Search Results (8,584)

Background: The aim of this study was to compare microbial diversity and compositional changes under digestive stress in yogurts produced using different culture strategies in a dynamic in vitro gastrointestinal system. Methods: Yogurts produced with probiotic starter cultures, standard yogurt cultures, and probiotic sachet supplementation were evaluated using a dynamic in vitro gastrointestinal system including mouth, stomach, and small intestinal phases under controlled pH, temperature, and digestion conditions. Microbial diversity and compositional changes before and after the in vitro gastrointestinal simulation were determined using a 16S rRNA amplicon-based Oxford Nanopore sequencing approach, and the resulting data were analyzed using bioinformatics, alpha-diversity, and beta-diversity metrics. Results: Probiotic sachet addition significantly increased microbial species richness, alpha diversity, and community balance compared to starter culture yogurts (Shannon and Inverse Simpson, p < 0.05). In vitro gastrointestinal system led to a reduction to a Firmicutes-dominant core microbiota in all samples; in contrast, beta diversity and PCoA analyses showed that the post-digestive microbial structure of sachet-supplemented yogurts was significantly different (PERMANOVA, p = 0.001). Conclusions: Consequently, it was demonstrated that adding probiotic sachets to yogurt increases the diversity and resilience of beneficial bacteria throughout the digestive tract, making the product a more robust and functional food for health. Full article

Cascading failures in complex networks occur when local node or edge failures propagate to trigger large-scale collapse. Traditional pairwise network models cannot adequately capture group coordination and multi-agent higher-order interactions. Higher-order networks incorporating simplicial structures more accurately represent group and multi-node interactions, providing a new framework to study cascading failures and network robustness. The paper proposes a higher-order coupled map lattice (CML) model to characterize cascading failures in simplicial complex networks and analyze the influence of higher-order structures on network robustness. Further experiments on fourth-order simplicial networks investigate robustness differences under various topologies and attack strategies. Results indicate that fourth-order simplicial networks are vulnerable to targeted attacks but robust against random failures, regardless of network type. Furthermore in single-order networks, the higher simplex dimensions, the greater robustness. The theoretical perturbation thresholds for third-order networks show a negative correlation between the critical perturbation and the sum of network coupling parameters. These results are validated by analysis of simplices added to ordinary networks, destructive experiments, and empirical networks. This study deepens the understanding of cascading failure mechanisms and robustness in higher-order networks, and provides theoretical guidance for designing resilient networks based on higher-order structures. Full article

The emergence of glucagon-like peptide-1 receptor agonists (GLP-1RAs) and dual incretin-based therapies has fundamentally transformed obesity pharmacotherapy, enabling magnitudes of non-surgical weight loss that were previously unattainable. Yet, the clinical success of these treatments cannot be measured in kilograms alone. Total body weight is a composite, tissue-nonspecific endpoint that fails to distinguish between adipose reduction and losses in skeletal muscle mass, strength, and physical function—compartments of direct relevance to metabolic health, functional independence, and long-term resilience. This narrative review builds on and extends existing conceptualizations of weight loss quality by proposing a clinically oriented, multidimensional framework of high-quality weight loss. Within this framework, preferential adiposity reduction is achieved while preserving skeletal muscle mass, neuromuscular function, dietary adequacy, and cardiometabolic health. We examine the physiological and clinical consequences of lean tissue loss during pharmacological energy restriction, with specific attention to phenotypes at greatest risk (i.e., older adults, individuals with sarcopenic obesity, and those with type 2 diabetes). We then evaluate the evidence supporting precision protein nutrition, dietary fiber adequacy, and gastrointestinal tolerability management as nutritional countermeasures, followed by a mechanistic and clinical analysis of resistance training as the primary exercise strategy for preserving lean mass and function. Finally, we discuss body composition monitoring, integrated multidisciplinary care, and unresolved research gaps. The future of obesity treatment lies not in greater weight loss per se, but in achieving better weight loss—defined as metabolically favorable, functionally responsible, and clinically sustainable. Bone health is treated as a further dimension of high-quality weight loss, since pharmacologically driven energy restriction can adversely affect areal bone mineral density and microarchitecture, and adequate protein intake combined with mechanical loading is required to preserve skeletal integrity alongside lean mass. Full article

Efficient operation of rainwater pumping stations is essential for mitigating urban flooding under climate change. This study focuses on the Samcheok Osipcheon watershed, located in Gangwon-do, South Korea, and proposes a deep learning-based inflow prediction framework for the Samcheok-si drainage system using SWMM-simulated datasets. A total of 900 rainfall scenarios were generated and used to train three models: ANN, CNN, and LSTM. All models reproduced inflow hydrographs with high accuracy, but the CNN model showed overfitting with oscillations in the recession limb. The LSTM model demonstrated the best performance, achieving an NSE of 0.97 and a PPE of 3.45%. Based on the predicted inflow, two pump operation strategies were evaluated. The proactive operation considering upstream surcharge conditions, combined with second-level control, reduced peak water levels from 2.585 m to 2.439 m (approximately 5.6%) compared to the conventional operation. In addition, second-level pump operation reduced excessive discharge and stabilized detention basin water levels. The results indicate that the proposed framework can support real-time pump operation, enhance the resilience and sustainability of urban drainage systems, and contribute to sustainable urban flood mitigation. Full article

Ground deformation monitoring is critical for safety and environmental management in modern mining. Active mining sites are highly exposed to terrain instabilities and subsidence, risking infrastructure integrity, disrupting operations, and posing hazards to communities. In this context, Differential Synthetic Aperture Radar Interferometry (DInSAR) techniques provide an effective and non-invasive tool capable of detecting millimetric surface displacements. This study implements the Small Baseline Subset (SBAS) technique through an open-source workflow based on the Python package hyp3_sbas, enabling semi-automated and reproducible interferometric processing by combining HyP3 with MintPy. The workflow is applied to the Björkdal gold mine (Sweden), a pilot site of the Horizon Europe XTRACT project focused on enhancing resilience in critical raw material supply chains. Integrating Sentinel-1 viewing geometries resolves the true vertical deformation field, yielding an overall mean velocity of −3.99 mm/year across the mining complex, with significant displacement rates concentrated below the 25th percentile (Q1) at −11.07 mm/year. Sector-specific analysis reveals localised subsidence accelerating over underground footprints and tailings storage facilities (mean velocities of −6.56 and −3.98 mm/year; Q1 thresholds near −13.00 mm/year), contrasting with the geomechanical stability observed at the open-pit area (mean: −0.45 mm/year). The proposed open-source framework shows strong potential for operational satellite-based monitoring, supporting predictive maintenance and early-warning strategies for risk management in mining environments while simplifying and standardising the interferometric processing workflow. Full article

This study investigated the potential of microbial-assisted phytoremediation using Eichhornia crassipes (water hyacinth) to reduce heavy metal and salinity pollution in produced water collected from Aadi Oil Field in Gujar Khan, Pakistan. Produced water was analyzed for physicochemical parameters and heavy metal content using Inductively Coupled Plasma–Optical Emission Spectrometry (ICP-OES) to establish baseline data. E. crassipes plants augmented with indigenous, contaminant-tolerant microbial isolates were employed in a 15-day laboratory experiment. The results showed a resilient growth response, with plant height increasing to approximately 11–15 cm and root length extending up to 10–13 cm across treatments. Biomass also improved, with wet weights reaching 21–24 g from an initial 20 g. The treatment effectively reduced key physicochemical parameters: pH was stabilized from an initial alkaline value of 9.14 to near-neutral values (7.0–7.5), and total dissolved solids (TDSs) were reduced by approximately 50%. Heavy metal removal rates varied, with the highest efficiency of 79.2% for Silver (Ag) and the lowest (18.5%) for Mercury (Hg) This study demonstrates that E. crassipes actively participated in phytoremediation by absorbing and accumulating heavy metals and reducing salinity. The association with contaminant-tolerant microbes appeared to enhance the plant’s tolerance and overall treatment efficacy, indicating that plant–microbe interactions offer a sustainable strategy for the treatment of produced water. Full article

Background: The transition from linear to circular supply chains (CSC) is critical for advancing sustainability, resilience, and resource efficiency, while supporting the UN Sustainable Development Goals (SDGs). However, existing studies rarely integrate internal operational performance with external PESTEL factors under the Benefits, Opportunities, Costs, and Risks (BOCR) perspective, limiting the ability to prioritize circular strategies holistically. Methods: This study develops a decision-making framework that combines the Best-Worst Method (BWM) and Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS), enabling reliable prioritization of interdependent sustainability criteria. Results: A case analysis in the chemical industry demonstrates the applicability of the framework, enhancing transparency and reducing subjectivity in CSC evaluation. Findings highlight quality as the key operational attribute and social as the dominant PESTEL dimension, reinforcing the integration of internal and external factors toward SDG-oriented strategies. Conclusions: The study contributes theoretically by bridging operational and contextual dimensions in CSC evaluation under the BOCR perspective, and methodologically by advancing hybrid MCDM applications to address uncertainty. Managerially, the framework provides a structured tool for aligning circular supply chain strategies with organizational objectives and SDGs, supporting decision-making that strengthens environmental sustainability, stakeholder legitimacy, and resilience. Full article

Modern flight decks incorporate advanced automation and real-time data systems to improve safety and efficiency. However, maintaining situational awareness under a high workload remains a critical challenge, particularly when attentional resources are stretched. This study investigates how adaptive interfaces, using real-time eye-tracking data, can help pilots maintain their concentration and stay aware of all indications on large-area displays. To this end, we developed a gaze-responsive display that monitors whether the pilot has visually focused on objects displayed on a moving map. When the system detects that the pilot has not noticed an object on the map, it automatically adjusts the interface, highlighting the missed object, thus helping to prevent critical information from being overlooked. The interface was evaluated in a controlled simulation study with 34 participants. By capturing pilots’ gaze behaviour, the system reveals how attention shifts under workload and how adaptive visual cues can complement natural scanning patterns. Participant feedback indicated that adaptive behaviour provided supportive guidance without imposing additional cognitive load. Overall, the study highlights the potential of adaptive gaze-based interfaces to enhance attention strategies and contribute to more resilient situational awareness in aviation. Full article

The rapid global deployment of wind energy requires robust and efficient control strategies for doubly fed induction generator (DFIG)-based systems. Conventional proportional–integral (PI) controllers tuned by classical or metaheuristic methods often exhibit poor adaptability to turbulence, parameter drift, and grid disturbances. This paper introduces an adaptive owl search optimisation (A-OSO) algorithm for the real-time tuning of PI controllers in DFIG back-to-back converters. Unlike traditional OSO, the adaptive variant dynamically adjusts its exploration–exploitation balance based on the severity of system disturbances, enabling faster convergence and improved resilience. The study benchmarks the proposed method against particle swarm optimisation (PSO), genetic algorithm (GA), simulated annealing (SA), and owl search optimisation (OSO) simulation studies in MATLAB/Simulink, coupled with hardware-in-the-loop (HIL) tests on an FPGA platform, demonstrating that A-OSO achieves superior efficiency (97.1%), lower power losses (35 kW), faster low voltage ride-through (LVRT) recovery (less than 150 ms), and reduced total harmonic distortion (THD) (2.4%). These findings establish A-OSO as a practical, grid-compliant optimisation strategy for next-generation smart wind farms. Full article

High-performance and ultra-high-performance fiber-reinforced concretes (HPFRC/UHPFRC) have emerged as advanced cementitious composites capable of achieving superior mechanical performance, durability, and structural efficiency compared with conventional concrete. However, their widespread adoption remains challenged by relatively high material costs and significant embodied environmental impacts associated with elevated binder and fiber contents. This study presents a comprehensive life-cycle review of advanced high-performance cementitious composites, evaluating their sustainability from raw material extraction and mix design to structural application, service life, and end-of-life considerations. The review synthesizes current knowledge on material composition, production processes, structural performance, durability characteristics, and environmental impacts through the framework of life-cycle assessment (LCA). Particular attention is given to the influence of mix-design parameters, including binder composition, supplementary cementitious materials (SCMs), aggregate systems, and fiber type, on embodied carbon, energy demand, and mechanical performance. A dataset compiled from published experimental studies covering high-performance and ultra-high-performance concrete mixtures is analyzed to examine relationships between compressive strength, embodied energy, and carbon footprint, highlighting the dominant role of cementitious binders and fiber production in environmental impacts. Although advanced fiber-reinforced concretes generally exhibit higher cradle-to-gate emissions than conventional concrete, their superior mechanical properties, improved durability, reduced material demand, and extended service life can substantially reduce life-cycle environmental impacts at the structural level. The review further discusses emerging strategies for developing low-carbon high-performance cementitious composites, including clinker reduction, recycled and alternative fibers, optimized particle packing, and AI-assisted mix design. Finally, key research gaps are identified, particularly regarding standardized LCA methodologies, long-term durability data, harmonized performance-based functional units, and circular-economy strategies for material recycling and reuse. The findings highlight that performance-based life-cycle evaluation is essential for accurately assessing the sustainability potential of advanced high-performance cementitious composites in resilient and low-carbon infrastructure systems. Full article

In commercial pig production systems, early weaning imposes abrupt nutritional, environmental and social challenges before full gastrointestinal maturation has occurred, increasing susceptibility to post-weaning diarrhoea (PWD) and impaired growth performance. Although enterotoxigenic Escherichia coli (ETEC) is frequently implicated in PWD, pathogen presence alone does not adequately explain variation in disease expression among pigs and production systems. Increasing evidence indicates that gastrointestinal stability following weaning is determined by interactions among digestive capacity, substrate flow, microbial metabolism, epithelial integrity and host immune responses. In this review, substrate flow refers to the quantity, composition and regional distribution of undigested dietary and endogenous substrates moving through the gastrointestinal tract (GIT) and becoming available for microbial fermentation. The review proposes substrate flow as the central mechanistic interface linking digestive physiology with microbial metabolic activity during the post-weaning transition. Commercial weaning frequently occurs before complete adaptation to cereal- and plant-based diets has developed. Reduced feed intake, elevated gastric pH, incomplete pancreatic adaptation and reduced brush-border enzyme activity impair nutrient digestion during this transition, increasing nutrient overflow to the distal intestine. Under these conditions, microbial metabolism shifts from predominantly saccharolytic fermentation towards proteolytic pathways associated with production of ammonia, phenols, indoles and branched-chain fatty acids. These metabolites impair epithelial integrity, alter luminal conditions and favour proliferation of opportunistic bacteria. Conversely, effective digestion supports saccharolytic fermentation, short-chain fatty acid production, epithelial integrity and microbial stability. Microbial dysbiosis is therefore more accurately interpreted as a metabolic consequence of altered substrate availability and fermentation dynamics rather than solely as a compositional imbalance of bacterial taxa. By integrating digestive physiology, microbial ecology and nutritional management, the substrate-flow concept provides a mechanistic framework for development of more biologically coherent nutritional strategies aimed at improving gastrointestinal resilience and reducing antimicrobial reliance in modern pig production systems. Full article

Beer is one of the oldest and most widely consumed fermented beverages in the world. However, barley production is increasingly vulnerable to agricultural and socioeconomic pressures, particularly in temperate growing regions where rising temperatures threaten yield stability. In contrast, rice is projected to experience comparatively smaller yield declines, highlighting its potential as a more climate-resilient starch source for brewing. This opportunity is especially relevant in the United States, where Arkansas produces approximately half of the nation’s rice supply. Large commercial breweries have successfully incorporated rice through the use of cereal cookers, but these systems are often impractical for smaller operations because of their cost and space requirements. In addition, rice supplied to the brewing industry is often sourced as a byproduct of the edible rice market, where multiple cultivars may be blended, reducing consistency and obscuring cultivar-specific effects that influence brewing performance. This manuscript reviews variation among rice cultivars in the physical, chemical, and agronomic properties relevant to brewing and examines how these differences affect extract yield and processability. Particular emphasis is placed on practical strategies to overcome technical barriers, including alternative mashing approaches and the use of heat-stable exogenous enzymes to facilitate the use of milled rice without dedicated cereal-cooking infrastructure. Full article

The present paper addresses the problem of modeling the resilient operation of a cryptocurrency exchange (CEX) under delayed quantum attacks of the harvest-now-decrypt-later type. The proposed model diverges from extant approaches in its conceptualization of the quantum threat. Whereas extant approaches treat the quantum threat as an external shock, the proposed model conceptualizes the accumulation of cryptographically vulnerable data as an internal state variable of the system. The framework under consideration is formulated as a system of nonlinear differential equations linking the exchange’s liquidity, the intensity of post-quantum cryptography (PQC) adoption, and the volume of accumulated threat. The analytical conditions for asymptotic stability are derived. The resolution of the system enables the identification of a region of admissible defense strategies, which is interpreted in the paper as a “survival dome.” Numerical simulations demonstrate that both delayed and excessively aggressive migration strategies toward post-quantum cryptography may lead to the degradation of the exchange. The findings indicate that a balanced and adaptive transition strategy, aimed at mitigating quantum risks, can preserve liquidity while minimizing long-term losses. The findings establish a theoretical framework for the development of migration strategies for financial platforms undergoing a transition to post-quantum security standards. Full article

The greater cane rat algorithm (GCRA) represents an emerging swarm intelligence paradigm derived from the instinctual survival patterns exhibited by greater cane rats (GCRs), which simulates the typical male-dominated survival patterns of the GCR species, including rainy-season mating and reproduction behaviors, dry-season behavioral differentiation of solitary males and clustered females, and their nonlinear adaptive foraging characteristics. Nevertheless, the original GCRA suffers from inherent defects in complex and high-dimensional optimization scenarios, encompassing premature convergence phenomena, inadequate local exploitation proficiency, constrained convergence precision, and a proneness to stagnation at local optima, which severely restrict its practical engineering application. To address the aforementioned limitations, this work introduces an enhanced hybrid variant of the greater cane rat algorithm, amalgamated with Teaching-and-Learning-Based Optimization (TLBO) and designated as the TLGCRA, incorporating three pivotal targeted innovations. Specifically, the TLGCRA innovatively introduces the two-stage teacher–student interactive learning mechanism of TLBO on the basis of retaining the core evolutionary and behavioral characteristics of the original GCRA, which effectively compensates for the insufficient local disturbance capability of the original algorithm and enriches population diversity to avoid local optimum stagnation. Furthermore, an adaptive parameter tuning strategy is innovatively designed and embedded in the iterative optimization process, which dynamically balances the global exploration and local exploitation capabilities of the algorithm, fundamentally improving the low learning efficiency and weak mining performance of the GCRA. A suite of computational simulations is conducted across 23 canonical benchmark functions and six representative constrained engineering design optimization scenarios. The introduced TLGCRA is benchmarked against the canonical GCRA, LPSO, and ten cutting-edge metaheuristic approaches. Empirical outcomes substantiate that the TLGCRA attains marked performance advantages in terms of convergence velocity, solution precision, and algorithmic resilience. In particular, the optimized design effectively improves the optimal solution precision of the algorithm in complex multimodal function optimization, and the standard deviation of multiple independent runs in six engineering application cases is close to zero, verifying its excellent stability. Statistical verification employing the Friedman test and Wilcoxon signed-rank test additionally corroborates that the TLGCRA exhibits statistically robust and dependable optimization efficacy. In summary, the proposed innovative fusion strategies endow the TLGCRA with stronger environmental adaptability and comprehensive optimization performance, enabling it to realize faster convergence speed and higher computational accuracy, as well as outstanding stability and robustness, thus furnishing a viable resolution framework for intricate constrained engineering optimization challenges. Full article

Punjab, Pakistan, is experiencing severe groundwater depletion due to excessive and unplanned extraction, declining surface water availability, rapid population growth, and increasing climate variability. Groundwater has become the primary source of irrigation and drinking water across the province, contributing about 50%, 90% and 95% of the requirements of agricultural, domestic, and industrial water demands. Natural recharge rates have been reduced due to construction, pavements, and the lining of irrigation channels. This study presents the first pilot-scale Managed Aquifer Recharge (MAR) initiative implemented by the Irrigation Research Institute (IRI) of the Punjab Irrigation department. Floodwater has been diverted into the bed of the abandoned Old Mailsi Canal (OMC), which off-takes from Islam Headworks. About 144 recharge wells have been constructed in the bed of the OMC. During the 2025 flood season, approximately 12,000 acre-feet of floodwater was diverted and stored through engineered ponding, canal-bed rehabilitation, and recharge wells. A comprehensive monitoring program was established, including piezometers, automated data loggers, groundwater quality sampling, pumping tests, geophysical surveys, and sediment analyses. The results indicate a maximum groundwater level rise of up to 11 ft., with average increases ranging from 2.6 to 5.2 ft across the recharge ponds. Groundwater quality also showed an improvement following MAR implementation; electrical conductivity decreased from 900 to 650 μS/cm in Pond-I and from 850 to 750 μS/cm in Pond-III. These findings demonstrate that repurposing abandoned canal infrastructure for floodwater-based MAR provides a technically feasible, environmentally sustainable, and climate-resilient strategy for enhancing groundwater availability for sustainable management in Punjab and other water-stressed regions. Full article