We introduce a simple yet efficient approach to optimize the modal fleet size of urban public transportation services, considering both user- and operator-oriented factors. This is envisaged to enhance the potential for achieving sustainable urban transportation systems and, eventually, opportunities to create sustainable cities. The presented constraint optimization approach can be described as follows. First, the expected passenger demand and the cycle time for the public transportation service are estimated. Next, the desired constraints and parameters, such as those related to the headway and seat supply, are determined. Finally, the optimal combination of different vehicle classes and the number of trips satisfying all the defined constraints are determined. The case of an urban area in a developing country is considered. The resulting solution determines the optimal numbers of public transportation trips and vehicles, by mode, required to meet the expected passenger demand, provide a high-quality service with acceptable headways for passengers, and, at the same time, reduce the service providers’ costs as well as the environmental impacts. It is also concluded that a fleet composed of different modes can better facilitate the achievement of the optimal solution for passengers and service providers compared with the one-mode fleet. Full article

Deep learning algorithms, such as convolutional neural networks (CNNs), have been widely studied and applied in various fields including agriculture. Agriculture is the most important source of food and income in human life. In most countries, the backbone of the economy is based on agriculture. Pests are one of the major challenges in crop production worldwide. To reduce the overall production and economic loss from pests, advancement in computer vision and artificial intelligence may lead to early and small pest detection with greater accuracy and speed. In this paper, an approach for early pest detection using deep learning and convolutional neural networks has been presented. Object detection is applied on a dataset with images of thistle caterpillars, red beetles, and citrus psylla. The input dataset contains 9875 images of all the pests under different illumination conditions. State-of-the-art Yolo v3, Yolov3-Tiny, Yolov4, Yolov4-Tiny, Yolov6, and Yolov8 have been adopted in this study for detection. All of these models were selected based on their performance in object detection. The images were annotated in the Yolo format. Yolov8 achieved the highest mAP of 84.7% with an average loss of 0.7939, which is better than the results reported in other works when compared to small pest detection. The Yolov8 model was further integrated in an Android application for real time pest detection. This paper contributes the implementation of novel deep learning models, analytical methodology, and a workflow to detect pests in crops for effective pest management. Full article

Progress in schooling using competence-based teaching for students is a priority in setting up a quality-centered educational process. Thus, using ICT tools as teaching–learning techniques represents an important objective in reaching scholastic performance and the capacity to offer students various experiences in teaching. Starting with an analysis of research on integrating ICT tools in the educational environment, this study is aimed at presenting teaching opportunities for all students, seeing education through the lens of those instruments specific to a secondary school level, referencing the subject of geography. The applied research design includes mixed methods applied to a total of 674 students, based on results received before and after a written evaluation, to determine the level of knowledge of the students. The present study evaluates the learning environment in which a successful and practical integration of ICT tools is anticipated in the teaching and learning process, from the perspective of the comparative measurement of the impact on the tests, resulting from the formative assessment. The conducted experiment and the Google Forms questionnaires suggest, through the degree of involvement of all students, how technology can facilitate the teaching–learning process. The results showed that there are statistically significant differences between the experimental and the control group, and that information and communication technologies (ICT) represent an important tool for improving performance and developing participatory skills, having the ability to provide students with positive results. Full article

During the last years, renewable energy strategies for sustainable development perform as best practices and strategic insights necessary to support large scale organizations’ approach to sustainability. Power purchase agreements (PPAs) enhance the value of such initiatives. A renewable PPA contract delivers green energy efficiently to organizations that seek sustainability benefits. Consequently, various approaches that define PPAs are utilized to motivate both interested parties to participate in such deals. Highlighting sustainability tactics during negotiations and identifying relative impacts during the projects’ implementation are both necessary. Following the methodological approach of this study, open-ended criteria are used to identify vital negotiation pricing patterns that permit practitioners to deliver win–win outcomes. The aim of this research is to introduce a multicriteria decision analysis-based method, namely PROMETHEE, to evaluate the implementation of sustainability-related strategies using PPAs. This paper proposes a method for ranking alternative sustainable energy strategies in order for decision-makers to be able to select the most appropriate PPA structure for an organization. Full article

Globalization has prompted enterprises worldwide to increasingly seek the optimal supply chain configuration. However, outsourcing, shortened product life cycles, and a reduced supply base severely weaken supply chain risk tolerance. With the emergence of blockchain, enterprises see an opportunity to mitigate supply chain risks. The purpose of our research is to explore supply chain managers’ intention to adopt blockchain technology from the perspective of supply chain risk management. Using a survey sample of 203 managers in China and the USA, we explored the impact of four perceived benefits of blockchain technology on supply chain risk resistance by extending the technology acceptance model. The results show that the traceability, transparency, information sharing, and decentralization of blockchain can enhance the perceived usefulness of blockchain in supply chain resilience and responsiveness, and the ability to withstand disruption risks and supply and demand coordination risks encountered in the supply chain, thus promoting the adoption of the technology. In addition, the relationships between supply chain resilience and blockchain technology adoption and between supply chain responsiveness and blockchain technology adoption are more salient for managers with high levels of uncertainty avoidance. Full article

In the current competitive context, innovation has become a necessity, to the point that it seems that no company can survive without developing innovation processes. One of the great concerns of small and medium-sized companies is about the investment of resources in innovation activities and their success. Therefore, this article analyzes the influence of different innovation activities performed by small and medium-sized enterprises (SMEs) of the Colombian Caribbean region on their product innovation outcomes and thereby provides a solid groundwork for the design of innovation strategies. To achieve this objective, a data collection instrument was first validated in 56 SMEs, followed by the application of a reflective measurement model with convergent validity analysis. The results of the model application revealed that investment in Research and Development (R&D) and innovation, knowledge transfer, technology adoption, and financial aspects explain to a greater extent the introduction of new products to the market. Full article

Agriculture and forestry are fundamental industries. With the development of the ESG concept, stakeholders are increasingly concerned about the relationship between ESG and agricultural and forestry corporate performance. This paper examines 156 listed agricultural and forestry companies to explore the impact of ESG on corporate performance, both theoretically and empirically, using two-stage least squares. Heterogeneity is explored from the perspective of three sub-dimensions of ESG and industry comparison, respectively. Finally, the impact mechanism of ESG is analyzed from three perspectives: government, market, and company. Results indicate that (1) ESG and corporate performance are significantly and positively correlated, and higher ESG ratings are beneficial to corporate performance improvement. (2) Compared with E performance, S and G performance are more conducive to promoting corporate performance growth. (3) There is no significant difference in the effect of ESG on corporate performance between listed companies in agriculture and forestry. (4) Tax incentives and the regional marketization degree have a negative moderating effect, but the proportion of female executives plays a positive moderating role. These findings provide useful insights for listed companies in agriculture and forestry to improve ESG performance and, consequently, corporate performance, and also promote listed companies to play a greater leading role in green development. Full article

Microplastic contamination has become a concerning topic of study in recent decades. This review discusses the development of microplastic pollution based on a selection of South Asian countries consisting of Bangladesh, Iran, Philippines, Thailand, India, Indonesia, and Vietnam. The condition of microplastic pollution related to the abundance of microplastic found in various environments as well as the presence of microplastics in food and the air, is covered in this review. Several reports found that drinking water sourced from taps was found to have about 83% of microplastic particles in the year 2017 based on results from 14 nations, and in the year 2018, 260 bodies of water for human consumption in 11 countries were found to have about 93% of microplastic particles. Micro debris pollution in seas and oceans worldwide is predicted to be at an amount of 236,000 metric tons based on a statistical report. A mean value of 30 micro debris per liter of glacier water was recovered from the top of Mount Everest, whereas about 2200 small particles per liter were discovered in the deep waters of the Mariana Trench. The main environments that are severely microplastic-contaminated are water-based places such as rivers, estuaries, and beaches. The presence of microplastics in food items, such as tea bags, sugar, shrimp paste, and salt packets, has been reported. In terms of impacts on the environment, microplastic contamination includes the ingestion of microplastics by aquatic creatures in water environments. The impacts on terrestrial environments relate to microplastics sinking into the soil, leading to the alteration of the physicochemical parameters of soil. Meanwhile, the impacts on the atmospheric environment include the settling of microplastics on the external bodies of animals and humans. Full article

A social bot is an intelligent computer program that acts like a human and carries out various activities in a social network. A Twitter bot is one of the most common forms of social bots. The detection of Twitter bots has become imperative to draw lines between real and unreal Twitter users. In this research study, the main aim is to detect Twitter bots based on diverse content-specific feature sets and explore the use of state-of-the-art machine learning classifiers. The real-world data from Twitter is scrapped using Twitter API and is pre-processed using standard procedure. To analyze the content of tweets, several feature sets are proposed, such as message-based, part-of-speech, special characters, and sentiment-based feature sets. Min-max normalization is considered for data normalization and then feature selection methods are applied to rank the top features within each feature set. For empirical analysis, robust machine learning algorithms such as deep learning (DL), multilayer perceptron (MLP), random forest (RF), naïve Bayes (NB), and rule-based classification (RBC) are applied. The performance evaluation based on standard metrics of precision, accuracy, recall, and f-measure reveals that the proposed approach outperforms the existing studies in the relevant literature. In addition, we explore the effectiveness of each feature set for the detection of Twitter bots. Full article

Resilience measurement is an emerging topic in the field of disaster risk reduction. However, its application in Global South cities has proven to be a challenge due to the uniqueness of southern urbanisms and data challenges. As a result, the Resilience Benchmarking Assessment and Impact Toolkit (RABIT) framework has recently been developed to support resilience assessment in informal, marginalized, and disaster-prone contexts of southern cities. This paper asserts the relevance of the RABIT framework and uses it to assess the resilience of Manggahan residences, a resettled marginalized community in Pasig City, Metro Manila. Drawing on a quantitative approach and using exploratory factor analysis (EFA), the study revealed that scale, robustness, and learning attributes of the RABIT framework are strong contributors to the community’s resilience. Self-organization, diversity, and redundancy have similar levels of contribution. Equality and rapidity were found to have the weakest relative contribution. The study findings emphasize the need to view resilience in resettled communities holistically and adopt an integrated and comprehensive approach that considers the multiple aspects of everyday life to proactively build adaptive and future resilient capacities. Full article

The Pashtun, Tajik, and Hazara ethnic groups, divided into three zones, make up most of the ethnic segments in Kabul, the capital of Afghanistan. However, each ethnic settlement is subdivided into smaller geographical communities. In this research, we want to validate the existence of these ethnic communities and how these communities are valuable to the residents of Kabul city. We also aimed to analyze what facilities the community shares and why it is crucial to consider these communities while redeveloping the informal settlements in Kabul city. We did a literature review on ethnic segmentation, chose a case study in district 13 of Kabul, interviewed the residents and head of the community, and distributed socio-economic questionnaires made by the Ministry of urban development and land Afghanistan in three streets to determine how the ethnicities are subdivided. Ethnicities are divided into smaller sub-ethnic communities based on their migrated rural districts. People live together because of a communal identity known as Qawm. Each community shares a mosque, and to redevelop their areas, they want to be relocated near their current community of ethnicity. Moreover, ethnicity is a significant factor in choosing where to live in Kabul. Hence, the government policymaker must consider the importance of these communities in redeveloping informal settlements. Full article

The COVID-19 pandemic has significantly impacted the tourism and hospitality industry. This study aims to reconceptualize the concept of customer perceived value (CPV) in Isfahan’s five-star hotels during the pandemic using the grounded theory (GT) approach in the context of qualitative research. The objective of this study is to explore the key dimensions of CPV and identify the main strategies that enhance the value perceived by hotel customers. Data were collected with interviews with 30 experts in the hotel and hospitality industry, and MAXQDA software was used to analyze the data. The dominant themes that emerged from the content analysis included health-oriented self-gratification value, financial value, quality value, emotional value, social value, epistemic value, information value, and health security value. Additionally, the subthemes identified were trust, satisfaction, and information access. All these themes could potentially be employed by hotel sectors as solutions to enhance customer satisfaction during the pandemic. From a practical standpoint, this study provides insights to hotel managers or practitioners to implement updated strategies that contribute to knowledge development about customer perceived value in the hotel sector, which can lead to enhanced customer satisfaction and loyalty. The article makes theoretical contributions to the literature on CPV by identifying new dimensions that are relevant to the pandemic. This extension of the literature provides a more comprehensive understanding of CPV in crisis situations and can inform future research on the topic. The article also discusses future research directions. Full article

Transportation systems globally face challenges related to congestion, decreased quality of life, limited accessibility, increased harmful emissions and costs, growing use of private cars and in some cases lack of intra and intermodal integration. Smart Mobility is believed to be a solution to some of these challenges by providing comprehensive and intelligent mobility services, decreasing transportation costs, promoting safety, and combating pollution and traffic congestion. Despite this potential, there is still uncertainty surrounding what smart mobility is and whether it is moving toward improving the quality of life and making cities more sustainable. To address this gap, this paper conducts a bibliometric review of 3223 Web of Science Core Collection-indexed documents to provide a comprehensive understanding of smart mobility research. The findings reveal a lack of multi-disciplinary approaches in previous studies with a strong emphasis on technological aspects and limited social or economic considerations in current research. The review identifies four distinct periods of smart mobility research, with recent interest sparked by advancements in big data, deep learning, artificial intelligence, and real-time technologies in transport systems. However, there is a dearth of research on smart mobility in developing countries, where urban populations are rapidly increasing. Thus, the review proposes a research agenda to address the current gaps in knowledge. Furthermore, the review provides an updated and integrated definition of smart mobility as the use of advanced technologies, such as the Internet of Things (IoT), big data analytics, and artificial intelligence, to improve transportation efficiency, mobility for all, and sustainability while safeguarding the quality of life. The primary challenge for smart mobility is the co-evolution with existing transport systems, making further research on integration with these systems and real-time technologies essential for advancing smart mobility research. The paper’s main contribution is an integrated conceptualisation of smart mobility research and novel research topics that build on this unified base. Full article

Due to environmental degradation, using environment-friendly products has become necessary to reduce carbon emissions. However, the consumption of such products is still below expectations because these products are usually costlier than their traditional counterparts. The current study aims to investigate consumer behavior towards environment-friendly products using Ajzen’s theory of planned behavior as a theoretical model. The study seeks to examine the role of the key determinates of environmentally conscious purchase behavior, such as ethical obligation and green self-identity. A total of 386 responses were collected from consumers living in a few major cities of northern India using purposive sampling. The data were analyzed using structural equation modeling in Amos 22.0. The results demonstrated that attitudes towards environment-friendly products perceived behavioral control and green self-identity as the major determinants of green purchase intentions. In addition, attitude was reported to mediate the effect of ethical obligation on green purchase intentions and green self-identity was found to moderate the effect of attitude on green purchase intentions. Additionally, green self-identity was also reported to moderate the relationship between ethical obligation and attitude. The study adds value to the existing literature by signifying the role of green self-identity and ethical obligation in stimulating consumers’ green purchase intentions. The findings of the study are also meaningful for marketers and policymakers. Full article

Nowadays, a noticeable trend in society is the search for more and more healthy food products. This is also reflected in the interest in plant-based ingredients replacing animal ones, which are more caloric, difficult to digest, and have more negative environmental impact. The purpose of this study was to determine the carbon footprint (CF) of technological process of ice cream, made with traditional ingredients as well as with fat and sugar substitute ingredients, under laboratory and handcraft conditions. Process-line portable metering was designed and implemented. Emission and production data were recorded for different ice blends; at a laboratory-scale, the determined technological process, CF_tech, of traditional ice cream was 0.360 and for ice cream with substitutes 0.385 kg CO₂/kg product. The pasteurization process accounted for the largest share in CF_tech of ice cream with different contents of substitutes. Under handicraft conditions, the CF_tech of traditional ice cream as well as ice cream with fat and sugar substitutes were 0.253 and 0.248 kg CO₂/kg product, respectively. In contrast, for standard a handcraft, CF was the lowest at 0.234 kg CO₂/kg product. CF_tech of laboratory-scale ice cream production is larger than for handcraft production. Pasteurization along with homogenization and ripening accounted for the largest share of CO₂ emissions. Full article