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The purpose of this paper is to develop a coupled lattice Boltzmann model for the simulation of the freezing process in unsaturated porous media.

In the developed model, the porous structure with complexity and disorder was generated by using a stochastic growth method, and then the Shan-Chen multiphase model and enthalpy-based phase change model were coupled by introducing a freezing interface force to describe the variation of phase interface. The pore size of porous media in freezing process was considered as an influential factor to phase transition temperature, and the variation of the interfacial force formed with phase change on the interface was described.

The larger porosity (0.2 and 0.8) will enlarge the unfrozen area from 42 mm to 70 mm, and the rest space of porous medium was occupied by the solid particles. The larger specific surface area (0.168 and 0.315) has a more fluctuated volume fraction distribution.

The concept of interfacial force was first introduced in the solid–liquid phase transition to describe the freezing process of frozen soil, enabling the formulation of a distribution equation based on enthalpy to depict the changes in the water film. The increased interfacial force serves to diminish ice formation and effectively absorb air during the freezing process. A greater surface area enhances the ability to counteract liquid migration.

This research is aimed at preparing landslide susceptibility using spatial analysis and soft computing machine learning techniques based on convolutional neural networks (CNNs), artificial neural networks (ANNs) and logistic regression (LR) models.

Using the Geographical Information System (GIS), a spatial database including topographic, hydrologic, geological and landuse data is created for the study area. The data are randomly divided between a training set (70%), a validation (10%) and a test set (20%).

The validation findings demonstrate that the CNN model (has an 89% success rate and an 84% prediction rate). The ANN model (with an 84% success rate and an 81% prediction rate) predicts landslides better than the LR model (with a success rate of 82% and a prediction rate of 79%). In comparison, the CNN proves to be more accurate than the logistic regression and is utilized for final susceptibility.

Land cover data and geological data are limited in largescale, making it challenging to develop accurate and comprehensive susceptibility maps.

It helps to identify areas with a higher likelihood of experiencing landslides. This information is crucial for assessing the risk posed to human lives, infrastructure and properties in these areas. It allows authorities and stakeholders to prioritize risk management efforts and allocate resources more effectively.

The social implications of a landslide susceptibility map are profound, as it provides vital information for disaster preparedness, risk mitigation and landuse planning. Communities can utilize these maps to identify vulnerable areas, implement zoning regulations and develop evacuation plans, ultimately safeguarding lives and property. Additionally, access to such information promotes public awareness and education about landslide risks, fostering a proactive approach to disaster management. However, reliance solely on these maps may also create a false sense of security, necessitating continuous updates and integration with other risk assessment measures to ensure effective disaster resilience strategies are in place.

Landslide susceptibility mapping provides a proactive approach to identifying areas at higher risk of landslides before any significant events occur. Researchers continually explore new data sources, modeling techniques and validation approaches, leading to a better understanding of landslide dynamics and susceptibility factors.

The learning outcomes of this case study are to understand the business model canvas and value propositions and apply advanced business innovation tools in electric vehicle business models; evaluate the current cargo vehicle scenarios at national and global levels and draw out the possibilities and costs for a new player; extrapolate the future scenario of the cargo economy, its electrification and positioning in a business-to-business (B2B) and business-to-customer (B2C) segment, especially for a developing economy; and improve the student’s ability to get organisational buy-in and execute new business models.

LoadExx is a fully electrified electric cargo service focusing on logistics in Kolkata, a metropolitan city in the eastern part of the country. The service of LoadExx commenced in January 2021 in the B2B segment after overcoming its then issues of driver hesitancy and customer anxiety and financial issues to adopt electrified cargo systems. The conundrum faced by LoadExx in its commencement thus had been solved under the able guidance of its owner Amit Arora. The case study was positioned four months after the commencement of LoadExx. To gain market power and traction, Arora and his team came up with the idea of market expansion. However, the current conundrum was whether LoadExx would enter the B2C segment in its current location or expand with the same business model to other parts of the country. The expansion was to be implemented in the immediate future to retain its rarity and reduce the imitability of the business model of LoadExx. This case study details the logistics and market operations of the cargo sector, especially electric cargo, in a developing economy, especially India. A teaching note supplementing the “Cracking the conundrum of e-cargo logistics: curious case of LoadExx” case study has been provided.

This case study is designed for undergraduate and postgraduate students and senior management professionals in executive education programmes undertaking courses in logistics management and supply chain operations and related cargo logistics courses. This case study denotes integrating key processes from end-users and gaining the trust of drivers, thereby showing the perspective of the plight and conundrums of a cargo aggregator working in the B2C segment. This case study could be used to discuss concepts related to not-for-profit firms, aggregators, policymakers and think tanks.

Teaching notes are available for educators only.

CSS 9: Operations and logistics.

Supporting distant teams is a frequent phenomenon. Through the lens of the social identity theory, this research aims to examine differences between local and distant fans regarding drivers of team identification.

A multigroup structural equation model was employed. The data were collected through an online survey with 1,285 sports fans.

Team distinctiveness constitutes an important aspect fueling identification for all fans, whereas congruence between own and team personality is important for local and displaced fans only. Team prestige does not impact identification for either group.

To build up a base of highly identified supporters, clubs should emphasize those aspects of team brands that fans consider distinctive. When targeting local fans, clubs should also focus on communicating the brand's unique personality aspects.

This is the first study that assesses the potential differences behind fans' social identification with local and distant teams.

The purpose of this study is to analyze the horizontal expansion of Burayu Town between 1990 and 2020. The study typically acts as a baseline for integrated spatial planning in small- and medium-sized towns, which will help to plan sustainable utilization of land.

Landsat5-TM, Landsat7 ETM+, Landsat5 TM and Landsat8 OLI were used in the study, along with other auxiliary data. The LULC map classifications were generated using the Random Forest Package from the Comprehensive R Archive Network. Post-classification, spatial metrics, and per capita land consumption rate were used to understand the manner and rate of expansion of Burayu Town. Focus group discussions and key informant interviews were also used to validate land use classes through triangulation.

The study found that the built-up area was the most dynamic LULC category (85.1%) as it increased by over 4,000 ha between 1990 and 2020. Furthermore, population increase did not result in density increase as per capita land consumption increased from 0.024 to 0.040 during the same period.

As a result of financial limitations, there were no high-resolution satellite images available, making it challenging to pinpoint the truth as it is on the ground. Including senior citizens in the study region allowed this study to overcome these restrictions and detect every type of land use and cover.

Data on urban growth are useful for planning land uses, estimating growth rates and advising the government on how best to use land. This can be achieved by monitoring and reviewing development plans using satellite imaging data and GIS tools.

The use of Random Forest for image classification and the employment of local knowledge to validate the accuracy of land cover classification is a novel approach to properly customize remote sensing applications.

This case study has been developed based on the primary data obtained through a series of interviews held with the senior management of Cargills, and the secondary data obtained from the company’s corporate website www.cargillsceylon.com/,annual reports and publicly available sources of information such as newspaper articles.

This case study focuses on the strategic responses employed by Cargills (Ceylon) PLC – a leading business conglomerate in Sri Lanka – in response to the challenges posed by the COVID-19 pandemic. The duration of this case study is from January 2020 to September 2021. The case study particularly examines the key business sectors of Cargills (Ceylon) PLC – retail, food manufacturing and quick service restaurants – which elaborate on the change management practices and strategies deployed by the company in each of these sectors during this challenging period. This study is based on the primary data gathered from the interviews held with the Cargills (Ceylon) PLC team, and the secondary data obtained from the corporate website of Cargills (Ceylon) PLC. This case study is most suitable to be taught in academic courses related to strategic change management.

The case is most suited to be discussed with undergraduates (3rd year and 4th year) following business and management studies related disciplines. While the pivotal area around which the case has been developed is strategic change management, covering environmental analysis, strategic analysis and process of change management, the case could also be used in strategic management classes, to discuss environmental analysis, strategic planning approaches and business and corporate level strategies.

CSS 11: Strategy.

Although the concept of destination is often described as an economic term that describes places of interest for tourists and visitors, currently, there is still little awareness in the extant literature about regional, city, village, resort, or even standalone tourist destinations. This chapter aims to clarify the meaning of destinations. It distinguishes the differences between common locations and tourist destinations. It uses case studies to describe places, placemaking, and the experiencescapes of various destinations. This contribution implies that tourist attractions differentiate themselves from other places, as they offer accessible attractions with amenities.

To be able to continuously provide affordable services to consumers, managers of critical infrastructure (CI) maintenance supply networks have to balance investments in resilience with costs. At the same time, CI providers need to consider factors that influence resilience such as the geographical spread or the location of the network. This study aims to contextualize supply chain resilience knowledge by exploring how maintenance resource configurations impact resilience and costs in CI supply networks.

An in-depth longitudinal single case study of a representative CI provider that has centralized its maintenance supply network is used. Data were collected before and after the change to evaluate the effect of the changes on the maintenance supply network.

This study shows that in this specific CI maintenance context, structural resource choices such as the quantity or location of spare parts and tools, the creation and exploitation of tacit knowledge and staff motivation impact both resilience and costs due to geographical spread, network location and other network properties.

This study extends general supply chain resilience knowledge to a new setting (i.e. CI) and shows how existing insights apply in this context. More specifically, it is shown that even in engineered supply networks there is a need to consider the effect of human agency on resilience as the creation and exploitation of tacit knowledge are of immense importance in managing the network. In addition, the relationship between normal accidents theory and high reliability theory (HRT) is revisited as findings indicate that HRT is also important after a disruption has taken place.