The impacts of heavy metal pollution on the planet are among the major environmental problems, with oil facilities having been singled out as the key origin of the heavy metal emissions. Hence, the current study was conducted to investigate the levels of heavy metals in the soil of oil installations and centers for distribution of Anbar Province, Iraq. The samples of soil were done and performed by AAS (Flame Atomic Absorption Spectroscopy). Additionally, some environmental pollution indicators were measured to find their origins over space and time. The results reveal that the average heavy metal concentrations exceeded the limit value set by the World Health Organization and the U.S. Environmental Protection Agency, respectively. Pollution indicators, such as Contamination Factor (CF), Pollution Load Index (PLI), and Geoaccumulation Index (Igeo), were used to examine the level of contamination, which revealed that the area is either uncontaminated or moderately contaminated. Human activities, particularly the increase in air pollution driven by rapid population growth, have been identified as the primary contributors to soil contamination. This research sheds light on the nature and the sources of heavy metal pollution in oil facility sites, exploring the possible solutions to this environmental problem.
This study focuses on enhancing energy resilience in Repulse Bay, a remote community in Nunavut, facing significant power challenges due to the reliance on aging diesel generators. To address these issues, this research explores the development of sustainable hybrid energy systems using HOMER Pro. The optimization results identify a configuration integrating wind turbines and solar panels as the optimal techno-economic solution. The expected outcomes include a substantial reduction in greenhouse gas emissions, increased reliability of energy supply, and significant economic benefits. The proposed hybrid energy system achieves a net present cost of $20.66 million while significantly increasing the renewable energy fraction to 75%. This system drastically reduces greenhouse gas emissions by 60%, aligning with Canada’s goal of achieving net-zero emissions by 2050. Additionally, the implementation of this system is projected to create 149 jobs, thus supporting local economic growth. The findings highlight the potential for similar Arctic communities to transition to renewable energy, contributing to global renewable energy efforts. This study not only demonstrates the viability of hybrid energy systems in reducing environmental impact and operational costs but also underscores their broader implications for sustainable development in other remote Arctic regions and beyond. By adopting such systems, Arctic communities can significantly enhance their energy resilience, reduce their carbon footprint, and stimulate economic growth, thereby aligning local initiatives with global sustainability goals.
The 6.0 moment magnitude scale (Mw) earthquake that struck Ranau, Sabah, on June 5, 2015, resulted in seismic intensities of VI to VII, significantly increasing the seismic vulnerability of buildings in the region. This study presents an analysis of the site-specific seismic ground response and liquefaction potential for the Ranau District, East Malaysia. Ground response spectra were generated for 15 borehole sites, applying a 5% damping factor at ground level using both global and local input ground motions. Seven global and five local seismic records were processed using a one-dimensional equivalent linear approach via DEEPSOIL software. The LiqIT software, based on the Boulanger and Idriss method, was employed for the liquefaction analysis. Ground amplification in Ranau was found to range between 1.281 and 5.132, with peak ground acceleration (PGA) reaching an average maximum of 0.314 g at the surface. Soil periods across the region varied from 0.05s to 1s, consistent with the specifications outlined in the Malaysian National Annex for Sabah (MS EN 1998-1:2015). The results confirmed that the Ranau District is not prone to liquefaction, offering valuable insights for the structural design of future constructions in the area.
The industrial revolution had begun in the 20th century, which resulted in pollution, the appearance of epidemics and diseases, and a demand for sustainability. Numerous cities have adopted the smart city concept to improve energy efficiency, manage services, improve the quality of life for users, and lessen and resolve environmental problems. Since it may be a route to the smart city, a smart university can be thought of as a mini city, and smart city strategies can be implemented in it. Digitization is a catalyst for transforming a traditional university that is based primarily on human practices into a smart university that uses artificial intelligence integrating intelligent learning platforms. The Smart Campus initiative aims to create a university where technology aids academics, staff, students, and visitors in performing daily responsibilities more efficiently and effectively and makes life inside a modern-campus easier. The smart campus has to be understood to identify and validate a framework and ultimately apply it to a university using the smart infrastructure technologies. In this Research; a comprehensive review of retrofitting universities to reflect the modern worldwide technological movement in renovating a sustainable technological architectural design and construction for a university to create a smart educational environment.
To maintain competitiveness and ensure long-term sustainability in the automotive sector, understanding the determinants of profit growth is crucial. This study empirically examines the impact of the Current Ratio (CR) and Net Profit Margin (NPM) on profit growth from 2018 to 2022, focusing on ten automotive companies listed on the Indonesia Stock Exchange. A quantitative methodology, utilizing panel data regression analysis and specifically the Fixed Effects Model (FEM), was employed to uncover significant insights. It was found that the CR positively influences profit growth, whereas the NPM exhibits a negative effect. These empirical findings offer valuable insights into financial management practices within the automotive industry. By understanding the impact of key financial metrics on profitability, investors, managers, and policymakers are better equipped to make informed decisions to optimize financial strategies for profit growth. This study contributes to the existing literature by addressing the relationship between the CR and NPM within the context of the automotive sector, an area where comprehensive analysis has been lacking. These insights are vital for informing strategic financial decisions and supporting the long-term health of the industry in a fiercely competitive global market.
The integration of artificial intelligence (AI) and robotics into the warehouse management system (WMS) has substantially advanced supply chain (SC) operations, offering notable improvements in efficiency, accuracy, and economic resilience. In warehousing environments, AI algorithms and robotized systems enable rapid and precise product retrieval from storage while optimizing routing and packaging, thereby reducing order preparation time and enhancing delivery reliability. The implementation of these advanced technologies also results in fewer errors, improved customer satisfaction, and streamlined SC processes, empowering organizations to better manage inventory and respond swiftly to fluctuating market demands. Such innovations allow for reduced operating costs, enhanced productivity, and increased sustainability. Autonomous mobile robots (AMRs), automated guided vehicles (AGVs), and drones, among other cutting-edge solutions, are increasingly incorporated into the WMS to minimize physical labor and mitigate workplace injuries. Despite these benefits, considerable challenges remain, including the high initial costs and requisite technical expertise for ongoing maintenance. The integration of new AI and robotic technologies into pre-existing systems necessitates careful evaluation, substantial employee training, and process adaptation. Nonetheless, these technologies play a crucial role in fostering environmentally and socially sustainable operations within warehouses and broader SCs, contributing to reduced carbon emissions and the elimination of hazardous tasks for human workers. This study aims to identify the most effective AI and robotic technologies for a sustainable WMS, with recommendations tailored to maximize SC value through automation. A detailed examination of existing warehouse practices is essential to pinpoint areas where automation can yield the most substantial impact and deliver long-term resilience and value for SCs.
Separately excited direct current (DC) motors, renowned for their linear characteristics and controllability, are extensively employed in various industrial applications. Effective speed control of these motors can be achieved through multiple methods, with fuzzy logic being a particularly robust approach. This study focuses on evaluating the transient responses of current and voltage in relation to the rotational speed of a DC motor under two distinct control schemes: field control and armature control, both subjected to similar load disturbances. A simulation-based methodology was employed using a DC motor speed control system combined with a fuzzy logic controller (FLC) designed with the Mamdani min-max method. The system was implemented in Simulink. In this framework, the FLC processes speed error signals and field current ($I_f$) errors as inputs to generate a field voltage control signal, which is then utilized by the armature voltage (Va) regulator to modulate the armature voltage. The results demonstrate that the FLC effectively stabilizes motor speed, quickly and accurately following speed references, even under load disturbances. Moreover, the system effectively mitigates speed fluctuations induced by load variations. A comparison between the two control schemes reveals that the field control approach exhibits a slower response time, taking 2.93 seconds to reach a steady state, whereas the armature control achieves this in a significantly faster time of 0.144 seconds. These findings underscore the efficacy of fuzzy logic in maintaining stable and responsive speed control in DC motors, with the armature control method displaying superior transient performance.
This comprehensive review investigates the ethical implications of artificial intelligence (AI)-driven predictive analytics in healthcare, with a focus on patient privacy, algorithmic bias, equitable access, and transparency. The study further explores the integration of these ethical considerations into educational frameworks to enhance the training and preparedness of healthcare professionals in the responsible use of AI technologies. A systematic literature review was conducted using databases such as PubMed, Scopus, and Google Scholar, employing keywords related to AI, predictive analytics, healthcare, education, and ethics. Articles published from 2017 onwards, discussing the ethical challenges and applications of AI in healthcare and educational settings, were included. Thematic analysis of selected articles revealed significant ethical concerns, including patient privacy, algorithmic bias, and equitable access to AI technologies. Findings underscored the necessity for robust data protection mechanisms, transparent algorithm development, and equitable access policies. The study also highlighted the importance of incorporating AI literacy and ethical training in medical education. An ethical framework was proposed, outlining strategies to address these challenges in both healthcare practice and educational curricula. This framework aims to ensure the responsible use of AI technologies, promote transparency, and mitigate biases in healthcare settings. By addressing a critical gap in understanding the ethical implications of AI-driven predictive analytics in healthcare and its integration into education, the study contributes to the development of guidelines and policies for the equitable and transparent deployment of AI. The proposed ethical framework provides actionable recommendations for stakeholders, aiming to enhance medical education and improve patient outcomes while upholding essential ethical principles.
Warehousing serves as a critical component in the logistics chain, functioning as an intersection for inbound and outbound flows of goods before distribution to end customers. Given the complexity of warehousing operations, which involve numerous processes, activities, and workforce engagement, significant risks are inherently present. Consequently, a comprehensive risk analysis is imperative for effective risk management. Such analysis informs risk evaluation and facilitates the determination of appropriate mitigation strategies, with the goal of prioritising risks based on their potential impact. The objective of this study is to present a novel approach for risk assessment in warehouses operated by third-party logistics (3PL) companies, employing a combination of Failure Modes, Effects, and Criticality Analysis (FMECA) and Data Envelopment Analysis (DEA). The proposed framework aims to optimise risk prioritisation and to support the implementation of targeted preventive and corrective measures, thereby enhancing workplace safety and operational efficiency. This approach has been applied to a case study of a 3PL provider operating in the Serbian market, where 14 specific risks were identified and assessed. The most critical risks included falls from height, items falling from shelves during handling, forklift operations, and machinery-related risks involving packaging machines, electrical equipment, industrial cleaners, heaters, and forklift battery charging—particularly with regard to potential explosion hazards due to hydrogen gas release and acid spills. Based on the risk assessment, a series of preventive and corrective measures were formulated to mitigate the identified risks, thereby reducing the likelihood of occupational incidents, injuries, and fatalities. The integration of FMECA and DEA has been demonstrated as an effective methodology for systematically evaluating risks in warehouse operations, offering a robust basis for improving safety measures in logistics environments.
Remediate groundwater (GW) contaminants (anions, T.D.S, Cr6) to utilize (GW) for irrigation purposes, and implement experimental findings using adsorption technology to minimize pollutants concentration in (GW). Banana peels activated carbon (BPAC) modified to flash as-synthesized graphene (FG) adsorbent. Synthesization of (FG) by transmutation (BPAC) into graphene in a burst of light through an electro-flash reactor technique producing (5gm) of (FG) each time by exposing (BPAC) to manual circuit break of (8 - 10) shocks in the reactor. The adsorption process in batch mode remediates the (GW) samples stabilizing one parameter either (FG) dosage, agitation speed, PH value, or contact time for each experiment and varying the others. Characterization of (FG) The samples’ composition is analyzed using an FTIR spectrometer, SEM, and XRD analysis. Adsorption capacity improved by creating a high internal pores structure with a powerful capacity of adsorption due to its functional surface area. (0.717 m2/gm), and Remediation conducted for (T.D.S, SO4, NO3, and Cr6) values to be proportion to Iraqi’s and FAO standards of irrigation water.
This study examines the predatory efficiency and biological characteristics of Phytoseiulus persimilis in managing Tetranychus urticae (two-spotted spider mite) populations under laboratory and greenhouse conditions. Laboratory assessments were conducted to evaluate feeding preferences and reproductive performance by providing Ph. persimilis with different prey types, including T. urticae eggs, Sitotroga cerealella eggs, and decapsulated Artemia salina cysts. Findings indicated a marked preference for T. urticae eggs, with Ph. persimilis consuming an average of 23.5 eggs per day, significantly surpassing other prey types in consumption rate. Greenhouse trials in cucumber cultivation systems evaluated the predator's efficacy in reducing T. urticae populations at a predator-to-prey ratio of 1:10. Within 10 days, Ph. persimilis achieved a reduction of over 70% in T. urticae populations, underscoring its effectiveness as a biological control agent in greenhouse settings. Statistical analyses, conducted using dispersion analysis via Microsoft Excel and SigmaStat 3.1 software, validated these findings. Controls comprised untreated greenhouse sections and laboratory containers devoid of predators to ensure accurate comparative assessments. The results support Ph. persimilis as a highly effective biological control agent, demonstrating significant predation rates and reproductive success, which underscores its potential to reduce chemical pesticide reliance and promote sustainable, eco-friendly pest management in integrated pest management (IPM) frameworks.
Open Access
