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The potential of vehicle ad hoc networks (VANETs) to improve driver and passenger safety and security has made them a hot topic in the field of intelligent transportation systems (ITSs). VANETs have different characteristics and system architectures from mobile ad hoc networks (MANETs), with a primary focus on vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. But protecting VANETs from malicious assaults is crucial because they can undermine network security and safety.

The black hole attack is a well-known danger to VANETs. It occurs when a hostile node introduces phony routing tables into the network, potentially damaging it and interfering with communication. A safe ad hoc on-demand distance vector (AODV) routing protocol has been created in response to this issue. By adding cryptographic features for source and target node verification to the route request (RREQ) and route reply (RREP) packets, this protocol improves upon the original AODV routing system.

Through the use of cryptographic-based encryption and decryption techniques, the suggested method fortifies the VANET connection. In addition, other network metrics are taken into account to assess the effectiveness of the secure AODV routing protocol under black hole attacks, including packet loss, end-to-end latency, packet delivery ratio (PDR) and routing request overhead. Results from simulations using an NS-2.33 simulator show how well the suggested fix works to enhance system performance and lessen the effects of black hole assaults on VANETs.

All things considered, the safe AODV routing protocol provides a strong method for improving security and dependability in VANET systems, protecting against malevolent attacks and guaranteeing smooth communication between cars and infrastructure.

This study explores the consequences of digitalization in the field of education, particularly in relation to teachers’ course processes in higher education institutions. It emphasizes the importance of understanding how information systems (IS) support not only individual tasks but also processes as a whole. The results reveal that process practices have not been considered comprehensively and even core processes may be unseen.

A systematic literature review was conducted to explore the extent to which teachers’ processes are discussed in the literature. A qualitative case study was then conducted at a Finnish higher education institution to identify course processes and their relationships to IS.

Teachers’ processes have scarcely been discussed in the literature, and the process support provided by ISs is remarkably limited. It seems that course processes, which are core to education, are a blind spot in education digitalization. To support evaluating the level of support by IS, novel course process indicators were introduced.

Developing core processes, teachers’ course processes and thesis processes in education field, supports improving service quality. In all industries, organizations should consider whether processes are properly recognized and whether IS support not only individual tasks but also processes as a whole. We recommend recognizing and applying business process management practices to better support teachers’ work and to improve overall efficiency in education.

To the best of our knowledge, this is the first education sector study that attends to teacher’s work as a comprehensive process.

Thin cracks on the surface, such as those found in nuclear power plant concrete structures, are difficult to identify because they tend to be thin. This paper aims to design a novel segmentation network, called U-shaped contextual aggregation network (UCAN), for better recognition of weak cracks.

UCAN uses dilated convolutional layers with exponentially changing dilation rates to extract additional contextual features of thin cracks while preserving resolution. Furthermore, this paper has developed a topology-based loss function, called ℓ_cl Dice, which enhances the crack segmentation’s connectivity.

This paper generated five data sets with varying crack widths to evaluate the performance of multiple algorithms. The results show that the UCAN network proposed in this study achieves the highest F1-Score on thinner cracks. Additionally, training the UCAN network with the ℓ_cl Dice improves the F1-Scores compared to using the cross-entropy function alone. These findings demonstrate the effectiveness of the UCAN network and the value of incorporating the ℓ_cl Dice in crack segmentation tasks.

In this paper, an exponentially dilated convolutional layer is constructed to replace the commonly used pooling layer to improve the model receptive field. To address the challenge of preserving fracture connectivity segmentation, this paper introduces ℓ_cl Dice. This design enables UCAN to extract more contextual features while maintaining resolution, thus improving the crack segmentation performance. The proposed method is evaluated using extensive experiments where the results demonstrate the effectiveness of the algorithm.

This case study is intended to help students to evaluate Kavak’s business model, examine the global expansion strategy of Kavak, analyze the competitive strategy adopted by Kavak, recognize the ways in which Kavak leveraged technology in all its business operations, examine the key challenges faced by Kavak in the fragmented Latin American as well as global used car market and explore strategies that Kavak can adopt in future to maintain its dominance in the global used car market.

This case study is about the meteoric rise of Kavak, a Mexican used car retailer that aimed to disrupt the emerging pre-owned car markets with its unique value propositions and compelling global expansion strategy. Co-founded in 2016 by Carlos García Ottati (Ottati), in Mexico City, Kavak emerged as an end-to-end solution to buy, manage, sell and finance pre-owned cars. Using pricing algorithms driven by artificial intelligence and machine learning-based inspection tools and personalized recommendations, Kavak reshaped the mobility sector in the Latin American and Middle Eastern regions. In a mere six years of operation, the company established its presence in nine countries: Argentina, Brazil, Chile, Colombia, Mexico, Peru, Turkey, the UAE and Oman. Kavak’s innovative yet simple business model ensured transparency and guarantees in all its transactions where reconditioned vehicles were sold to thousands of customers through its e-commerce platform as well as a network of brick-and-mortar hubs. Its in-house financing arm Kavak Capital was at the core of its business model, as it offered affordable leasing options, making car ownership possible for both first- and second-time car owners within just a few minutes of applying. The platform had an inventory of 40,000 vehicles as of 2023 with more than 50% of Kavak’s sales being financed by Kavak Capital. The case study discusses the challenges faced by Kavak in the fragmented used car market including rising interest rates for vehicle loans, managing capital-intensive operations, rising competition and external economic headwinds such as inflation and slowing economic growth. Going forward, the challenge before Ottati and his team was how to make profits, build customer trust, attract customers and achieve global success.

This case study is suitable for MBA/MS level and is designed to be a part of the business strategy/and international business curriculum.

CSS: 5: International business.

Teaching notes are available for educators only.

With Industry 4.0 and the extensive rise of smart technologies, we are seeing remarkable transformations in work practices and workplaces. Scholars report the phenomenal progress of smart technologies. At the same time, we can hear the rhetoric emphasising their potential threats. This study focusses on how and where intelligent machines are leveraged in the workplace, how humans co-working with intelligent machines are affected and what they believe can be done to mitigate the risks of the increased use of intelligent machines.

We conducted in-depth interviews with 15 respondents working in various leadership capacities associated with intelligent machines and technologies. Using NVivo, we coded and churned out the themes from the qualitative data collected.

This study shows how intelligent machines are leveraged across different industries, ranging from chatbots, intelligent sensors, cognitive systems and computer vision to the replica of the entire human being. They are used end-to-end in the value chain, increasing productivity, complementing human workers’ skillsets and augmenting decisions made by human workers. Human workers experience a blend of positive and negative emotions whilst co-working with intelligent machines, which influences their job satisfaction level. Organisations adopt several anticipatory strategies, like transforming into a learning organisation, identifying futuristic technologies and upskilling their human workers, regularly conducting social learning events and designing accelerated career paths to embrace intelligent technologies.

This study seeks to understand the emotional and practical implications of the use of intelligent machines by humans and how both entities can integrate and complement each other. These insights can help organisations and employees understand what future workplaces and practices will look like and how to remain relevant in this transformation.

This paper aims to examine the relationship between board structure and risk-taking, exploring how this association is influenced by advanced technologies in the banking sector.

This study uses a panel sample of 22 Pakistani banks from 2011 to 2018. To test the authors’ hypothesis, the authors use regression analysis with two-way cluster robust standard errors. Further, the authors also check the robustness of the authors’ findings using alternate proxies of board structure and bank risk-taking behavior. To address endogeneity concerns, the authors use the two-stage least square technique.

In the era of the Fourth Industrial Revolution, Pakistani banks’ digitalization is modeled by the presence of Temenos-T24/Oracle as their core banking system (software providing end-to-end operational integration). Its interactional effect with corporate governance is evaluated to implicate informed risk-taking by the board as a result of improved information access and analysis. The authors find that board size has a positive association with risk-taking, and the use of modern technology reshapes this association in the banking sector.

The contribution of this paper is twofold. First, the impact of board structure on bank risk-taking has not been extensively researched in Pakistan – a highly volatile and unpredictable economy. Second, the evaluation of the role of technology on bank risk is being researched for the very first time – a uniqueness of this paper.

The problem of long delay and waiting time in Disabled Facilities Grants (DFG) housing adaptation has been ongoing for years. This study aimed at constructing an innovative smart solution to streamline the housing adaptation process to prevent lengthy delays for disabled and elderly people.

The Adapt-ABLE approach is suggested based on a constructive research approach, where extensive theoretical development of the Adapt-ABLE concept is developed. It consists of four integrated platforms that undergo theoretical and analogical development and validations through applicable theories, a workshop, four brainstorming sessions and a focus group.

The proposed Adapt-ABLE approach utilises process optimisation techniques through an IT system for streamlining the process. The merits of the semi-automated system include the development of a preventive measure that allows measurement of suitability index of homes for the occupants, indicative assessment that shorten the application duration, procurement and contracting platform that utilises principles based on framework agreement and call-off contract, and a platform that standardised performance management for continuous improvement.

The Adapt-ABLE solution will cut the application journey of non-qualified applicants and suggest where help can be sought. The qualified applicants' application journey will also be shortened through an online indicative assessment regime and early online resources (means) testing. Overall, the proposed system reduces the waiting time, and timely delivery improves the applicant's quality of life by living independently. It will potentially save the NHS billions of pounds used to replace hips and residential care costs due to lengthy delays in the housing adaptations process.

This paper proposes a self-supervised monocular depth estimation algorithm under multiple constraints, which can generate the corresponding depth map end-to-end based on RGB images. On this basis, based on the traditional visual simultaneous localisation and mapping (VSLAM) framework, a dynamic object detection framework based on deep learning is introduced, and dynamic objects in the scene are culled during mapping.

Typical SLAM algorithms or data sets assume a static environment and do not consider the potential consequences of accidentally adding dynamic objects to a 3D map. This shortcoming limits the applicability of VSLAM in many practical cases, such as long-term mapping. In light of the aforementioned considerations, this paper presents a self-supervised monocular depth estimation algorithm based on deep learning. Furthermore, this paper introduces the YOLOv5 dynamic detection framework into the traditional ORBSLAM2 algorithm for the purpose of removing dynamic objects.

Compared with Dyna-SLAM, the algorithm proposed in this paper reduces the error by about 13%, and compared with ORB-SLAM2 by about 54.9%. In addition, the algorithm in this paper can process a single frame of image at a speed of 15–20 FPS on GeForce RTX 2080s, far exceeding Dyna-SLAM in real-time performance.

This paper proposes a VSLAM algorithm that can be applied to dynamic environments. The algorithm consists of a self-supervised monocular depth estimation part under multiple constraints and the introduction of a dynamic object detection framework based on YOLOv5.

The Internet of Things (IoT) cloud platforms provide end-to-end solutions that integrate various capabilities such as application development, device and connectivity management, data storage, data analysis and data visualization. The high use of these platforms results in their huge availability provided by different capabilities. Therefore, choosing the optimal IoT cloud platform to develop IoT applications successfully has become crucial. The key purpose of the present study is to implement a hybrid multi-attribute decision-making approach (MADM) to evaluate and select IoT cloud platforms.

The optimal selection of the IoT cloud platforms seems to be dependent on multiple attributes. Hence, the optimal selection of IoT cloud platforms problem is modeled as a MADM problem, and a hybrid approach named neutrosophic fuzzy set-Euclidean taxicab distance-based approach (NFS-ETDBA) is implemented to solve the same. NFS-ETDBA works on the calculation of assessment score for each alternative, i.e. IoT cloud platforms, by combining two different measures: Euclidean and taxicab distance.

A case study to illustrate the working of the proposed NFS-ETDBA for optimal selection of IoT cloud platforms is given. The results obtained on the basis of calculated assessment scores depict that “Azure IoT suite” is the most preferable IoT cloud platform, whereas “Salesman IoT cloud” is the least preferable.

The proposed NFS-ETDBA methodology for the IoT cloud platform selection is implemented for the first time in this field. ETDBA is highly capable of handling the large number of alternatives and the selection attributes involved in any decision-making process. Further, the use of fuzzy set theory (FST) makes it very easy to handle the impreciseness that may occur during the data collection through a questionnaire from a group of experts.