Abstract Lightweight mechanical metastructures are widely used in the automotive industry and related transportation equipment due to their high specific strength, high specific stiffness, and designability. This study presents an auxetic cylindrical metastructure designed as a lightweight mechanical system with tunable stiffness and strength, offering promising applications in the automotive industry for energy absorption and structural components. The structure employs polylactic acid as the matrix material owing to its favorable printability and mechanical properties. Multiple samples with varying geometric parameters are fabricated via fused deposition modeling to validate the corresponding finite element models. Axial and lateral compression tests reveal distinct deformation mechanisms. Under axial loading, the metastructure exhibits significant necking behavior accompanied by auxetic expansion, with parameter T 3 critically influencing axial stiffness and strength through control of local buckling patterns. During lateral compression, pronounced bending deformation emerges, forming high elastic strain regions near connecting nodes and enabling adjustable lateral deformation. Parameters T 2 and T 3 primarily govern lateral nominal stiffness and strength by affecting deformation mode transitions. Multi-objective optimization using NSGA-II demonstrates a trade-off between the volume fraction and mechanical performance. The optimized design achieves significant mechanical property improvement, as numerically confirmed. This auxetic metastructure provides a novel approach for programmable lightweight system design, with potential applications spanning automotive industry, aerospace, biomedical, and impact-absorption engineering fields.

This study examines the evolving competencies required for quality engineers in contemporary industry. A non-anonymous questionnaire was distributed among members of the Israeli Society for Quality. Since respondents provided personal and professional details, the verified group of professionals was treated as an expert panel representing recognized knowledge and experience in the field. The questionnaire included items on routine competencies (e.g. statistics, auditing, communication) and emerging ones (e.g. risk management, big data analytics, sustainability). Data were analyzed using statistical tests, reporting the relative importance and significance of different competencies. Findings show that quality professionals assign the highest importance to interpersonal and quality management skills, while traditional technical competencies are ranked lower. Among emerging competencies, risk management was identified as the most critical, surpassing digitalization and environmental protection. Overall, the results highlight a gap between academic preparation, which still emphasizes technical expertise, and practical industry requirements for hybrid profiles that combine managerial, interpersonal, and resilience-oriented competencies. The study suggests that curricula should rebalance technical training with greater focus on quality management and risk management competencies to better align graduates with market needs.

Accurate prediction of failure in industrial machinery and engines is critical for minimizing unexpected downtimes and enabling cost-effective maintenance. Existing predictive models often struggle to generalize across diverse datasets and require extensive hyperparameter tuning, while conventional optimization methods are prone to local optima, limiting predictive performance. To address these limitations, this study proposes a hybrid optimization framework combining Harris Hawks Optimization (HHO) and Wild Horse Optimization (WHO) to fine-tune the hyperparameters of ResNet, Bi-LSTM, Bi-GRU, CNN, DNN, VAE, and Transformer-GRU models. The framework leverages HHO’s global exploration and WHO’s local exploitation to overcome local optima and optimize predictive performance. Following hybrid optimization, the Transformer-GRU model consistently outperformed all other models across four benchmark datasets, including time-to-failure (TTF), intelligent maintenance system (IMS), C-MAPSS FD001, and FD003. On the TTF dataset, mean absolute error (MAE) decreased from 0.72 to 0.15, and root mean square error (RMSE) from 1.31 to 0.23. On the IMS dataset, MAE decreased from 0.04 to 0.01, and RMSE from 0.06 to 0.02. On C-MAPSS FD001, MAE decreased from 11.45 to 9.97, RMSE from 16.02 to 13.56, and score from 410.1 to 254.3. On C-MAPSS FD003, MAE decreased from 11.28 to 9.98, RMSE from 15.33 to 14.57, and score from 352.3 to 320.8. These results confirm that the hybrid HHO–WHO optimized Transformer-GRU framework significantly improves prediction performance, robustness, stability, and generalization, providing a reliable solution for predictive maintenance.

Multi-objective optimization (MOO) plays a critical role in mechanical and industrial engineering, where conflicting design goals must be balanced under complex constraints. In this study, we introduce the Multi-Objective Giant Trevally Optimizer (MOGTO), a novel extension of the Giant Trevally Optimizer inspired by predatory foraging dynamics. MOGTO integrates predation-regime switching into a Pareto-based framework, enhanced with feasibility-aware archiving, knee-biased selection, and adaptive constraint handling. We benchmark MOGTO against established algorithms—NSGA-II, SPEA2, MOEA/D, and ParetoSearch—using synthetic test suites (ZDT1–3, DTLZ2) and classical engineering problems (welded beam, spring, and pressure vessel). Performance was assessed with Hypervolume (HV), Inverted Generational Distance (IGD), Spacing, and coverage metrics across 30 independent runs. The results demonstrate that MOGTO consistently achieves competitive or superior HV and IGD, maintains more uniform spacing, and generates larger feasible archives than the baselines. Particularly on constrained engineering problems, MOGTO yields more feasible non-dominated solutions, confirming its robustness and industrial applicability. These findings establish MOGTO as a reliable and general-purpose metaheuristic for multi-objective optimization in engineering design.

The Engineering Insurance Branch contributes to supporting the economic development movement of both developed and developing countries, because of its distinct role in providing insurance protection to construction and industrial projects during the implementation phase, and to industrial organizations during the actual operation phase of losses resulting from sudden and unforeseen (accidental) accidents. Nevertheless, the sales of industrial insurance, (one of the most important types of engineering insurance), continues to fluctuate and decline in the sales of insurance policies among insurance companies operating in the insurance markets of the developing countries. On this basis, the subject (Factors affecting the sales of industrial engineering policies) was surveyed at the National Insurance Company (NIC), by selecting a sample of 50 NIC producers. The researchers used a questionnaire as the main data-collection tool. The research hypotheses were tested using the relevant statistical methods. For the purpose of testing the relationship hypotheses, the Spearman Ranking Correlation Factor was adopted. The research revealed a series of conclusions, the most important of which was the emphasis placed by the management of the NIC on marketing engineering construction policies more than its focus on industrial engineering insurance policies, owing to their almost mandatory nature Finally, the researchers made a number of recommendations, the most prominent of which was the decline in the results of the engineering insurance work for the period considered. They recommended intensifying efforts to increase the contribution of industrial insurance to the engineering branch of the NIC.

Driven by the digital age, the chemical and chemical engineering industry is gradually transforming towards green, refined and intelligent development, thus posing new demands on talents' data literacy, innovative thinking ,and interdisciplinary capabilities. Taking the Applied Chemistry major as an example, this paper explores the paths and methods of promoting educational reform and construction through digital and intelligent technologies. First, in the term of teaching modes, personalized and interactive teaching is realized through intelligent platforms and virtual simulation technologies. Second, in the practical system, a new paradigm of "virtual simulation - simulation calculation - physical verification" is constructed. Third, in the course content, new technological elements such as Python, chemoinformatics and artificial intelligence are integrated. Fourth, in the evaluation system, a multi-dimensional and full-process evaluation mechanism is built based on learning data analysis. Fifth, in the faculty construction, teachers are encouraged to transform into "mentors, designers and analysts". Sixth, in professional management, a data-driven decision-making mechanism is established. In conclusion, digital and intelligent technologies will drive the Applied Chemistry major to shift from cultivating traditional "chemists" to nurturing "composite talents in chemistry and data", providing important support for building a high-quality education system.

Using Machine Learning (ML) in educational management transforms higher education strategy. This study examines students’ views on machine learning (ML) technologies and how they might be used to plan, monitor, and predict student performance. The Faculty of Industrial Engineering and Robotics surveyed 118 third-year undergraduates. It featured closed- and open-ended questions to collect quantitative and qualitative data. Descriptive statistics showed broad patterns, inferential tests (Chi-square, t-test, ANOVA) showed group differences, regression models predicted school outcomes, and exploratory factor analysis (EFA) and clustering found hidden attitudes and student profiles. A multi-method quantitative approach combining descriptive statistics, inferential tests, regression modeling, and exploratory techniques (EFA and clustering) was employed. The findings show that most students realize that ML may help them be more productive, adapt their study pathways, and learn about the future. Concerns remain regarding its accuracy, overreliance, and morality. The findings indicate that ML can both support and challenge educational management, depending on how responsibly it is implemented. Results show that institutions may utilize ML as a strategic tool to boost academic progress and make better judgments, provided they incorporate it responsibly and follow ethical rules and training.

This work applies a probabilistic reliability model for industrial diesel engines based on lubricant degradation to the MDU-06 Hyundai H21/32 engine at thermal power station, Ecuador. The engine operated seamlessly from August 2023 until October 2025, generating power continuously. Thus, the model predicts dependability based on oil condition behavior rather than mechanical faults. The study offers a non-linear and multivariate degradation model to track the associated evolution of TBN, Sulfation, Nickel, and Vanadium. Stochastic degradation modeling and Weibull reliability estimation were used to estimate lubricant life under real operating conditions. Results show a Weibull shape parameter β = 8.3 and scale parameter η = 1920 hours, with dependability drastically decreasing after 2000 operational hours. These results demonstrate that lubricant degradation can accurately forecast engine health, allowing predictive maintenance without interruption. Condition-based maintenance (CBM) procedures are optimized to maintain system dependability over 80% while lowering premature wear and maintenance costs with the suggested framework.

Marine macroalgae represent a versatile and sustainable platform within blue biotechnology, offering structurally diverse polysaccharides that are making significant contributions to next-generation therapeutical applications. Algae are rich sources of high-value biomolecules, including polysaccharides, vitamins, minerals, proteins, antioxidants, pigments and fibers. Algal biomolecules are widely explored in modern pharmaceuticals due to their range of physiochemical and biological properties. Recently, algal polysaccharides have gained increasing attention in nanomedicine due to their biocompatibility, biodegradability and tunable bioactivity. The nanomedical applications of algal polysaccharides pertain to their anti-coagulant, antiviral, anti-inflammatory, antimicrobial and anti-cancer properties. In this review, we discuss some major macroalgal polysaccharides, such as agar, agarose, funoran, porphyran, carrageenan, alginate and fucoidan, as well as their structure, uses, and applications in nanomedical systems. Both sulfated and non-sulfated polysaccharides demonstrate significant therapeutic properties when engineered into their nanotherapeutic forms. Previous studies show antimicrobial potential of 80-90% antiviral activity > 70%, significant anticoagulant activity, and excellent anticancer responses (up to 80% reductions in cancer cell viability have been reported in nanoformulated versions of polysaccharides). This review discusses structure-function relationships, bioactivities, nanomaterial synthesis and nanomedical applications (e.g., drug delivery, tissue engineering, biosensing, bioimaging, and nanotheranostics). Overall, this review reflects the potential of algal polysaccharides as building blocks in sustainable biomedical engineering in the future healthcare industry.

A lattice-ordered T-bipolar soft group framework for smart manufacturing system optimization in industrial engineering

Precision quantification is a core metric in industrial engineering (e.g., production quality control, sensor data calibration, automated assembly accuracy), where the traditional assumption of isotropic (homoscedastic) error variances often fails to capture real-world heteroscedastic characteristics (e.g., uneven measurement errors in assembly lines, divergent process variations in mass production). To address this critical discrepancy, this study develops a rigorous probabilistic framework for precision quantification in heteroscedastic normal populations, leveraging advanced distribution theory and numerical optimization. For the first time, the closed-form probability density function (pdf) and cumulative distribution function (cdf) of the planar precision index (PPI, defined as the modulus of a 2D heteroscedastic normal vector for industrial measurement data) are derived by integrating polar coordinate transformation with modified Bessel function theory. This resolves the long-standing absence of a strict analytical representation for this fundamental distribution, establishing a "first-principle" mathematical basis for industrial precision assessment. Building on this distributional foundation, a dual-tier computational framework is proposed: (1) A benchmark numerical solver that combines the bisection method (for convergence guarantee) and Brent’s algorithm (for superlinear efficiency) to yield exact precision index values, suitable for offline industrial system calibration; (2) A theoretically grounded linear approximation derived via moment matching and small-parameter perturbation, optimized for real-time production quality monitoring. This framework advances precision quantification from "ideal assumption-dependent models" to "data-driven, physics-consistent computation," and extends seamlessly to complex error structures in industrial scenarios (e.g., correlated sensor data, multimodal process variations). Theoretical analyses demonstrate that within the engineering-relevant variance ratio range (0.3–3.0), the average relative error of the approximation is constrained to <5%, with maximum error below 10%—well within industrial acPPItance thresholds. Validation via Monte Carlo simulations (100,000 trials) and field tests of automated welding processes confirms the method’s accuracy (mean absolute error <0.5%) and robustness. Compared to traditional homoscedastic methods, this approach reduces systematic bias in product qualification rate prediction by up to 23%, providing a reliable tool for industrial quality control and system certification.

ABSTRACT Starch is a significant food product and a versatile biomaterial utilized globally across several industrial sectors, including food, healthcare, textiles, chemicals, and engineering. The physicochemical qualities and functioning of starch primarily determine its adaptability in industrial applications. Native or unmodified starch has limited functionality and applicability. The physical and chemical characteristics of unmodified starch influence its effectiveness, which is a key reason why it is commonly used as an excipient in the pharmaceutical industry, particularly as a binder and disintegrant. The polyhydroxy glucose monomers that make up starch are chemically reactive and can undergo several reactions, which allows for their chemical modification. Various processes, including hydrolysis, esterification, etherification, and oxidation, can be performed on starch. Baked goods, sweets, soups, and salad dressings can all benefit from the changed starches that result from these reactions. This overview discusses the many chemical reactions that starch undergoes, the associated changes in functionality, and the applications of chemically modified starches in the pharmaceutical sector.

This study presents a comprehensive analysis of global methane (CH<sub>4</sub>) emissions using advanced data exploration and machine learning techniques, with an emphasis on identifying key sectoral contributors, geographic emission hotspots, and the performance of mitigation technologies. Employing methods such as random forest regression, geospatial mapping, and multi-dimensional visual analytics, the research highlights the energy sector’s dominant role in methane output and reveals detailed emission patterns across U.S. states. The analytical framework includes time-series feature engineering, synthetic data augmentation for localized insights, and 3D surface modeling to examine the relationships between energy production levels, temporal trends, and emission intensities. The results provide actionable insights for policymakers by identifying critical points of intervention and advocating for the integration of artificial intelligence-driven exhaust after-treatment systems to reduce methane emissions. This work offers a scalable, reproducible approach for environmental monitoring and supports global decarbonization efforts in line with U.S. clean energy objectives. The random forest model used in this study achieved a mean absolute error of 2.71 and an R² score of 0.81, demonstrating strong predictive accuracy for methane emissions trends based on regional and sectoral data.

X-ray radiation is widely used in industry, medicine, and space engineering, but its potential hazards necessitate effective shielding materials to protect both humans and the environment. Although lead (Pb) has been the conventional choice due to its high density (∼11.5 g cm-3) and atomic number (Z = 82), concerns over toxicity and weight have driven the search for alternative materials, including metal/metal oxide-polymer composites. Here, for the first time, we report the development of novel non-lead X-ray shielding nanocomposites by integrating a gadolinium-based metal-organic framework (Gd-MOF) with bismuth halides. The synthesized Gd-MOF and Gd-MOF/PDMS/BiI3 composites were characterized using XRD, FT-IR, HR-TEM, XPS, BET, and FE-SEM with EDX mapping. XRD confirmed the presence of Gd at the (100) plane, while FT-IR identified O-H, CC, and C-H bonds, verifying successful MOF synthesis. The Gd-MOF exhibited a surface area of 158.3 m2 g-1, and SEM revealed uniform dispersion of Gd-MOF and BiI3 within the PDMS sponge. The resulting composite is lightweight, flexible, and environmentally friendly. By combining the high shielding efficiency of bismuth halides with the porous structure of Gd-MOF, the 3 mm-thick sponge achieved X-ray attenuation efficiencies of ∼94% at 60 kV and 78% at 100 kV. With a low density of 1.21 g cm-3 and a simple fabrication process, this composite represents a promising non-toxic alternative to conventional heavy-metal shielding materials.

Reusable and modular combinatorial libraries for iterative metabolic engineering of Saccharomyces cerevisiae.

Effect of ammonia-hydrogen fuel ratio on combustion stability, performance and emissions of an industrial diesel engine

Applications of computational statistics in industrial engineering optimization

ABSTRACT Robotic and artificial intelligence (AI) systems in industrial settings are becoming increasingly collaborative and interconnected. This shift raises complex ethical concerns, particularly regarding safety and security. While a growing number of ethics guidelines and frameworks are issued, they largely remain abstract and difficult to apply in practice. Concrete examples of challenges, practices and actionable solutions in industrial engineering are still scarce. This paper presents the findings of an empirical study conducted in Austria and the Netherlands, involving 29 qualitative data items. In contrast to most studies in robot ethics, this work exemplifies real-world practices of ethical engineering. It focuses on two key challenge dimensions: power and economic pressure, and accountability and governance. Concludingly, the paper proposes two practical solutions: the establishment of a new professional role, Ethical Robotic and AI Systems (EROS) integrators and the introduction of the governance concept of ‘technologies of the collective’ to facilitate successful ethics integration.

Question: The architecture, engineering and construction industry (AEC) has an increasing interest in achieving better situational awareness (SA) in complex projects. The implementation of digital visual management (DVM) tools as a means of communication to increase SA in AEC projects has the potential to simplify information dissemination. What are the barriers faced during the implementation of DVM? Is it possible to identify factors that can facilitate implementation? Purpose: The purpose of this paper is to identify the barriers encountered during the implementation of DVM and to identify possible countermeasures to overcome such barriers. Research Method: 21 interviews were conducted with project management professionals who implemented DVM in four projects in Finland. Findings: Barriers include technological limitations on data collection and sharing of information, cultural mistrust among project participants, and increased work for data collection. On the other hand, the findings show that lean principles, such as the openness of the environment to share information, the standardization of work, and continuous improvement, have the potential to facilitate DVM implementation. Limitations: Interviews as a research method are limited due to their subjective nature. In addition, the interviews were limited to Finnish projects and complex infrastructure project management professionals. Thus, the results and explanations do not necessarily generalize to other construction industries or project types. Implications: Project teams that desire to implement DVM or that are implementing DVM can now identify expected barriers and plan possible ways of overcoming them during implementation in advance. The identified enablers contribute to knowledge about DVM and can offer practical guidance during the implementation of these strategies. Value for authors: This paper identifies barriers and enablers to the adoption and implementation of DVM. Keywords: visual management, digital visual management, situational awareness, construction, Lean Construction Paper type: Full paper

Question: How can sustainability criteria be integrated into a decision model to enhance the application of reverse logistics in construction? Purpose: The study explored how economic, social, and environmental sustainability criteria could be integrated into a decision model for reverse logistics design. Research Method: A field study tested a decision model based on multiple-criteria decision analysis at an industrialized housebuilding firm in Sweden. Through theory, observations, interviews, and expert validation, a practical decision model was developed and tested to enhance transport and weatherproofing of modules. Findings: The theoretical framework primarily developed for the engineering and automotive industries can be adapted for reverse logistics in industrialized housebuilding by using a decision model based on multiple-criteria decision analysis. Limitations: Further research is needed to validate and refine the model, and to extend its application to other construction products and processes. The findings may be more specific to the process examined in the field study rather than across the industry. Implications: The criteria of the decision model support decision-making in the weather and transportation protection process, though they can be further refined to provide a more precise basis for decisions. Value for practitioners: Understanding the importance of systematic decision-making in reverse logistics design can improve sustainability and ensure compliance. Keywords: Reverse Logistics, Green Logistics, Closed Loop Supply chain, Construction Supply Chain Management, Decision-making, Reuse, Sustainability, Lean Construction Paper type: Full paper