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The purpose of this paper is to create optimally located Facilitation Centers on this tourist circuit, evaluated through network analysis, thus creating an effective linkage between tourism and economic activities of the craftsmen who are the custodians of the cultural heritage of Puri.

The craft villages lying in and around this tourist circuit are surveyed to establish socio-economic condition of artisans, significance of the craft and spatial distribution of craft villages and the willingness of artisans to travel closer to the transport spine. Network analysis is used to assess the suitability of Facilitation Center location using travel time and distance as parameters. Finally, the sustainability of the Facilitation Centers is evaluated using a cost-benefit analysis (CBA).

The Facilitation Centers can be spatially developed at the strategic locations to expand tourist market. This will help in leveraging the economic benefits of tourism to a marginalized rural artisan community by creating a sustainable model.

The focus on festival can help to protect local cultural traditions, develop tourism and promote the economic, social and cultural developments of the destination. Dispersal strategies adopted aim to increase visitors' satisfaction with the product and thus entice them to stay longer in the destination.

The purpose of this paper is to gain a better understanding on how metasurfaces behave, in terms of currents in each unit cell. A better knowledge of their behavior could lead to an ad-hoc design for specific applications.

The methodology used is both theoretical and numerical; it is based on circuit theory and on an optimization procedure.

The results show that when the knowledge of the current in each unit cell of a metasurface is needed, the most common approximations currently used are often not accurate. Furthermore, a procedure for the termination of a metasurface, with application-driven goals, is given.

This paper investigates the distribution of the currents in a 2D metamaterial realized with magnetically coupled resonant coils. Different models for the analysis of these structures are illustrated, and the effects of the approximations they introduce on the current values are shown and discussed. Furthermore, proper terminations of the resonators on the boundaries have been investigated by implementing a numerical optimization procedure with the purpose of achieving a uniform distribution of the resonator currents. The results show that the behavior of a metasurface (in terms of currents in each single resonator) depends on different properties; as a consequence, their design is not a trivial task and is dependent on the specific applications they are designed for. A design strategy, with lumped impedance termination, is here proposed.

To address the challenges of the agricultural sector, innovation is necessary. This study aims to focus on knowledge circulation as a basis to facilitate innovation in viticulture in the context of climate change.

We have conducted interviews with viticulture stakeholders in Central Spain (Madrid region) on their perceptions and concerns about climate change, knowledge on practices to mitigate its effects on this crop and their relationship with each other for knowledge exchange. A map showing the knowledge nodes and their relationships with other stakeholders has been drawn based on the answers obtained.

Winegrowers have already noticed the effects of climate change, and they are changing some agricultural practices. Drip irrigation was the most frequently mentioned option to minimize these effects. The map of knowledge identifies the main nodes in the information flow. Results also highlight different approaches to climate change and interesting nuances in the maps of knowledge among winegrowers with and without winery.

This paper is focused on the Madrid region, a territory that is still consolidating its wine sector at the economic and marketing levels. We understand that regions with more consolidated or stronger sectors involve maps of knowledge more complex than that obtained in this study.

Showing the nodes of knowledge, as well as the weaknesses and strengths of the information circuit in the wine sector in the Madrid region, is very relevant to developing strategies aimed at supporting innovation in this sector. From a practical point of view, strategies for knowledge generation and circulation are only one part of the innovation process – policies for financial and technical support are key complementary measures.

Identification of key agents in the innovation process in the wine sector is essential to foster innovation processes. Ultimately, this will lead to more efficient adaptation to new challenges in the sector.

The Agriculture Knowledge and Innovation Systems (AKIS) approach has a consolidated theoretical framework that pays great attention to knowledge flows, but specific studies are needed to capture the reality of AKIS by sector and by region.

The rapid development of urban areas in Sleman District, Indonesia, has created new challenges for firefighting response services. One of the primary challenges is to identify the optimal locations for new fire stations, to improve service quality and maximize service coverage within the specified time.

This paper proposes a method for precisely calculating travel time that integrates delay time caused by traffic lights, intersections and congestion. The study highlights the importance of precise calculation of travel time in order to provide a more accurate understanding of the service area covered by the fire stations. The proposed method utilizes network analysis in ArcGIS, the analytical hierarchy process (AHP) and simple additive weighting (SAW) to accurately calculate travel time and to identify the best locations for new fire stations. The identification of new site was based on service safety, service quality, service costs and demographic factors and applied to the Sleman district in Indonesia.

The results showed that the total area covered by old and new fire stations decreased from 61% to 31.8% of the study area when the adjusted default speed scenario was implemented.

The results indicated that the default speed scenario could provide misleading information about the service area, while the adjusted default speed scenario improved service quality and maximized service coverage.

The proposed method provides decision-makers with an effective tool to make informed decisions on optimal locations for new fire stations and thus enhance emergency response and public safety.

The purpose of this paper is to develop a blockchain-based data capture and transmission system that will collect real-time power consumption data from a household electrical appliance and transfer it securely to a local server for energy analytics such as forecasting.

The data capture system is composed of two current transformer (CT) sensors connected to two different electrical appliances. The CT sensors send the power readings to two Arduino microcontrollers which in turn connect to a Raspberry-Pi for aggregating the data. Blockchain is then enabled onto the Raspberry-Pi through a Java API so that the data are transmitted securely to a server. The server provides real-time visualization of the data as well as prediction using the multi-layer perceptron (MLP) and long short term memory (LSTM) algorithms.

The results for the blockchain analysis demonstrate that when the data readings are transmitted in smaller blocks, the security is much greater as compared with blocks of larger size. To assess the accuracy of the prediction algorithms data were collected for a 20 min interval to train the model and the algorithms were evaluated using the sliding window approach. The mean average percentage error (MAPE) was used to assess the accuracy of the algorithms and a MAPE of 1.62% and 1.99% was obtained for the LSTM and MLP algorithms, respectively.

A detailed performance analysis of the blockchain-based transmission model using time complexity, throughput and latency as well as energy forecasting has been performed.

This paper examines how key actors in the UK food system (FS) understand the role of the local food sector in relation to FS resilience.

Discourse analysis was used to assess and compare the framings of the UK FS in 36 publications released during Covid-19 from alternative food networks (AFNs) actors and from other more mainstream FS actors, including the UK government.

The analysis shows that AFNs actors perceive the UK FS as not resilient and identify local FSs as a route towards greater resilience (“systemic” framing). In contrast, other food actors perceive the UK FS as already resilient, with the role of local food limited to specific functions within the existing system (“add-on” framing). The two groups converge on the importance of dynamic public procurement and local abattoir provision, but this convergence does not undermine the fundamental divergence in the understanding of the role of “the local” in resilient UK FSs. The local food sector’s messages appear to have gone largely unheard in mainstream policy.

The paper presents an analysis of public sector reports focused on the UK FS released during the Covid-19 pandemic years 2020–2021. The corpus inclusion criteria mean that publications during this period which focus on other food sector issues, such social injustices, climate change and health, were not included in the analysis, although they may have touched upon local food issues. The authors further recognise that Covid-19 had a longer lasting effect on FSs than the years 2020–2021, and that many other publications on FSs have been published since. The time span chosen targets the time at which FSs were most disrupted and therefore aims to capture emerging issues and solutions for the UK FS. The authors’ insights should be further validated through a more complete review of both public reports and academic papers covering a wider base of food-related issues and sectors as well as a broader timespan.

A comparison of how different FS actors understand the importance of local food, especially in relation to resilience, has not been undertaken to date. The findings raise important questions about the disconnect between AFN actors and other actors in the framing of resilience. Considering the need to ensure resilience of the UK FS, this study's findings raise important insights for UK food policy about the “local food blindspot” and for food movement actors wishing to progress their vision of transformative change.

This study employs Stein Rokkan's methodological approach to analyse state formation in the Greater Middle East. It develops a conceptual framework distinguishing colonial, populist and democratic pacts, suitable for analysis of state formation and nation-building through to the present period. The framework relies on historical institutionalism. The methodology, however, is Rokkan's. The initial conceptual analysis also specifies differences between European and the Middle Eastern state formation processes. It is followed by a brief and selective discussion of historical preconditions. Next, the method of plotting singular cases into conceptual-typological maps is applied to 20 cases in the Greater Middle East (including Afghanistan, Iran and Turkey). For reasons of space, the empirical analysis is limited to the colonial period (1870s to the end of World War 1). Three typologies are combined into one conceptual-typological map of this period. The vertical left-hand axis provides a composite typology that clarifies cultural-territorial preconditions. The horizontal axis specifies transformations of the region's agrarian class structures since the mid-19th century reforms. The right-hand vertical axis provides a four-layered typology of processes of external intervention. A final section presents selected comparative case reconstructions. To the authors' knowledge, this is the first time such a Rokkan-style conceptual-typological map has been constructed for a non-European region.

Railways are a well-known example of complex critical infrastructure, incorporating socio-technical systems with humans such as drivers, signallers, maintainers and passengers at the core. The technological evolution including interconnectedness and new ways of interaction lead to new security and safety risks that can be realised, both in terms of human error, and malicious and non-malicious behaviour. This study aims to identify the human factors (HF) and cyber-security risks relating to the role of signallers on the railways and explores strategies for the improvement of “Digital Resilience” – for the concept of a resilient railway.

Overall, 26 interviews were conducted with 21 participants from industry and academia.

The results showed that due to increased automation, both cyber-related threats and human error can impact signallers’ day-to-day operations – directly or indirectly (e.g. workload and safety-critical communications) – which could disrupt the railway services and potentially lead to safety-related catastrophic consequences. This study identifies cyber-related problems, including external threats; engineers not considering the human element in designs when specifying security controls; lack of security awareness among the rail industry; training gaps; organisational issues; and many unknown “unknowns”.

The authors discuss socio-technical principles through a hexagonal socio-technical framework and training needs analysis to mitigate against cyber-security issues and identify the predictive training needs of the signallers. This is supported by a systematic approach which considers both, safety and security factors, rather than waiting to learn from a cyber-attack retrospectively.

Agriculture is the backbone of a country, contributing more than half of the sector of economy throughout the world. The need for precision agriculture is essential in evaluating the conditions of the crops with the aim of determining the proper selection of pesticides. The conventional method of pest detection fails to be stable and provides limited accuracy in the prediction. This paper aims to propose an automatic pest detection module for the accurate detection of pests using the hybrid optimization controlled deep learning model.

The paper proposes an advanced pest detection strategy based on deep learning strategy through wireless sensor network (WSN) in the agricultural fields. Initially, the WSN consisting of number of nodes and a sink are clustered as number of clusters. Each cluster comprises a cluster head (CH) and a number of nodes, where the CH involves in the transfer of data to the sink node of the WSN and the CH is selected using the fractional ant bee colony optimization (FABC) algorithm. The routing process is executed using the protruder optimization algorithm that helps in the transfer of image data to the sink node through the optimal CH. The sink node acts as the data aggregator and the collection of image data thus obtained acts as the input database to be processed to find the type of pest in the agricultural field. The image data is pre-processed to remove the artifacts present in the image and the pre-processed image is then subjected to feature extraction process, through which the significant local directional pattern, local binary pattern, local optimal-oriented pattern (LOOP) and local ternary pattern (LTP) features are extracted. The extracted features are then fed to the deep-convolutional neural network (CNN) in such a way to detect the type of pests in the agricultural field. The weights of the deep-CNN are tuned optimally using the proposed MFGHO optimization algorithm that is developed with the combined characteristics of navigating search agents and the swarming search agents.

The analysis using insect identification from habitus image Database based on the performance metrics, such as accuracy, specificity and sensitivity, reveals the effectiveness of the proposed MFGHO-based deep-CNN in detecting the pests in crops. The analysis proves that the proposed classifier using the FABC+protruder optimization-based data aggregation strategy obtains an accuracy of 94.3482%, sensitivity of 93.3247% and the specificity of 94.5263%, which is high as compared to the existing methods.

The proposed MFGHO optimization-based deep-CNN is used for the detection of pest in the crop fields to ensure the better selection of proper cost-effective pesticides for the crop fields in such a way to increase the production. The proposed MFGHO algorithm is developed with the integrated characteristic features of navigating search agents and the swarming search agents in such a way to facilitate the optimal tuning of the hyperparameters in the deep-CNN classifier for the detection of pests in the crop fields.

In Morocco, as in many developing countries, environmental responsibility is not well integrated into corporate management at the operational, tactical, and strategic levels. While the management literature offers a rich body of knowledge on Corporate Social Responsibility (CSR) strategies and practices, less attention has been paid to exploring the complexity of environmental responsibility through the lens of corporate culture. This research aims to address this gap by examining the influence of cultural factors on the deployment of environmental responsibility using Johnson's (2000) model of corporate culture. This model identifies seven components of corporate culture: stories or myths, symbols, power structures, organizational structures, control systems, rituals and routines, and paradigms. Through a Moroccan industrial group case study, this chapter presents the successful deployment of environmental responsibility and describes how managing cultural factors facilitated this transition. This chapter also identifies the unique aspects of the group's culture that allowed redesigning the company's management systems. These insights offer valuable implications for managers and policymakers seeking to improve the environmental performance of large enterprises in developing countries.