A novel MEREC-MULTIMOOSRAL MADA framework for the strategic selection of edible oils: an optimization model for health sustainability.

Development of a risk prediction model for relapse in patients with schizophrenia based on a systematic review and meta-analysis.

Isomorphism or differentiation? A game-theoretic analysis of product upgrade decisions for lower-quality hotels

IPASDU: A DSS based on MAUT to evaluate intervention programs based on physical activity and sport for the prevention of drug use.

Health technology assessment (HTA) informs evidence-based decision-making for resource allocation in healthcare; however, the topic selection phase that determines which technologies proceed to full assessment is underexplored and often lacks fairness and transparency, particularly disadvantaging orphan medicinal products (OMPs) for rare diseases. We drew on selectively identified literature and illustrative HTA experiences to advance an opinion-driven argument about equity in topic selection for OMPs, grouping insights into three domains: structural challenges, conceptual frameworks and policy strategies. Structural barriers in topic selection include a lack of institutionalised, participatory processes, overreliance on prioritisation criteria implicitly aligned with population-wide impact and minimal engagement with stakeholders representing the rare disease community, resulting in the systematic under-prioritisation of OMPs. Procedural and value frameworks, including multi-criteria decision analysis (MCDA), evidence-informed deliberative processes (EDPs), accountability for reasonableness (A4R), equity weighting and Health Equity Impact Assessment (HEIA), can help agencies incorporate social value and fairness before formal appraisal, but remain unevenly operationalised. Policy strategies such as equity-oriented horizon scanning, open nomination systems, and special pathways for OMPs are highlighted as feasible approaches to promote fairness. Achieving equity in HTA topic selection for OMPs requires both policy and cultural change, and embedding pluralistic value frameworks, engaging stakeholders and adopting specific policy tools are essential steps towards ensuring that rarity is met with heightened ethical attention rather than exclusion.

Selection of alarm displays for time- and error-critical tasks: A multi-hierarchy decision framework integrating network structure and multi-criteria decision analysis

Prioritization of interventions to reduce antimicrobial use in dairy cows: A multicriteria decision analysis.

As of 2024, South Korea accounts for approximately 33% of the global display market; however, the environmental occurrence, bioaccumulation, and ecological risks of organic compounds associated with the display industry remain poorly understood. This study investigated the occurrence of display-related organic compounds (DROCs) in sediments from Asan Bay and four adjacent artificial lakes, as well as in freshwater fish collected seasonally from Lake Asan, Korea, and identified priority pollutants. Target compounds included 15 liquid crystal monomers and one organic light-emitting material, quantified using gas chromatography-tandem mass spectrometry (GC-MS/MS). Mean DROC concentrations were 5.8 ng g-1 dry mass (dm) in artificial lakes and 4.7 ng g-1 dm in Asan Bay. Benzo[b]anthracene (BbA, 0.56 ng g-1 dm) exhibited the highest median concentration. Dilution factor analysis revealed that BbA and 4'-hexyloxy-4-biphenylcarbonitrile (6OCB) showed high mobility from freshwater to marine environments. Hazard quotient analysis identified 4,4'-bis(4-propylcyclohexyl)biphenyl (BPCHB) as the primary contributor to predicted ecological risk. DROC concentrations in fish declined seasonally from 860 ng g-1 lipid mass (lm) in April to 65 ng g-1 lm in October. Concentrations of all DROCs in fish decreased with increasing trophic position. Multicriteria decision analysis, based on the results of this study, identified BbA, BPCHB, and 6OCB as priority pollutants. These findings provide new insights into the environmental behavior of DROCs and inform their prioritization for environmental management in a global display manufacturing hotspot.

Small Island Developing States (SIDS) such as Cabo Verde (CV) face persistent energy insecurity due to heavy reliance on imported fossil fuels and limited deployment of land-based renewables. This study develops a transferable, risk-informed multicriteria decision analysis (MCDA) framework for ocean current energy site selection, integrating high-resolution hydrodynamic modeling, environmental constraints, techno-economic factors, and tropical cyclone (TC) exposure. Using a high-resolution ocean circulation model that resolves tidal and inter-island flow dynamics, we characterize the spatial and temporal variability of surface currents and associated power densities across CV. These resource layers are combined with depth, distance-to-coast, protected areas, shipping routes, and TC hazard fields within an MCDA structure supported by sensitivity analysis. Results show that the strongest and most persistent currents occur within the northwestern inter-island channels, where seasonal mean speeds exceed 0.7m/s and power densities surpass 200 W/m2, with 95th-percentile speeds above 2m/s. Several locations meet key feasibility thresholds with mean currents near or above 1m/s, suitable depths (10-20m), proximity to shore, and relatively low variability while also lying outside high-TC-risk zones, low wave conditions, and major maritime corridors. The proposed framework demonstrates how hazard-aware, multi-criteria evaluation can support resilient OCE planning in SIDS and is transferable to other cyclone-exposed coastal systems.

Biodiversity Hotspots in Nepal Himalaya: Endemic Plants, Threats, and Conservation Priorities for Bagmati Province, Nepal.

Although THA is routinely offered to patients age 85 years and older, surgeons might hesitate to operate on patients deemed "too young." This study tests whether such hesitancy reflects a consistent standard. (1) How much utility (net benefit) can be expected from THA, as a function of patient age and operation-related clinical variables? (2) What relative cost must be attributed to revision of a THA such that the imposition of this penalty renders the expected utility of the operation too low to justify it in younger people? A calculator was programmed using a state-transition Markov model to estimate the expected lifetime benefit of THA as a function of patient age and other user-specified parameters. A novel unit of benefit, the joint-adequate life year (JALY), was defined. One JALY is earned in each postoperative year that the patient has an intact prosthesis and adequate clinical outcome. JALYs can also be forfeited when a patient enters a revision state. Thus, previously accumulated JALYs may be partially or completely offset, resulting in a net lifetime total that is lower than the total years of an adequate outcome. Users of the calculator specify patient sex and age at the time of THA (we modeled sex rather than gender in order to be able to derive life expectancy from sex-specific life tables published by the Social Security Administration), the anticipated fraction of patients expected to have a clinically adequate outcome (termed here the "satisfaction fraction"), prosthetic life expectancy, the expected annual risk of revision attributed to catastrophic events such as fracture or infection, and a discount rate, which is used to convert future gains and losses to their net present value. The prosthetic life expectancy parameter was used to calculate an annual risk of needing revision for wear. Users also assign a negative value to the revision state, representing the number of JALYs that a patient would be willing to forgo to avoid that state. This penalty was applied in the first instance the failure state was encountered, and a value 1.5 times this penalty was applied for any subsequent encounter. The model was then interrogated to provide the expected JALYs earned as a function of failure-state penalties ranging from 0 to 40 JALYs. For this calculation, prosthetic life expectancy was set to 15 years, the anticipated satisfaction fraction was set to 90%, the annual rate of catastrophic failure was set to 0.5%, and no temporal discounting was applied. Sensitivity analyses were then performed for different values of prosthetic life expectancy, satisfaction fraction, catastrophic failure rate, second-revision penalty ratio, and with 2% and 10% temporal discounting applied. Based on the common use of THA in 85-year-old patients (a group with a life expectancy of about 6 years after THA), a benchmark of six JALYs was established, as logically no patient can have more "adequate joint years" than total years of life. We then determined the penalty that must be assigned to the revision state, such that a 45-year-old male's expected benefit would fall below this six-JALY benchmark. A cohort of 45-year-old males can be expected to earn 25 lifetime JALYs under the base case assumptions and a revision penalty of five JALYs. Lifetime JALYs for this cohort do not drop below the threshold of six until the revision penalty reaches 25 JALYs. Sensitivity analysis demonstrated that this finding was robust across wide variation in clinically plausible values for prosthetic life expectancy, satisfaction, failure rates, and discounting. Hesitation to offer THA to 45-year-old patients with impairing hip disease is logically inconsistent with the procedure's broad acceptance in 85-year-old patients. This is because under plausible assumptions of the clinical parameters, 45-year-old patients stand to accrue considerably more lifetime benefit. Indeed, even accounting for the inevitable failures younger patients may encounter (and the penalties these failures impose), such patients can expect to accrue more than six JALYs across their lifetimes unless implausibly large failure penalties are assumed. Level II, Economic and Decision Analyses.

Preoperative identification of infectious nonunion remains challenging, particularly in patients without overt clinical signs of infection. Conventional inflammatory markers alone often provide insufficient diagnostic accuracy, creating uncertainty in surgical planning. This study aimed to develop and internally evaluate a predictive model combining novel and conventional biomarkers for preoperative diagnosis of infectious nonunion in long bone fractures. We hypothesized that integrating procalcitonin (PCT) with classical inflammatory markers would enhance diagnostic accuracy. We retrospectively analyzed 276 patients treated for femoral or tibial nonunion after internal fixation (2018-2023). Patients were randomly divided into a model-development cohort (n = 221) and a hold-out test cohort (n = 55). A multivariable logistic regression model was built using serum levels of PCT, interleukin-6 (IL-6), C-reactive protein (CRP), and erythrocyte sedimentation rate (ESR). Model performance was assessed by AUC, calibration plots, and decision curve analysis. In the model-development cohort, 110 patients had infectious nonunion and 111 had aseptic nonunion. Infected cases had significantly higher biomarker levels (p < 0.001). Among single markers, ESR had the best performance (AUC ~0.79), followed by PCT (~0.71), IL-6 (~0.62), and CRP (~0.61). The best multivariable model included PCT, CRP, and ESR, achieving an AUC of ~0.85 (95% CI ~0.80-0.90). Bootstrapping confirmed minimal overfitting (corrected AUC ~0.84). In the hold-out test cohort, this model maintained strong performance (AUC ~0.85, 95% CI ~0.72-0.96), with ~75% sensitivity and 85% specificity-superior to any single biomarker. Calibration plots showed good agreement, and decision analysis demonstrated higher clinical benefit than using individual markers or treating all patients. The PCT + CRP + ESR model reliably distinguishes infectious from aseptic nonunion, outperforming traditional markers alone. It offers a practical, accurate preoperative tool for infection screening, supporting more informed surgical decision-making. In clinical use, interpretation should be based on the predicted probability generated by the combined model rather than isolated elevation of individual biomarkers. Further prospective and external validation is warranted.

Precise segmentation of medical images plays a crucial role in modern clinical practice, providing important foundations for the quantitative analysis of medical images and clinical decision making. However, although deep learning techniques have achieved significant success in conventional medical image segmentation, they still exhibit obvious limitations when faced with complex structure segmentation tasks such as colorectal cancer: high-quality medical data acquisition is not only difficult, but also even when data is relatively sufficient, the diverse morphological features of lesions cannot be adequately represented due to their high variability, which severely limits the generalization capability of traditional data-driven segmentation methods. To address these challenges, we propose a feature-driven segmentation model that improves performance by deeply mining intrinsic data information rather than relying on parameter stacking. Specifically, we introduce a spectrum-prior-boundary triple modeling paradigm, where frequency domain reconstruction and modulation is employed to establish mappings between different frequency bands and heterogeneous signals to identify ambiguous signals, a level set-based segmentation algorithm is used to construct abdominal anatomical distance fields to incorporate morphological priors, and an auxiliary edge branch is designed by integrating deep semantic and shallow detail features to strengthen boundary awareness. Extensive experiments on colorectal cancer segmentation demonstrate that the proposed method achieves significant improvements in both segmentation accuracy and boundary perception over other state-of-the-art methods. Furthermore, evaluations on lung cancer and breast cancer segmentation tasks validate its strong generalization capability across diverse lesion types.

Background/Objectives: Patient-specific subperiosteal implants are increasingly used to treat severely atrophic ridges due to advances in digital planning and additive manufacturing. This study aimed to evaluate the effects of material type and implant configuration on stress distribution in subperiosteal implant systems and to compare their overall biomechanical performance using a multi-criteria decision framework. Methods: A three-dimensional model of a severely atrophic maxilla was reconstructed to simulate four clinical scenarios combining two configurations (one-piece and two-piece) and two materials (titanium and 60% carbon fiber-reinforced polyetheretherketone). Finite element analysis was conducted to assess stress distribution within the implant body, fixation screws, prosthetic framework, and surrounding bone under vertical and oblique loading conditions. Maximum and minimum principal stresses were evaluated in bone, whereas von Mises stresses were calculated for implant components. The resulting biomechanical indicators were subsequently integrated using an entropy weight-TOPSIS multi-criteria decision analysis. Results: Principal stresses in the surrounding bone showed minimal variation between titanium and 60% carbon fiber-reinforced polyetheretherketone across all configurations. Implant configuration had a more pronounced effect on implant body stress. Under oblique loading, the two-piece configuration demonstrated substantially higher implant stresses than the one-piece design, whereas under vertical loading, lower implant stresses were observed in the two-piece configuration. The multi-criteria analysis ranked the one-piece titanium model highest under oblique loading and the two-piece titanium model highest under vertical loading. Conclusions: Implant configuration and loading direction influenced biomechanical behavior more than material selection in patient-specific subperiosteal implants.

This paper presents a novel framework for addressing complex multi-criteria decision analysis (MCDA) problems by integrating Prospect Theory with the recently developed interval-valued intuitionistic quadri-partitioned neutrosophic soft set (IVIQPNSS) structure. The proposed model effectively incorporates both subjective preferences and objective criteria weights by leveraging Prospect Decision Theory, which models human behavior under risk and uncertainty based on gains and losses relative to a reference point. A newly formulated score function (SF) is introduced to transform the quadri-partitioned interval-valued neutrosophic information—comprising truth, indeterminacy, contradiction, and falsity—into precise numerical measures. This enables a refined ranking mechanism among alternatives. The proposed methodology merges expert judgment with data-driven insights, offering a robust algorithmic structure for decision-makers. The practical efficiency and reliability of the approach are demonstrated through a real-world application, ma\-king it a promising tool in the context of uncertain and ambiguous decision-making environments.

Geospatial and machine learning approaches for malaria risk mapping in flood-prone districts: Implications for public health decision-making.

Unlicensed medical practices (UMPs) pose a substantial threat to patient safety and public health, but their clandestine nature makes them difficult to monitor through conventional surveillance systems. Legal epidemiology offers a framework for using judicial data to study hidden health-related misconduct, and machine learning (ML) may help convert unstructured legal texts into analyzable public health information. This study aimed to characterize prosecuted UMP cases in South Korea using a legal infoveillance framework and evaluate the utility of ML-assisted extraction from criminal court decisions for public health surveillance. We conducted a retrospective observational study of 1532 criminal court decisions involving UMP-related convictions in South Korea between 2005 and 2023. Using an ML-assisted extraction pipeline with human-in-the-loop verification, we transformed unstructured judicial texts into structured legal and medical variables. Analyses were conducted at the case, charge, defendant category, and legal ruling levels. In addition to descriptive analyses, we performed exploratory inferential analyses to examine factors associated with legal rulings and professionals' involvement. Of 1718 charge entries, 987 (57.5%) were related to Article 5 of the Act on Special Measures for the Control of Public Health Crimes, and 731 (42.5%) were related to Article 27(1) of the Medical Service Act. Profit motive was coded in 91.6% (1404/1532) of the cases. At the legal ruling level (n=2004 entries), suspended sentences, meaning sentences whose execution was conditionally suspended under Korean criminal law, were the most common outcome (1261/2004, 62.9%), followed by fines (421/2004, 21%) and imprisonment without suspension (209/2004, 10.4%). Of 1716 defendant category entries, ordinary persons accounted for 1294 (75.4%), health care professionals accounted for 264 (15.4%), and health care providers accounted for 158 (9.2%). Physicians were the largest subgroup among health care professionals. In exploratory multinomial models, licensed personnel-only and mixed ordinary person and licensed personnel cases were more likely than ordinary person-only cases to result in fines or imprisonment without suspension rather than suspended sentences. Secondary exploratory analyses also suggested distinctive patterns of professional involvement and possible scope-of-practice or delegation-related boundary violations. ML-assisted analysis of criminal court decisions can serve as a useful supplementary surveillance method for hidden UMPs. In South Korea, prosecuted UMPs were predominantly profit driven and involved both ordinary persons and licensed personnel. The findings support closer monitoring of scope-of-practice and delegation-related violations and demonstrate the value of judicial records as a source of public health intelligence.

The Eastern Desert of Egypt is a hyper-arid region where groundwater serves as the primary alternative to surface water for sustainable development. However, comprehensive assessments of groundwater potential across this vast and geologically complex region remain limited. This study addresses this gap by developing a spatial model for delineating groundwater potential zones (GWPZ) using an integrated approach that combines remote sensing, Geographic Information Systems (GIS), and the Analytical Hierarchy Process (AHP). Seven key thematic layers, precipitation, lithology, slope, drainage density, soil type, land use/land cover (LULC), and lineament density, are selected, standardized, and weighted based on hydrogeological relevance. These layers are integrated through a weighted overlay analysis in a GIS environment. The resulting GWPZ map is initially classified into five categories: very high, high, moderate, low, and very low potential. The very high and very low potential zones have very small areas in the final output due to the region’s arid conditions and hydrogeological limitations. The remaining zones covered the study area as follows: high potential (7.7%), moderate (54.5%), and low potential (37.7%). Model reliability is assessed through two complementary validation approaches. First, 370 groundwater wells with available location data are spatially overlaid on the GWPZ map, showing limited overlap with high recharge zones, as most wells target deep fossil aquifers not influenced by present-day surface conditions. Second, a supplementary validation using three independent surface-derived indicators: Topographic Wetness Index, curvature, and lineament–stream intersection density, demonstrated strong agreement with the GWPZ output. The integration of these two validation methods confirms the robustness of the model for mapping shallow groundwater recharge potential in arid environments. This framework offers a scalable, data-driven approach to support groundwater exploration and strategic water resource planning in similar regions worldwide.

A method for forensic-ready intrusion detection using explainable learning, prediction-aware graph modeling, and counterfactual analysis

This study aims to juridically analyze the criminal act of sexual abuse against children committed by Indonesian National Army officers and assess the application of the principle of the rule of law in military justice based on Decision Number 88-K/PMT-BDG/AL/X/2024. The research method used is normative legal research with a legislative approach and a case approach, using secondary data obtained through literature studies and analysis of court decisions, then analyzed qualitatively. The results of the study showed that the perpetrator's actions had fulfilled the elements of criminal offenses, namely sexual acts, intentional elements, and physical, psychological, and moral losses experienced by child victims. The military court's decision in the case reflects the application of the principle of the rule of law and equality before the law because the perpetrators are still processed and sentenced to death without any special treatment despite their status as state officials. However, there are still weaknesses in the aspect of victim protection, especially related to psychological recovery and transparency of the military justice process. Therefore, it is necessary to strengthen child protection mechanisms, increase the accountability of military law enforcement officials, and more firm and consistent law enforcement to ensure justice and legal certainty.