Sufficiency of the BOT-2 short form to screen motor competency in preschool children with strabismus

Background Strabismus is one of the most common visual disorders in children, with a reported prevalence of 2.48% in preschoolers. Additionally, up to 89.9% of preschool children with strabismus do not have normal stereopsis. Whether this lack of normal stereopsis affects the motor competency of preschool children with strabismus is unknown. The Bruininks-Oseretsky Test of Motor Proficiency Second Edition short form (BOT-2 SF) can be a useful tool for screening; however, its sufficiency as a diagnostic tool for children with various disorders is controversial. Objective The aims of this study were thus to examine motor competency in preschool children with strabismus by using the BOT-2 and to evaluate the usefulness of the BOT-2 SF to identify those at risk for motor competency issues. Methods Forty preschool children (aged 5–7 years) with strabismus were recruited, all of whom had abnormal stereopsis. The BOT-2 complete form (CF) was administered to all children. The BOT-2 CF was administered to all children. The scores of the BOT-2 SF were extracted from the relevant items of the BOT-2 CF for further analysis. Results The prevalence of children with strabismus who had below average performance in the composites of “Fine Manual Control”, “Manual Coordination”,”Body Coordination”, and “Strength and Agility” were 15%, 70%, 32.5%, and 5%, respectively, on the BOT-2 CF. Compared with these results, the sensitivity of the BOT-2 SF was 33.33% (95% CI = 7.49%–70.07%) and the specificity was 100% (95% CI = 88.78%–100%). Conclusion Preschool children with strabismus had a high prevalence of impaired motor competency, especially in fine motor competency. The BOT-2 SF was not as sensitive in identifying motor difficulties in preschool children with strabismus. Therefore, the BOT-2 CF is recommended for evaluating motor proficiency in preschool children with strabismus.

Real-Time Detection of Freezing Motions in Parkinson's Patients for Adaptive Gait Phase Synchronous Cueing

Parkinson's disease is the second most common neurodegenerative disease worldwide reducing cognitive and motoric abilities of affected persons. Freezing of Gait (FoG) is one of the severe symptoms that is observed in the late stages of the disease and considerably impairs the mobility of the person and raises the risk of falls. Due to the pathology and heterogeneity of the Parkinsonian gait cycle, especially in the case of freezing episodes, the detection of the gait phases with wearables is challenging in Parkinson's disease. This is addressed by introducing a state-automaton-based algorithm for the detection of the foot's motion phases using a shoe-placed inertial sensor. Machine-learning-based methods are investigated to classify the actual motion phase as normal or FoG-affected and to predict the outcome for the next motion phase. For this purpose, spatio-temporal gait and signal parameters are determined from the segmented movement phases. In this context, inertial sensor fusion is applied to the foot's 3D acceleration and rate of turn. Support Vector Machine (SVM) and AdaBoost classifiers have been trained on the data of 16 Parkinson's patients who had shown FoG episodes during a clinical freezing-provoking assessment course. Two clinical experts rated the video-recorded trials and marked episodes with festination, shank trembling, shuffling, or akinesia. Motion phases inside such episodes were labeled as FoG-affected. The classifiers were evaluated using leave-one-patient-out cross-validation. No statistically significant differences could be observed between the different classifiers for FoG detection (p>0.05). An SVM model with 10 features of the actual and two preceding motion phases achieved the highest average performance with 88.5 ± 5.8% sensitivity, 83.3 ± 17.1% specificity, and 92.8 ± 5.9% Area Under the Curve (AUC). The performance of predicting the behavior of the next motion phase was significantly lower compared to the detection classifiers. No statistically significant differences were found between all prediction models. An SVM-predictor with features from the two preceding motion phases had with 81.6 ± 7.7% sensitivity, 70.3 ± 18.4% specificity, and 82.8 ± 7.1% AUC the best average performance. The developed methods enable motion-phase-based FoG detection and prediction and can be utilized for closed-loop systems that provide on-demand gait-phase-synchronous cueing to mitigate FoG symptoms and to prevent complete motoric blockades.

Effects of the COVID-19 Pandemic Outbreak on the Performance of Sign Language Interpreters

In general, stress affects the efficiency of workers’ performance. With the coronavirus disease 2019 pandemic outbreak, sign language interpreters experience increased stress due to various factors. This study aims to determine the stressors faced by sign language interpreters during the pandemic. To achieve this goal, we prepared a questionnaire consisting of 15 paragraphs covering psychological, health, cognitive, linguistic and environmental aspects. Then, we surveyed 57 sign language interpreters in the Arab region. In the analysis, we calculated the average performance levels in addition to the differences between participants’ average scores. We also divided the stress levels into three categories: simple, moderate and severe. Results indicated that the stress was medium on average and no statistically significant differences in the performance average in accordance with the study variables (gender, experience and workplace).

Reduction of Energy Consumption for Tire Test Room

This article presents a software-implemented 3-dimensional simulated analysis of a 4-tire test room and the 6-tire test room. The results of the average performance through the simulated analysis of 100 iterations were obtained. The simulation showed the temperature distribution in the test rooms. This objective of this study was to assess the efficiency of the start-up process in each test room and to find the most efficient setup. A promising improvement would be to install the heaters at the bottom of the room under the drums instead of at the ceiling. The results of the simulation will be compared to the data of temperature logging of the tire test rooms once there is availability upon the lifting of the Covid pandemic lockdown restrictions.

Performance of early literacy students in cognitive-linguistic skills during the pandemic

Introduction: The Covid-19 pandemic made discrepancies between the different educational realities more evident for schoolchildren in the beginning of literacy. Objective: to characterize the performance of cognitive-linguistic skills of students in early literacy phases during the pandemic. Methods: Twenty-two elementary school students participated in this study, distributed in GI 1st year students and 2nd year GII students, submitted to the application of the Cognitive-Linguistic Skills Assessment Protocol for students in the initial stage of literacy. Results: Students from GI and GII showed average performance for writing the name and writing the alphabet in sequence. The GI presented a refusal response for the subtests of word dictation, pseudoword dictation and picture dictation, word repetition and visual sequential memory of shapes and poor performance for alphabet recognition in random order and average performance for alphabet recognition in sequence. GII showed lower performance for the subtests of word dictation, pseudoword dictation, picture dictation and superior performance for alphabet recognition in random order, alphabet in sequence and visual sequential memory of shapes. Discussion: The appropriation of the letter-sound relationship mechanism raises questions, since it evidenced the difficulty of all students in cognitive-linguistic skills necessary for the full development of reading and writing in an alphabetic writing system such as Brazilian Portuguese . Conclusion: Students in the 1st and 2nd years showed lower performance in cognitive-linguistic skills important for learning reading and writing.

Greedy Matching in Bipartite Random Graphs

This paper studies the performance of greedy matching algorithms on bipartite graphs [Formula: see text]. We focus primarily on three classical algorithms: [Formula: see text], which sequentially selects random edges from [Formula: see text]; [Formula: see text], which sequentially matches random vertices in [Formula: see text] to random neighbors; and [Formula: see text], which generates a random priority order over vertices in [Formula: see text] and then sequentially matches random vertices in [Formula: see text] to their highest-priority remaining neighbor. Prior work has focused on identifying the worst-case approximation ratio for each algorithm. This guarantee is highest for [Formula: see text] and lowest for [Formula: see text]. Our work instead studies the average performance of these algorithms when the edge set [Formula: see text] is random. Our first result compares [Formula: see text] and [Formula: see text] and shows that on average, [Formula: see text] produces more matches. This result holds for finite graphs (in contrast to previous asymptotic results) and also applies to “many to one” matching in which each vertex in [Formula: see text] can match with multiple vertices in [Formula: see text]. Our second result compares [Formula: see text] and [Formula: see text] and shows that the better worst-case guarantee of [Formula: see text] does not translate into better average performance. In “one to one” settings where each vertex in [Formula: see text] can match with only one vertex in [Formula: see text], the algorithms result in the same number of matches. When each vertex in [Formula: see text] can match with two vertices in [Formula: see text] produces more matches than [Formula: see text].

Homogeneity of the photocathode in the Hamamatsu Photomultiplier Tube

It is common practice to test the optical properties of photomultiplier tubes (PMTs) by illuminating the entire photocathode region from the front at once and measuring the average performance. However, for optimal utilisation of the PMT performance in experiments, especially in the single-photon region, it is essential to also know the systematic variations across the photocathode, which requires measurements with focused light sources that illuminate only small regions of the PMT. We present a detailed uniformity characterisation of the gain, transit time, transit time spread, and pulse shape of the 80 mm Hamamatsu R15458-02 PMT. We find that the parameters exhibit asymmetry along one axis, likely caused by the position and geometry of the dynode system. For all parameters except the transit time, the observed variations are small given the intrinsic variation of the parameters. For positions with shifted transit time we observe on average underamplified pulses which can potentially be exploited to improve the pulse reconstruction.

Performance evaluation and ranking of regional primary health care and public health Systems in Iran

Background The present study has been undertaken with the aim to evaluate performance and ranking of various universities of medical sciences that are responsible for providing public health services and primary health care in Iran. Methods Four models; Weighted Factor Analysis (WFA), Equal Weighting (EW), Stochastic Frontier Analysis (SFA), and Data Envelopment Analysis (DEA) have been applied for evaluating the performance of universities of medical sciences. This study was commenced based on the statistical reports of the Ministry of Health and Medical Education (MOHME), census data from the Statistical Center of Iran, indicators of Vital Statistics, results of Multiple Indicator of Demographic and Health Survey 2010, and results of the National Survey of Risk Factors of non-communicable diseases. Results The average performance scores in WFA, EW, SFA, and DEA methods for the universities were 0.611, 0.663, 0.736 and 0.838, respectively. In all 4 models, the performance scores of universities were different (range from 0.56–1, 0.53–1, 0.73–1 and 0.83–1 in WFA, EW, SFA and DEA models, respectively). Gilan and Rafsanjan universities with the average ranking score of 4.75 and 41 had the highest and lowest rank among universities, respectively. The universities of Gilan, Ardabil and Bojnourd in all four models had the highest performance among the top 15 universities, while the universities of Rafsanjan, Ahvaz, Kerman and Jiroft showed poor performance in all models. Conclusions The average performance scores have varied based on different measurement methods, so judging the performance of universities based solely on the results of a model can be misleading. In all models, the performance of universities has been different, which indicates the need for planning to balance the performance improvement of universities based on learning from the experiences of well-performing universities.

A Robust Mathematical Model for Sustainable and Resilient Supply Chain Network Design: Preparing a Supply Chain to Deal with Disruptions

Supply chains suffer from serious vulnerabilities and disruptions with increasing global crises, including pandemics and natural disasters. Dynamic and complex supply chain environments have constantly led companies to modern management approaches such as resilience to address disruptions. Besides, the sustainability approach enhances the strength of the supply chain in disruptions by considering economic, social, and environmental aspects. This paper develops a mathematical model for designing a supply chain network considering resilience and sustainability. In this model, suppliers were exposed to disruption with different probabilities. The model has three objectives: minimizing total costs and maximizing suppliers’ social and environmental scores. A robust scenario-based stochastic programming approach has been used for potential disruption scenarios. The multiobjective model is solved by the ε -constraint method in GAMS software. The numerical results show the performance of the model in a different situation. Also, the robust scenario-based stochastic programming approach allows the average performance of the supply chain in each objective to improve.

Data Optimization in IoT-Assisted Sensor Networks on Cloud Platform

This article presents a new scheme for data optimization in IoT assister sensor networks. The various components of IoT assisted cloud platform are discussed. In addition, a new architecture for IoT assisted sensor networks is presented. Further, a model for data optimization in IoT assisted sensor networks is proposed. A novel Membership inducing Dynamic Data Optimization (MIDDO) algorithm for IoT assisted sensor network is proposed in this research. The proposed algorithm considers every node data and utilized membership function for the optimized data allocation. The proposed framework is compared with two stage optimization, dynamic stochastic optimization and sparsity inducing optimization and evaluated in terms of performance ratio, reliability ratio, coverage ratio and sensing error. It was inferred that the proposed MIDDO algorithm achieves an average performance ratio of 76.55%, reliability ratio of 94.74%, coverage ratio of 85.75% and sensing error of 0.154.