Online learning and technology acceptance has become a highly significant subject in the field of information technology. The challenges of eLearning acceptance and adoption in higher education are complex and multifaceted: it is important to carefully consider the environmental, social, and economic implications of eLearning implementation and to work toward ensuring that eLearning programs are accessible, equitable, and sustainable over the long term. Many theories and models have been proposed over the years to explain individual usage and behavior and measure the degree of acceptance and satisfaction toward technology acceptance and online learning. This study reviews the challenges and limitations of online learning acceptance and adoption for the last ten years (2012–2022). Lack of technical support, awareness, institution readiness, quality online course content, and less information technology skill of faculty members in the early years present challenges. Further, self-efficacy, financial and technology factors, pedagogical learning, socio-economic evolution, digital competence and compatibility, and lack of technological infrastructure have significantly affected the adoption of eLearning in higher education institutions in recent years.

The urgent penetration of the Industrial Internet of Things (IIoT) in industrial sectors has fostered unmatched benefits in terms of efficiency, productivity, and overall performance. Although current security frameworks already offer a sound level of protection, they do not adequately scale to the growing diversity and number of devices integrated into the Industrial Internet of Things. They rely on static, centralized network organization and management, which cannot ensure data integrity across a huge number of highly distributed devices. The proposed research provides insight into an innovative end-to-end security solution model, which caters to the needs of IIoT via the introduction of the blockchain. The current security solutions seem to fail in terms of security, scalability, data efficiency, integrity, and the actual needs of IIoT. As a result, the top chance of a cyberattack or similar threat remains high. The proposed work demonstrates that IIoTs may benefit from blockchain frameworks because blockchains reuse existing security mechanisms, enable transaction validation without central trust, and implement appropriate authorization. Additionally, the IIoT environment can benefit from implementing security frameworks that reflect their characteristics. In this paper, an overall solution that employed the decentralized setup of blockchain was proposed for authentication, authorization, and data integrity for all devices. Our approach is free from the need for any central authority. That mechanism is based on smart contracts that enforce security policies. Our framework allows for the switching of any access rule at any time, as well as it can immediately react to some suspicious behavior. In our paper, we proposed some lightweight cryptographic schemes, and Data management overhead which could fit any device. Moreover, our algorithm for the blockchain implementation is hybrid because this approach incorporates all the best features of the private and public implementation due to the individual blocks containing feasible information rather than the entire history.

Holographic Multiple-Input Multiple-Output (HMIMO) is becoming a major enabling technology for next-generation IoT-enabled consumer devices and future 6G communications. For accurate electromagnetic (EM) wave manipulation, HMIMO uses densely packed antenna elements across a fixed physical aperture, creating an electromagnetically confined surface. This is in contrast to typical massive MIMO. For small, power-constrained consumer IoT devices, this architecture’s disruptive potential includes higher spatial resolution, extremely directional beamforming, and increased energy economy. With inter-element spacing less than half the wavelength, HMIMO arrays’ ultra-dense nature introduces substantial electromagnetic coupling that defies accepted channel assumptions based on independently dispersed and identically distributed models. The design and implementation of HMIMO-based IoT systems thus face a major challenge: realistic and computationally efficient channel modeling. We investigate four sophisticated channel modeling approaches based on electromagnetic field theory in order to address this. The first offers a precise but computationally demanding approach to modeling point-to-surface communication and is based on planar Green’s function. Particularly for far-field and near-field scenarios, the second and third methods offer trade-offs between complexity and modeling quality by using plane wave and spherical wave expansions, respectively. The last technique makes use of stochastic Green’s function to represent the unpredictability present in Rayleigh or rich-scattering situations. These models open the door for the incorporation of holographic communication into consumer devices for the Internet of Things, allowing for intelligent, context-aware, and high-capacity connectivity. Future studies must investigate how to integrate these models with AI-driven control and adaptive beamforming methods, as well as better improve them for real-world implementation.

The study titled " Adopting the UTAUT model to understand academic use of emerging technologies among Moroccan nursing students" addresses a pertinent issue in the context of the COVID-19 pandemic and its impact on education. The research employs the...

Background: Healthcare systems strive to provide high-quality services while optimizing resource utilization and cost-effectiveness. Achieving these goals necessitates using scientific tools or methodologies to assess costs before implementing cost-reduction measures. Diagnosis related group DRGs) which is also called case-mix is a costing method in health. This study focused on a secondary hospital located in the Jazan region of Saudi Arabia. Our goal was to conduct a patient-level healthcare service cost analysis using the Case-mix system. The purpose of this current study was to identify the cost of DRG for each inpatient and compare it with previous price of services of inpatients. Materials & Methods: To achieve this, we conducted a retrospective cross-sectional analysis, classifying healthcare service costs they incurred throughout the year 2018 for inpatients diagnosis and methods. Results: Our findings revealed a notable disparity between DRG-based costing, amounting to SAR 138,147,926, and the average costing, which stood at SAR 118,799,359. The DRG-based costing method demonstrated excellent reliability and better representation of patient services. As a result, we recommend its use for reimbursement purposes.