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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.

This paper aims to examine the processes of sustainability reporting assurance (SRA) and the influence they have on shaping perception from disclosures. Given the evidence of inconsistencies and ambiguities in assurance processes, this paper examines how legitimacy is attained and maintained at different stages of SRA.

Evidence collected from 23 semi-structured interviews with assurance providers (APs), consultants, professionals and non-governmental organisations (NGOs) (non-APs) was used to conduct a thematic analysis from the perspectives of interviewees.

APs and non-APs are united in recognising the value of SRA, although, perspectives on transparency between the two groups differ. Experience and industry knowledge are essential to SRA delivery with non-APs preferring accounting APs. Nevertheless, non-APs are concerned about the role of companies in deciding assurance scope, as it can affect scrutiny. APs favour data accuracy (as opposed to data relevance) assurance due to team dynamics and internal review influences, with the latter also restricting assurance innovation. APs are interested in accessing better evidence and stakeholder engagement evaluations. Providing advisory services was not rejected by all APs. The perspectives of APs and non-APs demonstrate how progress in SRA has gained pragmatic legitimacy with noticeable gaps that serve to undermine attainment of moral legitimacy.

SRA is a developing practice that will adopt changes as it continues to mature; some of these changes could impact findings in this research. General perspectives on SRA were sought from interviewees, this affected the ability for an in-depth focus on any of the range of interesting SRA issues that arose over the course of the research. Interviews were conducted with relevant parties in the SRA space that operate in the UK. Perspectives from parties outside the UK were not solicited.

Companies make an important decision to commission SRA. Findings in this research have highlighted specific non-APs issues of concern that can be useful in structuring operations and reporting regimes to facilitate assurance procedures. The findings will also be helpful to APs as they can direct more emphasis on stakeholder concerns towards demonstrating greater stakeholder accountability. Regulatory and standard setters can enact appropriate policies that can potentially drive the practice forward for assessment of cognitive legitimacy.

The findings provide relevant account of stakeholder voices on the quality of corporate disclosures that has a direct effect on the wellbeing of communities and sustainability of societies. Collective stakeholder input on expectations can shape sustainability discourse.

This research demonstrates the applicability of financial audit quality indicators in SRA processes, extends the debate around the effectiveness of new audit fields and highlights the challenges of maintaining legitimacy with different audiences.

Material extrusion-based additive manufacturing is a prominent manufacturing technique to fabricate complex geometrical three-dimensional (3D) parts. Despite the indisputable advantages of material extrusion-based technique, the poor surface and subsurface integrity hinder the industrial application of this technology. The purpose of this study is introducing the hot air jet treatment (HAJ) technique for surface treatment of additive manufactured parts.

In the presented research, novel theoretical formulation and finite element models are developed to study and model the polishing mechanism of printed parts surface through the HAJ technique. The model correlates reflow material volume, layer width and layer height. The reflow material volume is a function of treatment temperature, treatment velocity and HAJ velocity. The values of reflow material volume are obtained through the finite element modeling model due to the complexity of the interactions between thermal and mechanical phenomena. The theoretical model presumptions are validated through experiments, and the results show that the treatment parameters have a significant impact on the surface characteristics, hardness and dimensional variations of the treated surface.

The results demonstrate that the average value of error between the calculated theoretical results and experimental results is 14.3%. Meanwhile, the 3D plots of Ra and Rq revealed that the maximum values of Ra and Rq reduction percentages at 255°C, 270°C, 285°C and 300°C treatment temperatures are (35.9%, 33.9%), (77.6%,76.4%), (94%, 93.8%) and (85.1%, 84%), respectively. The scanning electron microscope results illustrate three different treatment zones and the treatment-induced and manufacturing-induced entrapped air relief phenomenon. The measured results of hardness variation percentages and dimensional deviation percentages at different regimes are (8.33%, 0.19%), (10.55%, 0.31%) and (−0.27%, 0.34%), respectively.

While some studies have investigated the effect of the HAJ process on the structural integrity of manufactured items, there is a dearth of research on the underlying treatment mechanism, the integrity of the treated surface and the subsurface characteristics of the treated surface.

Reducing the number of generated mutants by clustering redundant mutants, reducing the execution time by decreasing the number of generated mutants and reducing the cost of mutation testing are the main goals of this study.

In this study, a method is suggested to identify and prone the redundant mutants. In the method, first, the program source code is analyzed by the developed parser to filter out the effectless instructions; then the remaining instructions are mutated by the standard mutation operators. The single-line mutants are partially executed by the developed instruction evaluator. Next, a clustering method is used to group the single-line mutants with the same results. There is only one complete run per cluster.

The results of experiments on the Java benchmarks indicate that the proposed method causes a 53.51 per cent reduction in the number of mutants and a 57.64 per cent time reduction compared to similar experiments in the MuJava and MuClipse tools.

Developing a classifier that takes the source code of the program and classifies the programs' instructions into effective and effectless classes using a dependency graph; filtering out the effectless instructions reduces the total number of mutants generated; Developing and implementing an instruction parser and instruction-level mutant generator for Java programs; the mutant generator takes instruction in the original program as a string and generates its single-line mutants based on the standard mutation operators in MuJava; Developing a stack-based evaluator that takes an instruction (original or mutant) and the test data and evaluates its result without executing the whole program.

Prior research on meeting or beating earnings thresholds documents that firms with earnings momentum are awarded with valuation premiums. However, it is unclear from this strand of literature why this is the case. Therefore, this study aims to investigate the effects of time-varying earnings persistence on earnings momentum and their pricing effects.

This study exploits a firm that reports earnings momentum as research setting to examine whether earnings persistence is significantly higher for firms with consecutive earnings increases. In addition, it investigates a relation between earnings momentum and fundamentals-driven earnings persistence and estimates return associations of earnings momentum conditional on economic-based persistence of earnings.

The empirical evidence suggests that firms with earnings momentum reflect higher time-varying earnings persistence. It further reveals that longer duration of earnings momentum is associated with higher fundamentals-driven earnings persistence. More importantly, valuation premiums are exclusively assigned to earnings momentum determined by strong firm fundamentals, not momentum itself.

This study provides new empirical evidence that valuation premiums accrued to firms with earnings momentum are conditional on time-varying earnings persistence. The research implications are relevant to investors, regulators and auditors, as the results bring conclusions that earnings momentum reflects successful business models not poor accounting quality. This leads to a more complete view of earnings momentum and helps allocate resources when evaluating earnings-momentum firms.

Face is the most intuitive and representative feature at the individual level. Many studies show that beautiful faces help individuals and enterprises obtain economic benefits and form a high economic premium, but the discussion of their potential social value is insufficient. This study aims to focus on the impact of the personal characteristics of executives. It mainly analyzes the impact mechanism of CEO facial attractiveness on corporate social responsibility (CSR) decision-making, clarifying the social value of beauty from the perspective of CSR.

The authors use the regression model to analyze the panel data set, which was conducted by a sample of Chinese publicly listed firms from 2016 to 2018.

The study found that CEOs with high facial attractiveness are more active in fulfilling CSR, which can usually bring higher social benefits. CEOs with beautiful faces are prone to overconfidence, are optimistic about their ability and the future development of the enterprise and are more willing to increase their investment in CSR. CEO duality can positively regulate the positive correlation between a CEO’s facial attractiveness and CSR.

Based on the perspective of upper echelons theory, this paper explores the mechanism of CEO facial attractiveness on CSR. This study enriches the perspective of the upper echelon’s theoretical research and has essential enlightenment for CEO selection and training practice.

This study aims to explore how sustainable business models (SBM) can be developed within agri-innovation systems (AIS) and emphasize an integration of the two with a systemic understanding for reducing food loss and value loss in postharvest agri-food supply chain.

This study conducted longitudinal qualitative research in a developing country with food loss challenges in the postharvest supply chain. This study collected data through multiple rounds of fieldwork, interviews and focus groups over four years. Thematic analysis and “sensemaking” were used for inductive data analysis to generate rich contextual knowledge by drawing upon the lived realities of the agri-food supply chain actors.

First, this study finds that the value losses are varied in the supply chain, encompassing production value, intrinsic value, extrinsic value, market value, institutional value and future food value. This happens through two cumulative effects including multiplier losses, where losses in one model cascade into others, amplifying their impact and stacking losses, where the absence of data stacks or infrastructure pools hampers the realisation of food value. Thereafter, this study proposes four strategies for moving from the loss-incurring current business model to a networked SBM for mitigating losses. This emphasises the need to redefine ownership as stewardship, enable formal and informal beneficiary identification, strengthen value addition and build capacities for empowering communities to benefit from networked SBM with AIS initiatives. Finally, this study puts forth ten propositions for future research in aligning AIS with networked SBM.

This study contributes to understanding the interplay between AIS and SBM; emphasising the integration of the two to effectively address food loss challenges in the early stages of agri-food supply chains. The identified strategies and research propositions provide implications for researchers and practitioners seeking to accelerate sustainable practices for reducing food loss and waste in agri-food supply chains.

The Covid-19 pandemic appears to have engendered heterogeneous effects on individuals’ labour market prospects. This paper focuses on two possible sources of a heterogeneous exposition to labour market risks associated with the pandemic outbreak: the routine task content of the job and the teleworkability. To evaluate whether these dimensions played a crucial role in amplifying employment and wage gaps among workers, we focus on the case of Italy, the first EU country hit by Covid-19.

Investigating the actual effect of the pandemic on workers employed in jobs with a different degree of teleworkability and routinization, using real microdata, is currently unfeasible. This is because longitudinal datasets collecting annual earnings and the detailed information about occupations needed to capture a job’s routine task content and teleworkability are not presently available. To simulate changes in the wage distribution for the year 2020, we have employed a static microsimulation model. This model is built on data from the Statistics on Income and Living Conditions (IT-SILC) survey, which has been enriched with administrative data and aligned with monthly observed labour market dynamics by industries and regions.

We measure the degree of job teleworkability and routinization with the teleworkability index (TWA) built by Sostero et al. (2020) and the routine-task-intensity index (RTI) developed by Cirillo et al. (2021), respectively. We find that RTI and TWA are negatively and positively associated with wages, respectively, and they are correlated with higher (respectively lower) risks of a large labour income drop due to the pandemic. Our evidence suggests that labour market risks related to the pandemic – and the associated new types of earnings inequality that may derive – are shaped by various factors (including TWA and RTI) instead of by a single dimension. However, differences in income drop risks for workers in jobs with varying degrees of teleworkability and routinization largely reduce when income support measures are considered, thus suggesting that the redistributive effect of the emergency measures implemented by the Italian government was rather effective.

No studies have so far investigated the effect of the pandemic on workers employed in jobs with a different degree of routinization and teleworkability in Italy. We thus investigate whether income drop risks in Italy in 2020 – before and after income support measures – differed among workers whose jobs are characterized by a different degree of RTI and TWA.

This study aims to identify calendar anomalies that can affect stock returns and asymmetric volatility. Thus, the objective of this study is twofold: on the one hand, it examines the impact of calendar anomalies on the returns of both conventional and Islamic indices in Indonesia, and on the other hand, it analyzes the impact of these anomalies on return volatility and whether this impact differs between the two indices.

The authors apply the GJR-generalized autoregressive conditional heteroskedasticity model to daily data of the Jakarta Composite Index (JCI) and the Jakarta Islamic Index for the period ranging from October 6, 2000 to March 4, 2022.

The authors provide evidence that the turn-of-the-month (TOM) effect is present in both conventional and Islamic indices, whereas the January effect is present only for the conventional index and the Monday effect is present only for the Islamic index. The month of Ramadan exhibits a positive effect for the Islamic index and a negative effect for the conventional index. Conversely, the crisis effect seems to be the same for the two indices. Overall, the results suggest that the impact of market anomalies on returns and volatility differs significantly between conventional and Islamic indices.

This study provides useful information for understanding the characteristics of the Indonesian stock market and can help investors to make their choice between Islamic and conventional equities. Given the presence of some calendar anomalies in the Indonesia stock market, investors could obtain abnormal returns by optimizing an investment strategy based on seasonal return patterns. Regarding the day-of-the-week effect, it is found that Friday’s mean returns are the highest among the weekdays for both indices which implies that investors in the Indonesian stock market should trade more on Fridays. Similarly, the TOM effect is significantly positive for both indices, suggesting that for investors are called to concentrate their transactions from the last day of the month to the fourth day of the following month. The January effect is positive and statistically significant only for the conventional index (JCI) which implies that it is more beneficial for investors to invest only in conventional assets. In contrast, it seems that it is more advantageous for investors to invest only in Islamic assets during Ramadan. In addition, the findings reveal that the two indices exhibit lower returns and higher volatility, which implies that it is recommended for investors to find other assets that can serve as a safe refuge during turbulent periods. Overall, the existence of these calendar anomalies implies that policymakers are called to implement the required measures to increase market efficiency.

The existing literature on calendar anomalies is abundant, but it is mostly focused on conventional stocks and has not been sufficiently extended to address the presence of these anomalies in Shariah-compliant stocks. To the best of the authors’ knowledge, no study to date has examined the presence of calendar anomalies and asymmetric volatility in both Islamic and conventional stock indices in Indonesia.

Since conducting agile strategies provides sustainable passenger satisfaction and revenue by replacing applied policies with more profitable ones rapidly, the focus of this study is to evaluate agile attributes for managing low-cost carriers (LCCs) operations by means of resources and competences based on dynamic capabilities built on resource-based view (RBV) theory and to achieve sustainable competitive advantage in a volatile and dynamic air transport environment. LCCs in Turkey are also evaluated in this study since the competition among LCCs is high to gain market share and they can adapt quickly to all kinds of circumstances.

Two well-known Multi-Criteria Decision-Making Methods (MCDM) named as the Stepwise Weight Assessment Ratio Analysis (SWARA) and multi-attributive border approximation area comparison (MABAC) methods by employing Picture fuzzy sets (PiFS) are employed to determine weight of agile attributes and superiority of LCCs based on agile attributes in the market, respectively. To check the consistency and robustness of the results for the proposed approach, comparative and sensitivity analysis are performed at the end of the study.

While the ranking orders of agile attributes are Strategic Responsiveness (AG1), Financial Management (AG4), Quality (AG2), Digital integration (AG3) and Reliability (AG5), respectively, LCC2 is selected as the best agile airline company in Turkey with respect to agile attributes. SWARA and MABAC method based on PiFS is appropriate and effective method to evaluate agile attributes that has important reference value for the airline companies in aviation industry.

The findings of this study will support managers in the airline industry to conduct airline operations more flexibly and effectively to take sustainable competitive advantage in unexpected and dynamic environment.

To the author' best knowledge, this study is the first developed to identify the attributes necessary to increase agility in LCCs. Thus, as a systematic tool, a framework is developed for the implementation of agile attributes to achieve sustainable competitive advantage in the airline industry and presented a roadmap for airline managers to deal with crises and challenging situations by satisfying customer and increasing competitiveness.