Abstract

This article presents an innovative solution for the development of FAZZA’AH, a technological advancement designed specifically for the elderly and volunteer enthusiasts in Bahrain. The proposed system aims to simplify daily tasks for the elderly, enhancing their quality of life through convenience and accessibility. Simultaneously, it fosters a culture of volunteerism among youth by offering meaningful engagement opportunities across various service categories. FAZZA’AH is highly adaptable and effective in bridging generational divides, inspiring social responsibility, and empowering both the elderly and youth to lead purposeful lives rooted in connection and service. Implementation results demonstrate that FAZZA’AH is a reliable, adaptable, and efficient system with significant potential to enhance the quality of life for its users and set a benchmark for elderly care technologies in the region.

Abstract

The “NOOKHATHA” application is a technology-driven platform designed to support Bahraini fishermen by simplifying administrative processes, improving their quality of life, and aligning with Bahrain’s Vision 2030. It streamlines fishing license applications, eliminates paper-based procedures, and includes a real-time safety tracking system to monitor fishermen’s safety during maritime trips. The app also features a direct sales platform, boosting fishermen’s income by reducing reliance on intermediaries. As a multi-stakeholder platform serving Bahrain’s fishing industry, “NOOKHATHA” provides essential information on maritime laws, weather updates, and geographical boundaries, promoting awareness of marine resource conservation and sustainability. By integrating technology with tradition, the platform preserves Bahrain’s maritime heritage while addressing global challenges and transforming traditional industries through digital innovation. This research examines the development, implementation, and societal impact of “NOOKHATHA” in safety, sustainability, and economic empowerment, aiming to inspire new digital initiatives and inform best practices for e-government transformation.

Abstract

The increasing need for sustainable energy solutions has paved the way for innovative approaches to harness renewable energy sources. This research work focuses on the implementation of a Pico Hydro Turbine (PHT) system integrated with Internet of Things (IoT) technology within the traditional Falaj irrigation channels of Oman. The PHT system utilizes a 500W/24V AC generator to convert the potential energy of flowing water into electrical energy, which can be monitored and analysed remotely. The project aims to demonstrate the feasibility of utilizing small-scale hydroelectric systems in regions with limited water flow, leveraging IoT technology to enhance efficiency and operational transparency. The AC voltage generated by the turbine is measured using an Arduino R4 Wi-Fi equipped with an AC voltage sensor module. Then this generated voltage is converted into DC regulated voltage using AC-DC regulator. Data is transmitted in real-time to a cloud platform, ThingSpeak, enabling remote monitoring and data visualization. The study addresses key aspects such as system design, sensor calibration, data acquisition, and IoT integration. Emphasis is placed on ensuring reliability and safety in voltage measurement, especially given the variable nature of water flow in Falaj channels. The solution provides insights into the operational performance of the PHT system while promoting sustainable energy practices within local communities. The results highlight the potential of PHT systems as a cost-effective and eco-friendly energy solution in similar settings worldwide.

Abstract

Desalination has long been vital for providing clean water for consumption and agriculture. Recently, seawater desalination has emerged as a sustainable freshwater source, necessitating operational optimization. This project aimed to identify and optimize a suitable seawater desalination technology, selecting Reverse Osmosis (RO). The project involved designing, simulating, and optimizing the plant, followed by economic evaluations. Additionally, the integration of solar energy systems was analyzed for economic viability and CO2 emissions. Four alternatives were simulated to optimize a seawater desalination plant with a capacity of 500,000 m³/day, focusing on maintaining Total Dissolved Solids (TDS) below 300 ppm and specific energy consumption under 5 kWh/m³. The fourth alternative, deemed the best, achieved a TDS of 164.7 ppm and the lowest specific energy consumption of 4.33 kWh/m³. Economic analyses assessed the viability of ultrafiltration and desalination processes with and without 10% reliance on renewable energy. Two approaches were used: one excluding labor and land costs, and another including them. The first approach estimated the cost of producing 1 m³ of drinking water at BD 0.246/m³ without renewables, yielding a Net Present Value (NPV) of 373 million Bahraini Dinars. With renewables, the cost rose to BD 0.331/m³, with an NPV of 232.6 million Bahraini Dinars. The second approach, accounting for land and labor costs, calculated the cost at BD 0.252/m³ without renewables (NPV of 363 million Bahraini Dinars) and BD 0.3374/m³ with 10% renewables (NPV of 231.6 million Bahraini Dinars). Increasing renewable reliance to 20% raised the cost to BD 0.42788/m³ and reduced the NPV to 82 million Bahraini Dinars. Carbon footprint analysis showed lower emissions for the renewable-integrated design, with direct and indirect emissions of 1.6 and 0.72 kg CO2/m³, respectively, compared to the original design’s 1.79 and 0.78 kg CO2/m³.

Abstract

Fault detection, isolation, and accommodation (FDIA) systems play a critical role in ensuring the smooth operation of complex industrial processes with no faults.
However, traditional model-based approaches face challenges in capturing and maintaining the complexity of modern systems.
This paper presents a data-driven alternative for FDI and faulttolerant control (FTC) systems that overcome these limitations.
This paper tackles the fault identification and detection in the Shell heavy oil fractionator using two control approaches, the Model Predictive Control (MPC) and Proportional-Integral (PI) controller. Two types of fault behaviors (drift and bias faults) are applied to the validated model that was validated with historical data collected during normal process operation. To indicate potential faults in the measurement system, the Squared Prediction Error (SPE) is calculated to observe the possible faults. While introducing the two faults, the constructed FDI unit showed successful detection of the fault using PCA. For identification, the fault is isolated and identified through SPE calculations, where the top endpoint y1 clearly showed a high spike of 240 and 18 compared to the other outputs (for drift fault and bias fault, respectively). Finally, fault-tolerant control is constructed and applied to compensate for the fault behaviors introduced. The two FTC techniques used are measurement reconstruction and measurement replacement in both MPC and PI control algorithms. The two techniques showed great results in compensating faults, where the fault compensation for bias fault occurred at time 905 min after introducing the fault and
around 25-30 minutes after introducing the fault measurement as drift fault. The results demonstrate the system’s adaptability
and versatility in real-world industrial settings. When combined with PI and MPC controllers, the FDI system exhibits robust
performance, providing valuable insights into its capabilities.
By leveraging data-driven methodologies and assessing their performance in a simulated environment, this paper paves the way for more effective FDI systems in complex industrial processes.

Abstract

The project focuses on the development of a
comprehensive system aimed at enhancing workplace safety and monitoring employee well-being in industrial environments.
Utilizing a combination of computer vision techniques, wearable health monitoring systems, and data visualization platforms, the
project addresses critical aspects of safety and health monitoring. The initial phase involves the utilization of MATLAB’s Image Labeler tool to accurately detect and identify
essential personal protective equipment worn by employees. By training machine learning models with annotated images, the system can effectively recognize items such as helmets, safety jackets, and earmuffs. Data augmentation techniques further enhance the model’s robustness by introducing variations in the appearance of personal protective equipment items, ensuring accurate detection in diverse scenarios. The Aggregate Channel Features object detector, integrated into the system, leverages channel features and boosting techniques to achieve high accuracy in personal protective equipment detection tasks. The algorithm’s ability to capture object appearance and context information plays a crucial role in ensuring reliable safety compliance monitoring. In the second phase, a wearable health monitoring system is implemented using ESP32 Wi-Fi modules and sensors to track vital health parameters like humidity, room temperature, body temperature, heart rate, and oxygen levels in real-time. The two phases of the projects are combined by integrating the sensors into the design of earmuffs worn by the industrial workers. The collected data is transmitted to Power BI for analysis and visualization, providing insights into employee health trends and enabling proactive interventions.
The Power BI dashboard showcases detailed tables, average health metrics, and dynamic visualizations, facilitating real-time monitoring and analysis. The project’s innovative approach to integrating advanced technologies for safety and health monitoring in industrial settings sets a precedent for effective workplace safety solutions. By emphasizing data-driven insights and user-friendly interfaces, the system aims to create safer and healthier work environments while ensuring compliance with safety protocols and regulations.

Abstract

With a main objective of enhancing process
stability and control, this paper explores the development and use of a real-time soft sensor for predicting product composition
in Crude Distillation Units (CDUs). Developing a machinelearning model able to continually analyze and forecast product composition during crude oil distillation takes front stage. The basis of the research is experimental data derived from dynamic simulations of the CDU process employing Aspen-HYSYS form.
Time Series Linear Regression (TSLR), Time Series Partial Least Squares (TSPLS), and Time Series Neural Networks (TSNN) are among the approaches used in several soft sensor
models created. Performance measures, including root mean square (RMS) and the coefficient of determination (R-squared),
guide evaluation of these models. Thanks to its better accuracy and predictive capabilities, where it obtained the lowest Root Mean Square (RMS) error of 0.8006 and the highest coefficient of determination (R-squared), the Time-series Neural Network (TSNN) stands out among the developed models as the best
option for distillation endpoint estimate in CDUs. Then linked into the Aspen HYSYS modelling plant, the TSNN soft sensor estimated diesel molar flow in real-time. Integrated with the simulated plant, the model was trained on real-time data originating from an Aspen HYSYS simulation of a crude oil distillation unit, allowing continuous live estimates.

Abstract

This report investigates the feasibility and efficiency of Direct Air Carbon Capture (DAC) using aqueous sodium hydroxide (NaOH) and potassium hydroxide (KOH) solutions. Through simulations performed in Aspen Plus v.14 software, the study evaluates process performance and energy efficiency. The results demonstrate that KOH outperforms NaOH in carbon dioxide (CO₂) capture efficiency (89.8% vs. 86.4%) due to its higher solubility and ease of regeneration.
A key focus of the study is energy optimization, achieved through pinch analysis, identifying a minimum temperature difference (ΔTmin) of 15°C. This optimization reduced heating and cooling demands to 51 MW and 103 MW, respectively, in the optimal scenario. Additional thermal recovery from the slaker reactor offset 54.7% of heating requirements for KOH systems and 54.4% for NaOH systems, resulting in residual heating demands of 79.282 MW and 80.657 MW, respectively.
The findings underscore the potential of DAC as a viable solution for atmospheric CO₂ removal, especially when paired with energy-efficient designs. Future work will refine process designs, explore alternative absorbents, and integrate renewable energy sources to enhance scalability and sustainability.

Abstract

Plastic waste is a global environmental problem that needs better management and use. Based on the 2030 vision, Bahrain aims to reduce the percentage of carbon dioxide emissions. Unfortunately, the current methods of dealing with plastic waste, such as landfills and incineration, do not serve this goal; on the contrary, they increase environmental damage. Solar gasification is an innovative and long-term solution to these long-standing issues.
In this work, we investigated a solution to the plastic waste problem by using a solar gasification plastic waste-to-energy converter, a new idea in the Bahrain market. Solar gasification is an innovative and environmentally friendly method for managing plastic waste. The solar dish-type thermal collector is modeled to produce thermal energy at a very high temperature, which energizes a decomposition process to produce gas, leading to power production in a gas power cycle. The gasification and gas power cycle was simulated in Aspen Plus to estimate the power produced.

Abstract

Solar thermal collectors are the main energy generation component of any solar-driven heating and cooling system type and configuration. The presented work aims to
design and develop two configurations of liquid solar thermal collectors consisting of a serpentine and spiral tube attached to a flat absorber sheet to conduct performance evaluation and comparison to demonstrate solar energy’s potential for potential low- to medium-temperature thermal applications. The work was executed in three phases. The first phase was to mathematically model the working of the liquid solar thermal collector to analyze their thermal performance for a range of input parameters. The energy balance equations were formulated and solved iteratively to compute the energy gain of the fluid and estimate the unknown fluid, absorber plate, and glazing temperatures. The influence of a wide range of parameters, including inlet fluid temperature, fluid flow rate, solar radiation intensity, ambient temperature, etc., was studied on the collector’s performance factors. In the second phase, both solar collector components were manufactured and/or procured from the local market, and the collectors were assembled. In the final phase, experiments were performed by measuring different physical parameters,
such as fluid and absorber plate temperature and mass flow rate, to estimate and document the thermal performance of the
solar collectors. The results showed that the spiral tube design performs better than the serpentine-type collector, achieving an average thermal efficiency of 48.2%, about 9% higher than the serpentine collector.

Abstract

The research investigates the application of Universal Design principles in residential and educational environments, focusing on both interior and outdoor spaces. By analyzing design elements that enhance accessibility, usability, and inclusivity, the research evaluates the integration of these principles in different spaces to accommodate diverse user needs. A qualitative methodology is used to conduct this study. Case studies in Residential and Educational Environments were conducted to assess compliance with Universal Design standards, highlighting best practices and identifying gaps in implementation. The findings underscore the importance of applying a holistic design approach that fosters equity and functionality, which offers recommendations for architects, interior designers, and policymakers to create more inclusive environments. This research contributes to the growing discourse on sustainable, user-centered design, emphasizing its crucial role in shaping adaptable and equitable living and educational spaces.

Abstract

Pesticides used in agricultural and industrial sectors can be infiltrated into the soil and water causing certain illnesses such as cancer, birth defects, nausea, and weakened immune system. Therefore, studies are directed on methods to deal with contaminated agricultural and industrial wastes. Chemical degradation is one of the most studied methods recently utilized in order to remove these pesticides. The catalyst synthesized using natural resources are proven to be effective and eco-friendly. This study aims to minimize the side effects of the chemical-based nanoparticles and utilize a more sustainable approach such as green synthesis of Ca-Fe3O4, Ag- Fe3O4, Ca-MgO and Ag-MgO using extracts of Henna, Neem, and Knar leaves, to test these nanoparticle’s potential applicability for degrading Dazomet, and to enhance the degradation by synthesizing composite nanoparticles. The degradation was performed using adsorption on 5 mg catalyst in 100 ml of 0.04 M Dazomet solution for a duration of 30 minutes, the results indicated that the Ferrite-based catalysts degraded Dazomet quite effectively than the Magnesium oxide. The Ag- Fe3O4 synthesized by Neem and Knar exhibited 67.25% and 54.15% degradation, while via Henna extract displayed leeching thus making it impractical to be compared further. Moving on, the degradation of Ca- Fe3O4 prepared with all extracts was relatively insignificant. To optimize Ca- Fe3O4, nanocomposite particles of Ag-Ca- Fe3O4 in a ratio of 50:50 for Ag :Ca by Neem and Knar were formulated. This illustrated that Knar nanocomposite particles are more efficient with a degradation of 81.09% within 30 minutes.

Abstract

This project focused on the design, simulation, and optimization of a coronary stent tailored for treating coronary artery diseases while meeting clinical requirements. A balloon-expandable, open-cell stent design was developed using 316L stainless steel, offering a balance between flexibility and radial strength. Advanced simulation tests, including expansion, radial compression, and fatigue tests, were performed to evaluate the stent’s performance under physiological conditions. The optimized design achieved a radial strength of 429 MPa, ensuring sufficient support against vessel collapse, and a fatigue usage factor of 0.458, demonstrating durability under repetitive loading.
Key performance indicators such as dogboning, foreshortening, and radial recoil were optimized within ranges consistent with clinical standards, ensuring a balance between structural integrity and adaptability. Multi-objective optimization was utilized to adjust strut width (0.07–0.09 mm) and length (1–1.8 mm), allowing for trade-offs between conflicting objectives such as strength and flexibility. The final design achieved 26.7% artery coverage when fully expanded, restoring the artery’s diameter to a healthy 4 mm.
This project integrates engineering principles and medical requirements to produce high-performance stent. The outcomes highlight the potential for improving patient outcomes through meticulous design and optimization while adhering to clinical and manufacturing constraints.

Abstract

In the last two decades, a spectacular development in the superconducting qubits has been accomplished experimentally as well as theoretically. The main reason for that is the realization that superconducting qubits can play a crucial role in the emergent field of quantum information processing and quantum computing. The interaction of superconducting qubits with the quantized electromagnetic field leads to make the circuit quantum electrodynamics, inspired by cavity quantum electrodynamics.
The field of superconducting qubits and circuit QED is fast growing and branching to diverse applications in multidisciplinary fields. This makes it harder for first-time readers to follow-up.
Therefore, this article is targeting to introduce in an intuitive way the topic of superconducting qubit and circuit QED by giving all the mathematical background, Lagrangian formalism for electric circuits, interaction Hamiltonian, and contextual physical interpretation essential to anew scientists and engineers planning to be specialized in this field as well as portraying a wide vantage for higher studies through the suggested recommendation resulted from the simulated examples and written code based on Python packages and Qiskit software. In this article, the superconducting
qubits, transmon and fluxonium, are reviewed after introducing the abstract definition of a quantum bit. Moreover, light-matter interaction in fluxonium qubit and quantization of transmission line resonator are discussed briefly. Some of the simulated systems such as the energy levels of fluxonium as a function of offset flux are presented. Finally, applications of superconducting qubits in quantum computing and other fields such as quantum transistors, quantum machines and metamaterials are discussed.  Index Terms—Superconducting qubits, circuit QED, quantum logic gate, fluxonium, transmon.

Abstract

In the last two decades, a spectacular development in the superconducting qubits has been accomplished experimentally as well as theoretically. The main reason for that is the realization that superconducting qubits can play a crucial role in the emergent field of quantum information processing and quantum computing. The interaction of superconducting qubits with the quantized electromagnetic field leads to make the circuit quantum electrodynamics, inspired by cavity quantum electrodynamics.
The field of superconducting qubits and circuit QED is fast growing and branching to diverse applications in multidisciplinary fields. This makes it harder for first-time readers to follow-up.
Therefore, this article is targeting to introduce in an intuitive way the topic of superconducting qubit and circuit QED by giving all the mathematical background, Lagrangian formalism for electric circuits, interaction Hamiltonian, and contextual physical interpretation essential to anew scientists and engineers planning to be specialized in this field as well as portraying a wide vantage for higher studies through the suggested recommendation resulted from the simulated examples and written code based on Python packages and Qiskit software. In this article, the superconducting
qubits, transmon and fluxonium, are reviewed after introducing the abstract definition of a quantum bit. Moreover, light-matter interaction in fluxonium qubit and quantization of transmission line resonator are discussed briefly. Some of the simulated systems such as the energy levels of fluxonium as a function of offset flux are presented. Finally, applications of superconducting qubits in quantum computing and other fields such as quantum transistors, quantum machines and metamaterials are discussed.
Index Terms—Superconducting qubits, circuit QED, quantum logic gate, fluxonium, transmon.