Fabrication processes using additive manufacturing (AM) have the potential to create a variety of new products. For this reason, research and development is actively being conducted within this area. Parts with large and complex shapes are suitable for wire arc additive manufacturing (WAAM), an AM technique based on arc welding, which is classified as directed energy deposition. Studies on WAAM are being conducted within various fields, including studies examining their mechanical properties, heat input conditions, material microstructures, post-processing, artificial intelligence techniques, repairs, and the development of hybrid systems with machining. However, many of these studies are evaluations that use simple shapes such as walls. The evaluations using simple shapes are important for fundamental engineering. However, as a fabrication technology, WAAM requires various evaluations using actual part shapes that are used in industry as test pieces in order to develop industrial applications.
The purpose of this review is to further clarify the industrial application value of WAAM. First, a literature review on the results of studies on WAAM for industrial parts was conducted to summarize the current literature. Then, based on the study results obtained through the literature review, the main current issues are summarized. In addition, a discussion is conducted on how WAAM can be used to improve the development of various industries. The conclusion is that WAAM has the potential to develop further into a technology that will be one of the key factors to achieve industrial innovation.

Beams, as flat slender structures primarily subjected to bending and transverse shear stresses and likely used in every engineering structure, are among the most important topics in mechanical and structural engineering training and practice today. Despite the long history of man's understanding of structural behavior and the various shear deformation theories for beams proposed, the Euler–Bernoulli beam theory (or classical beam theory) is still the most widely used engineering approach today. Although the finite element method (FEM) is now the standard engineering method for analyzing all types of beam problems, meshfree methods have also been used to analyze beams in recent years. The assumption that any function can be written as an expansion of a set of continuously differentiable basis functions is a simple, easy to implement, and very popular non-symmetric meshless method for solving partial differential equations (PDEs) nowadays which, provided the basis coefficients are properly determined by a collocation method, can, in general, be used as an approximation scheme for the solution of PDEs. This article addresses radial point collocation numerical technologies for the static analysis of Euler–Bernoulli beams involving fourth-order spatial derivatives, including how to apply the method to uniform and isotropic beams with arbitrary boundary conditions and loadings, as well as a performance comparison of the meshless approach to traditional analytical and FEM solutions, demonstrating its appeal and competitiveness for a broader engineering application.

Optimizing industrial energy systems is vital for meeting sustainability targets, reducing operational costs, and enhancing overall system performance. This paper explores the integration of advanced statistical methods—including regression analysis, time-series forecasting, Monte Carlo simulations, and machine learning algorithms—to optimize energy utilization and drive efficiency gains in industrial settings. A comprehensive analysis of energy data demonstrates significant improvements in efficiency through precise demand forecasting, reductions in energy consumption, and cost-effective operational strategies. Machine learning-driven predictive maintenance models effectively forecasted equipment malfunctions, reducing downtime and maximizing energy use efficiency. This study emphasizes the power of data-driven strategies to identify inefficiencies, forecast energy requirements, and enhance resource allocation. Techniques such as regression and time-series models offered precise demand insights, while Monte Carlo simulations provided robust risk assessments amid operational uncertainties. Machine learning-based predictive maintenance reinforced system reliability by proactively addressing potential breakdowns and improving resource utilization.
Key challenges, including data quality issues, system complexity, and model scalability, are examined, highlighting the necessity of enhanced data integration and improved model interpretability. These factors are critical for the widespread adoption of statistical optimization approaches in industrial energy systems. The findings underscore the transformative role of statistical techniques in energy management, yielding substantial cost reductions and advancing sustainability efforts. The integration of these approaches with emerging technologies such as IoT and AI holds significant potential to further optimize system efficiency, bolster resilience, and drive sustainable industrial practices.

Knowledge of future land use changes is crucial, as they are interlinked to various factors of human-environmental systems. Land use changes have a profound impact on urban planning, environmental sustainability, resource management, and overall quality of life. Spatial data can often be computationally heavy, so the provision of accessible and ready-to-use tools is crucial for the analysis of land use changes in any case study. In this work, a Cellular Automata Markov (CAM) model is presented and applied through a combination of Geographic Information Systems (GIS) and Python, to predict land changes and provide future land use maps. The inputs are historical land use maps at a five-year time-step from 2006 to 2021, and the outputs include future land use maps until 2051. The Cedar Creek Watershed (CCW) in Indiana, US, is used as a case study; it is an area of great natural beauty, mainly consisting of agricultural lands, forests and water bodies. Various validation techniques are explored for the predicted maps, based on the historical data, including Accuracy, Mean Absolute Error (MAE), Root Mean Square Error (RMSE), the Kappa coefficient (κ), and Confusion Matrix statistics. The results indicated a gradual increase in urban areas at the expense of agricultural land. Forested areas showed stability with minimal change, while water bodies maintained consistent coverage. Some minor shifts from barren land to both urban and forested categories were also observed. Validation results showed a high accuracy of 99.63%, a mean absolute error (MAE) of 0.0094, and a root mean square error (RMSE) of 0.1613. The Kappa coefficient indicated strong agreement at 0.9925. A step-by-step GIS guide and the Python script are provided to contribute to the reproducibility and improvement of the model. Similar analyses can find multiple applications in a variety of studies on human-environmental systems (including water, agriculture, economy).

Methanol synthesis from CO₂ is a key strategy for carbon capture and utilization, offering a viable solution to mitigate climate change. Direct synthesis of methanol not only reduces greenhouse gases but also produces valuable chemicals for industrial applications. The aim of this study is to model and optimize the methanol synthesis process from CO₂, focusing on maximizing methanol yield while minimizing CO₂ content in the product stream. In this work, a detailed methanol synthesis process simulation was developed using the Soave-Redlich-Kwong equation of state in the Aspen Plus commercial software environment. Pure CO₂ stream which comes out of post-combustion carbon capture process and renewable hydrogen stream are used. The results are compared with open literature sources. Apart from that, a sensitivity analysis was employed to evaluate the effects of pressure, temperature, and recirculation fraction on process efficiency. The results showed that the highest methanol yield of 76,838 kg/h was obtained at 80 bar, 276°C, and a recirculation fraction of 0.9. The lowest CO₂ content in the final product (73 kg/h) occurred at 80 bar, 220°C, and a recirculation fraction of 0.6. These findings demonstrate the trade-off between maximizing methanol output and reducing unreacted CO₂. In conclusion, optimal operating conditions for both high yield and low CO₂ content were identified, providing a foundation for further process refinement. Future work will involve developing a more complex multi-reactor model and conducting economic assessments for large-scale industrial implementation.

This research investigates the evolution of sediment quality guidelines (SQGs) since the 1980s, highlighting a shift from basic comparisons of contaminant concentrations to more sophisticated assessments that account for ecological impacts. Historically, the reliance on background or reference values for sediment contamination assessments neglected the biological diversity and potential adverse effects on aquatic organisms. In response, various national and regional agencies have developed comprehensive SQGs aimed at safeguarding sediment-dwelling organisms within freshwater ecosystems. These guidelines are categorized into threshold effect concentrations (TECs) and probable effect concentrations (PECs). Utilizing statistical analyses performed with Statgraphics 19 and Microsoft Excel 2019, this study reveals notable variability in PECs, ranked as follows: chromium (Cr) at 11.00%, Nickel (Ni) at 30.04%, zinc (Zn) at 32.84%, copper (Cu) at 33.11%, lead (Pb) at 40.44%, cadmium (Cd) at 43.47%, arsenic (As) at 48.48%, and mercury (Hg) at 52%. These results indicate that chromium is the least variable contaminant, while mercury exhibits the highest variability. Additionally, the toxicity levels of various metals (loids) were investigated using box-and-whisker plots, revealing that mercury was the most toxic, whereas zinc was found to be the least toxic. For TECs, the ranking is lead (11.43%), mercury (16.67%), zinc (18.70%), nickel (20.00%), arsenic (25.79%), cadmium (29.58%), chromium (30.29%), and copper (37.14%), indicating lead as the least variable and copper as the most among the examined guidelines.

Solar radiation data is essential for evaluating solar energy potential; designing, optimizing and developing predictive models for solar energy systems; and other applications. While weather stations provide reliable data, their high installation and maintenance costs lead to data gaps in many regions. Satellite-derived data presents a cost-effective alternative, offering broad coverage. However, satellite-derived data require continuous evaluation to prove them as reliable substitutes for ground measurements. This study compared satellite-derived (SD) solar irradiation data from two sources, NASA's POWER and PVGIS, against ground-measured (GM) data from the World Radiation Data Centre (WRDC). The comparison relied on data from 171 WRDC stations spanning 2005 to 2020. The Bland-Altman method was the primary statistical measure used because of its ability to determine agreement between data sources and identify systematic bias; this involved constructing Limits of Agreement (LOA), within which the most differences between the two data sources are expected to lie. Additional statistical measures, including r-correlation, root mean square error analysis, and t-value analysis, were employed to validate the BA findings and to investigate the influences of latitude, diurnal periods and annual seasons on the agreement between the SD and GM data. The results of the study showed that, when compared with the WRDC data, the POWER data exhibited limits of agreement (LOA) of 0.45±4.78 MJ/m²/day, while PVGIS data had LOA of 0.47±5.11, with the range of LOA being 9.55 and 10.23, respectively. In addition, distinct relationships between the range of LOA and latitude and season were visually identified from the plots, indicating that these factors affect the agreement between the SD and GM data. The narrower LOA range of the POWER data suggested it to be the more reliable substitute for GM data.

Decision-makers have begun to take an increasing interest in identifying the environmental impacts through the application of Life Cycle Assessment (LCA) for the management of contaminated sites. The analysis of scientific literature shows that impact assessment studies using life cycle assessment are rare for bioremediation processes using biosorbents. More studies are therefore needed to develop sustainable bioremediation processes for the removal of pollutants such as heavy metals that can subsequently be successfully applied in large-scale facilities. We propose an impact analysis for the wastewater treatment by biosorption of 1 liter of water contaminated with Cr(VI) at a concentration of 25 mg/L. We evaluated the environmental impacts potentially generated by Cr(VI) bioremoval in batch and column conditions to demonstrate the environmental and human health impacts and benefits of this type of bioremediation process. Biomass of Rhizobium viscosum CECT 908, previously classified as Arthrobacter viscosus was used as biosorbent for Cr(VI). Environmental impacts were quantified through LCA methodology, within the Sphera Product Sustainability Solutions Software. All major factors that can lead to environmental impacts, such as transportation or electricity, have been considered. The lowest environmental impact value was quantified for the impact category human toxicity cancer (HTc) for Cr(VI) ions removed by biosorption in the batch and continuous systems. The category human toxicity cancer (HTc), is the category with a negative value, and therefore indicates no quantified environmental impact. The study has shown that energy consumption needs to be reduced in the process in order to record lower environmental impacts.

Thermoelectric (TE) devices reliably convert electricity to heat (and vice versa) without moving parts. They can be integrated into solar energy devices to improve thermal energy conversion in various applications. This study aimed to experimentally investigate the improvement in the efficiency of a solar air heater (SAH) by incorporating TE modules. Eleven TEC1-12706 TE modules, with their cold sides affixed to the rear of the SAH absorber plate, were installed in the model SAH we assessed. Photovoltaic modules provided direct current to the TE modules to create a temperature difference across the surface of the TE modules. This propelled heat transmission to the air moving beneath the absorber plate as the TE modules extracted heat from the absorber plate through their cold to their hot sides. Under the same ambient conditions of 38.6°C maximum ambient temperature and maximum insolation of 380.6 W/m², this thermoelectric heat-pumping solar air heater (TE-SAH) demonstrated a notable gain in efficiency over the classic SAH, with an average efficiency of 23% compared to the latter's 18%. The maximum collector outlet temperatures were 61°C and 56.5°C, respectively. These indicated mean efficiency and outlet temperature gains of 31.5% and 8%, respectively. At an air mass flow rate of 0.013 kg/s, the TE-SAH achieved a peak efficiency of 74%, whereas the standard SAH recorded a peak efficiency of 57%. This work introduces a new strategy for enhancing the performance of SAH systems. It shows the significant improvement in efficiency that thermoelectric technology can produce when paired with a conventional SAH system.

This study investigates the unprecedented electricity consumption in Croatia, which was driven by an intense heatwave in July 2024. Daytime temperatures consistently exceeded 30 °C, and the intense tourist season caused air conditioning usage to skyrocket. The previously recorded maximum from August 2023 was surpassed on several occasions during July 2024. In the evening hours of July 17, 2024, a new record high demand of 3381 MW was recorded. More troublesome, in the hours of high demand, about 50% of the electricity had to be imported because domestic power plants could not generate the entire demand. As a consequence, electricity prices went up to 480 EUR/MWh, four times the daily average price in Croatia. In response to power peaks and increased electricity imports, Croatia has intensified efforts to expand the share of renewable energy in the electricity mix. From 44.8% in 2020, the share of renewables increased to 58.5% in 2023. This marks a significant increase, mostly driven by the expansion of the wind and solar markets. However, as of 2023, Croatia's per capita electricity generation from wind and solar PV combined was 676 kWh per person, which is only about half the EU-27 average (1410 kWh per capita). Croatia and Southern Europe alike will continue to experience hotter summers, and power systems will have to handle higher peak loads. As the energy system transitions to a larger share of renewables, power grid flexibility will become crucial. Flexible power generation could be used to fill gaps in the renewable output. Pumped hydro and batteries could store excess renewable energy and release it during demand peaks. Demand response is another option, as shifting electricity usage to periods when wind and solar generation are high could help adapt to their intermittent nature.