Inadequacy Of Model Research Articles

Machine learning-based infection diagnostic and prognostic models in post-acute care settings: a systematic review

Abstract Objectives This study aims to (1) review machine learning (ML)-based models for early infection diagnostic and prognosis prediction in post-acute care (PAC) settings, (2) identify key risk predictors influencing infection-related outcomes, and (3) examine the quality and limitations of these models. Materials and Methods PubMed, Web of Science, Scopus, IEEE Xplore, CINAHL, and ACM digital library were searched in February 2024. Eligible studies leveraged PAC data to develop and evaluate ML models for infection-related risks. Data extraction followed the CHARMS checklist. Quality appraisal followed the PROBAST tool. Data synthesis was guided by the socio-ecological conceptual framework. Results Thirteen studies were included, mainly focusing on respiratory infections and nursing homes. Most used regression models with structured electronic health record data. Since 2020, there has been a shift toward advanced ML algorithms and multimodal data, biosensors, and clinical notes being significant sources of unstructured data. Despite these advances, there is insufficient evidence to support performance improvements over traditional models. Individual-level risk predictors, like impaired cognition, declined function, and tachycardia, were commonly used, while contextual-level predictors were barely utilized, consequently limiting model fairness. Major sources of bias included lack of external validation, inadequate model calibration, and insufficient consideration of data complexity. Discussion and Conclusion Despite the growth of advanced modeling approaches in infection-related models in PAC settings, evidence supporting their superiority remains limited. Future research should leverage a socio-ecological lens for predictor selection and model construction, exploring optimal data modalities and ML model usage in PAC, while ensuring rigorous methodologies and fairness considerations.

A Brief Study on Indian Startups- Analytic Research on the Success and Failure Factors of Swiggy, Boat, Doodhwala, And Shopx

India’s startup ecosystem has witnessed unprecedented growth in recent years, emerging as one of the most dynamic and fast-paced environments globally. This research paper examines the primary factors leading to the high failure rate of Indian startups, focusing on familiar challenges such as inadequate business models, poor product-market fit, cash flow problems, regulatory hurdles, and inefficient management. Through case studies of successful ventures like Swiggy, and BoAT, this paper highlights critical success factors that have enabled these companies to thrive, including data-driven decision-making, customer-centric strategies, adaptability, and strong strategic partnerships. The study also explores the pivotal role of venture capitalists (VCs) in shaping startup trajectories, from early seed funding to advanced growth stages. Additionally, the paper delves into the influence of Indian government initiatives, such as Startup India and the Atal Innovation Mission, in fostering a supportive ecosystem that encourages entrepreneurship and innovation. Keywords: Entrepreneur, Start-up, Economy, Future market, Challenges

Parental obesity, health determinants, and cardiometabolic risk according to sleep duration in schoolchildren: analysis through structural equations

Backgroundthe independent association between parental obesity, sleep and lifestyle habits with cardiometabolic risk factors in children and adolescents has been widely explored in the literature. Our study represents a novel approach to comprehensively investigate a complex model encompassing various aspects associated with cardiometabolic risk in youth. Thus, the aim of the present study was to verify the relationship between parental obesity and cardiometabolic risk in children and adolescents, considering the mediator role of health indicators according to sleep time.MethodsThis cross-sectional study was conducted on 3,973 children and adolescents aged 6 to 17 years attending public and private schools in a city located in Southern Brazil. Sleep duration, lifestyle, physical activity, natural food consumption, and parental obesity were evaluated through self-reported questionnaires. Physical fitness was evaluated according to the protocols of Projeto Esporte Brasil. The body fat percentage was evaluated through the measures of tricipital and subscapular folds, and the body mass index was calculated. The clustered metabolic risk score (cMetS) considered the summing z-scores of waist circumference, systolic blood pressure, triglycerides, total cholesterol/high-density lipoprotein cholesterol ratio, and fasting glucose, divided by five. A structural equation model was applied for statistical analysis. Results: The relationship between parental obesity and cardiometabolic risk was observed in children and adolescents with adequate sleep, being significant (p < 0.05) mediated by physical activity, natural food, physical fitness, and lifestyle. This was also observed when parental education was included in the inadequate sleep cluster model. Conclusion: Present findings underscore the importance of sufficient sleep duration as a critical factor in understanding the complex interplay between parental obesity and cardiometabolic risk in children and adolescents.

NFSA-DTI: A Novel Drug–Target Interaction Prediction Model Using Neural Fingerprint and Self-Attention Mechanism

Existing deep learning methods have shown outstanding performance in predicting drug–target interactions. However, they still have limitations: (1) the over-reliance on locally extracted features by some single encoders, with insufficient consideration of global features, and (2) the inadequate modeling and learning of local crucial interaction sites in drug–target interaction pairs. In this study, we propose a novel drug–target interaction prediction model called the Neural Fingerprint and Self-Attention Mechanism (NFSA-DTI), which effectively integrates the local information of drug molecules and target sequences with their respective global features. The neural fingerprint method is used in this model to extract global features of drug molecules, while the self-attention mechanism is utilized to enhance CNN’s capability in capturing the long-distance dependencies between the subsequences in the target amino acid sequence. In the feature fusion module, we improve the bilinear attention network by incorporating attention pooling, which enhances the model’s ability to learn local crucial interaction sites in the drug–target pair. The experimental results on three benchmark datasets demonstrated that NFSA-DTI outperformed all baseline models in predictive performance. Furthermore, case studies illustrated that our model could provide valuable insights for drug discovery. Moreover, our model offers molecular-level interpretations.

Development of New Electricity System Marginal Price Forecasting Models Using Statistical and Artificial Intelligence Methods

The System Marginal Price (SMP) is the cost of the last unit of electricity supplied to the grid, reflecting the supply–demand equilibrium and serving as a key indicator of market conditions. Accurate SMP forecasting is essential for ensuring market stability and economic efficiency. This study addresses the challenges of SMP prediction in Turkey by proposing a comprehensive forecasting framework that integrates machine learning, deep learning, and statistical models. Advanced feature selection techniques, such as Minimum Redundancy Maximum Relevance (mRMR) and Maximum Likelihood Feature Selector (MLFS), are employed to refine model inputs. The framework incorporates time series methods like Multilayer Perceptron (MLP), Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Convolutional LSTM (ConvLSTM) to capture complex temporal patterns, alongside models such as Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Extreme Learning Machine (ELM) for modeling non-linear relationships. Model performance was evaluated using the Mean Absolute Percentage Error (MAPE) across regular weekdays, weekends, and public holidays. XGBoost combined with MLFS consistently achieved the lowest MAPE values, demonstrating exceptional accuracy and robustness. Among all of the models, XGBoost combined with MLFS consistently achieved the lowest MAPE values, demonstrating superior accuracy and robustness. The results highlight the inadequacy of traditional models like ARIMA and SARIMA in capturing non-linear and highly volatile patterns, reinforcing the necessity of using advanced techniques for effective SMP forecasting. Overall, this study presents a novel and comprehensive approach tailored for complex electricity markets, significantly enhancing predictive reliability by incorporating economic indicators and sophisticated feature selection methods.

Regularization by double complementary priors for full waveform inversion

Full-waveform inversion (FWI) represents an advanced geophysical imaging technique focused on intricately depicting subsurface physical properties by iteratively minimizing the differences between the simulated and observed seismograms. Unfortunately, the conventional FWI utilizing a least-squares loss function suffers from various drawbacks, including the challenge of local minima and the necessity for human intervention in parameter fine-tuning. It is particularly problematic when handling noisy data and inadequate initial models. Recent works have exhibited promising performance in two-dimensional FWI by integrating structural sparse representation to procure adaptive dictionaries. Drawing inspiration from the competitiveness of structural sparse representation, we introduce a paradigm of group sparse residuals that integrates two types of complementary prior information by harnessing both the internal and external subsurface media models. The proposed algorithm is based on an alternate minimization algorithm to guarantee workflow flexibility and efficient optimization capabilities. We experimentally validate our method for two baseline geological models, and a comparison of the results demonstrates that the proposed algorithm faithfully recovers the velocity models and consistently outperforms other traditional or learning-based algorithms. A further benefit from the group sparse coding used in this method is that it reduces the sensitivity to data noise.

Evaluation of Drivers’ Mental Model, Trust, and Reliance Toward Level 2 Automated Vehicles

Level 2 automated vehicles (AVs) have been commercially available for years, yet the extent to which drivers are cognizant of their capabilities and limitations remains largely unknown. This study aimed to assess the public’s mental model of Level 2 AVs and investigate the relationships between mental model, trust, and reliance. Moreover, it sought to explore driver heterogeneity through cluster analysis to identify typical driver classes. Employing Tesla Autopilot as a representative Level 2 automation system, we designed a questionnaire measuring mental model from three dimensions: functional condition, sensorimotor preparation, and automation monitoring. This questionnaire was administered to both Tesla Autopilot owners (N = 357) and non-owners (N = 357). The results showed that, on average, drivers possessed an inadequate mental model regarding Level 2 AVs, and those with a relatively better mental model exhibited lower levels of trust and reliance. Owning a Level 2 AV did not result in a better understanding of the system, but it led to a higher trust level. Moreover, the results suggested that drivers’ trust and reliance in AVs tended to increase within the initial 6 months of usage and then kept stabilized after that. Through cluster analysis, we identified three distinct driver classes that exhibited significant differences in their mental model levels and attitudes towards AVs: Class 1 (blurred positive), Class 2 (sober neutral), and Class 3 (cautiously positive). This further validates the presence of heterogeneity among drivers. The insights gained from this study can serve as a valuable resource for supporting and guiding the development and implementation of training programs aimed at enhancing drivers’ comprehension of Level 2 AVs.

Semantic Segmentation of Satellite Images for Landslide Detection Using Foreground-Aware and Multi-Scale Convolutional Attention Mechanism.

Advancements in satellite and aerial imagery technology have made it easier to obtain high-resolution remote sensing images, leading to widespread research and applications in various fields. Remote sensing image semantic segmentation is a crucial task that provides semantic and localization information for target objects. In addition to the large-scale variation issues common in most semantic segmentation datasets, aerial images present unique challenges, including high background complexity and imbalanced foreground-background ratios. However, general semantic segmentation methods primarily address scale variations in natural scenes and often neglect the specific challenges in remote sensing images, such as inadequate foreground modeling. In this paper, we present a foreground-aware remote sensing semantic segmentation model. The model introduces a multi-scale convolutional attention mechanism and utilizes a feature pyramid network architecture to extract multi-scale features, addressing the multi-scale problem. Additionally, we introduce a Foreground-Scene Relation Module to mitigate false alarms. The model enhances the foreground features by modeling the relationship between the foreground and the scene. In the loss function, a Soft Focal Loss is employed to focus on foreground samples during training, alleviating the foreground-background imbalance issue. Experimental results indicate that our proposed method outperforms current state-of-the-art general semantic segmentation methods and transformer-based methods on the LS dataset benchmark.

Non-Parametric Fuzzy Regression Discontinuity Design in Modelling Viral Load Suppression Among People Living With HIV/AIDS (PLWHA)

Adherence to antiretroviral therapy (ART) is a significant determinant of viral load suppression in HIV patients. There are inadequate statistical models that bring out the direct effects of ART on the suppression of HIV/AIDS. Traditional regression models address the general determinants of viral load suppression. Regression discontinuity designs, on the other hand, bring out the causal effects of ART on viral load suppression based on various thresholds. This study used the non-parametric fuzzy regression discontinuity design (FRDD) to model viral load suppression in PLWHA. The study began with developing a non-parametric FRDD, simulating the model to assess its performance, and applying the model to the Quality-of-Care dataset from Kaggle. The study focused on viral load suppression as the outcome variable, CD4 count and age as the running variables, gender, and whether a patient received counseling as additional covariates. The optimal thresholds were 40.5 years and 320 cubic millimeters for the CD4. There was an increasing negative treatment effect of ART on viral load suppression as the cutoff points for CD4 count increase. At the same time, there was an increasing negative treatment effect of ART on viral load suppression with increasing age. The compliance ratios for respondents increased with the negative increase in the treatment effect. Other analyses, such as the McCrary density test, bunching test, and manipulation test, indicated that the non-parametric fuzzy regression discontinuity design is effective in modeling viral load suppression.

Development of a Microfluidic Vascularized Osteochondral Model as a Drug Testing Platform for Osteoarthritis.

Osteoarthritis (OA) is a degenerative joint disease characterized by changes in cartilage and subchondral bone. To date, there are no available drugs that can counteract the progression of OA, partly due to the inadequacy of current models to recapitulate the relevant cellular complexity. In this study, an osteochondral microfluidic model is developed using human primary cells to mimic an OA-like microenvironment and this study validates it as a drug testing platform. In the model, the cartilage compartment is created by embedding articular chondrocytes in fibrin hydrogel while the bone compartment is obtained by embedding osteoblasts, osteoclasts, endothelial cells, and mesenchymal stem cells in a fibrin hydrogel enriched with calcium phosphate nanoparticles. After developing and characterizing the model, Interleukin-1β is applied to induce OA-like conditions. Subsequently, the model potential is evaluated as a drug testing platform by assessing the effect of two anti-inflammatory drugs (Interleukin-1 Receptor antagonist and Celecoxib) on the regulation of inflammation- and matrix degradation-related markers. The model responded to inflammation and demonstrated differences in drug efficacy. Finally, it compares the behavior of the "Cartilage" and "Cartilage+Bone" models, emphasizing the necessity of incorporating both cartilage and bone compartments to capture the complex pathophysiology of OA.

MixUNETR: A U-shaped network based on W-MSA and depth-wise convolution with channel and spatial interactions for zonal prostate segmentation in MRI

Magnetic resonance imaging (MRI) plays a pivotal role in diagnosing and staging prostate cancer. Precise delineation of the peripheral zone (PZ) and transition zone (TZ) within prostate MRI is essential for accurate diagnosis and subsequent artificial intelligence-driven analysis. However, existing segmentation methods are limited by ambiguous boundaries, shape variations and texture complexities between PZ and TZ. Moreover, they suffer from inadequate modeling capabilities and limited receptive fields. To address these challenges, we propose a Enhanced MixFormer, which integrates window-based multi-head self-attention (W-MSA) and depth-wise convolution with parallel design and cross-branch bidirectional interaction. We further introduce MixUNETR, which use multiple Enhanced MixFormers as encoder to extract features from both PZ and TZ in prostate MRI. This augmentation effectively enlarges the receptive field and enhances the modeling capability of W-MSA, ultimately improving the extraction of both global and local feature information from PZ and TZ, thereby addressing mis-segmentation and challenges in delineating boundaries between them. Extensive experiments were conducted, comparing MixUNETR with several state-of-the-art methods on the Prostate158, ProstateX public datasets and private dataset. The results consistently demonstrate the accuracy and robustness of MixUNETR in MRI prostate segmentation. Our code of methods is available at https://github.com/skyous779/MixUNETR.git.

Figurative Language and Sensory Perception: Corpus-Based Computer-Assisted Study of the Nature and Motivation of Synesthetic Metaphors in Olive Oil Tasting Notes

ABSTRACT Meaning in sensory language is often built through figurative mechanisms, such as synesthetic metaphors, where a sensorial domain is used to talk about perceptions from a different sense, as in sweet [ taste ] texture [ touch ]. The motivation of synesthetic transfers of meaning has been studied in general and literary language, resulting in attempts to reveal universal patterns regarding the directionality of meaning transfer and sensorial conceptual preference. However, those universals have not been proven in any sensory Language for Specific Purposes (LSP). We a sensory LSP corpus of olive oil tasing notes in English to explore the nature of these metaphors, test existent models and explanatory accounts and identify tendencies present in synesthetic meaning transfers. The computer-assisted semi-automatic scalable methodology followed consists of the innovative quasi-simultaneous identification of semantic incongruences and the classification of synesthetic expressions in the discourse according to the source and target sensorial domains. Results show the inadequacy of existent models to explain synesthetic behavior in olive oil tasting language. The patterns found are discussed in the light of cognitive constraints and LSP genre analysis to conclude that a multi-causal approach is needed to explain the motivation of synesthetic transfers of meaning.

Why do things expand on heating? A discussion on the need for asymmetric potentials to understand thermal expansion

Abstract Understanding thermal expansion is pivotal in various contexts such as comprehending sea level rise due to climate change. Considering the importance of these topics for physics education, this article aims to clarify the characteristics of the models used to represent and explain thermal expansion. Limitations of the linear expansion equation commonly found in textbooks are shown, emphasising the importance of conceptual discussion about thermal expansion. Microscopic models are also explored, demonstrating the inadequacy of the harmonic oscillator model in explaining thermal expansion and introducing the necessity of employing asymmetric potentials. Through the graphic of the Lennard-Jones potential and an algebraic deduction akin to Enrico Fermi’s approach, the thermal expansion of solids and its relation with asymmetric interactions is explored.

Discrepancies in precipitation changes over the Southwest River Basin of China based on ISIMIP3b

Selecting appropriate global climate models (GCMs) is crucial for minimizing uncertainty in regional climate projections under future scenarios. Previous studies have predominantly assessed the modeling capability of GCMs for regional precipitation climatology and its long-term patterns based on annual and seasonal precipitation data. Building upon these, we primally evaluated the performance of five GCMs from phase 3b of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3b) in simulating precipitation concentration and its variations in the Southwest River Basin (SWRB) of China using the precipitation concentration index (PCI). The results indicate that: (1) The 5 GCMs generally capture the spatial distribution of annual average precipitation in the SWRB but significantly overestimate its magnitude, with a maximum regional average deviation of 207.80 mm. Furthermore, all models tend to overestimate the overall drying trend in the SWRB and show limited capability in simulating interdecadal variations of annual precipitation. (2) While the 5 GCMs reasonably simulate the spatial distribution of annual average PCI in the SWRB, they tend to overestimate its values, with a maximum regional average deviation of 1.54. Additionally, their simulation performance in capturing PCI trends and interdecadal variations is also limited. (3) The 5 GCMs tend to overestimate seasonal precipitation in the SWRB, with the best simulation performance for the distribution of autumn precipitation, followed by spring and summer, and the poorest for winter. Significant differences exist in the simulation performance of the models for seasonal precipitation proportions, which result in discrepancies in the models’ representation of PCI. Moreover, the models’ poor simulation performance of PCI trends is partly due to their inadequate modeling of trends in seasonal precipitation proportions. The findings will contribute to laying the foundation for meteorological hydrological research and water resource management in the SWRB.

How to mathematically model a drill-string: lumped or continuous models?

This work compares the efficacy of lumped and continuous models in simulating drill-string dynamics. We aim to critically explore the inadequacy of lumped models and challenge their perpetuated use in the literature. Lumped models represent the drill-string with a pre-determined number of discrete masses, sometimes as few as one or two. This inherently restricts their ability to capture the distributed nature of drill-strings, compromising their accuracy. In this paper, these limitations are made evident by comparing simulations of lumped models with simulations of continuous models that increase in complexity. First, predictions of the axial behaviour obtained with two models, one lumped and another continuous, are analysed. Second, simulations involving a 3-D fully coupled continuous model are evaluated. The simulations demonstrate the inaccuracy of lumped models. Also, they refute the frequent assumption of representing the bottom hole assembly (BHA) as a single lumped mass. Beyond these results, theoretical arguments based on fundamental wave-reflection phenomena are employed to further support the inadequacy of lumped formulations for this purpose. Third, this paper also discusses the prevalent strategy of validating lumped models through comparisons with laboratory experiments. This approach suffers from being a makeshift validation, as similitude principles are often violated in laboratory setups, resulting in a practice that may have helped perpetuate lumped models in the literature. Consequently, using lumped models for general drill-string dynamics prediction is not appropriate. Recognising their limitations and exploring alternative approaches, such as continuous models, is crucial for accurate drill-string dynamics prediction.

Does the Merger of the Indonesian Islamic Bank Matter for Its Social Mission of Economic Empowerment?

Research Originality: Economic empowerment through financing Micro Small Medium Enterprises (MSMEs) is one dimension of the social mission of sharia banking in Indonesia. The merger action of Bank of Sharia Indonesia (BSI) raises the question of whetherthe performance of economic empowerment has changed.Research Objectives: This research aims to analyse the differences in the BSI’s performance of economic empowerment during preand post-merger periods and analyse the issues and strategies for improving BSI's post-merger economic empowerment performance.Research Methods: It is a research using mixed method combining a quantitative explorative analysis approach using Wilcoxon Test, and a qualitative one using Analytic Network Process.Empirical Results: This research finds that BSI's economic empowerment performance has increased in terms of the quantity but decreased in terms of proportion in the post-merger period. ANP research demonstrates that risk management is a priority issue for the BSI post-merger period. The social mission of economic empowerment indicates several priority issues, namely, the relatively higher rate of profit margin than interest, the inadequate supervision model, the low rate of risk management literacy, and the relatively higher risk of profit loss sharing (PLS)-based financing. The strategy analysis indicates that policy intervention for BSI’s MSMEs financing is a top priority in improving the performance of its economic empowerment.Implications: This research recommends some recommendations for sharia banks’ MSMEs financing equivalent to the one of conventional banks. Economic digitalization as technological means to widen accessibility of economic empowerment and distribution segmentation focusing more on the halal sector.JEL Classification: A13, G21, G34How to Cite:Isman, A. F., &amp; Hidayah, N. (2024). Does the Merger of the Indonesian Islamic Bank Matter for Its Social Mission of Economic Empowerment? Etikonomi, 23(2), 333 – 352. https://doi.org/10.15408/etk.v23i2.39810.

METHOD TO DEVELOP HYBRID MANAGEMENT MODELS FOR R&amp;D PROJECTS BY USING A MORPHOLOGICAL ANALYSIS MATRIX

Management professionals are increasingly adopting hybrid implementation models for applicative R&amp;D projects, specific to dynamic environments, for rapidly adapting to various challenges. In this paper, being aware of the inadequacy of traditional management models in today's volatile and innovative socio-economic landscape, we made somehow a literature review and we adapted a method that, based on the use of a morphological analysis matrix, allowed us to prepare, by implementing in the first phase a focus-group pilot, the elaboration of personalized hybrid models, targeting mainly, as an original approach, research – development - innovation (RDI) projects. In the future, we intend to validate the method, through a case study evaluation, aiming to provide professionals with appropriate managerial solutions by developing a guide for selecting the best practices and adopting high-performing hybrid management models.

Computational fluid dynamics simulation of ammonia leakage scenarios during ship-to-ship bunkering

Ammonia is emerging as a potential marine fuel, yet its toxicity and risk of accidental leaks pose significant safety challenges compared to other alternatives. This study focuses on the critical ship-to-ship bunkering process using Computational Fluid Dynamics (CFD) simulations to analyse diverse ammonia leakage scenarios considering various leak orientations relative to the wind and the ships. The numerical setup was initially validated against controlled experiments, demonstrating satisfactory agreement. Results indicate that atmospheric ammonia dispersion is significantly influenced by obstacles like the superstructure and hull in the near field, with diminishing effect in the far field. Conventional Gaussian plume models are inadequate within the first few hundred metres from the release source due to the complex near-field flow patterns and increased turbulence generated by the wakes of the hull and superstructure. These findings underscore the necessity for improved ship-scale dispersion models to accurately evaluate the impact of obstacles on ammonia dispersion. The consequence analysis reveals that upward leaks create larger risk zones with potential for irreversible health effects, while horizontal leaks may expose personnel to life-threatening concentrations. Vertical leaks in crosswind configurations generate lethal zones covering tens of metres on deck. These results highlight the importance of considering the exact location of the transfer hose relative to the ship and all potential wind orientations in risk assessments. Given the extensive range of potential dispersion patterns and the inadequacy of conventional Gaussian dispersion models in this context, we strongly recommend vessel-specific CFD simulations before certifying ammonia bunkering operations as safe. This approach is crucial for accurate risk assessment at the ship scale, where complex geometries and multi-directional flows invalidate the assumptions of traditional point-source, uni-directional dispersion models.

Clinician Perspectives on Palliative Care for Older Adults With Serious Mental Illnesses: A Multisite Qualitative Study

ObjectivesApproximately 5.5% of the population live with serious mental illnesses (SMI). Older adults with SMI experience a high burden of serious medical illnesses and disparities in advance care planning, symptom management, and caregiver support. The objectives of this study are to explore interdisciplinary clinician perspectives on the palliative care needs of older adults with SMI and serious medical illnesses. Design, setting, and participantsThis qualitative study utilized thematic analysis of semi-structured interviews of interdisciplinary clinicians practicing palliative care, geriatrics, or geriatric/consultation-liaison psychiatry at four hospitals within an urban health system. MeasurementsThemes related to care of older adults with serious mental illness and serious medical illness with respect to clinician experiences, challenges in care, and opportunities to improve care. ResultsThe authors interviewed 45 clinicians. Major themes identified were: (1) Current paradigms of palliative care do not meet the needs of patients with SMI; (2) Clinicians are motivated to care for this population but require more training and interdisciplinary practice; (3) There is a need for structural integration of psychiatric and palliative care services. ConclusionsThe study underscores the inadequacy of current palliative care models in meeting the unique needs of older adults with SMI. Models of integrated psychiatric and serious illness care and enhanced training are needed to improve the delivery of palliative care. Integrated care models and workforce development at the interface of serious illness care and psychiatric have the potential to improve outcomes for this vulnerable population.

An AHP-based evaluation framework for automotive stamping suppliers

PurposeThis study aims to establish a robust evaluation framework for suppliers within the automotive supply chain, specifically in the stamping sector. The primary objectives are to elucidate the performance criteria of suppliers, identify indicators and scales for measuring these criteria and find the importance of the criteria.Design/methodology/approachThe evaluation framework comprises a criteria hierarchy and indicators developed based on the evaluation criteria of major automotive manufacturers. Specific indicators and measurement scales are recommended for assessing suppliers. Importance weights for the criteria are assigned based on the input of nine experts using the Analytic Hierarchy Process (AHP). Finally, four sheet metal stamping tooling (SMST) suppliers are evaluated by four specialists using the proposed evaluation framework.FindingsThe study introduces a novel classification of criteria, encompassing financial and commercial perspectives, delivery capability, supplier facility and cultural approaches and business process necessities. The findings underscore the significance of financial and commercial stability in the selection of SMST suppliers, emphasizing their role in mitigating risks associated with disruptions, bankruptcies and unforeseen events. Additionally, several SMST evaluation factors identified in this study contribute to the development of resilience capabilities, highlighting the crucial importance of their inclusion and assessment in the proposed evaluation framework.Originality/valueThis research presents a comprehensive model for evaluating SMST suppliers, which tackles the multidisciplinary challenges within the automotive supply chain. Given the inadequacy or nonexistence of current SMTS selection models, this study bridges the gap by exploring potential and necessary criteria, alongside 116 specific indicators and measurement scales.