Search Results (3,524)

This article presents an integrated design studio pedagogy for flood-resilient urban waterfronts that employs groundscape strategies, treating the ground as an active design medium to generate hybrid structures integrating landscape, architecture, and infrastructure. Implemented at the Fay Jones School of Architecture and Design (Fall 2024), the studio challenged students to transform North Little Rock’s flood-vulnerable riverfront by replacing conventional levee infrastructure with ground-based public architectural interventions. The study adopts a pedagogical case-study approach, examining a studio cohort in which all projects were developed under shared site conditions, design constraints, and instructional frameworks. Five assignments progressed from collaborative precedent analysis to individual technical development, integrating computational modeling, performance simulations, and expert consultations across structural, envelope, MEP, and site engineering. Student work is analyzed through comparative sectional diagrams and selected in-depth project studies to evaluate how groundscape functioned as a shared solution type for multiscalar integration. The results show that groundscape operates productively when tested against specific site constraints rather than deployed as a generalized esthetic. In response to flood elevations, degraded ecology, and limited public access, students developed distinct ground-based operations—such as embedding, lifting, and integrating flood walls as spatial thresholds—demonstrating architecture’s capacity to mediate between civic space, environmental performance, and flood protection. By situating groundscape within a problem-oriented pedagogy, the study consolidates modernist, postmodern, and contemporary groundscape discourse and demonstrates how architectural education can engage productively with climate-adaptation challenges. Full article

Background/Objectives: Understanding the genetic basis of food consumption is a key step toward precision nutrition, viewed as a long-term future perspective. This study aimed to investigate genetic variants associated with grapefruit (Citrus paradisi) intake and to evaluate their potential relationship with obesity risk. Methods: A genome-wide association study (GWAS) was conducted on 19,653 European-ancestry participants from two prospective cohorts, the Nurses’ Health Study (NHS) and the Health Professionals Follow-Up Study (HPFS). We employed a functional annotation strategy to select a suggestive locus for follow-up analysis, and computationally derived molecular docking simulations explored a plausible functional link between grapefruit’s bioactive compounds and the candidate gene product. Results: Although falling short of the conventional threshold for genome-wide significance, a suggestive locus was prioritized on chromosome 14, with the lead single nucleotide polymorphism (SNP), rs2124 (p < 5 × 10⁻⁶), located within the metabolic gene ADCK1 (aarF domain containing kinase 1). Molecular docking simulations supported a plausible mechanistic hypothesis, indicating that key bioactive compounds in grapefruit could bind with high affinity to the ADCK1 protein. Consistent with the GWAS finding, individuals with the CC genotype reported lower mean grapefruit intake. This genotype was also associated with other lifestyle factors, notably, lower physical activity in women. In age- and multivariate-adjusted models, the CC genotype was associated with a modestly increased risk of incident obesity in females, but not in males. Conclusions: Our exploratory findings suggest a prioritized candidate locus associated with grapefruit intake, and its link to obesity risk may be mediated by the metabolic gene ADCK1. However, given the lack of genome-wide significance and independent replication, these findings should be considered preliminary and exploratory. These hypothesis-generating results support the integration of genetics and dietary habits, warranting further mechanistic validation. Full article

Achieving Great Britain’s 2050 net-zero target requires strategic integration of hydrogen into the national energy system. This study evaluates the system-wide impacts of hydrogen blending (0–100%) using a bi-level optimisation framework that combines long-term cooperative investment planning with short-term operational Optimal Power and Gas Flow (OPGF) simulation. The strategic layer models infrastructure investment decisions under a cooperative game-theoretic structure, where system value is allocated among electricity, hydrogen production, and storage technologies using the Shapley-value payoff mechanism. Contrary to traditional centralised cost-minimisation models, our findings demonstrate that a cooperative planning structure identifies superior transition pathways. Comparative results reveal that at 100% hydrogen penetration, the cooperative framework reduces total system CO${}_2$ emissions by 31%, lowers operational costs by 26%, and decreases total electricity supply requirements by 8% relative to centralised planning. Furthermore, the cooperative approach significantly enhances economic resilience, yielding a more robust Net Present Value (NPV) across all blending levels compared to centralised planning, while ensuring project profitability at lower blending thresholds (20%) where traditional models remain loss-making. Simulation results indicate that hydrogen blending up to 20% maintains operational stability with manageable increases in operational cost. Full hydrogen conversion (100%) increases peak electricity supply requirements by approximately 30% relative to low-blending scenarios due to electrolysis-driven load expansion and conversion losses. The findings demonstrate that hydrogen blending represents a viable transitional pathway when supported by integrated infrastructure development and cooperative stakeholder coordination, enabling a more efficient and economically sustainable phased progression towards Great Britain’s 2050 net-zero target. Full article

Biogenic amines (BAs) are important qualitative indicators of quality, as they are produced by specific microbial strains and can therefore reflect the activity of specific spoilage organisms (SSOs). Their presence in food, including wine, provides valuable information on processing conditions, hygiene practices and storage management throughout the production chain. In wine, the accumulation of BAs—particularly histamine, tyramine and putrescine—is mainly associated with microbial activity during fermentation, especially malolactic fermentation, and may pose potential risks to consumer health. Despite the recognized toxicological relevance of BAs, current European Union (EU) regulations only establish limits for histamine in certain fish products, with no specific legal thresholds defined for wine. However, growing evidence on the interactions and adverse effects of BAs highlights the need to better address their occurrence in wine and to improve consumer awareness regarding safety and quality aspects. In addition to safety concerns, the implementation of good hygiene and manufacturing practices across the entire production process plays a crucial role in controlling BA levels in the final product. These factors, together with the intrinsic characteristics of wine, may influence consumer perception and choice, integrating aspects of health, production methods and product quality. Recent findings suggest a shift in perspective, where BAs are not only considered risk markers but also useful indicators for assessing wine quality and enhancing consumer safety. Full article

Automated fruit harvesting is crucial for alleviating labor shortages and enhancing agricultural productivity. In this context, it is crucial to obtain information on fruit poses before picking in order to avoid damaging the fruit and/or the plant. However, the complex and unstructured orchard environment poses significant challenges regarding the pose estimation task. In this study, a dragon fruit pose estimation (DFPE) framework using a single RGB image is proposed for dragon fruit automated harvesting, which includes three key components: dataset annotation processing, keypoint detection, and geometric pose estimation. First, a multi-source dataset consisting of 8467 images is constructed to enhance the estimation model’s generalizability. A pseudo four-keypoint annotation strategy is designed to fit the annotation rules of mainstream single-class keypoint detection models and mitigate the inherent limitations of multi-target keypoint detection in agricultural scenarios. This strategy implicitly encodes the fruit’s orientation using bounding box group IDs, while preserving geometric information for pose inference. Then, the fruit body and its two core keypoints (navel and stem) are detected via a real-time keypoint detection model. Notably, the proposed DFPE framework is detector-agnostic: other mainstream keypoint detection models can also be plugged into the subsequent geometric pose inference stage, which guarantees the generality and scalability of the framework. Finally, a dragon fruit pose estimation algorithm based on customized geometric constraints is designed, which takes the detected pose information as the input and outputs the posture of dragon fruit. The results of experiments conducted in natural orchard and laboratory environments demonstrate that the ellipses fitted using the proposed DFPE framework closely aligned with fruit contours, even under foliage occlusion conditions. In the laboratory environment, roll errors reached a maximum of 14.8°, whereas yaw errors peaked at 13.4°. Crucially, all roll and yaw errors remained consistently below 15°, which is well within the tolerance threshold required for non-destructive picking operations using a harvesting robot. In summary, this work presents a low-cost solution for dragon fruit pose estimation from a single RGB image, which can potentially be extended to other ellipsoid crops and is suitable for implementation in harvesting robots operating in orchards. Full article

Systemic lupus erythematosus (SLE) is a heterogeneous autoimmune disease in which B-cell dysregulation plays a central pathogenic role beyond autoantibody production. Advances in B-cell biology have led to the development of targeted therapies, including inhibition of the B-cell activating factor (BAFF) pathway. Belimumab, a monoclonal antibody that neutralizes soluble BAFF, modulates B-cell survival signals upstream, promoting progressive immunologic remodeling rather than rapid depletion. This review integrates current knowledge on BAFF-dependent B-cell biology with mechanistic, pharmacokinetic, and clinical data to provide a comprehensive framework for understanding belimumab’s effects in SLE and lupus nephritis (LN). Belimumab preferentially reduces transitional and naïve B cells, while memory B cells show a relative transient increase followed by a gradual return to baseline levels, reflecting redistribution rather than expansion, and long-lived plasma cells are largely unaffected. These effects result in progressive remodeling of B-cell compartment dynamics and contribute to broader modulation of adaptive immune amplification pathways. Pharmacokinetic data support a threshold-based model of BAFF neutralization, with exposure influenced by disease-related factors such as proteinuria in LN. Clinical response is primarily determined by baseline disease biology, with greater benefit observed in patients with serologically active disease and less established organ involvement. Across clinical trials and real-world studies, belimumab reduces disease activity and flares, enables glucocorticoid tapering, and slows organ damage accrual. In LN, it improves renal outcomes and reduces the risk of kidney-related events. Collectively, these findings support belimumab as a disease-modifying therapy in SLE. Further research is needed to refine patient selection and optimize treatment sequencing and combination strategies. Full article

We propose a sequential design method aiming at the estimation of an extreme quantile based on a sample of binary data corresponding to peaks over a given threshold. This study is motivated by an industrial challenge in material reliability and consists of estimating a failure quantile from trials whose outcomes are reduced to indicators of whether the specimen has failed at the tested stress levels. The proposed approach relies on a splitting strategy that decomposes the target extreme probability into a product of higher-order conditional probabilities, enabling a progressive exploration of the tail of the distribution through sampling under truncated laws. We consider GEV and Weibull models for the underlying distribution, and the sequential estimation of their parameters is carried out using an enhanced maximum likelihood procedure specifically adapted to binary data, addressing the substantial uncertainty inherent to such limited information. Full article

Conserved lands play a central role in sustaining ecological functions within working agricultural regions, yet their capacity to maintain wetland conditions varies widely depending on hydrologic persistence and seasonal dynamics. This study assesses the hydrologic performance of Nebraska’s major conservation programs using multi-year Sentinel-2 satellite observations spanning from 2018 to 2024. Five land-protection categories were evaluated: the Wetlands Reserve Program (WRP), Wildlife Management Areas (WMAs), Waterfowl Production Areas (WPAs), the Conservation Reserve Program (CRP), and additional protected lands mapped in the Protected Areas Database of the United States (PAD-US). To capture hydrologic dynamics across scales, we quantified parcel-level inundation percentages alongside program-level wetness metrics that represent cumulative surface-water extent. Lands enrolled in WRP and WPA generally exhibited higher inundation levels at the 0% threshold across annual and seasonal periods, with variability across programs, reflecting their role in wetland restoration and habitat provision. WMAs showed greater seasonal variability but retained water under higher persistence thresholds (≥25% and ≥50%), underscoring their importance in maintaining semi-permanent wetland conditions during drier periods. Wetland-associated CRP lands provide essential short-duration wetness that supports regional hydrologic connectivity across working agricultural landscapes. Similar seasonal patterns were observed across other protected lands, which generally contributed to episodic surface water rather than long-term hydrologic storage. Seasonal analyses highlighted strong intra-annual variability driven by snowmelt, precipitation regimes, and evapotranspiration. Collectively, the results demonstrate substantial differences in hydrologic function among conservation programs and provide an empirical basis for prioritizing investments toward lands that most effectively sustain wetland habitats and water-quality benefits. Full article

Exercise adaptation and training maladaptation arise from overlapping metabolic, redox, inflammatory, endocrine, and tissue-remodeling processes, so the translational question is not whether biomarkers change but when, where, and for which decision they become informative. This narrative review develops a decision-linked framework for minimally invasive biomarkers across the recovery–overload continuum and treats biomarker meaning as a molecule–matrix–time–decision relationship rather than as a stand-alone peak. The framework is organized around five coupled layers: stimulus architecture, signaling and release biology, sampling matrix and pre-analytics, bout-relative kinetics, and the monitoring decision to be supported. Current evidence indicates that no single biomarker reliably separates productive remodeling from delayed recovery, tissue strain, non-functional overreaching, or early maladaptation. Classical chemistry remains useful for bounded tasks, especially delayed tissue strain and stress reactivity; cfDNA appears promising for rapid load sensitivity; targeted metabolite panels are strongest for recovery phenotyping; and circulating RNAs and extracellular-vesicle cargo add mechanistic depth but remain constrained by pre-analytical fragility and incomplete standardization. The central practical implication is that overload is better interpreted as progressive loss of signal resolution than as threshold-crossing and that sparse temporally staggered panels are more likely to aid monitoring decisions than isolated markers or untimed high-dimensional profiles. Progress will depend on purpose-specific panels, transparent analytical standards, and prospective validation against symptoms, performance, and established measures across sex, hormonal, circadian, and training contexts. Full article

The development of a green hydrogen industry is a strategic priority for Russia’s energy transition, yet the dynamics of scaling up this nascent sector remain poorly understood. This study uses agent-based modeling (ABM) to simulate the co-evolution of Russia’s electricity, hydrogen, and electrolyzer sectors over 2024–2050. The model incorporates three types of heterogeneous agents (power producers, hydrogen producers, and electrolyzer manufacturers) operating under bounded rationality. Four scenarios are examined across 50 Monte Carlo runs each, varying the electrolyzer learning rate (10–25%), willingness to pay for green hydrogen (2–6 $/kg), and government support intensity. The results reveal an endogenous three-phase development pattern: Phase I (2024–2028) dominated by renewable capacity build-up reaching ~30 GW; Phase II (2029–2040) characterized by rapid electrolyzer deployment scaling to 14.5 GW; and Phase III (2041–2050) marked by stabilization at approximately 30 GW producing 1.12 Mt/year at 3.1 $/kg. Two critical thresholds are identified: renewable capacity exceeding 30–38 GW and low-cost electricity above 4–7 TWh/year. The electrolyzer learning rate emerges as the most influential parameter, while the pessimistic scenario confirms market failure without a green premium (WTP < 2 $/kg). Strategic investment losses of 2–6 billion USD are necessary catalysts for industry emergence. Russia’s 2030 production target (0.55 Mt) is found structurally infeasible under all scenarios. Full article

The automated production of large-scale labeled datasets from concrete X-ray computed tomography (CT) images is a fundamental prerequisite for training and validating deep learning-based segmentation models. However, existing methods either require extensive manual annotation or rely on domain-specific deep learning models that themselves demand labeled data—a circular dependency. This paper presents a parameter-driven three-class segmentation framework that automatically classifies each pixel in a concrete CT slice into one of three material phases: void (air pores and cracks), coarse aggregate, and mortar matrix, generating annotation masks suitable for large-scale dataset production without manual labeling. The proposed method combines: (1) fixed-threshold void detection calibrated to concrete CT grayscale characteristics; (2) adaptive percentile-based initial segmentation responsive to image-specific statistics; (3) multi-criteria connected component scoring based on area, shape descriptors (circularity, solidity, compactness, extent, aspect ratio), intensity distribution, and boundary gradient; (4) material science-informed size constraints aligned with concrete phase volume fractions; and (5) a material continuity enforcement module that applies topological hole-filling and conditional convex-hull consolidation to eliminate internal contamination within accepted aggregate regions, reducing boundary roughness by 7.6% and recovering misclassified boundary pixels. All parameters are centralized in a configuration file, enabling reproducible batch processing of 224 × 224 pixel CT slices at 0.07–1.12 s per image. Evaluated on 1007 224 × 224 concrete CT patches cropped from 200 representative scan frames, the framework produces three-class segmentation masks with physically consistent void fractions (mean 3.2%), aggregate fractions (mean 32.4%), and mortar fractions (mean 64.4%), all within ranges reported in the concrete CT literature (used as a dataset-scale QC screen, not a validation metric). Primary outputs and the archived image–mask pairs for this work are provided as an 8-bit patch archive. For pixel-wise validation, we report IoU, Dice, and pixel accuracy on an independently labeled subset that can be unambiguously paired with the released predictions: averaged over 57 matched patches, mean pixel accuracy is 88.6%, macro-mean IoU is 74.7%, and macro-mean Dice is 84.9%. The framework provides a fully automated annotation pipeline for dataset production, eliminating manual labeling costs for concrete CT image collections. The generated datasets are suitable for training semantic segmentation networks such as U-Net and its variants. Full article

Komagataella phaffii (formerly Pichia pastoris) is a prominent platform for recombinant protein production, yet secretion efficiency often remains a critical bottleneck. In this study, we validated three candidate genes—WHI3, CLPP, and PMP20—previously identified through genome-wide CRISPR activation screening, for their potential to enhance heterologous protein secretion. Overexpression of these factors under the control of the methanol-inducible AOX1 promoter increased the secretion of human serum albumin (HSA), with WHI3 and CLPP yielding improvements of 18.3% and 17.9%, respectively. Furthermore, applying this strategy to human lactoferrin (hLF) revealed that WHI3 overexpression robustly enhanced hLF secretion by approximately 70%. Comparative analysis of different promoters (AOX1, GAP, and CAT) indicated that the AOX1 promoter remains the most effective driver for these enhancers, suggesting a threshold-dependent regulatory mechanism. These results demonstrate the protein-dependent nature of secretion optimization and identify WHI3, CLPP, and PMP20 as novel, effective co-expression factors for improving recombinant protein yields in K. phaffii. Full article

This study proposes an integrated artificial intelligence-based framework for modeling and predicting quay crane productivity and operational delays in conventional container terminals, addressing key limitations in the existing port analytics literature. The research introduces a novel dual-mode machine learning architecture that explicitly separates retrospective prediction (forecast mode) from pre-operational decision support (decision mode), addressing a critical gap in existing literature where predictive models are rarely aligned with real-world informational constraints. The framework is applied to a high-resolution, real-world dataset comprising ship-level operations over a three-year period (2023–2025), incorporating a structured representation of 27 delay types and multiple resource allocation variables. A multi-indicator modeling strategy is employed, simultaneously analyzing four productivity metrics (RQCP, GMPH, WBMPH and NMPH), thus allowing for a systematic comparison of their structural sensitivities to delays, congestion, and equipment utilization. The results reveal a clear hierarchy of predictability and operational behavior: structurally driven indicators such as RQCP and GMPH exhibit high predictive stability, while delay-sensitive indicators such as NMPH display greater variability, reflecting real-time operational disruptions. The consistent model performance in forecasting and decision-making indicates significant predictive value in pre-operational variables, endorsing its utility for uncertain decision-making. Sensitivity analysis reveals a critical nonlinear congestion threshold affecting predictive accuracy under extreme operational strain. Employing a combination of multi-indicator productivity modeling, structured delay classification, and ensemble learning within an integrated analytical framework, this research enhances both methodological and practical insights into port operations, aiding in merging predictive analytics with operational decision-making in container terminals to enhance resource allocation, delay handling, and container terminal efficiency. Full article

Portable devices are powered in direct current (DC) or by batteries (primary battery), accumulators (secondary battery), and now supercapacitors, which can also be used for energy storage. The European Portable Battery Association states that approximately 239,000 tons of batteries were placed on the market in the European Economic Area (EEA) plus Switzerland in 2022. Even if they were all disposed of correctly respecting the 3R paradigm (Reduce, Reuse and Recycle), non-rechargeable batteries create an environmental problem because they do not discharge completely with an obvious waste of energy. Secondary batteries and supercapacitors can be recharged because they use reversible chemical/physical processes while primary batteries cannot be recharged because they are based on irreversible redox reactions; nevertheless, it is possible to try to recover their residual charge if this is higher than a threshold beyond which the reactions can be reversible. The most used batteries are alkaline zinc/manganese dioxide and they are non-rechargeable; an inappropriate recharge attempt can lead to serious harm to the operator and the environment. This paper describes a simple Arduino-based circuit and the protocol to measure and graph the residual charge of an alkaline battery in order to establish if it can be recharged. The circuit, design, the Arduino Uno R3 sketch (i.e., microprocessor software) and the full protocol are here presented under the open source license (Copyright Creative Commons Public license, CC BY-NC-ND 4.0 EN) so that they could become a pilot system and then a commercial product. The residual charge of 158 batteries, obtained after discharging those that, by eye, appeared damaged, was measured. Results evidenced that 49% of batteries had a residual voltage, under low load, between 1.2 and 1.6 V, making them good candidates for a recharge attempt. Full article

Cajanus cajan (L.) Huth (Fabaceae) is a food legume of considerable nutritional and functional significance. This study examined the comparative effects of salt stress on seed germination, hydroponic growth, and phytochemical accumulation across two developmental stages: 10-day-old germinated seeds and 45-day-old hydroponically grown plants, using NaCl solutions at concentrations of 0, 25, 50, 75, 100, and 150 mM. Both germination rate and growth were greatest at 0–25 mM NaCl, with performance declining at higher concentrations. LC–MS/MS analysis of free amino acids in 10-day-germinated seeds revealed a salt-induced metabolic shift. Proline, leucine, and phenylalanine were the dominant free amino acids and increased progressively with rising NaCl concentrations. Phytochemical profiling by HPLC identified gallic acid, catechin, and genistin as the major compounds, with increased levels under salinity stress. Germinated seeds at 150 mM NaCl, germinated seeds exhibited the highest phytochemical accumulation, with total phenolic content (TPC), total flavonoid content (TFC), and DPPH activity reaching 18.192 ± 0.020 mg GAE/g extract, 8.519 ± 0.026 mg QE/g extract, and 11.623 ± 0.284 mg AAE/g extract, respectively. Phytochemical responses in 45-day hydroponic plants varied by tissue type. Leaves exhibited declining TPC and TFC with increasing NaCl (from 29 to 16 mg GAE/g and 41 mg QE/g extract), while stems showed the opposite trend, reaching 18 mg GAE/g and 21 mg QE/g extract at 50 mM. Root tissues maintained comparatively low phytochemical levels throughout. Notably, DPPH scavenging capacity increased across all tissues under salt stress, with peak values of 12–13 µg AAE/g extract recorded at 50 mM NaCl. These results indicate that salt stress exerts stage- and organ-dependent effects on phytochemical accumulation in C. cajan. High salinity during germination stimulates bioactive compound production, whereas moderate salinity appears to be the threshold at which antioxidant capacity is maximized in hydroponic systems. These observations point to the practical utility of controlled salt elicitation as a strategy for enriching pigeon pea with health-promoting phytochemicals, reinforcing its potential as a functional food crop. Full article