Search Results (62,831)

Robust numerical frameworks are essential for the simulation, design, monitoring, and control of membrane-based separation units, particularly under highly nonlinear and industrially relevant operating conditions. In this context, a comprehensive phenomenological and numerical framework is proposed for the simulation of hollow-fiber membrane modules, incorporating coupled mass, momentum (through pressure drop), and energy transport equations. The governing equations are discretized using a rigorous orthogonal collocation formulation, and the performances of two numerical solution strategies are systematically investigated for the first time to allow the in-line and real-time implementation of the model: a steady-state approach based on the Newton–Raphson method with careful treatment of initial estimates, and a pseudotransient formulation. Particularly, an original and consistent numerical treatment is introduced for the energy balance at boundaries where the permeate flow vanishes, enabling the stable incorporation of thermal effects and Joule–Thomson phenomena. The results clearly show that the steady-state Newton–Raphson approach provides the best overall performance in terms of computational efficiency, numerical robustness, and accuracy when physically consistent initial profiles are employed. In particular, the combination of a linear initial guess and a numerical mesh constituted of four collocation points yielded the most favorable balance between convergence speed, numerical robustness, and accuracy for the base-case sensitivity analysis. For monitoring-oriented applications, the numerical choice should be weighted primarily toward computational performance once physical consistency and convergence criteria are satisfied, rather than toward maximum mesh-refinement accuracy. In this context, small differences in internal-fiber profiles can be compensated through real-time permeance estimation and are negligible when compared with measurement uncertainty in real industrial processes. Under extreme operating conditions involving low concentrations, low flow rates, and highly permeable species, the pseudotransient formulation proved to be a reliable auxiliary strategy, enabling robust convergence when suitable initial guesses were not readily available. The proposed framework is validated against experimental data from the literature and subjected to extensive convergence and sensitivity analyses, providing a reliable basis for simulation and for assessing computational feasibility in in-line and real-time monitoring-oriented applications. A full demonstration of digital-twin integration, online parameter updating, reduced-order coupling, and closed-loop control is beyond the scope of the present study and will be addressed in future work. Full article

Down syndrome (DS), caused by trisomy 21, is the most prevalent genetic condition associated with accelerated aging and near-universal development of early-onset Alzheimer’s disease (AD). Beyond gene-dosage imbalance, trisomy 21 induces widespread transcriptional, metabolic, and proteomic remodeling that establishes a chronic state of proteotoxic and oxidative stress from early development. Increasing evidence identifies DS as a disorder of proteostasis network failure, in which sustained translational pressure, redox disequilibrium, and degradation pathway insufficiency progressively erode cellular resilience. In the DS brain, persistent endoplasmic reticulum stress with PERK-dominant signaling, mitochondrial dysfunction characterized by oxidative phosphorylation deficits and excessive reactive oxygen species production, and impaired antioxidant responses create a highly vulnerable intracellular environment. Concomitantly, degradation systems become compromised: proteasomal catalytic activity declines, ubiquitin-dependent signaling is remodeled, and chronic mTOR hyperactivation suppresses autophagic and mitophagic flux. The coordinated impairment of the ubiquitin–proteasome system and autophagy establish a feed-forward cycle of proteotoxic accumulation and redox amplification. Within this framework, Alzheimer-like neuropathology in DS emerges not solely from amyloid precursor protein triplication but as the late manifestation of decades-long proteostasis exhaustion. Therapeutic strategies aimed at restoring global proteostasis and redox balance may therefore represent a more effective systems-level approach to mitigating neurodegeneration in DS. Full article

The human gut microbiota represents a complex and dynamic ecosystem of trillions of microorganisms that play a fundamental role in maintaining physiological homeostasis, regulating metabolism, and modulating the immune system. This narrative review explores the biochemical intricacies of the gut microbiome, focusing on the dominant phyla (Firmicutes, Bacteroidetes, Actinobacteria, Proteobacteria, Verrucomicrobia, Fusobacteria) and their specific contributions to host health. A critical emphasis is placed on the metabolic outputs of these microorganisms, such as short-chain fatty acids (SCFAs) like butyrate, which serve as vital energy sources and anti-inflammatory signaling molecules. Conversely, the review examines how dysbiosis, the disruption of microbial balance, is mechanistically linked to the pathogenesis of diverse conditions, including obesity, diabetes mellitus, inflammatory bowel disease (IBD), and gout. Furthermore, it highlights the profound impact of dietary interventions on microbial architecture, notably, how non-digestible carbohydrates promote beneficial taxa and eubiosis, while high-fat and high-sugar diets drive metabolic endotoxemia and systemic inflammation. By synthesizing current knowledge on microbial biotransformations of proteins and polyphenols, this work underscores the bidirectional relationship between nutrition and the microbiome. Ultimately, understanding these biochemical interactions is essential for developing targeted probiotic, prebiotic, and nutritional strategies to prevent and manage chronic metabolic and inflammatory disorders. Full article

Due to the inherent limitations of both hyperspectral and multispectral imagery, balancing high spatial resolution with high spectral fidelity has become one of the fundamental challenges in remote sensing image processing. A prevailing strategy is to fuse these two types of data to reconstruct images that jointly preserve their respective advantages. However, existing reconstruction approaches still suffer from complex coupling between spatial and spectral information, and limited feature extraction capabilities. To address these issues, this study proposes PMSwinNet (Pyramid Multi-scale Swin Transformer Network), a novel architecture that integrates pyramid-based feature enhancement with Transformer mechanisms. The PMSwinNet incorporates multi-scale pyramid feature fusion and window-based self-attention. Through a progressive multi-stage design and three complementary components—feature extraction and reconstruction modules—the Transformer branch leverages window partitioning and shifting operations to capture long-range spatial dependencies and local contextual cues, while the pyramid features extract both global and local information across multiple spatial scales. In addition, a high-frequency branch is introduced, which employs lightweight convolutions to enhance edges, textures, and other high-frequency details, effectively suppressing blurring and artifacts during reconstruction. Experimental evaluations on multiple public hyperspectral datasets demonstrate that the PMSwinNet outperforms state-of-the-art methods, particularly in terms of detail preservation, spectral distortion suppression, and robustness. Full article

Reform and sustainability of the defined benefit pension system has received considerable attention because it addresses the challenges of an aging population and the risk of fund insolvency. However, previous studies have little consideration to the effects of reemployed retired laborers and the sensitivity of each key reform element. The study established a financial forecasting model incorporating reemployed retired laborers and employed comparative static analysis to examine the effects of several variables on Taiwan’s public labor pension. In Scenario Three, the fund balance was projected to remain positive until 2064. Furthermore, increasing the premium rate to 17% had the strongest positive effect on the fund balance with 16-year delay, while an annual government subsidy of NT$100 billion had the second-most positive effect with 10-year delay. Moreover, solely reducing the old-age annuity amount by 10% had a positive impact on the fund balance with 7-year delay. Furthermore, allowing 50% of reemployed retired laborers to reenroll in the system had the positive effect with 9-year delay before bankruptcy. Finally, the study proposes a comprehensive reform plan for the public labor pension and offers valuable insights for other countries. Full article

Fine-grained bird identification is crucial for ecosystem monitoring, species conservation, and habitat assessment. However, in real-world environments, there are challenges such as imbalances in modality quality and interference from background noise. To improve fine-grained audio-visual bird classification under heterogeneous modality conditions, we propose an audio-visual feature fusion framework named CABIF-Net. This framework introduces a confidence-based Top-K mean pooling module to select key frames to optimize the visual representations at the video level. Through a Confidence Calibration module, it dynamically assesses the reliability of the visual and audio modalities and integrates a Bidirectional Inter-modulation Fusion module to achieve controllable cross-modal information interaction. Experiments were conducted on the publicly available SSW60 dataset, characterized by severe noise and imbalance in modality quality, and the self-built Birds21 dataset with balanced modality quality. The experimental results show that the classification accuracies were 85.76% and 96.67%, respectively, outperforming existing unimodal methods and several mainstream fusion strategies. Weight distribution and visualization analyses further indicate that the proposed method can adaptively adjust the modality contributions based on discriminative evidence at the sample level. This study provides an effective framework for fine-grained audio-visual bird species recognition. Full article

Green communities play a critical role in advancing sustainable development; however, evaluating their performance and identifying appropriate improvement strategies remain challenging due to uncertain, incomplete, and multidimensional information. This study formalizes three key processes essential to green community governance—sustainability evaluation, attribute reduction, and decision-rule generation—and proposes a rough set-based decision framework that integrates quantitative indicators, expert knowledge, and rule-based reasoning. Using empirical assessment data from Nantou County, the framework identifies the most influential determinants of community performance, including accessibility-related facilities, remote-area status, and socioeconomic conditions. The results reveal clear drivers of sustainable community performance. Remote villages lacking community hubs face structural barriers to participation. Communities without facilities supporting vulnerable groups tend to stall at the registration stage, while bronze-level villages require equity-focused engagement despite possessing stronger resource endowments. Notably, silver-level performance is consistently associated with moderate income levels and moderate income disparity, underscoring socioeconomic balance—rather than economic extremes—as a key precondition for stable sustainability advancement. Together, these findings provide interpretable, evidence-based guidance for policymakers and community managers to identify performance gaps and allocate resources more effectively. Full article

Transcatheter structural heart interventions, including aortic, mitral and tricuspid valve replacement or repair, and patent foramen ovale, atrial septal defect, and left atrial appendage closure, have dramatically expanded over the past two decades, providing substantial improvements in both clinical outcomes and quality of life. These interventions are performed in a high-risk patient population, which is at risk for both thrombotic and bleeding complications. The introduction of prosthetic devices into the arterial or venous circulation under heterogeneous hemodynamic conditions inevitably increases the risk for thrombotic events and thromboembolic complications. Consequently, the selection of antithrombotic therapy (AT) regimen and its duration is complex and should be tailored to each patient’s risk profile, balancing the expected risk and benefits. This state-of-the-art review critically examines the thrombotic risks inherent to transcatheter structural heart interventions, synthesizes available evidence and current guidelines recommendations on antithrombotic management, and defines persisting gaps in knowledge while discussing the most relevant ongoing clinical trials. Full article

Despite the growing use of tessellated and patterned façades in contemporary architecture, their thermal performance, particularly in cooling-dominated educational buildings, remains insufficiently quantified, with existing studies largely prioritizing daylighting or aesthetic outcomes over energy-driven thermal behavior. This study aims to systematically evaluate how different tessellated façade geometries and perforation ratios influence thermal performance and cooling demand in a Mediterranean climate, and to identify an optimal façade configuration that balances multiple thermal objectives. Three tessellation typologies—nature-inspired (Voronoi), Islamic geometric, and folded origami-based patterns—were parametrically generated and applied as external shading screens to an educational building. Annual thermal simulations were conducted using Climate Studio to assess four performance metrics: solar heat gain, energy use intensity, hours of overheating derived from operative temperature, and peak cooling demand. A post-simulation, data-driven, multi-objective, decision-support approach was applied using Compromise Programming to systematically evaluate and rank discrete façade alternatives based on multiple thermal performance criteria. Results indicate that all tessellated façades reduce solar heat gain and peak cooling demand relative to the unshaded baseline, with performance strongly dependent on both geometry and perforation ratio. Lower perforation ratios (20%) consistently outperform more open configurations, while Voronoi-based façades achieve the most balanced overall thermal performance across all evaluated criteria and emerging as the top-ranked solution. The study’s novelty lies in its comparative, cooling-focused evaluation of fundamentally different tessellation logics using transparent, decision-oriented optimization rather than subjective comfort indices or computationally intensive evolutionary algorithms. Beyond its specific findings, the research provides a transferable methodological framework for integrating geometry-informed façade design into early-stage decision-making, supporting climate-responsive and energy-efficient educational architecture in Mediterranean and similar climates. Full article

Colorectal cancer (CRC), characterized by epidermal growth factor receptor (EGFR) overexpression, is often associated with poor prognosis and limited therapeutic response to conventional chemotherapy. In this study, we developed EGFR-targeted extracellular vesicles (EGFR-tEVs) by transiently engineering donor cells to display the GE11 peptide, aiming to enhance the precision of doxorubicin (Dox) delivery. The physicochemical properties of EGFR-tEVs were characterized using TEM, NTA, and Western blot. In vitro, EGFR-tEV-Dox exhibited increased cellular uptake in EGFR-overexpressing HCT-116 cells, leading to the activation of the p53-Bax-cleaved PARP1 apoptotic pathway. Notably, while Dox treatment induced p53 in normal colon fibroblasts (CCD18-Co), it did not trigger significant Bax activation or PARP1 cleavage, suggesting a preference for survival-related signaling in non-malignant cells. In a xenograft mouse model, EGFR-tEVs + Dox administration resulted in a 33.1% reduction in tumor volume and an 82.8% decrease in Ki-67 expression compared to the control group. These results indicate that transient receptor-mediated targeting enhances functional drug delivery to malignant tissues while minimizing pro-apoptotic induction in normal cells. Our findings suggest that EGFR-tEVs + Dox represents a balanced therapeutic strategy that improves antitumor efficacy with a favorable safety profile for EGFR-positive colorectal cancer. Full article

Since 2007, the porosity–to–cement relationship has been widely used as a unified parameter to predict mechanical strength, durability, expansion, and stiffness of stabilized soils. In this formulation, the volumetric binder content is adjusted by an internal exponent x, typically ranging between 0 and 1, to balance the relative contributions of porosity and cementation. Traditionally, the parameters of this relationship have been obtained using manual regression procedures. This study proposes an automated calibration methodology for the porosity–binder index, where the parameters A, B, and x are determined through an iterative optimization framework based on minimization of the sum of absolute errors (SAE) combined with a Monte Carlo search algorithm. The methodology is applied to a cement-stabilized clay blended with ground glass (GG), recycled gypsum (GY), and limestone residues (CLW). The predictive capability of the calibrated model is evaluated using unconfined compressive strength (q_u) and initial shear stiffness (Go) datasets. Two calibration strategies are considered: Calibration Process No. 1, based on CLW mixtures and q_u values only, and Calibration Process No. 2, incorporating all mixtures (CLW, GG, and GY) and both q_u and Go responses. The results indicate that Calibration Process No. 2 provides a more robust and physically consistent parameter set, yielding coefficients of determination of 0.9318 and 0.9412 for q_u and Go, respectively. The proposed algorithm-driven calibration framework improves predictive capability and provides a systematic approach for determining the parameters of the porosity–binder relationship. Full article

A nano-CuO/ZnO desulfurizer was successfully prepared via a homogeneous precipitation method, and the effects of Cu doping on its microstructure, oxygen species, desulfurization performance, and reaction mechanism were systematically investigated. The results show that an appropriate Cu doping amount (TZ2, Cu:Zn = 1:18.40) significantly reduces the particle size (to ~10.9 nm) compared with pure ZnO (14.3 nm), leading to an increased number of surface-active sites. XPS and TG analyses reveal that Cu incorporation increases the proportion of lattice oxygen and decreases the concentration of oxygen vacancies, indicating that the modification effect of Cu dominates over the particle size effect in regulating surface oxygen species. Despite the reduced oxygen vacancy concentration, the desulfurization performance is markedly enhanced, with TZ2 exhibiting the longest breakthrough time under oxygen-free conditions at room temperature. This improvement is attributed to the strong interaction between highly dispersed Cu species and the ZnO matrix, which promotes H₂S adsorption and activation. Mechanistic studies demonstrate that, unlike pure nano-ZnO, where oxygen vacancy-mediated reactions dominate, the CuO/ZnO system follows a chemisorption-driven pathway involving the formation of copper sulfides and highly reactive polysulfide intermediates. Furthermore, the presence of oxygen significantly influences the reaction behavior, with an optimal oxygen concentration (~10%) maximizing desulfurization performance by balancing the generation of reactive oxygen species and sulfur intermediates. This work provides new insights into the design of high-performance ZnO-based desulfurizers and highlights the critical role of Cu-induced mechanism transformation. Full article

This study addresses a critical methodological gap in evaluating building envelope performance in hot, arid climates, the overreliance on annual energy indicators, which fail to capture transient thermal behavior during peak-load periods. In such environments, instantaneous heat gains, their intensity, and temporal distribution are decisive factors for cooling demand, occupant comfort, and grid stability. To overcome this limitation, a dynamic evaluation framework—the Thermal Adaptation Rating (TAC) system—is proposed. TAC integrates three interrelated indices—peak temperature reduction (ΔT_peak), relative peak cooling load reduction (ΔP_peak, %), and peak thermal delay (Δt_delay), representing thermal damping, load intensity mitigation, and temporal redistribution, respectively. A typical residential building in Karbala was modeled in DesignBuilder using the EnergyPlus engine, with inputs documented and calibration performed against real consumption data following ASHRAE standards (MBE and CV(RMSE)) to ensure reliability. The study examined advanced envelope systems, including thermochromic glass (TG), phase-change materials (PCMs), aerogel materials (AMs), and hybrid combinations. Results revealed that while AM achieved the greatest annual energy savings, its impact on instantaneous cooling load was limited. PCM, by contrast, effectively mitigated and delayed peak loads, enhancing thermal comfort (PMV/PPD). Hybrid systems, particularly TG-PCM, delivered the most balanced performance, simultaneously reducing peak cooling load and shifting its occurrence to reshape the cooling demand curve during critical periods. These findings demonstrate that annual indices alone are insufficient for evaluating envelope performance in extreme climates. Peak-condition analysis, expressed in terms of instantaneous cooling load, as operationalized through TAC, provides a more accurate representation of thermal behavior and offers a practical tool to guide envelope design decisions in hot, dry regions. Full article

Rose hip processing generates seed-rich by-products that remain underexplored beyond oil extraction, despite their potential as a source of phenolic compounds and antioxidant activity. This study investigates the effect of bioprocessing (short-term fermentation) on the phenolic composition and antioxidant activity of rose hip (Rosa spp.) seed by-products, with relevance to cosmetic-oriented applications related to oxidative stress modulation. Rose hip seeds were obtained after juice production and subjected to short-term fermentation (14 days at 21 °C) using Saccharomyces cerevisiae, followed by mechanical separation and drying. Non-fermented and bioprocessed seeds were analyzed for individual phenolic compounds and antioxidant activity (DPPH, ABTS, FRAP), and correlation and multivariate analyses were conducted. Bioprocessing reduced total identified phenolics from 15.79 to 10.72 mg/g DW (≈32%), primarily due to a decrease in epigallocatechin (10.89 to 6.50 mg/g DW). In parallel, the relative contribution of phenolic acids increased, including gallic acid (0.50 to 0.60 mg/g DW) and salicylic acid (0.98 to 1.20 mg/g DW), indicating a selective compositional redistribution accompanied by partial degradation. Antioxidant activity decreased after bioprocessing (DPPH ~340 to ~250 µmol TE/g DW) but remained substantial. Correlation analysis identified epigallocatechin as the main contributor to antioxidant capacity. These findings show that rose hip seeds behave as a process-sensitive phenolic matrix in which bioprocessing alters the balance of individual compounds without complete loss of antioxidant activity. The results indicate that seed-derived by-products retain functional potential for further valorization in cosmetic-oriented applications. Full article

Optimized dispatch of microgrids is crucial for improving the economic performance and long-term sustainability of modern low-carbon power systems. In particular, accurate economic dispatch modeling for battery energy storage systems (BESSs) is essential for properly evaluating the operational benefits and lifetime costs of microgrids. However, when both battery cycle aging and calendar aging are considered, the resulting scheduling model becomes highly nonlinear, high-dimensional, non-convex, and multimodal, which poses substantial challenges to conventional optimization methods. To alleviate the above problem, a symmetry-guided multi-strategy differential hybrid slime mold algorithm (MDHSMA) is introduced for the day-ahead economic dispatch of microgrids under a refined battery degradation framework. First, a chaotic bimodal mirrored Latin hypercube sampling strategy is designed to exploit symmetry during population initialization, thereby enhancing diversity and improving structured coverage of the search space. Second, a history-driven adaptive differential evolution mechanism is integrated to balance global exploration and local exploitation more effectively during the iterative search process. Third, a state-aware stagnation handling framework is incorporated to maintain population vitality and further improve convergence accuracy and robustness. MDHSMA is evaluated against 12 state-of-the-art optimizers on the CEC2017 and CEC2022 benchmark suites and two representative engineering optimization problems to verify its overall performance. In addition, it is applied to a microgrid case study with refined BESS degradation modeling. The results show that MDHSMA achieves the lowest comprehensive operating cost by effectively coordinating electricity arbitrage and battery life consumption. Moreover, it guides the energy storage system toward shallow charge–-discharge patterns, thereby mitigating accelerated degradation caused by excessive cycling. These results confirm the effectiveness and practical value of the proposed method for sustainable microgrid dispatch in complex nonconvex optimization scenarios. Full article