Risk Scenarios Research Articles

Bio-organic substitution in tobacco (Nicotiana tabacum L) cultivation: Optimum strategy to lower carbon footprint and boost net ecosystem economic benefit

The partial substitution of organic manure for chemical nitrogen fertilizers, known as organic substitution, is widely regarded as a cleaner and more sustainable production strategy. However, few studies have quantified greenhouse gas emissions, product income and net ecosystem economic benefit (NEEB) using a life cycle assessment (LCA) approach, particularly for typical tobacco (Nicotiana tabacum L.) production. Here, we quantified the yield and quality of a typical tobacco production in Qujing, Yunnan, China, through field experiments and calculated its carbon footprint and NEEB using the LCA approach. Four organic substitution strategies were established with equal nitrogen inputs, including synthesized chemical fertilizer (SN), farmyard organic manure (NF), commercial organic manure (NC), and bio-organic (Trichoderma viride Pers.) manure (NT), each substituting 15% of synthesized nitrogen fertilizer. Compared to the SN strategy, the NT strategy significantly increased yield and income by 10.3% and 9.6%, respectively. In contrast, the NF strategy significantly reduced income, while the NC strategy showed no significant difference. Both the NC and NT strategies significantly reduced N2O cumulative emissions (by 15.9% and 8.0%, respectively), increased δSOC (by 38.4% and 15.0%, respectively), and decreased carbon footprint compared to the SN strategy. However, the NF strategy significantly increased the income-scaled carbon footprint, even though it also notably reduced N2O cumulative emissions (by 22.6%) and increased δSOC (by 7.9%). The NT strategy achieved a win-win scenario of low environmental risk and high economic returns of tobacco production with significantly increased NEEB (by 10.6%) compared to the SN strategy (37.60 × 103 CNY yr−1). This suggests that the bio-organic Trichoderma manure substituting 15% synthesized nitrogen fertilizer is the best organic substitution strategy for sustainable tobacco production.

Evaluation of ground motion models for Northern Vietnam based on ground motion records of the 2020 Mw5.0 Moc-Chau earthquake sequence

It is challenging to select ground motion models (GMMs) for seismic hazard assessments for a region with sparse recorded data. In this study, data on the 2020 Mw5 Moc-Chau earthquake and its aftershocks were used to select an appropriate GMM for northern Vietnam (NVN). The 204 strong motion records were collected from 32 seismic stations and then used to compare eleven non-Vietnamese and two simplified Vietnamese local GMMs to assess their model prediction efficiencies. Among all the candidates, the global NGA-West2 GMMs performed the best fit with the data. Our analyses revealed the possibility of damage resulting from shaking in the Hanoi metropolitan area caused by recognized earthquake sources in NVN. In our examination of total residuals of differences between the GMM predictions and observed data, the average standard deviation from ASK14 was slightly higher than the limit accepted for modern seismic hazard assessments. ASK14 was further adjusted by the spatially varying coefficients that were derived from observations ground motion of this event. The adjusted ASK14 was used to evaluate seismic risk scenarios of large earthquakes in NVN and compared with the structures’ design spectra of the Hanoi area. To increase the prediction efficiency, additional local data are required to develop a region-specific GMM for NVN. We suggest that GMM be developed in the near future by regionalizing the ASK14 GMM according to additional local data further collected from existing broadband seismic observations and new accumulating continuous recording data from Vietnam’s broadband seismic networks.

Assessment of coastal flood risk scenarios on infrastructure in the Keta municipality in Ghana using a GIS approach

Coastal flooding and erosion, caused by climate change-induced sea level rise, pose significant threats to low-lying coastal areas worldwide. The African continent, including Ghana, has experienced severe impacts from these hazards, affecting the socio-economic development of coastal communities. This research focuses on the Keta municipality in Ghana. The Keta municipality is highly vulnerable to coastal flooding and erosion due to its low elevation and the construction of the Akosombo hydroelectric dam. This research aims to predict the impact of coastal flooding on infrastructure in the Keta municipality using different flood scenarios. It also fills the existing knowledge gap in understanding how future flood risk can impact on critical infrastructure and consequently on other vital socio-economic sectors in the coastal community of Keta. The study utilizes a Geographic Information System (GIS)-based approach, combining various datasets, including topographic data, administrative shapefiles, field survey points, and infrastructure data. Two flood scenarios were considered to assess areas at risk: a 2.5-m sea level flood scenario representing potential climate change events and a worst-case 5-m sea level flood scenario. The findings reveal that 3.3 km2 (9 %) of coastal land area, 3.9 km of roads, and 69 structures are at risk under a 2.5m sea level rise scenario, increasing to 7.1 km2 (19.4 %), 13.6 km of roads, and 667 structures under a 5m scenario. The study highlights the urgency of addressing these risks to protect coastal communities' socio-economic development and livelihoods. Recommendations include implementing appropriate coastal management strategies, improving infrastructure resilience, and promoting sustainable land-use practices. By understanding and addressing the future impact of coastal flooding, decision-makers can mitigate climate change's adverse effects on coastal areas and ensure the region's long-term sustainability.

Presentación del Volumen 5 Número 3 - Octubre 2024 - enero 2025

Latest issue of Volume 5 of the journal, which highlights aspects of national security, reaffirming that in Peru there are multiple dimensions of the national reality in which risk scenarios develop. The national peculiarity lies in the increase of problems –in the internal sphere– that affect national security and development, which leads to maintaining the diversity of studies and research that highlight the interdependence of security and development, but does not neglect the analysis of the advance of war, as the case of the use of drones, mercenaries and nuclear plants in the conflict between the Russian Federation and Ukraine. In the anniversary month of CAEN-EPG, its important role in benefiting proposals to optimize our national security, overcoming the adversaries of the Homeland, is highlighted.

One health and social research in disease ecology: A social contextual study of vector-borne diseases in the Yucatan Peninsula, Mexico

Although disease ecology recognizes the multiplicity of factors involved in the (re)emergence of vector-borne diseases (VBD), it is necessary to strengthen attention to the social context and the social determinants of health. It is essential to integrate a sociocultural approach to health into the biological analysis of VBD ecology. To implement a coherent One Health approach in the prevention and control of VBD, it is pivotal to first understand the social and ecological interactions of the local context. Between October 2021 and June 2022, a social contextual study was made through surveys, semi-structured interviews and participatory activities in 12 localities with diverse ecological, economic and socio-cultural contexts in the Yucatan Peninsula. Through the perceptions of the populations on health and VBD, specifically mosquitoes, we gained knowledge on socio-cultural dynamics that influence people's relationship with pathogens. Local knowledge, management, and control of vectors; interactions with domestic and wild animals; and health-landscape relationship, were identified as factors that determine health-disease processes. This study contributed to a better understanding of local contexts and therefore, to the design of socially pertinent strategies for the reduction of vector-borne diseases risk scenarios in the study sites with an integrated approach.

Developing Methods for Assessing the Impact of Digital Innovations on the Economic Efficiency of Enterprises: Methodology for Measuring ROI of Digital Projects

This study aims to develop methodologies for assessing the return on investment (ROI) of digital projects, crucial for understanding the economic impact of digital innovations on enterprises. Traditional ROI metrics often fail to capture the comprehensive value generated by digital projects. The proposed integrative framework employs advanced analytical methods, including cost-benefit analysis, real options analysis, and predictive analytics, to evaluate both tangible and intangible benefits. It incorporates dynamic risk management and scenario planning to address uncertainties and future challenges. Continuous feedback and iterative improvements ensure the framework's accuracy and relevance, enabling enterprises to optimize economic efficiency and strategic decision-making. This holistic approach offers an understanding of the multifaceted value of digital innovations.

Burj Rashid: a tale of two tides – rising waters and changing traditions

PurposeThis paper delves into the traditional ecological knowledge (TEK) and practices of Burj Rashid, an ancient historical city on Egypt’s northern coast, which stands at the meeting point of the Nile’s western branch and the Mediterranean Sea. Burj Rashid boasts a strategic location and rich natural resources and has a long history of relationships between land, people, river, sea and climate change, serving as a model for residents’ adaptation to their ever-changing surroundings.Design/methodology/approachClimate studies have exposed the village’s vulnerability to climate and topographical hazards such as rising temperatures, shifting weather patterns, decreasing precipitation, encroaching seas due to sea level rise, coastal erosion and high soil salinization. These factors pose a high risk of water scarcity, crop failure in the medium term, potential famine in the long term and declining fish populations, threatening fishing communities. To address these challenges, the Net Zero: Heritage for Climate Action project - launched by the International Centre for the Study of the Preservation and Restoration of Cultural Property (ICCROM) and the First Aid and Resilience for Culture in Times of Crisis program, funded by Swedish Postcode - proposes a research and development methodology through a platform that weaves together heritage knowledge and climate science. The Egyptian Heritage Rescue Foundation has implemented a platform in Burj Rashid as an innovative site to study risks, vulnerabilities and capacities.FindingsThe project will explore root causes, identify risk scenarios and establish a stakeholder map to guide the development of mitigation strategies and resilience-building measures.Originality/valueBy harnessing the wisdom of TEK and integrating it with scientific knowledge, the project paves the way for innovative climate change adaptation strategies that ensure the long-term sustainability of Burj Rashid’s unique cultural heritage.

Confirmatory Factor Analysis of the Sustainable Development Goals Derived Gentrification Scale in the Context of the Pandemic

In the context of human development goals, gentrification has emerged as a significant and unpredictable phenomenon amid the ongoing health crisis, potentially impacting the sustainable development goals (SDGs). This study aimed to analyze the theoretical framework of gentrification and its empirical indicators in the unique context of the pandemic, foreseeing potential risk scenarios. A cross-sectional, psychometric, and correlational study was conducted with 100 university students dedicated to the SDGs during the pandemic. The findings revealed the prevalence of two out of seven factors associated with poverty alleviation and institutional justice for peace. Given the observation of seven factors in the current literature, this study recommends expanding the indicators to enhance the variance percentage and aligning the theoretical model with empirical evidence. It underscores the pressing need for collaborative efforts in urban and sustainable development.

What do trees reveal about the sliding of the lateral moraine of the Belvedere Glacier (western Italian Alps)?

The debris-covered Belvedere Glacier is an iconic place for investigating glacier dynamics and geomorphological processes typical of high mountain environments. Moreover, being located in an area highly suited to tourism, glacial and geomorphological hazards can evolve into risk scenarios. Particular attention has been paid during this research to the surge-type event that occurred at the beginning of the 21st century, and to the recent sliding of a lateral moraine nearby the chairlift station. Tree sampling was performed (19 trees on the lateral moraine; 10 undisturbed trees), and the results were compared with morphometric measurements on orthophotos of different years. Besides sampling trunks, the six available exposed roots (13 samples) from a tree located along the sliding niche were sampled to identify the exposure time. Morphometric measurements of the touristic trail dislocation indicate a sliding rate of 1.87 m/y – 1.98 m/y (2018–2023), while the regression rate of the sliding niche is 1.70 m/y (2021–2023). The age of trees along the trench is variable (14–49 years), as is the signal of compression wood, enhancing differentially the passage of the surge wave and the subsequent glacier downwasting. The beginning of root exposure occurred between 2017 and 2019, before the effective evidence of large fractures in the ground. Moreover, the roots show traumatic resin ducts in the period between 2020 and 2022, confirming the tree disturbance. In conclusion, the investigated events are recorded differentially in the sampled trees, especially in roots, anticipating the actual commencement of ground failure. A multidisciplinary approach, including remote sensing, field survey, and dendrogeomorphological analysis is essential to define the dynamics of complex systems.

Holistic Information Security Management and Compliance Framework

The growing complexity of cybersecurity threats demands a robust framework that integrates various security domains, addressing the issue of disjointed security practices that fail to comply with evolving regulations. This paper introduces a novel information security management and compliance framework that integrates operational, technical, human, and physical security domains. The aim of this framework is to enable organizations to identify the requisite information security controls and legislative compliance needs effectively. Unlike traditional approaches, this framework systematically aligns with both current and emerging security legislation, including GDPR, NIS2 Directive, and the Artificial Intelligence Act, offering a unified approach to comprehensive security management. The experimental methodology involves evaluating the framework against five distinct risk scenarios to test its effectiveness and adaptability. Each scenario assesses the framework’s capability to manage and ensure compliance with specific security controls and regulations. The results demonstrate that the proposed framework not only meets compliance requirements across multiple security domains but also provides a scalable solution for adapting to new threats and regulations efficiently. These findings represent a significant step forward in holistic security management, indicating that organizations can enhance their security posture and legislative compliance simultaneously through this integrated framework.

Towards integrated multi-risk reduction strategies: A catalog of flood and earthquake risk mitigation measures at the building and neighborhood scales

The integration of risk reduction measures for earthquakes and floods is a promising concept in the field of multi-hazard disaster risk reduction. However, existing studies mostly discuss it conceptually, often missing a focus on decision-making. This paper introduces a novel approach by examining synergies and trade-offs between earthquake and flood risk reduction measures. The paper first conducts a detailed analysis of selected measures for these two hazards. It then explores how these individual risk mitigation measures can be strategically integrated at the building level to maximize synergies, potentially resulting in time and cost savings. Furthermore, it discusses the limitations of current decision-making procedures for single-hazard risk mitigation selection when applied to multi-hazard risk (MHR) scenarios. To address these challenges, the study proposes new decision variables to be incorporated into existing Multi-Criteria Decision-Making frameworks with the aim of facilitating strategic risk reduction prioritization for MHR scenarios, ensuring sustainability and community resilience. To aid implementation (i.e., the phase in which the measure would be selected and tested in a series of case study buildings), a catalog of analyzed risk reduction measures is developed and provided as Supplementary Material. In future developments, this catalog will facilitate the integration of these measures into decision-support tools for evidence-based multi-hazard disaster risk reduction.

The effects of the Phlaegrean Bradyseism on building systems: Field research applied in Pozzuoli

The contribution presents the results of field research aimed at assessing the effects of the Phlaegrean Bradyseism phenomena on a building system located in the historic centre of Pozzuoli (Italy). The study falls within the scope of building façade vulnerability analyses conducted by the authors to support the Public Administration in managing bradyseismic emergencies.Considering that the seismic-deformation phenomena connected to Bradyseism affect the performance and integrity of façade components, the research focused on studying its impact on the technical elements within the Technological Unit Classes of “Load-bearing Structure”, “Enclosure”, and “External Partition”, which directly project onto the external environment and collectively constitute the Building Envelope. The methodology for impact assessment was developed by correlating data acquired from a monitoring system installed on the façade of a surveyed building with characteristic parameters related to seismic events and soil deformations in a specific reference period. The analyses conducted excluded any significant impact of these seismic-deformation forcings on the building's Load-bearing Structure, both in terms of displacements and damage. On the other hand, significant impacts were found on the technical elements of the building envelope, which, due to their lower resistance and ductility, represent a constant hazard for the exposed urban system's safety, configuring a Building Risk scenario.

A seismic-risk-based bi-objective stochastic optimization framework for the pre-disaster allocation of earthquake search and rescue units

Accurately predicting earthquakes' time, location and size is nearly impossible with today’s technology. Severe earthquakes require prompt and effective mobilization of available resources, as the speed of intervention has a direct impact on the number of people rescued alive. This, in turn, calls for a strategic pre-disaster allocation of search and rescue (SAR) units, both teams and equipment, to make the deployment of resources as quick and equitable as possible. In this paper, a seismic risk-based framework is introduced that takes into account distance-based contingencies between cities. This framework is then integrated into a mixed-integer non-linear programming (MINLP) problem for the allocation of SAR units under uncertainty. The two minimization objectives considered are the expected maximum deployment time of different SAR units and the expected mean absolute deviation of the fulfillment rates. We recover the best vulnerability-adjusted routes for each size-location scenario as input to the optimization model using the dynamic programming (DP) approach as part of the broader area of reinforcement learning (RL). The results of the hypothetical example indicate that the comprehensive model is feasible in various risk scenarios and can be used to make allocation-deployment decisions under uncertainty. The results of the sensitivity analysis verify that the model behaves reasonably against changes in selected parameters, namely the number of allowed facilities and weights of individual objectives. Under the assumption that the two objectives are equally important, the model achieves a total deviation of %3.5 from the objectives with an expected maximum dispatch time of 1.1327 hours and an expected mean absolute deviation of 0.01.

Data-driven corporate growth: A dynamic financial modelling framework for strategic agility

This research aimed to develop a Dynamic Financial Growth Model (DFGM) to enhance corporate growth by promoting strategic agility through data-driven decision-making. The main objective was to optimize corporate value by integrating real-time data, dynamic decision-making, risk management, and scenario analysis. The research employed a mathematical modelling framework that combined predictive analytics, real options theory, and scenario-based optimization to represent dynamic corporate financial decisions. The numerical example demonstrated how the model adjusts strategic decisions in response to changes in market data and evaluates corporate value under optimistic, pessimistic, and baseline scenarios. The main results indicated that the DFGM is effective in optimizing corporate value by allowing for continuous adjustments and strategic flexibility, distinguishing itself from traditional static financial models that lack real-time adaptability. The findings highlighted the value of incorporating risk constraints and scenario analysis, resulting in a balanced approach that manages both growth and uncertainty. However, the study identified limitations, including the need for empirical validation, more complex predictive analytics, and accounting for behavioral factors affecting decision-making. The conclusion emphasizes that the DFGM provides an adaptable and data-driven framework that enhances corporate strategic agility, making it a valuable tool for managing growth in rapidly changing environments, while also suggesting future research to refine the model's practical application

A hybrid BWM–CRITIC–VIKOR approach for assessing oil and gas risk scenarios in probabilistic linguistic term set

The oil and gas industry plays a crucial role in supporting energy supply and economic development. Mitigating risks in oil and gas projects is essential for reducing decision-making uncertainties, optimizing resource allocation, and fostering the competitiveness and sustainable growth of enterprises. Therefore, evaluating decision-making risks in the oil and gas sector is of significant practical importance. This paper proposes a comprehensive approach based on the BWM–CRITIC–VIKOR multi-criteria decision-making (MCDM) evaluation model. An evaluation index system encompassing six critical dimensions—technology, economics, environment, security, societal impact, and political factors—is established. To enhance assessment precision, the AHP interval estimation method is employed to identify and eliminate indicators with low contributions. Subsequently, the probabilistic linguistic term set (PLTS) is introduced into the best–worst method (BWM) and CRITIC methods to calculate the weights for each index. This approach helps to circumvent the subjectivity inherent in single subjective weighting methods. To evaluate and rank alternatives, the PLTS is used as an evaluation linguistic tool within the VIKOR framework. Finally, this methodology is applied to decision-making in the oil and gas industry, and its effectiveness and superiority are compared with the AHP–TOPSIS method and the BWM–CRITIC method.

Proactive Prevention Strategies for Grid Risk Scenarios Based on Pumped Storage Participation

Given the increasing importance of cascading trip prevention, this study presents the pumped storage characteristics to cooperate with thermal units and further ensure the safety of grid operation. The proposed model comprises two phases: the first phase is the day-ahead decision-making phase, which synergistically optimizes thermal units and pumped storage to improve the flexibility of grid operation; the second phase is the intra-day adjustment phase considering the uncertainty of the wind-photovoltaic and transmission line failure outage to enhance the robustness. Furthermore, this model employs a column and constraint generation algorithm to solve the two-phase, three-layer optimization problem, thereby minimizing the adjustment cost under the riskiest scenarios. The experimental results show that adding pumped storage reduces the total system operating cost by 3.99%, the renewable energy abandonment rate to 0.65%, and the thermal power unit output standard deviation to 74.13 MW.

Research on Obstacle Avoidance Strategy of Automated Heavy Vehicle Platoon in High-Speed Scenarios

Amidst the rapid progression of the road transport industry, the safety and efficiency of heavy-vehicle platoons have garnered significant attention. The study tackles the challenge of obstacle avoidance presented by vehicles owing to their considerable mass, delayed response times, and line-of-sight impediments, by introducing a cooperative obstacle avoidance system for heavy-vehicle platoons based on deep reinforcement learning. The system comprises three primary modules: perception, decision-making, and control. Initially, the perception module acquires real-time environmental data. Subsequently, the decision-making module formulates obstacle avoidance decisions based on the acquired data. Specifically, it implements a two-stage braking obstacle avoidance strategy under low collision risk scenarios, while employing a fifth-degree polynomial for planning and tracking obstacle avoidance paths under high collision risk conditions suitable for steering maneuvers. The control module utilizes the local multi-agent deep deterministic policy gradient (LADDPG) algorithm to train the heavy-vehicle platoon agents, ensuring the formation’s maintenance while mitigating collisions with other vehicles and obstacles. The effectiveness of the proposed system is substantiated through simulation experiments, demonstrating its adaptability to various traffic conditions, selection of suitable obstacle avoidance strategies, and significant enhancement of obstacle avoidance performance and heavy-vehicle platoon stability.

Modelling Risk Dependencies in Insurance Using Survival Clayton Copula

Our aim in this paper is to show the use of survival Clayton copula as a suitable tool for modelling risk dependencies in insurance. A purpose-built simulation of an adequate upper tail dependence can be an important part of the aggregation of risks in an insurer’s internal models. The occurrence of extreme values of the aggregate random variable might have a very negative impact on the insurer when securing coverage of unexpected losses. The upper conditional quantile exceedance probability of the copula is a suitable indicator. In addition an analysis of its effect on the level of modelling of the risk scenario is available. This effect is measured using the Tail Value at Risk of the aggregate random variable. To simplify our description of the given principle for aggregating risks we will in this paper only consider the two-dimensional case. The programming language R was used to simulate the values of the joint distribution of the marginal random variables.

Influence of stocking density and interaction variability on disease progression of white spot syndrome virus-infected shrimp under different risk scenarios

White spot syndrome virus (WSSV), the causative agent of white spot disease (WSD), is the most significant viral pathogen affecting farmed shrimp. As the culture model for whiteleg shrimp has evolved into a higher-intensity model, the need to study the influence of stocking density on disease progression in pond culture systems has been reinforced. Therefore, we assessed WSD progression by deriving a direct correlation between seasonal disease outbreak risk, stocking density, and viral shedding rate (WSSV genome copies/L/g) using three different stocking densities: high (210 shrimp/m2), medium (140 shrimp/m2), and low (70 shrimp/m2). In Experiment 1 (three stocking densities at 25 °C) and Experiment 2 (stocking density × temperature conditions), shrimp showed relatively high cumulative mortality within the temperature range of optimal WSSV replication (23–29 °C). Notably, the mortality trend, peak viral loads, and viral shedding of WSSV-infected shrimp occurred earlier under high-density conditions, regardless of temperature. Thereafter, this study established WSD risk scenarios based on water temperature conditions and confirmed a significant difference in mortality trends between vulnerable and less vulnerable water temperature scenarios under high- and low-density conditions. The vulnerable water temperature scenario included constant (25 °C), shifting-up (20 °C to 30 °C), and shifting-down (30 °C to 20 °C) conditions, whereas the less vulnerable water temperature scenario included constant 20 °C and 30 °C conditions. Based on mortality trends, the WSD risk scenarios revealed that the times to 50 % mortality for shrimp at high, medium, and low densities were 4, 5, and 6 days post-infection (dpi) in the vulnerable water temperature scenario, and 6, 7, and 9 dpi in the less vulnerable water temperature scenario. Viral shedding variability was evaluated based on the correlation between viral loads and viral shedding rate, and linear regression analysis revealed a significant positive correlation between viral shedding rates and viral loads in shrimp, regardless of WSD risk scenarios or stocking density conditions. Notably, a relatively enhanced viral shedding rate was observed at higher stocking densities (from low- to high-density conditions) and under vulnerable water temperature scenarios. This study suggests that increased stocking density and vulnerable water temperature scenarios could induce greater interactions among WSSV-infected shrimp. It also confirmed the potential role of risk scenarios, stocking density, and viral shedding in WSD progression, providing insights into the disease outbreak mechanisms on shrimp farms.

Regulatory solutions to alleviate the risks of generative AI models in qualitative research

ABSTRACT Generative AI models, with their enhanced capacity for conversation, will soon find widespread applications in qualitative research, especially in the disciplines of social science and public policy. Although researchers guarantee the confidentiality of the data, the tools they use for data analysis are largely their choice and remain unregulated, raising serious ethical concerns. Prior research has established the potentially hazardous effects of such transformative architecture on research integrity and ethics; however, the interventions required to alleviate the risks that impact the 3Rs – Reviewers, Researchers, and Research Respondents – have not yet been studied. Initially, we analysed the potential risks associated with Large Language Models (such as GPTs) by examining scientific publications. We then had a ‘risk workshop’ with four qualitative researchers, followed by open-ended interviews with seven individuals from the 3 R impact groups to develop the various risk scenarios. We compare these risks to the AI-related policies of the European Union, Singapore, the United States, the United Kingdom and China to identify regulatory gaps. The research output illustrates potential regulatory interventions on a continuum, with nodality-based soft laws at one end and more extensive regulatory interventions (hard laws) at the other for various LLM applications in qualitative research.