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Multi-objective optimization problems (MOPs) are ubiquitous in scientific research and engineering applications, where multiple conflicting objectives must be optimized simultaneously. Unlike traditional multi-objective evolutionary algorithms, which aim to approximate the entire Pareto front, preference-based multi-objective evolutionary algorithms (PBMOEAs) incorporate decision maker preferences to guide the search toward a region of interest (ROI). This paper presents a focused survey of PBMOEAs using a three-dimensional analytical framework consisting of preference articulation mode, preference modeling approach, and preference incorporation mechanism. Under this framework, the survey reviews major preference modeling approaches and preference incorporation mechanisms, and further discusses three practical issues critical to PBMOEA design: the effectiveness of preference incorporation, the practical limits of interactive methods, and the role of visualization in ROI-oriented decision support. Findings indicate that while PBMOEAs enhance decision-making efficiency, their performance hinges on preference quality, problem characteristics, cognitive load, and method suitability. Future directions include unified modeling, comprehensive evaluation, human-centered design, and integrating surrogate models, explainable learning, and LLM-assisted interfaces. Full article

Dynamic cables, serving as the critical link between floating wind turbines and submarine cables, are subjected to significant tension fluctuations and bending deformations under environmental loading. While deep-water systems have been widely studied, investigations of large-capacity wind turbines in shallow water environments remain limited. This study establishes a coupled numerical model of an IEA 15 MW floating wind turbine and its dynamic cable system at a water depth of 50 m. The platform’s six-degree-of-freedom motions were calculated under 0°, 90°, and 180° loading directions, followed by a systematic analysis of lazy-wave dynamic cable response characteristics. Results indicate that platform motions and dynamic cable responses are strongly direction-dependent in shallow water, with the 0° loading direction identified as the governing design case due to peak curvature and tension levels. Analysis reveals that the touchdown point location is the primary driver of tension response, while cable length increments predominantly influence bending. Utilizing these insights, a multi-objective fitness function was integrated with a Particle Swarm Optimization (PSO) algorithm. The optimized configuration significantly reduced peak curvature and total cable length, providing a theoretical framework and engineering guidance for the design of high-capacity floating wind systems in shallow-water regions. Full article

As the integrated energy market evolves toward a multi-stakeholder coexistence model, balancing economic efficiency, user well-being, and system-level sustainability among interacting stakeholders has become a key challenge, particularly in the rapidly developing regional integrated energy markets in China. Thus, to satisfy user comfort and energy substitution requirements while achieving cost-effective electricity and heating supply, this study proposes a Stackelberg game-based market trading framework involving an integrated energy producer (IEP), an integrated energy operator (IEO), and a load aggregator (LA). First, the integrated energy market framework and transaction modes are established, and the profit models of IEP and IEO are formulated. Considering users’ energy substitution behavior, user comfort is quantified to explicitly reflect user welfare in market decision making, and a consumer surplus model is developed for LA participating in market transactions. Second, a Stackelberg game framework is constructed to coordinate the strategies of all participants by incorporating source–load energy flows, and the equilibrium solution is proven to be unique and solvable using quadratic programming. Finally, a case study based on historical data from Hebei Province, China, is conducted to validate the proposed strategy. The results demonstrate that the proposed method effectively coordinates the interests of all stakeholders, enhances demand response capability without reducing user comfort, and improves economic benefits for both supply and demand sides in regional integrated energy markets. Full article

Drought stress significantly constrains crop productivity and yield stability. Sorghum (Sorghum bicolor L. Moench), a C4 cereal widely cultivated in arid and semi-arid regions, exhibits high water-use efficiency and remarkable drought tolerance. Understanding both the impacts of drought and the plant’s response mechanisms is essential for enhancing drought resilience in this crop. In this study, physiological changes and differential protein accumulation were analyzed in leaves of the sorghum inbred line BT × 623 under 10% PEG-6000-induced drought stress. The physiological adaptation to drought was characterized by improved water retention and mitigation of oxidative damage through the synergistic action of antioxidant enzymes. Using two-dimensional electrophoresis (2-DE) and MALDI-TOF-TOF mass spectrometry, 43 protein spots were successfully identified, corresponding to 38 unique proteins differentially expressed under osmotic stress. These proteins function in diverse biological processes, including protein synthesis, processing, and degradation; photosynthesis; carbohydrate and energy metabolism; transcriptional regulation; stress and defense; lipid and membrane metabolism; and amino acid metabolism. Proteomic profiling revealed that the coordinated modulation of multiple functional groups, such as those involved in photosynthesis, energy metabolism, transcriptional adjustment, ROS scavenging, and protein turnover, underpins sorghum’s osmotic stress adaptation. These findings provide key insights into the drought resistance mechanisms of sorghum at both physiological and proteomic levels. Full article

Ukraine’s reconstruction, estimated at $524 billion over the next decade, presents an unprecedented opportunity to embed circular economy principles into industrial rebuilding, but the financial architecture currently deployed for reconstruction is structurally blind to circular outcomes. This paper examines how digital tools and innovative financing mechanisms can channel investment toward circular industrial reconstruction in Ukraine, drawing on Germany’s National Circular Economy Strategy (NCES, adopted December 2024) as a reference model. A comparative institutional analysis combines a documentary review of Ukrainian reconstruction policy frameworks (Ukraine Plan 2024–2027, RDNA4, Ukraine Facility) and German NCES instruments with the construction of a financing−technology pathway typology. Five pathways are proposed: circular bond issuance with Digital Product Passport integration; blended finance with blockchain impact verification; EU Facility conditionality with AI-driven resource management; war risk insurance with circular construction standards; and SME digitalisation credit with circular economy competency building. Each pathway is assessed against five criteria: investment scale, risk mitigation, circular measurement, digital readiness, and institutional feasibility, and applied to four industrial corridors (Dnipro region, Zaporizhzhia region, Kharkiv region, and Donetsk region). The analysis reveals that no single pathway is sufficient; a layered strategy differentiating by region is required. Digital tools, particularly the Digital Product Passport and blockchain traceability, serve as partial substitutes for institutional trust in post-conflict settings, reducing information asymmetry between investors and project operators. The paper contributes a practically oriented framework at the under-theorised intersection of post-conflict reconstruction finance and circular economy scholarship. Full article

Background/Objectives: MSS-CRC comprises a heterogeneous group of tumors generally considered “immune cold” due to limited neoantigen generation and T-cell exclusion or inactivation. Current evidence indicates that the composition of T and B immune cells within the tumor microenvironment represents a prognostically relevant factor, significantly associated with both tumor expression profiles and molecular subtypes. Methods: We conducted an exploratory analysis to identify prognostically relevant immune cell components in this group of tumors and to investigate corresponding differences in RNA-based bulk expression and high-resolution spatial transcriptomic profiles. Results: A total of 254 localized mismatch repair-proficient colorectal cancer cases were evaluated. Our findings revealed PD-L1 expression as a robust independent prognostic biomarker associated with favorable outcomes in this specific population. Bulk RNA expression analysis showed that PD-L1-negative tumors exhibited an expression profile consistent with abundant cancer-associated fibroblast infiltration, increased matrix stiffness, and impaired immune activation—features consistent with tumor progression and poorer clinical outcomes. In contrast, PD-L1-positive tumors displayed stromal programs enriched in immune activation and controlled remodeling, consistent with an immunologically active microenvironment. Spatial transcriptomics added an additional layer of evidence, revealing that epithelial to mesenchymal transition-related programs can dominate stromal niches in PD-L1-negative tumors, particularly within macrophage-enriched stromal regions. Conclusions: Our observations suggest an association between PD-L1 expression on immune cells and immune-activated versus mesenchymal-dominant states, potentially occurring within macrophage-enriched stromal niches. These results provide insight into the biological mechanisms underlying disease progression and highlight tumor-associated macrophages as a potential therapeutic target to overcome immune resistance, particularly in PD-L1-negative MSS-CRC tumors. Full article

This study presents a comparison between the results of process parameter optimization for the deep drawing of an AA5754-O automotive fuel tank, which utilizes two different objective functions. The first objective function is the maximum thinning percentage (max. %Thinning) of the formed part, which is a conventional formability index. The second is Q-value, a metric derived from the Thinning Limit Diagram that accounts for both necking-prone (excessive thinning) and wrinkling-prone (thickening) regions. The experiments were conducted using finite element simulation to model the forming behavior under an inscribed central composite design within the response surface methodology. Three process parameters, which are well known to be important for controlling material flow and achieving a balance between wrinkling and excessive thinning in deep drawing, were varied: blank holder pressure, the height of the male drawbead, and the radius of the female drawbead. Refined second-order response surface models were developed for both objective functions. Optimization based on the response surface models showed that, for the max. %Thinning objective function, the final part exhibited 19.46% maximum thinning but suffered from substantially higher wrinkling, as indicated by a maximum thickening of 36.39%. In contrast, the Q-value-based optimization resulted in a more balanced formability condition, with maximum thinning of 21.74% and maximum thickening of 13.17%. Moreover, the normalized density of elements in the safe zone of the Thinning Limit Diagram was higher, indicating an improvement in formability robustness. Therefore, this study highlights the limitations of conventional thinning-based optimization and demonstrates the potential of the Q-value as an extended practical quantitative formability tool that can simultaneously address necking and wrinkling in sheet metal forming, as presented through the studied automotive fuel tank on behalf of complex components. Full article

Streptococcus canis is an opportunistic pathogen that colonises the mucosal surfaces and skin of its host. Though predominantly a veterinary pathogen affecting cats and dogs, S. canis has also been identified as the causative agent in severe human disease. IdeC is a secreted cysteine protease of S. canis that has a high specificity for IgG, cleaving at the hinge region. We show here that the protein binds back to the surface of the bacteria. Additionally, the protein contains a conserved Arg-Gly-Asp (RGD) motif, the minimal peptide sequence required for integrin binding. Several bacterial proteins containing RGD motifs have been implicated in adhesion and invasion of host cells. This RGD motif along with the ability of IdeC to bind back to the bacterial surface after secretion is the basis for this study into a potential secondary function of IdeC in adhesion and/or invasion. We used protein-coated latex beads to investigate the interaction of IdeC with epithelial and endothelial cells and, further, the extent to which the RGD motif is involved in this interaction by utilising an RGD->RGE recombinant protein. We also report here that the deletion of IdeC in S. canis results in a significant reduction in invasion into epithelial cells. Full article

The construction sector’s transition to carbon neutrality requires innovative strategies to address the performance and cost challenges of advanced building designs, such as passive-oriented nearly zero-energy buildings. This study proposes an artificial intelligence-based multi-objective optimization framework to reduce both energy consumption and construction costs for residential building envelopes in Chengdu’s hot summer and cold winter climate. The framework uses the NSGA-II genetic algorithm within DesignBuilder to explore trade-offs between energy efficiency and economic cost. Key design parameters (wall insulation thickness, roof insulation thickness, and window glazing type) are optimized to obtain a Pareto-optimal front. A subsequent global incremental cost analysis of the non-dominated solutions identifies the optimal balance where significant energy savings are achieved before diminishing returns set in. The research results show that by combining the NSGA-II algorithm with the global incremental cost method in the Chengdu area, the parameters of the enclosure structure can be systematically optimized, and the optimal balance point between energy conservation and cost can be effectively identified. Based on this, an “energy-saving optimal—trade-off optimal—cost optimal” template set design path based on dual objectives of energy consumption and cost can be obtained, which is applicable to different demand-oriented engineering scenarios. This research provides a quantifiable decision-making basis for the design of buildings with passive design strategies that achieve near-zero energy consumption in hot summer and cold winter regions, helping to achieve the coordinated optimization of energy efficiency goals and economic feasibility, and promoting the reliable promotion and application of near-zero energy buildings. Full article

Accurate and scalable wetland mapping remains challenging due to strong spatial heterogeneity and limited availability of reference data. Spatial transferability of classification algorithms offers a promising solution by enabling models trained in one region to be applied to other sites, but its effectiveness depends on the degree of domain shift, algorithm robustness, and data representation. In this study, we evaluate this ability for wetland mapping using multitemporal Sentinel-2 data across two wetland systems in France: the Camargue and the Étangs de la Champagne humide. Classification is performed for three main land-cover classes—open water, aquatic vegetation, and terrestrial vegetation—using one neural network (MLP), one deep-learning model (InceptionTime), and two machine-learning algorithms (Random Forest and XGBoost), and three feature configurations (spectral bands, spectral indices, and their combination). Results reveal that when models are trained on Camargue and applied to Champagne, the highest OA reaches 90% (using InceptionTime and XGBoost), when models are trained on Champagne and applied to Camargue, the highest OA reaches 84% (using InceptionTime and XGBoost), corresponding to a decrease of 6% in OA. Within the selected algorithms, InceptionTime and XGBoost achieve the highest OA in both transfer directions. Combining spectral bands and indices improves classification performance of InceptionTime and MLP by up to 8%, while XGBoost and RF perform better using band data (5% higher OA than the combination). Class-wise analysis highlights substantial differences in transferability. Terrestrial vegetation shows the highest and most stable performance across the tested configurations, with F1-scores up to 92%, followed by open water (F1 up to 88%), while aquatic vegetation remains the most challenging class to transfer, with F1-scores up to 85% depending on algorithm and configuration. Annual time series benefit aquatic vegetation, whereas shorter series covering only the vegetation growing season remain sufficient for more stable LC classes (terrestrial vegetation). InceptionTime and MLP show higher performance using annual time series, while RF and XGBoost perform better using short time series. Overall, these results highlight the potential of spatial transferability for wetland mapping within the context of the two studied sites, although further validation across a broader range of wetlands is required. Full article

Anti-A and anti-B antibodies are essential for monitoring adverse reactions in organ transplants and transfusions. However, their importance is also growing due to their involvement in the pathophysiology of various human diseases, such as infections, although this is currently the subject of heated debate. A characteristic heterogeneity in the titers and classes of anti-A and anti-B antibodies is observed among individuals. Several factors appear to be responsible, such as everyone’s specific immune profile, age, sex, microbiota composition, lifestyle, and health status. The immune profile, the result of a specific genetic predisposition and mediated and controlled by cytokines, shows a bidirectional relationship with ABO antigen expression, the gut microbiota, and the levels and class switching of anti-ABO antibodies. Associations between ABO groups and circulating levels of cytokines and chemokines further highlight this complex interaction. To better understand the role of the immune profile in this context, we evaluated, for the first time, the possible association between polymorphic variants in the regulatory regions of the genes encoding the cytokines IL-8, IL-1, IL-4, IL-6, IFN-γ, and IL-10 and anti-A and anti-B antibody titers and classes by group and in total. We also assessed the levels of these cytokines in each group and their correlations with anti-A and anti-B antibodies, as well as with age and associations with gender. Significant data were obtained that may contribute to a better understanding of the other roles of ABO antibody titers. Full article

Glucagon-like peptide-1 (GLP-1) and dual GIP/GLP-1 receptor agonists, originally introduced for the management of type 2 diabetes mellitus and obesity, are increasingly recognized for their broader actions within the central nervous system, with emerging implications in neuropsychiatry and neurodegeneration. This review integrates current preclinical and clinical evidence, emphasizing their pharmacodynamic profile, central receptor distribution, and the molecular pathways linking metabolic signaling to neural function. Evidence suggests that GLP-1 receptor activation across key brain regions involved in energy balance and reward modulates multiple neurotransmitter systems, including dopamine and serotonin, as well as glutamatergic and GABAergic transmission, thereby influencing behavior, affective processes, and cognitive function. In parallel, these agents exhibit neuroprotective properties through improved neuronal insulin sensitivity, attenuation of neuroinflammatory pathways, and support of neuroplasticity, alongside effects on limiting pathological protein aggregation. Dual GIP/GLP-1 agonism may further potentiate these central actions through complementary metabolic and synaptic mechanisms. Although pharmacovigilance data have identified isolated neuropsychiatric adverse events, current clinical evidence does not support a consistent causal association. Collectively, incretin-based therapies represent a promising translational approach at the interface of metabolic and neuropsychiatric disorders, warranting further investigation into their long-term central safety, therapeutic efficacy, and clinical relevance. Full article

Despite recent advances in polycyclic aromatic hydrocarbon (PAH) research on the Tibetan Plateau (TP), studies investigating the transport potential and accumulation dynamics of these contaminants in the Hengduan Mountains, especially in forest soils which are important sinks for atmospheric PAHs, remain scarce. In the present study, soil and lichen samples (partially located under the forest canopy) were concurrently collected from 62 sampling sites across the Hengduan Mountains to characterize the occurrence, spatial distribution patterns, and underlying controlling factors of PAHs. The total concentrations of the 16 US EPA priority PAHs (∑₁₆PAHs) in soils and lichens ranged from 59.8 to 1163 ng/g and 174 to 3362 ng/g, respectively—values consistently higher than those reported in corresponding matrices from the northern and northwestern TP. Further, concentrations of PAHs in both soil and lichen under the forest canopy are significantly higher than those on the leeward slope without forest. Compositional fractionation of PAHs along the longitudinal and latitudinal gradients of sampling locations indicates significant modulation of PAH distribution by both the Indian monsoon and East Asian monsoon, a pattern further corroborated by air mass backward trajectory analysis. Our results confirm that PAHs can be transported to the southeastern TP slope via long-range atmospheric transport (LRAT). Notably, the combined effects of mountain cold-trapping and forest filtering jointly govern the deposition and spatial distribution of PAHs in this region. Full article

Rapid urbanization and agricultural expansion in arid regions have profoundly altered carbon cycles and landscape stability. Focusing on the Hexi Corridor, China, this study integrates multi-source geospatial data (1990–2020) to analyze the spatiotemporal evolution and driving factors of land-use carbon emissions (LUCE) and landscape ecological risks (LER). By integrating carbon accounting, LER assessment, bivariate spatial autocorrelation, and the Optimal Parameter Geographic Detector (OPGD), we quantify the intricate relationship between carbon dynamics and landscape integrity. Results indicate a transformative pattern of anthropogenic expansion and natural contraction, with a 2315.49 km² net loss of unused land. Net carbon emissions surged 4.6-fold, while forest and grassland sinks exhibited a significant “lock-in effect” due to fragile ecological foundations. Simultaneously, LER followed an “inverted U-shaped” trajectory; the refined 5 × 5 km grid scale revealed a significant drop in high-risk areas from 44.65% to 10.96% following ecological restoration. Spatial analysis reveals a significant “spatial mismatch” between LUCE and LER, with oases manifesting “high carbon–low risk” clustering. Driver detection confirms a driving asymmetry. LUCE is dominated by anthropogenic factors (nighttime light, q > 0.90), whereas LER is profoundly constrained by natural backgrounds. Future governance must shift toward a collaborative system centered on source-based emission control and precise regional management to synergize low-carbon transition with landscape security. Full article

Small ruminant lentiviruses are longstanding viral infections affecting sheep and goats worldwide, resulting in reduced efficiency and economic losses. In Portugal, updated epidemiological data are scarce. The aim of this study was to assess the seroprevalence and risk factors for SRLV in Portugal. The study was conducted in Portuguese flocks of ovine and caprine species. Flocks were randomly chosen, and producers were invited to answer a questionnaire. The indirect ELISA test, ID Screen^® MVV/CAEV Indirect, was made to detect infection. We collected samples from 59 flocks, of which 55.93% (CI 95%: 43.26–68.60%) had at least one positive animal. Of these flocks, 1302 individual samples presented a seroprevalence of 32.95% (CI 95%: 30.08–35.81%). Regarding the risk factor analysis, the multivariable mixed-effects logistic regression model at the individual level identified variables with increased odds of SRLV seropositivity. Caprine species (OR = 2.47; CI 95%: 1.01–6.03), non-autochthonous breed (OR = 2.95; CI 95%: 1.23–7.06), animals older than two years old (OR = 1.95; CI 95%: 1.29–2.94), dairy aptitude (OR = 8.15; CI 95%: 2.53–26.24), unknown serostatus of newly acquired animals (OR = 9.41; CI 95%: 2.93–30.23) and participation in livestock competitions (OR = 4.25; CI 95%: 1.42–12.73) were significantly associated with increased odds of seropositivity. SRLV has been confirmed in both regions of Portugal studied, with a significant regional disparity that is likely attributable to differences in management practices. The identification of risk factors specific to each production system is crucial for the development and implementation of voluntary control programs. Full article