Evidence for the Reliability and Validity of the Arabic Version of the Student Risk Screening Scale for Internalizing and Externalizing Behaviors (SRSS-IE)

School-based universal screening for behavioral/emotional risk is a necessary first step to providing services in an educational setting for students with emotional and behavioral disorders (EBDs). Psychometric properties are critical to making decisions about choosing a screening instrument. The purpose of the present study was to examine the psychometric properties of the student risk screening scale for internalizing and externalizing behaviors (SRSS-IE). Participants included 3145 students and their teachers. Item-level analyses of the current sample supported the retention of all items. The internal consistency of the SRSS items ranged from 0.83 to 0.85. Convergent validity between the SRSS-IE and a well-established screening tool, the strength and difficulties questionnaire (SDQ), was found for the total score (r = 0.70). Additionally, the results of this study demonstrate strong social validity, suggesting the SRSS-IE to be a useful and functional screening tool. We conclude that the SRSS-IE is a valid and reliable instrument for assessing the level of emotional and behavioral difficulties among elementary students.

Video-Based Interventions for Adolescents and Young Adults with Autism Spectrum Disorder:A Systematic

Many individuals with autism spectrum disorder(ASD)experience delays in the development of social and communications skills,which can limit their opportunities in higher education and employment resulting in an overall negative impact to their quality of life.This systematic review identifies 15 studies that explored the effectiveness of Video-Based Interventions(VBIs)for those with ASD during the critical years of adolescence and young adulthood.The 15 studies described herein found this to be an effective intervention for this population for the improvement of their vocational,daily living,and academic skills.In addition,VBIs allow for the maintenance and generalization of the different target behaviors that were examined.The majority of the studies located by this review also investigated the social validity of the intervention method with participants and caregivers and found these VBIs to have high social validity.Although a few studies that implemented VBIs to improve academic skills were located,the research on their use in this area was found to be lacking,indicating a gap in the research on VBIs.Increased usage of VBIs—including video modeling and video prompting—with the target population of those aged 15–28 with ASD is recommended with specific attention given to the use of VBIs to improve the academic and social skills of adolescents and young adults with ASD.

Deep Transfer Learning Driven Automated Fall Detection for Quality of Living of Disabled Persons

Mobile communication and the Internet of Things(IoT)technologies have recently been established to collect data from human beings and the environment.The data collected can be leveraged to provide intelligent services through different applications.It is an extreme challenge to monitor disabled people from remote locations.It is because day-to-day events like falls heavily result in accidents.For a person with disabilities,a fall event is an important cause of mortality and post-traumatic complications.Therefore,detecting the fall events of disabled persons in smart homes at early stages is essential to provide the necessary support and increase their survival rate.The current study introduces a Whale Optimization Algorithm Deep Transfer Learning-DrivenAutomated Fall Detection(WOADTL-AFD)technique to improve the Quality of Life for persons with disabilities.The primary aim of the presented WOADTL-AFD technique is to identify and classify the fall events to help disabled individuals.To attain this,the proposed WOADTL-AFDmodel initially uses amodified SqueezeNet feature extractor which proficiently extracts the feature vectors.In addition,the WOADTLAFD technique classifies the fall events using an extreme Gradient Boosting(XGBoost)classifier.In the presented WOADTL-AFD technique,the WOA approach is used to fine-tune the hyperparameters involved in the modified SqueezeNet model.The proposedWOADTL-AFD technique was experimentally validated using the benchmark datasets,and the results confirmed the superior performance of the proposedWOADTL-AFD method compared to other recent approaches.

IoT-Driven Optimal Lightweight RetinaNet-Based Object Detection for Visually Impaired People

Visual impairment is one of the major problems among people of all age groups across the globe.Visually Impaired Persons(VIPs)require help from others to carry out their day-to-day tasks.Since they experience several problems in their daily lives,technical intervention can help them resolve the challenges.In this background,an automatic object detection tool is the need of the hour to empower VIPs with safe navigation.The recent advances in the Internet of Things(IoT)and Deep Learning(DL)techniques make it possible.The current study proposes IoT-assisted Transient Search Optimization with a Lightweight RetinaNetbased object detection(TSOLWR-ODVIP)model to help VIPs.The primary aim of the presented TSOLWR-ODVIP technique is to identify different objects surrounding VIPs and to convey the information via audio message to them.For data acquisition,IoT devices are used in this study.Then,the Lightweight RetinaNet(LWR)model is applied to detect objects accurately.Next,the TSO algorithm is employed for fine-tuning the hyperparameters involved in the LWR model.Finally,the Long Short-Term Memory(LSTM)model is exploited for classifying objects.The performance of the proposed TSOLWR-ODVIP technique was evaluated using a set of objects,and the results were examined under distinct aspects.The comparison study outcomes confirmed that the TSOLWR-ODVIP model could effectually detect and classify the objects,enhancing the quality of life of VIPs.

Deep Learning Driven Arabic Text to Speech Synthesizer for Visually Challenged People

Text-To-Speech(TTS)is a speech processing tool that is highly helpful for visually-challenged people.The TTS tool is applied to transform the texts into human-like sounds.However,it is highly challenging to accomplish the TTS out-comes for the non-diacritized text of the Arabic language since it has multiple unique features and rules.Some special characters like gemination and diacritic signs that correspondingly indicate consonant doubling and short vowels greatly impact the precise pronunciation of the Arabic language.But,such signs are not frequently used in the texts written in the Arabic language since its speakers and readers can guess them from the context itself.In this background,the current research article introduces an Optimal Deep Learning-driven Arab Text-to-Speech Synthesizer(ODLD-ATSS)model to help the visually-challenged people in the Kingdom of Saudi Arabia.The prime aim of the presented ODLD-ATSS model is to convert the text into speech signals for visually-challenged people.To attain this,the presented ODLD-ATSS model initially designs a Gated Recurrent Unit(GRU)-based prediction model for diacritic and gemination signs.Besides,the Buckwalter code is utilized to capture,store and display the Arabic texts.To improve the TSS performance of the GRU method,the Aquila Optimization Algorithm(AOA)is used,which shows the novelty of the work.To illustrate the enhanced performance of the proposed ODLD-ATSS model,further experi-mental analyses were conducted.The proposed model achieved a maximum accu-racy of 96.35%,and the experimental outcomes infer the improved performance of the proposed ODLD-ATSS model over other DL-based TSS models.

Embodying Deeply Held Values in Education:Seeking a More Equitable World for Both Humans and Non-Humans

Highlights In this article,we contend that the bedrock of an equitable world lies in the profound recognition of love as the fundamental force permeating the cosmos.We believe that love is built into the essence of who we are.We posit that genuine progress toward an equitable world is elusive unless we place love,both for one another and for the natural world,at the core of our educational endeavors.

A Deep Learning-Based Recognition Approach for the Conversion of Multilingual Braille Images

Braille-assistive technologies have helped blind people to write,read,learn,and communicate with sighted individuals for many years.These technologies enable blind people to engage with society and help break down communication barriers in their lives.The Optical Braille Recognition(OBR)system is one example of these technologies.It plays an important role in facilitating communication between sighted and blind people and assists sighted individuals in the reading and understanding of the documents of Braille cells.However,a clear gap exists in current OBR systems regarding asymmetric multilingual conversion of Braille documents.Few systems allow sighted people to read and understand Braille documents for self-learning applications.In this study,we propose a deep learning-based approach to convert Braille images into multilingual texts.This is achieved through a set of effective steps that start with image acquisition and preprocessing and end with a Braille multilingual mapping step.We develop a deep convolutional neural network(DCNN)model that takes its inputs from the second step of the approach for recognizing Braille cells.Several experiments are conducted on two datasets of Braille images to evaluate the performance of the DCNN model.The rst dataset contains 1,404 labeled images of 27 Braille symbols representing the alphabet characters.The second dataset consists of 5,420 labeled images of 37 Braille symbols that represent alphabet characters,numbers,and punctuation.The proposed model achieved a classication accuracy of 99.28%on the test set of the rst dataset and 98.99%on the test set of the second dataset.These results conrm the applicability of the DCNN model used in our proposed approach for multilingual Braille conversion in communicating with sighted people.

Effect of vision loss on plasticity of the head and neck proprioception

AIM:To investigate whether head and neck proprioception and motor control could be compensatory enhanced by long-term vision loss or impairment.METHODS:Individuals who were blind,low vision or sighted were included in the study,which would undergo the head repositioning test(HRT).The constant error(CE),absolute error(AE),variable error(VE)and root mean square error(RMSE)of each subject were statistically analyzed.Data were analyzed using the SAS 9.4.Tukey-Kramer for one-way ANOVA was used for comparison of blind,low vision,and sighted subjects,as well as to compare subjects with balanced vision,strong vision in the left eye and strong vision in the right eye.Independent sample t-test was used to compare subjects with congenital blindness and acquired blindness,as well as left and right hand dominance subjects.RESULTS:A total of 90 individuals(25 blind subjects,31 low vision subjects,and 34 sighted subjects)were included in the study.Among the blind subjects,14 cases had congenital blindness and 11 cases had acquired blindness.Among the blind and low vision subjects,21 cases had balanced binocular vision,17 cases had strong vision in the left eye and 18 cases had strong vision in the right eye.Among all subjects,11 cases were left hand dominance,and 79 cases were right hand dominance.There were significant differences in AE,VE,and RMSE in head rotation between blind,low vision,and sighted subjects(P<0.01),in AE,VE,and RMSE between blind and sighted(P<0.01),and in VE and RMSE between low vision and sighted(P<0.05).No significant difference between blind and low vision(P>0.05).Significant differences in CE and AE of head right rotation and CE of general head rotation between congenital and acquired(P<0.05).No significant differences between left and right hand dominance and in balance or not of binocular vision(P>0.05).CONCLUSION:Long-term vision loss or impairment does not lead to compensatory enhancement of head and neck proprioception and motor control.Acquired experience contributes to HRT performance in the blind and has long-lasting effects on plasticity in the development of proprioception and sensorimotor control.

An Exploratory Investigation of Difficulties in Applying Functional Behavior Assessment and Implementing Behavioral Intervention Plans in ADHD Programs in Saudi Arabia

Functional behavior assessment(FBA)and behavioral intervention plans(BIPs)can be effective for students with attention deficit hyperactivity disorder(ADHD);however,teachers may face difficulties when implementing FBA procedures and,in turn,BIPs because of lack of time,insufficient training,and multiplicity of beliefs.Thus,it is important to identify the difficulties teachers may face and the obstacles that can deter them from implementing intervention plans.This is a worthwhile endeavor because nearly all classrooms will have students with behavioral problems who will benefit from specifically designed educational interventions.This study aimed to identify the difficulties in applying FBA and the barriers in implementing BIPs among Saudi teachers in specialized ADHD programs.Using the descriptive-analytical approach,a questionnaire targeting difficulties and barriers was admi-nistered to 209 public school teachers from specialized ADHD programs in Saudi Arabia.The results revealed that the difficulty level of applying FBA in ADHD programs was high.The level of barriers in implementing BIPs was moderate.Finally,the study indicated that barriers in implementing BIPs could be predicted by the difficulties of applying FBA.The results suggest the importance offinding better practices,restructuring the provision of ADHD programs,training teachers,improving assessment tool availability,and ensuring cooperation between school administrators and teachers.

Study on the Cooperative E-commerce Model between Enterprises based on the Value Chain

The real e-commerce between enterprises is based on the internal departments of enterprises and the cooperative interaction between enterprise and its partners. In this paper, on the basis of the theory of value chain, 11 cooperative e-commerce models between enterprises have been classified according to the activities of the cooperation between enterprises, and then every cooperative e-commerce model between enterprises is discussed. In practice, cooperative e-commerce between enterprises can be a combination of one or more e-commerce models between enterprises.

Neuromorphic transcutaneous electrical nerve stimulation(nTENS)induces efficient tactile-related cortical networks in forearm amputees

Conventional transcutaneous electrical nerve stimulation(cTENS),which uses a modulated square waveform as stimuli,has been generally used in testing and eliciting artificial tactile perception in forearm amputees.However,a novel neuromorphic TENS(nTENS)model based on neural signals has been largely ignored.In this study,we further explore the effect of nTENS patterns to elicit tactile perception in forearm amputees.Four forearm amputees were recruited to test discriminate tactile perception elicited by different TENS patterns with electroencephalography(EEG)recording at the following four stimulated sites:the index finger and the little finger on both phantom and real sides.Finally,we compared the results of cortical networks in six frequency bands at different stimulated sites between forearm amputees and able-bodied subjects.Behavioral results suggested that n TENS patterns required a lower electric charge at each stimulated site than cTENS patterns.And forearm amputees required a higher intensity in each TENS pattern than able-bodied subjects.Moreover,amputees showed a lower clustering coefficient(aCP),global efficiency(aEG),local efficiency(aEL),and a longer path length(aLP)than able-bodied subjects in all six frequency bands when stimulation was accessed.Specifically,the SMU pattern showed a higher functional network efficiency in real fingers than at phantom sites in theta,alpha,and high gamma bands.This study highlighted the characteristics of n TENS patterns in eliciting tactile perception among forearm amputees,which provided insights into evaluating the neural mechanism of tactile information processing in forearm amputees and building tactile perceptual systems for sensory rehabilitation.

A Multilevel Analysis of Asian Immigrant Children’s Reading Achievement in the Early Years:Evidence from the ECLS-K Data

This study investigates 1.5 generation1 Asian immigrant children’s(n=264)early literacy achievement patterns,treating them as a heterogeneous group.Specifically,the within-group variances in reading achievement from kindergarten to third grade are examined,drawing on four waves of data from the ECLS-K class of 1998-1999.Our analysis shows that ethnicity plays a role in shaping the children’s initial reading readiness and later growth,but the effects of languages spoken at home are not significant.Our analysis also demonstrates a persistent achievement gap between low-and high-socioeconomic status(SES)Asian groups.However,gender difference in terms of children’s reading development is not found to be significant.There is also no interaction between SES and the other factors such as gender,ethnicity and language backgrounds.