CABI Abstracts

Mass building projects play a key role in the economic prosperity of any country. Furthermore, these projects are among the main drivers of environmental and social problems. In recent years, with the spread of the concept of sustainable development in the life cycle of construction projects and the dynamic and eventful nature of these projects, the issue of risk management in the sustainable construction industry has received more and more attention among researchers. The construction industry, like other industries, faces various risks. Therefore, it is crucial to identify and evaluate risks in mass construction projects due to the high volume of work. In this study, an integrated model based on fuzzy Shannon entropy and fuzzy EDAS is proposed for risk assessment in large-scale building projects. Initially, by reviewing related articles, 66 effective sub-indicators are identified and classified into 18 risk categories, including 6 external risks and 12 internal risks. Subsequently, a questionnaire is designed to assess the three factors of detection, probability of occurrence, and severity risks for each risk index. This questionnaire distributes to 15 mass production companies in the construction field in Isfahan. The fuzzy Shannon entropy method is then applied to determine the weight of risk factors. The weights of each factor, detection, probability of occurrence, and severity, are calculated as 0.386, 0.342, and 0.273, respectively. These weights are used in the fuzzy EDAS method to prioritize the identified risks in mass-building projects. The results of the fuzzy EDAS method determined the three most critical risks: “inflation rate volatility”, “import/export restrictions”, and “unforeseen climatic conditions”. Additionally, three low-risk sub-indicators are obtained: “limitation on working hours”, “collapse of the structure”, and “unpredictable fire”.

Low carbon dioxide (CO2) emissions make nuclear energy crucial in decarbonizing the economy. In this context, nuclear safety, and especially the operation of nuclear power plants, remains a critical issue. This article presents a new fractal cluster method of control that improves the quality of assessing fuel element cladding integrity, which is critical for nuclear and environmental safety. The proposed non-destructive testing method allows for detecting defects on the inner and outer cladding surfaces without removing the elements from the nuclear reactor, which ensures prompt response and prevention of radiation leakage. Studies have shown that the fractal dimension of the cladding surface, which varies from 2.1 to 2.5, indicates significant heterogeneity caused by mechanical damage or corrosion, which can affect its integrity. The density analysis of defect clusters allows quantifying their concentration per unit area, which is an important indicator for assessing the risks associated with the operation of nuclear facilities. The data obtained are used to assess the impact of defects on the vessel’s integrity and, in turn, on nuclear safety. The monitoring results are transmitted in real time to the operator’s automated workstation, allowing for timely decision making to prevent radioactive releases and improve environmental safety. The proposed method is a promising tool for ensuring reliable quality control of the fuel element cladding condition and improving nuclear and environmental safety. While the study is based on VVER-1000 reactor data, the flexibility of the proposed methodology suggests its potential applicability to other reactor types, opening avenues for broader implementation in diverse nuclear systems.

This article investigates the influence of different solvents on the mechanical properties of biocompatible and biodegradable polycaprolactone (PCL) scaffolds. During the research, using electrospinning technology, 27 samples of polycaprolactone nanofibers exposed to different solvents were produced. A tensile test was performed on the produced nanofiber samples, and the nanofiber mechanical properties, yield strength, elastic modulus, and elastic elongation were calculated, and load–displacement and stress–strain dependence diagrams were compared from the obtained results. The strongest nanofiber was singled out, and its mechanical properties were compared with those of biological tissues and its application in tissue engineering. The structure was determined using a scanning electron microscope, and the structures of nanofibers exposed to different solvents were compared. After calculating the influence of different solvents on the mechanical properties of the nanofibers, the strongest structure was identified, PCL and chloroform, which has an elastic modulus of 9.86 MPa and a yield strength of 1.11 ± 0.32 MPa. The type of solvent used in the production of the solution affects the homogeneity of the fibre and the shape of the filaments. In solvents with lower viscosity, the fibre filaments are more homogeneous and more evenly distributed.

Waste tire textile fiber (WTTF), a secondary product from the processing of end-of-life tires, is predominantly disposed of through incineration or landfilling—both of which present significant environmental hazards. The incineration process emits large quantities of greenhouse gases (GHGs) as well as harmful substances such as dioxins and heavy metals, exacerbating air pollution and contributing to climate change. Conversely, landfilling WTTF results in long-term environmental degradation, as the synthetic fibers are non-biodegradable and can leach pollutants into the surrounding soil and water systems. These detrimental impacts emphasize the pressing need for environmentally sustainable disposal and reuse strategies. We found that 80% of WTTF was used for the production of thermal insulation mats. The other part, i.e., 20% of the raw material, used for the twining, stabilization, and improvement of the properties of the mats, consisted of recycled polyester fiber (RPES), bicomponent polyester fiber (BiPES), and hollow polyester fiber (HPES). The research shows that 80% of WTTF produces a stable filament for sustainable thermal insulating mat formation. The studies on sustainable thermal insulating mats show that the thermal conductivity of the product varies from 0.0412 W/(m∙K) to 0.0338 W/(m∙K). The tensile strength measured parallel to the direction of formation ranges from 5.60 kPa to 13.8 kPa, and, perpendicular to the direction of formation, it ranges from 7.0 kPa to 23 kPa. In addition, the fibers, as well as the finished product, were characterized by low water absorption values, which, depending on the composition, ranged from 1.5% to 4.3%. This research is practically significant because it demonstrates that WTTF can be used to produce insulating materials using non-woven technology. The obtained thermal conductivity values are comparable to those of conventional insulating materials, and the measured mechanical properties meet the requirements for insulating mats.

In today’s world, everything changes at lightning speed, making what is relevant today potentially obsolete tomorrow. This author’s scientific article addresses the issues of energy resilience and cybersecurity in railway signaling. A new proposal based on artificial intelligence is made to improve the fault tolerance of rail transport signaling infrastructure by ensuring increased energy efficiency and detecting cyber-attacks in real time. A linearly coupled neural network model was designed and implemented in a railway signaling simulation to simultaneously track the energy characteristics of signaling and detect abnormal behavior. The authors’ model was validated based on MATLAB(24.2.0.2863752 (R2024b) Update 5) simulations of a real double-track railway line under normal operating conditions and in a ransomware cyber-attack scenario. The AI simulation model correctly predicted the resilience of the signaling system, achieving an average absolute error of 0.0331 in predicting the fundamental performance indicator, and successfully identified an upcoming cyber-attack 20 min before the incident. This study demonstrates the promising architecture of the AI-based signaling system, which provides a significant increase in resilience to emergency situations in relation to power supply and cyber-attacks. By optimizing the signaling infrastructure with AI, it is possible to ensure safe and continuous movement of trains, including emergency situations, representing a promising approach to improving the resilience and safety of railways.

Monitoring insect activity at hive entrances is essential for advancing precision beekeeping practices by enabling non-invasive, real-time assessment of the colony’s health and early detection of potential threats. This study evaluates deep learning models for detecting worker bees, pollen-bearing bees, drones, and wasps, comparing different YOLO-based architectures optimized for real-time inference on an RTX 4080 Super and Jetson AGX Orin. A new publicly available dataset with diverse environmental conditions was used for training and validation. Performance comparisons showed that modified YOLOv8 models achieved a better precision–speed trade-off relative to other YOLO-based architectures, enabling efficient deployment on embedded platforms. Results indicate that model modifications enhance detection accuracy while reducing inference time, particularly for small object classes such as pollen. The study explores the impact of different annotation strategies on classification performance and tracking consistency. The findings demonstrate the feasibility of deploying AI-powered hive monitoring systems on embedded platforms, with potential applications in precision beekeeping and pollination surveillance.

In a scientific context, the main focus of sustainable supply chain management is on the creation and optimization of product and information flows; however, the management of financial flows receives insufficient attention. All effectively developed supply chain activities may collapse as a result of inadequate management of sustainable supply chain financial processes. In order to successfully develop systematically functioning processes of the international supply chain, it is necessary to analyze how to apply financing instruments in a targeted and effective manner. Adequate financing of the sustainable supply chain is the effect of great prospects and competitive advantage not only on a national scale but also in international markets. The aim of this research was to assess the importance of financing instruments used in international sustainable supply chain finance. Correlation-regression analysis was chosen for the research, which was designed to assess the factors of financial instruments of the dairy industry sustainable supply chain using the example of a company. The results showed that the key factor in the supply chain processes of the dairy products production company was the turnover ratio of buyers’ debts; therefore, in order for the company to improve the indicators of the sustainable supply chain, it should allocate more financing specifically to the turnover ratio of buyers’ debts.

Pultruded GFRP (glass fiber-reinforced polymer) materials are widely used in structural engineering because of their lightweight, corrosion immunity, and electromagnetic transparency. However, the design of load-bearing components facing substantial compressive stresses, e.g., columns, must be more stringent than steel structures due to excessive deformability, material heterogeneity, and vulnerability to stress concentration. This manuscript investigates the failure performance of locally produced GFRP materials, focusing on the material heterogeneity effect on the mechanical resistance of a support joint of a pultruded tubular GFRP column. This experimental campaign employs relatively short rectangular profile fragments to isolate the support behavior and verify a simplified numerical finite element model, which neglects the nonlinearity of GFRP material. This work determines the material failure mechanisms behind the mechanical performance of pultruded profiles subjected to longitudinal compression for various column lengths.

The transition to a circular economy is important in achieving sustainability, promoting resource efficiency, and reducing environmental impact. This paper aims to assess the development of a CE in EU countries and highlight the use of environmentally friendly practices for its development. Decision-making methods based on various criteria provide a solid basis for evaluating complex and multidimensional circular economy (CE) initiatives. Simple AdditiveWeighting (SAW), the widely used MCDM method, facilitates sorting and selection according to the generalised results obtained according to weighted criteria. Due to its simplicity and ease of use, this method is particularly useful for assessing CE development in different countries. The evaluation will be based on a comprehensive overview of the available literature and empirical data, allowing for a comprehensive assessment of the CE’s development initiatives in the European context using the SAW method. The results show that while significant progress has been made in the EU’s transition to a circular economy (CE), disparities among Member States and data limitations hinder a comprehensive assessment. Italy, Germany, France, the Netherlands, and Spain are among the most advanced countries in achieving a circular economy’s objectives. The study proposes a novel MCDM-based framework that effectively evaluates CE performance, identifying key strengths and weaknesses across countries. By focusing on competitiveness and innovation indicators and incorporating environmental factors, the framework offers valuable insights for policymakers and stakeholders.

This study focused on evaluating the sensitivity and limitations of the simplified equipment used in the Digital Image Correlation (DIC) technique, comparing them with the analog extensometer, based on the mechanical property data of a composite made of fiberglass and epoxy resin. The objectives included establishing a methodology based on the literature, fabricating samples through manual lamination, conducting mechanical tests according to the ASTM D3039 and D3518 standards, comparing DIC with the analog extensometer of the testing machine, and contrasting the experimental results with classical laminate theory. Three composite plates with specific stacking sequences ([0]3, [90]4, and [±45]3) were fabricated, and samples were extracted for testing to determine tensile strength, modulus of elasticity, and other properties. DIC was used to capture deformation fields during testing. Comparisons between data obtained from the analog extensometer and DIC revealed differences of 11.1% for the longitudinal modulus of elasticity 𝐸1 and 5.6% for 𝐸2. Under low deformation conditions, DIC showed lower efficiency due to equipment limitations. Finally, a theoretical analysis based on classical laminate theory, conducted using a Python script, estimated the longitudinal modulus of elasticity 𝐸𝑥 and the shear strength of the [±45]3 laminate, highlighting a relative difference of 31.2% between the theoretical value of 7136 MPa and the experimental value of 5208 MPa for 𝐸𝑥.