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Primary hyperparathyroidism has emerged as a prevalent endocrine disorder in clinical settings, necessitating in most cases, surgical intervention for the removal of the diseased gland. This condition is characterised by overactivity of the parathyroid glands, resulting in excessive parathyroid hormone production and subsequent disturbances in calcium homeostasis. The primary mode of management is surgical treatment, relying on the accurate localisation of the pathological parathyroid gland. Precise identification is paramount to ensuring that the surgical intervention effectively targets and removes the diseased gland, alleviating the hyperfunctioning state. However, localising the gland becomes challenging, as discrepancies between the clinical manifestation of active parathyroid and radiological identification are common. Based on our current knowledge, to date, no comprehensive review has been conducted that considers all factors collectively. This comprehensive review delves into the factors contributing to false-negative 99mTc-Sestamibi scans. Our research involved an exhaustive search in the PubMed database for hyperparathyroidism, with the identified literature meticulously filtered and reviewed by the authors. The results highlighted various factors, including multiple parathyroid diseases, nodular goitre, mild disease, or the presence of an ectopic gland that causes discordance. Hence, a thorough consideration of these factors is crucial during the diagnostic workup of hyperparathyroidism. Employing intraoperative PTH assays can significantly contribute to a successful cure of the disease, thereby providing a more comprehensive approach to managing this prevalent endocrine disorder.

The exponential growth of digital information, particularly user-generated content on social media and blogging platforms, has underscored the importance of sentiment analysis (SA). Arabic language sentiment analysis (ASA) involves identifying the orientation of ideas, feelings, emotions, and attitudes within Arabic text to determine whether they convey a positive, negative, or neutral sentiment. This paper presents a comprehensive review of the past decade, focusing on the utilization of SA in the Arabic language. It examines various applications, methodologies, and challenges associated with ASA, highlighting gaps and limitations in existing approaches, lexicons, and annotated datasets. The primary objective of this review is to assist researchers in identifying these gaps and limitations while offering accessible annotated datasets, preprocessing techniques, and procedures. We defined specific criteria for selecting research publications from the last 10 years, including 150 papers in our review process, while excluding earlier publications. The review utilized multiple databases, including Google Scholar, Scopus, and Web of Science. The inherent complexity of the Arabic language, due to its unique traits and diverse dialects, presents significant challenges in ASA. Moreover, the lack of annotated datasets, lexicon resources, and programming tools further complicates sentiment analysis in Arabic. The morphological variations within Arabic make it linguistically challenging. To address these issues, it is crucial to develop additional resources and construct new Arabic sentiment lexicons that account for the various dialects within Modern Standard Arabic (MSA). Our findings reveal that there is no standard public lexicon that adequately enhances the calculation of ASA across different domains, such as e-commerce, politics, public health, and marketing.

Abstract The genus Enterobacter belongs to the ESKAPE group, which includes Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter spp. This group is characterized by the development of resistance to various antibiotics. In recent years, Enterobacter cloacae (E. cloacae) has emerged as a clinically important pathogen responsible for a wide range of healthcare-associated illnesses. Identifying Enterobacter species can be challenging due to their similar phenotypic characteristics. The emergence of multidrug-resistant E. cloacae is also a significant problem in healthcare settings. Therefore, our study aimed to identify and differentiate E. cloacae using Matrix-assisted laser desorption ionization–time of flight mass spectrometry (MALDI-TOF MS) as a fast and precise proteomic analytical technique. We also tested hospital-acquired E. cloacae isolates that produce Extended-spectrum beta-lactamases (ESBL) against commonly used antibiotics for treating urinary tract infections (UTIs). We used a total of 189 E. cloacae isolates from 2300 urine samples of patients with UTIs in our investigation. We employed culturing techniques, as well as the BD Phoenix™ automated identification system (Becton, Dickinson) and Analytical Profile Index (API) system for the biochemical identification of E. cloacae isolates. We used the MALDI Biotyper (MBT) device for peptide mass fingerprinting analysis of all isolates. We utilized the single peak intensities and Principal Component Analysis (PCA) created by MBT Compass software to discriminate and cluster the E. cloacae isolates. Additionally, we evaluated the sensitivity and resistance of ESBL-E. cloacae isolates using the Kirby Bauer method. Out of the 189 E. cloacae isolates, the BD Phoenix system correctly identified 180 (95.24%) isolates, while the API system correctly identified 165 (87.30%) isolates. However, the MBT accurately identified 185 (98.95%) isolates with a score of 2.00 or higher. PCA positively discriminated the identified E. cloacae isolates into one group, and prominent peaks were noticed between 4230 mass-to-charge ratio (m/z) and 8500 m/z. The ESBL-E. cloacae isolates exhibited a higher degree of resistance to ampicillin, amoxicillin-clavulanate, cephalothin, cefuroxime, and cefoxitin. Several isolates were susceptible to carbapenems (meropenem, imipenem, and ertapenem); however, potential future resistance against carbapenems should be taken into consideration. In conclusion, MALDI-TOF MS is a powerful and precise technology that can be routinely used to recognize and differentiate various pathogens in clinical samples. Additionally, the growing antimicrobial resistance of this bacterium may pose a significant risk to human health.

In this work, we propose a novel Gate and Drain Engineered Schottky Barrier (SB) FET (GDE-SBFET) for biosensing application with significant sensitivity improvement. Two different gate materials are employed by the proposed SB device having work functions of 3.9 eV (Al) and 4.72 eV (Cu) and electrostatically doped drain (work function of 3.9 eV). Etching out the oxide on both sides of the gate creates a nano-gap cavity that is used to detect the biomolecule. The biomolecule electrical characteristics such as charge density and dielectric constant can modify the SB on either side of the gate, which can alter the device‘s driving current. The Drain Current sensitivity ( $S_{drain}$ ) parameter are extensively analyzed at $V_{DS} = V_{GS} = 0.5$ V and a comparison between state of the art devices and conventional devices has been carried out. From the obtained results, it can be concluded that proposed device sensitivity is much superior for negatively charged and neutral biomolecules (maximum of 460 for biomolecules with negative charge, maximum of 128 for neutral biomolecule and maximum of 35 for positively charged biomolecules, at K = 12). These are the highest values of sensitivity observed for SB-FETs.

Information Technology or IT is a combination of technology itself and a collection of IT services that ensure the effective implementation of overall Information Technology in an organization. Like other services, Information Technology Service Management (ITSM) has become a global subject because managing IT is only possible through efficient and effective ITSM protocols. A well-implemented ITSM delivery system improves the quality of IT services, which eventually enhances the organization’s overall capacity and output. Public service delivery is closely dependent on the quality IT services that can be acquired through ITSM. This research study reviews some of the established ITSM standards and frameworks like FitSM, ITIL, CobiT, and ISO/IEC 20000 and proposes a most suitable and sustainable tool and its implementation roadmap for the digital transformation of public sector governance. For this pilot study, a public sector organization (PSO) working under the Government of Punjab (GoPb) was selected. This study focuses on an important area that has not yet been adequately addressed, where ITSM delivery can produce tremendous results. This study contributes to academics and researchers since it discusses the selection and implementation of a service management system (SMS) for a PSO while focusing on its needs, requirements, and available resources. In general, the qualitative analysis methodology is used in this research study, and specifically, it can be identified as applied research. The data is collected through a questionnaire, and the results are shown and discussed in the later sections.

BACKGROUND: Therapeutic plasma exchange (TPE) is a treatment option used in many medical conditions. Response rates are variable as some disorders clearly benefit from TPE as first- or second-line therapy. This study aims to summarize our experience at King Abdulaziz University Hospital at King Abdulaziz University Hospital, Jeddah, Saudi Arabia. MATERIALS AND METHODS: In a retrospective fashion, wdemographic data, underlying disease, apheresis-related parameters, and outcomes were collected. RESULTS: Between January 2005 and March 2020, 159 patients with 177 episodes underwent a total of 945 sessions of TPE. The majority of patients (96.8%) undergoing TPE in our center are of categories I to III according to 2019 American Society for Apheresis guidelines. Most patients had neurologic disorders, 74 (46.5%), where myasthenia gravis was the most common indication, 34 (21.4%) patients with response in 44/45 (97.8%) episodes, followed by thrombotic microangiopathies 31 (17.5%) patients with response in 17/34 (50%) episodes, Guillain–Barré syndrome 27 (16.9%) patients with recovery in 20/27 (74.1%) patients, and systemic lupus erythematous 25 (15.7%) patients with recovery in only 11/25 (44%) patients. Complications included hypotension in 29/945 (3.06%) sessions and citrate-induced symptoms in 6/945 (0.6%) sessions. CONCLUSION: Our center complies with the recommended standards of indications for initiating TPE. Neurologic conditions constitute the largest group of patients requiring TPE.

Mobile ad hoc networks (MANETs) are wireless multi-hop networks that do not rely on any fixed infrastructure, unlike traditional networks. Nodes in MANETs are formed dynamically and are free to move in any direction at variable speeds. The special characteristics of MANETs make them vulnerable to flooding attacks, which can have a negative impact on their performance. Moreover, due to their nature, employing solutions designed for traditional networks is not feasible. One potential solution to enhance the performance of MANETs in the face of network attacks is to implement trust management. This paper evaluates the performance of Ad hoc On-Demand Distance Vector (AODV) Routing in the presence of a flooding attack. We propose a direct trust management scheme to detect and isolate malicious nodes and implement this scheme on AODV. We name the modified protocol Trusted AODV (TAODV) and, finally, compare the performance of AODV and TAODV when both are under a flooding attack to measure the improvement achieved by our suggested scheme.

Background: Diabetic emergencies are serious acute life-threatening complications of diabetes mellitus (DM). The Hajj season requires the health system in Saudi Arabia to prepare efficiently for the healthcare of millions of pilgrims, particularly for diabetic emergencies. Thus, diabetic emergencies need rapid recognition, diagnosis and treatment. This study aimed to explore the frequency and associated factors of diabetic emergencies among the pilgrim's patients with DM during Hajj, Mecca 2019. Methods: This is a prospective study which was conducted on 153 patients with DM. They were selected from three major healthcare-providing facilities during Hajj, which are Arafat, Muzdelefah and Muna healthcare centres. The study was conducted from Aug 5 to 12, 2019. All the patients who presented with any of the hypoglycaemic or hyperglycaemic emergencies had their demographic and clinical characteristics recorded to estimate the prevalence of each emergency and identify its significant associated factors. Result: More than 90% of the study participants were patients with type 2 diabetes mellitus (T2DM), while around 7% had type 1 diabetes mellitus (T1DM). Diabetic ketoacidosis (DKA) and hyperosmolar hyperglycaemic state (HHS) and hypoglycaemia were presented in (n = 11, 7.2%), (n = 19, 12.4%) and (n = 28, 18%), respectively, of the participants. Moreover, the study found that “younger age” (odds = 30.4, P = 0.0115) and “type of medication” are significantly associated with DKA. Furthermore, “older age”, “type of medication”, “having Cardiovascular Disease (CVD)” and “diabetes duration” were found to have a significant association with HHS. Moreover, hypoglycaemia was associated with neuropathy complication (odds = 3.54948, P = 0.0187). Conclusions: Among the pilgrims with diabetes participating in Hajj, a considerable proportion with a range of diabetic emergencies present to the onsite medical facilities. Preparation is required in terms of logistics and health education about diabetic emergencies to meet the needs of pilgrims with DM, especially those who use insulin and have longstanding diabetes. Further research on DM and the creation of guidelines for health providers and patients with DM during Hajj are important focuses for the future.

Smart cities have emerged as a specialized domain encompassing various technologies, transitioning from civil engineering to technology-driven solutions. The accelerated development of technologies, such as the Internet of Things (IoT), software-defined networks (SDN), 5G, artificial intelligence, cognitive science, and analytics, has played a crucial role in providing solutions for smart cities. Smart cities heavily rely on devices, ad hoc networks, and cloud computing to integrate and streamline various activities towards common goals. However, the complexity arising from multiple cloud service providers offering myriad services necessitates a stable and coherent platform for sustainable operations. The Smart City Operational Platform Ecology (SCOPE) model has been developed to address the growing demands, and incorporates machine learning, cognitive correlates, ecosystem management, and security. SCOPE provides an ecosystem that establishes a balance for achieving sustainability and progress. In the context of smart cities, Internet of Things (IoT) devices play a significant role in enabling automation and data capture. This research paper focuses on a specific module of SCOPE, which deals with data processing and learning mechanisms for object identification in smart cities. Specifically, it presents a car parking system that utilizes smart identification techniques to identify vacant slots. The learning controller in SCOPE employs a two-tier approach, and utilizes two different models, namely Alex Net and YOLO, to ensure procedural stability and improvement.

Sustainable urban water management is essential to handle water scarcity, leakage, and inefficient distribution. This paper covers water management in urban areas, including an introduction, an overview of water management practices, the characteristics and functioning of water distribution systems, monitoring and control systems for efficient distribution, smart systems for optimization, strategies for water conservation and waste management, per capita water demand analysis, and desalination plant overviews. The article proposes a blockchain-based water management architecture with IoT sensors for accurate reporting. The framework uses blockchain technology to authenticate and share real-time data between sensors and the water distribution dashboard. It also has a modular API for water leakage detection and flow control to decrease water waste and enhance distribution. The suggested approach might enhance water management; however, its execution is complex. Maintaining the framework’s efficacy is advised. The research provides insights into water management and proposes a technology solution employing blockchain and IoT sensors for trustworthy data reporting and effective water distribution to promote sustainable urban water management.

Fresh dates have a limited shelf life and are susceptible to spoilage, which can lead to economic losses for producers and suppliers. The problem of accurate shelf life estimation for fresh dates is essential for various stakeholders involved in the production, supply, and consumption of dates. Modified atmosphere packaging (MAP) is one of the essential methods that improves the quality and increases the shelf life of fresh dates by reducing the rate of ripening. Therefore, this study aims to apply fast and cost-effective non-destructive techniques based on machine learning (ML) to predict and estimate the shelf life of stored fresh date fruits under different conditions. Predicting and estimating the shelf life of stored date fruits is essential for scheduling them for consumption at the right time in the supply chain to benefit from the nutritional advantages of fresh dates. The study observed the physicochemical attributes of fresh date fruits, including moisture content, total soluble solids, sugar content, tannin content, pH, and firmness, during storage in a vacuum and MAP at 5 and 24 ∘C every 7 days to determine the shelf life using a non-destructive approach. TinyML-compatible regression models were employed to predict the stages of fruit development during the storage period. The decrease in the shelf life of the fruits begins when they transition from the Khalal stage to the Rutab stage, and the shelf life ends when they start to spoil or ripen to the Tamr stage. Low-cost Visible–Near–Infrared (VisNIR) spectral sensors (AS7265x—multi-spectral) were used to capture the internal physicochemical attributes of the fresh fruit. Regression models were employed for shelf life estimation. The findings indicated that vacuum and modified atmosphere packaging with 20% CO2 and N balance efficiently increased the shelf life of the stored fresh fruit to 53 days and 44 days, respectively, when maintained at 5 ∘C. However, the shelf life decreased to 44 and 23 days when the vacuum and modified atmosphere packaging with 20% CO2 and N balance were maintained at room temperature (24 ∘C). Edge Impulse supports the training and deployment of models on low-cost microcontrollers, which can be used to predict real-time estimations of the shelf life of fresh dates using TinyML sensors.

Cognitive radio technology was introduced as a possible solution for spectrum scarcity by exploiting dynamic spectrum access. In the last two decades, most researchers focused on enabling cognitive radios for managing the spectrum. However, due to their intelligent nature, cognitive radios can scan the radio frequency environment and change their transmission parameters accordingly on-the-fly. Such capabilities make it suitable for the design of both advanced jamming and anti-jamming systems. In this context, our work presents a novel, robust algorithm for spectrum characterisation in wideband radios. The proposed algorithm considers that a wideband spectrum is sensed by a cognitive radio terminal. The wideband is constituted of different narrowband signals that could either be licit signals or signals jammed by stealthy jammers. Cyclostationary feature detection is adopted to measure the spectral correlation density function of each narrowband signal. Then, cyclic and angular frequency profiles are obtained from the spectral correlation density function, concatenated, and used as the feature sets for the artificial neural network, which characterise each narrowband signal as a licit signal with a particular modulation scheme or a signal jammed by a specific stealthy jammer. The algorithm is tested under both multi-tone and modulated stealthy jamming attacks. Results show that the classification accuracy of our novel algorithm is superior when compared with recently proposed signal classifications and jamming detection algorithms. The applications of the algorithm can be found in both commercial and military communication systems.

This work presents a robust watermarking technique in hybrid domain for the copyright claim of medical images. The scheme is a fusion of three popular transforms: Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT) and Singular Value Decomposition (SVD). The input image is first separated into region of interest (ROI) and region of non-interest (RONI). The DWT is applied on RONI to get low and high frequency bands. The low frequency band is then segmented into $4\times 4$ blocks. The Human Visual System (HVS) is applied to select the potential blocks for implanting watermark content. Each $4\times 4$ selected block is further subdivided into four $2\times 2$ carrier matrices. The SVD is applied to each carrier matrix. Finally, the hidden information is implanted by altering the largest diagonal singular values of four $2\times 2$ matrices. The technique is blind, so host image is not needed for the extraction of hidden information. The proposed scheme achieves higher values of imperceptibility as well as robustness. Experimental results reveal that the proposed technique outperforms the techniques currently reported in the literature by achieving higher values of imperceptibility in the form of PSNR with value of 44.0567 decibels (dB) and SSIM value of 0.9800. At the same time it achieves excellent values of robustness with maximum NCC value of 1.000 and minimum BER with value of 0.000.

Breast cancer is one of the most prevalent types of cancer that mainly affects the women population. chances of effective treatment increase with early diagnosis. Mammography is considered one of the effective and proven techniques for the early diagnosis of breast cancer. Tissues around masses look identical in a mammogram, which makes the automatic detection process a very challenging task; they are indistinguishable from the surrounding parenchyma. In this paper, we present an efficient and automated approach to segment masses in mammograms. The proposed method uses hierarchical clustering to isolate the salient area followed by extraction of features to reject false detection. We applied our method to two popular publicly available datasets (mini-MIAS and DDSM). A total of 56 images from the mini-mias database and 76 images from DDSM were randomly selected. Results are explained in terms of ROC (Receiver Operating Characteristics) curves and compared with other state-of-the-art techniques. Experimental results demonstrate the efficiency and advantages of the proposed system in automatic mass identification in mammograms.

In remote sensing data processing, cover classification on decimeter-level data is a well-studied but tough subject that has been well-documented. The majority of currently existent works make use of orthographic photographs or orthophotos and digital surface models that go with them (DSMs). Urban land cover classification plays a significant role in the field of remote sensing to enhance the quality of different applications including environment protection, sustainable development, and resource management and planning. Novelty of the research done in this area is focused on extracting features from high-resolution satellite images to be used in the classification process. However, it is well known in machine learning literature that some of the extracted features are irrelevant to the classification process with a negative or no effect on its accuracy. In this work, a genetic algorithm-based feature selection approach is used to enhance the performance of urban land cover classification. Neural networks (NNs) and random forest (RF) classifiers were used to evaluate the proposed approach on a recent urban land cover dataset of nine different classes. Experimental results show that the proposed approach achieved better performance with RF classifier using only 27% of the features. The random forest tree has achieved highest accuracy 84.27%; it is concluded that the RF algorithm is an appropriate algorithm for classifying cover land.

The propagation of digital media over the Internet has helped improve digitization, which has given an excessive lead to copyright issues. Digital watermarking techniques have been applied to address copyright issues. In research, a system is being developed to handle veracious types of watermarked attacks, for obtaining extreme security and an adequate level of visibility and robustness. The discrete wave transform (DWT) and singular value decomposition (SVD) approaches were applied to analyze veracious types of attacks. The DWT method was used to embed the host image in four levels; this level was processed using the SVD method. The peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) were applied to measure the invisibility, and the normalization correlation (NC) was used to examine the robustness of watermarked images. The empirical results showed that the proposed DWT-SVD achieved superior accuracy in identifying the various attacks. The proposed DWT-SVD performance was confirmed during the training process, and the proposed system was shown to have high invisibility and robustness against various types of attacks on watermarked images. Finally, the results of the proposed system were compared to existing systems, and it was shown that DWT-SVD achieved better performance in terms of pixel-value modification attacks.

Augmented reality (AR) has gained enormous popularity and acceptance in the past few years. AR is indeed a combination of different immersive experiences and solutions that serve as integrated components to assemble and accelerate the augmented reality phenomena as a workable and marvelous adaptive solution for many realms. These solutions of AR include tracking as a means for keeping track of the point of reference to make virtual objects visible in a real scene. Similarly, display technologies combine the virtual and real world with the user’s eye. Authoring tools provide platforms to develop AR applications by providing access to low-level libraries. The libraries can thereafter interact with the hardware of tracking sensors, cameras, and other technologies. In addition to this, advances in distributed computing and collaborative augmented reality also need stable solutions. The various participants can collaborate in an AR setting. The authors of this research have explored many solutions in this regard and present a comprehensive review to aid in doing research and improving different business transformations. However, during the course of this study, we identified that there is a lack of security solutions in various areas of collaborative AR (CAR), specifically in the area of distributed trust management in CAR. This research study also proposed a trusted CAR architecture with a use-case of tourism that can be used as a model for researchers with an interest in making secure AR-based remote communication sessions.

In the application of teleradiology, radiologists located at different geographical locations around the globe, exchange Computed Tomography (CT) scan images along with relevant patient information for diagnosis as well as therapeutic measures. However, when these images are exchanged over public networks like the Internet, two important issues are created: content authentication and copyright protection. In this paper, a novel high capacity reversible watermarking scheme for CT Scan images is presented which is based on the region of interest (ROI) and region of non-interest (RONI) watermarking. The scheme embeds and extracts the secret data into the input CT scan image as well as restores the image to its pristine state at the receiving end. After segmenting the host image into the ROI and RONI regions, the scheme implants a fragile watermark (FW) into the ROI to check the integrity of the ROI and a robust watermark (RW) into the RONI for copyright protection. The novelty of the proposed system is in two fold. First, it takes advantage of the segmentation algorithm to avoid the additional overhead of information about the vertices usually required at the receiving end in order to select the ROI. Secondly, it takes advantage of the prediction-based reversible watermarking to avoid additional overhead of location map, which is usually required for reversibility in most of the reversible medical image watermarking techniques. Experimental results show that the proposed system outperforms medical image watermarking techniques recently reported in the literature in terms of embedding capacity and imperceptibility of watermarked image.

Transactions related to vehicles include manufacturing, buying, selling, paying insurance(takaful), obtaining regular inspection, leasing a vehicle from banks, getting in an accident, engaging in a traffic violation, calculating price predictions and renting a vehicle. Many people perform transactions related to vehicles in their daily life; transportation authorities also perform vehicle transactions as part of managing vehicle fleets. But tracking these transactions is a challenging task. There are countrywide solutions that uses centralized systems. However, these solutions have problems with trust management, transparency, and access control. Therefore, we believe there is still room for integrated automation of various vehicle-related transactions. In this paper, we present a blockchain-based framework for vehicle tracking that incorporates the mentioned features. Moreover, blockchain is customized to enable usage control for additional transactions, such as inspection, renting and islamic insurance. The usage control model is integrated with IoT devices to continuously monitor the vehicles for certain conditions and remotely revoke access if needed. The complete transaction set is recorded over an immutable ledger that provides trust, transparency and a complete history of record. In this paper, we also presents a prototype implementation of a permissioned blockchain, which will be made available under the GNUv3 General Public License. Performance analysis is performed on the newly proposed framework implementation over the permissioned blockchain to measure its adoption and suitability.

The unprecedented growth in Augmented Reality (AR) has captured the focus of researchers and the industrial sector. The development of AR applications and their implementation in various domains is broadening. One of the advancements in the field of AR is Collaborative AR, which provides ample opportunities for the members of a team to work on a particular project remotely. The various activities carried out remotely, in a collaborative fashion, are based on the active interaction and transmission of data and applications across a communication channel that constitutes a mesh of frequently interacting applications, thus providing a real feeling of working together physically in the purportedly same demographic area. However, in the integration of different roles, remotely working in collaborative AR has a great chance of being intruded upon and manipulated. Consequently, the intrusion may explore novel vulnerabilities to various sensitive collaborative projects. One of the security concerns for collaborative and interconnected remote applications is to have pristine environments, where the participants of the collaborative AR can reliably trust each other during the execution of the various processes. This paper presents an integrity-aware CAR-Tourist (Collaborative Augmented reality for Tourism) framework wherein the unauthorized user’s access is denied and the remote participants of the network are provided with a secure environment through the state-of-the-art Blockchain architecture. This study further provides a use-case implementation of a tourism application. Each tourist has the chance to hire a remote guide for collaborative guidance over a blockchain-trusted network. Moreover, the proposed framework is lightweight, as the only necessary communication between the tourist and guide is recorded in the blockchain network. Each user has to register on a permission blockchain to be allowed to perform certain activities on our proposed CAR-Tourist framework. The decentralized Blockchain approach provides a consensus mechanism based on which not every participant is free to intrude on ongoing communication. Thus, through the proposed framework, all the participants in the collaborative Augmented Reality will have the essential trust of working remotely without external intrusion.

The digital transformation disrupts the various professional domains in different ways, though one aspect is common: the unified platform known as cloud computing. Corporate solutions, IoT systems, analytics, business intelligence, and numerous tools, solutions and systems use cloud computing as a global platform. The migrations to the cloud are increasing, causing it to face new challenges and complexities. One of the essential segments is related to data storage. Data storage on the cloud is neither simplistic nor conventional; rather, it is becoming more and more complex due to the versatility and volume of data. The inspiration of this research is based on the development of a framework that can provide a comprehensive solution for cloud computing storage in terms of replication, and instead of using formal recovery channels, erasure coding has been proposed for this framework, which in the past proved itself as a trustworthy mechanism for the job. The proposed framework provides a hybrid approach to combine the benefits of replication and erasure coding to attain the optimal solution for storage, specifically focused on reliability and recovery. Learning and training mechanisms were developed to provide dynamic structure building in the future and test the data model. RAID architecture is used to formulate different configurations for the experiments. RAID-1 to RAID-6 are divided into two groups, with RAID-1 to 4 in the first group while RAID-5 and 6 are in the second group, further categorized based on FTT, parity, failure range and capacity. Reliability and recovery are evaluated on the rest of the data on the server side, and for the data in transit at the virtual level. The overall results show the significant impact of the proposed hybrid framework on cloud storage performance. RAID-6c at the server side came out as the best configuration for optimal performance. The mirroring for replication using RAID-6 and erasure coding for recovery work in complete coherence provide good results for the current framework while highlighting the interesting and challenging paths for future research

Locating a missing child or elderly person in a large gathering through face recognition in videos is still challenging because of various dynamic factors. In this paper, we present an intelligent mechanism for tracking missing persons in an unconstrained large gathering scenario of Al-Nabawi Mosque, Madinah, KSA. The proposed mechanism in this paper is unique in two aspects. First, there are various proposals existing in the literature that deal with face detection and recognition in high-quality images of a large crowd but none of them tested tracking of a missing person in low resolution images of a large gathering scenario. Secondly, our proposed mechanism is unique in the sense that it employs four phases: (a) report missing person online through web and mobile app based on spatio-temporal features; (b) geo fence set estimation for reducing search space; (c) face detection using the fusion of Viola Jones cascades LBP, CART, and HAAR to optimize the results of the localization of face regions; and (d) face recognition to find a missing person based on the profile image of reported missing person. The overall results of our proposed intelligent tracking mechanism suggest good performance when tested on a challenging dataset of 2208 low resolution images of large crowd gathering.

In the past few years, the implementation of blockchain technology for various applications has been widely discussed in the research community and the industry. There are sufficient number of articles that discuss the possibility of applying blockchain technology in various areas, such as, healthcare, IoT, and business. However, in this article, we present a comparative analysis of core blockchain architecture, its fundamental concepts, and its applications in three major areas: the Internet-of-Things (IoT), healthcare, business and vehicular industry. For each area, we discuss in detail, challenges and solutions that have been proposed from the research community and industry. This research studies also presented the complete ecosystem of blockchain of all the papers we reviewed and summarized. Moreover, analysis is performed of various blockchain platforms, their consensus models, and applications. Finally, we discuss key aspects that are required for the widespread future adoption of blockchain technology in these major areas.

Traffic management in a smart city mainly relies on video feeds from various sources such as street cameras, car dash cams, traffic signal cameras, and so on. Ensuring the confidentiality of these video feeds during transmission is necessary. However, due to these devices’ poor processing power and memory capacity, the applicability of traditional encryption algorithms is not feasible. Therefore, a selective encryption system based on singular value decomposition (SVD) and chaotic maps is presented in this study. The proposed cryptosystem can be used in smart traffic management. We apply SVD to identify the most significant parts of each frame of the video feed for encryption. Chaotic systems were deployed to achieve high diffusion and confusion properties in the resulted cipher. Our results suggest that the computational overhead is significantly less than that of the traditional approaches with no compromise on the strength of the encryption.

The probability of losing vulnerable companions, such as children or older ones, in large gatherings is high, and their tracking is challenging. We proposed a novel integration of face-recognition algorithms with a soft voting scheme, which was applied, on low-resolution cropped images of detected faces, in order to locate missing persons in a challenging large-crowd gathering. We considered the large-crowd gathering scenarios at Al Nabvi mosque Madinah. It is a highly uncontrolled environment with a low-resolution-images data set gathered from moving cameras. The proposed model first performs real-time face-detection from camera-captured images, and then it uses the missing person’s profile face image and applies well-known face-recognition algorithms for personal identification, and their predictions are further combined to obtain more mature prediction. The presence of a missing person is determined by a small set of consecutive frames. The novelty of this work lies in using several recognition algorithms in parallel and combining their predictions by a unique soft-voting scheme, which in return not only provides a mature prediction with spatio-temporal values but also mitigates the false results of individual recognition algorithms. The experimental results of our model showed reasonably good accuracy of missing person’s identification in an extremely challenging large-gathering scenario.

Embedded multi-core systems are implemented as systems-on-chip that rely on packet storeand-forward networks-on-chip for communications. These systems do not use buses or global clock. Instead routers are used to move data between the cores, and each core uses its own local clock. This implies concurrent asynchronous computing. Implementing algorithms in such systems is very much facilitated using dataflow concepts. In this paper, we propose a methodology for implementing algorithms on dataflow platforms. The methodology can be applied to multi-threaded, multi-core platforms or a combination of these platforms as well. This methodology is based on a novel dataflow graph representation of the algorithm. We applied the proposed methodology to obtain a novel dataflow multi-core computing model for the secure hash algorithm-3. The resulting hardware was implemented in field-programmable gate array to verify the performance parameters. The proposed model of computation has advantages, such as flexible I/O timing in term of scheduling policy, execution of tasks as soon as possible, and self-timed event driven system. In other words, I/O timing and correctness of algorithm evaluation are dissociated in this paper. The main advantage of this proposal is ability to dynamically obfuscate algorithm evaluation to thwart side-channel attacks without having to redesign the system. This has important implications for cryptographic applications.

Health care workers (HCW's) are always at an increased risk of contracting tuberculosis (TB) infection. In Saudi Arabia, Interferon Gamma Release Assay (IGRA) has not been evaluated as a screening tool for latent TB infection (LTBI) among HCW's considering their high demographic diversity. During February 2012 to January 2015 a cross sectional study has been conducted in a tertiary care center with maximum demographically diverse staff population in the capital city-Riyadh. After a short interview and consenting, all the candidates were subjected to tuberculin skin test (TST) and QuantiFERON TB gold In-tube test (QFT). A logistic regression analysis was carried out for establishing the associations between putative risk factors and the diagnostic tests. The candidates were classified according to geographical origin and a detailed analysis was conducted on the impact of their origin towards the results of TST and QFT. Of the 1595 candidates enrolled, 90.6% were BCG vaccinated, female (67.9%) and mainly nurses (53.2%). Candidates with high risk of suspected or confirmed TB patient exposure were 56.1% and 76.5% of them had

Background: Self-care practices are particularly important for maintaining health and preventing its recurrence, and they can begin after the acute phase of myocardial infarction has passed. Aim of the study: to assess self-care for Saudi patients with myocardial infarction. Research design: Descriptive research design was used to conduct this study. Setting: The present study conducted at Cardiac Out Patient Clinics in Saudi University Hospital. Sample: Simple random sample was used in this study. The total number of sample was 254 myocardial infarction patients. Tools: One tool was used to collected data Part (I): An interviewing questionnaire to assess patients' socio demographic characteristics. Part (II): About patients' medical history. Part (III): Patients' knowledge regarding myocardial infarction and self-care. Part (IV): Patients' self-care reported practices. Results: 63.8% of patients had total average knowledge regarding myocardial infarction and self-care, while 65% had unsatisfactory reported self-care practices. Conclusion: There were highly statistical significant relation between studied myocardial infarction patients' total knowledge and their socio demographic characteristic regarding educational level, while there were highly statistically significant relations between the total self-care practices of studied myocardial infarction patients and their total knowledge. Recommendation: Health educational program should be developed and implemented for patients with myocardial infarction to improve, and updated knowledge and self-care practices. [ABSTRACT FROM AUTHOR]

Background:Risk management aims to proactively identify and mitigate potential risks through systemic interventions, staff education, and the integration of technology. By fostering a culture of safety, these programs contribute to better patient outcomes and reduced healthcare costs. Aim: To investigate the effectiveness of a risk management program on the frequency of the medication errors at children hospital in Saudi Arabia. Methods:The study will use a quasi-experimental design to evaluate a risk management program aimed at reducing medication errors among nursing staff in a pediatric hospital. The study population will consist of 100 full-time nurses. Data will be collected using a self-report survey and an observational checklist to assess medication errors and documentation practices. Statistical analysis will be conducted using SPSS to compare pre- and post-intervention results with paired t-tests and Chi-square tests. Results:The study compared an intervention group that received a risk management protocol with a comparison group that did not. The intervention group demonstrated a significant reduction in medication errors, with their post-test scores improving from 33.43 to 37.69 (p < 0.001). In contrast, the comparison group showed no significant change, with scores improving marginally from 32.08 to 33.87 (p = 0.312). Key findings included that 71.69% of participants identified immediate reporting as the best response to errors, while 91.57% believed errors were preventable through improved training and communication. The most common error found in the stages of medication management include incorrect dosage. Procedural and systematic approaches for instance check-lists were established as suitable means of intervention. Overall the findings stress on the need to have specific risk management plans especially in the environment where high risks are involved; staff training and understanding of risks, errors, and the overall culture of error reporting all these in the pursuit of optimal safety for pediatric medication administration. Hence, these results suggest that structured interventions should be implemented to reduce risk factors. Conclusion:In conclusion, the present study showed that the risk management protocol minimizes medication errors in the intervention group. Therefore, the systematic intervention, staff education, and strong errorreporting tools are effective measures to minimize medication errors. The results of the current study provide evidence for targeted risk management interventions to strengthen medication safety and safety culture in pediatrics. [ABSTRACT FROM AUTHOR]