Search Results (8,525)

Backgrounds/Objectives: The aging of the population is reflected in the increasing number of elderly patients admitted to intensive care units (ICUs), where assessing prognosis and the potential benefit of intensive care is challenging. The aim of this study was to clinically characterize ICU patients aged ≥80 years in the National Medical Institute of the Ministry of Internal Affairs and Administration in Warsaw, Poland. Methods: We retrospectively analyzed ICU patients admitted between 2018 and 2022, considering comorbidities, prognostic scores, the treatment methods and outcomes. Results: We included 476 patients (median age 84 [range 80–103 years], female 54.4%, median ICU stay 8 days) with a high incidence of various comorbidities. The overall risk of death was very high (76.4%) but was independent of sex and, surprisingly, of age. Advanced frailty was common, as indicated by the Clinical Frailty Scale (CFS) score (median 7, n = 189), which was identified as a significant risk factor for death independent of age, sex, and APACHE score (odds ratio for the 1-point CFS increase: 1.08, 95% CI 1.01–1.15, n = 103), but not of SAPS and SOFA scores. Organ support techniques were frequently used (invasive mechanical ventilation in 90.9%, pharmacological cardiovascular support in 83.2%, and renal replacement therapy in 14.1% of patients), with high associated mortality rates (80%, 79%, and 88%, respectively). Conclusions: Our results confirm the value of the prognostic scales used on admission to the ICU, but also highlight the need for individualized assessment of the expected benefit of ICU treatment in elderly patients, considering specific comorbidities, previous treatment and frailty. Full article

This study examines the impact of sociodemographic factors on the attitudes of EU citizens towards animal welfare and their implications for trade policy. Variations in animal welfare legislation across major commercial blocks create ethical and economic challenges, with the EU implementing stringent standards compared with other countries. Data were drawn from the Special Eurobarometer 533 survey, conducted between the 3rd and 26th March 2023, which employed a multistage, clustered sampling method across all 27 EU Member States, yielding a representative sample of 26,368 respondents. The survey collected detailed information on attitudes toward animal welfare alongside sociodemographic characteristics. The results revealed substantial public support for stricter regulations and informative labelling, with attitudes differing with age (p < 0.001), education (p < 0.001), income (p < 0.001), and political orientation (p < 0.001), according to the multinomial regression applied to each of the independent variables. These results emphasise the importance of these factors in shaping consumer expectations. The findings highlight the need for policymakers to integrate ethical and societal values into trade negotiations, ensuring that policies reflect public concerns, promote fair competition, and encourage higher animal welfare standards internationally. Additionally, understanding the perspectives and motivations of livestock industry stakeholders remains critical for implementing effective welfare strategies. By aligning EU trade policy with citizen values and stakeholder practices, it is possible to advance animal welfare globally while balancing economic and ethical considerations and promoting a fair trade. Full article

Biogas, a promising fuel for present and future generations, is produced from the anaerobic digestion of organic waste generated by the condominium itself. Therefore, this work aims to evaluate the exergoeconomic performance of a biogas cogeneration unit designed to meet the electrical and thermal energy demands of a residential condominium in the city of Teresina, Piauí, Northeast Brazil. The cogeneration unit consists of an internal combustion engine (ICE) coupled to an electric generator (genset) to produce electricity, and a heat exchanger that recovers part of the exhaust-gas heat to heat water. The analysis was conducted based on the concepts of Thermodynamics and Exergoeconomics, using the SPECO (Specific Exergy Costing) methodology to define the exergetic costs of the system. The novelty of the work lies in applying the SPECO exergoeconomic analysis to a small-scale biogas cogeneration unit fueled by residential organic waste. The achieved electricity production was 167.40 kW, and the heat transfer rate at the exchange rate was 51.55 kW. The results revealed that the exergy destroyed in the internal combustion chamber (ICE) was 223.65 kW, whereas that in the heat exchanger was significantly higher at 45.67 kW. The exergy efficiency of the ICE reached 39.19%, and the heat exchanger efficiency was around 9%. In financial terms, the cost of exergy destroyed in the ICEC was USD/h 135, but in the heat exchanger, it was dramatically higher at USD/h 158.40. The cost of producing hot water (product) was considered extremely high (USD/GJ 38.98). The relative difference parameter in the heat exchanger also has a value much higher than expected (10.240). This is because the product’s cost (USD/GJ 38.98) is well above the cost of fuel (USD/GJ 3.468). This study concludes that the cogeneration unit is more justifiable by the savings generated through thermal energy production than by electricity production alone, since the cogeneration system significantly enhances performance, raising both the energetic and exergetic efficiencies to 55% and 48%, respectively, thereby confirming the added value of the simultaneous utilization of heat and power. Full article

Isolated microgrids with green hydrogen storage offer a promising solution for supplying electricity to remote communities where conventional grid expansion is infeasible. Designing such systems requires balancing two conflicting objectives: minimizing installation and operation costs while maximizing supply reliability. This paper proposes a multi-objective optimization methodology, based on the Non-dominated Sorting Genetic Algorithm II, to determine the optimal sizing of multiple microgrid components. This sizing explicitly addresses both the power capacities (kW) (for photovoltaic panels, wind turbines, electrolyzers, and fuel cells) and the energy storage capacities (kWh and kg) (for batteries and hydrogen tanks, respectively), aiming to generate Pareto-optimal solutions that explore this trade-off. The proposed method evaluates the trade-off by minimizing two objectives: the Net Present Value, which includes investment, replacement, and maintenance costs, and the total expected interruption hours, derived from an hourly energy balance analysis. The methodology’s effectiveness is validated using four distinct case studies. Three of these are based on real locations with specific load profiles and climate data. To test the method’s robustness, a fourth case study uses a fictitious load profile, designed with pronounced seasonal variations and a clear distinction between weekday and weekend consumption. Our results demonstrate the method’s ability to identify efficient hybrid renewable topologies combining photovoltaic and/or wind generation, batteries, and hydrogen systems (electrolyzer, storage tank, and fuel cell). The obtained cost–reliability curves provide practical decision-support tools for system planners. Full article

Novel highly compressible and stretchable nanocomposite (NC) hydrogels were obtained by the free radical polymerization of N-vinylformamide (NVF) in aqueous solution in the presence of Laponite XLG (XLG) as the crosslinker and 2,2′-azobis(2-methylpropionitrile) as the initiator. The expected composition of the NC hydrogels was proved by FTIR, TEM, XRD, and TGA analyses. Swelling degree (SD) and mechanical measurements showed that the properties of the PNVF NC hydrogels were largely different from those of both PNVF hydrogels covalently crosslinked by N,N’-methylenebisacrylamide (MBA) and equivalent poly(N-vinyl-2-pyrrolidone) (PNVP) NC hydrogels. After an initial fast swelling stage, the PNVF NC hydrogels displayed a slow, but steady, SD increase with time, unlike the MBA-crosslinked and NVP hydrogels, which exhibited a much smaller SD change during their second swelling stage. The mechanical testing of the synthesized hydrogels by uniaxial compressive and tensile measurements showed much higher compressibility (>90%) and stretchability (up to ≈840%) in the PNVF NC hydrogels than both PNVP and MBA-crosslinked PNVF hydrogels (compressibility < 80%; stretchability up to ≈114%). Cyclic compression tests revealed higher values for both elastic character and mechanical stability in the PNVF NC hydrogels in comparison to the MBA-crosslinked and PNVP ones. These different mechanical properties were explained by the PNVF NC gels possessing a network made of homogeneously distributed crosslinking sites and flexible polymer chains, thus avoiding extensive chain breakage up to larger stress values. The PNVF NC hydrogels described here may find applications for water purification, due to their high clay content, as well as in the biomedical field based on the biocompatibility of both the polymer and crosslinking agent. Full article

This study examines the suitability of multifamily housing for young families in Riyadh in the context of rapid urban growth and rising housing demand. Although apartments are increasingly promoted as efficient and sustainable housing options, limited evidence exists on whether they align with the cultural expectations, affordability constraints, and spatial needs of young Saudi households. A questionnaire of 639 respondents was conducted between January and March 2024, and the data were analysed using a combination of non-parametric and regression-based statistical techniques. Findings show that affordability is the primary barrier to ownership, with married and larger households experiencing the greatest financial pressure. Construction quality, security, room number, and location were the most valued apartment attributes, while stylistic features were less influential. Preferences for full-service gated complexes reflect the importance of privacy, safety, and structured community environments. The data analysis indicates that family size and perceived price-income suitability shape ownership intentions, although the overall model fit suggests additional institutional and market constraints. The study concludes that improving multifamily housing for young families requires integrated policy responses linking affordability measures, governance and building management reforms, and culturally responsive design standards to support the aims of sustainable urban development. Full article

The cosmological constant (CC, $\Lambda$) problem stands as one of the most profound puzzles in the theory of gravity, representing a remarkable discrepancy of about 120 orders of magnitude between the observed value of dark energy and its natural expectation from quantum field theory. This paper synthesizes two innovative paradigms—holographic naturalness (HN) and pre-geometric gravity (PGG)—to propose a unified and natural resolution to the problem. The HN framework posits that the stability of the CC is not a matter of radiative corrections but rather of quantum information and entropy. The large entropy $S_{\mathrm{dS}}\sim M_{\mathrm{P}}^2/\Lambda$ of the de Sitter (dS) vacuum (with $M_{\mathrm{P}}$ being the Planck mass) acts as an entropic barrier, exponentially suppressing any quantum transitions that would otherwise destabilize the vacuum. This explains why the universe remains in a state with high entropy and relatively low CC. We then embed this principle within a pre-geometric theory of gravity, where the spacetime geometry and the Einstein–Hilbert action are not fundamental, but emerge dynamically from the spontaneous symmetry breaking of a larger gauge group, SO($1,4$)→SO($1,3$), driven by a Higgs-like field ${\varphi }^A$. In this mechanism, both $M_{\mathrm{P}}$ and $\Lambda$ are generated from more fundamental parameters. Crucially, we establish a direct correspondence between the vacuum expectation value (VEV) v of the pre-geometric Higgs field and the de Sitter entropy: $S_{\mathrm{dS}}\sim v$ (or $v^3$). Thus, the field responsible for generating spacetime itself also encodes its information content. The smallness of $\Lambda$ is therefore a direct consequence of the largeness of the entropy $S_{\mathrm{dS}}$, which is itself a manifestation of a large Higgs VEV v. The CC is stable for the same reason a large-entropy state is stable: the decay of such state is exponentially suppressed. Our study shows that new semi-classical quantum gravity effects dynamically generate particles we call “hairons”, whose mass is tied to the CC. These particles interact with Standard Model matter and can form a cold condensate. The instability of the dS space, driven by the time evolution of a quantum condensate, points at a dynamical origin for dark energy. This paper provides a comprehensive framework where the emergence of geometry, the hierarchy of scales and the quantum-information structure of spacetime are inextricably linked, thereby providing a novel and compelling path toward solving the CC problem. Full article

To address the need for robust investment strategies in an increasingly uncertain global market, this study focuses on an ambiguity-averse insurer facing exchange rate uncertainty while investing in a foreign stock market. The insurer’s surplus is modeled via a classical compound Poisson process, and exchange rate dynamics are captured using an Ornstein–Uhlenbeck process for the drift component. Within the framework of maximizing expected exponential utility of terminal wealth, we derive and solve the Hamilton–Jacobi–Bellman equation to characterize the optimal investment strategy and the associated value function. Finally, a numerical example illustrates how varying model parameters influences the insurer’s optimal investment behavior. Full article

Raw material price volatility, freight rates, and foreign exchange all pose significant uncertainty for lithium-ion battery manufacturers, jeopardising procurement planning and financial stability. In this paper, we formulate a discrete Conditional Value-at-Risk (CVaR) optimisation model to design implementable robust hedging strategies for multi-factor cost exposure. Unlike conventional continuous hedge models, which are often severely parameter-sensitive and require frequent rebalancing, the discrete approach takes hedge ratios to be fixed at a finite implementable grid (0%, 50%, 100%) and simultaneously minimises the expected cost and tail risk. We conduct two case studies: the first evaluates the model behaviour under stochastic price shocks using a multi-market simulation data set, and the second subjects the model to stress testing on correlation drift and tail amplification in order to examine systemic robustness. Our results show that, compared with an OLS-based hedge or a fully hedged benchmark, the discrete CVaR framework yields smoother hedge patterns, lower tail losses, and improved liquidity stability; in addition, our results indicate that, when combined with tail-risk penalisation, decision discretisation can endogenously confer robustness to the industrial procurement horizon. This work contributes to the stochastic optimisation literature and provides a practical tool for mitigating volatility in the global lithium supply chain. Full article

This study investigates the relationship between spectral reduction and structural demand using different equivalent viscous damping approaches for existing reinforced concrete (RC) buildings. In that regard, 20 existing reinforced concrete buildings ranging from three to seven stories were selected. Equivalent viscous damping ratios were obtained based on the building period and ductility values using Applied Technology Council (ATC) 40 and Priestley et al. approaches. Subsequently, the corresponding spectral reduction factors were computed using various spectral reduction models existing in the literature. These reduction factors were then applied to design spectra defined for different soil classes in Turkish Building Earthquake Code (TBEC) 2018 to estimate the inelastic spectral demands. Finally, a comparison was conducted in terms of the obtained spectral reduction coefficients and spectral demands, highlighting the influence of different damping models on expected structural response. Full article

Increasing periods of zero-discharge and large fluctuations in discharge are expected in future hydropower operations due to changes in the electricity system, including greater reliance on solar and wind power, as well as increased variability in precipitation driven by climate change. In this study, several types of zero-flow periods were analyzed in a regulated northern river in Sweden. The results highlight different mitigation measures that may be suitable for reducing ecological impacts associated with hydropeaking. The study also evaluates potential improvements that could be achieved by implementing a mean annual low flow instead of zero flow. Overall, the findings demonstrate the value of conducting detailed river-specific analyses to identify effective ecological restoration measures in regulated river systems. Full article

Background: Optical coherence tomography (OCT) can quantify the morphology and dimensions of a macular hole for diagnosis and treatment planning. Objective: The aim of this study was to perform automatic segmentation of macular holes (MHs) and cysts from OCT macular volumes using a deep learning-based model and to quantitatively evaluate decision reliability using the model’s focus regions and GradientSHAP-based explainability. Methods: In this study, we automatically segmented MHs and cysts in OCT images from the open-access OIMHS dataset. The dataset comprises 125 eyes from 119 patients and 3859 OCT B-scans. OCT B-scan slices were input to a UNet-48-based model with a 2.5D stacking strategy. Performance was evaluated using Dice and intersection-over-union (IoU), boundary accuracy was evaluated using the 95th-percentile Hausdorff distance (HD95), and calibration was evaluated using the expected calibration error (ECE). Explainability was quantified from GradientSHAP maps using lesion coverage and spatial focus metrics: Attribution Precision in Lesion (APILτ), which is the proportion of attributions (SHAP contributions) falling inside the lesion; Attribution Recall in Lesion (ARILτ), which is the proportion of the true lesion covered by the attributions; and leakage (Leakτ = 1 − APILτ), which is the proportion of attributions falling outside the lesion. Spatial focus was monitored using the center-of-mass distance (COM-dist), which is the Euclidean distance between the attribution center and the segmentation center. All metrics were calculated using the top τ% of the pixels with the highest SHAP values. SHAP features were clustered using PCA and k-means. Explanations were calculated using the clinical mask in ground truth (GT) mode and the model segmentation in prediction (Pred) mode. Results: The Dice/IoU values for holes and cysts were 0.94/0.91 and 0.87/0.81, respectively. Across lesion classes, HD95 = 6 px and ECE = 0.008, indicating good boundary accuracy and calibration. In GT mode (τ = 20), three regimes were observed: (i) retina-dominant: high ARIL (hole: 0.659; cyst: 0.654), high Leak (hole: 0.983; cyst: 0.988), and low COM-dist (hole: 7.84 px; cyst: 6.91 px), with the focus lying within the retina and largely confined to the retinal tissue; (ii) peri-lesional: highest ARIL (hole: 0.684; cyst: 0.719), relatively lower Leak (hole: 0.917; cyst: 0.940), and medium/high COM-dist (hole: 16.22 px; cyst: 10.17 px), with the focus located around the lesion; (iii) narrow-coverage: primarily seen for cysts in GT mode (ARIL: 0.494; Leak: 1.000; COM-dist: 52.02 px), with markedly reduced coverage. In Pred mode, the ARIL20 for holes increased in the retina-dominant cluster (0.758) and COM-dist decreased (6.24 px), indicating better agreement with the model segmentation. Conclusions: The model exhibited high accuracy and good calibration for MH and cyst segmentation in OCT images. Quantitative characterization of SHAP validated the model results. In the clinic, peri-lesion and narrow-coverage conditions are the key situations that require careful interpretation. Full article

We formalize an inverse, data-conditioned variant of the Variational Quantum Eigensolver (VQE) for clinical biomarker discovery. Given patient-encoded quantum states, we construct a task-specific Hamiltonian whose coefficients are inferred from clinical associations and interpret its expectation value as a calibrated energy score for prognosis and treatment monitoring. The method integrates coefficient estimation, ansatz specification with basis rotations, commuting-group measurements, and a practical shot budget analysis. Evaluated on public infectious disease datasets under severe class imbalance, the approach yields consistent gains in balanced accuracy and precision–recall over strong classical baselines, with stability across random seeds and feature ablations. This variational energy scoring framework bridges Hamiltonian learning and clinical risk modeling, offering a compact, interpretable, and reproducible route to biomarker prioritization and decision support. Full article

Understanding the genetic diversity and structure of regional cacao and its close relatives is essential for strengthening conservation strategies and enhancing the resilience of Amazonian agroforestry systems. This study evaluated the genetic diversity, population structure, and varietal relationships of 48 sexually derived regional accessions of Theobroma cacao, T. grandiflorum, and T. bicolor with desirable morpho-agronomic traits, together with eight universal T. cacao reference clones, all cultivated in farmer-managed agroforests of the northwestern Colombian Amazon, using a panel of 15 SSR markers. The loci exhibited substantial allelic richness (mean Na = 8.53) and consistently high expected heterozygosity (Hexp = 0.74), with numerous private alleles indicating species- and lineage-specific divergence. Bayesian clustering, ΔK inference, and minimum spanning networks identified four genetically coherent subpopulations corresponding to the three species and a distinct lineage within T. cacao, strongly aligned with the discriminant analysis of principal components (DAPC) results. Analysis of Molecular Variance (AMOVA) revealed that most genetic variation occurred among subpopulations (56.68%), while pairwise FST (Wright’s fixation index) values confirmed strong interspecific differentiation and significant divergence within T. cacao. No isolation-by-distance pattern was detected. These findings demonstrate that regional Theobroma germplasm maintained in smallholder agroforests constitutes a valuable reservoir of genetic diversity that complements universal reference clones. By documenting species-level divergence and lineage-specific variation, this study supports the integration of farmer-managed genetic resources into conservation planning and highlights their importance for the long-term resilience of Amazonian cacao-based agroforestry landscapes. Full article

The maintenance operations in the wind sector (particularly offshore) are extremely costly, and thus technologies for optimization of maintenance need to take the high risks related to these costs into account. On the topic of predictive maintenance, any false prediction can lead to high cost incursions, and even a few false predictions can invalidate the predictive maintenance strategy. The costs and benefits of correct and false predictions are not uniform or symmetric, leading the conventional performance metrics to misalign the perceived model performance with the actual business impact. This problem leads us to introduce a cost-aware deep learning framework that accounts for the costs and benefits of predictions. This framework is achieved by loss functions designed to adjust the parameters according to the cost–benefit and not to optimize model accuracy. For the case study of wind turbine generator gearbox failure prediction, several cost-aware loss functions are tested, along with a novel fine-tuning approach that combines them. The case study shows that using cost-aware expected-value loss functions improves cost–benefit performance by an average of 30–57% compared with models trained with conventional loss functions. Full article