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Surface transportation has evolved through technology advancements using parallel knowledge areas such as machine learning (ML). However, the transportation industry has not yet taken full advantage of ML. To evaluate this gap, we utilized a literature review approach to locate, categorize, and synthesize the principal concepts of research papers regarding surface transportation systems using ML algorithms, and we then decomposed them into their fundamental elements. We explored more than 100 articles, literature review papers, and books. The results show that 74% of the papers concentrate on forecasting, while multilayer perceptions, long short-term memory, random forest, supporting vector machine, XGBoost, and deep convolutional neural networks are the most preferred ML algorithms. However, sophisticated ML algorithms have been minimally used. The root-cause analysis revealed a lack of effective collaboration between the ML and transportation experts, resulting in the most accessible transportation applications being used as a case study to test or enhance a given ML algorithm and not necessarily to enhance a mobility or safety issue. Additionally, the transportation community does not define transportation issues clearly and does not provide publicly available transportation datasets. The transportation sector must offer an open-source platform to showcase the sector's concerns and build spatiotemporal datasets for ML experts to accelerate technology advancements. [ABSTRACT FROM AUTHOR]

Cigarette butt littering poses a significant environmental challenge, with billions of butts discarded each year, fouling ecosystems with slow-to-decompose cellulose acetate filters that absorb and release harmful compounds. In response, an innovative, sustainable approach for valorizing ballot bin waste (BBW) by extracting high-quality cellulose acetate from cigarette butts was investigated. This green approach eliminates the need for hazardous acids and toxic solvents, resulting in a yield of 30% (w/w) and a degree of substitution (DS) of 2.0–2.5, which is comparable to pure cellulose acetate. The following four essential processes are involved in this process: filter separation, water washing to remove impurities, ethanol purification, and acetone precipitation of the cellulose acetate. This approach not only mitigates environmental harm, but also supports circular economy goals by transforming waste into valuable resources. [ABSTRACT FROM AUTHOR]

Vinyl ester resins are widely used as thermoset matrix materials for laminated composites, particularly in naval and automotive applications, due to their strength, chemical resistance, and ease of processing. However, their brittleness limits their use, especially in cold conditions. This study investigates the toughness of core–shell rubber (CSR)-modified resins in composites with natural fibers. This research compares the properties of the neat resin matrix and the CSR-modified matrix. After optimizing the resin curing process with catalysts, various treatments were tested to analyze their mechanical and thermal properties. Using the vacuum bagging process, flax and glass fibers were used as reinforcements to assess the effects of matrix modifications. Flax fibers were chosen for their sustainability as a potential alternative to glass fibers. Mechanical testing was performed, comparing the performance of flax-based composites to those with glass fibers. Water absorption tests on flax composites followed the ISO 62 standard. Additionally, interlaminar shear strength and SEM micrography studies were conducted to examine the morphology and fiber–matrix adhesion, linking the microscopic structure to mechanical properties. Results indicate that while glass-reinforced composites have superior properties, flax composites offer a sustainable alternative, making them a promising choice for future applications. [ABSTRACT FROM AUTHOR]

Vicia sativa L., commonly known as the common vetch, is an annual, self-pollinating legume used primarily as fodder both by livestock and wildlife. Additionally, it contributes to environmental balance through nitrogen fixation and the improvement of soil properties. The phenotypic, genetic, and metabolite variability among four advanced lines (BK45, BK29, BK23, BK27) and two commercial varieties (M-6900, BI-65) of V. sativa were evaluated in order to be used for future breeding programs aimed at producing genetically improved varieties. BK45 was the most promising line due to its high genetic polymorphism, but also because it exhibited a significant amount of seed production and high seed quality based on its metabolomics profile. A stepwise multiple regression analysis (MRA) revealed a relationship between SCoT alleles, seed, and biomass yield. Additionally, several statistically significant marker bands linked to metabolites were found using the SCoT marker analysis. Hence, data assessed via MRA may be helpful in marker-assisted breeding programs. Finally, the two commercial varieties can be further exploited in breeding programs due to their high genetic diversity. [ABSTRACT FROM AUTHOR]

The concept of intelligence has many applications, such as in coatings and cyber security. Smart coatings have the ability to sense and/or respond to external stimuli and generally interact with their environment. Self-healing coatings represent a significant advance in improving material durability and performance using microcapsules and nanocontainers loaded with self-healing agents, catalysts, corrosion inhibitors, and water-repellents. These smart coatings can repair damage on their own and restore mechanical properties without external intervention and are inspired by biological systems. Properties that are affected by either momentary or continuous external stimuli in smart coatings include corrosion, fouling, fungal, self-healing, piezoelectric, and microbiological properties. These coating properties can be obtained via combinations of either organic or inorganic polymer phases, additives, and pigments. In this article, a review of the advancements in micro/nanocapsules for self-healing coatings is reported from the aspect of extrinsic self-healing ability. The concept of extrinsic self-healing coatings is based on the use of capsules or multichannel vascular systems loaded with healing agents/inhibitors. The result is that self-healing coatings exhibit improved properties compared to traditional coatings. Self-healing anticorrosive coating not only enhances passive barrier function but also realizes active defense. As a result, there is a significant improvement in the service life and overall performance of the coating. Future research should be devoted to refining self-healing mechanisms and developing cost-effective solutions for a wide range of industrial applications. [ABSTRACT FROM AUTHOR]

Commercially available 316L (1.4404) stainless steel is commonly used for industrial filtration due to its combination of good material properties, particularly its corrosion resistance, which is a critical factor for filters in corrosive (e.g., saltwater) environments. Recently, laser powder bed fusion (LPBF) has enabled new more complex and efficient filtration pieces to be manufactured from this material. However, it is critical to know how the corrosion resistance is affected by this manufacturing strategy. Here, the corrosion resistance of LPBF manufactured 316L stainless steel is compared with wrought 316L sheet. The corrosion of the samples in saltwater was assessed with asymmetric electrochemical noise, potentiodynamic polarisation curve, and electrochemical impedance spectroscopy. The samples before and after corrosion were examined with scanning electron microscopy and energy-dispersive spectroscopy. The LPBF samples had higher corrosion resistance than the sheet samples and were more noble. The corrosion resistance of the LPBF sample increased with time, while the wrought sample corrosion resistance reduced over time. The corrosion mechanism of both samples was stable with time, formed of a passive film process and a bared material process. This paper presents the first study about the temporal evolution of the LPBF 316L stainless steel corrosion mechanism. [ABSTRACT FROM AUTHOR]

The integrative potential of LPBF-printed parts for various innovative applications depends upon the robustness and infallibility of the part quality. Eliminating or sufficiently reducing factors contributing to the formation of defects is an integral step to achieving satisfiable part quality. Significant research efforts have been conducted to understand and quantify the triggers and origins of LPBF defects by investigating the material properties and process parameters for LPBF-printed geometries using various sensing technologies and techniques. Frequently, combinations of sensing techniques are applied to deepen the understanding of the investigated phenomena. The main objectives of this review are to cover the roles of selective sensing technologies by (1) providing a summary of LPBF metal print defects and their corresponding causes, (2) informing readers of the vast number and types of technologies and methodologies available to detect defects in LPBF-printed parts, and (3) equipping readers with publications geared towards defect detection using combinations of sensing technologies. Due to the large pool of developed sensing technology in the last few years for LPBF-printed parts that may be designed for targeting a specific defect in metal alloys, the article herein focuses on sensing technology that is common and applicable to most common defects and has been utilized in characterization for an extended period with proven efficiency and applicability to LPBF metal parts defect detection. [ABSTRACT FROM AUTHOR]

Air pollution is a critical issue impacting urban environments, leading to severe health problems and environmental degradation. This comprehensive review examines the potential of green systems—specifically green walls, active green walls, and urban greenery systems—to mitigate atmospheric pollutants such as particulate matter (PM), volatile organic compounds (VOCs), and carbon dioxide (CO2). By systematically analyzing 44 recent studies, this review highlights the pollutant capture efficiency of various green technologies and plant species in both indoor and outdoor settings. Active green walls, particularly those utilizing plant species such as Chlorophytum comosum and Sansevieria trifasciata, were found to be highly effective, with VOC reduction efficiencies of up to 96.34%, PM reductions of 65.42%, and CO2 reduction rates reaching 4.8% under optimal conditions. This review identifies key strengths in current research, including diverse experimental setups and the use of sophisticated measurement techniques, but also notes significant limitations such as variability in experimental conditions and a lack of long-term performance data. This study underscores the importance of proper maintenance to sustain green systems' efficacy and highlights the potential issue of pollutant resuspension, which remains under-researched. Practical implications for urban planning are discussed, advocating for the integration of effective green systems into urban infrastructure to enhance air quality and public health. Recommendations for future research include the need for standardized metrics, long-term studies, economic feasibility analyses, and real-world validation of simulation models to better understand and optimize green systems for urban air pollution mitigation. [ABSTRACT FROM AUTHOR]

Accurate remaining useful life (RUL) prediction is one of the most challenging problems in the prognostics of turbofan engines. Recently, RUL prediction methods for turbofan engines mainly involve data-driven models. Preprocessing the sensor data is essential for the performance of the prognostic models. Most studies on turbofan engines use piecewise linear (PwL) labeling, which starts with a constant initial RUL value in normal/healthy operating time. In this study, we designed a prognostic procedure that includes difference-based feature construction, change-point-detection-based PwL labeling, and a 1D-CNN-LSTM (one-dimensional–convolutional neural network–long short-term memory) hybrid neural network model for RUL prediction. The procedure was evaluated on the subset FD001 of the C-MAPSS dataset. The proposed procedure was compared with machine learning and deep learning models with and without the new difference feature. Also, the results were compared with the studies that used similar labeling approaches. Our analysis of the numerical results underscores the clear superiority of the proposed 1D-CNN-LSTM model with the difference feature in RUL prediction, with a score of 437.2 and an RMSE value of 16.1. This result illustrates the superior predictive capability of the 1D-CNN-LSTM model, which outperformed traditional machine learning methods and one of the earliest deep learning methods. These findings emphasize the superior predictive capability of the 1D-CNN-LSTM model and underline the potential of the feature engineering process for more accurate and robust RUL prediction in the context of turbofan engine prognostics. [ABSTRACT FROM AUTHOR]

Science and engineering research studies are currently concentrating on synthesizing, designing, producing, and consuming ecologically benign chemical species to replace harmful chemicals. This is due to the increasing demands of conservation knowledge and strict ecological regulations. Numerous environmentally friendly substitutes produced from natural resources, including biopolymers, plant extracts, chemical pharmaceuticals (drugs), and so on, are now frequently used as inhibitors to replace dangerous corrosion inhibitors. Many compounds have been extensively used. A range of methods, including physisorption, chemisorption, barrier protection, thin-film growth, and electrochemical procedures, will be used to provide corrosion resistance. The various kinds of corrosion inhibitors (CIs), the mechanisms underlying inhibition, and the evaluation procedures have all been covered in-depth. This review provides an overview of the relevant literature in which researchers and scientists used different types of CIs, the effect of CIs on metals, and information about designs and mechanisms used to minimize corrosion in a variety of equipment composed of alloys or metals, along with electrochemical evaluation studies. This review will provide scholars with fresh insights to advance the discipline. [ABSTRACT FROM AUTHOR]

Recent advances in additive manufacturing (AM) technology provide the potential for on-demand and rapid production of spare parts during urgent repair times. Recently, big oil and gas companies have shown early progress in using additive technology in manufacturing specific heat exchangers, downhole cleanout tool nozzles, offshore risers, gas turbine nozzles, and subsea chemical stick injection tools. Despite the mentioned progress, the current adoption level of additive technology for the offshore oil and gas industry is very limited. Non-destructive and destructive evaluation methods of additively manufactured metallic components have been studied extensively. However, the technique selection procedure and scope of the required test methods have not been studied sufficiently. This paper discusses various elements related to the qualification of additively manufactured components for application in the oil and gas industry. A risk-based qualification method for identifying the scopes of required non-destructive and destructive tests and the resulting qualification procedure for additively manufactured spare components in offshore oil and gas applications is suggested. [ABSTRACT FROM AUTHOR]

To investigate the bioconcentration and potential health risks of metal(loid)s in a particular agro-ecological area, 230 pairs of soil and corresponding crop grain samples were collected from typical corn and wheat plants. The geo-accumulation index (Igeo), bioconcentration factors (BCF), health risk assessment (the target hazard quotient), and Receptor Prediction Model (PCS-SMLR) analysis were adopted to study the spatial distribution, assess the health risks, and predict the relationship between metal(loid)s and soil properties. It was found that the mean concentrations of Cu, Zn, Pb, Hg, and Cd in the study area's agricultural soils exceeded the background soil concentrations, especially for Cd (0.2 mg/kg), Meanwhile, the corresponding Cd concentration in wheat samples was higher than the food quality limit. The results of the Igeo showed that the samples with a value higher than 0 for Cd and Hg accounted for 47.83% and 33.48%, respectively. The results of BCF of Cu, Zn, Cd, and As were higher in wheat than in corn, except for Ni. The target hazard quotient (TTHQ) of health risk of wheat, corn, and soil were higher for children (2.48) than adults (1.78), showing a potential health risk for individuals who mostly consume wheat. In addition, the PCS-SMLR analysis of the BCF prediction model for Cu, Zn, As, and soil properties showed differences in terms of the influences from wheat and corn. These results provide valuable information that not only can help local residents improve the staple food structure, but also can get provide a reference metal(loid)s concentration level for agricultural soils in the study area and restore a sustainable agro-ecological environment. [ABSTRACT FROM AUTHOR]