With the abundance of public transportation in highly urbanized areas, it is common for passengers to make inefficient or flawed transport decisions due to a lack of information. The exact arrival time of a bus is an example of such information that can aid passengers in making better decisions. The purpose of this study is to provide a method for predicting path-based bus travel time, thereby assisting accurate bus arrival and departure time predictions at each bus stop. Specifically, we develop a Geo-conv Long Short-term Memory (LSTM) model that (1) extracts subsequent spatial features through a 1D Convolution Neural Network (CNN) for the entire bus travel sequence and (2) captures the temporal dependencies between subsequences through the LSTM network. Additionally, this study utilizes additional variables that affect two components of bus travel time (dwelling time and transit time) to precisely predict travel time. The constructed model is then evaluated by the practical application to two bus lines operating in Seoul, Korea. The results show that our model outperforms three other baseline models. Two bus lines with different types of operation show different model performance patterns that are dependent on travel distance. Interestingly, we find that the variable related to the link of the stop location appears to play an important role in predicting bus travel time. We believe that these novel findings will contribute to the literature on transportation and, in particular, on deep learning-based travel time prediction. Full article

Protected cultivation is an innovative way of raising seasonal and off-seasonal crops under a controlled environment. Vegetables and flower crops have tremendous potential to augment productivity, generate employment, utilize land efficiently and enhance export. This study was undertaken to assess the economic feasibility of protected cultivation in the high export potential zones of the Pune and Nasik districts of Maharashtra, India, by employing project analytical tools and the regression model. The results revealed that the cultivation of flowers and vegetables under protected cultivation was highly lucrative with high investment. The protected cultivation of rose and capsicum had higher cultivation cost (300%), gross return (250%) and net return (190%) as compared to open cultivation. Moreover, most of the crops grown in polyhouses are highly profitable at different discount rates (7%, 10% and 12%), whereas a few crops were rewarding under shade net condition with subsidies. Factors such as literacy (p < 0.05), income (p < 0.05), access to subsidy (p < 0.05) and the risk orientation index (p < 0.01) were found statistically significant in technology adoption. In the context of a changing climate and shrinking land resources, water scarcity, incidence of pests and diseases, an ever-increasing population, low productivity under open conditions and changes in consumer’s preference are the drivers for switching over to protected cultivation. In the recent past, protected cultivation has been gaining importance in different parts of the country, including Maharashtra. The policy implications are creating modern infrastructure, enhanced application of ICTs, maximum crop production with minimum utilization of land and institutional support to promote technology on a commercial scale. Full article

Weakly rigid drilling systems, such as the industrial robot, are widely used in aerospace, military, and other fields due to its good flexibility and large scope of operation. However, the weak rigidity can easily cause burrs, seriously affecting the precision of parts and product performance. To reduce the heavy deburring process and to improve continuous production and sustainable processing capacity, accurate prediction of burr quality is a prerequisite. Traditional burr forming theory cannot accurately predict the drilling defects. Data-driven approaches can be independent of prior knowledge and discover relationships between process parameters and machining precision directly from the data structure itself. Therefore, to take advantage of both approaches, a fusion model was established for burr classified prediction. On the one hand, the drilling and burr forming process was firstly modeled, and preliminary classification results for burrs were calculated. On the other hand, according to the measured data, the errors between initial calculation results and actual classification results were obtained and selected as the tag values of dataset, which served as inputs for the error compensation model of burrs. Finally, by training the network of TCN–DNN using the drilling data, the burr classified prediction in a weak rigid hole-making system was realized. Experimental results showed that compared with traditional drilling theory, the prediction accuracy of the proposed model improved by 25%, reaching 91.67%. The results can provide a basis for judging the process of burr post-treatment, which has practical guiding significance. This method is beneficial to reduce the heavy deburring process and to improve sustainable processing capacity. Full article

The complexity in the power system topology, together with the new paradigm in generation and demand, make achieving an adequate level of supply quality a complicated goal for distribution companies. The electrical system power quality is subject to different regulations. On one hand, EN-50160 establishes the characteristics of the voltage supplied by public electricity networks, therefore affecting distribution companies. On the other hand, the EN-61000 series of standards regulates the electromagnetic compatibility of devices connected to the network, therefore affecting the loads. Power companies and device manufacturers are both responsible and affected in the issue of quality of supply. Despite the regulations, there are certain aspects of the supply quality that are not solved. One of the most important is the location of the disturbance’s origin. This paper presents a review of the main techniques to locate the disturbance’s origin in the electric network through two approaches: identification of the disturbance’s cause and the location of the origin. Full article

Thailand’s 4.0 strategy intends to achieve prosperity, security, and sustainability for its citizens both today and in the distant future. By focusing on the public sector and its civil servants, the government is driving innovation in the public sector by using design thinking (DT) to train government officials to improve public sector innovation (PSI). However, despite studies being conducted on DT and its increasing popularity among scholars, there is still a gap between knowledge and practice with regard to the learning effectiveness of the DT methodology in terms of fostering public sector innovation. Therefore, the authors’ objective for this study was to explore the effectiveness of DT in the Thai PSI and measure the learning effectiveness of the design thinking method (DTM). The researchers conducted action research through a series of workshops and in-class activities involving both qualitative and quantitative approaches. The sample was made up of government officials participating in the Thai Government Innovation Lab (GIL) project in 2020 and 2021. The results on the effectiveness of the program showed that fostering government innovation through DT education led to highly sustainable and highly effective outcomes. This study contributes to the development of public innovation by implementing the DTM as a core methodology for fostering innovation in public organizations. Full article

Land use conversion is the main cause for soil degradation, influencing the sustainability of agricultural activities in the Ecuadorian Andean region. The possibility to identify the quality based on the spectral properties allows remote sensing methods to offer an alternative form of monitoring the environment. This study used laboratory spectroscopy and multi-spectral images (Sentinel 2) with environmental covariates (physicochemical parameters) to find an affordable method that can be used to present spatial prediction models as a tool for the evaluation of the quality of Andean soils. The models were developed using statistical techniques of logistic regression and linear discriminant analysis to generate an index based on soil order and three indexes based on the combination of soil order and land use. This combined approach offers an effective method, relative to traditional laboratory methods, to derive estimates of the content and composition of soil constituents, such as electrical conductivity (CE), organic matter (OM), pH, and soil moisture (HU). For Mollisol index.3 with Páramo land use, a value of organic matter (OM) ≥8.6% was obtained, whereas for Mollisol index.4 with Shrub land use, OM was ≥6.1%. These results reveal good predictive (estimation) capabilities for these soil order–land use groups. This provides a new way to monitor soil quality using remote sensing techniques, opening promising prospects for operational applications in land use planning. Full article

In the construction industry, sustainability is evaluated, not only in terms of harmful emissions generated during the operation phase, but also in terms of the embodied emissions belonging to building materials and technical equipment. As a consequence, the implementation of highly efficient building materials has become crucial. The objective of this study is to investigate an insulation system based on parallel air chambers embodied in rockwool panels, and to correlate the implications of its implementation compared to an existing insulation system. The analysis was conducted on the first administrative/public building completed in Romania, according to passive house standards. The study begins with experimental investigations of insulation systems under laboratory conditions. Thus, the influence of air layers on the thermal properties of existing rockwool panels was assessed. On the basis of the experimental results, the theoretical energy demand of the high school building and life cycle analysis are determined using simulation software for both insulation solutions: existing insulation composed of solid rockwool panels, and rockwool panels with embedded air layers. The thickness of the insulating air layers is optimized, and with the help of Rayleigh–Bénard equations for each of the five climate zones that were further determined. Taken together, it is expected to achieve a better insulation system by maintaining constant embedded emissions. In conclusion, assuming a 50-year life cycle for the high school building, the insulation system composed of rockwool with embedded air layers brings about a reduction in the total energy consumption of approximately 9.82%, compared to the case of a standard insulation system based on solid rockwool panels without additional air layers. Full article

This article discusses how to sustainably manage and protect the Alpine landscape from the risk of extreme weather events due to climate change. The authors present the results achieved by applying the Conservation and Management Plan (CMP) method in the case of a large terraced healing park built in the Central Alps in the 1930s and damaged in 2018 by the effects of Storm Adrian (also known as Vaia). We thoroughly analysed the state of preservation through historical documentation, aerial shooting with a drone, a tree-by-tree evaluation, and a GIS platform for integrated information management. Such a CMP approach is usually applied to assess the state of decay and plan for the sustainable conservation of historic buildings and gardens. The method of analysis and the performed activities allowed us to evaluate the park’s landscape values and identify the elements of risk, leading to the drafting of a monitoring map and guidelines of intervention. This experience made it possible to test its effectiveness in protecting the Alpine landscape and its specific fragility. Full article

Due to globalization in this modern age of technology and other uncontrollable influences, transportation parameters can differ within a certain range of a given period. In this situation, a managerial position’s objective is to make appropriate decisions for the decision-makers. However, in general, the determination of an exact solution to the interval data-based transportation problem (IDTP) becomes an NP-hard problem as the number of choices within their respective ranges increases enormously when the number of suppliers and buyers increases. So, in practice, it is difficult for an exact method to find the exact solution to the IDTP in a reasonable time, specifically the large-sized problems with large interval sizes. This paper introduces solutions to the IDTP where supply, demand, and cost are all in interval numbers. One of the best interval approximations, namely the closed interval approximation of pentagonal fuzzy number, is proposed for solving the IDTP. First, in the proposed closed interval approximation method (Method-1), the pentagonal fuzzification method converts the IDTP to a fuzzy transportation problem (FTP). Subsequently, two new ranking methods based on centroid and in-center triangle concepts are presented to transfer the pentagonal fuzzy number into the corresponding crisp (non-fuzzy) value. Thereafter, the optimal solution was obtained using Vogel’s approximation method coupled with the modified distribution method. The proposed Method-1 is reported against a recent method and shows superior performance over the aforementioned and a proposed Method-2 via benchmark instances and new instances. Full article

When activity locations were shut down in the first lockdown to prevent the spread of COVID-19 in Austria, people reduced their trips accordingly. Based on a dataset obtained through a weeklong mobility and activity survey we analyse mobility and time use changes, as well as changes in activity locations and secondary activities. Regression analysis is used to analyse differences in time use changes between socio-demographic groups. We show that trip rates and distances as well as public transport use dropped significantly during the lockdown and did not recover fully in the subsequent opening phase. Former travel time was used for additional leisure, sleep, domestic tasks, and eating in the lockdown, but only the latter two retained their increases in the opening phase. The lockdown resulted in a convergence of time use of socio-demographic groups with formerly different patterns, but the differences reappeared in the opening phase. Our findings are consistent with results from the literature but offer an integrated perspective on mobility and time use not found in either mobility- or time use-focussed studies. We conclude that there is a potential for trip reduction through a shift to virtual performance of activities, but the extent of this shift in post-pandemic times remains unclear. Full article

This paper uses the three-stage DEA method to measure the input–output efficiency of China’s 23 listed power generation companies (mainly thermal power generation) in 2019, and uses the SFA regression model to eliminate environmental elements and random disturbances. The results show that in a non-homogeneous environment, the scale efficiencies of most power generation companies are greater than or equal to their pure technical efficiencies. These companies should first improve management and technical levels, and then optimize the scale of investment. Furthermore, after removing environmental variables, half of the companies should turn to increasing economies of scale instead of diminishing economies of scale. It can be seen that environmental factors, such as the degree of regional development and IPO time, have reduced the economies of scale of enterprises, so they should strengthen the communication between different regions, and the government should provide assistance to companies that are listed late. Full article

This paper builds Pharmaceutical Manufacturer Stackelberg, Pharmacy Stackelberg and Nash game models with and without price cap regulation. The optimal pricing, performance and social welfare are derived and compared in three different power structures to find out how price cap regulation and power structures affect the drug supply chain. More power over other supply chain members always allows the pharmaceutical manufacturer to obtain more profits. However, the pharmacy cannot always benefit from its dominant position in the market with changes in the wholesale price cap. Additionally, the balanced market structure may harm social welfare under certain conditions. Another interesting finding is that the restricted wholesale price cap deeply affects the financial performance and social welfare in the manufacturer-dominated and pharmacy-dominated markets. The research results can provide important management insights, which will be beneficial to the government to design smart price-limiting policies that take into account the power relationships of the supply chain. Full article

Multiple systemic crises have highlighted the vulnerabilities of our globalised food system, raising the demand for more resilient and ecologically sustainable alternatives, and fuelling engagement in practices such as community-supported agriculture (CSA). In CSA, local farmers and households share the costs and products of farming, allowing them to organise food provision non-commercially around short supply chains. While this may prefigure alternatives to the dominant food system, CSA is considered limited in regard to its scalability and accessibility. While these shortcomings apply to individual CSAs, we know little about whether multi-CSA networks can tackle them by expanding and institutionalising their practices at scale. This paper alleviates this blind spot by investigating local CSA networks in Wales and Germany through a lens of ‘food movement networks’, identifying their scaling practices and encountered challenges. It draws on semi-structured interviews with CSA actors and observations at network gatherings. The paper shows that local collaboration enables CSAs to integrate their supply chains (scaling out), engage their communities (scaling deep), and participate in food councils (scaling up), while further networking at regional level helps new initiatives start up. It also reveals competitive tensions between neighbouring CSAs, which constitutes a hitherto unknown challenge to CSA’s potential scalability. Full article

Biochar has been suggested for application in acidic soils for increasing agricultural productivity, as it may result in the benefits of sustainable carbon offset into soils and of increasing soil fertility improvement. However, the role of biochar in enhancing nutrient bioavailability and plant performance is manifested through the complex interactions of biochar-soil-plant. Moreover, it is not yet known how a crop-residue-derived biochar would perform in acidic soil when applied with a reduced rate of lime and phosphorus. Here, we examined the performance of maize with different combinations of biochar, lime, and phosphorus (P) application rates under field conditions. Specifically, rice husk biochar (10 t ha⁻¹) was applied with 75% of the required lime and three rates of phosphorus fertilizer (100%, 75%, and 50%). The results showed that incorporation of biochar and lime, irrespective of the rates of P application, significantly increased soil nutrient (nitrogen and P) availability, while aluminum (Al) and iron (Fe) concentrations in soil were reduced. Furthermore, when biochar was combined with a lower amount of lime (75% of the recommended amount) and half of the required P, maize production increased by 62.38% compared to the control. Similarly, nutrient uptake in plants increased significantly in the same treatment (e.g., P uptake increased by 231.88%). However, soil respiration (CO₂ emission) increased with lime only and the combined application of lime with biochar compared to the control; these treatments resulted in a higher carbon loss, as CO₂ from the soil (84.94% and 67.50% from only lime treatment (T₂), and rice husk biochar (RHB) and lime with 50% triple superphosphate (TSP) (T₅), respectively). Overall, our findings imply that biochar application may sustain productivity in acid soils even when lime and P fertilizer applications are made at a reduced rate. Full article

We may translate anthropogenic climate change as a reaction of our planet to our unsustainable economic activities. This research explores whether environmental policies have been impacted by extreme climatic events like droughts, floods, storms, tornados, and wildfires. We use yearly panel data from 1990 to 2017 for the OECD (Organization for Economic Cooperation and Development) countries to examine such a relationship. To have an impartial analysis, we control major variables influencing environmental policies such as energy consumption, gross domestic product (GDP), population, technology, head of the state’s political affiliation, carbon emission, and waste generation. The analysis results suggest that policymakers make more stringent environmental decisions as the death rate increases and environmental threats become more imminent putting human life is at risk; this correlation is stronger in the case of European Countries. Full article