A theoretical study is done to treat Baghdad biomedical wastes (5.21 tons/day) using many treatment methods according to construction and maintenance costs, energy and water consumption and decreasing waste volume and mass. Previous published experimental data results and experimental operation conditions are considered in the current research. The thermal energy and generated wastes are computed from the complete combustion of biomedical wastes (100 kg/h) from two hospitals in Baghdad. Survey data shows that the Medical City and Al-Yarmouk Hospital generate about (700 kg /day) of biomedical waste. A multi-chamber incinerator with a capacity of (100 kg/h) has been designed to burn biomedical wastes using diesel fuel to reach (1100˚C) as a burning temperature. The resulting thermal energy was computed to be (0.7178565 MW) which is emitted directly with flue gases to the ambient without any off-gas system. The mass of flue gases is found to be (1895.8175 kg/h) and the gases are (193.20872 kg/h for CO2 and 1.6352 kg/h for HCl) through using excess air ratios at 150% for solid wastes burning and 20% for liquid fuel burning. The required cement and water are determined for resulting ash to produce concrete suitable for hot weather countries. The results show that incineration has the less costs in construction and operational costs among other modern technologies with a decreasing waste mass to 91.5%.

Globally, the focus is on maximizing the energy yield and efficiency of PV systems as an essential renewable energy source. Different methods have been proposed to achieve this goal. Where the sun’s location in the sky is changed during the day, a tracking mechanism has been proposed as a promising technology to harness the maximum amount of solar radiation by PV system as compared to the fixed panels. In this study, the design and implementation of a polar single-axis tracking system is presented to improve the energy efficiency of PV system through angular variation during the day using the proposed tracking system. This is achieved by interconnecting some devices that include GPS sensors and satellite dish actuators as the main components that are managed and controlled using astronomical equations. The designed system is tracked in a discrete manner that can be adjusted automatically during the day related to each degree change of azimuth angle of the sun from sunrise to sunset. The system has been field tested in Sulaymaniyah, Iraq, to evaluate its performance in different weather conditions and compare it with a fixed PV system. The test results for the clear sky indicated that the increased amount of energy production for the tracking system is 36.6% as compared with the stationary panel. Whereas, for a mostly cloudy day the measured amount of increase was 18.5%. This improvement of the harnessed energy for PV systems is important to make the system more efficient and sustainable.

The rapid integration of cyberphysical systems (CPSs) and the Industrial Internet of Things (IIoT) has transformed industrial environments, driving advancements in autonomy, flexibility, and interconnectedness. However, this evolution introduces significant cybersecurity challenges, including vulnerability to sophisticated cyberattacks targeting critical infrastructure. Artificial immune systems (AISs), inspired by the human immune system′s adaptive and self‐regulating mechanisms, provide a promising approach for intrusion detection in dynamic industrial networks. This study conducted a systematic literature review (SLR) following the PRISMA methodology to investigate the state‐of‐the‐art applications, challenges, and research trends of AIS in industrial intrusion detection systems (IDSs). From an initial pool of 1320 records retrieved from databases like IEEE Xplore, Scopus, and Web of Science, 50 peer‐reviewed studies were selected for in‐depth analysis. The results highlight a growing adoption of AIS algorithms, including the negative selection algorithm (NSA), clonal selection algorithm (CSA), and dendritic cell algorithm (DCA), for anomaly detection, fault tolerance, and distributed control in industrial settings. Despite promising simulation‐based outcomes, challenges such as real‐time scalability, model interpretability, and infrastructure compatibility remain significant barriers. To address these gaps, this study proposes a novel conceptual framework that integrates AIS with multiagent systems (MASs) and blockchain technology, enabling autonomous detection, context‐aware decision‐making, and secure, decentralized data exchange. Future research should prioritize enhancing AIS scalability, reducing algorithmic complexity, and exploring integration with emerging technologies like explainable AI and blockchain to ensure robust, adaptive, and secure industrial systems. These findings offer critical insights for researchers and practitioners aiming to develop intelligent and resilient industrial cybersecurity solutions.

Big data analytics’ incorporation into customized cancer care is a revolutionary development in medical science. Using knowledge from genomic sequencing, electronic health records (EHRs), clinical trial databases, and social determinants of health, this systematic review investigates the relationship between artificial intelligence (AI), machine learning, and big data analysis in customizing cancer treatments for each patient. The study highlights how AI‐driven prediction models might improve treatment outcomes, reduce side effects, and increase patient safety by synthesizing insights from the last 10 years of academic research. A systematic literature review that follows PRISMA guidelines and includes both quantitative and qualitative evaluations of academic publications is among the key approaches. The findings show that multiomics approaches, which combine transcriptomics, proteomics, metabolomics, and genomics, are becoming increasingly important for customized medicine. Real‐time data analytics and wearable technology are also noted as promising resources for prompt responses. Notwithstanding obstacles, including data heterogeneity, moral dilemmas, and validation problems, the results highlight how crucial AI is to treating tumor complexity and developing precision medicine. In order to improve regulatory frameworks, promote interdisciplinary collaboration, and optimize AI applications in cancer treatment planning, the study concludes by suggesting future research areas. For scientists, physicians, and legislators hoping to transform cancer treatment using big data analytics–driven tailored medicine, this review provides insightful information.

The influence of an oblique angle of incidence of a laser beam on the position of the formed melt track in a powder bed is investigated experimentally. We introduce techniques for an accurate determination of geometric displacement effects to measure its precise position and shape. With increasing oblique incidence, an expanding lateral displacement is found that cannot be explained solely by geometric projection effects. Possible sources are considered and discussed, such as powder consolidation, intensity dependent displacement of the weld root, and a remaining contribution supposedly resulting from complex process dynamics during the interaction of laser and alloy in the melt zone. Compensation of the track displacement is necessary to achieve dimensional accuracy of printed parts.

This experimental study investigates the impact of biodiesel derived from Jatropha oil and multiwalled carbon nanotubes (CNTs) on the performance and emissions of a diesel engine. The analysis involved five fuel conditions: pure diesel (D), diesel blended with 50% biodiesel (DB50), diesel blended with 80% biodiesel (DB80), diesel blended with 50% biodiesel and CNTs (DB50C), and diesel blended with 80% biodiesel and CNTs (DB80C). The results indicate that biodiesel blends, particularly those enhanced with CNTs, significantly improve fuel efficiency and engine performance. Blends with additives (DB50C and DB80C) exhibited higher brake power (BP) and lower brake‐specific fuel consumption (BSFC) across various engine speeds, indicating more effective energy conversion and better fuel economy. Moreover, emissions analysis revealed that biodiesel blends with CNTs significantly reduce harmful emissions. Emissions of carbon monoxide (CO), unburned hydrocarbons (UHCs), and nitrogen oxide (NO x ) were consistently lower for DB50C and DB80C compared to D. The most notable reduction of 23.9% in NO x emissions occurred at an engine speed of 2200, while CO and UHC emissions were reduced by 17.8% and 15.4%, respectively, at engine speeds of 1600 rpm. These findings suggest that the use of biodiesel with CNT additives not only increases combustion efficiency but also contributes to cleaner engine operation. This study emphasizes the potential of biodiesel, especially when combined with advanced nanoparticles, as a practical and environmentally friendly alternative to conventional diesel fuel.

The physical and social structure of urban residential areas is affected by public open spaces in positive and negative ways. Public spaces serve as the basis for the growth of urban life that promotes physical activities and social relations. The research addressed the problem of lack of knowledge about the mechanisms that contribute to achieving healthy and lively public places according to spatial characteristics. It aimed to develop and improve public places through a more comprehensive knowledge of the effective spatial characteristics that contribute to enhancing the value of public places. The research assumed the existence of a correlation between vital spatial characteristics and healthy environments. For the purpose of testing the hypothesis, the theoretical framework extracted was applied to models of public places in international countries, Such as Canada, Japan, Algeria. The research adopted a qualitative descriptive approach to verify the hypothesis. The results showed that there is a relationship between health and vital environments that helped provide dynamism to the functioning of urban space and can be used as a criterion for measuring the effectiveness of public places.

An ergonomically designed workstation is important in preventing repetitive strain and other health‐related problems. However, its importance in enhancing employees’ productivity is rarely studied. Thus, the main aim of this study was to assess the impact of workstation design on perceived productivity. Data were collected through a structured questionnaire, observational checklist, and anthropometric measurements. A sample of 398 operators and 22 supervisors in six garment manufacturing companies in Ethiopia were considered in the survey. Among the 398 operators, 361 were willing for anthropometric measurements of 37 body dimensions. Seven different furniture (chairs and tables) in the sewing sections of the six companies were measured and evaluated against standards and compared with the anthropometry of the subjects. Descriptive and correlation analyses were used to evaluate the sewing workstations, while one‐sample t ‐tests were conducted to compare anthropometric measurements. The result showed that the existing furniture, working posture and space, and equipment negatively impacted the productivity of operators. The dimensions of the workstation furniture were neither within a standard nor matched to anthropometric measurements of the subjects. The mismatch of furniture dimensions with the anthropometry of the subjects along with the significant mean differences in anthropometry of workers across nations showed that domestic standard is necessary. Finally, an ergonomic chair was proposed with dimensions matching the anthropometry of the subjects mostly.

The numerical investigation of mixed convection heat transfer in a three‐dimensional ventilated enclosure containing a T‐shaped heat fin is crucial to understanding the complex heat transfer mechanisms involved in such systems and optimizing their efficiency for various engineering applications. This research focuses on numerically examining mixed convective heat transfer within a 3D rectangular ventilated enclosure including a T‐shaped heat fin heated at a constant temperature. The study spans a broad range of parameters, including the Prandtl number (0.71 ≤ Pr ≤ 7.10), Richardson number (0.1 ≤ Ri ≤ 10), Reynolds number (10 ≤ Re ≤ 1500), and fluid–solid thermal conductivity ratio (0.1 ≤ Κ ≤ 10). Numerical simulations utilizing the ANSYS Fluent CFD code reveal that the thermal distribution, the fluid flow field, the rate of heat transfer, and the average Nusselt number Nu ave associated with the heat fin surface and the dominance of a particular convection regime are significantly influenced by these parameters. The outcomes are succinctly summarized in a correlation (Nu ave = a Re b Ri c ), holding implications for engineering design.

Currently, there is an important demand for the mastery of engineering students in certain components proposed by the productive model of Industry 4.0, 5.0, and the Colombian qualification system. The focus of this research is mainly directed towards the domains of robotics, computer vision, distributed computing, and simulation of intricate systems. A methodology was proposed to enhance the objectives of modern educational paradigms and to favor training processes. This methodology employs an active learning strategy to provide both teachers and students with the necessary tools for the advancement of the subjects within the automation and control engineering program. Additionally, it fosters a comprehensive and interconnected understanding of real‐world problems, thus allowing the paradigms that Industry 5.0 brings with it to be addressed by having the student as the center or target of the application of this methodology to enrich their training process. To support the implementation and validation of this methodology, a teaching and learning environment was developed. This environment was created specifically to address the task of manipulating objects, including the actions of picking, placing, and carrying. The design of this environment incorporated several methodological and conceptual components derived from the fields of mobile robotics, manipulative robotics, and computer vision. The findings of the study indicate a marked improvement in the level of autonomy within the training procedures, as well as a marked improvement in the inclination to recognize and address problems. This resource provides students with the tools necessary to address practical challenges by applying the knowledge acquired within an academic environment.