Search Results (6,584)

Post-conflict reconstruction research has examined façade materiality and symbolism, yet the process conditions under which aesthetic specifications are systematically overridden during construction remain neglected. This study investigates why designed architectural aesthetics fail to survive implementation in post-2017 Mosul, Iraq. A mixed-methods design combined formal visual analysis of 12 recently completed residential façades with a structured survey of 45 practicing architects. Survey data reveal that designers are excluded from construction supervision in 76% of projects and that clients intervene in material and color selection in 70% of cases. Visual analysis identifies a sophisticated design language—orthogonal massing articulated through contrasting materials—that is rarely realized in built form. Where designers retain supervisory authority, projects most consistently achieve material–form coherence. The study advances the concept of an aesthetics of interruption (the systematic degradation of designed form–material relationships through the fragmentation of professional authority during construction). Exclusion produces four distinct pathologies: material substitution, execution degradation, language override, and ornamental hollowing. The findings demonstrate that aesthetic degradation in post-conflict reconstruction stems not from design incapacity but from broken process structures. Safeguarding architectural quality requires contractual frameworks mandating designer supervision and material-substitution protocols that protect design intent. Full article

In contemporary Spanish, there are three types of sequences with consecutive meaning that contain the nouns forma, manera, modo, and suerte: the sequence de tal {forma/modo/manera/suerte} que, which forms part of a prepositional phrase that contains a consecutive construction formed by a quantifying phrase and a subordinate clause headed by que; the phrasal conjunction de {forma/modo/manera/suerte} que, which can introduce a consecutive (resultative) subordinate clause included in the predicate verb phrase of the main clause or a consecutive clause modifying the entire matrix clause; and the phrasal preposition de {forma/modo/manera/suerte} de, which generally has an infinitive clause as its complement. This paper explores the analyzability of these sequences and their evolution towards the current state of Spanish, where all three variants coexist. As will be demonstrated, the latter two variants, which emerged from the first, are the result of a grammaticalization process. This process, operating through reanalysis and analogy, has led to two distinct lexical splits. Full article

Accurate and up-to-date mapping of built-up areas is of great importance for sustainable urban planning, disaster management, and the monitoring of environmental changes. In this study, a residual U-Net-based deep learning architecture named FiveBandTTA is proposed for built-up area segmentation from Sentinel-2 multispectral satellite imagery. The proposed model aims to simultaneously learn spatial and spectral features by jointly processing RGB, NIR (B8), and SWIR (B11) bands within the same encoder–decoder structure. The model incorporates standard residual blocks following the conventional residual learning principle, multi-level skip connection mechanisms, and TTA-based inference strategies. Within the scope of the study, a multi-temporal built-up area dataset was constructed from Sentinel-2 imagery acquired over Kocaeli Province. The performance of the proposed model was comparatively evaluated against RGB Baseline, FiveBand Single, DeepLabV3+, and SegFormer models. Experimental results demonstrated that the proposed model achieved the highest segmentation performance among all compared approaches, obtaining 0.8447 IoU, 0.9124 Dice, and 0.9249 Precision scores. It was observed that the use of multispectral bands together with the residual encoder–decoder structure may contribute to improved representation of small-scale built-up regions and complex boundary structures. Furthermore, the comparative experiments indicated that the NIR and SWIR bands provide complementary spectral information for distinguishing built-up areas, while the TTA-based inference strategy may contribute to improved segmentation stability and prediction consistency. Overall, the obtained results demonstrate that the proposed approach is an effective and robust method for built-up area segmentation from medium-resolution Sentinel-2 imagery. Full article

Multi-target path planning for Automated Guided Vehicle (AGV) in complex logistics environments requires balancing planning efficiency, obstacle avoidance capability, and trajectory smoothness. To address these challenges, this paper proposes a bi-level collaborative framework integrating Particle Swarm Optimization (PSO) with the Bidirectional Rapidly Exploring Random Tree (Bi-RRT). The framework unifies adaptive sampling, online parameter optimization, and trajectory smoothing within a single planning architecture. Specifically, the framework constructs a five-dimensional particle encoding that includes the expansion step size and multi-level strategy switching thresholds. During the Bi-RRT expansion process, an expansion-failure-driven adaptive sampling mechanism is introduced to enhance search performance in cluttered environments, while local-density-based suppression and directional dispersion are employed to reduce redundant exploration. In addition, a lightweight PSO-based monitoring mechanism enables online adaptive parameter adjustment. For multi-target scheduling, a greedy heuristic based on a hybrid weighted graph determines the visitation sequence. Trajectory smoothness is further improved using cubic B-spline interpolation combined with bounded perturbation optimization. Experimental results demonstrate that the proposed framework improves planning efficiency while maintaining stable performance across environments with different obstacle densities. These results demonstrate the effectiveness of the proposed framework for multi-target AGV path planning in complex warehouse environments. Full article

Localized optical absorption by nanoscale inclusions can profoundly alter energy deposition in optical traps, giving rise to nonlinear and long-timescale dynamics. Recent experiments have reported the formation of expanding optically darkened regions and episodic plasma-like emission during pulsed near-infrared optical trapping of magnetic beads interacting with biological cells. Here, we develop a reduced-order mechanistic model to investigate whether absorber-driven optical–thermal feedback associated with Fe₃O₄ inclusions is sufficient to reproduce the observed pre-plasma darkening dynamics. The model is constructed progressively from first-principles electromagnetic absorption and pulse-scale thermal diffusion to nonlinear feedback mediated by an evolving optically modified region. Single-pulse and multi-pulse simulations demonstrate that isolated iron-oxide absorbers cool too rapidly to sustain long-timescale thermal accumulation through linear heating alone. However, incorporation of a bubble-mediated optical feedback channel produces bounded growth, partial optical darkening, and slow relaxation dynamics consistent with experimentally observed minute-scale evolution. Electromagnetic absorption was computed using full core–shell Mie theory, yielding absorption cross-sections sufficient to support strong localized optical attenuation under experimentally relevant trapping conditions. The resulting reduced-order feedback framework reproduces stable growth–relaxation cycles, finite transmission plateaus, and self-limited optical darkening without requiring runaway heating or catastrophic cavitation. To evaluate the model quantitatively, simulated transmission dynamics were compared against experimentally measured normalized transmission traces digitized from previously reported optical trapping experiments. The fitted model reproduced the observed finite transmission plateau and slow post-activation relaxation with good agreement ($R^2\approx 0.86$, RMSE $\approx 1.3\times {10}^{-2}$). These results support the interpretation that experimentally observed optical darkening arises from a feedback-regulated optical–thermal process involving slowly evolving structural modification of the trapping region rather than cumulative thermal storage within isolated absorbers. The present framework provides a quantitatively constrained reduced-order description of feedback-mediated optical darkening under pulsed optical trapping conditions and establishes iron-oxide absorption as a physically plausible ignition mechanism for dark-state formation in the pre-plasma regime. Full article

Carbon dioxide (CO₂) capture through adsorption using porous materials has emerged as a promising strategy for mitigating industrial greenhouse gas emissions. However, selecting an optimal adsorbent material under varying operating conditions remains a complex and time-consuming process when relying solely on experimental studies. In this project, a machine-learning-based framework is developed to predict CO₂ adsorption capacity and identify the most suitable adsorbent material using process and material parameters. A comprehensive dataset was constructed comprising multiple classes of adsorbent materials including activated carbon, zeolites, metal–organic frameworks (MOFs), porous organic polymers (POPs), alumina/silica, and amine-functionalized sorbents. The dataset includes key parameters such as temperature, pressure, CO₂ mole fraction, humidity, BET surface area, micropore characteristics, amine loading, heat of adsorption, particle density, pellet diameter, and bed void fraction. Two machine learning models based on the XGBoost algorithm were implemented. An XGBoost Regressor was used to predict the experimental CO₂ adsorption capacity, while an XGBoost Classifier was trained to identify the type of adsorbent used based on the input parameters. The models were trained and validated using a train–test split approach to ensure reliable performance evaluation. The results demonstrate that gradient boosting models can accurately capture complex nonlinear relationships between adsorption conditions, material properties, and adsorption performance. The developed framework provides a fast and efficient predictive tool that can assist researchers and engineers in screening adsorbent materials and optimizing CO₂ capture systems for industrial applications. Using this model, one can predict the adsorption capacity of any adsorbent used in the training dataset and predict its type with 95% accuracy. Full article

Adding inspection trains to existing railway timetables is a complex task that must balance operational efficiency and service reliability, which are essential for the sustainable operation and maintenance of high-speed railway infrastructure. To address this challenge, a feasible region-based space–time network modeling approach is proposed for incorporating Comprehensive Inspection Trains (CITs) into existing railway schedules, aiming to enhance inspection efficiency while minimizing operational disruptions. Firstly, the constraints that need to be considered when scheduling for CIT are comprehensively analysed and modelled, and a mixed-integer nonlinear model with the objective of minimizing the total number of stops is constructed. In order to eliminate the difficulty of solving this model, based on the original space–time network method, more kinds of train event arcs are introduced to accurately portray the train operation process; in particular, the extra time consumed due to the acceleration and deceleration process is also reflected in the network construction process. The feasibility of various event arcs is evaluated with time windows, and the original problem finally transforms into the equivalent shortest path problem on a feasible event arc network. The processing procedure includes key stages, such as station space–time discretization, interval operation event processing, station capacity handling, and network simplification. The experimental results indicate that the approach effectively resolves all station capacity conflicts, compresses inspection durations, and optimizes the number of stops. Remarkably, the number of non-full-speed inspection sections is reduced by 43.16%, demonstrating the model’s efficiency. Additionally, the proposed approach is computationally efficient, improves timetable capacity utilization for infrastructure inspection, and supports the sustainable operation of high-speed railway systems. Full article

Traditional research was mostly focused on the effects of emulsions on the mechanical properties and durability of cement mortar, while studies on the regulation mechanism of emulsions on the rheological properties of cement-based materials and the coupling mechanism with the hydration process were rarely conducted. In this paper, a novel core–shell structured emulsion was prepared by free radical polymerization. The regulation of cement mortar yield stress, creep recovery, dynamic viscosity, and thixotropy by different dosages (0–10%) of the emulsion admixture was systematically investigated, and combined with characterization by scanning electron microscopy (SEM), X-ray diffraction (XRD), and Fourier transform infrared spectroscopy (FTIR), the microscopic action mechanism of the emulsion was elucidated. It was demonstrated that the Bingham fluid behavior of cement mortar was not altered by the core–shell emulsion, whereas a significant dosage-dependent regulatory effect on its rheological parameters was observed, and a critical regulation interval of 4–6% was identified. At an emulsion dosage of 10%, the yield stress of the mortar was increased by 937.0% compared to that of the control group. At dosages of 2–4%, the static structural stability and construction flowability of the mortar were synergistically optimized, and the weakest thixotropy and the best structural stability were exhibited at an emulsion dosage of 4%. A more pronounced shear-thinning behavior was shown by all modified mortars, and their high-shear flowability was not affected. Microstructural analysis confirmed that no chemical reaction occurred between the emulsion and the cement hydration products. Through the triple effects of “hydration retardation by physical coating, pore filling and densification, and composite network enhancement”, a film was formed on the surface of cement particles by the emulsion, which hindered the diffusion of water and ions, thereby regulating the cement hydration process and microstructural evolution. Full article

Dense underwater aquaculture poses significant challenges for intelligent image processing because asymmetric occlusion, turbidity, aeration-like bubbles, and motion blur frequently degrade fish contours and quasi-periodic scale textures. These disturbances often cause conventional detectors to miss detections, merge bounding boxes, experience feature collapse, and exhibit unstable counting. To address this problem, we propose DenseFish-v13, a symmetry-aware NMS-free YOLOv13-Mamba framework for dense underwater fish detection and bio-kinematic behavior recognition. The framework integrates a Bio-Harmonic Frequency Gate to preserve biological texture patterns while suppressing bubble-like frequency noise, a Bi-directional Multi-scale Wavelet Mamba backbone for global occlusion-aware structure recovery, and an asymmetry-aware density repulsion strategy to separate highly overlapping fish instances during bipartite matching. In addition, a lightweight Bio-Kinematic Behavior Head converts continuous detections into interpretable trajectory descriptors for behavior-state recognition. Experiments on the Dense-Aqua benchmark, constructed from public aquaculture datasets, show that DenseFish-v13 achieves 64.8% mAP@50:95 and a Counting MAE of 3.7 on the overall test set, while reaching 64.2% mAP@50:95 and a Counting MAE of 4.1 on the extreme-density split. Under a strong synthetic bubble perturbation, the model shows only a 1.3 percentage-point drop in mAP and maintains 125 FPS on Jetson Orin NX. These results demonstrate its effectiveness in robust, real-time underwater aquaculture monitoring. Full article

This paper proposes the Continuous Operational Feedback Loop (COFL) architecture, a fully localized, event-driven operational monitoring and response system for Direct-to-Consumer (D2C) commerce. The architecture integrates the n8n workflow engine with on-premise large language model (LLM) inference via the Ollama framework, forming a containerized stack deployable on commodity CPU-only edge hardware (~USD 1640). Using a multi-source dataset of 1800 records constructed from publicly available e-commerce corpora and evaluated with a silver-standard automated labeling protocol, empirical validation demonstrates an end-to-end latency of 3.22 s and a macro-F1 sentiment classification score of 0.836—representing 98.2% of the full-precision baseline and 94.0% of cloud GPT-4o API generation quality measured by ROUGE-L—at approximately 1/200th of the per-request inference cost. A systematic quantization ablation study across six model-quantization configurations establishes LLaMA 3 8B Q4_K_M as the Pareto-optimal selection for the target hardware. An Analytic Hierarchy Process (AHP) multi-criteria framework with criterion weights derived from published literature confirms the COFL implementation achieves a higher composite score than cloud API deployment under the stated evaluation assumptions. Failure mode and effects analysis (FMEA) is summarized to characterize system reliability under identified failure scenarios. Full article

The present study aims to address critical research gaps in duckweed–microbe symbiotic systems specifically applied to high-load livestock and poultry breeding wastewater. These gaps include the insufficient development of well-characterized, multi-functional, complex microbial consortia adapted to complex livestock wastewater matrices, and the technical challenge of achieving simultaneous efficient wastewater purification and duckweed feed quality enhancement. This study is motivated by the pressing issue of agricultural non-point source pollution, which is caused by large-scale livestock and poultry breeding wastewater discharge, and the high external dependence of the feed industry on protein raw materials. The present study utilised Spirodela as the fundamental material, and a functionally complementary complex symbiotic bacterial consortium consisting of Bacillus subtilis, Bacillus tequilensis and Pseudomonas fluorescens was screened and constructed. An experiment was conducted over a 14-day period in which a range of inoculation ratios were systematically explored. The aim of this experiment was to ascertain the purification efficiency of the duckweed–bacteria symbiotic system on high-load livestock and poultry breeding wastewater. Furthermore, the experiment sought to determine the effect of this purification process on the feed value of duckweed. The results demonstrated that complex bacterial inoculation significantly enhanced wastewater purification efficiency. The final removal rate of ammonia nitrogen in all treatment groups exceeded 90% after 14 days, and the maximum removal rates of total nitrogen and total phosphorus reached 67.0% and 58.9%, respectively, thereby demonstrating superior purification performance in comparison to the control group. The inoculation ratio of 10:1 was identified as the optimal parameter for wastewater purification, while the 5:1 ratio was found to be the maximum for crude protein accumulation in duckweed. The maximum dry-based crude protein content recorded was 38.9% on day 14, representing an increase of 26.3% in comparison with the control group. The established duckweed–bacteria symbiotic system has the capacity to simultaneously achieve the efficient purification of livestock and poultry breeding wastewater and the high-value utilisation of duckweed. The optimal process parameters for a range of application scenarios have been determined. This study contributes to the theoretical framework of aquatic plant–microbe symbiotic remediation and provides technical support for the recycling of wastewater resources and the sustainable development of the livestock and poultry breeding industry. Full article

In recent years, it has become inevitable to dig underneath existing highways when excavating tunnels. The soil settlement induced by ground excavation may adversely affect existing highways. In this study, a settlement monitoring system is used to obtain the settlement sequence of multiple measurement points on the pavement. Multifractal detrended fluctuation analysis (MF-DFA) is used to focus on analyzing the multiple fractal features of the pavement settlement rate. The results show that the settlement rates of the highway caused by the tunnel excavation and construction process all show multiple fractal characteristics. The fluctuations in the measurement points above and near the entrance of the tunnel are more complex and intense. Based on the moving-average method (MA), convolutional neural network (CNN), and Extreme Learning Machine (ELM), MA-CNN and MA-ELM prediction models are constructed to predict the settlement value sequences of the fluctuating points. The results indicate that the MA-ELM prediction model demonstrates superior predictive performance (with R² values of 0.956, 0.950, and 0.979 on the test set). Further, with the help of the Dung Beetle Optimizer (DBO), a meta-heuristic algorithm for parameter optimization, the hybrid model DBO-MA-ELM greatly improves the prediction performance (R² of 0.975, 0.997, 0.998 for the testing set). Full article

This study presents a damage-based comparative assessment of reinforced concrete buildings affected by the 1992 Erzincan earthquake (Mw 6.8) and the 2023 Kahramanmaraş earthquake sequence (Pazarcık, Mw 7.7; Elbistan, Mw 7.6), two destructive earthquake events in Türkiye separated by nearly three decades. A distinctive contribution of the study is the presentation of original color photographs from the 1992 Erzincan earthquake, systematically documented and comparatively evaluated for the first time and directly compared with post-earthquake field observations from Malatya following the 2023 earthquake sequence. To complement the field-based evidence, representative strong ground motion records from both earthquake events were processed and compared using standard seismic intensity and spectral response parameters. The spectral evaluation indicates that the 1992 Erzincan ground motion and the 2023 Elbistan-related motion recorded in Malatya imposed comparable seismic demands relevant to typical reinforced concrete buildings, thereby providing a rational basis for cross-event damage interpretation. Despite substantial advances in Turkish seismic design codes, recurrent damage mechanisms were observed in both building stocks, particularly soft-story formation, short-column effects, inadequate transverse reinforcement, poor beam–column joint performance, and deficiencies in material quality and detailing. The findings demonstrate that seismic safety cannot be improved through code development alone unless design provisions are consistently translated into construction quality, detailing practice, inspection, and field implementation. Full article

The glycine-aspartic acid-serine-leucine (GDSL) esterase/lipase family is a functionally diverse group of hydrolytic enzymes involved in multiple plant biological processes, including stress adaptation and development. However, its evolutionary patterns, functional conservation, and stress-responsive mechanisms in grasses remain not fully elucidated. In this study, a comprehensive comparative genomic analysis was performed on the GDSL gene family across nine representative grass species and Arabidopsis thaliana. Genome-wide identification, phylogenetic analysis, duplication pattern detection, synteny analysis, cis-regulatory element prediction, protein–protein interaction (PPI) network construction, and RNA-seq-based expression profiling were employed. A total of 1707 GDSL genes were identified, with substantial expansion in grasses, especially hexaploid wheat. Whole-genome and segmental duplications were the major drivers of family expansion, with most duplicated genes under strong purifying selection. A grass-specific clade (C3-2) was identified, and extensive syntenic conservation was observed among closely related grasses. Promoter analysis revealed enrichment of stress- and hormone-responsive cis-elements, and RNA-seq showed dynamic GDSL expression under low-temperature stress in rice and wheat. These findings demonstrate that the expansion of the GDSL gene family in grasses is driven by polyploidization and lineage-specific duplication, accompanied by the emergence of a grass-specific clade (C3-2) and regulatory diversification, collectively shaping stress-responsive evolutionary innovation in Poaceae. Full article

The transition towards carbon-neutral construction materials requires innovative solutions that combine reduced embodied energy, enhanced durability and improved building energy efficiency. This study investigates and compares two novel bioaerated low-density composites—BAAC and BIOAERMAC—developed through biologically driven aeration processes incorporating industrial byproducts. BAAC is produced using Saccharomyces cerevisiae and hydrogen peroxide, replacing conventional aluminum powder and improving safety while enabling the valorization of waste-derived yeast. BIOAERMAC is a gypsum-based composite incorporating synthetic anhydrite, microorganisms, peroxides, and recycled rubber from end-of-life tires. The materials were characterized in terms of hygrothermal behavior and dimensional stability, and compared with commercial autoclaved aerated concrete under equivalent mechanical strength conditions. The results highlight significant differences in moisture transport and shrinkage, primarily governed by pore structure and connectivity. BAAC exhibits behavior comparable to conventional AAC, whereas BIOAERMAC shows reduced capillary and hygroscopic absorption, indicating limited pore connectivity, but higher drying shrinkage. These findings demonstrate the effectiveness of bioaeration in tailoring pore structure and controlling the trade-off between moisture transport, durability, and dimensional stability, highlighting the potential of bioaerated composites for low-carbon and energy-efficient building applications. Full article