Sustainability, Volume 14, Issue 17 (September-1 2022) – 656 articles

In the pursuit of economic survival in the current competitive conditions with the aim of long-term prosperity and sustainability in the market, many companies today approach significant strategic changes in the management of their business. The purpose of this study is the design of a systematic procedure for implementing strategy changes into internal business processes for a project-oriented production type of organization. The proposed methodology contains steps where the selection and verification of key performance indicators at individual levels of management takes place. Furthermore, their monitoring and quantification of the impact of the change in strategy on internal company processes. The result of the study explains how the management can monitor and evaluate the chosen processes in accordance with the fulfilment of the chosen strategy of the company, which supports the systematic introduction of changes in the processes with the aim of sustaining the company’s performance. Full article

The production of textiles has a strong impact on the environment due to both over-consumption and the practice of production processes requiring the use of substances to manufacture, treat, and dye fabrics. In this context, finding new ways and solutions to transform used textiles into by-products or inputs for production is a trump card for the future of the textile sector. This may be accomplished by developing a circular economy policy, which involves large investments with a payoff only in a medium to long-term perspective. The main aim of the present work is to provide a set of guidelines to guide textile industries in the transition from traditional production processes to a systemic approach in consideration of the circular economy. This could leverage the efficient use of regenerated wool, the reduction (or lack) of waste production, and the management of the end-of-life of the product. Full article

Integrated reporting (IR) by firms is intended to improve not only the quality of information available to external parties, but also internal managerial decision making. IR is considered useful to address the short-term orientation of firms caused by pressure from short-term oriented shareholders. This study examines whether the introduction of IR discourages real activities manipulation, a form of myopic behavior. Using a large sample of Japanese listed companies, the study empirically tests the effect of IR on real activities manipulation through panel data regression analysis. We find that the introduction of IR is related to higher level of abnormal cash flows from operations, lower level of abnormal production costs, and lower level of total activities manipulation. These results generally suggest that firms tend not to engage in real activities manipulation after IR is introduced. Our results also show that while there is insignificant difference in the degree of real activities manipulation between IR and non-IR firms immediately after the introduction of IR, the degree of real activities manipulation is generally smaller in IR firms than in non-IR firms after more time has passed since the introduction of IR, consistent with the view of practitioners that IR is a continuous improvement process of internal decision making. Regarding the non-financial aspects, additional analysis shows that introducing IR is positively associated with the performance of environmental, social and governance (ESG). Our findings suggest that IR could discourage companies’ short-term oriented behavior and promote long-term value creation, which is of interest to a wide range of stakeholders. Thus, our findings provide insightful evidence for researchers, practitioners, and policy makers interested in the role of IR in stakeholder-oriented corporate governance mechanisms. Full article

Developing localized climate mitigation strategies requires understanding how national consumption drives local carbon dioxide (CO₂) emissions from a sectoral perspective. Exploring the carbon footprint considering inter-provincial trade is vitally important; however, few studies have explored the production side of embodied carbon emissions and the drivers of embodied carbon. Here, we use the multi-regional input–output model to calculate the flow of embodied carbon between provincial departments on Shanghai’s production side in 2012, 2015, and 2017. We also establish a decomposition method for the embodied carbon index with small data demand. Our results show that from 2012 to 2017, the outflow increased and went to tertiary industries in neighboring and economically developed provinces. Among them, the activity effect drove the increase in carbon emissions, and the technique effect drove the reduction in embodied carbon. Surprisingly, we found that the low efficiency of the energy utilization of metal products and the unreasonable structure of other services increased embodied carbon emissions from 2012 to 2015. Sectors with high exogenous embodied carbon emissions are critical areas in which collaborative mitigation efforts between Shanghai and downstream provinces drive these emissions. Shanghai should avoid falling into the “low-carbon trap” of developing countries. It should continue to adjust its industrial structure and increase the use of low-carbon energy to achieve carbon reduction. Full article

Due to the generally poor planning of new university campuses in China today, students living in these places normally do not walk as often as they should, and with studies showing the significant impact of walking on physical health and productivity, there is an urgent need to improve university walkability. Walkability is a valuable tool for assessing the level of support for walking in a region, and there are few studies on walkability on university campuses. In this paper, we used a network distance-based Walk Score to evaluate and analyze the walkability of the Zijingang east campus, Zhejiang University. We improved some of the parameters of the Walk Score based on the actual travel characteristics of the students, formed a new calculation method based on a geographic information system (GIS) applicable to the university campus, and evaluated the applicability of this method. The results show that the new method can reflect the actual walking experience and provide a helpful design reference for designers. We also found that optimizing the distribution of facilities is very effective using the feature of the attenuation function. Full article

Greenhouse gas (GHG) emissions from agricultural soils can accelerate climate change, therefore, different soil fertilization techniques should be assessed before application to reduce GHG emissions. Pig slurry applications can greatly influence soil carbon dioxide (CO₂), nitrous oxide (N₂O), and ammonia (NH₃) emissions of arable fields; thus, it is important to find site-specific techniques to lessen any negative environmental impacts. In this study, we examined the short-term effect of pig slurry application techniques of spreading and injection on soil greenhouse gas and NH₃ emissions under different irrigation amounts. We used the dynamic chamber method with in-situ gas analyzers. Our study showed that there were elevated emissions during the first week after slurry application; however, the difference between GHG emissions of spreading and injection treatments were not significant. Elevated GHG emissions (213–338% and 250–594% in the case of CO₂ and N₂O emissions, respectively) were observed under dry circumstances compared to irrigated treatments, as well as significantly higher NH₃ emissions occurred for surface spreading under non-irrigated (dry) circumstances compared to other treatments. There were no statistically significant differences between the soil chemistry of different application techniques. However, pig slurry increased the available nitrogen forms (ammonium- and nitrate-nitrogen), which caused N₂O and NH₃ peaks regardless of treatment type. Leachate chemistry was more affected by irrigation strategies than application techniques. Our study highlights the importance of soil conditions at the time of application, rather than the application technique for fertilization using pig slurry. Full article

The outbreak of COVID-19 has forced Chinese international education to move online. An emerging number of studies have been published on online teaching and learning during the pandemic, few of which, however, focus on international students in China. This study examined the predictive effects of an online learning environment and student engagement on international students’ learning of Chinese as a foreign language (CFL). Self-reported data were collected in an online questionnaire survey involving 447 international CFL students at eight universities located in different geographical regions in China. Descriptive statistics revealed the participants’ favorable perceptions of an online learning environment, student engagement and Chinese learning achievement. The results of multiple linear regression revealed that three online learning environment factors, i.e., course accessibility, student interaction, course organization, and student engagement exerted significant positive effects on Chinese learning achievement. The implications of the study are discussed for the sustainable enhancement of the online learning environment to improve international students’ online language learning. Full article

Approaching the ecological agriculture topic in the context of European Policies to accelerate the conversion to those policies is an interdisciplinary challenge. The motivation to develop this subject is based on the longitudinal observation that the ecological agriculture evolution in Romania has been very slow, despite the policies aimed to accelerate the transition from the conventional to the ecological agriculture have been supported since the 2000s. The goal of the paper is to reframe the available data to evidence the slow dynamics of the organic farms’ certification. The methods used are descriptive and numerical analysis, supplemented by a qualitative-transversal interpretation. The research work has been carried out on the dynamic analysis of the ecological agriculture progress in Romania, based on the data with the ecological certification of the specialized companies (2019–2021). The main hypothesis: the slow dynamics are caused by subjective barriers. The results confirm the slow dynamics of ecological certifications due to some limits and barriers to understand the real role and benefits from the ecological agriculture. In this context, the European Union Organic Action Plan for 2021–2030 proves to be a stimulus for the Romanian ecological agriculture. Full article

Biochar is a carbon-based substance made by the pyrolysis of organic waste. The amount of biochar produced is determined by the type of feedstock and pyrolysis conditions. Biochar is frequently added to the soil for various reasons, including carbon sequestration, greenhouse gas mitigation, improved crop production by boosting soil fertility, removing harmful contaminants, and drought mitigation. Biochar may also be used for waste management and wastewater treatment. Biochar’s various advantages make it a potentially appealing instrument material for current science and technology. Although biochar’s impacts on soil chemical qualities and fertility have been extensively researched, little is known about its impact on enhancing soil physical qualities. This review is intended to describe biochar’s influence on some crucial soil physical and hydrological properties, including bulk density of soil, water holding capacity, soil porosity, soil hydraulic conductivity, soil water retention, water repellence–available plant water, water infiltration, soil temperature, soil color, and surface albedo. Therefore, we propose that the application of biochar in soils has considerable advantages, and this is especially true for arable soils with low fertility. Full article

In the current competitive scenario, SMEs need to adopt advanced technology in order to comprehend the dynamics of Industry 4.0 and the sustainable operations for operational excellence. The present study discusses the relationship between Industry 4.0 with sustainable operations and operational excellence. A research model is proposed and assessed through structural equation modelling (SEM). The current research shows that if SMEs adopt Industry 4.0 practices and the use of advanced robotics in order to minimize human intervention, as well as smart logistics that react to changes in production capacity, virtual reality and dynamic simulation techniques, then SMEs can significantly achieve sustainability in manufacturing operations. Furthermore, this study will help companies to attain an operational excellence with greater efficiency and productivity. Therefore, SMEs need to focus on sustainable manufacturing practices. Full article

The overuse of chemical fertilizers has caused various ecological problems in China, necessitating the development of organic alternative fertilizers. There are few studies on the rapidly emerging organic fertilizer rapeseed green manure, despite the importance of understanding its decomposition efficiency and impact on soil. In this study, using plant residue from 14 rapeseed cultivars, we examined the 30-day decomposition changes under conditions A and B (150 and 300 g of plant residue returned, respectively) and detected the effects of their decomposition on soil nitrogen, phosphorus, potassium, and microorganisms. Under condition B, the 30-day cumulative decomposition and nutrient release rates of rapeseed were higher than those under condition A, and the rapeseed decomposition rate exceeded 50% under both conditions, which is similar to results in legume green fertilizers. Moreover, the decomposition of rapeseed green manure significantly increased the soil nutrient content and effectively improved the soil bacterial community structure and diversity relative to the original soil, especially under condition B. Thiobacillus, Azotobacter, and Pseudomonas are bacteria that responded to plant decomposition, and the abundance of the three bacterial genera after plant decomposition was significantly correlated with the plant decomposition traits and soil nutrient content. In conclusion, rapeseed green manure has potential to offset the use of chemical fertilizers, promoting sustainable agricultural development, and this study provides a reference for such green fertilization measures. Full article

The global music market has witnessed the rapid rise of Korean pop music, K-pop, in recent years. While there has been an increased interest of scholars from various disciplines to account for the global success of K-pop, limited attention has been paid to the key players in the industry, music businesses. Based on a historical analysis of Korea’s music industry, we contend that the innovative production system of Korea’s music businesses has played a significant role in facilitating K-pop’s global success. In order to provide theoretical support to the argument, this paper critically reviews the existing literature to present debates on (i) the process of how value is created in distinctive stages in the music industry; (ii) cooperative and competitive interactions between firms within the music industry; and (iii) changes in the music industry’s competitive environment. Full article

The traditional multiple input multiple output (MIMO) systems cannot provide very high Spectral Efficiency (SE), Energy Efficiency (EE), and link reliability, which are critical to guaranteeing the desired Quality of Experience (QoE) in 5G and beyond 5G wireless networks. To bridge this gap, ultra-dense cell-free massive MIMO (UD CF-mMIMO) systems are exploited to boost cell-edge performance and provide ultra-low latency in emerging wireless communication systems. This paper attempts to provide critical insights on high EE operation and power control schemes for maximizing the performance of UD CF-mMIMO systems. First, the recent advances in UD CF-mMIMO systems and the associated models are elaborated. The power consumption model, power consumption parts, and energy maximization techniques are discussed extensively. Further, the various power control optimization techniques are discussed comprehensively. Key findings from this study indicate an unprecedented growth in high-rate demands, leading to a significant increase in energy consumption. Additionally, substantial gains in EE require efficient utilization of optimal energy maximization techniques, green design, and dense deployment of massive antenna arrays. Overall, this review provides an elaborate discussion of the research gaps and proposes several research directions, critical challenges, and useful recommendations for future works in wireless communication systems. Full article

The number and sizes of informal settlements are expected to increase drastically in the future, and dramatically so in Sub-Saharan Africa, where migration from rural to urban areas is increasing, and poverty and food insecurity threaten livelihoods. Data sources explaining livelihood factors in informal settlements are scarce, and often highly disputed. In this study, Kibera is investigated, one of the largest informal settlements in Africa. The main aim is to analyze differences in livelihood factors across the villages in Kibera, and to explain some of the existing discrepancies in food security levels among its population. In particular, livelihood factors such as tribe, welfare and trust can explain some of the variation in food security across 12 of the 13 villages located in Kibera. The analyses inform of significant differences across the villages when it comes to, among others, income, food insecurity, ownership of land in rural areas, tribal background and trust levels in strangers and community leads. To reach the millions of people living in informal settlements now, and increasingly so in the future, it is advised that research and implementation go hand in hand, with enhanced understanding of the complexities within rural–urban food systems to ensure solutions that are affordable and accessible to low-income groups. On this pathway to fight poverty and hunger in the future, today’s policies and programs must take such complexities into account to positively contribute to strengthening the resiliency and sustainability of rural–urban food systems by ensuring an increase in welfare levels with zero climate impact. Full article

The extensive use of dyes in numerous industries results in massive dye discharge in the wastewater, which is a major cause of water pollution. Globally, the consumption of dyes is near seven hundred thousand tons across different sectors, of which around 10–15% goes into the wastewater. Among the dye kinds, anionic dyes make up the main proportion, having a 32–90% share in the wastewater. Different plant-derived wastes, which are sustainable given their natural abundance, effectiveness, and low cost, are frequently proposed for dye separation. However, these adsorbents are inherently more suitable for cationic dyes than anionic dyes. In recent years, the modification of these wastes has been progressively considered to suit them to anionic dye removal. These modifications involve mechanical, thermal, or chemical treatments, or combinations. These attempts propose two-way benefits, as one abundant waste is being used to cure another severe problem, and eventually both could be diminished. This review has a key focus on the evaluation of plant-derived adsorbents and their modifications, and particularly for anionic dye adsorption. Overall, the mechanism of adsorption and the suitability of the current methods are discussed, and their future potential is explored. Full article

This study proposes a simultaneous optimization model that considers flow assignment and vehicle capacity for the problem of transit network design to determine the route structure and frequencies simultaneously. The problem is focused on reducing the total travel time and the number of transfers. A heuristic algorithm is developed to solve this problem. In the proposed algorithm, the initial routes are generated according to a changing demand matrix, which can reflect the real-time demand with transfers and ensure that the direction of route generation maximizes the percentage of direct service. A regulating method for a sequence of stops is used during route generation to guarantee the shortest trip time for a formed route. Vehicles are allocated to each route according to the flow share. The concept of vehicle difference is introduced to evaluate the distinction between actual allocated vehicles and required vehicles for each route. The optimization process of frequencies based on vehicle difference can ensure that the solution meets the constraints. Two scale networks are used to illustrate the performances of the proposed method. Results show that route structure and frequencies can be optimized simultaneously through the proposed method. Different scenarios are created to test the algorithm properties via various parameter values. The test result indicates that the upper bound is a key parameter to balance the proportion of direct service and average in-vehicle travel time (AIVTT), and the increased number of planning routes can improve the proportion of direct service. Full article

One of the most common health problems that threaten the transportation infrastructure construction workers in Hong Kong is heat stress. An effective way to reduce this problem is to design a proper work–rest schedule, and the key issue is predicting the maximum working duration given the different conditions of the workers and the surrounding environment, which is the research question of this study. Air temperature, an important input feature, is also determined by the maximum working duration itself, i.e., the input feature is a function of the prediction target. Therefore, the prediction model developed is different from ordinary prediction models and is hard to solve by standard statistical or machine learning models. For the prediction process, a trial-and-error algorithm is proposed to derive a solution based on two theorems that are rigorously proved; there exists a unique solution, and the solution is within a certain range in the prediction model. The proposed model and its solution approach were constructed and validated using simulated data; temperature data were collected from Hong Kong Observatory. The results showed that the mean squared error (MSE), mean absolute percentage error (MAPE), and R² of the test set were 0.1378, 0.1123, and 0.8182, respectively, showing that the prediction performance was generally accurate. This study can help construction practitioners and governments to rationally design the work–rest schedules of transportation infrastructure construction workers and thus protect them from the risks brought about by heat stress. Full article

The building sector is responsible for more than one-third of total global energy consumption; hence, increasingly efficient energy use in this sector will contribute to achieving carbon neutrality. Most existing building-energy-benchmarking methods evaluate building energy performance based on total energy use intensity; however, energy usage in buildings varies with the seasons, and as such, this approach renders the evaluation of cooling and heating energy difficult. In this study, an information gain-based temporal segmentation (IGTS) method was used to identify the seasonal transition times based on patterns of hourly weather and corresponding building energy use. Twelve commercial buildings were considered for the study and four seasons were identified using IGTS; base-load, cooling energy, and heating energy data were gathered. For the 12 buildings, the estimated and measured heating and cooling energy during the summer and winter periods showed a linear relationship (R² = 0.976), and the average of those differences was 9.07 kWh/m². In addition, differences in the benchmarking results based on these energies were marginal. The results indicated that the IGTS approach can be effectively used for determining the actual heating and cooling energy consumption in buildings, as well as for energy benchmarking. This can, in turn, improve building energy use, with positive implications for achieving carbon neutrality. Full article

Predicting household vehicle ownership (HVO) is a crucial component of travel demand forecasting. Furthermore, reliable HVO prediction is critical for achieving sustainable transportation development objectives in an era of rapid urbanization. This research predicted the HVO using a support vector machine (SVM) model optimized using the Bayesian Optimization (BO) algorithm. BO is used to determine the optimal SVM parameter values. This hybrid model was applied to two datasets derived from the US National Household Travel Survey dataset. Thus, two optimized SVM models were developed, namely SVMBO#1 and SVMBO#2. Using the confusion matrix, accuracy, receiver operating characteristic (ROC), and area under the ROC, the outcomes of these two hybrid models were examined. Additionally, the results of hybrid SVM models were compared with those of other machine learning models. The results demonstrated that the BO algorithm enhanced the performance of the standard SVM model for predicting the HVO. The BO method determined the Gaussian kernel to be the optimal kernel function for both datasets. The performance of the SVM#1 model was improved by 4.27% and 5.16% for the training and testing phases, respectively. For SVM#2 model, the performance of this model was improved by 1.20% and 2.14% for the training and testing phases, respectively. Moreover, the BO method enhanced the AUC of the SVM models used to predict the HVO. The hybrid SVM models also outperformed other machine learning models developed in this study. The findings of this study showed that SVM models hybridized with the BO algorithm can effectively predict the HVO and can be employed in the process of travel demand forecasting. Full article

Citizen science projects seem to have a high potential to provide systematized, high-quality biodiversity observations for science and other purposes. iNaturalist offers users purposeful participation by creating projects that allow observations to be grouped with a taxonomic and geographic focus, as well as on a given time scale. Between 2014 and December 2021, 720 iNaturalist projects have been created for Spain, most of them after the establishment of Natusfera—the Spanish branch of the iNaturalist global Community—in 2020. In this paper, we analyze how iNaturalist projects are created; how they perform in terms of engagement, data contribution, and impact; and assess the degree and possible causes of their success. A database with project descriptors and indicators was created for this purpose. We discovered that a high percentage of the projects (more than 25%) perform far short of expectations, and that bioblitzes are in general very successful in terms of creation, dissemination, and participation. Finally, we present some recommendations aiming to make these projects more effective. Full article

The information value (IV) model is a conventional method for landslide susceptibility prediction (LSP). However, it is inconsistent with the actual situation to regard all conditioning factors as equally weighted in the modeling process. In view of this, this paper studied the optimization effect of different weight calculation methods for IV model. Xingshan County, a typical landslide-prone area located in Hubei Province, China, was taken as a case study. The procedure was as follows: First, six conditioning factors, including elevation, slope angle, aspect, curvature, distance to river, and distance to road, were selected to form an evaluation factor library for analyzing the landslide susceptibility. Then, the weight of factors was calculated by fuzzy analytical hierarchy process (FAHP) and principal component analysis (PCA). On this basis, combined with the IV model, two weighted IV models (FAHP-IV model and PCA-IV model) were formed for LSP. The results shows that the optimization effect of PCA was the best. Moreover, compared with the IV-only model (AUC = 0.71), the FAHP-IV model (AUC = 0.76) and PCA-IV model (AUC = 0.79) performed better. The outcome also provided a feasible way for the study of regional LSP. Full article

Under the green sustainable development strategy, media attention has played a vital role in promoting green innovation of enterprises. Nevertheless, existing research mainly analyzes how media attention affects enterprise innovation behavior, while ignoring the role of environmental regulation and green financial policy. The main purpose of this study is to explore whether the media’s attention to environmental issues can promote the enterprise’s green innovation, whether the constraint policies of environmental regulatory and incentive policy of green financial can play regulatory roles, and whether these effects are heterogeneous among different types of enterprises. Based on the data of Chinese A-shared listed enterprises from 2010 to 2019, this paper draws the following conclusions by constructing the fixed effect models: First, media attention promotes the substantive and strategic green innovation of enterprises. Second, environmental regulation and green finance have positive regulatory effects on media attention and enterprise green innovation, and these regulation effects have a more significant impact on promoting substantive green innovation. Third, media attention is more sensitive in heavy pollution and state-owned enterprises green innovation. The results of the robustness test still support the conclusion, indicating that the conclusions are reliable. The research of this paper helps to clarify the role of environmental regulation and green finance in media attention and green innovation on a deeper level and puts forward targeted suggestions on how to stimulate enterprise green innovation from the perspective of media, government, and enterprises. Full article

Digitalization and digitization are topics for researchers and manufacturers. Integrating new technologies facilitates the collection of data from a company in real-time and processing them afterwards. In this context, the design and implementation of Maintenance 4.0 have become popular in the literature. Its objective is to minimize downtime, optimize energy consumption, and increase availability, utilization rate, and useful life of machines while ensuring environmental preservation and safety of personnel. Our contribution consists of setting up a specific digitalization methodology for companies wishing to switch to Maintenance 4.0 in order to contribute to sustainable development. The information obtained will be processed to carry out effective interventions to increase the reliability and availability of equipment. A case study of an industrial company was carried out where we implemented this methodology. As a result, we were able to increase the reliability of the machines, which has an impact on the environment by reducing energy consumption and the quantity of plastic waste. On the economic level, this led to an improvement in the Overall Equipment Effectiveness (OEE) and a reduction in product prices. Thanks to these technologies of digitizing maintenance documents (procedures, machine history, risk prevention) and the quick localization of machine failures, the hard work and risks are reduced. Full article

Sugarcane is a lignocellulosic crop and the juice extracted from its stalks provides the raw material for 86% of sugar production. Globally, sugarcane processing to obtain sugar and/or ethanol generates more than 279 million tons of solid and liquid waste annually, as well as by-products; namely, straws, bagasse, press mud, wastewater, ash from bagasse incineration, vinasse from ethanol distillation, and molasses. If not properly managed, this waste will pose risks to both environmental factors and human health. Lately, valorization of waste has gained momentum, having an important contribution to the fulfillment of policies and objectives related to sustainable development and circular bioeconomy. Various technologies are well-established and implemented for the valorization of waste and by-products from sugarcane processing, while other innovative technologies are still in the research and development stage, with encouraging prospects. We propose a sustainable sugarcane processing flow and present an analysis of the physico-chemical characteristics of generated wastes and by-products. We emphasize the available possibilities of valorizing each waste and by-product, considering that they are important biomass resources for obtaining biofuels and a wide range of other products with added value, which will contribute to the sustainability of the environment, agriculture, and human health worldwide. Full article

This review study circumscribes wind catchers as vernacular zero-energy systems of passive ventilation. The research reviews various types of wind catchers and analyses their design, effectiveness and utility in building design. Furthermore, the study documented some of the technological transformations of wind catchers and their adaptation (functional and symbolic) in various regions. In this context, the complex design data of various wind catchers were appraised, and adaptable design data is compiled in “Geographical and Regional Influences on Wind Catcher Design “ and “Performance Evaluation of Wind Catchers” of study. The design analysis uncovered interesting facts about the effectiveness of various types of wind catchers; for example, a wind catcher with one side could be employed only as a wind scoop, whereas a multi-sided wind catcher can work simultaneously as a wind scoop and a heat sink (exhaust). The study also revealed that, in the near past, wind catchers were extensively used for ventilation. However, in modern times they are being adapted as an element of urban and architectural (identity) rather than a functional element. Finally, the end results of this study present candid suggestions for using wind catchers in modern buildings and chalks out blueprints (design guidelines) to adapt wind catchers. Towards the adaptation of wind catchers in modern buildings, this study has worked out 14 key design modifications in different types of wind catcher. Most of these findings are related to improving wind intake, preventing dust and rain penetration, the size of a wind catcher’s opening and shaft. Full article

The Architecture, Engineering, and Construction industries are allocated 40–60% of the worldwide raw material extraction. Construction waste accounts for a significant share of the total waste volume. Therefore, careless handling reduces natural resources and waste deposits (landfills). Furthermore, material reuse and recycling can reduce resource and energy consumption and environmental emissions in some cases. Waste management concepts in the fields of Architecture, Engineering, and Construction are increasingly in the European Union and worldwide focus. A circular economy can be seen as a system in which resource input, waste, emission, and energy leakage are minimised due to closed material loops. Therefore, implementing a consistent Circular Economic requires a holistic approach in which material, emissions, and energy are put into context. This paper aims to analyse dismantling, recovery, and recycling processes and link relevant parameters to assess material sustainability. The technical effort must be made, and the associated costs are compared with the influence of eco-indicators. Furthermore, the data required can be used for the following three areas: Facilitating demolition planning and on-site waste management; resource management at the local/regional/state level; and governmental tax mechanisms. Full article

Urban water demand forecasting is beneficial for reducing the waste of water resources and enhancing environmental protection in sustainable water management. However, it is a challenging task to accurately predict water demand affected by a range of factors with nonlinear and uncertainty temporal patterns. This paper proposes a new hybrid framework for urban daily water demand with multiple variables, called the attention-based CNN-LSTM model, which combines convolutional neural network (CNN), long short-term memory (LSTM), attention mechanism (AM), and encoder-decoder network. CNN layers are used to learn the representation and correlation between multivariate variables. LSTM layers are utilized as the building blocks of the encoder-decoder network to capture temporal characteristics from the input sequence, while AM is introduced to the encoder-decoder network to assign corresponding attention according to the importance of water demand multivariable time series at different times. The new hybrid framework considers correlation between multiple variables and neglects irrelevant data points, which helps to improve the prediction accuracy of multivariable time series. The proposed model is contrasted with the LSTM model, the CNN-LSTM model, and the attention-based LSTM to predict the daily water demand time series in Suzhou, China. The results show that the hybrid model achieves higher prediction performance with the smallest mean absolute error (MAE), root mean squared error (RMSE), and mean absolute percentage error (MAPE), and largest correlation coefficient (R²). Full article

Walking reports numerous benefits for older persons, yet its practice can be hindered by the built environment. This article seeks to understand how and why certain elements of the built environment facilitate or impede the everyday trips older persons complete on foot. It reports the findings of a set of walking interviews conducted in four central neighbourhoods of Santiago de Chile, where forty older persons were invited to walk and talk about the trips they complete on foot and the aspects that facilitate or hinder them. The findings reveal that older persons are aware of the benefits of walking and travel regularly on foot despite the barriers they find in their neighbourhoods. The presence/absence of greenery, the conditions of the facades and the level of cleanliness of the streets affect older persons’ walking experience and can increase/diminish their willingness to walk. Damaged and poorly designed pedestrian infrastructure can cause fear, provoke accidents and become serious hazards. Older persons develop strategies to overcome these barriers, yet the data suggest that they see Santiago as a “disabling city” because it has obstacles that could be unsurmountable in a near future if an illness or an accident diminishes their abilities. Full article

The restrictions of the ongoing COVID-19 pandemic resulted in the downturn of various industries and in contrast a massive growth of the information technology industry. Consequently, more Filipinos are considering career changes to earn a living. However, more people still need to be upskilled. This study combines the extended Technology Acceptance Model and Task Technology Fit framework to determine factors affecting a career shifter’s use of software testing tools and its impact on perceived performance impact amidst the COVID-19 pandemic in the Philippines. A total of 150 software testers voluntarily participated and accomplished an online questionnaire consisting of 39 questions. The Structural Equation Modeling and Deep Learning Neural Network indicated that Task Technology Fit had a higher effect on Perceived Performance Impact. Moreover, Task Technology Fit positively influenced Perceived Usefulness. Computer Self-Efficacy was a strong predictor of Perceived Ease of Use. Perceived Ease of Use confirmed the Technology Acceptance Model framework as a strong predictor of Actual System Use. Intention to Use, Perceived Usefulness, Actual Use, and Subjective Norm were also significant factors affecting Perceived Performance Impact. This study is the first to explore the career shifter’s use of software testing tools in the Philippines. The framework would be very valuable in enhancing government policies for workforce upskilling, improving the private sector’s training and development practices, and developing a more competitive software testing tool that would hasten users’ adaptability. Lastly, the methodology, findings, and framework could be applied and extended to evaluate other technology adoption worldwide. Full article

Nowadays, photovoltaics (PV) has gained popularity among other renewable energy sources because of its excellent features. However, the instability of the system’s output has become a critical problem due to the high PV penetration into the existing distribution system. Hence, it is essential to have an accurate PV power output forecast to integrate more PV systems into the grid and to facilitate energy management further. In this regard, this paper proposes a stacked ensemble algorithm (Stack-ETR) to forecast PV output power one day ahead, utilizing three machine learning (ML) algorithms, namely, random forest regressor (RFR), extreme gradient boosting (XGBoost), and adaptive boosting (AdaBoost), as base models. In addition, an extra trees regressor (ETR) was used as a meta learner to integrate the predictions from the base models to improve the accuracy of the PV power output forecast. The proposed model was validated on three practical PV systems utilizing four years of meteorological data to provide a comprehensive evaluation. The performance of the proposed model was compared with other ensemble models, where RMSE and MAE are considered the performance metrics. The proposed Stack-ETR model surpassed the other models and reduced the RMSE by 24.49%, 40.2%, and 27.95% and MAE by 28.88%, 47.2%, and 40.88% compared to the base model ETR for thin-film (TF), monocrystalline (MC), and polycrystalline (PC) PV systems, respectively. Full article