University of Sharjah

Myopathy leads to skeletal and cardiac muscle degeneration which is a major cause of physical disability and heart failure. Despite the therapeutic advancement the prevalence of particularly cardiac diseases is rising at an alarming rate and novel therapeutic targets are required. Nicotinamide riboside kinase-2 (NRK-2 or NMRK2) is a muscle-specific β1-integrin binding protein abundantly expressed in the skeletal muscle while only a trace amount is detected in the healthy cardiac muscle. The level in cardiac tissue is profoundly upregulated under pathogenesis conditions such as ischemia and hypertension. NRK-2 was initially identified to regulate myoblast differentiation and to enhance the levels of NAD+, an important coenzyme that potentiates cellular energy production and stress resilience. Recent advancement has shown that NRK-2 critically regulates numerous cellular and molecular processes under pathogenic conditions to modulate the disease severity. Therefore, given its restricted expression in the cardiac and skeletal muscle, NRK-2 may serve as a unique therapeutic target. In this review, we provided a comprehensive overview of the multifaceted NRK-2 roles played in different cardiac and muscular diseases and discussed the underlying molecular mechanisms in detail. Moreover, this review precisely examined how NRK-2 regulates metabolism in cardiac muscle, and how dysfunctional NRK-2 is associated with energetic deficit and impaired muscle function, manifesting various cardiac and skeletal muscle disease conditions.

Objectives Hypertension guidelines recommend the use of single-pill combinations (SPCs) of antihypertensive drugs to improve treatment persistence and blood pressure control. This study aimed to investigate the long-term effects of ramipril/amlodipine (R/A) SPC versus free equivalent dose combinations (FEC) on cardiovascular outcomes and treatment persistence. Methods This retrospective, observational study analysed the database of the Hungarian National Health Insurance Fund. The study included patients with hypertension aged at least 18 years who were initiated on R/A SPC or FEC of different dose combinations (R/A 5/5, 5/10, 10/5 and 10/10 mg) between 2012 and 2018, with follow-up for up to 60 months. Imbalances in baseline characteristics were reduced with propensity score-based sub-classification. All analyses were performed with Cox proportional hazard model and propensity score sub-classification to adjust the imbalances in baseline characteristics. Drug persistence and MACEs were the primary and secondary endpoints, respectively. Results Overall, 104 882 patients with SPC and 68 324 patients with FEC-treated hypertension were included. The R/A 5/5 mg combination represented the largest proportion (62%). The nonpersistence rate was significantly lower with SPC than with FEC from month 1 to month 24 in the R/A 5/5 mg combination ( P < 0.001) and during the entire observation period in the remaining combinations. The MACE rate was significantly reduced with all R/A SPCs versus FECs. No effects on age and sex on both endpoints were noted. Conclusion This study further supports the beneficial effects of the use of SPC on 60-month persistence and MACEs in hypertension.

The recent o-ran specifications promote the evolution of ranran architecture by function disaggregation, adoption of open interfaces, and instantiation of a hierarchical closed-loop control architecture managed by ric entities. This paves the road to novel data-driven network management approaches based on programmable logic. Aided by ai and ml, novel solutions targeting traditionally unsolved ran management issues can be devised. Nevertheless, the adoption of such smart and autonomous systems is limited by the current inability of human operators to understand the decision process of such ai/ml solutions, affecting their trust in such novel tools. xai aims at solving this issue, enabling human users to better understand and effectively manage the emerging generation of artificially intelligent schemes, reducing the human-to-machine barrier. In this survey, we provide a summary of the xai methods and metrics before studying their deployment over the o-ran Alliance ran architecture along with its main building blocks. We then present various use-cases and discuss the automation of xai pipelines for o-ran as well as the underlying security aspects. We also review some projects/standards that tackle this area. Finally, we identify different challenges and research directions that may arise from the heavy adoption of ai/ml decision entities in this context, focusing on how xai can help to interpret, understand, and improve trust in o-ran operational networks.

Background Radiographs play a key role in diagnosis of periodontal diseases. Deep learning models have been explored for image analysis in periodontal diseases. However, there is lacuna of research in the deep learning model-based detection of furcation involvements [FI]. The objective of this study was to determine the accuracy of deep learning model in the detection of FI in axial CBCT images. Methodology We obtained initial dataset 285 axial CBCT images among which 143 were normal (without FI) and 142 were abnormal (with FI). Data augmentation technique was used to create 600(300 normal and 300 abnormal) images by using 200 images from the training dataset. Remaining 85(43 normal and 42 abnormal) images were kept for testing of model. ResNet101V2 with transfer learning was used employed for the analysis of images. Results Training accuracy of model is 98%, valid accuracy is 97% and test accuracy is 91%. The precision and F1 score were 0.98 and 0.98 respectively. The Area under curve (AUC) was reported at 0.98. The test loss was reported at 0.2170. Conclusion The deep learning model (ResNet101V2) can accurately detect the FI in axial CBCT images. However, since our study was preliminary in nature and carried out with relatively smaller dataset, a study with larger dataset will further confirm the accuracy of deep learning models.

Background Prior research conducted in the small bag manufacturing sector in Indonesia reported that occupational injuries occurred almost every month, with some workers reporting severe injuries that led to their fingers being amputated. Another study mentioned that the food manufacturing sectors tend to be more focused on improving their production activities than on paying attention to protecting their workers. Despite these conditions, employees are commonly seen by the owners as being responsible for their own safety at the workplace. Additionally, research examining how employees perceive occupational safety and health (OSH) and the current OSH programming available in SMB food and bag manufacturing in Indonesia is still limited. Objective This study aims to identify the perceptions among SMBs employees on OSH implementation in small and medium sized food and bag manufacturing businesses in Indonesia. Methods This qualitative study utilized in-depth interviews with employees of small and medium sized food and bag manufacturing businesses located in Bogor City, West Java Province. Results Occupational injuries happened to employees in almost all the businesses participated in the study. However, almost all the employees are not covered by insurance and accounted themselves to be responsible for both the injuries and to have the insurance. The employees often have casual or ‘family-like’ relationships with the business owners. Conclusions The ‘family-like’ relationship between business owners and employees in small and medium sized businesses can contribute to employees taking the responsibility for injuries that occur to themselves or their colleagues.

We propose a novel simulation algorithm for approximating ensembles of parameterized incompressible, non-isothermal flow problems in the presence of open boundaries. By adopting the idea of gPAV framework [Lin et al.: Comput. Methods Appl. Mech. Eng. 365, 112969 (2020)], we develop an efficient ensemble algorithm that only requires a single matrix assembly. We establish the unconditional stability results without any restricting assumptions on the variance of the diffusion coefficients or the time steps. Numerical tests on three benchmark flow problems show the accuracy and efficiency of our algorithm.

Surface-enhanced Raman spectroscopy (SERS) significantly amplifies the Raman scattering of molecules, enhancing their signals by several orders of magnitude. This emergent SERS-based optical detection strategies have attracted enormous research potential. In the current global context, the prevalence of environmental pollutants poses a critical concern that demands immediate attention, and hence this field requires the exploration of smart and advanced quality control tools. The advancement of SERS presents a promising path for monitoring and remediating environmental pollutants. By careful nanostructure design with enhanced surface plasmon resonance (SPR), SERS achieves rapid analysis, precise fingerprint specificity, and remarkable sensitivity nearing single molecule detection. Supported by portable Raman devices, SERS now emerges as a pivotal tool in addressing on-site environmental remediation challenges. In this review, we have summarized the developments in SERS-based strategies for environmental monitoring. Moreover, the correlation between SERS substrates and efficient environmental tracking is explored, addressing the need for precise monitoring both remotely and on-site. From a future perspective, the next level of research needed in advancing SERS sensors to practical purposes is also scrutinized.

Background Diabetic foot ulcers present a formidable challenge due to colonization by biofilm-forming microorganisms, heightened oxidative stress, and continuous wound maceration caused by excessive exudation. Methods To address these issues, we developed a robust, stretchable, electro-conductive, self-healing, antioxidant, and antibiofilm hydrogel. This hydrogel was synthesized through the crosslinking of polyvinyl alcohol (PVA) and chitosan (CH) with boric acid. To enhance its antimicrobial efficacy, graphene oxide (GO), produced via electrochemical exfoliation in a zinc ion-based electrolyte medium, was incorporated. For optimal antibiofilm performance, GO was functionalized with cranberry (CR) phenolic extracts, forming a graphene oxide-cranberry nanohybrid (GO-CR). Results The incorporation of GO-CR into the hydrogel significantly improved its stretchability (280% for PVA/CH/GO-CR compared to 200% for PVA/CH). Additionally, the hydrogel demonstrated efficient photothermal conversion under near-infrared (NIR) light, enabling dynamic exudate removal, which is expected to minimize retained exudate between the wound and the dressing, reducing the risk of wound maceration. The hydrogel effectively reduced levels of lipopolysaccharide (LPS)-induced skin inflammation markers, significantly lowering the expression of NLRP3, TNF-α, IL-6, and IL-1β by 39.2%, 31.9%, 41%, and 52.3%, respectively. Histopathological and immunohistochemical analyses further confirmed reduced inflammation and enhanced wound healing. Conclusion The PVA/CH/GO-CR hydrogel exhibits multifunctional properties that enhance wound healing ulcers. Its superior mechanical, antibacterial, and anti-inflammatory properties and ability to promote angiogenesis make it a promising candidate for effective wound management in diabetic patients.

This letter presents a novel and efficient hardware architecture to accelerate the computation of point multiplication (PM) primitive over arbitrary Montgomery curves. It is based on a new novel double field multiplier (DFM) that computes two field multiplications simultaneously. The DFM uses the interleaved multiplication technique, and it shortens the critical path of the circuit by computing two results at once. It is generic to work for any prime structure and curve parameters over the Montgomery curves. At the system level, a fast scheduling methodology is also presented to execute the field-level operations with the Montgomery ladder (ML) approach. Our ML and DFM designs perform the same operations regardless of the input values, which provides resistance to timing and simple power analysis side-channel attacks. It is synthesized and implemented over different FPGA platforms. The implementation results confirm that it outperforms the state-of-the-art in terms of area-time product and throughput/slice. To the best of the authors’ knowledge, it is the first fully LUT-based architecture for the arbitrary Montgomery curves.

It is time-consuming to obtain performance metrics for feasible topologies during the design of power electronic converters. This paper proposes a graph-theory-based algorithm that achieves fast loss calculation and waveform reconstruction for voltage source converters (VSCs) in steady-state operations. The VSC is modeled as a directed graph comprising a set of vertices connected by semiconductor devices or passive components. The current-conducting capacity of semiconductor devices is represented by a directed edge or a pair of directed edges in both directions. This allows all potential current paths to be identified by the graph search algorithm. With the current-path-based converter calculation model, the steady-state current of VSC is calculated in the frequency domain by voltage spectrum and load impedance. The loss distribution on each device is further calculated using the device loss model. Compared to state-of-the-art methods, the proposed method balances the requirements of high accuracy and speed. Finally, the calculation results are contrasted with experimental data from a back-to-back loss test platform, demonstrating that the calculated waveforms can well accurately describe the steady-state response of the converter and the relative loss error remains below 5%.

Ultra-wideband radar technology (UWB) has demonstrated its vital role through various applications in surveillance, search and rescue, health monitoring, and the military. Unlike conventional radars, UWB radars use high-frequency, wide-bandwidth pulses, enabling long-range detection and penetrating obstacles. This work presents an in-depth review of UWB radar systems for recognizing human activities in a room and through-the-wall (TTW) with other diverse applications. After briefly discussing different UWB radar working principles and architectures, the study explores their role in various TTW applications in real-world scenarios. An extensive performance comparison of the legacy studies is presented, focusing on detection tools, signal processing, and imaging algorithms. The discussion includes an analysis of the integration of machine learning models. The primary focus is on the detection, movement, monitoring of vital signs, and nonhuman classifications in the context of Through-The-Wall (TTW) scenarios. This study contributes to a better understanding of evolving technology capabilities by integrating artificial intelligence (AI) and robotics to automate and precisely locate the target in various scenarios. Furthermore, the discussion includes the impact of UWB technology on society, future industry trends, the commercial landscape, and ethical issues to understand and future research.

This paper addresses the problem of finding the source-to-destination path that maximizes the secrecy spectral efficiency in a multihop wireless network that contains a malicious eavesdropper node. Since the resulting routing metric is non-isotonic, the problem cannot be solved by standard shortest path algorithms. However, we provide a polynomial-time algorithm that provides guaranteed optimal solutions to the problem. Our algorithm hinges upon the divide-and-conquer principle from optimization theory, and a modification to the Bellman-Ford routing algorithm. Moreover, we illustrate that the case of multiple colluding eavesdropper nodes can be solved to optimality using the same framework of a single eavesdropper, but prove that the case of multiple non-colluding eavesdropper nodes is NP-complete to solve. For the latter, we show that a modified Dijkstra algorithm can produce approximate solutions that are guaranteed to have no cycles. Our numerical results further illustrate the efficiency of our proposed approach.

This study explores the development and comprehensive characterization of Al6061-Gd₂O₃ composites, designed to enhance radiation shielding and mechanical properties for critical applications in environments exposed to ionizing radiation. The purpose of this research is to assess the effectiveness of Gd2O3 as a reinforcing material for Al6061, with the aim of creating a dual-function composite that combines structural integrity with superior gamma-ray and neutron attenuation capabilities. Using X-ray diffraction (XRD) and scanning electron microscopy (SEM), we analyzed the microstructural effects of varying Gd2O3 content, observing homogeneous dispersion at low concentrations and clustering at higher levels. Mechanical properties demonstrated that increased Gd2O3 content reduces the elastic modulus, indicating a trade-off between stiffness and shielding efficiency. Radiation shielding parameters, including Transmission Factor (TF), mass attenuation coefficient (MAC), and Fast Neutron Removal Cross Section (FNRCS), were evaluated through Phy-X/PSD and PHITS Monte Carlo simulations. Results indicate that Gd2O3 reinforcement significantly improves gamma-ray and neutron shielding, particularly at higher concentrations, making the composite viable for non-structural applications prioritizing radiation protection. The findings from this study highlight the potential of Gd2O3-reinforced Al6061 composites to serve as effective shielding materials in environments where radiation exposure is a primary concern.

Dynare is a popular software for solving dynamic stochastic general equilibrium (DSGE) and overlapping generations (OLG) models. It is used along with Octave or Matlab. However, writing documents using Dynare outputs can be error-prone and time-consuming, as it requires copying and pasting the outputs into the document. To address this problem and ensure reproducibility, we create the DynareR R package. The package seamlessly integrates Dynare and R, extending R’s capabilities to estimate DSGE and OLG models using the Dynare engine. The package can be used with R, R Markdown and Quarto.

The use of the open publishing is expected to be the dominant model in the future. However, along with the use of this model, predatory journals are increasingly appearing. In the current study, the awareness of researchers in Jordan about predatory journals and the strategies utilized to avoid them was investigated. The study included 558 researchers from Jordan. A total of 34.0% of the participants reported a high ability to identify predatory journals, while 27.0% reported a low ability to identify predatory journals. Most participants (64.0%) apply “Think. Check. Submit.” strategy to avoid predatory journals. However, 11.9% of the sample reported being a victim of a predatory journal. Multinomial regression analysis showed gender, number of publications, using Beall’s list of predatory journals, and applying “Think. Check. Submit.” strategy were predictors of the high ability to identify predatory journals. Participants reported using databases such as Scopus, Clarivate, membership in the publishing ethics committee, and DOAJ to validate the journal before publication. Finally, most participants (88.4%) agreed to attend a training module on how to identify predatory journals. In conclusion, Jordanian researchers use valid strategies to avoid predatory journals. Implementing a training module may enhance researchers’ ability to identify predatory journals.

This research examines the impact of divestiture socialization on the well-being, authenticity, and work creativity of newcomer frontline employees. It is proposed that authenticity will mediate the effect of divestiture socialization on well-being and creativity while self-monitoring personality’s two dimensions of “other directedness” and “public performance” differently moderate the influence of divestiture socialization. “Public performance” can buffer the negative impact of divestiture socialization on authenticity, while “other directedness” can exacerbate the negative impact. The indirect effects of divestiture socialization on employees’ well-being and creativity through the expression of authenticity are also proposed to be moderated by the effects of self-monitoring dimensions. The study sample includes 217 newcomer frontline employees working in hospitality firms in the United Arab Emirates (UAE). A time-lagged research design is used to collect the data. The moderated mediation analyses support the proposed relationships. Theoretical and practical implications of the findings are discussed.

Objective To examine the relationship between etiologically-based preterm birth sub-groups and early postnatal growth according to gestational age at birth. Methods Prospective, multinational, cohort study involving 15 hospitals that monitored preterm newborns to hospital discharge. Measures/exposures: maternal demographics; etiologically-based preterm birth sub-groups; very, moderate and late preterm categories, and feeding. Primary outcomes: serial anthropometric measures expressed as z -scores of the INTERGROWTH-21 st preterm postnatal growth standards. Results We included 2320 singletons and 1180 twins: very=24.4% ( n = 856, including 178 < 28 weeks’ gestation); moderate=16.9% ( n = 592) and late preterm=58.6% ( n = 2052). The median (interquartile range) postmenstrual age at the last measure was 37 (36–38) weeks. The ‘no main condition’ sub-group percentage increased from early to late preterm; the ‘perinatal sepsis’ sub-group percentage decreased. ‘Perinatal sepsis’, ‘suspected IUGR’ and ‘fetal distress’ very and late preterm infants had lower postnatal growth patterns than the ‘no main condition’ reference sub-group. This pattern persisted in late but not very preterm infants when postnatal growth was corrected for weight z -score at birth. Conclusion The proportional contribution of etiologically-based preterm sub-groups and their postnatal growth trajectories vary by preterm category. Postnatal growth is partially independent of fetal growth in the majority of preterm infants (i.e., those born late preterm). Impact Preterm birth, the leading cause of under-5 mortality, is a highly heterogenous syndrome, with surviving infants at risk of suboptimal growth, morbidity, and impaired neurodevelopment. Both the proportional contribution of etiologically-based sub-groups and their postnatal growth trajectories vary by preterm category (very/moderate/late). The ‘perinatal sepsis’, ‘suspected IUGR’ and ‘fetal distress’ sub-groups amongst very and late preterm infants had lower postnatal growth than the ‘no main condition’ preterm infants. The pattern persisted after adjusting for birth size only in the late preterms. Postnatal growth is partially independent of fetal growth in the majority of preterm infants (i.e., those born late preterm).

Rapid growth and technological improvement in wireless communication, driven by engineers from various disciplines, have reached significant milestones. Unmanned Aerial vehicles (UAVs) and flying ad hoc networks (FANET) have undergone one of the biggest innovations. UAVs have drawn a lot of attention from research institutions. They are increasingly employed in various application fields, such as real‐time monitoring, precision farming, wireless coverage, military surveillance, climate monitoring, disaster surveillance, and monitoring and rescue operations. The primary characteristics of disasters are their unpredictability and the scarcity of resources in the affected areas. To reduce the loss of lives and livelihoods, disaster management has received much attention. Numerous methodologies and technologies have been developed to predict and handle disasters. UAVs are increasingly being used in disaster management. Additionally, artificial intelligence and collaborative machine learning techniques are gaining prominence among researchers, who are investigating the possibility of their use in disaster management tasks to better cope with the severe and frequently devastating effects of natural catastrophes. This paper provides a review of the relevant FANET research activities in disaster management and emerging artificial intelligence techniques, along with several observations and research challenges. The papers are categorized based on the disaster scenario‐related problems and their proposed solutions. FANET problems are receiving less attention from the research community, and the main challenges with FANETs are also highlighted. Finally, significant insights are presented that can aid in improving research related to the application of FANETs in disaster management.