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This chapter provides an overview on the traditions and values of teaching students with traumatic brain injury (TBI). First, we discuss the prevalence, identification, and characteristics associated with TBI and how those characteristics affect learning, behavior, and daily life functioning. Next, we focus on instructional and behavioral interventions used in maintaining the traditions in classrooms for working with students with TBI. Findings from a review of the literature conclude that there are no specific academic curriculums designed specifically for teaching students with TBI; however, direct instruction and strategy instruction have been shown to be effective educational interventions. Current research on students with TBI is predominately being conducted in medical centers and clinics focusing on area of impairments (e.g., memory, attention, processing speed) rather than academic achievement and classroom interventions. Finally, we conclude with a list of accommodations and a discussion of recommendations for future work in teaching students with TBI.

Untimely responses to emergency situations in urban areas contribute to a rising mortality rate and impact society's primary capital. The efficient dispatch and relocation of ambulances pose operational and momentary challenges, necessitating an optimal policy based on the system's real-time status. While previous studies have addressed these concerns, limited attention has been given to the optimal allocation of technicians to respond to emergency situation and minimize overall system costs.

In this paper, a bi-objective mathematical model is proposed to maximize system coverage and enable flexible movement across bases for location, dispatch and relocation of ambulances. Ambulances relocation involves two key decisions: (1) allocating ambulances to bases after completing services and (2) deciding to change the current ambulance location among existing bases to potentially improve response times to future emergencies. The model also considers the varying capabilities of technicians for proper allocation in emergency situations.

The Augmented Epsilon-Constrained (AEC) method is employed to solve the proposed model for small-sized problem. Due to the NP-Hardness of the model, the NSGA-II and MOPSO metaheuristic algorithms are utilized to obtain efficient solutions for large-sized problems. The findings demonstrate the superiority of the MOPSO algorithm.

This study can be useful for emergency medical centers and healthcare companies in providing more effective responses to emergency situations by sending technicians and ambulances.

In this study, a two-objective mathematical model is developed for ambulance location and dispatch and solved by using the AEC method as well as the NSGA-II and MOPSO metaheuristic algorithms. The mathematical model encompasses three primary types of decision-making: (1) Allocating ambulances to bases after completing their service, (2) deciding to relocate the current ambulance among existing bases to potentially enhance response times to future emergencies and (3) considering the diverse abilities of technicians for accurate allocation to emergency situations.

This chapter discusses what special education means for students with visual impairments (that is, those who are blind or have low vision) including what is being done and how traditions are maintained. More specifically, this chapter explores the importance of advancing values for the diverse population of students with visual impairments, focusing on cultivation of supportive, inclusive, and collaborative educational environments that continue to stand the test of time. This chapter highlights the increasing heterogeneity of this population of students and specific instructional strategies to support the cultural and linguistic diversity of learners with visual impairments in today's classrooms. This chapter also discusses the significance of promoting core concepts that are rooted in a traditional and specialized instructional framework for students who are visually impaired.

Every day, small and big incidents happen all over the world, and given the human, financial and spiritual damage they cause, proper planning should be sought to deal with them so they can be appropriately managed in times of crisis. This study aims to examine humanitarian supply chain models.

A new model is developed to pursue the necessary relations in an optimal way that will minimize human, financial and moral losses. In this developed model, in order to optimize the problem and minimize the amount of human and financial losses, the following subjects have been applied: magnitude of the areas in which an accident may occur as obtained by multiple attribute decision-making methods, the distances between relief centers, the number of available rescuers, the number of rescuers required and the risk level of each patient which is determined using previous data and machine learning (ML) algorithms.

For this purpose, a case study in the east of Tehran has been conducted. According to the results obtained from the algorithms, problem modeling and case study, the accuracy of the proposed model is evaluated very well.

Obtaining each injured person's priority using ML techniques and each area's importance or risk level, besides developing a bi-objective mathematical model and using multiple attribute decision-making methods, make this study unique among very few studies that concern ML in the humanitarian supply chain. Moreover, the findings validate the results and the model's functionality very well.

The study's objective was to address the factors impacting the employability attributes of fresh graduates in the Kingdom of Bahrain while considering the various challenges. This study used a quantitative approach which employed the questionnaire tool, and data were collected by using a convenience sampling method. The study sample comprised n = 385 respondents from different industries, including manufacturing, banking and finance, hospitality, healthcare, oil and gas, and real estate sectors of Bahrain. Data gathered from questionnaire were analyzed using Statistical Package for the Social Sciences (SPSS), where descriptive and inferential statistics was used to analyze the data. The results of the study showed for the major Hypothesis 1 that the demographic variables have no significant statistical impact on employment attributes of the new fresh graduates. Moreover, findings suggest that null hypothesis for major Hypothesis 2 has been rejected as applied academic skills and critical thinking skills have no significant impact on employability attribute of fresh graduates in the Kingdom of Bahrain. Null hypothesis for major Hypothesis 3 has been accepted as findings suggest that technology use skills (β ₁ = 0.080), system thinking skills (β ₂ = 0.210), communication skills (β ₃ = 0.402), and information skills (β ₄ = −0.100) which are an antecedent of workplace skills, have significant statistical impact on employability attribute of fresh graduates in Kingdom of Bahrain. Lastly, null hypothesis for major Hypothesis 4 has been accepted as findings suggest that interpersonal skills (β ₅ = 0.229) which are an antecedent of effective relationship have significant statistical impact on employability attribute of fresh graduates in the Kingdom of Bahrain.

The various quality improvement (QI) frameworks and maturity models described in the health services literature consider some aspects of QI while excluding others. This paper aims to present a concerted attempt to create a quality improvement maturity model (QIMM) derived from holistic principles underlying the successful implementation of system-wide QI programmes.

A hybrid methodology involving a systematic review (Phase 1) of over 270 empirical research articles and books developed the basis for the proposed QIMM. It was followed by expert interviews to refine the core constructs and ground the proposed QIMM in contemporary QI practice (Phase 2). The experts included academics in two academic conferences and 59 QI managers from the New Zealand health-care system. In-depth interviews were conducted with QI managers to ascertain their views on the QIMM and its applicability in their respective health organisations (HOs).

The QIMM consists of four dimensions of organisational maturity, namely, strategic, process, supply chain and philosophical maturity. These dimensions progress through six stages, namely, identification, ad-hoc, formal, process-driven, optimised enterprise and finally a way of life. The application of the QIMM by the QI managers revealed that the scope of QI and the breadth of the principles adopted by the QI managers and their HOs in New Zealand is limited.

The importance of QI in health systems cannot be overstated. The proposed QIMM can help HOs diagnose their current state and provide a guide to action achieving a desirable state of quality improvement maturity. This QIMM avoids reliance on any single QI methodology. HOs – using the QIMM – should retain full control over the process of selecting any QI methodology or may even cherry-pick principles to suit their needs as long as they understand and appreciate the true nature and scope of quality overstated. The proposed QIMM can help HOs diagnose their current state and provide a guide to action achieving a desirable state of quality improvement maturity. This QIMM avoids reliance on any single QI methodology. HOs – using the QIMM – should retain full control over the process of selecting any QI methodology or may even cherry-pick principles to suit their needs as long as they understand and appreciate the true nature and scope of quality.

This paper contributes new knowledge by presenting a maturity model with an integrated set of quality principles for HOs and their extended supply networks.

The purpose of the current study is to assess Omani teachers’ performance on tasks related to the stages of engineering design. To achieve this, data from an engineering design test was used, and demographic variables that are correlated with this performance were identified.

This descriptive study employed a cross-sectional design and the collection of quantitative data. A sample of preservice science teachers from Sultan Qaboos University (SQU) (n = 70) participated in this study.

Findings showed low and moderate levels of proficiency related to the stages of engineering design. Differences between males and females in terms of performance on engineering design tasks were found, with females scoring higher overall on the assessment. Biology preservice teachers scored higher than teachers from the other two majors (physics and chemistry) in two subscales. There were also differences between teachers studying in the Bachelor of Science (BSc) program and the teacher qualification diploma (TQD) program.

This study provides an overview, in an Arab setting, of preservice science teachers’ proficiency with engineering design process (EDP) tasks. It is hoped that the results may lead to improved instruction in science teacher training programs in similar contexts. Additionally, this research demonstrates how EDP competency relates to preservice teacher gender, major and preparation program. Findings from this study will contribute to the growing body of research investigating the strengths and shortcomings of teacher education programs in relation to science, technology, engineering and mathematics (STEM) education.

Rebar is the prime component of reinforced concrete structures, and rebar monitoring is a time-consuming and technical job. With the emergence of the fourth industrial revolution, the construction industry practices have evolved toward digitalization. Still, hesitation remains among stakeholders toward the adoption of advanced technologies and one of the significant reasons is the unavailability of knowledge frameworks and implementation guidelines. This study aims to investigate technical factors impacting automated monitoring of rebar for the understanding, confidence gain and effective implementation by construction industry stakeholders.

A structured study pipeline has been adopted, which includes a systematic literature collection, semistructured interviews, pilot survey, questionnaire survey and statistical analyses via merging two techniques, i.e. structural equation modeling and relative importance index.

The achieved model highlights “digital images” and “scanning” as two main categories being adopted for automated rebar monitoring. Moreover, “external influence”, “data-capturing”, “image quality”, and “environment” have been identified as the main factors under “digital images”. On the other hand, “object distance”, “rebar shape”, “occlusion” and “rebar spacing” have been highlighted as the main contributing factors under “scanning”.

The study provides a base guideline for the construction industry stakeholders to gain confidence in automated monitoring of rebar via vision-based technologies and effective implementation of the progress-monitoring processes. This study, via structured data collection, performed qualitative and quantitative analyses to investigate technical factors for effective rebar monitoring via vision-based technologies in the form of a mathematical model.

The purpose of this study is to look at the factors that drive people to participate in the sharing economy (SE). Based on the Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB), the study proposes a mathematical model. The study’s ultimate objective is to help businesses attract more involved customers and promote collaborative consumption as a sustainable alternative to typical consumption patterns. The study offers a conceptual framework established via a thorough literature review to examine Indian customers’ use behavior toward SE platforms. A one-sample two-tailed t-test is used to assess the framework’s efficacy. The research fills gap in the literature on the SE by investigating the factors that determine subjective norms (SN), attitudes (A), and perceived behavioral control (PBC). A framework is provided that takes behavioral intention (BI) contemplated as a mediating variable. The research improves TAM and TPB by including new factors such as technical characteristics. This research adds to the body of knowledge on the digital SE by underlining the relevance of usage behavior in comprehending Indian customers, where A, SN, and PBC are important aspects. The research presents a paradigm for better understanding customers’ attitudes and behaviors toward various SE platforms, which might help academics, practitioners, and policy makers situate their initiatives within the larger field of sharing. The study’s categorizations of Indian consumers’ A, SN, PBC, and BI toward the SE might potentially advise on future research and government policies.