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Background: Adolescent women's reproductive health is often neglected despite the high prevalence of early marriage. Since no appropriate scales were found to assess the health status of adolescent women, this study aimed to develop a reproductive health scale in married adolescent women in Iran and investigate its psychometric properties. Materials and Methods: An exploratory mixed-methods study was conducted in Ardabil healthcare centers (Ardabil City, Iran) between May 2017 and December 2018. In the qualitative phase, 14 semi-structured in-depth interviews were conducted with married adolescent women, and two focus group discussions were held with 12 key informants. In the quantitative phase, the initial scales were validated using face, content, and construct validities. In a cross-sectional study among 300 women, Exploratory Factor Analysis (EFA) was used to assess the construct validity. Internal consistency and test-retest methods were used to review. The initial scale was designed with 45 items, but only 30 items reached the construct validity stage. EFA revealed five factors that explained 50.96% of the variance. Cronbach's alpha coefficient of 0.75 estimates the reliability of the scale. Results: The qualitative study identified 76 items that reached 88 items through literature confirmed its reliability, and test-retest with a two-week interval confirmed its consistency (ICC = 0.99, p < 0.001). Finally, the scale was approved with 27 items and four domains: sexual, pregnancy and childbirth, psychosocial, and family planning. Conclusions: This valid and reliable scale with cultural sensitivity can be used to help health professionals to improve the reproductive health of married adolescent women.

In the current work, for the first time, the design equations for an array of rectangular slots axially placed on a cylindrical waveguide are driven and presented, making it possible to attain asymmetrical and non-broadside patterns with higher bandwidths. The method of least squares is then used for both the radiation pattern synthesis with the specified side lobe level and the array input impedance matching. For optimization, the error function comprised of the three terms of impedance matching, array pattern synthesis, and slot design equations is constructed. To minimize the error function, the genetic algorithm combined with the conjugate gradient method is utilized and the geometrical characteristic of the slots defined. This procedure increases the design speed and provides the means to synthesize any desired pattern for an array of axial rectangular slots on a cylindrical waveguide. Numerical and fabrication examples are presented as illustrations of the proposed synthesis method. The results of the proposed method are verified by using the commercial software High-Frequency Structure Simulator (HFSS) in comparison to measurement results obtained out of the proposed prototype of a fabricated antenna operating at the frequency of 9 GHz.

This study utilises a deep convolutional neural network (CNN) implementing regularisation and batch normalisation for the removal of mixed, random, impulse, and Gaussian noise of various levels from digital images. This deep CNN achieves minimal loss of detail and yet yields an optimal estimation of structural metrics when dealing with both known and unknown noise mixtures. Moreover, a comprehensive comparison of denoising filters through the use of different structural metrics is provided to highlight the merits of the proposed approach. Optimal denoising results were obtained by using a 20‐layer network with 40 × 40 patches trained on 400 180 × 180 images from the Berkeley segmentation data set (BSD) and tested on the BSD100 data set and an additional 12 images of general interest to the research community. The comparative results provide credence to the merits of the proposed filter and the comprehensive assessment of results highlights the novelty and performance of this CNN‐based approach.

With the advances in machine learning for the diagnosis of Alzheimer’s disease (AD), most studies have focused on either identifying the subject’s status through classification algorithms or on predicting their cognitive scores through regression methods, neglecting the potential association between these two tasks. Motivated by the need to enhance the prospects for early diagnosis along with the ability to predict future disease states, this study proposes a deep neural network based on modality fusion, kernelization, and tensorization that perform multiclass classification and longitudinal regression simultaneously within a unified multitask framework. This relationship between multiclass classification and longitudinal regression is found to boost the efficacy of the final model in dealing with both tasks. Different multimodality scenarios are investigated, and complementary aspects of the multimodal features are exploited to simultaneously delineate the subject’s label and predict related cognitive scores at future timepoints using baseline data. The main intent in this multitask framework is to consolidate the highest accuracy possible in terms of precision, sensitivity, F1 score, and area under the curve (AUC) in the multiclass classification task while maintaining the highest similarity in the MMSE score as measured through the correlation coefficient and the RMSE for all time points under the prediction task, with both tasks, run simultaneously under the same set of hyperparameters. The overall accuracy for multiclass classification of the proposed KTMnet method is 66.85 ± 3.77. The prediction results show an average RMSE of 2.32 ± 0.52 and a correlation of 0.71 ± 5.98 for predicting MMSE throughout the time points. These results are compared to state-of-the-art techniques reported in the literature. A discovery from the multitasking of this consolidated machine learning framework is that a set of hyperparameters that optimize the prediction results may not necessarily be the same as those that would optimize the multiclass classification. In other words, there is a breakpoint beyond which enhancing further the results of one process could lead to the downgrading in accuracy for the other.

The purpose of this study is to explore perception of social discrimination among ordinary people and health-care workers toward individuals with COVID-19 in Jordan. A cross-sectional descriptive-comparative design was used to collect data from a convenience sample of 272 ordinary people and 109 HCWs utilizing an online survey format. HCWs reported low to medium social discrimination (SDS) level, while ordinary people reported a higher level with statistical difference (t = 8.64, p

Background and Objectives: Cerebral Palsy (CP) can trouble caregivers in the families of children with cerebral palsy. This study aimed to investigate the factors affecting caregiver troubles of the families of children with CP. Methods: In this cross-sectional study, 121 children with CP and their parents participated. Factors such as gross motor function, manual ability, communication function, eating and drinking abilities, seizure, IQ, age, gender, auditory and visual problems were evaluated in children, and their predictive power to the caregiver difficulties was measured. Results: The Mean±SD of age of the children in the study was 9.7±4.6 years. The linear regression results showed that none of the evaluated factors were predictors of caregiver difficulties. Conclusion: The present study showed that none of the factors mentioned could predict caregivers’ troubles. Future studies on the factors affecting the caregiver difficulty should be conducted to provide additional information or to draw more complex models to describe caregivers’ difficulties in parents of children with CP in Iran.

This study was conducted to identify the factors and the interactions, which affect the load and elongation at the first break of gimp yarns. The experimental design had seven factors: two levels each. It was found that using two single yarns, instead of a similar ply yarn, for the core component, caused the increase of values of load and elongation at the first break. The effect component contributed positively to the load and elongation when it was a cotton yarn rather than a bamboo yarn. This study contributes to a broader understanding of process–structure–property relationships of fancy yarn.

Background and Aim: Pediculus humanus capitis has been prevalent throughout the world, especially in developing countries among elementary students and societies with a weak socio-economic status. This study aimed to forecast head lice (Pediculidae: P. capitis) infestation incidence hotspots based on spatial correlation analysis in Ardabil Province, Northwest Iran. Materials and Methods: In this retrospective analytical study, all cases of head lice infestations who were confirmed by Centers for Disease Control office have been studied from 2016 to 2018. Head lice infestation incidence hotspots in the province should be detected based on general G statistics in ArcMap GIS10.4.1. Furthermore, MaxEnt.3.3.3 model was used for modeling the high-risk areas. Results: The prevalence rate of pediculosis was 14.90/100,000 populations. The general G statistics revealed that the head lice infestation in this study area has a high cluster pattern. The analysis showed that the Parsabad and Germi counties were identified as a head lice infestation incidence hotspots. Statistical and spatial analyses of head lice infestation incidence showed a significant positive correlation with head lice infestation incidence hotspots and the altitudes (15-500 m), annual temperature range (14-16.5°C), and slope and average diurnal temperature (12-18°C). Conclusion: The results of this study showed that the most ecologically suitable areas of head lice occurrence were identified in two hotspots (Parsabad and Germi) in the Northern areas of Ardabil Province (Parsabad and Germi counties); in the borderline of Iran and the Republic of Azerbaijan.

Context: The fatigue is reported as the most common and annoying symptom in patients with cancer, timely diagnosis, and treatment can significantly influence the treatment and rehabilitation. It is crucial to have an appropriate tool to accurately assess fatigue status. Objectives: Our purpose was to assess psychometric properties of the Persian versions of fatigue scale by Original Scales from the viewpoint of children with cancer (Child Fatigue Scale [CFS]-24 h), their parents (Parent Fatigue Scale [PFS]-24 h), and staff (Staff Fatigue Scale FSF-24 h). Methods: Convenience sampling of the participants was conducted 100, including children with cancer within the age range of 7–12 years, their parents, and caregivers in medical staff. Test–retest reliability and internal consistency were evaluated using intraclass correlation (ICC) and Cronbach's alpha coefficient. Dimensionality was determined by factor analysis. The patients' fatigue was also assessed through visual analog scale-fatigue (VAS-F). Results: Test–retest (ICCCFS= 0.71, ICCPSF= 0.82, and ICCSFS= 0.78) was acceptable with a high level of internal consistency (αCFS= 0.80, αPFS= 0.83, and αSFS= 0.84). Factor analysis identified three, five, and two components for the CFS, PFS, and Staff Fatigue Scale (SFS), respectively. There was moderate correlation between CFS and VAS-F. Conclusions: Results of the current study indicated that CFS in children with cancer, PFS in their parents, and SFS in medical staff were valid and reliable instruments to assess fatigue from the viewpoint of children with cancer along with their parents and medical staff.

Purpose: In children, refractive error (RE), especially myopia, is influenced by both genetic and environmental factors. We compared the magnitude and determinants of RE among Saudi and Indian students in Riyadh, Saudi Arabia. Methods: This study was conducted in 2017–2018 and evaluated students in preparatory and secondary grades. A “Spot Screener” was used to determine if the child passed or failed a refraction test. Data on vision, RE, anisometropia, and strabismus were compared between Indian and Saudi students. P < 0.05 was considered statistically significant. Results: The study sample was comprised of 770 Indian and 708 Saudi students. There were more Indian students (35.3%) with moderate visual impairment (

Due to importance of measuring the degree of resemblance, the similarity measure is widely adopted in various areas of the information systems (e.g., medical informatics and information retrieval) and in several applications like medical diagnostic, image processing, and pattern recognition. However, most of the existing similarity measures focus mainly on the degree of similarity without consulting expert(s) about the results. In this paper, an efficient tool for measuring similarity and agreement of objects that embeds experts’ opinions is proposed to assess similarity among features and agreement of opinions among experts. To obtain such robust measuring tool, three construction steps were followed. Firstly, adapting soft expert set as a general structure that consists of four components: objects, attributes, experts, and experts’ opinions. Secondly, representing the soft expert set, without losing stored information, in such a way as to fit the proposed similarity-agreement measure and make it simpler and more meaningful than the similar existing measures. Thirdly, axiomatizing the similarity-agreement measure for the case of two experts to simplify the model. Further, a diagnostic prediction application and its algorithm is discussed in this context, along with analysis of the experimental results. Analysis of performance of the proposed similarity-agreement measure revealed that it has high accuracy, sensitivity, and value of the F-measure and that it has better performance than existing state-of-the-art tools.

BackgroundAsthma is one of the most common chronic illnesses among children and adolescents. It can severely affect their quality of life (QoL). Our study assessed the QoL and analyzed potential risk factors for poor QoL among asthmatic children and adolescents.MethodsThis was a cross-sectional comparative study. Pediatric Asthma Quality of Life Questionnaire (PAQLQ) was used to measure the QoL and Asthma Control Test (ACT) was used to evaluate asthma control. The Chi-square test and independent t-test were used to compare variables. We used Multivariate logistic regression to identify the association between determinants and outcomes. Statistical significance was set at pResultsWe recruited 132 participants. We found that 47 patients (35.6%) had controlled Asthma and 85 patients (64.3%) had uncontrolled Asthma. When compared to uncontrolled asthma individuals, participants with controlled asthma had improved QoL and scored significantly higher in the symptom domain (P = 0.002), activity domain (P = 0.004), emotional domain (P = 0.002), and overall PAQoL scores (P = 0.002). Hospital admission affects significantly all domains of PAQOL (PConclusionsThe results of the present study showed that in children and adolescents, uncontrolled asthma, disease severity, and previously hospitalized patients were associated with poor QoL. These factors must be considered when planning a comprehensive care plan for a better quality of life.

In this research study, Recycled Polymer (RP) composites are synthesized by using compression molding process, initial mixtures of RP and Date Palm Fibers (DPF) with four different lengths (e.g., 2.5, 5, 7.5, and 10 mm) and weight ratios (e.g., 5, 10, 15, and 20 wt%). The RP composites utilized in this study are polyethylene and polypropylene. The mixtures of RP and DPF are heated at 80°C and then poured into a priori prepared mold. The mold is designed to have three cavities for three specimens in order to characterize them through impact, creep, and tensile tests. The results showed that the hardness and impact increased with this process. In addition, an increase in the DPF up to 15 wt% was observed with a small increase in the DPF length. High creep resistance was also observed to be 10 mm with 20 wt% in the DPF specimens. The maximum strain was obtained in a 2.5 mm fiber length with 5% of DPF due to ductility of the plastic matrix. Moreover, with a small ratio of tough DPF, short fibers are unable to block or resist rapid plastic deformation in specimens. In fact, the DPF specimens of 10 mm length with 20 wt% exhibit a high tensile strength of 78 N/mm2 in comparison with other composite specimens. This is due to the length and content of fibers, which improve transferring action and microfailure modes.

Background: The functional reorganization of brain networks sustaining gait is poorly characterized in amyotrophic lateral sclerosis (ALS) despite ample evidence of progressive disconnection between brain regions. The main objective of this fMRI study is to assess gait imagery-specific networks in ALS patients using dynamic causal modeling (DCM) complemented by parametric empirical Bayes (PEB) framework. Method: Seventeen lower motor neuron predominant (LMNp) ALS patients, fourteen upper motor neuron predominant (UMNp) ALS patients and fourteen healthy controls participated in this study. Each subject performed a dual motor imagery task: normal and precision gait. The Movement Imagery Questionnaire (MIQ-rs) and imagery time (IT) were used to evaluate gait imagery in each participant. In a neurobiological computational model, the circuits involved in imagined gait and postural control were investigated by modelling the relationship between normal/precision gait and connection strengths. Results: Behavioral results showed significant increase in IT in UMNp patients compared to healthy controls (Pcorrected

Predictive maintenance is considered as one of the most important strategies for managing the utility systems of commercial buildings. This research focused on chilled water system (CWS) components and proposed a methodological framework to build a comprehensive predictive maintenance program in line with Industry 4.0/Quality 4.0 (PdM 4.0). This research followed a systematic literature review (SLR) study that addressed two research questions about the mechanism for handling CWS faults, as well as fault prediction methods. This research rectified the associated research gaps found in the SLR study, which were related to three points; namely fault handling, fault frequencies, and fault solutions. A framework was built based on the outcome of an industry survey study and contained three parts: setup, machine learning, and quality control. The first part explained the three arrangements required for preparing the framework. The second part proposed a decision tree (DT) model to predict CWS faults and listed the steps for building and training the model. In this part, two DT algorithms were proposed, C4.5 and CART. The last part, quality control, suggested managerial steps for controlling the maintenance program. The framework was implemented in a university, with encouraging outcomes, as the prediction accuracy of the presented prediction model was more than 98% for each CWS component. The DT model improved the fault prediction by more than 20% in all CWS components when compared to the existing control system at the university.

Overheating of photovoltaic (PV) panels decreases their efficiency and lifetime, and subsequently increases the levelized cost of energy (LCOE). Passive PV cooling would enhance the PV operational stability and durability. The cooling tower (CT) technology offers an attractive approach for zero-cost capability. In this work, we developed and customized a CT specific for passive PV cooling. Since the dense downdrafted cooled air gained high velocity, a turbine was installed at the bottom of the CT for power production. At the height’s ambient temperature, the CT cooled the air from 50 °C down to 30 °C. The cooled air at 30 °C has enough capacity to cool the PV panels. This cooling capacity improved the average annual efficiency of the PV panels by 6.83%. The design specifications of the CT have the highest performance, with the maximum radius of PV area of panels that can be cooled at 50 m. Furthermore, the current design could operate during the night for power production with minimum operational cost.

Abstract Introduction Culturally fair cognitive assessments sensitive to detecting changes associated with prodromal Alzheimer's disease are needed. Methods Performance of Hispanic and non‐Hispanic older adults on the Loewenstein‐Acevedo Scale of Semantic Interference and Learning (LASSI‐L) was examined in persons with amnestic mild cognitive impairment (aMCI) or normal cognition. The association between a novel cognitive marker, the failure to recover from proactive semantic interference (frPSI), and cortical thinning was explored. Results English‐speaking aMCI participants scored lower than cognitively normal participants on all LASSI‐L indices, while Spanish‐speaking aMCI participants scored lower in learning, frPSI, and delayed recall. Healthy controls obtained equivalent scores on all indices except retroactive semantic interference. English‐speaking and Spanish‐speaking aMCI participants had equivalent scores except English speaker's greater vulnerability to frPSI. Across aMCI groups, frPSI was associated with cortical thinning of the entorhinal cortex and precuneus (r = −0.45 to r = 0.52; P < .005). Discussion In diverse populations, LASSI‐L performance differentiated patients with aMCI from cognitively normal older adults and was associated with thinning in Alzheimer's disease–prone regions, suggesting its clinical utility.

Objectives: Flexion contracture in fingers of hands is a complication of Dupuytren’s contracture and cannot be stopped by the progressive course. The aim of this study was to review the effect of splinting in the correction of contracture after Dupuytren’s contracture operation. Methods: To carry out this research, a kind of systematic evidence-based review process (Duffy 2005) was used. For data gathering, we used electronic database and Persian- and English-language specific journals between 1990 and January 2019. The keywords were related to examine the effect of various splinting after Dupuytren’s contracture operation. Results: After data gathering, 26 articles about Dupuytren’s contracture were found; after checking and deep reviewing of those articles, 17 articles were removed from the study and 9 articles were included in the study for reviewing. The results of a review article based on using splint in Dupuytren’s contracture after operation were classified into 6 categories, including static splint, dynamic splint, day splint, night splint, the time duration of splinting, and prescribed time of splinting. Discussion: Splint alone has been ineffective in reducing contracture. Static splint and hand therapy (exercise) are together the most effective way for the treatment of Dupuytren’s contracture. Occasional and irregular use of splint also has no effect on pain relief and the reduction of contracture. The pressure and force of splint will be gradually increased, and the splint should be used at least for 3 months, too.

This study aims to show the impact of both the width of the base of the spinning triangle and the production speeds of hollow-spindle spinning machines on the structure of ultimate multiple-thread-structure bouclé yarns and similar fancy yarns. A hollow-spindle spinning machine was used and bouclé yarns were made of a core thread, an effect thread and a (multifilament) binder. Initially, five bouclé yarns were made by setting the widths of the base of the spinning triangle at five levels, i.e. 4.5 mm, 7.5 mm, 10 mm, 13 mm and 16 mm. A further six bouclé yarns were made to show the changes that occur to the spinning triangle at various production speeds. The resulting fancy bouclé yarns were assessed by measuring the size, number and circularity ratio of bouclé profiles. It was found that at low production speeds, i.e. at start-up, that the spinning triangle was unstable, which adversely affected the structure of the final bouclé yarns. However, at production speeds higher than 17 m/min, the spinning triangle became stable, though such a stable spinning triangle had no impact on the structure of the resulting fancy bouclé yarns. The results of this study may help fancy yarn manufacturers to avoid making defective fancy yarns.

Predictive Maintenance 4.0 (PdM 4.0) showed a highly positive impact on chilled water system (CWS) maintenance. This research followed the recommendations of a systematic literature review (SLR), which was performed on PdM 4.0 applications for CWS at commercial buildings. Per the SLR, and to start making an excellent PdM 4.0 program, the faults and their frequencies must be identified. Therefore, this research constructed an industry survey, which went through a pilot study, and then shared it with 761 maintenance officers in different commercial buildings. The first goal of this survey is to verify the faults reported by SLR, explore more faults, and suggest a managerial solution for each fault. The second goal is to determine the minimum and maximum frequencies of faults occurrence, while the third goal is to verify selected operational parameters, in which their data can be used in smart buildings applications. A total of 304 responses are considered in this study, which identified additional faults and provided faults solutions for all CWS components. Based on the survey outcomes, justifiable frequencies are proposed, which can be used in creating the dataset of any machine learning model, and then to control the CWS performance.

Green hydrogen is considered to be one of the best candidates for fossil fuels in the near future. Bio-hydrogen production from the dark fermentation of organic materials, including organic wastes, is one of the most cost-effective and promising methods for hydrogen production. One of the main challenges posed by this method is the low production rate. Therefore, optimizing the operating parameters, such as the initial pH value, operating temperature, N/C ratio, and organic concentration (xylose), plays a significant role in determining the hydrogen production rate. The experimental optimization of such parameters is complex, expensive, and lengthy. The present research used an experimental data asset, adaptive network fuzzy inference system (ANFIS) modeling, and particle swarm optimization to model and optimize hydrogen production. The coupling between ANFIS and PSO demonstrated a robust effect, which was evident through the improvement in the hydrogen production based on the four input parameters. The results were compared with the experimental and RSM optimization models. The proposed method demonstrated an increase in the biohydrogen production of 100 mL/L compared to the experimental results and a 200 mL/L increase compared to the results obtained using ANOVA.

The use of aqueous carbonate as an inorganic absorbent is not only inexpensive but also stable and environmentally friendly. However, the regeneration processes for aqueous carbonate sorbents require high regeneration heat duty; this energy intensity makes their wide utilization unaffordable. In this work, a low-temperature, energy-saving, and environmentally friendly carbon dioxide desorption method has been investigated in potassium bicarbonate-water-alcohol solutions. The addition of alcohol, particularly isopropanol, to the potassium bicarbonate-water solution can significantly increase carbon dioxide desorption capacity. The potassium bicarbonate-water-isopropanol solution used in this study (36 wt % isopropanol) resulted in 15.2 mmol of carbon dioxide desorption within 2400 s at 80 °C, which was 2000-fold higher than the potassium bicarbonate-water-solution. This research demonstrates a water-lean solvent-based carbon dioxide removal route with the potential to be economical, environmentally safe, and energy-efficient. CO2 sequestration, capture, and utilization technologies will play a key role in reducing CO2 emissions. The excellent desorption kinetics and relatively moderate desorption temperatures (80 °C) of water-lean solvent could help in reducing the cost of CO2 capture, particularly in terms of the heat demand at the regenerator.

Introduction: The diagnosis of a post–surgical uroenteric fistula can be challenging and may be delayed for months after symptoms begin. A normal anion gap metabolic acidosis has been reported in up to 100% of patients after ureterosigmoidostomy, and bladder substitution using small bowel and/or colonic segments. Here, we describe a rare case of a pediatric patient who developed a uroenteric fistula from the transplant ureters into the small bowel, after an en-bloc kidney transplantation resulting in profound acidosis and deceptive watery diarrhea.Case Presentation: The patient is an 8-year-old girl with end stage kidney disease (ESKD) secondary to focal segmental glomerulosclerosis. Through a right retroperitoneal approach, she underwent a right native nephrectomy and a pediatric deceased donor en-bloc kidney transplant including two separate ureters. One month later, she had a renal allograft biopsy for suspected rejection. During the week after the biopsy, she experienced abdominal pain followed by watery diarrhea and metabolic acidosis requiring continuous bicarbonate/acetate infusions. An extensive gastro-intestinal evaluation for the cause of the diarrhea including endoscopy was inconclusive. The urine output decreased to

Health promotion from an early age is key to preventing unhealthy weight development in childhood, and parental involvement is essential. The school-based Healthy School Start intervention aims to promote healthy dietary and activity habits in the home environment and prevent child obesity through parental support. This study evaluated the effectiveness of the third iteration of the programme on children's dietary and activity behaviours, and body composition through a cluster-randomised controlled trial. The trial included 17 schools (8 intervention) in disadvantaged areas in mid-Sweden with 353 families with 5- to 7-year-old children. The primary outcomes were intake of selected healthy and unhealthy foods and beverages measured using photography. Secondary outcomes were physical activity and sedentary time measured by accelerometry, and measured weight and height. All outcomes were assessed at baseline and post-intervention (8 months). Linear multi-level regression showed significant favourable effects of the intervention for intake of sweet beverages (b = - 0.17, p = 0.04), intake of healthy foods (b = 0.11, p = 0.04), and more time in moderate to vigorous physical activity during weekdays (b = 5.68, p = 0.02). An unfavourable sub-group effect of the intervention was found for children from families with low education regarding sedentary time on weekends (b = 23.04, p = 0.05). The results align with the previous two trials of the programme, indicating that school-based parental support is a useful approach for health promotion in young children in disadvantaged areas.

Early childhood teachers routinely facilitate play-based learning experiences in their physical classrooms; however, the pivot to virtual teaching platforms created a barrier for providing age appropriate, play-based learning opportunities during the COVID-19 pandemic. There are few studies exploring how to promote play in the virtual classroom or what types of activities and learning experiences promote play in synchronous and asynchronous settings. Therefore, this study explored the barriers and challenges to fostering play through virtual teaching and the types of play-based instruction teachers were effectively able to implement in their virtual classroom. This study used content analysis along with descriptive analysis of an online survey with open-ended prompts that early childhood teachers completed (n = 76). Findings revealed two major themes related to challenges and barriers in teachers' efforts to foster play-based learning through virtual formats. Even though teachers noted significant challenges and barriers they identified multiple play-based activities they were able to facilitate effectively through virtual formats. These activities were categorized through the theoretical framework of Piaget's stages of the development of play with the addition of guided play. Implications for how play can be fostered through virtual teaching in early childhood classrooms were discussed.

Drinking water is a possible source of humans’ illness when it contains chemicals and microorganisms especially from anthropogenic activities. The water supply from groundwater remains very important in Algeria. To assess the quality of groundwater in the region of Sedrata, analyses were carried out on 26 wells belonging to two neighbouring areas: one urban and the other rural. A study of physicochemical parameters has focused on the measurement of in situ temperature, electrical conductivity, pH and turbidity. Then the following parameters were analysed: hardness, and the elements: Ca2+, Mg2+, SO42−, PO43−, Cl−, NO2−, NO3−, NH4+ as well as metal trace elements Fe2+, Mn2+, Al3+. The samples taken for the bacteriological study were filtered and introduced into growth medium for the research and enumeration of total germs, faecal coliforms, faecal streptococci and sulphite reducing Clostridium. As a result, the contamination of the studied waters is almost general. Some of the most important obtained values are ranging from 4.8 to 76 mg∙dm−3 for nitrates, the recorded values for mesophilic germs vary from 1 to 1100 CFUs∙cm−3. Agricultural activity and livestock products on the one hand and the use of fertilizers on the other hand are the main sources of physicochemical and bacteriological pollution. Contaminated wells should be treated as soon as possible to limit contamination before spreading in the deep aquifers. In the future, it will be necessary not only to assess the health risks related to the level of contamination of these waters, but also to proceed with their treatment before supplying them to consumers.

Objectives: Stroke is one of the main causes of disability and mortality worldwide. Most survivors experience impairments in their upper limb motor function. Methods: This experimental study was performed as a clinical trial on 30 chronic stroke patients who experienced stroke from 6 to 96 months ago. Patients were non-randomly divided into the intervention (Virtual Reality besides conventional occupational therapy) and control (conventional occupational therapy) groups. Each treatment session lasted for one hour which was divided into conducting conventional occupational therapy techniques and Virtual Reality (VR) for the intervention group, and routine techniques for the control group. The intervention effectiveness was evaluated by the Fugl-Meyer Upper Extremity Scale, Stroke Impact Scale, Chedoke Arm and Hand Activity Inventory, Motricity Index, Modified Ashworth Scale and goniometer. Results were analyzed by SPSS and one-sample Kolmogorov-Smirnov, Shapiro-Wilk test, Independent Samples t-test and Mann Whitney U test were applied to assess the normality of data and to detect significant differences between study variables. Results: The results suggested that investigated parameters such as upper limb motor function, muscle tone and the range of motion were significantly different in the intervention group, compared to control group; however, there was no significant changes in none of the group’s daily living activities. Discussion: VR-based computer games in combination with routine occupational therapy interventions could improve upper extremities functional impairments in chronic stroke patients. However, it seems the mechanisms behind the effectiveness of video games and their impact on brain cortex as well as upper limbs function need to be further investigated.

Predictive maintenance plays an important role in managing commercial buildings. This article provides a systematic review of the literature on predictive maintenance applications of chilled water systems that are in line with Industry 4.0/Quality 4.0. The review is based on answering two research questions about understanding the mechanism of identifying the system’s faults during its operation and exploring the methods that were used to predict these faults. The research gaps are explained in this article and are related to three parts, which are faults description and handling, data collection and frequency, and the coverage of the proposed maintenance programs. This article suggests performing a mixed method study to try to fill in the aforementioned gaps.

In December 2019, the novel coronavirus disease 2019 (COVID-19) appeared. Being highly contagious and with no effective treatment available, the only solution was to detect and isolate infected patients to further break the chain of infection. The shortage of test kits and other drawbacks of lab tests motivated researchers to build an automated diagnosis system using chest X-rays and CT scanning. The reviewed works in this study use AI coupled with the radiological image processing of raw chest X-rays and CT images to train various CNN models. They use transfer learning and numerous types of binary and multi-class classifications. The models are trained and validated on several datasets, the attributes of which are also discussed. The obtained results of various algorithms are later compared using performance metrics such as accuracy, F1 score, and AUC. Major challenges faced in this research domain are the limited availability of COVID image data and the high accuracy of the prediction of the severity of patients using deep learning compared to well-known methods of COVID-19 detection such as PCR tests. These automated detection systems using CXR technology are reliable enough to help radiologists in the initial screening and in the immediate diagnosis of infected individuals. They are preferred because of their low cost, availability, and fast results.

Background and Objectives: Cerebral palsy is a non-progressive disorder in the growing brain that disrupts movement and ultimately limits the individual’s performance and participation in a variety of areas, such as everyday life, play, and school activities. Individualized occupational interventions help these children receive appropriate services and supports based on their needs, abilities, and disabilities. Current research aimed at investigating effect of individualized occupational therapy interventions on mobility in children with cerebral palsy in the schools. Methods: This is a Single-blind randomized clinical trial, in which 18 children with cerebral palsy at ages 6-12 were selected using convenience method, and were divided into control and intervention groups using block randomization method. During the research period, the control group like before the research received the conventional occupational therapy interventions for 5 weeks and 12 sessions. The intervention group received occupational interventions based on the individualized appropriate goals. Both groups were assessed by School Function Assessment (SFA). Results: The average age of participants was 10.17 years old. Of these children, 52% used wheelchair .Difference in changes of mobility variables was significant between control and intervention group (P