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The purpose of this study is to quantify the impact of electricity power outages on the local housing market in South Africa.

This study uses the autoregressive distributive lag (ARDL) and quantile autoregressive distributive lag (QARDL) models on annual time series data, for the period 1971–2014. The interest rate, real income and inflation were used as control variables to enable a multivariate framework.

The results from the ARDL model show that real income is the only factor influencing housing price over the long run, whereas other variables only have short-run effects. The estimates from the QARDL further reveal hidden cointegration relationship over the long run with higher quantile levels of distribution and transmission losses raising the residential price growth.

Overall, the findings of this study imply that the South African housing market is more vulnerable to property devaluation caused by power outages over the short run and yet remains resilient to loadshedding over the long run. Other macro-economic factors, such as real income and inflation, are more influential factors towards long-run developments in the residential market.

To the best of the authors’ knowledge, this is the first study to examine the empirical relationship between power outages and housing price growth.

This research aims to analyse the perceptions of practitioners in three regions regarding the challenges faced by their firms during the pandemic, considered a black-swan event. It examines the strategies implemented to mitigate and recover from risks, evaluates the effectiveness of these strategies and assesses the difficulties encountered in their implementation.

In the summer of 2022, an online survey was conducted among supply chain (SC) practitioners in France, Poland and the St. Louis, Missouri region of the USA. The survey aimed to understand the impact of COVID-19 on their firms and the SC strategies employed to sustain operations. These regions were selected due to their varying levels of SC development, including infrastructure, economic resources and expertise. Moreover, they exhibited different responses in safeguarding the well-being of their citizens during the pandemic.

The study reveals consistent perceptions among practitioners from the three regions regarding the impact of COVID-19 on SCs. Their actions to enhance SC resilience primarily relied on strengthening collaborative efforts within their firms and SCs, thus validating the tenets of the relational view.

COVID-19 is (hopefully) our black-swan pandemic occurrence during our lifetime. Nevertheless, the lessons learned from it can inform future SC risk management practices, particularly in dealing with rare crises. During times of crisis, leveraging existing SC structures may prove more effective and efficient than developing new ones. These findings underscore the significance of relationships in ensuring SC resilience.

The purpose of this article is to make a contribution to the existing knowledge by using the unique cross-jurisdiction data drawn from the FCA’s REP-CRIM submissions to explore dynamics behind firms’ perceptions on financial crime. Capturing firm’s sentiment is notoriously challenging, and any relevant regulatory data is usually not available in the public domain. A recent exception is the UK Financial Conduct Authority’s (FCA’s) financial crime data return (REP-CRIM) submissions which include the cross-country regulatory data on the UK financial institutions’ perceptions of jurisdiction risk. Despite a broad literature with respect to financial crime, there exists an important gap in the existing knowledge with respect to factors that are associated with the perceptions of firms with respect to jurisdiction risk, which this article aims to close.

Using cross-country regulatory data on the UK financial institutions’ perceptions of jurisdiction risk, this study empirically determines that perceptions of jurisdiction risk is significantly and positively associated with anti-money laundering and countering the financing of terrorism (AML/CFT) framework, as well as with tax burden on business and institutional and legal risk in the case of 165 jurisdictions.

The findings lend support to the proposition that unsystematic efforts and too much publicity may ascertain the high-risk image of a jurisdiction, deterring cross-border business. Policy implications that emerge from the study also add to the case for strengthening institutional and legal frameworks, as well as relieving the tax burden on doing business.

Findings of the present study should be interpreted with caution, as the dependent variable used in the present study reflects UK firms’ perceptions of jurisdiction risk, which may depend on various factors such as different risk appetites and the countries in which firms carry out business, and not necessarily the actual level of risks based on financial crime statistics. For example, a jurisdiction which may indeed be considered high risk, would not necessarily be ranking high on the FCA’s list of UK firms’ jurisdiction risk perceptions due to few firms operating in that particular country. As a result, the list could differ from the Financial Action Task Force’s black and grey lists. Findings based on the regulatory data on the UK financial institutions’ perceptions of jurisdiction risk should be considered preliminary in nature, given that they are based on a single year cross sectional data. As global and country-level AML/CFT efforts continue to intensify and as more regulatory data becomes publicly available, it would be imperative to bring further empirical evidence to bear on the question of whether financial crime perceptions are likely to be more pronounced for jurisdictions where AML/CFT efforts are more intensified. Likewise, from a policy standpoint, it would be equally important to explore further the role that institutional and legal risk, as well as tax burden on businesses, play in shaping firms’ perceptions of jurisdiction risk.

Findings lend support to the proposition that unsystematic efforts and too much publicity may ascertain the high-risk image of a jurisdiction, deterring cross-border business. Therefore, rather than waiting for more data to be made available by other financial regulators, which could lead to a more conclusive evidence in the future, on balance, the findings of this study add to the case for carefully designing and systematically implementing AML/CFT measures in a less publicized manner. Findings lend support to the theoretical postulation that disorderly efforts and undue publicity regarding AML/CFT efforts serve to ascertain the high-risk image of a jurisdiction, which could deter cross-border business and could be detrimental to how firms undertake due diligence. They also suggest that disorderly implementation of AML/CFT measures may hinder access to formal financial service and jeopardize authorities’ ability to trace the movement of funds, which may also add to negative perceptions of jurisdiction risk.

Findings are in line with the theoretical expectations that perceptions of jurisdiction risk would be expected to be higher in countries with inadequate disclosure rules, lax regulation and opacity jurisdiction. Likewise, results are aligned with the expectations that tax burden on business would be expected to be in a positive relationship with jurisdiction risk, as it would increase the likelihood of tax evasion, which incentivizes financial crime. Therefore, policy implications that emerge from the study also add to the case for strengthening institutional and legal frameworks and relieving the tax burden on doing business as part of efforts to improve the international image of jurisdictions with respect to financial crime risks.

Using the cross-country regulatory data on the UK financial institutions’ perceptions of jurisdiction risk, this study has empirically determined that perceptions of jurisdiction risk is significantly and positively associated with AML/CFT framework, as well as with tax burden on business and institutional and legal risk. These findings have implications from a policy standpoint.

This study aims to uncover apple preference and consumption in Indonesia, to disclose the risk of non-halal contamination of apples and the importance of maintaining the halal integrity of apples along the supply chain and to uncover the impacts of food miles of apples along supply chain segmentation.

This study adopted mixed research methods under a fully mixed sequential dominant status design (QUAN → qual). Data were collected through a survey in some Indonesian provinces (N = 396 respondents). Samples were collected randomly from individual consumers. The qualitative data were collected through interviews with 15 apple traders in Indonesia. Data were analysed using crosstab, chi-square and descriptive analysis.

First, Muslim consumers believe in the risk of chemical treatment of apples because it can affect the halal status of apples. Second, Indonesian consumers consider the importance of halal certification of chemical-treated apples and the additives for apple treatments. Third, the insignificance of domestic apple preference contributes to longer food miles at the first- and middle-mile stages (preference for imported apples). Fourth, apple consumption and shopping distance contribute to the longer food miles problem at the last-mile stage. Fifth, longer food miles have negative impacts, such as emissions and pollution, food loss and waste, food insecurity, financial loss, slow development of the local economy and food unsafety.

This research has implications for the governments, farmers, consumers (society) and business sectors.

This study proposes a framework of food miles under a halal supply chain (halal food miles) to reduce the risk of food miles and improve halal integrity. The findings from this research have theoretical implications for the development of the food mile theory, halal food supply chain and green supply chain.

Static inverters are very important for the emergency energy distribution system of aircraft and similar machines. At the same time, the electrical energy produced at high frequency for electrical devices is used to reduce the weight of the cables in the aircraft and spacecraft because of the skin effect. In the high-frequency system, a thinner cable cross-section is used, and a great weight reduction occurs in the aircraft. So, fuel economy, less and late wear of the materials (landing gear, etc.) can be obtained with decreasing weight. This paper aims to present the development of a functional multilevel inverter (FMLI) with fractional sinus pulse width modulation (FSPWM) and a reduced number of switches to provide high-frequency and quality electrical energy conversion.

After the production of FSPWM for FMLI with a reduced component, which, to the best of the authors’ knowledge, is presented for the first time in this study, is explained step by step, and eight operating states are given according to different FSPWMs operating the circuit. The designed inverter and modulation technique are compared by testing the conventional modular multilevel inverter on different loads.

According to application results, it is seen that there is a 50% reduction in cross-section from 100 Hz to 400 Hz with the skin effect. At 1000 Hz, there is a 90% cross-section reduction. The decrease can be in cable weights that may occur in aircraft from 10 kg to 100 kg according to different frequencies. It causes less harmonic distortion than conventional converters. This supports the safer operation of the system. Compared to the traditional system, the proposed system provides more amplitude in converting the source to alternating voltage and increases the efficiency.

FSPWM is developed for multilevel inverters with reduced components at the high frequency and cascaded switching studies in the power electronics of aircraft.

Although the proposed system has less current and power loss as mentioned in the previous sections, it contains fewer power elements than conventional inverters that are equivalent for different hardware levels. This not only reduces the cost of the system but also provides ease of maintenance. To reduce the cable load in aircraft and create more efficient working conditions, 400 Hz alternative voltage is used. The proposed system causes less losses and lower harmonic distortions than traditional systems. This will reduce possible malfunctions and contribute to aircraft reliability for passengers and cargo. As technology develops, it is revealed that the proposed inverter system will be more efficient than traditional inverters when devices operating at frequencies higher than 400 Hz are used. With the proposed inverter, safer operation will be ensured, while there will be less energy loss, less fuel consumption and less carbon emissions to the environment.

The proposed inverter structure shows that it can provide energy transmission for electrical devices in space and aircraft by using the skin effect. It also contains less power elements than the traditional inverters, which are equivalent for different levels of hardware.

The paper aims to tackle the classification of Remote Sensing Images (RSIs), which presents a significant challenge for computer algorithms due to the inherent characteristics of clustered ground objects and noisy backgrounds. Recent research typically leverages larger volume models to achieve advanced performance. However, the operating environments of remote sensing commonly cannot provide unconstrained computational and storage resources. It requires lightweight algorithms with exceptional generalization capabilities.

This study introduces an efficient knowledge distillation (KD) method to build a lightweight yet precise convolutional neural network (CNN) classifier. This method also aims to substantially decrease the training time expenses commonly linked with traditional KD techniques. This approach entails extensive alterations to both the model training framework and the distillation process, each tailored to the unique characteristics of RSIs. In particular, this study establishes a robust ensemble teacher by independently training two CNN models using a customized, efficient training algorithm. Following this, this study modifies a KD loss function to mitigate the suppression of non-target category predictions, which are essential for capturing the inter- and intra-similarity of RSIs.

This study validated the student model, termed KD-enhanced network (KDE-Net), obtained through the KD process on three benchmark RSI data sets. The KDE-Net surpasses 42 other state-of-the-art methods in the literature published from 2020 to 2023. Compared to the top-ranked method’s performance on the challenging NWPU45 data set, KDE-Net demonstrated a noticeable 0.4% increase in overall accuracy with a significant 88% reduction in parameters. Meanwhile, this study’s reformed KD framework significantly enhances the knowledge transfer speed by at least three times.

This study illustrates that the logit-based KD technique can effectively develop lightweight CNN classifiers for RSI classification without substantial sacrifices in computation and storage costs. Compared to neural architecture search or other methods aiming to provide lightweight solutions, this study’s KDE-Net, based on the inherent characteristics of RSIs, is currently more efficient in constructing accurate yet lightweight classifiers for RSI classification.

This paper investigates the effect of state-society relations on the industrially-related growth paths of developed countries.

It introduces a novel theoretical framework, the state-business-labor relations (SBLR) framework, where four main actors are identified: the state, big businesspersons or tycoons, owners and managers of small and medium enterprises (SMEs) or Entrepreneurs and labor. Different SBLR categories or modes are introduced depending on levels of coordination and power relations between the studied actors. The paper then investigates how these SBLR modes, through adopting various policies targeting the industrial sector, lead to different growth paths. Rather than focusing only on economic growth, this research regards a growth path as a matrix of the performance in long-run growth and equality of distribution.

Using regression analysis and statistical data, the results suggest that the Co-Balanced mode, having higher levels of coordination and lower favoritism, leads to the best growth path among the four introduced modes, especially with its emphasis on high levels of venture capital availability and easiness of starting business. while the Lib-Capture mode, characterized by lower coordination and higher favoritism, seems to have the worst growth path and the best implemented policy for this mode is suggested to be high profit taxes that seem to counter the negative impact of the existing high levels of favoritism.

Despite the important findings that this research has reached, this paper is mainly meant to open a further investigation into this topic and open this dimension that the research on VoC and political economy have under-researched. A deeper investigation of SBLR typologies that could only be possible by having richer datasets with more data on coordination for the whole world, rather than only the advanced economies, would further our understanding of the dynamics that shape the growth paths of different countries of the world.

To realize the best industrial growth path, fighting favoritism should be an important objective. The negative impact of favoritism on innovation could not be disregarded in the eve of the fourth industrial revolution, where innovation is increasingly pivotal to future industrial development. Actively engaging societal groups in the policymaking process is important in addressing their concerns and balancing them at the same time. This should lead to the double benefit of formulating better policies that should foster growth as well as provide better distribution of this growth. High levels of coordination should help in realizing this objective. Yet, this could only be possible if societal groups are free to associate and aggregate their power and when there are means of preventing one actor from gaining more favorite treatment and exclusive influence over policymakers. The presence of both powerful and broadly represented business associations and labor unions and the existence of a government interested in coordinating their efforts-rather than letting itself be controlled by one group at the expense of the others-should help in the realization of the best growth path. Thus, institutional reform that empowers societal groups and enables them to defend their interests as well as fights all forms of corruption should lead to the realization of a more prosperous and equitable industrial development, with the “re-industrialization” of the developed world being no exception. The technological and social challenges of intensive automation and digitalization accompanying the fourth industrial revolution make the envisaged institutional reform more urgent.

This paper is introducing a novel theoretical framework for studying the effect of state-society relations, particularly SBLR, on the industrial growth paths of developed countries. It integrates three important bodies of literature in order to build a more comprehensive understanding of the dynamics of state-society relations and their economic consequences. These are the Varieties of Capitalism (VoC), State-Business Relations (SBR) and Industrial Relations. The SBLR framework differentiates between tycoons and entrepreneurs, an important distinction that often goes unnoticed. Different SBLR categories or modes are introduced, depending on levels of coordination and power relations between the actors. It is proposed in this research that the effect on growth paths goes beyond the simple dichotomy between CMEs and LMEs usually present in the literature of VoC and that power relations provide an essential complementary dimension in explaining this causality.

This paper aims to investigate how congruent keywords are used in information security policies (ISPs) to pinpoint and guide clear actionable advice and suggest a metric for measuring the quality of keyword use in ISPs.

A qualitative content analysis of 15 ISPs from public agencies in Sweden was conducted with the aid of Orange Data Mining Software. The authors extracted 890 sentences from these ISPs that included one or more of the analyzed keywords. These sentences were analyzed using the new metric – keyword loss of specificity – to assess to what extent the selected keywords were used for pinpointing and guiding actionable advice. Thus, the authors classified the extracted sentences as either actionable advice or other information, depending on the type of information conveyed.

The results show a significant keyword loss of specificity in relation to pieces of actionable advice in ISPs provided by Swedish public agencies. About two-thirds of the sentences in which the analyzed keywords were used focused on information other than actionable advice. Such dual use of keywords reduces the possibility of pinpointing and communicating clear, actionable advice.

The suggested metric provides a means to assess the quality of how keywords are used in ISPs for different purposes. The results show that more research is needed on how keywords are used in ISPs.

The authors recommended that ISP designers exercise caution when using keywords in ISPs and maintain coherency in their use of keywords. ISP designers can use the suggested metrics to assess the quality of actionable advice in their ISPs.

The keyword loss of specificity metric adds to the few quantitative metrics available to assess ISP quality. To the best of the authors’ knowledge, applying this metric is a first attempt to measure the quality of actionable advice in ISPs.

Pre-positioning and distributing relief items are important parts of disaster management as it simultaneously considers activities from both pre- and post-disaster stages. This study aims to address this problem with a novel mathematical model.

In this research, a bi-objective mixed-integer linear programming model is developed to tackle pre-positioning and distributing relief items, and it is formulated as an integrated location-allocation-routing problem with uncertain parameters. The humanitarian supply chain consists of relief facilities (RFs) and demand points (DPs). Perishable and imperishable relief commodities (RCs), different types of vehicles, different transportation modes, a time window for delivering perishable commodities and the occurrence of unmet demand are considered. A scenario-based game theory is applied for purchasing RCs from different suppliers and an integrated best-worst method-technique for order of preference by similarity to ideal solution technique is implemented to determine the importance of DPs. The proposed model is used to solve several random test problems for verification, and to validate the model, Iran’s flood in 2019 is investigated as a case study for which useful managerial insights are provided.

Managers can effectively adjust their preferences towards response time and total cost of the network and use sensitivity analysis results in their decisions.

The model locates RFs, allocates DPs to RFs in the pre-disaster stage, and determines the routing of RCs from RFs to DPs in the post-disaster stage with respect to minimizing total costs and response time of the humanitarian logistics network.