglycemic agents which are worthy of further research.
Introducing the party-interest group relationships in contemporary democracies datasets

Few existing datasets on parties and interest groups include data from both sides and a wide variety of interest groups and parties. We contribute to filling this gap by making several interconnected new datasets publicly available. The Party-Interest Group Relationships in Contemporary Democracies (PAIRDEM) datasets include cross-national data from three different surveys of (1) central party organizations, (2) legislative party groups, and (3) interest groups. A fourth dataset based on coding of party statutes and party finance data was established together with the Political Party Database. The datasets contain novel indicators on party-group relationships in up to 21 mature democracies. In this research note, we first present the main content of the datasets and the research design. Second, we present descriptive statistics documenting the extent of organizational ties between parties and groups in contemporary democracies. Third, we illustrate more advanced usage through a simple application.
Physical and Dietary Intervention with Opuntia ficus-indica (Nopal) in Women with Obesity Improves Health Condition through Gut Microbiota Adjustment

Obesity is a multifactorial disease resulting in excessive accumulation of fat. Worldwide, obesity is an important public health problem, affecting a large proportion of the world population. The tender cactus Opuntia ficus-indica, commonly known in Mexico as “nopal”, is widely distributed in this country, Latin America, South Africa, and the Mediterranean area. Nopal cladodes are commonly marketed in different forms as fresh, frozen, or pre-cooked, and used as fresh green vegetable. The aim of this study was to evaluate the capability of nopal to improve the health condition of participants affected by obesity, in a physical and dietary intervention, through gut microbiota modification. These results were contrasted with the effect of nopal in the gut microbiota of normal weight participants. We describe the association among biochemical, anthropometric markers, and the gut microbiota diversity found in fecal samples of the obese and normal weight groups. The results presented in this work suggest that caloric restriction, addition of nopal to the diet and physical activity, promote changes in the gut microbiota in obese women, improving the host metabolism, as suggested by the correlation between some bacterial species with biochemical and anthropometrical parameters.
Motion recognition using deep convolutional neural network for Kinect-based NAO teleoperation

The capabilities of teleoperated robots can be enhanced with the ability to recognise and reproduce human-like behaviour. The proposed framework presents motion recognition for a Kinect-based NAO teleoperation. It allows the NAO robot to recognise the human motions and act as a human motion imitator. A stable whole-body imitation is still a challenging issue because of the difficulty in dynamic balancing of centre of mass (CoM). In this paper, a novel adaptive balancing technique for NAO (ABTN) is proposed to control the whole body in single as well as double supporting phases. It targets dynamic balancing of the humanoid robot by solving forward kinematics and applying a weighted average of mass with the CoMs of individual links with respect to the previous joint frames, which provides us with the dynamic CoM of the whole body. Our novel approach uses this dynamic CoM and calculates joint angles using proposed pitch and roll control algorithm to keep the dynamic CoM inside the stable region. Additionally, the NAO robot is capable of recognising human motions using the proposed 7-layer one-dimensional convolutional neural network (1D-CNN). To solve the problem of variable length of time sequences, Zero padding is introduced with 1D-CNN. It attains a recognition accuracy of 95% as compared to the hidden Markov model and neural network. The experimental results demonstrate that the developed teleoperation framework is robust and serves as potential support for the development and application of teleoperated robots.
Optimized 3D Bioprinting Technology Based on Machine Learning: A Review of Recent Trends and Advances

The need for organ transplants has risen, but the number of available organ donations for transplants has stagnated worldwide. Regenerative medicine has been developed to make natural organs or tissue-like structures with biocompatible materials and solve the donor shortage problem. Using biomaterials and embedded cells, a bioprinter enables the fabrication of complex and functional three-dimensional (3D) structures of the organs or tissues for regenerative medicine. Moreover, conventional surgical 3D models are made of rigid plastic or rubbers, preventing surgeons from interacting with real organ or tissue-like models. Thus, finding suitable biomaterials and printing methods will accelerate the printing of sophisticated organ structures and the development of realistic models to refine surgical techniques and tools before the surgery. In addition, printing parameters (e.g., printing speed, dispensing pressure, and nozzle diameter) considered in the bioprinting process should be optimized. Therefore, machine learning (ML) technology can be a powerful tool to optimize the numerous bioprinting parameters. Overall, this review paper is focused on various ideas on the ML applications of 3D printing and bioprinting to optimize parameters and procedures.