Decision Analysis Research Articles

Sustainable Shipping: Modeling Technological Pathways Toward Net-Zero Emissions in Maritime Transport (Part I)

Maritime transport accounts for approximately 3% of global greenhouse gas (GHG) emissions, a figure projected to rise by 17% by 2050 without effective mitigation measures. Achieving zero-emission shipping requires a comprehensive strategy that integrates regulatory frameworks, alternative fuels, and energy-saving technologies. However, existing studies often fail to provide an integrated analysis of regulatory constraints, economic incentives, and technological feasibility. This study bridges this gap by developing an integrated model tailored for international maritime transport, incorporating regulatory constraints, economic incentives, and technological feasibility into a unified framework. The model is developed using a predictive approach to assess decarbonization pathways for global shipping from 2018 to 2035. A multi-criterion decision analysis (MCDA) framework, coupled with techno-economic modeling, evaluates the cost-effectiveness, technology readiness, and adoption potential of alternative fuels, operational strategies, and market-based measures. The results indicate that technical and operational measures alone can reduce emissions by up to 44%, while market-based measures improve the diversity of sustainable fuel adoption. Biofuels, particularly BISVO and BIFAME, emerge as preferred alternatives due to cost-effectiveness, while green hydrogen, ammonia, and biomethanol remain unviable without additional policy support. A strict carbon levy increases transport costs by 46%, whereas flexible compliance mechanisms limit cost increases to 14–25%. The proposed approach provides a robust decision-support framework for policymakers and industry stakeholders, ensuring transparency in evaluating the trade-offs between emissions reductions and economic feasibility, thereby guiding future regulatory strategies.

Evaluation of the natural water vulnerability of watersheds through the integration of GIS and Fuzzy AHP.

Key factors, such as climate change and changes in land use and cover, amplify stress on water resources, making them vulnerable. The need to ensure these resources in sufficient quantity and quality to sustain their multiple uses highlights the relevance of determining their natural vulnerability to reverse extreme scenarios. Within the boundaries of a watershed, its assessment can contribute to addressing the challenges of the region's water security. In this context, this study aims to evaluate the natural water vulnerability in three major watersheds in Brazil, which are essential for supplying water for human consumption and industrial activities: the Doce, Paraíba do Sul, and Tietê river basins. The methodology employed involved the development of a Natural Water Vulnerability Index (NWVI), conceived from a multicriteria decision analysis approach using the Fuzzy AHP method. This index was integrated into Geographic Information Systems (GIS), allowing spatial analysis through five critical environmental factors: precipitation, land use and cover, elevation, slope, and soil texture. The results showed that precipitation is the most relevant factor, followed by land use and cover. The analysis revealed an increase in vulnerability in areas near the outlets of the basins. The Tietê river basin proved to be the most vulnerable, while the Doce river presented many areas with very high levels of vulnerability. This information indicates the need for intensive management strategies in the most vulnerable areas and the importance of continuously monitoring the less vulnerable areas to prevent future increases in vulnerability.

Flood risk susceptibility analysis in Larkana district Pakistan using multi criteria decision analysis and geospatial techniques

Flooding is one of the most devastating natural disasters in worldwide, with significant socioeconomic and environmental impacts. In such a scenario, flood susceptibility analysis is essential for successful risk management and disaster preparedness. The Larkana district located in densely populated area of Sindh province, approximately 1.8 million population with 0.32 million households, is highly susceptible to floods due to its geographical location, river systems, and hot climatic patterns with extremely high temperature in summer and mild winter. Larkana district often faces significant flood risks mainly due to monsoon rains and the overflow of the Indus River which often lead to widespread flooding. The research aims to identify and quantify key flood risk factors, including rainfall pattern, distance to river, topography, land use/land cover, soil texture, vegetation index, and drainage infrastructure. AHP was applied to prioritize these risk factors based on expert opinions and their relative significance in contributing to flood vulnerability. GIS employed for spatial analysis and mapping of risk zones, allowing for detailed visualization of high-risk areas across the district. For this study, the various data sources such as topographic data, land use and landcover information, rainfall and infrastructure data, were used to develop a comprehensive flood susceptibility model. The AHP method was employed to determine the relative weights with consistency ratio (CR) and GIS techniques to generate flood susceptibility maps by considering all nine flood risk factors. Flood risk levels were further classified into five different classes as very low, low, moderate, high and very high. By using AHP, the weights of each parameter were calculated as a percentage, and it was determined that four out of nine parameters had 79% impact on flood hazard. These factors are ranked as the most influential factors in flood hazards for study area, with rainfall, distance to river, slope, and elevation having the greatest influences, while LULC, TWI, NDVI, soil type, and curvature had 21% impacts, much less impact as compared to top ranked parameters. After that, each flood risk parameter maps were reclassified. These maps were superimposed by weighted overlay maps in order to show that 7.65% of the entire area is at very high flood risk, 63.89% is at high risk, 28.38% is at moderate risk, and 0.12% is at low risk. The findings and established complete flood susceptibility model will facilitate policymakers and disaster management authorities by identifying high-risk locations and prioritizing mitigation measures, ultimately reducing the impact of floods on local communities and infrastructure in Larkana district.

Suitable sites for fog water collection in Cape Town, South Africa

The worldwide shortage of fresh water is a critical issue, with two-thirds of the world’s freshwater being inaccessible, as it is locked up in frozen glaciers. Additionally, arid areas such as South Africa are experiencing increased water scarcity, with regions such as Cape Town having already announced “day zero”, the day when the city’s dams will run out of water. Fog water collection provides a sustainable and alternative source of fresh water. Nevertheless, existing methods for identifying suitable locations rely on manual and trial-based approaches. This study focuses on finding suitable locations for fog harvesting in Cape Town via geographic information system (GIS)-based multicriteria decision analysis. To accomplish this, relevant factors for fog harvesting were identified in the literature and then transformed into spatial data layers. Next, weights were assigned to criteria layers via the analytical hierarchy process method, ultimately creating a final suitability map through a weighted overlay of these criteria layers. The findings of this study indicated that regions near coastlines, with low temperatures and strong winds, elevations above 1000 m or below 200 m, and steep slopes facing the ocean are the most favourable locations for harvesting fog water. This approach could be replicated in other regions, but caution is necessary when determining criteria and thresholds because of the localised nature of fog.

MRI-based radiomics nomogram to predict complete response to definitive chemoradiation in patients with anal cancer.

Treatment response to definitive chemoradiation(dCRT) in patients with anal cancer varies significantly, with a subset experiencing persistent or progressive disease despite therapy. Radiomics extracts quantitative features from medical images, with the potential to develop predictive tools to assess treatment response. We aim to develop and validate an MRI-based radiomics nomogram to predict response to dCRT in patients with anal cancer. A single-institutional retrospective analysis of 45 patients with anal cancer treated with dCRT was performed. Radiomic features were extracted from pre-treatment T2-weighted MRI scans and predictive models were constructed. Clinical and radiomic features were analysed to develop the nomogram. Internal validation with 1000 bootstrap samples was performed to calculate optimism-corrected performance measures. 30/45(66.7%) achieved a complete treatment response. Male gender was found to be an independent predictor of incomplete response to dCRT (OR4.763,95%CI : 1.170-19.384,*P = 0.029). Two radiomic signatures emerged as strong predictors of treatment response to dCRT. The combined model outperformed the clinical and radiomic models. The combined model showed the highest predictive accuracy, achieving an apparent AUC : 0.87(0.75-0.99) and an optimism-corrected AUC: 0.85, mean absolute error : 0.029, PPV(0.68)and NPV(0.92), indicating excellent discriminative performance. It demonstrated a positive net benefit in decision analysis. The optimism-corrected calibration curves demonstrate that the radiomic and combined model provide well-calibrated predictions. This MRI-based radiomics nomogram offers a promising approach to predict response to dCRT in patients with anal cancer. This study is the first to integrate radiomics and clinical features into a validated predictive model for anal cancer.

Respirable dust and respirable crystalline silica exposures among workers at stone countertop fabrication shops in Georgia from 2017 through 2023.

This longitudinal study examines the severity of worker exposure to respirable crystalline silica (RCS) and respirable dust and demonstrates the need for increased education and implementation of both appropriate engineering controls and respiratory protection (RP) programs for stone fabricator shops, given the growing global number of accelerated silicosis cases associated with the fabrication of engineered stone (ES) countertops. Personal air sampling results and detailed job description notes obtained from 17 industrial hygiene air sampling visits conducted at 11 stone fabrication facilities between 2017 and 2023 in Georgia were used to align similar exposure groups (SEGs) for tasks for workers performing stone fabrication. Bayesian decision analysis was used to determine appropriate RP selection recommendations for the 4 proposed SEGs: SEG1-Support, SEG2-Automated Tool Operator, SEG 3-Small Tool Operator, and SEG 4-Fabrication/Lamination. The analysis concluded that all employees in stone fabrication shops that process ES should wear a respirator with a minimum assigned protection factor (APF) of 10, regardless of the engineering controls in place. For SEG 4, it is recommended that workers use respirators with an APF between 50 and 1,000. Among the 75 full-shift personal air samples for RCS dust, 41 samples (53%) exceeded the permissible exposure limit of 50 µg/m³. This is the first study to present the 4 SEG categories with sampling data to support the importance of including all employees (even support workers) in RP programs, exposure monitoring, and medical surveillance. Employers, occupational health professionals, and inspectors may use these SEG categories and corresponding RP recommendations to determine if employees have received appropriate RP for workers at stone countertop fabrication shops.

An equity-based financial framework for a sustainable healthcare system in Arab countries.

Disparities persist in access, quality and outcomes across different socioeconomic strata. Addressing these disparities requires a comprehensive understanding of the underlying factors contributing to healthcare inequities. Even though healthcare equity has been discussed in the literature, no comprehensive frameworks have been developed considering the given country's distinctive demographic cultural and socioeconomic variables. This study proposed an equity-based financial framework to enhance the sustainability of the healthcare system in Saudi Arabia, the United Arab Emirates (UAE) and Qatar. Moreover, this research aimed to examine the key factors influencing equitable access to healthcare services. A cross-sectional study design was employed, utilizing national health accounts, demographic surveys and health outcomes data from 3 Arab countries: Saudi Arabia, UAE and Qatar. The study included participants from 15 medical organizations, 500 policymakers and 10,000 patients. A stratified random sampling technique was employed to ensure a diverse and representative sample. The economic equity measurements included the principal component analysis (PCA) and Theil index. Financial sustainability was evaluated using techniques such as the cost-effectiveness analysis (CEA) and systems dynamics modeling techniques. This study identified a positive convergence in healthcare systems among Qatar, Saudi Arabia and the UAE. The Theil index value of 0.35 suggested a balanced distribution of healthcare resources across the 3 countries. Policy A had an incremental cost-effectiveness ratio (ICER) of $15,000 per quality-adjusted life year (QALY), making it more cost-effective compared to Policy B with an ICER of $20,000 per QALY and Policy C with an ICER of $25,000 per QALY. The Delphi technique achieved a consensus level of 90%, while Policy C emerged as the most preferred option in the multi-criteria decision analysis (MCDA), scoring a total of 85 points. Moreover, the PCA accounted for 60% of the variations related to healthcare equity in the specified countries. The findings of this study provide valuable insights for policymakers, offering a new roadmap for economic evaluation studies aimed at enhancing healthcare equity and sustainability in Arab countries.

The decision analysis of consumer purchasing for refurbished products.

Promoting refurbished products can positively impact environmental performance and play a vital role in reducing waste. This study employs the concept of the theory of perceived risk to examine the influence of social and financial risk, online retail channels and return policy on consumer-perceived risk. Additionally, it proposes that an online retail channel with a lenient return policy may enhance the intention of consumers to purchase refurbished products. The hypotheses were examined using partial least square structural equation modelling. The findings suggest that social risk, return policy and financial risk significantly enhance the perceived risk of purchasing refurbished products, but the online retail channel does not substantially impact perceived risk, which may be if both online and offline channels provide the same level of support and policy. The results also indicate that perceived risk mediates consumers' online purchase intention for refurbished products. This study is distinctive because it analyses the effects of social and financial risk, return policy and retail channel on consumers' perceived risk when purchasing refurbished products online. This study examines the concepts of online purchasing of refurbished products, which have received limited attention in existing literature. While previous studies have looked at these concepts separately, we aim to provide a comprehensive analysis by studying them together. By implementing the findings of this study, businesses can contribute to the circular economy, conserve resources, potentially reduce environmental imprints and enhance sustainability performance.

Risk Perception and Management Strategies Among Ecuadorian Cocoa Farmers: A Comprehensive Analysis of Attitudes and Decisions

Cocoa farming in Ecuador faces significant challenges due to market volatility and climate-related risks, necessitating effective risk management strategies. This study investigates the interplay between risk attitudes (RAs), risk perceptions (RPs), and risk management strategies (RMSs) among Ecuadorian cocoa farmers, examining how these factors influence decision-making under uncertainty. Combining experimental lotteries to assess risk and loss aversion, with partial least squares structural equation modeling (PLS-SEM) to analyze survey data, we explore how farmers prioritize perceived impacts over probabilities in their risk assessments. The findings reveal that farmers focus more on mitigating severe perceived impacts, such as price drops and production losses due to adverse weather, than on probability-based strategies, commonly opting for diversification and nonagricultural activities. These results highlight the importance of designing policies and tools that address the perceived impacts of risks, align support with farmers’ needs, and improve access to financial resources and tailored insurances. This approach offers valuable insights for policymakers aiming to enhance cocoa farmers’ resilience in volatile agricultural environments.

Examining the fate of frozen embryo inventory: an analysis of patients' embryo disposition decisions.

To determine factors associated with embryo disposition decisions at a large academic fertility center. We performed a single-center retrospective cohort study of patients who made final embryo disposition (discard or donation to research) between January 1, 2020, and February 28, 2024, via electronic consent. Demographic and cycle-specific variables were collected via chart review. Chi-square and Mann-Whitney U tests were used for data analysis (p < 0.05). Of 1280 patients, 900 (70.3%) discarded embryos and 380 (29.7%) donated to research. Patients who donated were more likely to have a diagnosis of recurrent pregnancy loss (6.1% vs 2.4%, p < 0.002). Patients who chose to donate had transferred more embryos (2 vs 1, p < 0.033) and had transferred more euploid embryos (44.7% vs 36.6%, p < 0.007). There was no difference in total number, number of euploids, or type of embryo disposed (p = 0.24, p = 0.96, p = 0.34). There was no difference observed among those who communicated with the center (p = 0.81) or those using donor gametes (egg p = 0.34, sperm p = 0.29). An additional analysis compared patients who achieved live birth (n = 902) to those who did not (n = 378), and those who donated were more likely to have achieved live birth (32.0% vs 24.1%, p < 0.005). At final embryo disposition, more patients discarded embryos than donated. Donators were more likely to have recurrent pregnancy loss as their reason for pursuing embryo creation, transfer more embryos across all cycles, and achieve a live birth. Discarders were more likely to have transferred untested or no embryos.

Economic Optimization Through Adherence to Best Practice Guidelines: A Decision Analysis of Traumatic Spinal Cord Injury Care Pathways in Australia.

Traumatic spinal cord injuries (TSCIs) have significant health, economic, and social effects on individuals, families, and society. In this economic analysis modeling study, we used record-linked administrative patient data from New South Wales, Australia, to construct a decision tree model to compare the economic cost of acute care for patients with TSCI under current clinical pathways with an optimal care (consensus guidelines-informed) modeled pathway. The optimal care pathway included direct transfer to a specialist SCI Unit (SCIU) or indirect transfer to SCIU within 24 h of injury, surgical intervention within 12 h of injury, and subsequent inpatient rehabilitation. Propensity score matching with inverse probability of treatment weighting (IPTW) was used to reduce potential confounding from baseline differences in patient characteristics. A generalized linear model regression with gamma distribution and log link, weighted with IPTW scores, was used for cost and length of stay (LoS) estimations to reduce any residual bias. Sensitivity analyses quantified the sensitivity of the findings to key model parameters. From the healthcare payer perspective, our economic analysis found acute TSCI care at an SCIU was more expensive, with delayed patient transfer pathways, surgery, and timing of surgery driving higher per-patient costs ($14,322 at specialist centers). Probabilistic sensitivity analysis (PSA) using 10,000 Monte Carlo iterations showed the modeled optimal pathway as the expensive option in the majority (86%) of stimulations. However, the modeled direct transfer care pathway demonstrated economic improvements compared to current care pathways, despite a higher upfront cost ($25,428 per patient), the modeled pathway reduced the episode LoS by 5 days (23 days vs. 28 days) on average, generating system-level savings of $20,628 per patient. In PSA, increasing the proportion of patients directly transferred to SCIU by 25%, the optimized pathway was preferred in 28.3% of the simulations. Furthermore, adopting this pathway lowered the incremental per patient cost to $17,157 while preserving a 5-day LoS benefit compared to current pathways (22 days vs. 27 days), which could generate potential savings of $3,471 per patient. Our findings show that guideline-based acute care management is initially resource-intensive but efficient in terms of patient LoS, with a higher proportion of direct transfers resulting in cost savings of $3,471 per patient, which represent system-level benefits from adopting the modeled pathway, rather than episode-level savings. Following consensus guidelines for acute care can provide an economically sustainable approach to resource-intensive patient needs while improving outcomes, as demonstrated in previous studies. In summary, while more intensive, adhering to clinical guidelines of direct transfer to SCIU demonstrates value for patients and health systems. Standardization to optimize time to surgery can achieve improved outcomes through earlier access to rehabilitation and substantial care efficiencies. These findings highlight the economic case for adherence to best practice care guidelines at the healthcare system level to inform future healthcare planning for patients with TSCI.

ACCOUNTABILITY OF PERPETRATORS OF EMBEZZLEMENT CRIMES WITH THEIR COLLEAGUES (Study of Decision Number 65/Pid.B/2024/PN.Sbr)

Abstract Background. Aims. This research aims to identify and analyze the causative factors of embezzlement committed by a colleague, as well as to analyze the criminal responsibility of the perpetrator in Decision Number 65/Pid.B/2024/PN.Sbr. Methods. This research employs a case study method on the aforementioned court decision. Result. The results indicate that several interconnected factors contributed to the embezzlement, including misunderstandings regarding the financial management of salt sales proceeds, the alleged motive of resentment from the victim, the lack of internal control in the company's financial management, and the close relationship and trust between the perpetrator and the victim, leading to less formal financial management practices. The analysis of the court decision shows that the defendant was legally and convincingly proven guilty of embezzlement as regulated in Article 372 of the Indonesian Criminal Code (KUHP). The Panel of Judges sentenced the defendant to one year and ten months of imprisonment, considering the elements of the crime in the article. Conclusion. This decision demonstrates the proportional application of the principles of legal certainty, justice, and expediency in law enforcement against embezzlement in the context of a working relationship. Implementation. Most importantly, regular communication between management and employees should be improved to create an open and trusting work environment. Embezzlement prevention requires a comprehensive approach that involves structural, cultural, and individual aspects within a company.

Evaluation of interrelationship between supply chain transparency and relative efficiency for logistics service providers in Thailand

ABSTRACT Logistics Service Providers are key players in logistics operations to support a seamless flow between stakeholders in the supply chain in this globalization age. One key strategy in supply chain management to improve supply chain efficiency is through the integration of supply chain transparency among key activities and operations in the supply chain. In this study, we investigate the complex, interrelationship between supply chain transparency and supply chain efficiency in supply chains using an integrated Multi-Criteria Decision Analysis approach. Seven key performance indicators were initially identified and analyzed using the Analytic Hierarchy Process approach based on the illustrated case study of logistics service provider in Thailand. Then, based on the selected key performance indicators, the efficiency optimization technique called the Data Envelopment Analysis is further applied to evaluate the performance of each logistics service provider sequentially. Finally, the transparency level of each logistics service provider is integrated to assess the effects on supply chain efficiency through the scenario analysis. Analyzed results suggest a practical analysis to comprehend how transparency and efficiency can be quantified in the supply chain and how their interrelationship plays a role in managerial implications relevant to supply chain managers and policyholders.

Silviculture Promotes Sustainability in Nothofagus antarctica Secondary Forests of Northern Patagonia, Argentina: A Multicriteria Analysis

Despite the growing recognition of sustainability in forest management, comprehensive multi-criteria evaluations of silvicultural practices remain scarce, particularly in Patagonia. In this study, we applied a multi-criteria decision analysis to evaluate the sustainability of different strip-cutting intensities in secondary Nothofagus antarctica forests in Northern Patagonia, Argentina. The performance of four management alternatives was assessed: no cutting, low cutting intensity, medium cutting intensity, and high cutting intensity. These alternatives were evaluated across 11 indicators of nature’s contributions to people. Indicator values were estimated from previous research across three contrasting sites, complemented by expert surveys to estimate weights and target values for each indicator. The results indicate that the key indicators included those associated with firewood harvesting, fire and invasions prevention, and timber species plantation performance. Medium cutting intensity consistently emerged as the most sustainable option across all sites, models, and scenarios. In contrast, no cutting performed poorly across most sites, models, and scenarios. These findings underscore the importance of integrating diverse ecological and socioeconomic indicators into forest management planning. The promotion of medium cutting intensity has the potential to enhance sustainability in N. antarctica forests, thereby contributing to the development of resilient and multifunctional landscapes in Northern Patagonia.

A systematic review of the current application status of decision-analytical models in the pharmacoeconomic evaluation of targeted therapies for pulmonary arterial hypertension

BackgroundThe implementation of targeted drug therapy results in a significant improvement in both survival rates and quality of life among patients diagnosed with pulmonary arterial hypertension (PAH), concurrently imposing a greater financial burden on them. The use of pharmacoeconomic evaluation based on decision-analytical models is extensively employed in the rational allocation of healthcare resources.ObjectivesThe present study conducted a systematic review of the literature on the pharmacoeconomic evaluation of drugs for treating PAH, with a focus on summarizing the composition and sources of parameters in decision-analytical models. This study aims to provide methodological guidance for future economic research.MethodsThe review was conducted across six databases (PubMed, Embase, the Cochrane Library, CNKI, VIP, WanFang Data) and two health technology assessment agency websites (NHS EED, INAHTA). The characteristics of each study and the compositional details of the decision-analytical models are extracted.ResultsIn total, 13 published studies were included. The pharmacoeconomic evaluation methods employed in the studies included cost-effectiveness analysis (CEA) and cost-utility analysis (CUA). The decision analysis models employed in all 13 studies were Markov models. The models were all constructed on the basis of the World Health Organization (WHO) functional class, with variations in parameter settings and sources.ConclusionsAll 13 Markov models provided useful insight into PAH modeling. Future research in this field can employ these research methods according to diverse research objectives. The utility values were derived from a single source; therefore, future studies should evaluate the quality of life in patients with PAH across varying disease severities.

Global trends and hotspots in robot-assisted arthroplasty: a CiteSpace-based bibliometric and visualized analysis.

With the increasing integration of robotic technology into medical practice, robotic-assisted arthroplasty (RAA) has emerged as a significant research focus. This study employs CiteSpace, a bibliometric analysis tool, to systematically examine the current state and future directions of RAA research on a global scale. By retrieving relevant literature from the Web of Science Core Collection database spanning from 1997 to 2024, a total of 229 articles were identified for analysis. The findings indicate a steady rise in the number of publications within this field. China, the United States, and South Korea are the leading contributors, with the Chinese People's Liberation Army General Hospital being the most prolific institution. Keyword clustering analysis reveals that key research areas include total hip arthroplasty, Markov decision analysis, total knee arthroplasty, robotic-assisted total knee arthroplasty, diverse assistive techniques, patient-reported outcome differences, robot-assisted total hip arthroplasty, and learning curves. Emerging trends are primarily centered around system development, navigation technologies, manual implantation methods, functional outcomes, and ossification processes.

A nomogram integrating the clinical and CT imaging characteristics for assessing spread through air spaces in clinical stage IA lung adenocarcinoma

PurposeThis study aimed to create a nomogram model to predict the spread through air spaces (STAS) in patients diagnosed with stage IA lung adenocarcinoma, utilizing a substantial sample size alongside a blend of clinical and imaging features. This model serves as a valuable reference for the preoperative planning process in these patients.Materials and methodsA total of 1244 individuals were included in the study. Individuals who received surgical intervention between January 2022 and May 2023 were categorized into a training cohort (n=950), whereas those treated from June 2023 to October 2023 were placed in a validation cohort (n=294). Data from clinical assessments and CT imaging were gathered from all participants. In the training cohort, analyses employing both multivariate and univariate logistic regression were performed to discern significant clinical and CT characteristics. The identified features were subsequently employed to develop a nomogram prediction model. The evaluation of the model’s discrimination, calibration, and clinical utility was conducted in both cohorts.ResultsIn the training cohort, multivariate logistic regression analysis revealed several independent risk factors associated with invasive adenocarcinoma: maximum diameter (OR=2.459, 95%CI: 1.833-3.298), nodule type (OR=4.024, 95%CI: 2.909-5.567), pleura traction sign (OR=2.031, 95%CI: 1.394-2.961), vascular convergence sign (OR=3.700, 95%CI: 1.668-8.210), and CEA (OR=1.942, 95%CI: 1.302-2.899). A nomogram model was constructed utilizing these factors to forecast the occurrence of STAS in stage IA lung adenocarcinoma. The Area Under the Curve (AUC) measured 0.835 (95% CI: 0.808–0.862) in the training cohort and 0.830 (95% CI: 0.782–0.878) in the validation cohort. The internal validation conducted through the bootstrap method yielded an AUC of 0.846 (95% CI: 0.818-0.881), demonstrating a robust capacity for discrimination. The Hosmer–Lemeshow goodness-of-fit test confirmed a satisfactory model fit in both groups (P &amp;gt; 0.05). Additionally, the calibration curve and decision analysis curve demonstrated high calibration and clinical applicability of the model in both cohorts.ConclusionBy integrating clinical and CT imaging characteristics, a nomogram model was developed to predict the occurrence of STAS, demonstrating robust predictive performance and providing valuable support for decision-making in patients with stage IA lung adenocarcinoma.

Using robust decision analysis to develop adaptive strategies for power system expansion in Bangladesh

Using robust decision analysis to develop adaptive strategies for power system expansion in Bangladesh

Co-operation, Competition and Market Structure from A Game-Theoretical and Austrian Perspective

While the Austrian School emphasises entrepreneurial discovery and the evolutionary nature of competition, game theory provides structured tools for analysing strategic interactions. This paper combines both perspectives to examine how voluntary co-operation influences market outcomes. Building on game theory concepts such as Nash equilibrium, repeated games and the coopetition approach, a theoretical analysis of strategic decisions is provided. These models are then compared with the Austrian view of dynamic market processes. Case studies from the platform economy, opensource development, the blockchain sector and the pharmaceutical and financial industries show real forms of cooperation without centralised control. A game-theoretical model is developed that depicts voluntary cooperation under different competitive conditions, taking into account trust, incentives and strategic expectation formation. This model is supplemented by simulation-based approaches that examine the long-term stability of cooperative structures in decentralised markets. The results show that voluntary co-operation can generate efficiency gains, but that it is largely dependent on institutional framework conditions and the repeatability of interactions. The combination of both theoretical approaches enables a deeper understanding of market behaviour beyond static equilibrium models. Competition and co-operation are not seen as opposites, but as complementary forces that together promote innovation processes, adaptability and market stability. The paper argues in favour of adaptive, institutionally embedded regulatory approaches that allow for competitive structures and at the same time support voluntary coordination. It thus contributes to the development of theory and offers practical implications for competition policy and corporate strategy in dynamic markets.

Flood risk assessment in the Swat river catchment through GIS-based multi-criteria decision analysis

Extreme climatic events, such as floods, are becoming increasingly frequent and severe worldwide, including in Pakistan. The Swat River Catchment (SRC), located in the eastern Hindukush region of Pakistan, is highly susceptible to flooding due to its unique geographical and climatic conditions. However, despite the region’s susceptibility, comprehensive flood risk assessments that integrate hazard, vulnerability, and exposure components remain limited. To address this gap, this study assesses flood risk in the SRC using 22 indicators distributed across the three core dimensions of flood risk: hazard, vulnerability, and exposure. Flood hazard was modeled using 11 indicators, broadly categorized into environmental, hydrological, and geographical aspects, while vulnerability was evaluated through socio-economic factors, geographical proximity, and land use characteristics. Exposure was analyzed based on population metrics and critical infrastructure. All data were converted into thematic layers in GIS, systematically weighted using the Analytical Hierarchy Process (AHP) and combined to produce hazard, vulnerability, and exposure maps respectively. These maps were then integrated through a risk equation to generate the final flood risk map. The results reveal that 31% of the study area is in a high flood risk zone, 27% in moderate risk zones, 23% in low risk, and 19% are safe areas. The results were validated using the Area Under the Curve (AUC) technique, yielding a value of 0.92, which indicates high reliability. By presenting the first integrated flood risk assessment for the SRC, this study provides valuable insights into flood-prone areas and risk distribution. These results highlight the urgent need for enhanced flood risk management, especially in urban areas. The developed methodology serves as a valuable tool for disaster management authorities and planners, helping them make risk-informed decisions, allocate resources efficiently, and implement targeted flood mitigation strategies.