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Accurately estimating the severity of derailment is a crucial step in quantifying train derailment consequences and, thereby, mitigating its impacts. The purpose of this paper is to propose a simplified approach aimed at addressing this research gap by developing a physics-informed 1-D model. The model is used to simulate train dynamics through a time-stepping algorithm, incorporating derailment data after the point of derailment.

In this study, a simplified approach is adopted that applies a 1-D kinematic analysis with data obtained from various derailments. These include the length and weight of the rail cars behind the point of derailment, the train braking effects, derailment blockage forces, the grade of the track and the train rolling and aerodynamic resistance. Since train braking/blockage effects and derailment blockage forces are not always available for historical or potential train derailment, it is also necessary to fit the historical data and find optimal parameters to estimate these two variables. Using these fitted parameters, a detailed comparison can be performed between the physics-informed 1-D model and previous statistical models to predict the derailment severity.

The results show that the proposed model outperforms the Truncated Geometric model (the latest statistical model used in prior research) in estimating derailment severity. The proposed model contributes to the understanding and prevention of train derailments and hazmat release consequences, offering improved accuracy for certain scenarios and train types

This paper presents a simplified physics-informed 1-D model, which could help understand the derailment mechanism and, thus, is expected to estimate train derailment severity more accurately for certain scenarios and train types compared with the latest statistical model. The performance of the braking response and the 1-D model is verified by comparing known ride-down profiles with estimated ones. This validation process ensures that both the braking response and the 1-D model accurately represent the expected behavior.

This article aims to investigate the impact of (1) the establishment of a refugee settlement, (2) the energy demand of a host and refugee population, (3) the residence time of refugees and (4) interventions in the energy sector on sustainable utilization of the forest.

Refugee movements from the Democratic Republic of Congo and settlement construction in a Zambian host society provide the setting. An agent-based model is developed. It uses survey data from 277 Zambian households, geographic information system coordinates and supplementary data inputs.

The future forest stock remains up to 30 years without an influx of refugees. Refugee developments completely deplete the forest over time. The settlement construction severely impacts the forest, while refugees' energy needs seem less significant. Compared with the repatriation of refugees, permanent integration has no influential impact on forest resources. Interventions in the energy sector through alternative sources slow down deforestation. Once a camp is constructed, tree cutting by hosts causes forest covers to decline even if alternative energy is provided.

The analysis is useful for comparable host–refugee settings and United Nations High Commissioner for Refugees interventions in settlement situations. Forest and energy sector interventions should involve host and refugee stakeholders.

This article adds value through an agent-based model in the Zambian deforestation–refugee context. The study has a pilot character within the United Nation's Comprehensive Refugee Response Framework. It fills a gap in long-term assessments of refugee presence in local host communities.

The purpose of this paper is to review the literature on logistics and supply chain costs to provide an analysis of sources of publication, citations and authorship using bibliometric analysis techniques (VOSviewer and CitNetExplorer tools).

A review of 756 articles published during the period 2014 to 2019 referenced in the Scopus database was performed. The review was limited to articles published in English and directly related to logistics and supply chain costs.

The research identified more than 2,000 authors representing more than 5,000 keywords and 10,000 references from a total of 155 journals investigated. A critical synthesis of the resulting data revealed several insights about various aspects of studies in this field. For instance, the review identified a scarcity of academic publications in three key areas, namely “supply chain,” “optimization” and “transportation”, which are concepts focused on the total supply chain.

This research highlights important areas of attention for both researchers and practitioners considering costs associated with logistics and supply chain operations and strategies. The results can also help identify thematic areas, journals and topics for future research. The paper identifies and proposes research areas to contribute to the literature when challenges to investigating logistics and supply chain costs are discussed.

As a crucial institutional form established since the Chinese economic reform, the system of competitive local governments has been shaping the characteristics of China's socialist market economy to a considerable degree.

This study not only adopts the view of existing studies that attribute the economic motive of local governments to rent and consider land public finance as a means through which local governments carry out strategic investment but also attempts to further develop the view within a Marxist analytical framework.

As a result, the local governments have helped to maintain an incredibly high investment rate over a considerable period of time, facilitating the continuous, rapid growth of the Chinese economy.

This study concludes that China's local governments function as the productive allocator and user of rent in the strategic investment based on land public finance and thereby embed themselves in the relative surplus-value production initially arising from competition amongst enterprises, forming the dual structure of relative surplus-value production unique to China's economy.

Covid-19 has caused many businesses to rethink their short- and potentially long-term workforce operations. The use of lateral flow serology can provide a clinically convenient approach for the assessment of prior infection with Covid-19. However, its widespread adoption in organisations seeking to use it to test for workforce immunity is controversial and confusing. This paper aims to explore the paradoxical dilemmas and dialectics immunity workforce testing creates.

This study involved capturing the ethnographical participation of a chief executive officer (CEO) dealing with the experience of managing the outcomes of Covid-19 workforce immunity testing. The aim was to take a snapshot in time of the CEO's empirical world, capturing their lived experiences to explore how management actions resulting from Covid-19 immunity testing can played out.

Providing staff with immunity tests at first glance appears sensible, decent and a caring action to take. Nevertheless, once such knowledge is personalised by employees, they can, through dialectic dialogue, feel disadvantaged and harbour feelings of unfairness. Subsequently, this paper suggests that immunity testing may only serve to raise awareness and deepen the original management dilemma of whether testing is a worthwhile activity.

This paper aims to be amongst the first works to empirically explore the workforce management challenges that arise within small businesses within the service sector following the completion of Covid-19 immunity testing of their staff. It seeks to achieve this via utilising the robust theoretical framework of the paradox theory to examine Covid-19's impact upon small business workforce management thinking and practice.

Several factors may play critical roles in alterations to product quality during storage of hog plum juice. This study aims to evaluate variations to physicochemical, antioxidant, anti-nutritional properties and microbial stability of hog plum juice during storage.

Juice was produced from hog plum fruits and stored for eight weeks at refrigerated and ambient conditions. Physicochemical, antioxidant properties, antinutritional factors and microbial properties of juices were determined using standard procedures

Degradation of ascorbic acid was higher in juices stored at ambient conditions (64.4%) compared to those stored by refrigeration (44.4%). Trends were similar for total phenolic, total flavonoid and total carotenoid contents. Total phenolic, total carotenoid and lycopene contents of fresh juice were 3.9 mg GAE/mL, 4.0 mg/mL and 1.3 mg/mL, which were not significantly different (at p = 0.08, 0.07 and 0.08, respectively) from the values at two weeks of storage at refrigerated conditions (3.9 mg GAE/mL, 3.9 mg/mL and 1.3 mg/mL). A sharp decrease of more than 40% (p = 0.02) in lycopene was recorded after four weeks, irrespective of storage temperature. Pasteurized hog plum juice showed no microbial growth until after four weeks of refrigerated storage when 1 CFU/mL each of bacterial and fungal growth were recorded. The juices, however, showed higher susceptibility to fungal growth as storage period increased.

Other variables not considered in this study such as nature of packaging materials may have significantly contributed to the observed data set. Further studies may, therefore, widen the scope of discussion to evaluate the associated relationship of these variables. Hog plum juice retained a considerable amount of bioactive components during refrigerated storage, which makes it a viable nutraceutical drink with industrial potentials and possible positive health implications for consumers.

This study provides new information that support the possible classification and use of hog plum juice as a safe functional beverage for human consumption.

Although the effect of storage temperature was significant in most of the properties studied, storage duration seems to have a greater influence on the stability of quality parameters during the storage of hog plum juice.

Dynamic advertising, including television and online video ads, demands new theory and tools developed to understand attention to moving stimuli. The purpose of this study is to empirically test the predictions of a new dynamic attention theory, Dynamic Human-Centred Communication Systems Theory, versus the predictions of salience theory.

An eye-tracking study used a sample of consumers to measure visual attention to potential areas of interest (AOIs) in a random selection of unfamiliar video ads. An eye-tracking software feature called intelligent bounding boxes (IBBs) was used to track attention to moving AOIs. AOIs were coded for the presence of static salience variables (size, brightness, colour and clutter) and dynamic attention theory dimensions (imminence, motivational relevance, task relevance and stability).

Static salience variables contributed 90% of explained variance in fixation and 57% in fixation duration. However, the data further supported the three-way interaction uniquely predicted by dynamic attention theory: between imminence (central vs peripheral), relevance (motivational or task relevant vs not) and stability (fleeting vs stable). The findings of this study indicate that viewers treat dynamic stimuli like real life, paying less attention to central, relevant and stable AOIs, which are available across time and space in the environment and so do not need to be memorised.

Despite the limitations of small samples of consumers and video ads, the results of this study demonstrate the potential of two relatively recent innovations, which have received limited emphasis in the marketing literature: dynamic attention theory and IBBs.

This study documents what does and does not attract attention to video advertising. What gets attention according to salience theory (e.g. central location) may not always get attention in dynamic advertising because of the effects of relevance and stability. To better understand how to execute video advertising to direct and retain attention to important AOIs, advertisers and advertising researchers are encouraged to use IBBs.

This study makes two original contributions: to marketing theory, by showing how dynamic attention theory can predict attention to video advertising better than salience theory, and to marketing research, showing the utility of tracking visual attention to moving objects in video advertising with IBBs, which appear underutilised in advertising research.

Modern meat processing requires automation and robotisation to remain sustainable and adapt to future challenges, including those brought by global infection events. Automation of all or many processes is seen as the way forward, with robots performing various tasks instead of people. Meat cutting is one of these tasks. Smart novel solutions, including smart knives, are required, with the smart knife being able to analyse and predict the meat it cuts. This paper aims to review technologies with the potential to be used as a so-called “smart knife” The criteria for a smart knife are also defined.

This paper reviews various technologies that can be used, either alone or in combination, for developing a future smart knife for robotic meat cutting, with possibilities for their integration into automatic meat processing. Optical methods, Near Infra-Red spectroscopy, electrical impedance spectroscopy, force sensing and electromagnetic wave-based sensing approaches are assessed against the defined criteria for a smart knife.

Optical methods are well established for meat quality and composition characterisation but lack speed and robustness for real-time use as part of a cutting tool. Combining these methods with artificial intelligence (AI) could improve the performance. Methods, such as electrical impedance measurements and rapid evaporative ionisation mass spectrometry, are invasive and not suitable in meat processing since they damage the meat. One attractive option is using athermal electromagnetic waves, although no commercially developed solutions exist that are readily adaptable to produce a smart knife with proven functionality, robustness or reliability.

This paper critically reviews and assesses a range of sensing technologies with very specific requirements: to be compatible with robotic assisted cutting in the meat industry. The concept of a smart knife that can benefit from these technologies to provide a real-time “feeling feedback” to the robot is at the centre of the discussion.

This paper aims to examine and overcome the barriers to the widespread adoption of blockchain technology, introducing a novel concept of sustainability in the fashion supply chain.

This work is an exploratory study of a well-known fashion company operating in the Veneto region (Italy). Data extracted from interviews and focus groups are coded using the (CAQDAS) software AQUAD. The outcome is then organized according to an adapted TOE view.

This exploration study's findings support the idea that the blockchain solution could be a valuable add-on in sustainable supply chains. However, a high understanding of technology and extensive communication with clients is required for successful integration.

Being the outcome of qualitative analysis, the findings require further investigation to be inferable at a broader scale. As the project is still incomplete, some managerial choices are always subject to change.

Focused on a practitioner approach, this paper should guide managers in the process of successfully implementing blockchain technology. Arguably, similar companies may opt for similar choices.

To the best of the authors' knowledge, this is the first paper to contextualize and address the blockchain adoption barriers in the fashion supply chain. Furthermore, it offers an overview of how blockchain affects sustainable production.

By extending Edmans et al.’s (2021) music sentiment measures to the Vietnam market, the authors aim to investigate the impacts of music sentiment on stock market returns and volatility.

The authors adopted Edmans et al.’s (2021) music-based sentiment to proxy for investor mood. The current study uses linear regression analysis.

The authors find that music sentiment is significantly and positively related to both stock returns and stock market volatility. The authors also show that music sentiment has a contagious effect: Global music sentiment and those in the United States, France and Hong Kong are significant drivers of the Vietnamese stock market. The authors also examine the effect on different industry returns and find that returns on stocks of firms in the communication services, consumer discretionary, consumer staples, energy, financials, healthcare, real-estate, information technology and utility sectors are significantly related to music sentiment. In addition to valence, the authors find that other Spotify audio features can be used to quantify music sentiment.

This study contributes to the behavioral finance literature that focuses on investor sentiment. The authors address this topic in Vietnam using high-frequency data.