Search Results (3,108)

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

Conductive core–shell superabsorbent polymers (SAPs) are emerging as multifunctional additives for cementitious materials, combining moisture management with electrical functionality. In cement-based systems, a swellable polymeric core enables internal curing and crack-sealing through controlled water uptake and release, while a conductive shell introduces ionic and/or electronic charge transport, addressing key limitations of conventional non-conductive SAPs. This dual functionality provides a pathway toward smart cementitious composites with enhanced durability, self-sensing capability, and moisture-responsive behavior. This review focuses on the physical chemistry mechanisms governing conductive core–shell SAPs in cementitious environments, with emphasis on swelling thermodynamics, water transport kinetics, interfacial phenomena, and charge transport mechanisms. The roles of osmotic pressure, elastic network constraints, ionic effects, and pore solution chemistry are critically discussed, together with their impact on conductivity, hydration processes, microstructure development, and long-term performance. The relative contributions of ionic and electronic conduction are examined in relation to hydration state, shell morphology, and percolation of conductive networks. In addition, the relevance of core–shell SAP architectures to sustainable packaging is briefly discussed as a secondary application, illustrating how similar physicochemical principles—such as moisture buffering and functional coatings—apply beyond construction materials. Finally, key knowledge gaps are identified, including long-term stability in highly alkaline environments, trade-offs between swelling capacity and conductivity, environmental impacts of conductive phases, and the need for integrated experimental and modeling approaches. Addressing these challenges is essential for the rational design and practical implementation of conductive core–shell SAPs in next-generation cementitious materials. Full article

The squat is a fundamental multi-joint movement widely studied in strength training and biomechanics. While numerous experimental and computational studies have examined squat kinematics and joint loading, the mechanisms governing how squat technique adapts to increasing external load remain insufficiently understood. In particular, inverse-dynamics-based approaches often overlook explicit constraints imposed by limited joint moment capacity. This study presents a computational framework for predicting load-dependent adaptations of squat posture. The human body was represented as a multi-segment rigid-body system, with joints modeled as nonlinear rotational elements with bounded moment capacity. A reference squat trajectory was first generated kinematically, and a constrained optimization procedure was then applied at each motion frame to determine a mechanically admissible posture under increasing barbell load. The results show that higher loads lead to systematic posture adaptations, including increased torso inclination and redistribution of rotational demand from the knee toward the hip joint. For the highest load, peak torso pitch increased from 30° to over 40°, while joint utilization exceeded unity, indicating the onset of yielding. These findings identify joint moment capacity as a key constraint governing squat technique and demonstrate the potential of the proposed framework for predictive biomechanical analysis. Full article

The rapid development of space exploration demands real-time backhaul of massive sensing payload data in space-ground integrated telemetry, tracking, and command (TT&C) networks. However, traditional narrow-band TT&C links suffer from severe congestion during massive data backhaul. Since most TT&C applications are inherently task-oriented and do not require pixel-perfect data reconstruction, we propose a task-oriented joint resource allocation framework based on semantic communications. Specifically, we introduce an adaptive semantic split computing mechanism that extracts and transmits only compact, decision-critical features instead of raw bitstreams, fundamentally mitigating the bandwidth bottleneck. The joint optimization of computation offloading, semantic splitting, and continuous on-board computing allocation is formulated as a stochastic mixed-integer nonlinear programming (MINLP) problem. We propose a decoupled algorithm based on Hierarchical Multi-Agent Proximal Policy Optimization (HMAPPO) to solve it. An outer layer employs multi-agent reinforcement learning (MARL) for distributed discrete decision-making, while an inner layer utilizes a Karush–Kuhn–Tucker (KKT)-based solver for continuous space-based computing allocation. This bi-level architecture overcomes the curse of dimensionality and mathematically guarantees zero-violation of physical capacity constraints. Simulations demonstrate that HMAPPO rapidly converges and sustains a high weighted success rate under heavy traffic congestion, significantly improving system utility compared to state-of-the-art baselines. Full article

We develop a topological classification of admissible reconstruction operations in generative systems where extended structure is built through repeated local extension subject to compatibility constraints. Reconstruction is formalized as a feasibility-governed process rather than a dynamical or metric one, with admissibility determined by the accumulation of obstruction under composition. Using loop diagnostics, we identify global incompatibilities that are invisible to local extension rules but become unavoidable under closed composition. Under mild and realization-independent assumptions, including indefinite continuation and finite interface capacity, we show that persistent nontrivial obstruction is possible only when it is supported on codimension-2 subsets of the reconstructed domain. This result induces a small number of topological universality classes distinguished by the existence and stability of loop-detectable obstruction. The framework is model-agnostic and applies equally to discrete, combinatorial, and continuum reconstructions, providing a topological explanation for the ubiquity of codimension-2 defects in generative systems. Full article

Continuous greenhouse watermelon cultivation is widely constrained by declining soil function, impaired nutrient cycling, and increasing soil-borne disease pressure. Developing biologically driven strategies to restore soil–crop coupling is therefore critical for sustainable protected horticulture. Here, we conducted a two-year field experiment (2024–2025) using a randomized block design with three treatments (CK, ST, and STE), three replicates per treatment, and a plot area of 22.5 m² to evaluate how straw application alone and in combination with earthworms regulate soil processes and crop performance in a continuous greenhouse watermelon system. Compared with CK and ST, earthworm–straw co-application (STE) exerted stronger effects, particularly during the mid-to-late growth stages. In 2024, STE increased soil organic matter by 25.34% and 30.28% relative to CK at the fruiting and harvest stages, respectively; in 2025, the corresponding increases were 25.22% and 27.62%. STE also significantly increased total nitrogen at nearly all growth stages, with the maximum increase reaching 67.23% relative to CK at harvest. In 2025, total phosphorus under STE was significantly higher than under CK and ST across all growth stages, with increases of 75.82% and 79.63%, respectively, at the fruiting stage. Neutral phosphatase activity was markedly enhanced, increasing by 292.24% at the fruiting stage in 2025. These improvements were accompanied by higher plot yield and lower wilt disease incidence, with yield increasing by 34.00% in 2024 and 21.29% in 2025 relative to CK, while disease incidence decreased by 41.46% and 56.06%, respectively. Integrative Mantel tests showed that total nitrogen was the factor most strongly associated with watermelon yield, with the correlation coefficient increasing from r = 0.490 (p = 0.001) in 2024 to r = 0.662 (p = 0.001) in 2025. Co-occurrence network analysis further revealed a strong positive correlation between yield and total nitrogen (r = 0.848 in 2024; r = 0.673 in 2025) and a negative correlation between disease incidence and total nitrogen (r = −0.661 in 2024; r = −0.822 in 2025), indicating progressively strengthened soil–plant functional coupling over time. Our findings demonstrate that earthworm–straw co-application strengthened soil nutrient transformation capacity and enhanced soil suppressiveness against wilt disease, thereby providing an effective ecology-based strategy for alleviating continuous-cropping constraints in greenhouse watermelon systems. Full article

Across California, the seven largest cities—Los Angeles, San Diego, San Jose, San Francisco, Fresno, Sacramento, and Long Beach—carry a disproportionate share of the state’s homelessness crisis, even though they operate under the same statewide policy framework. Each city’s homelessness system reflects its own history, political climate, and housing market conditions, and this study shows that a common set of structural forces especially severe housing scarcity, fragmented behavioral–health systems, and uneven local capacity shapes homelessness across these urban areas while producing different outcomes on the ground. Drawing on multidisciplinary research, statewide policy analyses, and municipal data, the analysis compares how cities interpret and implement key interventions, including permanent supportive housing, interim shelter expansion, prevention strategies, and enforcement-oriented responses. The findings make clear that California’s homelessness crisis cannot be reduced to a single cause; instead, understanding it requires a systems-oriented perspective that accounts for the intertwined economic, social, and policy forces shaping conditions in each community. By situating city-level strategies within broader statewide patterns, the study identifies points of convergence and divergence, as well as persistent structural constraints that limit the effectiveness of current responses, underscoring the need for coordinated, scalable, and context-responsive policy solutions. Full article

Semantic communications have emerged as a key paradigm for intelligent sixth-generation (6G) wireless networks, which aim to convey the meaning of information rather than accurate bit sequences. However, in open-space low Earth orbit (LEO) satellite links, the broadcast nature and wide beam coverage expose semantic transmissions to severe eavesdropping risks. This paper establishes a unified theoretical and algorithmic framework for secure semantic downlink transmission in satellite networks. In particular, we first develop an integrated mathematical model that couples the semantic representation process, physical-layer satellite propagation characteristics, and information-theoretic secrecy into a single analytical formulation. By defining a joint semantic security cost function, the antagonistic trade-off between semantic fidelity and secrecy capacity is quantitatively characterized under realistic power, beamforming, and propagation constraints. To balance semantic fidelity and information secrecy, a reinforcement-learning-based optimization framework is proposed, wherein an actor–critic agent learns optimal power allocation and semantic weighting strategies through continuous interaction with the environment. This learning-based optimization approach enables autonomous control without requiring explicit channel distribution knowledge or offline parameter tuning. Extended simulation results show that the proposed approach consistently enhances both semantic fidelity and secrecy performance compared with conventional power-control schemes and demonstrate its potential as a foundational architecture for secure and intelligent semantic communications in next-generation satellite networks. Full article

With the high penetration of distributed photovoltaics (PV) in distribution areas, transformer capacity limits and source–load fluctuations have become key factors constraining PV accommodation. To accurately assess the PV hosting capacity under energy storage regulation, this paper proposes an assessment method based on operating region analysis. First, a coordinated operation model for the distribution area is established, incorporating the transformer capacity, energy storage constraints, and power balance. On this basis, the calculation boundaries for the PV hosting capacity are discussed in two scenarios: Model 1 ignores power curve uncertainty, characterizing the geometry of the conventional operating region to find the maximum deterministic hosting capacity ($S_1$) that keeps the region non-empty. Model 2 introduces box-type uncertainty sets for the source and load, proposes the concept of a “Self-Balanced Operating Region”, and constructs a robust feasibility determination model ($f_3$) based on a Min–Max–Min structure. To solve this multi-layer nested non-convex model, an iterative algorithm based on duality theory and Benders decomposition is employed to determine the robust hosting capacity under uncertainty ($S_2$) at the critical point where $f_3$ shifts from zero to non-zero. Case studies show that source–load uncertainty leads to a significant contraction of the operating region, and the robust hosting capacity under uncertainty requirements is strictly less than the deterministic hosting capacity ($S_1>S_2$). This method quantifies the reduction effect of uncertainty on the accommodation capability, providing a theoretical basis for planning high-renewable penetration distribution areas and energy storage configuration. Full article

Against the backdrop of accelerating low-carbon transformation in the global energy system and decarbonization in the transportation sector, the widespread adoption of electric vehicles has intensified grid load imbalances and highlighted challenges in integrating intermittent renewable energy generation. Vehicle-to-Grid (V2G) technology has emerged as a key solution to these challenges. This paper systematically traces the global evolution of V2G technology from conceptualization to large-scale deployment, focusing on localized practices in China’s scaled V2G applications. It dissects the logic behind policy evolution, identifies three distinct Chinese V2G models—centralized, distributed, and battery-swapping—and validates the practical outcomes of representative pilot projects. Research reveals three core constraints hindering China’s large-scale V2G adoption: the absence of battery capacity degradation management mechanisms, fragmented standardization systems, and rigid market mechanisms. Based on this, the paper proposes recommendations for scaling V2G in China across three dimensions: power battery second-life utilization, standardization system construction, and market mechanism optimization. Furthermore, aligning with the global demand for large-scale V2G implementation, this paper proactively proposes innovative market models. These include establishing a coordinated trading mechanism between green power and V2G, developing a digitally driven distributed trust and transaction system, and exploring financialization and risk hedging models for battery assets. These concepts provide theoretical foundations and decision-making references for achieving high-quality, large-scale V2G applications worldwide. Full article

External electricity supplementation for islanded microgrids at expressway service areas is often settled under tiered electricity pricing based on cumulative energy consumption, where marginal prices increase discontinuously once tier thresholds are exceeded. This mechanism reshapes battery dispatch behavior and may alter economically optimal storage sizing. This paper proposes a unified planning–-operation optimization framework for wind–PV–battery microgrids that jointly determines the storage capacity and hourly scheduling while enforcing power balance, battery state-of-charge dynamics, and tiered settlement costs. By introducing tier-wise energy allocation variables and tier cap constraints, the nonlinear settlement rule is reformulated into an equivalent piecewise-linear structure, leading to a mixed-integer linear programming (MILP) model that can be solved using standard optimization solvers. A season-weighted annualized case study using four typical seasonal days reveals critical cross-tier dispatch behaviors, where charging–discharging schedules shift near tier boundaries and external electricity purchases are actively suppressed from entering higher-priced tiers. The proposed framework quantifies the premium-avoidance value of storage and provides a practical decision support tool for premium risk-aware sizing and operation of islanded expressway service-area microgrids. Full article

AI data-center (DC) growth is increasingly constrained by limited deliverable electricity, interconnection capacity, and cooling demand. This study develops a boundary-consistent screening framework for waste-to-energy (WtE)-coupled AI DC cooling, treating cooling as an energy service that can be supplied through grade matching rather than solely through electricity-driven mechanical chilling. The framework translates plant-side exportable heat into corridor-level planning objects by explicitly accounting for thermal attenuation, absorption-based conversion, and parasitic electricity associated with delivery and auxiliaries. Three results structure the analysis. First, a reference-case energy-service ledger shows how a representative regulated WtE plant with municipal solid-waste throughput of 1500 t/day and lower heating value of 10 MJ/kg yields ~78.1 MWth of exportable driving heat and, at a 20 km corridor, ~53.0 MWcool of delivered cooling and ~8.0 MWe of net avoided cooling electricity after parasitic debiting. Second, the coupled system is governed by operating regimes, not a single efficiency score. Under the baseline package, full thermal coverage is maintained up to ~20.9 km, the stricter quality-adjusted criterion remains positive to ~22.9 km, and the electricity–relief criterion remains positive to ~44.7 km. Third, deployment-scale translation for a 1 GW IT campus (u = 0.70, L = 5 km) implies a net grid relief of ~116.9–264.4 MW across scenario packages, while the required WtE footprint ranges from roughly three to 148 equivalent representative plants, or about 0.6–40 full-load-equivalent plants at a 25% displacement target. The contribution is a siting-ready planning framework that identifies when WtE-coupled cooling remains corridor-feasible, when it becomes hybrid and marginal, and when infrastructure scale rather than thermodynamic benefit becomes the binding constraint. It is intended as a screening tool for planning and comparison, not as a project-specific hydraulic or plant-cycle design. Full article

The retinal pigment epithelium (RPE) is a long-lived, highly polarised epithelial monolayer that performs essential functions in retinal homeostasis, including outer blood–retina barrier maintenance, visual cycle activity, metabolic exchange, phagocytic clearance of photoreceptor outer segments, and regulation of oxidative and immune balance. Because RPE cells persist for decades under conditions of sustained oxidative, metabolic, and phagocytic stress, this tissue provides a valuable model for examining how long-lived post-mitotic cells preserve function over time and how age-related dysfunction emerges when that balance weakens. Although much of the current literature on RPE ageing has been shaped by age-related macular degeneration (AMD), age-dependent change in the RPE should not be understood solely as a preclinical stage of disease. Rather, the ageing RPE offers a broader framework for studying cellular maintenance under chronic physiological load. In this review, we synthesise current evidence on RPE ageing across four interrelated domains: structural remodelling, mitochondrial and metabolic imbalance, proteostatic and lysosomal burden, and chronic inflammatory dysregulation. Across these processes, ageing in the RPE is expressed less as widespread cell loss than as progressive decline in cellular organisation, buffering capacity, and functional precision. Structural irregularity, altered mitochondrial regulation, incomplete degradative clearance, and persistent low-grade inflammatory signalling together reduce the ability of the RPE to maintain long-term homeostasis and increase vulnerability to age-related retinal dysfunction. We further argue that ageing in the RPE is best understood not as abrupt failure of isolated pathways, but as gradual loss of system coherence among interacting homeostatic systems that remain active while operating under increasing constraint. This view helps integrate diverse cellular and molecular findings and highlights the RPE as an informative model for understanding ageing in long-lived post-mitotic tissues. Full article

The rapid expansion of the Internet of Vehicles (IoV) necessitates high-capacity backhaul connectivity, yet the deployment of such networks under strict hardware and power constraints poses significant computational challenges for network optimization. To address this challenge, this paper investigates a joint transmit–receive beamforming optimization problem for narrowband wireless backhaul in IoV networks under constant-modulus constraints. Unlike ideal digital architectures, we focus on cost-effective analog phase shifters, which introduce strictly non-convex constant-modulus constraints, rendering the optimization problem mathematically intractable for standard solvers. Since the resulting problem is highly non-convex, we develop two structured numerical methods: an iterative alternating optimization (AO) method and a joint optimization (JO) method, where AO employs auxiliary WMMSE-guided alternating updates together with constant-modulus projection, while JO jointly updates both beamformers over the constant-modulus feasible set. We compare their achievable sum-rate performance with that of a CDO-based benchmark and analyze their dominant computational costs through representative Big-O complexity expressions. Furthermore, we examine the effect of SVD-based and random feasible initializations on empirical convergence behavior, runtime, and final achievable performance. Simulation results demonstrate that the proposed computational methods significantly improve achievable sum-rate performance compared with the CDO benchmark. Moreover, SVD-based initialization provides a more structured starting point and generally leads to better convergence behavior and lower runtime than random feasible initialization. The empirical timing results further show that AO exhibits faster empirical convergence and requires lower runtime, whereas JO achieves better final sum-rate performance after more iterations. Full article

The decoupled operation of electricity and water systems under variable demand conditions and tightly coupled operational constraints tends to increase total operating costs and reduce overall resource-use efficiency. In response, this study develops an integrated optimization framework for the short-term management of water–energy nexus systems composed of thermal generating units, co-production units, and a desalination plant. The proposed formulation is designed to simultaneously satisfy electricity and water demands while minimizing the total operating cost over a 24 h scheduling horizon. Methodologically, the problem is formulated as a mixed-integer nonlinear programming (MINLP) model implemented and solved in GAMS. The model explicitly incorporates electricity and water balance equations, generation-capacity limits, desalination bounds, thermal ramp-rate constraints, technical coupling relationships between electric power and water production in co-production units, and non-separable quadratic cost functions that preserve the techno-economic structure of joint production. The results confirm the technical and economic consistency of the integrated dispatch. In particular, the optimized solution satisfies an electricity demand of 45,491 MWh and a water demand of 7930 m³ with complete hourly balance consistency over the full scheduling horizon. Thermal units supply 59.4% of total electricity production, whereas co-production units contribute the remaining 40.6%. From the hydraulic perspective, the desalination plant provides 61.7% of total water demand, while co-production units supply 38.3%. The resulting total operating cost is USD 179,618.92. Relative to a decoupled benchmark, the integrated formulation reduces the total operating cost by USD 25,325.92, equivalent to 12.36%. These findings demonstrate that the proposed MINLP framework provides a robust and operationally relevant tool for the short-term planning of strongly coupled water–energy systems. Full article