Search Results (49)

This article examines how drama in English portrays men who anchor their self-worth to sexual potency and masculine performance, particularly when that confidence faces direct challenge or the quiet pressure of ageing. Through sustained close reading of Macbeth, Antony and Cleopatra, and Who’s Afraid of Virginia Woolf?, the analysis traces how characters negotiate pride, vulnerability, and the defence of manhood through dialogue, pacing, and silence. Existing criticism has often approached these moments through historical or ideological lenses; this reading instead attends to the psychological negotiation visible in the text itself. Drawing on established work on gender performance and narrative identity, the article treats masculine pride not as a fixed cultural pattern but as a dramatic act that characters repeat, defend, and sometimes surrender. The theoretical tools serve the text rather than dictate it, allowing the plays to reveal how men construct, protect, and adjust their sense of virility when it is called into question. The findings emerge from careful attention to scene structure, verbal exchange, and the unspoken tensions that shape dramatic portrayal. Full article

Advances in the decision-making field and the growing adoption of hybrid mathematical methods, combined with practitioners’ and managers’ interest in more efficient systems, have intensified the search for improved combinations of mathematical modeling capable of supporting robust, effective decision processes. In this context, it becomes possible to incorporate different methods for assigning substitution rates in multicriteria methods, thereby reducing uncertainty in the application of such models. The purpose of this study was to examine research that applies the MACBETH method (measuring attractiveness by a categorical-based evaluation technique) in combination with other weighting techniques, particularly the information entropy method. The literature mapping evaluated the evolution of studies on this topic, the temporal progression of research, the most productive authors, and the journals most aligned with the theme while also identifying the techniques and sectors in which they are applied. The review further highlighted benefits, limitations, and future challenges and developments in the field. The research was conducted through a systematic literature review, with analysis conducted using a bibliometric approach complemented by a meta-synthesis. The findings reveal a growing number of publications in recent years, along with the main techniques combined for assigning substitution rates. The results also highlight the sectors of application, leading journals, and emerging trends in the domain of hybrid multicriteria methods. Given the limited research on this topic, this is considered a valuable contribution. Full article

This article presents a comprehensive ranking of popular Multi-Criteria Decision Analysis (MCDA) methods, assessed across six dimensions of visibility and usage: general web presence, academic mentions, number of academic publications, citation impact, reported academic applications, and real-world (industrial and public sector) use cases. Rather than evaluating theoretical performance or methodological robustness, this study focuses on visibility and adoption, the extent to which an MCDA method has achieved prominence through name recognition, dissemination, academic use, and documented practical application. The observed ranking patterns are consistent with the possibility that method popularity is partly shaped by branding, dissemination dynamics, and cumulative visibility, rather than by demonstrated methodological superiority alone. AHP emerges as the most dominant method across all six criteria, followed by TOPSIS, PROMÉTHÉE, and VIKOR. Newer or more recently visible methods such as BWM demonstrate rapid academic growth, while software-supported methods such as PAPRIKA illustrate how dedicated tools can strengthen practical visibility. By contrast, established methods like ÉLECTRE and MACBETH, while academically respected, exhibit more niche or regionally concentrated use. The study also touches on the meta-cognitive irony that MCDA methods, designed to reduce bias and promote rational deliberation, may themselves be adopted in ways consistent with mechanisms such as availability, familiarity, and brand recognition. This raises concerns about the criteria by which methodological choices are made, particularly in academic modelling and industrial decision-support contexts. Ultimately, the findings suggest that while branding plays a legitimate role in method dissemination, it should not overshadow considerations of methodological transparency, theoretical coherence, and decision-context fit. By distinguishing between methodological performance and visibility/adoption, this article contributes to a more reflective discourse on the selection, teaching and development of MCDA methods and techniques. The ranking should therefore be interpreted as a comparative map of visibility and adoption, not as an assessment of methodological quality, theoretical soundness, or decision-analytic performance. Full article

In recent decades, residential mobility has emerged as a fundamental interpretative key lens for understanding contemporary urban transformations, particularly in polycentric and fragmented urban contexts. Movements between different residential settings reflect economic, social and cultural changes, impacting the organisation of urban spaces, the demand for services and mobility systems. In territories characterised by dispersed settlement patterns and strong functional polarisation, these dynamics tend to promote the intensive use of private means, with consequent negative impacts on environmental sustainability, social equity and economic efficiency. In response to these critical issues, there is growing interest in sustainable mobility models based on proximity and on the integration between daily travel, access to services and the quality of public space. Within this perspective, greenways are configured as hybrid infrastructures, capable of reorganising mobility while contributing to the regeneration of urban spaces. In the Caserta area, in the Campania region, the disused route of the former Alifana railway represents a topic of great interest, both for research and planning. Its potential strategic conversion into a greenway opens a broader perspective than that so far considered at the regional level, which has mainly focused on the infrastructure dimension. The paper analyses the strengths and weaknesses of an approach limited to infrastructural mobility, proposing a comparative evaluation of project scenarios—including the non-intervention hypothesis—both through the application of the MACBETH approach and preliminary parametric estimation of construction costs, in order to emphasise the importance of integrating social and environmental benefits, as well as quality of life, into decision-making processes. Full article

Background/Objectives: Esophageal cancer is one of the major global causes of cancer mortality, and the 5-year survival rate remains below 20% because many cases are detected late. In this study, a Spectral-Aided Vision Enhancer (SAVE) algorithm was utilized to convert conventional white-light endoscopic images (WLI) into hyperspectral-like narrow-band imaging (NBI) images for machine-learning classification of Dysplasia, Normal, and Squamous Cell Carcinoma (SCC). Methods: A total of 762 WLI images obtained from Kaohsiung Medical University were augmented to 1074 using the Al bumentations library, employing vertical flipping, horizontal flipping, and rotations. The SAVE conversion pipeline employs a 24-patch Macbeth color checker for calibration, γ-correction, CIE XYZ transformation, and multivariate regression to interpolate spectral bands, yielding an average color difference of 2.79 (CIEDE2000) from true NBI. The training outcomes and performance metrics illustrate the versatility of the machine learning/deep learning models—Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN)—which were trained and evaluated on both the original WLI and SAVE datasets. Performance metrics were analyzed based on precision, recall, accuracy, and F1-score. Results: The CNN sample achieved an accuracy of 100 percent on SAVE data, compared to 93 percent for WLI. The accuracy of RF improved, with WLI at 91% and SAVE at 96%, while SVM increased from 79% to 84%. These improvements indicate the diagnostically valuable spectral variations that can be amplified with SAVE, resulting in significant enhancements in pre-cancer/SCC sensitivity. Conclusions: The proposed SAVE method demonstrates significant potential for enhancing endoscopic imaging and advancing computer-aided diagnosis in esophageal cancer screening, with applicability in other gastrointestinal imaging scenarios as well. Full article

Patient manual handling during positioning is widely recognised to have a sparse evidence base, exposing healthcare practitioners (HCPs) to a high risk of work-related musculoskeletal disorders (WRMSDs). This study aimed to provide in-depth insight into the challenges of manual patient bed positioning in long-term care settings to identify best practices for optimising care. Semi-structured interviews were conducted with purposively recruited subject experts in the UK (n = 9; aged 30–62 years). Interviews focused on challenges, best practices, and solutions to patient manual handling positioning. Data were explored using thematic and framework analysis. Major gaps were evident in HCP training and in key aspects of positioning, including patient bed mobility, postural management, and turning patients into the side-lying position. Experts identified a need for realistic, comprehensive training for HCPs on the integrated, optimised use of low-tech equipment (e.g., wedges, breathable pillows, sliding systems, and sleep systems) for safe, single-handed patient positioning. This study provided novel recommendations for optimising practices in patient bed mobility, posture care, repositioning, and turning into side-lying, aimed at improving patient outcomes and mitigating occupational risks. Full article

The integration of environmental, social, and governance (ESG) principles into investment strategies represents a potential pathway for advancing financial sustainability and long-term market resilience. The usage of ESG screening techniques in portfolio construction is currently a subject of debate among practitioners and policymakers. This paper introduces a methodology that incorporates ESG scores into a low-volatility, Standard & Poor’s 500 index-based strategy without relying on traditional exclusionary screening. Rather than removing firms based solely on low ESG scores, we treat ESG as a predictive sustainability factor in identifying firms likely to experience extreme return volatility in the subsequent year, using a probit model and Fama–Macbeth estimation techniques. Firms with high ESG scores are found to be less likely to exhibit such behavior, suggesting an inverse relationship between ESG and risk. Our results show that portfolios constructed using this approach achieve higher average ESG scores, maintain returns equivalent to the benchmark, and reduce annualized return volatility by approximately 1.0%, a statistically significant reduction. By reframing ESG from a moral filter into a measurable risk mitigation mechanism, this study demonstrates how sustainability integration can enhance portfolio stability while supporting both financial and societal objectives. The proposed framework offers practical alternative for investors seeking exposure to sustainability-focused strategies while preserving traditional performance objectives. Full article

Background/Objectives: Immunotherapy has significantly improved clinical outcomes for patients with late-stage melanoma, yet a substantial portion of patients fail to respond to these treatments. The variability in responses to immunotherapy, both among individual patients and across different melanoma subtypes, underscores the need to explore the influence of circulating factors such as oxylipins on therapeutic outcomes. This study investigated the relationship between serum oxylipin profiles and response to immune checkpoint inhibitor therapy in melanoma subtypes to identify potential metabolic biomarkers for treatment response. Methods: In a retrospective cohort study, serum samples from 43 stage III and stage IV melanoma patients treated at the University of Colorado Hospital from 2010 to 2023 were analyzed via ultra-high-pressure liquid chromatography-mass spectrometry. Melanoma patients were treated with anti-PD-1 monotherapy or combination immune checkpoint inhibitor therapy, and response was assessed using RECIST 1.1 criteria. Results: We determined that global oxylipin metabolite profiles are largely uniform pre- and post-treatment across melanoma subtypes, including cutaneous, acral, mucosal, and uveal melanoma. Prostaglandin J₂ was more abundant in rare melanoma subtypes, including acral, mucosal, and uveal melanoma, compared to cutaneous melanoma. Conclusions: Despite limited variation in serum oxylipin molecular species by subtype and response status, we observed significant differences in prostaglandin J₂, which could serve as a potential biomarker for immune checkpoint inhibitor therapy response in melanoma. Full article

The sanitation sector faces significant challenges in achieving the universalisation goals established by the new 2020 regulatory framework. Prioritising these investments is essential due to the limited financial resources available, especially in sanitation projects. This article proposes a prioritisation model based on the Measuring Attractiveness by a Category-Based Evaluation Technique (MACBETH) method, aiming to order the execution of sewage projects by municipality when considering the perspectives of water and sewage concessionaires. The methodology involves brainstorming and Web-Delphi steps to identify criteria and subcriteria, as well as the use of the M-MACBETH software version 2.5.0 to define weights and value judgements. The research, conducted as a case study, employs a qualitative, quantitative, exploratory, and descriptive approach, emphasising interdisciplinary collaboration and model validation with experts. The conclusion highlights that the proposed model can be replicated in various contexts, enabling dealers to make more informed and effective decisions. Suggestions for future research include adapting the model to other areas of sanitation and integrating advanced technologies, such as artificial intelligence, for dynamic data analysis and management. Full article

Diagnosing gastrointestinal disorders (GIDs) remains a significant challenge, particularly when relying on wireless capsule endoscopy (WCE), which lacks advanced imaging enhancements like Narrow Band Imaging (NBI). To address this, we propose a novel framework, the Spectrum-Aided Vision Enhancer (SAVE), especially designed to transform standard white light (WLI) endoscopic images into spectrally enriched representations that emulate both hyperspectral imaging (HSI) and NBI formats. By leveraging color calibration through the Macbeth Color Checker, gamma correction, CIE 1931 XYZ transformation, and principal component analysis (PCA), SAVE reconstructs detailed spectral information from conventional RGB inputs. Performance was evaluated using the Kvasir-v2 dataset, which includes 6490 annotated images spanning eight GI-related categories. Deep learning models like Inception-Net V3, MobileNetV2, MobileNetV3, and AlexNet were trained on both original WLI- and SAVE-enhanced images. Among these, MobileNetV2 achieved an F1-score of 96% for polyp classification using SAVE, and AlexNet saw a notable increase in average accuracy to 84% when applied to enhanced images. Image quality assessment showed high structural similarity (SSIM scores of 93.99% for Olympus endoscopy and 90.68% for WCE), confirming the fidelity of the spectral transformations. Overall, the SAVE framework offers a practical, software-based enhancement strategy that significantly improves diagnostic accuracy in GI imaging, with strong implications for low-cost, non-invasive diagnostics using capsule endoscopy systems. Full article

Timely and accurate diagnosis of gastrointestinal diseases (GIDs) remains a critical bottleneck in clinical endoscopy, particularly due to the limited contrast and sensitivity of conventional white light imaging (WLI) in detecting early-stage mucosal abnormalities. To overcome this, this research presents Spectrum Aided Vision Enhancer (SAVE), an innovative, software-driven framework that transforms standard WLI into high-fidelity hyperspectral imaging (HSI) and simulated narrow-band imaging (NBI) without any hardware modification. SAVE leverages advanced spectral reconstruction techniques, including Macbeth Color Checker-based calibration, principal component analysis (PCA), and multivariate polynomial regression, achieving a root mean square error (RMSE) of 0.056 and structural similarity index (SSIM) exceeding 90%. Trained and validated on the Kvasir v2 dataset (n = 6490) using deep learning models like ResNet-50, ResNet-101, EfficientNet-B2, both EfficientNet-B5 and EfficientNetV2-B0 were used to assess diagnostic performance across six key GI conditions. Results demonstrated that SAVE enhanced imagery and consistently outperformed raw WLI across precision, recall, and F1-score metrics, with EfficientNet-B2 and EfficientNetV2-B0 achieving the highest classification accuracy. Notably, this performance gain was achieved without the need for specialized imaging hardware. These findings highlight SAVE as a transformative solution for augmenting GI diagnostics, with the potential to significantly improve early detection, streamline clinical workflows, and broaden access to advanced imaging especially in resource constrained settings. Full article

Over the past decade, multifactor models have shown enhanced capability compared to single-factor models in explaining asset return variability. Given the common assertion that higher risk tends to yield higher returns, this study empirically examines the augmented human capital six-factor model’s performance on thirty-two portfolios of non-financial firms sorted by size, value, profitability, investment, and labor income growth in the Indian market over the period July 2010 to June 2023. Moreover, the current study extends the Fama and French five-factor model by incorporating a human capital proxy by labor income growth as an additional factor thereby proposing an augmented six-factor asset pricing model (HC6FM). The Fama and MacBeth two-step estimation methodology is employed for the empirical analysis. The results reveal that small-cap portfolios yield significantly higher returns than large-cap portfolios. Moreover, all six factors significantly explain the time-series variation in excess portfolio returns. Our findings reveal that the Indian stock market experienced heightened volatility during the COVID-19 pandemic, leading to a decline in the six-factor model’s efficiency in explaining returns. Furthermore, Gibbons, Ross, and Shanken (GRS) test results reveal mispricing of portfolio returns during COVID-19, with a stronger rejection of portfolio efficiency across models. However, the HC6FM consistently shows lower pricing errors and better performance, specifically during and after the pandemic era. Overall, the results offer important insights for policymakers, investors, and portfolio managers in optimizing portfolio selection, particularly during periods of heightened market uncertainty. Full article

This research evaluates four hydrogen (H₂) production technologies via water electrolysis (WE): alkaline water electrolysis (AWE), proton exchange membrane electrolysis (PEME), anion exchange membrane electrolysis (AEME), and solid oxide electrolysis (SOE). Two scoring and ranking methods, the MACBETH method and the Pugh decision matrix, are utilized for this evaluation. The scoring process employs nine decision criteria: capital expenditure (CAPEX), operating expenditure (OPEX), operating efficiency (SOE), startup time (SuT), environmental impact (EI), technology readiness level (TRL), maintenance requirements (MRs), supply chain challenges (SCCs), and levelized cost of H₂ (LCOH). The MACBETH method involves pairwise technology comparisons for each decision criterion using seven qualitative judgment categories, which are converted into quantitative scores via M-MACBETH software (Version 3.2.0). The Pugh decision matrix benchmarks WE technologies using a baseline technology—SMR with CCS—and a three-point scoring scale (0 for the baseline, +1 for better, −1 for worse). Results from both methods indicate AWE as the leading H₂ production technology, which is followed by AEME, PEME, and SOE. AWE excels due to its lowest CAPEX and OPEX, highest TRL, and optimal operational efficiency (at ≈7 bars of pressure), which minimizes LCOH. AEME demonstrates balanced performance across the criteria. While PEME shows advantages in some areas, it requires improvements in others. SOE has the most areas needing enhancement. These insights can direct future R&D efforts toward the most promising H₂ production technologies to achieve the net-zero goal. Full article