Sustainability, Volume 14, Issue 21 (November-1 2022) – 968 articles

The present paper considers a manufacturing supply chain of deteriorating type inventories. The problem addresses the extra rented warehouse (RW) to store extra inventories if the manufacturer is producing more inventories than their owned warehouse (OW) capacity. Now, the problem is which inventories should be used first with minimum cost and minimum deterioration. To solve this problem, we have assumed a MFIFO (mixed first in first out) dispatching policy and constant demand rate over a finite time horizon. Along with these we have also assumed an inspection policy during the supply chain to separate deteriorated items and a carbon tax policy is also considered to control carbon emissions. The rate of deterioration depends on the number of inspections. If the number of inspections increases, it minimizes the rate of the decaying process. Due to the adoption of the inspection policy, the supply chain moves toward a green supply chain as it removes deteriorated inventories that minimize further decay by contact, and simultaneously separated deteriorated products can be utilized for other purposes that solve the problem of the disposal of deteriorating inventories and reduce emission generation. We have also established the uniqueness of the established model. The motto of solving the mathematical model is to find the values of the optimum value of $N$, the number of cycles, and $n,$ the number of inspections that helps to minimize total cost. At last, we illustrate the result with the help of a numerical example. Full article

The appropriate feature/predictor selection is as significant as building efficient estimation methods for the accurate estimation of power consumption, which is required for self-awareness and autonomous decision systems. Traditional methodologies define predictors by assessing whether there is a relationship between the predictors and the response variable. Contrarily, this study determines predictors based on their individual and group impacts on the estimation accuracy directly. To analyze the impact of predictors on the power-consumption estimation of an IT rack in a data center, estimations were carried out employing each prospective predictor separately using the measured data under the real-world workload. Then, the ratio of CPU usage was set as the default predictor, and the remaining variables were assigned as the second predictor one by one. By utilizing the same approach, the best combination of predictors was determined. As a result, it was discovered that some variables with a low correlation coefficient with power consumption improved the estimation accuracy, whereas some variables with high correlation coefficients worsened the estimation result. The CPU is the most power-consuming component in the server and one of the most used predictors in the literature. However, the estimation accuracy obtained using only the CPU is 10 times worse than the estimation result conducted by utilizing the predictor set determined at the end of the experiments. This study shows that instead of choosing predictors only from one point of view or one method, it is more convenient to select predictors by assessing their influence on estimation results. Examining the trend and characteristics of the estimated variable should also be considered. Full article

Urbanization and climate change have deteriorated the runoff water circulation and quality in urban areas worldwide. Consequently, low-impact development (LID) and green infrastructure (GI) techniques have been applied to manage impermeable land and non-point source pollutants. Herein, the impacts of urban characteristics, sewer system type, and precipitation intensity on surface runoff were analyzed using the Storm Water Management Model (SWMM) to derive an effective water circulation strategy for urban and complex areas through the optimal allocation of LID/GI strategies. The runoff rates were estimated to be 77.9%, 37.8%, and 61.7% for urban areas with separated and combined sewer systems and complex areas with combined sewer systems, respectively. During low rainfall, runoff was intercepted in areas with combined sewer systems, and runoff and pollutant load were lower than that in areas with separated sewer system. In contrast, wastewater was diluted during heavy rainfall; however, the total pollutant load was higher than in separated areas. The analysis of scenarios according to the regional distribution of each LID type resulted in high efficiency when combined sewers were applied during the distributed placement of catchment areas. Additionally, LID infrastructure was applied in areas with separated sewers when the placement was concentrated at the end of the basin. Full article

A constant power supply is a basic need for each consumer due to the increase in sensitive equipment day by day. As per IEEE standards, a 10% reduction in voltage from the supply voltage is not acceptable and may cause the failure of equipment. Previously, different techniques have been used to alleviate the voltage sag, such as STATCOM, DSTATCOM, SVC, and shunt capacitors, but these devices are connected in parallel, which compensates for the low value of voltage sag, and they have high maintenance costs involved. Compensation for the low and high values of voltage sag is possible through a series-connected device such as a dynamic voltage restorer. In this paper, a matrix converter is presented for DVR to convert AC to AC voltage directly and free from batteries, capacitors, and multiple conversions as needed in a voltage source inverter, resulting in a reduced cost of DVR topology. The DVR is meaningless in the absence of a controller, so it is necessary to select a suitable controller for the satisfactory operation of the DVR under a variation of system parameters. In this paper, the performance of a linear PID controller is analyzed and compared with a nonlinear controller, such as a sliding-mode controller, under variation of power system parameters inorder to select a robust controller that performs satisfactorily for DVR. Earlier trial-and-error methods were used to obtain the parameters of PID gains, but they require a large time to obtain the parameters of the PID gains and there is a chance of inaccuracy. A genetic algorithm was used to obtain the gain parameters, but it has more convergence time and the particle swarm optimization technique has involves less reliability. In this research paper, the sliding surface coefficient parameters such as and Ki for the PI sliding surface of SMC and PID gains are taken through an ant colony algorithm to obtain the robustness of the controllers. The purpose of this paper is to introduce the best DVR topology with reduced cost. MATLAB simulation software was utilized to analyze the performance of the DVR with PID and SMC controllers under different fault conditions and also the THD% of proposed controllers was analyzed through FFT. Full article

In order to avoid the influence of end restraint on triaxial stress and strain measurements, this study combines photogrammetry and computer technology to apply to the unconsolidated undrained test of triaxial soil samples. The novel method can establish an intuitive shear band evolution model of soil samples and help attain a clear and intuitive shear band evolution law. On the basis of the conventional triaxial test, this new method needs to paste the RAD (Ringed Automatically Detected) code points on the rubber film, plexiglass cover and brackets and then take pictures of the RAD code points in the loading process in a surrounding manner, with which the 3D shape of the restored soil samples can be determined. After eliminating the influence of refraction, the coded point cloud coordinates measured in the experiment can be used to calculate the axial deformation and radial deformation of the soil sample in the deformation process and determine the local stress–strain curve and three-dimensional displacement field diagram of the soil sample. The test shows that the new method can clearly determine the peak value and inflection point of the stress–strain curve in the middle region of soil samples, enabling it to reflect the shear change process of the soil sample more accurately. In addition, the displacement field can be used to directly observe the formation, development and penetration process of the shear band in soil samples. Full article

In recent years, the bitcoin market has developed rapidly and has been recognized as a new type of gold by many investors. It may replace gold as a hedge against inflation and become a new investment asset for financial management. The investment relationship with gold has increasingly important research value and practical significance. This paper modeled daily price flow data from 11 September 2016 to 10 September 2021 to help market traders determine whether they need to buy, hold, or sell assets in their portfolios daily. The model predicts price fluctuations through linear regression prediction of machine learning, K-Nearest Neighbor (KNN) algorithm. In the linear regression prediction, the goodness of fit of gold is 89.44%, and the goodness of fit of Bitcoin is 98.43%. In the test set prediction of KNN algorithm, the goodness of fit of gold is 97.25%, and the goodness of fit of Bitcoin is 95.06%. Based on this, the optimal investment strategy and the initial investment value are obtained. Empirical analysis shows that bitcoin price volatility and gold price volatility have a strong substitution effect; gold and currency used will be a suitable combination of hedging, which will bring momentum for the development of the market economy and become an important force in the sustainable development of a high-quality-driven economy. Full article

There have been several decades since the creative and cultural economy (CCE) was praised for its contributions to long-term socio-economic development and also for its sustainable approach concerning the main production factors involved by the creative industries—human intellect and talent—and cultural ones, and now, more than ever, the creative sector could be perceived as vital in facing and recovering from the several crises of recent years. In order to determine the “recipe” of a flourishing local CCE, our research continues analysing seven Romanian cities, by assessing several influential factors considered as catalysts of the CCE, such as: student populations, young populations, and local public expenditure on culture during 2008–2019 for the selected creative cities. In this paper, we will determine the connection between these catalysts of the local CCEs’ development as independent variables, and two economic dimensions, the number of employees and turnover, as the dependent variables. The determination of this correlation started by using the observation method and the method of multiple regression, but further investigation was needed, so the present paper deepens the research by approaching the panel data method. Our results prove an existing correlation between the analysed variables, some of them influencing positively, and others negatively. Full article

With the transmission of the COVID-19 epidemic at home and abroad, this paper considers the spread process in China, improves the classic epidemic SEIR model, and establishes a multistage time-delay control model (MTCM) for COVID-19 transmission. The MTCM divides the spread of COVID-19 into three periods: the outbreak period, the control period and the steady period. The classical SEIR model, the improved SEQIR model and the SEQIR Ⅱ model correspond to the three periods. The classical SEIR model was adopted for the outbreak period and yielded results that were consistent with the observed early propagation of COVID-19 transmission. In the control period, adding isolation measures and a time delay to the MTCM and adjusting the rates yielded a better simulation effect. In the steady period, the focus of consideration is the number of new patients, population movement (in-migration and out-migration of the population) and patient classification (symptomatic and asymptomatic patients). The MCTM was used for simulation, and the comparison results revealed that the simulated data of the MCTM (improved SEQIR model) and the actual data are similar in the control period. The control policy of isolation measures is effective. New infections, population flow and patients with symptomatic or asymptomatic symptoms are more consistent with the steady period characteristics. The multi-stage time-delay control model for COVID-19 transmission provides theoretical methods and good prevention and control measures for future epidemic policy formulation. Full article

Faba bean (Vicia faba L.) is an economically important crop cultivated globally for fulfilling human requirements. However, the productivity of the faba bean has declined due to poor management of soil, particularly under salt stress. Salt stress is a major constraint to crop productivity worldwide. Therefore, the objective of the present investigation is to check the behavior of faba bean genotypes on the basis of morphological and biochemical traits in response to salinity. In this study, we studied seven different treatments (including control) applied to faba bean under salt stress. Bioinoculants such as Trichoderma viride, Pseudomonas flourescens, Glomus mosseae, and Gigaspora gigantean, each separately and in combination, were tested for their efficacy under salinity stress. Data recorded on days to flowering (48.92 ± 1.15), days to maturity (144.56 ± 1.95), plant height (141.93 ± 4.81 cm), number of branches per plant (4.87 ± 0.09), number of clusters per plant (18.88 ± 0.24), number of pods per plant (48.33 ± 1.06), pod length (5.31 ± 0.02 cm), catalase (222.10 ± 2.76 mg), hydrogen peroxide (24 ± 4.58 mol/g), malondialdehyde (45 ± 1.00 mol/g), electrolyte leakage (54.67 ± 5.03), chlorophyll (51.67 ± 3.06 mg/g), proline content (2.96 ± 0.12 mg/g), and on other parameters indicated the combined inoculation of all the species (consortium) was taken to be highly effective even under salt stress. Overall, the consortium treatment comprising all of the bioinoculants was observed to be the most efficient treatment in improving all the morphological and biochemical traits of faba bean under salt stress. Although, other treatments also demonstrated considerable effects on faba bean as compared to one without bioinoculants under salt stress. Full article

Self-Determination Theory (SDT) has been studied to comprehend human motivation, particularly in education. Numerous studies have been conducted at universities regarding online learning as a technology to mitigate the effects of COVID-19. On the basis of these expansions, however, there is a knowledge gap regarding what constitutes advancement, future direction, and research gaps regarding SDT in university online learning. This new systematic literature review analyzed 49 articles using PRISMA to bridge the knowledge gap. Currently, SDT research in online learning at university does not extensively integrate other theories and models, but there is a trend toward acceptance models and cognitive theories. Future research should incorporate additional SDT factors such as intrinsic motivation, external regulation, identified regulation, and amotivation in addition to autonomy, competence, and relatedness. As most research samples students, a research gap involving lecturers and mixed groups is suggested. The future is anticipated to be dominated by quantitative research, leaving qualitative and mixed methods as points of exploration. This review sheds light on the advancements, future direction, and research gaps regarding SDT in university-level online learning. It could serve as a basis for future research in SDT within the context of online education. Full article

This article seeks to compare the performance of a LIDAR Windcube V2, manufactured by Leosphere, with that of a SODAR MFAS, manufactured by Scintec, in evaluating wind speed at different altitudes. The data from these two sensors were collected at three locations on the Brazilian equatorial margin in the state of Maranhão. The comparison of these sensors aims at their simultaneous use at different points. The horizontal velocity components, by altitude, showed Pearson correlation values above 0.9 and values for the vertical velocity component between 0.7 and 0.85. As for the sampling efficiency, the LIDAR had a performance slightly higher than that of SODAR, especially at the point closest to the coast. In general, both sensors showed similar values, despite the differences in sampling methods. The results showed that the joint performance of these sensors had good correlation, being reliable for application in estimating wind potential for power generation in coastal areas of the equatorial region. Full article

Fuel cells have lately received growing attention since they allow the use of non-precious metals as catalysts, which reduce the cost per kilowatt of power in fuel cell devices to some extent. Until recent years, the major barrier in the development of fuel cells was the obtainability of highly conductive anion exchange membranes (AEMs). On the other hand, improvements show that newly enhanced anion exchange membranes have already reached high conductivity levels, leading to the suitable presentation of the cell. Currently, an increasing number of studies have described the performance results of fuel cells. Much of the literature reporting cell performance is founded on hydrogen‒anion exchange membrane fuel cells (AEMFCs), though a growing number of studies have also reported utilizing fuels other than hydrogen—such as alcohols, non-alcohol C-based fuels, and N-based fuels. This article reviews the types, performance, utilized membranes, and operational conditions of anion exchange membranes for fuel cells. Full article

Indoor positioning systems have been of great importance, especially for applications that require the precise location of objects and users. Convolutional neural network-based indoor positioning systems (IPS) have garnered much interest in recent years due to their ability to achieve high positioning accuracy and low positioning error, regardless of signal fluctuation. Nevertheless, a powerful CNN framework comes with a high computational cost. Hence, there will be difficulty in deploying such a system on a computationally restricted device. Knowledge distillation has been an excellent solution which allows smaller networks to imitate the performance of larger networks. However, problems such as degradation in the student’s positioning performance, occur when a far more complex CNN is used to train a small CNN, because the small CNN does not have the ability to fully capture the knowledge that has been passed down. In this paper, we implemented the teacher-assistant framework to allow a simple CNN indoor positioning system to closely imitate a superior indoor positioning scheme. The framework involves transferring knowledge from a large pre-trained network to a small network by passing through an intermediate network. Based on our observation, the positioning error of a small network can be reduced to up to 38.79% by implementing the teacher-assistant knowledge distillation framework, while a typical knowledge distillation framework can only reduce the error to 30.18%. Full article

The proper maintenance of historical monuments and their use is one of the pillars of sustainable development. Over the years, historic architectural buildings have undergone numerous changes resulting from reconstruction, expansion, or damage caused both by natural and other disasters. Therefore, their contemporary appearance is the result of these changes. Thanks to the documentation of their transformations, one has the opportunity to get to know their history. Currently, thanks to advanced technology, it is becoming easier and easier to document various historical monuments. However, the method of their documentation, especially the possibility of their reconstruction and the creation of 3D models depends mostly on the data resources at one’s disposal. This article compares two extreme methods of recreating an architectural object that has undergone some changes throughout history. One of the methods is to reconstruct the object on the basis of a photograph using geometrical rules and computer aid, while the other is based on laser scanning. Due to the fact that the same object is being reconstructed by means of both methods, it is possible to evaluate and compare the applied methods and estimate their accuracy, as well as to draw conclusions about the transformations of the reconstructed object over the years. Full article

The fourth industrial revolution, also referred to as Industry 4.0, has resulted in many changes within the MedTech Industry. The MedTech industry is changing from interconnected manufacturing systems using cyber-physical systems to digital health technologies. The purpose of the study is to establish how Industry 4.0 can understand the impact Industry 4.0 is having on product lifecycle regulatory compliance and determine the effect Industry 4.0 is having on product lifecycle regulatory compliance. A qualitative research approach was utilised to gather data from the MedTech industry by conducting interviews with Medtech industry leaders. This research demonstrates that Industry 4.0 is easing product lifecycle regulatory compliance and that the impact is more positive than negative. Industry 4.0 offers many benefits to the MedTech Industry. This research will support organisations in demonstrating how digital technologies can positively impact product lifecycle regulatory compliance and support the industry in building a business case for future implementation of Industry 4.0 technologies. Full article

The existence of low-frequency oscillations in power systems is the cause of power angle instability, limiting the transmission of maximum tie-line power. One of the effective ways to improve the stability limits is by installing a power system stabilizer and supplementary excitation control to augment with an automatic voltage regulator (AVR) supplemental feedback stabilizing signal. This paper proposes a new strategy for simultaneously tuning the power system stabilizer (PSS) and FACTS controller, considering time delays. The design of the proposed controller is modeled as an optimization problem, and the parameters of the controller are optimized through the grasshopper optimization algorithm (GOA). The suggested controller’s efficacy is evaluated for both single-machine infinite bus systems and multi-machine power systems under various disturbances. It also investigated the performance of the proposed controller with variations in signal transmission delays. The results obtained from GOA optimized proposed controller are compared with those obtained from the differential evolution algorithm, genetic algorithm, and whale optimization algorithm. In this context, the proposed GOA optimized controller reduced the objective function value by 16.32%, 14.56%, and 13.72%, respectively, in the SMIB system and 1.41%, 9.98%, and 13.31%, respectively, for the multi-machine system compared with the recently published WOA, and the well-established GA and DE. Further, the proposed controller is found to be stable and effectively increases stability even under small disturbances. Full article

Local wisdom is frequently used by communities in managing their coastal resources without a precise measure of sustainability. As a result, the government must develop a standard for determining the wisdom of these practices. This study aimed to create such a standard, followed by a trial to evaluate management practices in Pangandaran coastal tourism. This qualitative case study included a literature review, direct observations, and in-depth interviews with fish farmers and fishers. They are standardizing instrument criteria for sustainable fishery resource management-defined wisdom. Such wisdom is divided into fundamental thinking (factual knowledge) and management practices (procedural knowledge). Each consists of five criteria: ecosystem and resource management, planning, governance, technology, and social and economic development. Each criterion has a specific rating indicator and parameter. The results show differences in the level of wisdom between the fish farmer and the fishers. Regarding basic thinking, fishers’ wisdom level is weak in three out of five criteria. Fishers reach a moderate wisdom level concerning fishing gear and technical criteria and a strong level on social and economic criteria. In contrast, the fish farmer is moderate to strong for four criteria and weak for the resources and ecosystems criterion. Regarding management practices, in general, fish farmers and fishers have the same level of wisdom. Both are weak in the ecosystem and resources, planning, and institutional criteria, while the fishing gear criteria reach moderate levels and the socio-economic criteria reach high levels. Full article

The post-pandemic era is full of instability and uncertainty, which brings new challenges and opportunities to the development of organization. As a sustainable feature of enterprises, improving employees’ adaptive performance levels is a necessary condition for enterprises to achieve the sustainable development goal. This study is based on self-determination theory, which focuses on new-generation employees as the key force of enterprise and incorporates harmonious work passion and core self-evaluation into the research framework to explore the influence of paradoxical leadership on adaptive performance. The survey data obtained from 519 new-generation employees shows that: paradoxical leadership is significantly and positively correlated with adaptive performance of new-generation employees; the relationship between paradoxical leadership and adaptive performance is partially mediated by harmonious work passion; core self-evaluation positively adjusts the relationship between paradoxical leadership and harmonious work passion. In addition, core self-evaluation also regulates the intermediary role of harmonious work passion—that is to say, the higher core self-evaluation of new-generation employees is, the stronger the intermediary role of harmonious work passion. The research results explain the connotation of how paradoxical leadership improves adaptive performance of new-generation employees, reveal the medium of the relationship between the two, and find both the role boundary of paradoxical leadership and the strategy to improve adaptive performance. Full article

Using local spatial statistics to explore local spatial association of geo-referenced data has attracted much attention. As is known, a local statistic is formulated at a particular sampling unit based on a prespecific proximity relationship and the observations in the neighborhood of this sampling unit. However, geostatistical data such as meteorological data and air pollution data are generally collected from meteorological or monitoring stations which are usually sparsely located or highly clustered over space. For such data, a local spatial statistic formulated at an isolate sampling point may be ineffective because of its distant neighbors, or the statistic is undefinable in the sub-regions where no observations are available, which limits the comprehensive exploration of local spatial association over the whole studied region. In order to overcome the predicament, a local-linear geographically weighted interpolation method is proposed in this paper to obtain the predictors of the underlying spatial process on a lattice spatial tessellation, on which a local spatial statistic can be well formulated at each interpolation point. Furthermore, the bootstrap test is suggested to identify the locations where local spatial association is significant using the interpolated-value-based local spatial statistics. Simulation with comparison to some existing interpolation and test methods is conducted to assess the performance of the proposed interpolation and the suggested test methods and a case study based on PM2.5 concentration data in Guangdong province, China, is used to demonstrate their applicability. The results show that the proposed interpolation method performs accurately in retrieving an underlying spatial process and the bootstrap test with the interpolated-value-based local statistics is powerful in identifying local patterns of spatial association. Full article

The Internet of Things (IoT) has redefined several aspects of our daily lives, including automation and control of the living environment, innovative healthcare services, and much more. Digital IoT devices and sensors, when integrated with home appliances, industrial systems, and online services in the physical world, have brought intense, disruptive changes in our lives. The industry and home users have widely embraced these ‘things’ on the Internet or IoT. However, the innate, intrinsic repercussions regarding security and data privacy are not evaluated. Security applies to Industrial IoT (IIoT) is in its infancy stage. Techniques from security and privacy research promise to address broad security goals, but attacks continue to emerge in industrial devices. This research explores the vulnerabilities of IIoT ecosystems not just as individual nodes but as the integrated infrastructure of digital and physical systems interacting with the domains. The authors propose a unique threat model framework to analyze the attacks on IIoT application environments. The authors identified sensitive data flows inside the IIoT devices to determine privacy risks at the application level and explored the device exchanges at the physical level. Both these risks lead to insecure ecosystems. The authors also performed a security analysis of physical domains to digital domains. Full article

Indoor occupancy prediction can play a vital role in the energy-efficient operation of building engineering systems and maintaining satisfactory indoor climate conditions at the lowest possible energy use by operating these systems on the basis of occupancy data. Many methods have been proposed to predict occupancy in residential buildings according to different data types, e.g., digital cameras, motion sensors, and indoor climate sensors. Among these proposed methods, those with indoor climate data as input have received significant interest due to their less intrusive and cost-effective approach. This paper proposes a deep learning method called CNN-XGBoost to predict occupancy using indoor climate data and compares the performance of the proposed method with a range of supervised and unsupervised machine learning algorithms plus artificial neural network algorithms. The comparison is performed using mean absolute error, confusion matrix, and F₁ score. Indoor climate data used in this work are CO₂, relative humidity, and temperature measured by sensors for 13 days in December 2021. We used inexpensive sensors in different rooms of a residential building with a balanced mechanical ventilation system located in northwest Copenhagen, Denmark. The proposed algorithm consists of two parts: a convolutional neural network that learns the features of the input data and a scalable end-to-end tree-boosting classifier. The result indicates that CNN-XGBoost outperforms other algorithms in predicting occupancy levels in all rooms of the test building. In this experiment, we achieved the highest accuracy in occupancy detection using inexpensive indoor climate sensors in a mechanically ventilated residential building with minimum privacy invasion. Full article

Engineering education plays a key role in the progress toward achieving the 17 Sustainable Development Goals (SDGs). However, engineering education faces many challenges worldwide, and the issues are becoming increasingly complicated because of the COVID-19 pandemic. To deal with these challenges and achieve the SDGs by 2030, governance that aligns engineering education and SDGs is badly needed. The International Center for Engineering Education (ICEE) has taken a series of governance actions to align engineering education and sustainable development. This research presents the contribution of these governance actions, analyzes the governance types and their relevance to the SDGs, and explores the key mechanisms of these governance actions and challenges. This research can provide useful information for the global community to understand China’s participation in global engineering-educational governance and promote engineering education for sustainable development. Full article

Average temperatures continue to rise throughout the world due to climate change and, thus, also in Europe, often occurring as heat waves. The negative effects of climate change-related heat waves can be observed, especially in urban areas where land sealing is the greatest and so is population density. Past studies have indicated that green volume can provide climate improvement by balancing humidity and regulating temperature. This study aims to estimate the distribution of surface heat islands and green volume and test the relationship between these variables in a case study of Bochum, Germany. A method to develop a temporally longitudinal 30-m Landsat 8-based land surface temperature (LST) analysis and 30-m LiDAR-based green volume dataset are presented, and their relationship is tested using Pearson’s correlation (n = 148,204). The results show that heat islands are moderately negatively correlated with green volume (r = −0.482; p < 0.05), LST can vary as much as 28 degrees °C between heat islands and densely vegetated areas, and the distribution is heterogeneous across Bochum. Full article

Steel wire rope is essential to many rope transport devices. As steel ropes are used, they become damaged, the identification of which is often very difficult or time-consuming. The criteria for retiring steel wire ropes are rigorous—sometimes, ropes that remain fit for further operation are replaced. This article aims to define a novel method of identifying the condition of steel ropes based on their residual magnetic field measurements and their potential use in other industries in the event of damage. This article presents a methodology for detecting discontinuities in steel ropes, which allows for determination of their suitability for further operation. The work uses a rope as a load-bearing element of a personal lift. The initial signal was recorded with a SpinMeter-3D magnetometer. The obtained results were subjected to the extraction of features, the analysis of which allowed identifying the damage. The obtained results enable us to conclude that this procedure is crucial in the context of sustainable development. Full article

Self-compacting concrete (SCC) has been developed as a type of concrete capable of filling narrow gaps in highly reinforced areas of a mold without internal or external vibration. Bleeding and segregation in SCC can be prevented by the addition of superplasticizers. Due to these favorable properties, SCC has been adopted worldwide. The workability of SCC is closely related to its yield stress and plastic viscosity levels. Therefore, the accurate prediction of yield stress and plastic viscosity of SCC has certain advantages. Predictions of the shear stress and plastic viscosity of SCC is presented in the current study using four different ensemble machine learning techniques: Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), random forest, and Categorical Gradient Boosting (CatBoost). A new database containing the results of slump flow, V-funnel, and L-Box tests with the corresponding shear stress and plastic viscosity values was curated from the literature to develop these ensemble learning models. The performances of these algorithms were compared using state-of-the-art statistical measures of accuracy. Afterward, the output of these ensemble learning algorithms was interpreted with the help of SHapley Additive exPlanations (SHAP) analysis and individual conditional expectation (ICE) plots. Each input variable’s effect on the predictions of the model and their interdependencies have been illustrated. Highly accurate predictions could be achieved with a coefficient of determination greater than 0.96 for both shear stress and plastic viscosity. Full article

With the upgrade of additive manufacturing (AM) equipment, pure copper and various Cu-based alloys with almost full density have been successfully produced, maintaining their excellent thermal and electrical conductivity and good mechanical properties at high temperatures as well. In this paper, a model with a series of inclined surface structures was designed and fabricated to investigate the structural design on the formability of CuCrZr alloy produced by laser powder bed fusion (LPBF). The typical structure dimensions of the as-built samples were measured and compared with their corresponding dimensions and the inclined angle (α) and the relative angle (γ) between the inclined surface and recoating directions. The results demonstrate that the inclined structures with α < 50° were fabricated either with varying buckling deformation and powder adhesion or in failure for severe distortion. The differences (L_d) between the typical structure dimensions and their models increase with the decreasing of α. It has been observed that L_d reaches 1 mm when α is 20° and drastically reduces to around 200 μm when α is above 50°. When α < 50°, L_d is generally increasing with a rising γ value from 0° to 180°, significantly affecting the dimensional accuracy. Full article

In this study, the possibility of applying the Life Cycle Thinking approach to structural design, considering all aspects and phases of the structure’s life, is investigated. The idea is to develop a procedure for the analysis of the economic and environmental impacts of structures in their life cycle, including not only ordinary costs along life cycle phases but also the extraordinary costs resulting from damage and anticipated end-of-life caused by unexpected natural hazards. The building performance under extraordinary conditions is calculated according to a time-based Loss Assessment Analysis. Such analysis provides the probable performance of a building and its components over a given period of time, considering all the hazardous events that can occur in that period, the probability of occurrence of each event, and the related effects. The outlined approach is applied to a case study of a single-story steel office building located in Italy. Two LC scenarios, having a duration of 2 years and 50 years, are considered. Results show that contributions of environmental impacts and benefits related to end-of-life management and economic losses for natural hazards are significant and not negligible. It is highlighted that the greatest challenge faced when using such a comprehensive approach is represented by data availability and representativeness that deeply limits the possibility of its implementation. Full article

Concrete materials are widely used in tunnel engineering. In China, the cold regions have gradually become the main area for highway and railway construction. Affected by high altitude, low temperature, turbulent wind, and other conditions, freezing damage, such as tunnel icing, occurs in concrete materials, which seriously affects the quality and operational safety of tunnels in cold regions. Therefore, it is necessary to carry out a quantitative analysis of various factors affecting freezing damage to protect concrete materials in tunnels. This paper summarizes various freezing damage phenomena in tunnels in cold regions and divides them into three types: water seepage and hanging ice type freezing damage, lining interface type freezing damage, and tunnel foundation ice accumulation type freezing damage. Based on the qualitative evaluation of each factor, the affiliation of each factor was divided. Then, the influence weight of each factor on freezing damage was obtained through the analytic hierarchy process, and then each factor was ranked. This study is helpful to the selection of anti-freezing measures for tunnels in cold regions. Full article

Achieving an economically feasible and environmentally robust model in agriculture while satisfying the expanding population’s food demands is a global challenge. Hence, a three-year (2014–2017) study was conducted at Punjab Agricultural University, Ludhiana to design environmentally clean, energy-efficient, and profitable cropping systems. Twelve cropping systems viz., rice-wheat (CS₁), basmati rice-hayola (transplanted)-mung bean (CS₂), basmati rice-radish-maize (CS₃), maize-potato-maize (CS₄), maize + turmeric-barley + linseed (CS₅), maize + turmeric-wheat + linseed (CS₆), maize + radish-wheat + linseed-mung bean (CS₇), groundnut + pigeon pea (5:1)-wheat + sarson (9:1) (CS₈), maize + black gram-pea (bed) + celery (furrows) (CS₉),_: maize + pigeon pea-chickpea (bed) + gobhi sarson (furrows) (CS₁₀), maize (green cobs) + vegetable cowpea + dhaincha (Sesbania spp.)-chickpea + gobhi sarson (CS₁₁) and sorghum + cowpea (fodder)-wheat + gobhi sarson (9:1) (CS₁₂) were tested in a four-times-replicated randomized block design. CS₁₁ had the maximum system productivity (28.57 Mg ha⁻¹), production efficiency (78.27 Kg Day⁻¹ ha⁻¹), irrigation water use efficiency (2.38 kg m⁻³), system net returns (4413.3 US$ ha⁻¹), and benefit to cost (B:C) ratio (2.83) over others. In comparison to the CS₁ system, this cropping system required ~78% less irrigation water for a unit economic production. However, the cultivation of CS₁₂ registered the highest energy use efficiency (49.06%), net energy returns (6.46 × 10³ MJ ha⁻¹), and global warming potential (GWP) (Mg CO₂ e ha⁻¹) at spatial scale. Among all the intensified systems, CS₁₁ had the lowest GHGI (0.29 kg CO₂ e kg⁻¹). Furthermore, cultivation of CS₆ resulted in the maximum bacterial and actinomycetes population in the soil, while CS₅ yielded the highest fungal count (23.8 × 10³ cfu g⁻¹ dry soil) in soil. Our study suggests that the cultivation of CS₁₁ is a resource-efficient, economically viable, and environmentally clean production system and could be a potential alternative to rice-wheat systems for developing a green economy policy for agricultural development in the Indo-Gangetic Plains (IGP) of India. Full article

Health care is an essential factor in the social sustainability of the university; therefore, it is a challenge and a responsibility to monitor a safe return to school that ensures the support of the physical and emotional well-being of students. In this sense, the Maslach Burnout Inventory-Student Survey (MBI-SS) is a validated resource with robust techniques in several regions of the world to diagnose school burnout. However, few efforts appear in the literature to validate it from a predictive approach in the Latin region. This study aims to validate, from a predictive approach, measures of school burnout in Latino university students from Mexico and Colombia. A total of 235 surveys were administered (Mx. n = 127, Co. n = 108), and a Partial Least Squares (PLS) measurement model was validated using the statistical program SmartPLS 3.3.7. As a result, 22 valid items were obtained in four reliable subconstructs: burnout, family cynicism, inefficacy, and somatization. The value of this research is its contribution to filling two gaps related to the MBI-SS scale (1) to contribute to the validation of the MBI-SS in a Latin context and (2) the use of the nonparametric statistical technique PLS focused on prediction. Full article