University of Bahrain
  • Madīnat ‘Īsá, Bahrain
Recent publications
Capacitive deionization (CDI) is a novel, cost‐effective and environmentally friendly desalination technology that has garnered significant attention in recent years. Carbon materials, owing to their excellent properties, have become the preferred electrode materials for CDI. Given the significant differences between different ions, ion‐selective performance has emerged as a critical aspect of CDI applications. However, comprehensive reviews on the selective ion separation capabilities of carbon materials for CDI remain scarce. This review examines the progress in developing carbon materials for ion‐selective separation in CDI, focusing on regulatory mechanisms and representative materials. It also discusses the applications of selective CDI carbon materials in areas such as heavy metal removal, nutrient recovery, seawater desalination resourcing, and water softening. Furthermore, the challenges and future prospects for advancing carbon materials in CDI are explored. This review aims to provide theoretical insights and practical guidance for utilising carbon materials in wastewater treatment and resource recovery.
Introduction Cervical cancer presents a significant global public health challenge, particularly affecting low- and middle-income countries like Egypt. Despite the availability of effective screening methods such as Pap smears and HPV testing, the incidence of cervical cancer remains high in Egypt. Health literacy, which refers to the ability to access, understand, and utilize basic health information and services to make informed decisions, is crucial in influencing individuals’ health behaviors, including their participation in cancer screening programs. Objectives To examine the correlation between health literacy levels and cervical cancer screening behaviors among women. Methods This study employed a multi-site cross-sectional research design from September 2023 to January 2024. The research was conducted at four primary health care (PHC) facilities in the Damanhur district of Egypt. Three hundred fifty women participated in the study, completing a comprehensive questionnaire that included a Woman’s Social and Health Form, a Cervical Cancer Knowledge Scale, a Cervical Cancer Screening Behaviors Scale, and a Health Literacy Scale (HLS-SF12). Results The study revealed significant relationships between the importance of health literacy (HL) in understanding cervical cancer (CC) knowledge and screening behaviors among Egyptian women. A positive correlation was found between Knowledge and HL (r = 0.507, p < 0.001). Conversely, perceived barriers negatively correlated with knowledge and HL (r = -0.172, p < 0.05; r = -0.277, p < 0.01). The regression analysis revealed that higher levels of HL were significantly associated with greater knowledge about CC (B = 0.148, p < 0.001). Conversely, knowledge about CC was also found to be a strong predictor of higher HL levels (B = 1.205, p < 0.001). These results highlight the bidirectional relationship between HL and knowledge, where improvements in one can enhance the other. Conclusion Addressing misconceptions and increasing knowledge about the importance of regular screenings, mainly through accessible and culturally appropriate channels, could lead to an improved uptake of cervical cancer screening services. Overall, this study lays a foundation for future research to continue exploring ways to improve cervical cancer prevention and control efforts among women. Clinical trial number Not applicable.
This paper presents a numerical assessment of a minichannel heat sink integrated with multiple heat transfer enhancement techniques. The primary objective is to evaluate the hydrothermal performance of minichannel incorporated with secondary inclined channels and triangular pins (MC-SICTP), compared with other heat sink designs, namely minichannel with triangle pins (MC-TP), minichannel with secondary inclined channels (MC-SIC), and straight rectangular minichannels (MC-RC). Also, an optimization study is conducted to test various position distances of pins (λ) for selecting the optimum pin position, demonstrating the highest hydrothermal performance. Three different types of working fluids are studied, namely distilled water, water-based zinc oxide-multi-walled carbon nanotubes (ZnO-MWCNT), and water-based cerium oxide-multi-walled carbon nanotubes (CeO2-MWCNT). Combining multiple heat transfer enhancement techniques offers a novel approach to enhancing the hydrothermal performance of minichannel heat sinks, contributing to the development of more efficient thermal management systems. The range of Reynolds number (Re) is considered to be from 200 to 1000, and a wide range of hybrid nanofluids volume concentrations of 0.5–2% is investigated. Computational fluid dynamics in ANSYS R21 is used, and the numerical simulations are conducted utilizing the finite volume method, solving the governing equations for heat transfer and fluid flow with applied boundary conditions. According to the findings, it is revealed that the MC-SICTP exhibits superior hydrothermal performance as compared with other designs. The MC-SICTP with (λ = 2) attains the highest performance with Nusselt number enhancement of 1.92 and performance evaluation coefficient (PEC) of 1.27 among other pin positions, at Re = 1000. In addition, the hybrid ZnO-MWCNT nanofluid outperformed the hybrid CeO2-MWCNT nanofluid, reaching a maximum Nusselt number improvement of 2.25 with a maximum PEC of 1.49, at a volume fraction of 2%.
This study examines the news impact, persistence and asymmetric effects of stock, oil and cryptocurrency markets in Gulf Cooperation Council (GCC) countries. The diagonal BEKK method is applied to the daily trading prices of three major cryptocurrencies, crude oil and four stock market indices from January 2018 to February 2024. The empirical results indicate a strong, significant volatility spillover between cryptocurrencies, oil and stock prices, but no return spillover effect among these asset classes. A negative news shock in cryptocurrency markets generates more volatility in GCC stock prices than positive news. The study suggests that cryptocurrency price movements are independent of other asset classes, providing portfolio diversification opportunities for investors in GCC countries.
Additive manufacturing (AM) techniques make fabricating complex designs, prototypes, and end-user products possible. Conductive polymer composites find applications in flexible electronics, sensor fabrication, and electrical circuits. In this study, thermoplastic polyurethane (TPU)-based conductive polymer composite samples were fabricated via fused filament fabrication (FFF). The effects of three important process parameters, including infill density (ID), layer thickness (LT), and fan speed (FS), on various mechanical properties (tensile and compressive properties) were investigated. It was observed that all the considered process parameters affect the mechanical properties, and they are significant parameters, as per the analysis of variance (ANOVA). From scanning electron microscopy (SEM) and optical microscopy, various combinations of parameters such as low ID, high LT, and high FS resulted in the formation of defects such as voids, cracks, and warping, which resulted in low mechanical properties. Finally, process parameter optimization was performed, resulting in a conductive polymer composite with the best possible combination of mechanical properties at high ID, low LT, and medium FS.
The objective of this paper is to use the synthetic control method (SCM) with an event study framework to estimate the economic effect of the Great East Japanese Earthquake and Tsunami in Tohoku on the Fukushima, Iwate, and Miyagi prefectures in Japan. Using data from 41 prefectures, the SCM was applied to provide a proxy for normal performance in these prefectures in terms of Prefecture Per Capita Income and Gross Prefecture Product. Using these metrics, the Miyagi and Iwate prefectures had positive and statistically significant increases in the three years that followed the event, whereas Fukushima performed in line with our synthetic control estimates. Our results indicate that the impact of the tsunami and earthquake from an economic standpoint was relatively short-lived and this geologic disaster was positively corrected with short- and intermediate-term economic growth in the three prefectures most affected. Our findings provide a contrast to studies on the 1995 earthquake in Kobe, Japan, in which DuPont and Noy (Econ Dev Cult Change 63: 777–812, 2015) illustrated that even massive infusions of resources could not overcome the negative economic impact of that particular geological event over the short- and medium-term. Koshimura et al. (Phys Eng Sci 373: 140–373, 2015) indicated that the tsunami and earthquake of Tohoku led to a paradigm shift in Japan’s disaster management policy, and it seems as though it was effective. The Japanese Government’s plan ‘Act on Development of Tsunami-resilient Communities’ called for a combination of urban planning, housing reconstruction, structural prevention/mitigation, and tsunami disaster mitigation plans all seem to have been effective in accomplishing their goals to support people affected by the event, reconstruct homes and cities, and to revitalize industries and livelihoods. There were some limitations to our study, which include reliance on certain prefectures to construct the synthetic control and whether other areas that were included in the construction of the synthetic control were also impacted by the event. In the study, we targeted the prefectures most affected by the event and conducted robustness checks to ensure that the most appropriate synthetic control was used based on a comparison of prefecture level predictor variables such as the average monthly rental cost, population, and average income.
Financial institutions are reforming their conventional financial operations to meet customers’ demands through the implementation of AI technology within their operations. The incorporation of AI-driven chatbots, particularly, has garnered momentum for its potential to offer personalized, customer-centric processes in ways that are yet to be achieved by the traditional approaches of financial institutions. Chatbots implement communication between users and AI-trained models, mimicking human dialogue-exchange by conversing in natural real-world language with the user through various channels—such as websites and mobile applications—in the place of traditional face-to-face representative services within the fintech sector. Through a systematic literary review and analysis, the paper provides a comprehensive understanding and extensive explanation of both the benefits and challenges associated with the integration of AI chatbots in FinTech customer service. The paper also identifies and elucidates the specific features and intricate functionalities within chatbots that significantly influence customer and user satisfaction, loyalty, and engagement levels. The study concluded that the prospects of AI-driven chatbots as conversational agents include the enhancement of user-experience through real-time, personalized customer service as well as efficient self-service and feasibility of bank and finance related operations. However, such novel technology possesses limitations to its extent in engaging in authentic conversation. Chatbot use comes with the added risk of plausible misinterpretation of context and exchange of false information and uncertainties surrounding security and protection of data privacy and confidentiality.
Background Educational research highlights active approaches to learning are more effective in knowledge retention and problem-solving. It has long been acknowledged that adapting to more active ways of learning form part of the challenge for new university students as the pedagogical distance between the didactical approach largely followed by secondary school systems the world over differs quite significantly from the often more student-led, critical approach taken by universities. University students encounter various learning challenges, particularly during the transition from secondary school to university. Poor adaptation and low performance in the first year of tertiary education can lead to higher failure rates and potential withdrawal from study programmes. Adopting active learning strategies early in this transition phase is crucial for supporting students’ adaptation and success. Gaining student engagement with active learning can be a significant challenge when there is an expectation to participate in a discussion or voice an opinion. Case-based learning (CBL), with its scaffolded form of learning, is an approach that could provide the support needed to help multicultural learners adapt to their new learning environment in a non-threatening classroom-based setting. The research question in this study was: what features of CBL support active learning? Methods Data was collected using Structured Group Feedback Sessions (SGFS) from 36 students from 12 different countries. Students were placed in eight Structured Group Feedback sessions, a method that facilitates structured discussions and is effect in curriculum evaluation and feedback. The Experience Based Learning model was used as the conceptual framework to guide the analysis, which was completed using the framework analysis method. Results Themes were derived from the Experience Based Learning model: affective, pedagogical, and organisational and analysed according to the research question. We found CBL can be used to facilitate active learning with all students at a multicultural medical university. We identified six learning points to highlight features of CBL that support active learning: CBL increased contact with peers and facilitated student bonding; students need to feel psychologically safe to participate; prior learning can enhance confidence to participate; facilitators need to be aware of their role, know about psychological safety, and manage student participation including the dominant voice; some students have a lower tolerance of uncertainty and need additional clarity at the end either via the facilitator or additional notes that provide the key learning points to take away; students became more engaged when a case is aligned to a real patient case giving it authenticity. Conclusions This study explores how CBL can support active learning in a multicultural medical school. We identified that CBL did facilitate active learning and students engaged with it and enjoyed it. We identified six learning points to support others going forward.
A water circulation system consisting of a heat dissipation plate was designed to assist laser cladding. The impact of scanning rate under constant cooling conditions on the structure and properties of Fe-based amorphous composite coatings prepared by laser cladding was investigated. The results show that with increasing scanning rate, the coating exhibits different characteristics. At 17 mm/s, the coating has the fastest cooling rate, minimal heat impact, and features predominantly amorphous phase, high hardness, and excellent wear and corrosion resistance. At 11 mm/s and 13 mm/s, local high hardness phases appear, but overall hardness is poor. At 15 mm/s, the coating has the highest hardness (630HV) due to increased amorphous content and solid solution strengthening of supersaturated α-Fe phase. Wear tests reveal low and stable wear rates for samples at 15 mm/s and 17 mm/s, correlating with their uniform hardness. Corrosion resistance tests show the best performance for the 11 mm/s samples, with excellent corrosion resistance. As scanning rate increases, the content of strengthening phases decreases, and the supersaturation of α-Fe phase increases, leading to decreased corrosion resistance. The 17 mm/s samples, with significantly higher amorphous phase content and lower overall content of supersaturated α-Fe phase, exhibit excellent corrosion resistance.
Incremental forming technology is gaining increasing attention from various industrial sectors owing to its adaptability and potential for customization. Nonetheless, some challenges such as poor geometric precision are impeding the industrial widespread of this method. Hybridization, cooperation between mechanisms/processes/energies/approaches, has lately emerged as a powerful approach to elevate the manufacturing competence. To this end, hybridization idea has been implemented in the area of incremental forming to address the pertinent challenges, and conducive outcomes have been achieved. The present article reviews advancements made in hybrid incremental forming. The article begins with a comparative analysis of contemporary and traditional incremental forming processes from multiple viewpoints. Subsequently, recent developments in energy-assisted incremental forming processes, categorized into cryogenic temperature-assisted, thermal-assisted and special energy field-assisted techniques (which encompass ultrasonic, electric, and electromagnetic fields), are summarized. The mechanisms through which special energy fields interact with materials and their subsequent impact on the forming quality of workpieces are systematically examined. Additionally, the benefits and drawbacks of various assisted methods are analyzed. Furthermore, the current state of research regarding the hybridization of incremental forming with other forming processes is outlined, which offers novel avenues for enhancing productivity and the quality of workpiece deformation. Lastly, the prospective advancements in hybrid incremental forming technology are presented to support its application in large-scale industrial contexts.
In this study, the quantum kernel is utilized as an indicator of the security of encoded information within classical strings. The impact of the Hadamard gate on the behavior of single, control, and all quantum strings is analyzed. To illustrate this concept, two distinct classical strings are examined. Our findings reveal that the position of the string significantly influences the level of security of the encoded information, depending on whether it acts as a control or target string. Furthermore, the quantum string's capacity remains almost stable when it functions as a target string during the entanglement process, whereas an unstable behavior is observed when it serves as the control string.
Tomatoes are essential fruits in numerous nations for their vast demand. It is very important to maintain the freshness of tomatoes. One of the primary challenges in the recent culinary landscape is accurately identifying healthy tomatoes while effectively eliminating damaged or rejected ones. Existing approaches employ various strategies for categorizing tomato fruit, but they often suffer from inaccuracies, slow detection, and suboptimal performance. Thus, motivated by this gap, in this paper, we propose a novel machine learning (ML) framework, ViT-SENet-Tom, which is a hybrid vision transformer (ViT) model with squeeze and excitation (SENet) block network for fast, accurate, and efficient tomato fruit classification. The framework works on three tomato classes, respectively, the ripe, unripe, and reject. In developing the proposed model, we utilized advanced and newly designed layers and functions. This integration created a more complex and sophisticated neural network, significantly enhancing efficiency and contributing to the model’s novelty. Our chosen dataset was small initially, but we implemented augmentation techniques to increase its size. This approach made our system more reliable, efficient, and effective. The hybrid ViT-SENet framework employs encoders and self-attention networks with squeeze and excitation channel functions to allow precise, robust, fast, and efficient tomato classification. In simulation, the framework achieves a training accuracy of 99.87% and validation accuracy of 93.87%, indicating the precise classification of tomatoes. Besides, this work tests accuracy using fivefold cross-validation. The highest accuracy seen at fold-5 is 99.90%. These testing results demonstrate the efficacy of the proposed framework in real-deployment scenarios. The implementation has the potential to provide enhanced and more sustainable food security and safety in future.
An Adaptive Neuro-Fuzzy Inference System (ANFIS) for assessing cognitive load in Brain-Machine Interfaces (BMIs) is suggested to be developed and evaluated in this work. EEG characteristics and task-related metrics are only two examples of the pertinent input variables that the ANFIS model uses to forecast and evaluate cognitive load levels. In order to deal with the inherent imprecision and uncertainty in cognitive load data, fuzzy learning is utilised to convert discrete input values into fuzzy sets through the use of predefined membership functions. To represent the complex interactions between input variables and cognitive load, rule activation and inference mimic the cognitive process of integrating data from several rules. The resulting fuzzy output is then defuzzed using techniques like centroid or mean of maxima to yield an understandable and straightforward measure of cognitive burden. The efficacy of the ANFIS model in offering a comprehensive and precise assessment of cognitive load levels in BMIs is demonstrated. The ANFIS model has potential as a reliable and flexible method for evaluating cognitive stress in the context of brain-machine interfaces. The study shows superior performance over an existing KNN method with an accuracy of 90%, precision of 88%, recall of 82%, and sensitivity of 85%.
Waste classification remains pivotal to environmental sustainability along with proper waste management. Traditional approaches such as CNNs and LSTM networks prove to be inadequate in properly capturing the spatial and temporal correlations in waste images. To cover for this, the study puts forward a new waste classification strategy that leverages CNNs, GRUs and transfer learning for increased classification performance. It is worth mentioning that proposed approach relies on CNNs for the spatial feature extraction, GRUs for temporal sequence learning and transfer learning for utilizing pre-trained models for both feature extraction and sequential learning. The proposed framework is developed in Python and tested on the waste classification dataset with accuracy of 97% which is superior to the traditional CNN (89%) and LSTM (92%). This result underlines the capability of applying the transfer learning of convolution neural network (CNN-GRU) that has the capability to develop an effective framework for waste classification efficiently. The research finds that use of such techniques enhances development of AI based solutions towards efficient waste management and environment protection.
Based on Biopharmaceutics Classification System, Ciprofloxacin (CIP) is class IV active pharmaceutical ingredient which exhibits poor solubility and permeability in water. It showed limited solubility due to formation of strong crystal lattice, as positive charged piperazine group interacts with negatively charged carboxylate group to form chains of molecules. Due to better performance, it is used very frequently for many infections treatment. Though, it is very useful antibiotic, but it is photo‐chemically unstable and development of antimicrobial resistance also reported in literature. Due to these serious concerns, the development of CIP formulation is a challenging task for pharmaceutical industries. Currently, several strategies are used to boost pharmaceutical properties of CIP such as solubility, dissolution and antibacterial activity. In this review we have discussed the approaches through which pharmaceutical properties of CIP can be improved such as solubility, dissolution rate and antibacterial activity and so forth. Salts, Co‐crystals, salt‐Co‐crystals, metal complexes, and nanoparticles formations are some prominent strategies for altering CIP pharmaceutical properties. Preliminary, this review is design for CIP, but it can also be helpful for other antibiotics (especially fluoroquinolones class).
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3,817 members
Ebrahim Abdulla Mattar
  • Department of Electrical and Electronics Engineering
Ossama Omar
  • College of Engineering
Mustafa Aytekin
  • Department of Civil Engineering and Architecture
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Madīnat ‘Īsá, Bahrain