Search Results (990)

Urban historic districts often present archaeological sites and historic buildings in a fragmented way, posing significant challenges for public understanding and enhancing heritage value. Solely physical conservation fails to fully communicate their cultural significance, while excessive commercialization often results in the erosion of cultural authenticity and the displacement of local communities. Drawing from cultural capital theory in sociology and cultural economics, this study redefines historical districts as sustainable urban cultural capital, comprising habituated, objectified, and institutionalized components. A Value Chain Model of Cultural Capital (VCMCC) is developed, consisting of three stages: cultural resource excavation, cultural asset cultivation, and cultural capital management. This model aims to empower heritage adaptive reuse and foster synergy between cultural heritage and economic development. Utilizing an embedded single-case design with longitudinal ethnography, the research focuses on the Western Han Archaeological Sites Historical District (WHAS HD) in Hanzhong, China. It involves multiple rounds of mixed-data collection from 2023 to 2025, on which design-based research is performed. This study operationalizes VCMCC through a series of spatially and socially grounded strategies. In the cultural resource excavation stage, superior resources are identified through a systematic review of historical archives, archaeological reports, and local gazetteers, along with surveys of architectural remains and spatial mapping. In the cultural asset cultivation stage, these resources are transformed into experiential and communicable cultural assets via a “one courtyard, one strategy” approach for activating courtyard functions, developing dual-theme heritage routes, and deploying digital interpretation tools. In the cultural capital management stage, a multi-stakeholder community committee is established, and binding institutional safeguards are integrated to ensure sustainable heritage adaptive reuse. Concurrently, a baseline indicator system covering three dimensions, cultural, social, and economic benefits, is developed to provide benchmarks for future post-intervention benefit evaluation and verification. The proposed and implemented VCMCC model translates cultural capital theory from an abstract explanatory framework into an actionable pathway for heritage adaptive reuse, offering theoretical and methodological guidance for the adaptive reuse of similar small and medium-sized historic districts. Full article

Gold nanoparticles (AuNPs) have emerged as versatile antiviral nanoplatforms because their size, morphology, plasmonic properties, and surface chemistry can be precisely engineered. In this review, we summarize the core design principles of antiviral AuNPs from a structure–function–mechanism perspective. We first outline representative synthetic and interface-programming routes for AuNP preparation, including citrate reduction, Brust–Schiffrin synthesis, seed-mediated growth, green synthesis, direct thiol-conjugation, and mixed-ligand shell strategies, emphasizing how these approaches define particle size, morphology, surface accessibility, interfacial composition, and downstream biofunctionalization potential. We then discuss major surface engineering strategies, including polyethylene glycol, nucleic acids, antibodies and nanobodies, peptides, glycans, antiviral drugs, and biomimetic coatings, with particular attention to how ligand density, orientation, flexibility, and interfacial stability determine biological performance. Next, we examine how functionalized AuNPs inhibit different stages of the viral life cycle, including viral attachment and entry, intracellular replication, assembly and egress, photothermal inactivation, and immune modulation or vaccine delivery. Finally, we highlight current challenges, including incomplete structure–activity relationships, dynamic nano–bio interactions under physiological conditions, limited standardization across studies, and translational barriers related to safety, reproducibility, and scale-up. This review provides a conceptual framework for the rational development of next-generation AuNP-based antiviral nanotherapeutics. Full article

Truck–drone collaborative delivery (TDCD) refers to a coordinated logistics paradigm in which drones are deployed from delivery trucks to serve nearby customers, enabling parallelized last-mile operations. Much of the existing TDCD literature relies on synthetic datasets and manufacturer-declared drone specifications, which may overestimate performance in real-world operations. This study develops an empirically informed, route-based Mixed-Integer Linear Programming (MILP) framework that integrates empirically derived drone performance models with constrained fleet allocation decisions. Using delivery routes from the Amazon Last Mile Routing Dataset (2021), we consider three electric trucks departing from a common depot, each equipped with drones drawn from a shared fleet of 10 units. Drone flight time and energy consumption are modeled using regression functions calibrated with real flight test data from a DJI Matrice 100 platform, capturing observed variations due to payload and operational conditions. The optimization jointly determines truck stop selection, customer assignments, and drone allocation while minimizing a weighted combination of route makespan, total energy consumption, and fleet size under operational and energy constraints. The results indicate that coordinated truck–drone delivery can achieve substantial reductions in both delivery completion time and energy consumption relative to conventional truck-only delivery. These findings demonstrate the effectiveness of coordinated truck–drone operations under realistic constraints and highlight the importance of data-driven modeling and fleet-level resource allocation in improving last-mile delivery performance. Full article

Green synthesis of metal oxide nanoparticles is a promising route to reduce toxic reagents and energy consumption while enabling biocompatible nanomaterials for biomedical use. In this work, cerium oxide (CeO₂) nanoparticles were synthesized using lemon juice and sucrose as bio-based chelating, capping and stabilizing agents. Three synthesis routes were designed by varying the use of lemon juice, sucrose, or their combination. The synthesized materials were characterized using thermal analysis (DSC—Differential Scanning Calorimetry and TGA—Thermogravimetric Analysis), X-ray diffraction (XRD), Raman spectroscopy, and scanning electron microscopy (SEM). Additionally, their antibacterial activity was assessed against Gram positive bacterium Staphylococcus aureus (S. aureus). Thermal analysis showed that heat treatment at 600 °C promotes high crystallinity, as evidenced by the development of sharp diffraction peaks associated with the cubic fluorite CeO₂ structure, and a dominant F_2g Raman mode at 463 cm⁻¹. SEM micrographs revealed nanometric particles and highlighted that combining lemon juice and sucrose effectively suppresses coalesced structures, yielding more homogeneous morphologies. Crystallite size calculations gave average sizes of 17.2 nm, with the lemon juice-only route producing the largest crystallites. Antibacterial tests revealed a clear dose-dependent inhibition of S. aureus, with marked inhibition of bacterial growth at concentrations ≥5 mg/mL and a plateau effect above 25 mg/mL. This study confirms the feasibility of using plant-based extracts as sustainable reagents for CeO₂ nanoparticle synthesis, with promising structural and biological performance for potential biomedical applications. Full article

Frankincense is a natural oleo-gum resin obtained from several Boswellia tree species, playing important roles in supporting the spiritual, cultural, and socioeconomic livelihoods of communities across East Africa. Despite their cultural and economic value, the Ethiopian market still lacks scientifically based criteria to evaluate and properly classify this raw material, with traditional grading relying on gum size, color, collection area, and impurity content. Frankincense-derived essential oil value is much higher than that of gum, making this valorization route very enticing. This work compares the extraction potential and chemical profiles of hydrodistilled essential oils from various commercial grades and also different Ethiopian harvest locations (Afar, Humera, Assosa, Shire, Metema, South Omo, Borena and Jigjiga). The essential oils were extracted using hydrodistillation with a Clevenger-type apparatus, and their chemical composition was identified with GC-MS. The results revealed no substantial quantitative and qualitative differences among commercial grades, showing that essential oils can be obtained indiscriminately from classification. As for harvesting locations, both the extraction yield and essential oil compositions varied substantially. With the economic value of frankincense essential oil around six times that of the raw resin required to obtain it, these results show the importance of revising the commercial grading system to reflect chemical composition and promote the value-added processing of both black and white frankincense, rather than relying mainly on raw resin exports. Full article

Polyethylene microplastics (PE-MPs) are ubiquitous constituents of waste activated sludge (WAS), acting as a major land-based source threatening coastal environmental integrity. However, how particle size and dose govern the methanogenic outcome during WAS digestion remains poorly defined. This study evaluated two particle sizes (50 vs. 300 µm) and doses (100 vs. 200 particles/gTS) to elucidate the differential effects of PE-MPs on methane yield and the underlying biological mechanisms. The results show that, while low-dose treatments either slightly inhibited methane yield (RS1) or had no significant effect (RL1), high-dose treatments (RS2 and RL2) achieved a net positive effect, with significant increases of 10.2% (p < 0.05) and 9.0% (p < 0.05) relative to the control, respectively. Nevertheless, RS2 and RL2 achieved methanogenic enhancement via distinctly different biological pathways. RS2 harnessed the stress of reactive oxygen species (ROS) (110.5% of the control) to drive community restructuring and biomass accrual (positive correlation between ROS intensity and total VS, Pearson’s r = 0.99). Key syntrophic and electrogenic taxa (e.g., Syntrophales, Bacteroidetes vadinHA17) exhibited a fully interconnected, decentralized network, thereby achieving tight coupling between hydrolysis and methanogenesis. RL2 leveraged the physical carrier effect to promote granulation and biomass growth, enriching Syntrophobacter to enhance propionate degradation. This culminated in a highly modular, sparse network characterized by localized competitive interactions. Together, dosage governs the net methanogenic effect of PE MPs, whereas particle size dictates the mechanistic routes of action. This work offers a mechanistic framework to optimize energy recovery from PE-MP-contaminated sludge while mitigating secondary environmental risks, providing a science-based strategy for the sustainable management of plastic-laden sludge that reconciles renewable energy recovery with pollution control. Full article

This paper presents a comparative analysis of three workspace discretization strategies, Cartesian, cylindrical and spherical, integrated into a graph-based path planning application developed in Python and connected to RoboDK. The study starts from the observation that the workspace of an articulated industrial robot is not naturally aligned with a uniform Cartesian partitioning, and this aspect can influence the internal structure of the graph and the planning effort. For the initial analysis, the three discretizations were tested for the same start-goal pair and for resolutions ranging from 1500 mm to 600 mm. All three variants led to the same validated route, with a length of 3292.215 mm, which shows that the main differences did not occur at the level of the final geometric solution, but at the level of the internal structure of the graph. On average, the spherical discretization generated the most compact graph, with 101.7 nodes and 256.4 edges, compared to 277.3 nodes and 724.9 edges for the Cartesian discretization. The average planning time was also shorter for the spherical discretization, 0.0069 s, compared to 0.0150 s for the Cartesian discretization and 0.0127 s for the cylindrical discretization. At the 600 mm resolution, the spherical discretization used approximately 63% fewer nodes and 66% fewer edges than the Cartesian discretization, while retaining a larger number of candidate routes. The evaluation was then extended by 180 additional trials, performed on two scenarios and on several start-goal pairs. Of these, 151 led to valid routes, corresponding to an overall success rate of 83.9%. The results show that the spatial representation influences the graph size, connectivity, planning time and length of validated routes. However, additional tests also show that these effects depend on the scenario and the criterion analyzed. The spherical discretization produced the most compact graphs, but did not lead in all cases to the shortest routes or the highest success rate. Therefore, the contribution of the paper consists in a controlled comparative evaluation of the influence of the spatial representation on a graph-based planning pipeline, not in demonstrating the universal superiority of a single discretization. Full article

Tobramycin is a highly hydrophilic aminoglycoside antibiotic with limited cellular permeability and negligible oral bioavailability, necessitating parenteral administration. This study aimed to develop drug delivery systems based on nano-sized colloidal assemblies (NCAs) incorporating tobramycin ion pairs to enhance its lipophilicity, potential for transition to the oral route, and antimicrobial activities. Tobramycin was ionically paired with oleic acid, lauric acid, and fluorescein and formulated into NCA preconcentrates (F1–F5) using combinations of Tween 80, DMSO, and propylene glycol. The resulting formulations formed stable nanodroplets upon dilution (9.50–16.30 nm) with narrow size distributions (polydispersity index; PDI < 0.3) and moderate negative zeta potentials (−4.99 to −11.13 mV). In vitro release studies indicated sustained drug release for ion-paired systems compared to the rapid release of free tobramycin. Cytotoxicity evaluation in Caco-2 cells demonstrated high biocompatibility at 1:10,000 and 2:10,000 dilutions, while concentration-dependent toxicity at higher doses suggested enhanced intracellular delivery. Cellular uptake studies revealed significantly higher tobramycin internalization (p < 0.001) from formulations F1–F3, with uptake values in the range of 81.76–96.14% compared to free drug, which showed zero or negligible uptake. Fluorescein-labeled formulations (F4 and F5) further confirmed enhanced uptake, demonstrating strong intracellular fluorescence. This was supported by visual observation, UV–Vis absorbance (70.5–84.8% relative to positive control), and confocal microscopy imaging. Antimicrobial activities against P. aeruginosa and S. aureus were comparable between formulations F1–F5 and free tobramycin (inhibition zones of 16–18 mm), utilizing the same tobramycin concentration in the diluting medium. These findings validate the effectiveness of the formulated NCAs in facilitating intracellular delivery of tobramycin while preserving biocompatibility and similar antimicrobial activities. Moreover, the uptake of fluorescein provides indirect evidence supporting the enhanced internalization of tobramycin in analogous ion-paired formulations. This strategy holds promise for overcoming intestinal barriers and improving oral bioavailability, potentially enabling the transition of tobramycin from parenteral to oral administration. Full article

Extended producer responsibility (EPR) is intended to link producer design decisions to end-of-life costs, but collective EPR schemes typically weaken this link by routing funding through producer responsibility organisations. We develop a multi-level framework of consumer-integrated environmental protection (CIEP) and argue that individual producer responsibility (IPR), where producers bear product-specific end-of-life liability, can function as a governance mechanism that reconnects design, consumer behaviour and waste governance. This paper is a qualitative multiple-case research study—not a systematic review—which draws on three funded research projects: (i) small and medium-sized enterprise (SME) tools for design-for-recyclability, (ii) an artificial intelligence (AI) application for household waste sorting, and (iii) closed-loop recycling of fishing gear in Vietnam. Within the first project (ToCoReRaM), a PRISMA-based systematic review of web-accessible circular economy tools finds that only 2 of 23 tools are SME-accessible through standard web searches. The AI-based waste-sorting application achieves approximately 75% classification accuracy under real-world conditions. The fishing gear study demonstrates technical and economic viability of closed-loop recycling, and a survey of more than 1500 Vietnamese fishers finds 95.8% willingness to return used gear given appropriate incentives. Together, the cases show that effective circular governance requires four complementary elements: IPR-based producer accountability, SME-accessible design tools, digital consumer guidance at the point of disposal, and context-sensitive governance capacity. These findings inform policy pathways for Sustainable Development Goal (SDG) 12 and SDG 14. Full article

Nickel ferrite (NiFe₂O₄) is one of the most studied inverse-spinel ferrites because it combines moderate saturation magnetization, comparatively high electrical resistivity, chemical stability, and broad synthesis flexibility. Yet the literature shows that the measured structure and magnetism of NiFe₂O₄ are not intrinsic constants; they evolve strongly with dimensionality, size, thickness, strain state, cation distribution, surface spin disorder, and synthesis pathway. This review develops a unified theoretical and literature-based interpretation of how dimensionality reshapes the structural and magnetic behavior of NiFe₂O₄ across bulk ceramics, nanoparticles, one-dimensional nanostructures, polycrystalline thin films, and ultrathin epitaxial films. The review is anchored in the two uploaded nickel ferrite attachments and expanded using internet-sourced journal literature on spinel inversion, surface effects, mechanochemical synthesis, sputtered and pulsed laser deposited thin films, and epitaxial ultrathin-film anomalies. The central novelty of this article is the formulation of a dimensionality-dependent framework in which the observed magnetic response is governed by a competition among three coupled factors: (i) the cation-distribution function, which controls the A–B superexchange balance and therefore the net ferrimagnetic moment; (ii) the microstructural coherence function, which measures how crystallinity, strain, defects, and anti-phase boundaries preserve or degrade exchange continuity; and (iii) the surface/interface spin-order parameter, which quantifies the loss or reconfiguration of magnetic order at free surfaces and buried interfaces. Within this framework, bulk NiFe₂O₄ behaves as a near-equilibrium inverse spinel with relatively stable magnetization, whereas nanoscale NiFe₂O₄ experiences strong spin canting and finite-size suppression due to the growing fraction of disordered surface spins. Thin films introduce a distinct regime in which strain, texture, anti-phase boundaries, substrate mismatch, and growth kinetics determine both anisotropy and magnetization. In ultrathin epitaxial films, off-equilibrium cation redistribution and interface-controlled electronic reconstruction may even generate magnetization values far above bulk expectations. The review also compares major synthesis routes—solid-state reaction, sol–gel, co-precipitation, hydrothermal growth, reactive milling, combustion, pulsed laser deposition, and radio-frequency sputtering—and explains why each route biases the final dimensionality-dependent properties differently. A set of word-style equations is provided to formalize spinel inversion, finite-size suppression, anisotropy scaling, coercivity trends, and superparamagnetic crossover. Beyond summarizing the field, the review proposes a regime map linking dimensionality to characteristic structural defects and magnetic signatures, and it identifies unresolved questions concerning the true origin of enhanced magnetization in ultrathin NiFe₂O₄, the interplay between anti-phase boundaries and strain, and the distinction between intrinsic inversion changes and extrinsic substrate artifacts. The resulting article offers a submission-ready, originality-focused review that positions dimensionality as the master variable governing structure–magnetism correlations in nickel ferrite. Full article

This study proposes a canopy-adaptive TAD-IRRT* (target-biased sampling, artificial potential field, and dynamic step-size informed rapidly-exploring random tree star) algorithm to solve the collision-free 3D path-planning problem for a 6-DOF apple-harvesting robotic arm. To improve computational speed and search directionality, the method integrates target-biased sampling and a distance-regulated artificial potential field (APF) into the Informed-RRT* framework. Furthermore, an obstacle-distance-based dynamic step-size mechanism is introduced to optimize spatial exploration. The generated routes undergo greedy path pruning and cubic B-spline smoothing to ensure kinematic executability. The simulation results in complicated ROS-based scenarios demonstrate that the TAD-IRRT* algorithm achieves a 100% planning success rate, reducing the average computational time and joint-space path length by approximately 60.1% and 15.6%, respectively, compared to the standard Informed-RRT*. Kinematic analysis via Fourier curve fitting ($R^2=0.9849$) confirms continuous angular velocity and acceleration without high-frequency chattering. Physical prototype experiments in the dense-obstacle scenarios show that the proposed method increases the path execution success rate by 36.7% and reduces the average execution time by 41% compared to the standard Informed-RRT* algorithm. The proposed approach effectively balances high-quality path generation with low computational overhead, providing a reliable and safe solution that significantly reduces mechanical wear. Full article

The Annual Delivery Program (ADP) for Liquefied Natural Gas (LNG) represents a complex maritime inventory-routing problem that requires the precise synchronization of production and distribution. This study introduces a novel Mixed Integer Linear Programming (MILP) model designed to optimize vessel routing and scheduling over a one-year horizon under a direct-shipment assumption. The model minimizes total logistics costs, encompassing both fixed annual fleet costs and daily operating costs. The novelty of the model can be summarized in two aspects. First, it simultaneously optimizes several decisions: the assignment of frequency of deliveries to customers, the assignment of vessels to customers, cargo load sizes, and vessel routing and scheduling. The key distinction is that, unlike existing formulations that take the frequency of deliveries to customers as a fixed parameter, this frequency is itself a decision variable selected from a customer-specific discrete set; the selected frequency partitions the planning horizon into uniform windows and sets each delivery’s cargo load size to the exact demand accumulated over its window from daily demand data. Second, it incorporates several relaxations of selected variables and valid inequalities that enable us to solve the complex model for moderate size problems within a reasonable computational time using the exact optimization approach. Using this novel model, we carried out extensive numerical analysis based on cost and operational parameter scenarios and developed important insights for the characteristics of a solution to the problem. Full article

Institutional shuttle fleets with fixed routes and predictable terminal parking are well-suited to charging photovoltaic–battery energy storage system (PV–BESS) charging for sustainable campus mobility. However, siting and sizing are often solved numerically without identifying the physical constraints that determine the optimum. This study develops a sustainability-oriented framework for converting a 10-van diesel shuttle fleet at Rajamangala University of Technology Lanna into an electric fleet supported by distributed PV–BESS charging stations. A centralized one-station layout is compared with a distributed two-station layout, and a closed-form active-constraint sizing rule is derived using Karush–Kuhn–Tucker (KKT) analysis. Results show that the distributed configuration eliminates dead-run travel and provides higher lifecycle value than the centralized case. KKT analysis identifies two binding constraints: the PV rooftop-area limit and the BESS one-day autonomy requirement. Under base-case assumptions, the transition achieves positive lifecycle value and substantial CO₂ reduction relative to the diesel baseline. Monte Carlo analysis confirms financial robustness within the uncertainty ranges, while deterministic stress tests show sensitivity to diesel prices, PV electricity credit values, discount rate, and fleet utilization. The framework provides an interpretable decision-support method for institutional fleet electrification in solar-rich campus settings, contributing to SDGs 7, 11, and 13 through clean-energy adoption, sustainable transportation, and CO₂-emission reduction. Full article

Order picking is one of the most resource-intensive warehouse operations; therefore, improving routing efficiency remains an important challenge. Deep reinforcement learning (DRL) has shown promise in complex optimization problems. However, its application to warehouse order picking is still limited, and graph-based representation learning using graph neural networks (GNNs) in this context remains largely unexplored. This paper proposes a GNN-based DRL method that models warehouse layouts as graphs to optimize order-picking paths while simultaneously learning graph-based structural embeddings of storage locations. The approach is evaluated against exact optimal solutions for smaller instances and against classical heuristic baselines, including the Lin–Kernighan algorithm, in simulated warehouse environments of different scales. The results show that the proposed GNN–DRL approach produces routing solutions with low optimality gaps across different order sizes and remains effective across different warehouse sizes when fine-tuned. In addition, a preliminary small-scale multi-picker experiment illustrates that the proposed framework could be extended toward more complex warehouse optimization settings. Moreover, the learned node embeddings capture meaningful structural properties of warehouse layouts and adapt to different operational contexts, highlighting the potential of integrating GNNs and DRL as a flexible foundation for advanced warehouse optimization. Full article

To reveal the overtopping dam-break mechanism under extreme rainfall conditions and assess downstream flood risk, a series of dam-break flume tests, flood routing simulations and inundation risk assessments were conducted. Using the Hubuling Reservoir in Rizhao City, Shandong Province as a case study, a circulating extreme rainfall dam-break flume system with a controllable reservoir water level was constructed at a geometric similarity scale of 1:70. Four test conditions were designed: no rainfall and 50-year, 100-year and 2000-year rainfall return periods. Pore water pressure, earth pressure and water content sensors were embedded in critical dam sections to monitor real-time internal dynamic responses. The results show that, due to the combined effect of the highest rainfall intensity, rapid reservoir water-level rise, progressive infiltration-induced weakening and concentrated surface erosion, a dam-break occurs only under the 2000-year rainfall return period. The failure process is divided into four stages: initial infiltration, slope surface scour, overtopping initiation and rapid breach development. Based on dam-break parameters obtained by physical model tests, a two-dimensional numerical using HEC-RAS was conducted. The results show that, under the 2000-year rainfall return period, the flood reaches the downstream area at 80 min after dam failure. The maximum inundation area reaches 15.20 km² at 200 min, with a maximum inundation depth of 11.80 m and a maximum inundation duration of 144 h. By integrating the maximum inundation depth, inundation duration and land use conditions, the expected economic loss is estimated to be 690 million yuan. The results provide important support for dam-break early warnings, emergency management and disaster mitigation of similar small- and medium-sized reservoirs. Full article