Search Results (35)

Agricultural cold chain logistics is characterized by inherent challenges—product perishability, high carbon emissions, and stringent time windows—which are further exacerbated by dynamic disruptions. Existing methods suffer from slow adaptability, unstable multi-objective convergence, and severe cold-start issues. This work falls within the broad scope of biomimetics—the science of emulating nature’s time-tested strategies to solve complex engineering problems—and bio-inspired data-driven methods and their applications in engineering control, optimization, and artificial intelligence. The proposed H-MODRL framework embodies core biomimetic principles: the Genetic Algorithm (GA) mimics Darwinian natural selection and genetic inheritance, the Sparrow Search Algorithm (SSA) abstracts the cooperative foraging and anti-predation behaviors of sparrow populations in nature, and the Arrhenius-based freshness-decay model captures the biochemical kinetics governing perishable biological products. By synergistically integrating these biological evolution principles, swarm intelligence, and deep learning, the framework tackles real-world logistics complexity in a manner directly inspired by living systems. This study presents a well-organized hybrid optimization framework (H-MODRL) that couples a three-stage hybrid evolutionary mechanism, synergistically integrating heuristic warm-start, evolutionary policy guidance, and deep reinforcement learning decision-making. First, an improved genetic algorithm combined with the earliest deadline first strategy constructs a feasible initial population satisfying hard time-window constraints. Second, a large neighborhood search-enhanced chaotic sparrow search algorithm builds a high-quality elite guidance set for policy learning. Third, a physics-based multi-objective proximal policy optimization model embedded with Arrhenius equation-derived freshness-decay kinetics performs online decision-making. Experiments demonstrate that pre-computed all-pairs shortest paths and an O(1) hash-based dynamic-disruption indexing mechanism support fast online replanning. On heterogeneous simulated terrains based on real Chinese geospatial data, H-MODRL outperforms state-of-the-art algorithms across four objectives—logistics cost, carbon emissions, terminal freshness, and delivery time—while exhibiting compact, low-variance performance distributions, thereby validating its engineering robustness and practical value in complex agricultural cold chain environments. Full article

Background/Objectives: In critically ill patients, endotracheal intubation (EI) is often performed to secure the airway or mechanical ventilation. Accurately predicting the timing of intubation significantly affects patient outcomes. We developed an artificial intelligence (AI) model designed for real-time risk stratification of patients requiring EI. Methods: We utilized the Medical Information Mart for Intensive Care-IV (MIMIC-IV) 2.2 dataset and performed model development using 15 clinical variables, including vital signs, Glasgow Coma Scale (GCS) score, and arterial blood gas analysis results. Patients intubated before or within 1 h of intensive care unit (ICU) admission were excluded. Clinical data from the ICU inherently consists of continuous time-series measurements. Traditional machine learning models often treat this information as static tabular data, neglecting vital temporal dynamics and patient history. Conversely, deep learning time-series approaches can capture these complex patterns over time. Thus, we applied the Gated Recurrent Unit with Decay++ (GRU-D++) model to predict the need for EI. GRU-D++ is an extension of the GRU and GRU-D. It builds upon the GRU-D to provide improved performance when handling datasets with exceptionally high rates of missing values. GRU-D++ is a time series deep learning model with an automatic mechanism for imputing missing values. This built-in capability eliminates the need for additional data preprocessing and has previously demonstrated high predictive performance. Using the 15 variables, we evaluated the optimal timing for EI in ICU-admitted patients by applying various AI models. Results: Among these, the GRU-D++ model demonstrated AUROC of 0.888, AUPR of 0.481, sensitivity of 0.474, specificity of 0.995, precision of 0.511, and F1 score of 0.491 on MIMIC-IV dataset. For KNUH dataset, the model demonstrated AUROC of 0.913, AUPR of 0.063, sensitivity of 0.162, specificity of 0.997, precision of 0.137, and F1 score of 0.147 within the 2 h in advance scenario. Furthermore, when compared with conventional scoring systems such as the Heart rate, Acidosis, Consciousness, Oxygenation, Respiratory rate (HACOR) score and Respiratory rate-Oxygenation (ROX) index, the GRU-D++ model also showed better performance predictive accuracy. Conclusions: The AI-based intubation prediction model developed in this study holds potential as a real-time risk stratification tool, providing timely risk assessments regarding the need EI. While operational threshold recalibration is essential prior to clinical deployment, further prospective multicenter studies are required to validate the clinical utility of this model in real-time practice. Full article

Historical small towns (HSTs) embody irreplaceable cultural heritage and territorial identity, facing depopulation, economic marginalization, and infrastructure decay. Improving their liveability and attractiveness is essential to reverse these trends and boost sustainable development. In this context, HSTs are potential drivers of circular and sustainable socio-technical systems, where the circular economy (CE) offers a framework for local sustainability. However, HSTs lack adequate sustainable CE implementation tools. This study, the culmination of the H-SMA-CE project, develops a Decision Support System (DSS) to assist local policymakers in planning CE transitions in Italian HSTs. The DSS integrates three building blocks: context analysis (metabolic flows, stakeholder networks), an intervention library with cost–benefit data, and a composite Municipal Circular Economy Index (MCEI). The tool enables users to assess baseline circularity, simulate scenarios, and identify optimal investment portfolios through multi-objective optimization. This approach allows for the simultaneous evaluation of the benefits of each sustainability aspect, i.e., environmental, economic and social. Tested on the municipality of Taurasi (Italy), an HST with a wine-based economy, the results show that balanced intervention strategies yield greater circularity improvements than single-objective approaches. The paper contributes to the discourse on digital tools for sustainability transitions, offering a replicable model for evidence-based CE governance in heritage-rich territorial contexts. Full article

In the face of the dual challenges of global climate change and rapid urbanization, optimizing the ecosystem services of urban green spaces has become a key strategy for building resilient and sustainable cities. This is particularly crucial in ecologically fragile arid and semi-arid regions. To accurately assess the thermal regulation function of urban green spaces, this study selected 20 parks in Xi’an, China. Combining remote sensing and Geographic Information System (GIS) technology, we adopted four established cooling indicators—Park Cooling Area (PCA), Park Cooling Efficiency (PCE), Park Cooling Intensity (PCI), and Park Cooling Gradient (PCG)—to systematically evaluate the thermal regulation functions of urban parks and their landscape-driving mechanisms. The results indicated that the average cooling amplitude of the parks was 2.53 °C, with an effective influence distance reaching 323.9 m, exhibiting a significant spatial gradient decay. We found a non-linear trade-off between green space scale and efficiency: while large parks provided a wider absolute cooling range, small and medium-sized parks demonstrated higher efficiency per unit area. Furthermore, a blue-green synergistic configuration significantly enhanced the mitigation of the urban heat island effect. The study confirmed that Park Area (PA), Park Perimeter (PP), and the Normalized Difference Vegetation Index (NDVI) significantly promoted cooling effects, whereas landscape fragmentation inhibited ecological benefits. This study elucidates the comprehensive regulation mechanism of urban parks on the urban microclimate, providing planning guidance for implementing Nature-based Solutions (NbS) and achieving climate-adaptive development in arid and semi-arid cities within the context of urban renewal. Full article

Indoor environmental quality (IEQ), influenced by ventilation and acoustic conditions, directly affects human health and comfort. Existing studies often concern either ventilation or sound insulation alone, neglecting the impact of the trickle ventilator's internal structure and its combination with windows on overall performance. This study introduced a double-chamber model to quantify the ventilation performance of three trickle ventilators using tracer-gas-decay and pressure-difference methods. We calculated the flow coefficient ($C_d$) and flow exponent ($n$) to reveal differences in pressure sensitivity, with trickle ventilator TV2 showing the highest-pressure sensitivity ($C_d$ = 1.34, $n$ = 0.89). The weighted sound reduction index ($R_W$) and weighted sound insulation index for traffic-noise correction ($R_W +{ C}_{tr}$) were measured, showing trickle ventilators TV1-1 and TV1-2, and TV2 were 29 dB, 30 dB, and 34 dB, respectively. And the sound insulation and ventilation performance of window-trickle ventilator combinations were analyzed. Trickle ventilators could enhance acoustic performance for low-insulation windows but reduce it for high-insulation windows. The study also quantitatively balanced ventilation and acoustics. This research provides data support and theoretical guidance for the synergistic optimization of ventilation and sound insulation in building environments and provides guidance on ventilation and noise control strategies suited to different floor levels and outdoor noise environments. Full article

Building clustering is a key challenge in cartographic generalization, where the goal is to group spatially related buildings into semantically coherent clusters while preserving the true distribution patterns of urban structures. Existing methods often rely on either spatial distance or building feature similarity alone, leading to clusters that sacrifice either accuracy or spatial continuity. Moreover, most deep learning-based approaches, including graph attention networks (GATs), fail to explicitly incorporate spatial distance constraints and typically restrict message passing to first-order neighborhoods, limiting their ability to capture long-range structural dependencies. To address these issues, this paper proposes LA-GATs, a multi-feature constrained and spatially adaptive building clustering network. First, a Delaunay triangulation is constructed based on nearest-neighbor distances to represent spatial topology, and a heterogeneous feature matrix is built by integrating architectural spatial features, including compactness, orientation, color, and height. Then, a spatial distance-constrained attention mechanism is designed, where attention weights are adjusted using a distance decay function to enhance local spatial correlation. A second-order neighborhood aggregation strategy is further introduced to extend message propagation and mitigate the impact of triangulation errors. Finally, spectral clustering is performed on the learned similarity matrix. Comprehensive experimental validation on real-world datasets from Xi’an and Beijing, showing that LA-GATs outperforms existing clustering methods in both compactness, silhouette coefficient and adjusted rand index, with up to about 21% improvement in residential clustering accuracy. Full article

The relationship between architectural design and outdoor acoustic environments remains underexplored, particularly in educational spaces where noise levels impact comfort and usability. This study investigates the impact of building facade height on the outdoor acoustic environment in university courtyards. Acoustic measurements were conducted in two courtyards at Batna 1 University, each surrounded by buildings with distinct facade heights. Key acoustic parameters, including reverberation time (RT), early decay time (EDT), rapid speech transmission index (RaSTI), Definition (D50), and sound pressure level (SPL) attenuation were evaluated at specified source-receiver distances. The results reveal a strong correlation between RT20 and distance at higher frequencies due to building facade reflections, while lower frequencies are more influenced by geometric configuration and material absorption properties. The results demonstrate that RT and EDT increase logarithmically or polynomially with distance, especially at higher frequencies (2000–4000 Hz), due to the decrease in direct sound energy and increase in reflected sound amplitude. Taller building facades lead to longer RT and EDT values compared to lower heights. D50 and RaSTI decrease polynomially with increasing source–receiver distance, with lower values observed in the courtyard with taller facades, indicating reduced speech clarity. The SPL attenuation is influenced by surrounding geometry, with the least reduction in the courtyard with lower facade heights, followed by the taller facade courtyard, contrasting with semi-free field conditions. These findings highlight the significant role of building facade height and architectural elements in shaping the acoustic characteristics of outdoor spaces, providing valuable insights for designing acoustically comfortable urban environments, particularly in educational settings. Full article

In this study, the passive acoustic performance of Necmettin Erbakan University Köyceğiz Campus Masjid was investigated. Designed as the largest masjid of the city with a capacity of 15,000 people and a volume of 43,200 m³, the masjid, which has traces of Seljuk, Ottoman and Modern architecture. is built as a complex at a location overlooking the city in the Meram District of Konya City, Turkiye. The aim of the study is to determine the acoustic comfort conditions by considering all the activities in the masjids as a whole. Within the scope of the study, the acoustic performance of the masjid was evaluated by determining different source and receiver points for each mode of activity. As a method, the chosen masjid was simulated with ODEON Room Acoustics Software Ver. 14.04 software. Objective room acoustic parameters were analysed in three groups. These are sound energy ratio parameters (reverberation time (RT), early decay time (EDT), clarity (C50, C80), lateral fraction (LF80)), speech intelligibility parameters (definition (D50), speech transmission index (STI)) and sound strength parameters (strength (G)). The results obtained were compared with precedent studies in the literature. In comparison with the acoustic values obtained in other masjid/mosque buildings, it was reported that, while the speech intelligibility of other masjids/mosques was at a satisfactory level, the masjid under consideration was at a poor level in both fully occupied and unoccupied conditions. In the analysis made for reverberation time, it was seen that the masjid discussed in this study showed similar characteristics to other masjids/mosques globally. As a result, it was determined that the dimensions of the surfaces forming the mihrab, the minbar design and the depths of the mahfil overhangs are effective regarding the acoustics of the masjid, and the design of curved surfaces should be carried out in a way that does not cause focusing problems. In addition, suggestions that can give guidelines to modern masjid designs have been put forward. Full article

To implement the geographical and future climate adaptation of building moisture design for building projects, practitioners need efficient tools, such as precalculated climate indices to assess climate loads. Among them, the Frost Decay Exposure Index (FDEI) describes the risk of freezing damage for clay bricks in facades. Previously, the FDEI has been calculated for 12 locations in Norway using 1961–1990 measurements. The purpose of this study is both updating the FDEI values with new climate data and future scenarios and assessing how such indices may be suitable as a climate adaptation tool in building moisture safety design. The validity of FDEI as an expression of frost decay potential is outside the scope of this study. Historical data from the last normal period as well as future estimated climate data based on 10 different climate models forced by two emission scenarios (representative concentration pathways 4.5 and 8.5) have been analyzed. The results indicate an overall decline in FDEI values on average, due to increased winter temperatures leading to fewer freezing events. Further, the variability between climate models and scenarios necessitates explicit uncertainty evaluations, as single climate model calculations may result in misleading conclusions due to high variability between models. Full article

The decay of radon gas in soil and buildings produces alpha radiation, which is the second leading cause of lung cancer in humans. Therefore, by conveniently detecting radon gas in the environment, potential sources of danger can be identified early, and necessary measures can be taken to protect human health. Solid-state nuclear track detectors prepared from polyallyl diglycol carbonate (PADC) resin are the most sensitive detectors for alpha radiation released by radon gas. The traditional method of preparing PADC resin involves free radical thermal polymerization, which suffers from issues such as low polymerization efficiency, long processing time, and the occurrence of defects in the product. In this study, PADC resin was efficiently prepared using a UV initiator. Starting from the polymerization mechanism, experiments were designed using a controlled variable approach, and a rational polymerization apparatus was devised. By comparing the double bond conversion rate, transparency, hardness, and yellowness index of the polymers, the optimal initiator for PADC resin, 2-hydroxy-2-methyl-1-phenyl-1-propanone (1173), was selected. The influence of irradiation intensity, irradiation time, and UV initiator dosage was investigated. The performance of the polymers, including double bond conversion rate, optical properties, dynamic mechanical properties, etching rate, and track detection efficiency, was analyzed. The experimental conditions for preparing PADC resin were optimized: irradiation intensity of 12 mW/cm², irradiation time of 25 min, and UV initiator dosage of 5 parts. The resulting resin polymer had a double bond conversion rate of 93.2% and a track detection efficiency of 0.714. Full article

Sesame, Sesamum indicum L., is one of the oldest domesticated crops used for its oil and protein in many parts of the world. To build genomic resources for sesame that could be used to improve sesame productivity and responses to stresses, a USDA sesame germplasm collection of 501 accessions originating from 36 countries was used in this study. The panel was genotyped using genotyping-by-sequencing (GBS) technology to explore its genetic diversity and population structure and the relatedness among its accessions. A total of 24,735 high-quality single-nucleotide polymorphism (SNP) markers were identified over the 13 chromosomes. The marker density was 1900 SNP per chromosome, with an average polymorphism information content (PIC) value of 0.267. The marker polymorphisms and heterozygosity estimators indicated the usefulness of the identified SNPs to be used in future genetic studies and breeding activities. The population structure, principal components analysis (PCA), and unrooted neighbor-joining phylogenetic tree analyses classified two distinct subpopulations, indicating a wide genetic diversity within the USDA sesame collection. Analysis of molecular variance (AMOVA) revealed that 29.5% of the variation in this population was due to subpopulations, while 57.5% of the variation was due to variation among the accessions within the subpopulations. These results showed the degree of differentiation between the two subpopulations as well as within each subpopulation. The high fixation index (F_ST) between the distinguished subpopulations indicates a wide genetic diversity and high genetic differentiation among and within the identified subpopulations. The linkage disequilibrium (LD) pattern averaged 161 Kbp for the whole sesame genome, while the LD decay ranged from 168 Kbp at chromosome LG09 to 123 Kbp in chromosome LG05. These findings could explain the complications of linkage drag among the traits during selections. The selected accessions and genotyped SNPs provide tools to enhance genetic gain in sesame breeding programs through molecular approaches. Full article

Cultural heritage assets face significant threats from climate change and land subsidence, leading to extensive social, economic, and environmental losses, and damage to artistic and monumental heritage in Italian coastal cities. In particular, addressing these challenges in the Venetian context necessitates the development of an adaptation plan for the lagoon area and the identification of targeted intervention strategies to preserve cultural and territorial heritage. To address these objectives, a systematic study was conducted to investigate the deterioration patterns exhibited by the most representative lithologies used in Venetian buildings. Thirty samples of five carbonate stone varieties subjected to natural aging were monitored in six different areas of Venice’s historic center and on Torcello Island, selected based on altimetry relative to tidal zero and exposure to environmental forces. An integrated multi-analytical approach was employed to identify and map macro- and micro-morphologies of stone surfaces related to chemical weathering and physical decay. Stones underwent evaluation during nine monitoring periods using various tests (ultrasound P-wave velocity and colorimetric measures) and analyses (µX-Ray Fluorescence, X-ray powder diffraction, stereomicroscope observations, and recognition of biological patinas). Data processing aimed to elucidate how microclimate and intrinsic stone features influence the occurrence and progression of deterioration phenomena. From the experimental findings, a Stone Deterioration Index and Intervention Procedures (SDIi) were proposed to estimate deterioration rates and assess the need for targeted intervention through conservative actions. Full article

Doctrinal texts on architectural heritage conservation emphasize the importance of fully understanding the structural and material characteristics and utilizing information systems. Photogrammetry allows for the generation of detailed, geo-referenced Digital Elevation Models of architectural elements at a low cost, while GIS software enables the addition of layers of material characteristic data to these models, creating different property maps that can be combined through map algebra. This paper presents the results of the mechanical characterization of materials and salt-related decay forms of the polygonal apse of the 13th-century monastery of Santa María de Bonaval (Guadalajara, Spain), which is primarily affected by salt crystallization. Rock strength is estimated using on-site nondestructive testing (ultrasound pulse velocity and Leeb hardness). They are mapped and combined through map algebra to derive a single mechanical soundness index (MSI) to determine whether the decay of the walls could be dependent on the orientation. The presented results show that salt decay in the building is anisotropic, with the south-facing side of the apse displaying an overall lower MSI than the others. The relative overheating of the south-facing side of the apse enhances the effect of salt crystallization, thereby promoting phase transitions between epsomite and hexahydrite. Full article

Hydrated H-Apophyllite (HH-Apo) and H-carletonite (H-Car) were synthesized at 0 °C by leaching an apophyllite and a carletonite single crystal in a large surplus of 1.2 molar hydrochloric acid. The XRD powder patterns of HH-Apo and H-Car were indexed with space group symmetries of P4/ncc and I4/mcm and lattice parameters of a = 8.4872(2) Å, c = 16.8684(8) Å and a = 13.8972(3) Å, c = 20.4677(21) Å, respectively. The crystal structures were solved based on model building of the structures of the precursors and a physico-chemical characterization. Rietveld structure refinements confirmed the structure models. HH-Apo and H-Car are among the very few crystalline silicic acids whose structures have been determined and confirmed based on a structure refinement. The structure of HH-Apo contains thin silicate monolayers that can be regarded as constructed by rings of interconnected [SiO₃OH] tetrahedra which form a puckered silicate layer. A sheet of water molecules is intercalated between the silicate layers. There are no direct hydrogen bonds between the silanol groups, but there are hydrogen bonds of different strengths between the terminal O atoms of the silicate layers and the intercalated water molecules. The ¹H MAS NMR spectrum presents a strong signal at 4.9 ppm related to the aforementioned bonds and interactions between the water molecules, as well as a small signal at 22.5 ppm corresponding to an extremely strong hydrogen bond with d(O...O) ≈ 2.2 Å. The structure of H-Car is free of structural water and consists exclusively of microporous silicate double-layers with 4-connected [SiO₄] and 3-connected [SiO₃OH] tetrahedra in a ratio of 1:1 and a thickness of 9.2 Å. Neighboring layers are connected to each other by medium–strong hydrogen bonds with O...O distances of 2.56 Å. The structure of HH-Apo decays within several hours while H-Car is stable. A topotactic condensation reaction applied to H-Car forms an irregularly condensed silicate which still contains the layers in a distorted form as building blocks. Full article

The planning of interventions aimed at preserving the built heritage of inner areas is a complex process due to the fragility of these contexts. It should stem from adequate knowledge to support the recognition of qualities, resources, and potentials, and the reinterpretation of residual values. From the perspective of an axiological approach to the built heritage, it is possible to foster the resemantization of such values based on a rigorous cognitive model. This research proposed a cognitive model of the built heritage of the historic neighbourhood of Granfonte in Leonforte (Enna). The knowledge model, developed in Excel, has a hierarchical type of structure characterized by domain, classes, properties, and the attribution of values to properties. The approach makes it possible to execute queries that arise from specific relationships between classes. In this study, we developed both simple queries to measure the percentages of certain characteristics of the building units and complex queries for the estimation of aggregate indices to define the degree of transformation and loss of integrity $I_{\mathrm{T}\mathrm{I}}$ and degradation $I_{\mathrm{D}}$ of the building units or to identify those most exposed to the risk of ruination and contagion. The proposed model can be framed within the framework of ontologies supporting structured heritage knowledge. Full article