Search Results (27,364)

Interest in microalgae-based technologies has emerged in recent years as a response to environmental challenges and the global food crisis, for providing alternative and sustainable food products. This study used temperature variations between 18 and 32 °C and nitrogen-to-phosphorus (N:P) ratios between 1.9 and 42.6 to model and optimize growth and composition of Chlorella vulgaris, a nutritionally interesting species. Lower temperatures appear ideal for this strain. An increase in average biomass productivity was observed with decreasing temperature, leading to a maximum of 122.27 mg_dw L⁻¹ d⁻¹ at 18 °C on the fourth day of cultivation. The maximum productivities for total proteins, fatty acids, carbohydrates, and pigments were, respectively, 26.9 mg L⁻¹ d⁻¹, 26.4 mg L⁻¹ d⁻¹, 16.0 mg L⁻¹ d⁻¹, and 2.41 mg L⁻¹ d⁻¹, all referring to 18 °C. The fatty acid, carotenoid, and amino acid profiles were also ascertained; several indicators suggested that cultivation of these microalgae under the aforementioned optimal conditions holds potential for the food industry. The high proportion of polyunsaturated fatty acids—including two essential fatty acids; the high production of lutein, and the presence of several essential amino acids are among the favorable indicators. Overall, the information generated by this study is helpful to support future pilot studies aimed at the commercial production of microalgae-derived products. Full article

Accurate monitoring of tomato maturity and fruit number is essential for improving crop management and supporting accurate yield estimation in greenhouse environments. However, variations in lighting conditions, occlusions, and overlapping fruits often make reliable maturity classification and fruit counting challenging. This paper presents an integrated approach for tomato maturity classification and fruit number estimation using RGB and multispectral images. The proposed approach consists of tomato detection, tomato tracking and counting, and maturity classification of tomatoes. The detection model identifies tomatoes in each frame, the tracking module associates individual tomatoes across image sequences to avoid duplicate counting, and the classification models determine maturity levels. Experiments are conducted on three tomato datasets collected in greenhouse environments. The results show that the proposed method achieves a maximum maturity classification accuracy of 81%. In addition, the proposed approach facilitates consistent fruit counting across image sequences, supporting practical applications in greenhouse monitoring. These findings demonstrate the potential of integrating RGB and multispectral information for automated tomato maturity classification and fruit counting in precision agriculture. Full article

Drought is a major constraint on crop productivity, and microbial inoculants are increasingly explored to mitigate plant water stress. However, most inoculant discovery pipelines rely on trait-based screening performed outside the ecological context in which beneficial plant-microbe interactions naturally arise. In natural soils, drought-exposed plants can reshape the rhizosphere environment by altering carbon allocation and root exudation, thereby selectively recruiting microorganisms compatible with water-limited conditions and effectively performing an ecological pre-selection. Here, we captured this process during early seedling establishment and leveraged drought-driven rhizosphere recruitment as a nature-guided strategy to nominate bacterial inoculant candidates. Tomato seedlings were grown in natural agricultural soil microcosms under well-watered and drought-stressed regimes, and cultivable bacteria were comparatively isolated from rhizosphere and bulk soil fractions. Recruitment-prioritized isolates were subsequently characterized through biochemical assays and genome-informed analyses to provide functional and taxonomic context and were evaluated in early inoculation assays under water stress. Drought-recruited isolates displayed distinct plant-associated responses, and genome-scale taxonomy indicated that one drought-associated isolate represents a genomically distinct lineage within the genus Paracoccus. Together, these findings highlight drought-driven rhizosphere recruitment as an ecologically grounded framework for identifying stress-compatible bacterial candidates and uncovering previously undescribed rhizosphere diversity. Full article

Interest in the genomic sequencing of healthy newborns has raised a discussion on whether this technology should be introduced into existing newborn screening (NBS) programs. This qualitative study explores a multi-stakeholder perspective on the future of genomic sequencing in NBS. Semi-structured interviews were conducted with 26 professionals involved in NBS or in clinical genome sequencing in the Netherlands. Participants highlighted opportunities such as the possibility to use one test for a wide range of genetic conditions, reducing diagnostic odyssey, expanding the scope of NBS, and increasing program efficiency. Challenges were raised regarding genetic variant interpretation, expected increased parental anxiety, data privacy issues, difficulties with information provision, and high costs. Three areas of tension between participants’ perspectives were identified: screening strategy, screening performance, and roles and responsibilities. It was emphasized that implementing genomic sequencing should not risk reducing the current high NBS participation, and that enhancing knowledge, communication, and collaboration between all stakeholders is needed. Although most participants did not believe genomic sequencing as a first-tier test is currently desirable and feasible, they acknowledged it has a role to play in the future of NBS. Future decision-making should consider the potential impact on the participation rate, program quality, and balancing benefits and harms. Full article

Remote sensing images acquired by UAVs under nighttime or low-illumination conditions suffer from insufficient illumination, leading to degraded image quality, detail loss, and noise, which restrict their application in public security and disaster emergency scenarios. Although existing machine learning-based enhancement methods can recover part of the missing information, they often cause color distortion and texture inconsistency. This study proposes an improved low-illumination image enhancement method based on a Weakly Paired Image Dataset (WPID), combining the Hierarchical Deep Convolutional Generative Adversarial Network (HDCGAN) with a low-rank image fusion strategy to enhance the quality of low-illumination UAV remote sensing images. First, YCbCr color channel separation is applied to preserve color information from visible images. Then, a Low-Rank Representation Fusion Network (LRRNet) is employed to perform structure-aware fusion between thermal infrared (TIR) and visible images, thereby enabling effective preservation of structural details and realistic color appearance. Furthermore, a weakly paired training mechanism is incorporated into HDCGAN to enhance detail restoration and structural fidelity. To achieve objective evaluation, a structural consistency assessment framework is constructed based on semantic segmentation results from the Segment Anything Model (SAM). Experimental results demonstrate that the proposed method outperforms state-of-the-art approaches in both visual quality and application-oriented evaluation metrics. Full article

This study examines the determinants of city brand equity in the context of Riyadh Season, a large-scale cultural and entertainment festival in Saudi Arabia. Drawing on Aaker’s customer-based brand equity framework adapted to the city-brand context and informed by Source Credibility Theory (SCT), the study tests the direct effects of brand association, brand awareness, brand loyalty, and customer satisfaction on city brand equity, as well as the moderating role of online influencers. Survey data were collected from 991 attendees and analyzed using structural equation modeling (SEM). The results indicate that brand awareness and brand loyalty significantly enhance city brand equity, whereas brand association and customer satisfaction have no significant effects. Contrary to prevailing assumptions in tourism and digital branding research, online influencers do not moderate the relationships between brand equity dimensions and overall city brand equity. These findings identify boundary conditions for influencer effectiveness and suggest that, in experience-intensive and time-bound mega-events, city brand equity is driven more by recognition and repeat attachment than by influencer-mediated communication or post-event satisfaction. The study refines city brand equity theory and offers practical guidance for policymakers and event organizers seeking to build sustainable city brands beyond influencer-centric strategies. Full article

Under high renewable energy penetration, wind power forecasting faces pronounced challenges due to strong randomness and uncertainty, making conventional point-forecast-centric paradigms insufficient for risk-aware and reliable power system scheduling. An uncertainty-aware temporal transformer framework for wind power forecasting is presented, integrating probabilistic modeling with deep temporal representation learning to jointly optimize prediction accuracy and uncertainty characterization. Crucially, rather than treating uncertainty quantification merely as a post-processing step, the central conceptual contribution lies in modularizing uncertainty directly within the attention mechanism. A probability-driven temporal attention mechanism is incorporated at the encoding stage to emphasize high-variability and high-risk time slices during feature aggregation, while a multi-quantile output and interval modeling strategy is adopted at the prediction stage to directly learn the conditional distribution of wind power, enabling simultaneous point and interval forecasts with statistical confidence. Extensive experiments on multiple public wind power datasets demonstrate that the proposed method consistently outperforms traditional statistical models, deep temporal models, and deterministic transformers, as validated by formal statistical significance testing. Specifically, the method achieves an MAE of $0.089$, an RMSE of $0.132$, and a MAPE of $10.84\%$ on the test set, corresponding to reductions of approximately $8\%$–$10\%$ relative to the deterministic transformer. In uncertainty evaluation, a PICP of $0.91$ is attained while compressing the MPIW to $0.221$ and reducing the CWC to $0.241$, indicating a favorable balance between coverage reliability and interval compactness. Compared with mainstream probabilistic forecasting methods, the model further reduces RMSE while maintaining coverage levels close to the 90% target, effectively mitigating excessive interval conservatism. Moreover, by adaptively generating heteroscedastic intervals that widen during high-volatility events and narrow under stable conditions, the model achieves a highly focused and effective capture of critical uncertainty information. Full article

The retrofit of existing residential buildings plays a critical role in reducing energy consumption and carbon emissions in the building sector. However, previous retrofit evaluations often fail to account for the age-related thermal and lighting requirements of residents in aging residential buildings, thereby overlooking the substantial behavioral heterogeneity that shapes retrofit effectiveness. This study evaluates the comprehensive performance of different building envelope retrofit strategies, considering occupants’ thermal and visual comfort, from the perspectives of energy efficiency, economic feasibility, and environmental sustainability. First, age-specific differences in occupancy patterns, thermal preferences, and lighting requirements between elderly and non-elderly comparison group occupants were systematically extracted from the literature. Then, a typical high-rise residential building was modeled in EnergyPlus to serve as the reference building, within which the differentiated occupant behavior models were implemented, and the pre-retrofit condition was defined as the baseline scenario. Next, six commonly applied exterior wall insulation materials and different glass configurations and window frames were parameterized and evaluated under varying insulation thicknesses and remaining building service life scenarios. Finally, the energy-saving performance, economic benefits, and carbon reduction potential of envelope retrofit measures were quantitatively assessed across three primary functional zones (bedroom, living room, and study), using area-normalized indicators. The results indicate that, in the retrofit of existing residential buildings, bedrooms and study rooms exhibit greater retrofit benefits than living rooms, primarily due to longer occupancy durations and higher heating demand. In terms of retrofit strategies, exterior wall insulation consistently outperforms window retrofitting in energy-saving potential, with energy-saving rates of approximately 3.2–4.3% depending on functional zone, material type, and insulation thickness. Among the evaluated materials, vitrified microbead insulation performs best overall in terms of energy, economic, and carbon benefits at 40–60 mm thickness. These findings support occupant-informed, low-carbon retrofit decision-making for existing residential buildings. Full article

Anti-unmanned aerial vehicle (Anti-UAV) detection is critical for airspace security, yet existing single-modality approaches suffer from severe performance degradation under adverse illumination, thermal crossover, and extreme scale variation. In this paper, we propose CSFADet, a dual-modal detection framework that jointly exploits visible and infrared imagery through four tightly integrated modules. First, a Cross-Spectral Feature Alignment (CSFA) module performs early-stage spectral calibration by computing cross-modal query–value attention maps, generating modality-aware channel descriptors that re-weight and concatenate the two spectral streams. Second, a Dual-path Texture Enhancement Module (DTEM) enriches fine-grained spatial details via cascaded convolutions with residual connections. Third, a Dual-path Cross-Attention Module (DCAM) introduces a feature-shrinking token generation strategy followed by symmetric cross-attention branches with learnable scaling factors, Squeeze-and-Excitation recalibration, and a $1\times 1$ convolution fusion head, enabling deep bidirectional interaction between modalities. Fourth, a Dual-path Information Refinement Module (DIRM) embeds Adaptive Residual Groups (ARGs) that cascade Multi-modal Spatial Attention Blocks (MSABs) with channel and dynamic spatial attention, culminating in a Multi-scale Scale-aware Fusion Refinement (MSFR) unit that employs three parallel multi-head attention branches with a Scale Reasoning Gate and Channel Fusion Layer to produce scale-discriminative enhanced features. Experiments on the public Anti-UAV300 benchmark show that CSFADet achieves 91.4% mAP@0.5 and 58.7% mAP@0.5:0.95, surpassing fifteen representative detectors spanning single-stage, two-stage, YOLO-family, and Transformer-based categories. Ablation studies confirm the complementary contributions of each module, and heatmap visualizations verify the model’s capacity to focus on small, distant UAV targets under challenging conditions. Full article

Industrial-scale Anaerobic Digestion (AD) is a key technology for the energy recovery of agro-industrial and municipal waste and for the mitigation of greenhouse gas emissions; however, the actual operational performance of industrial biodigesters continues to show significant discrepancies with respect to the theoretical values reported in the scientific literature. In this context, there is still a lack of systematic analysis to identify which operating parameters are consistently monitored in industrial settings and which remain insufficiently explored, particularly those that describe the overall state of the digestion environment. To address this gap, a systematic literature review was conducted in the Scopus database for the period 2000–2026, complemented by a bibliometric analysis using VOSviewer software v1.6.18. 3. After applying inclusion criteria focused exclusively on industrial-scale and pilot systems, 1327 documents corresponding to the category of operating parameters were selected and analyzed using keyword co-occurrence networks and evaluation of occurrence frequencies and total link intensities. The analysis shows a marked concentration of the literature on a small set of classic parameters, highlighting pH (154 occurrences, 3667 link intensities), temperature (147 occurrences, 3255 link intensities), and ammonia (131 occurrences, 2824 link intensities) as the most recurrent variables in the industrial operation of anaerobic digesters. Complementarily, parameters such as chemical oxygen demand, total and volatile solids, and hydrogen sulfide have progressively increased their presence since 2015, mainly associated with effluent quality assessment, nutrient recovery, and overall process sustainability. In contrast, variables that integrate the state of the environment, such as electrical conductivity, oxidation-reduction potential, and the rheological properties of digestate, appear in less than $5\%$ of the studies analyzed, despite their ability to integrate information on stability, buffer capacity, and overall operating conditions. Taken together, these findings highlight an imbalance between the intensive use of traditional parameters and the limited incorporation of integrative indicators in industrial monitoring, suggesting that their systematic inclusion, together with the development of soft sensors and predictive models, could contribute to improving operational control and reducing the gap between the theoretical performance and actual behavior of industrial biodigesters. Full article

This study proposes a principal component analysis-based convolutional neural network (PC-CNN) to correct atmospheric turbulence-induced aberrations. Unlike traditional Zernike polynomials (ZPs)-based methods (ZP-CNN), PC-CNN avoids mode aliasing and cross-coupling via the strict orthogonality of principal components (PCs). A coefficient magnification strategy is incorporated to further enhance efficacy, maximally preserving the intrinsic physical information within the PCs coefficients. A series of systematic experiments was conducted under conditions from weak to strong turbulence, characterized by $D/r_0$ from 1 to 25, where $D$ is the pupil diameter and $r_0$ is the atmospheric coherence length. Quantitative results show PC-CNN achieves a lower mean relative error (MRE) in coefficient prediction than ZP-CNN under equivalent conditions. It also yields a higher Strehl ratio, reduced speckles, and enhanced spot clarity while requiring fewer basis terms, demonstrating high stability and robustness in strong turbulence. These findings emphasize that basis function orthogonality and physically informed preprocessing are critical design principles for deep-learning-based wavefront sensor-less adaptive optics (AO), establishing a robust foundation for real-time intelligent AO systems in astronomy and free-space optical communications. Full article

In structural health monitoring, purely data-driven methods for deformation prediction are often susceptible to time-varying boundary conditions under complex operating scenarios, leading to insufficient physical interpretability and limited generalization across different conditions. To address these challenges, this study proposes a Physics-Informed Dual-branch Long Short-Term Memory framework (PINN-DualSHM). The framework employs dual-branch LSTMs to separately extract temporal features of structural mechanical responses and environmental thermal effects. Dynamic decoupling and fusion of these heterogeneous features are achieved through an adaptive cross-attention mechanism. Furthermore, physical priors, including the thermodynamic superposition principle and structural settlement monotonicity, are embedded into the loss function as regularization terms, complemented by a dual uncertainty quantification system based on heteroscedastic regression and MC Dropout. Experimental results based on long-term measured data from an industrial base project in Shenzhen demonstrate that PINN-DualSHM significantly outperforms baseline models such as LSTM, CNN-LSTM, and GAT-LSTM. Specifically, the Root Mean Square Error (RMSE) is reduced by 65.25%, and the coefficient of determination (R²) reaches 0.925. Physical consistency analysis confirms that the introduction of physical constraints effectively suppresses anomalous predictive fluctuations that violate mechanical laws. Uncertainty decomposition reveals that aleatoric uncertainty is dominant (93.7%), objectively indicating that the current system’s accuracy bottleneck lies in sensor noise rather than model capability. By enhancing prediction accuracy while providing credible quantitative assessments and physical interpretability, the proposed method provides a scientific basis for the operation, maintenance optimization, and upgrading decisions of SHM systems. Full article

Understanding the hemodynamics of the carotid artery is essential for assessing atherosclerotic disease progression and identifying regions vulnerable to plaque formation. Background: Disturbed flow patterns and abnormal shear stresses, particularly near the carotid bifurcation, are known to influence endothelial dysfunction; therefore, this study aims to quantify the impact of patient-specific carotid artery geometry on key hemodynamic parameters associated with atherosclerotic risk. Methods: Four patient-specific carotid artery geometries were reconstructed from medical imaging data, processed using MIMICS, and analyzed using computational fluid dynamics in ANSYS Fluent, with blood modeled as an incompressible non-Newtonian fluid using the Carreau–Yasuda viscosity model under pulsatile flow conditions; velocity streamlines, pressure distribution, time-averaged wall shear stress (TAWSS), and oscillatory shear index (OSI) were evaluated at early systole, peak systole, and peak diastole. Results: The simulations revealed complex flow behaviour, including flow reversal, pressure build-up, and low-shear regions concentrated near the carotid bulb and bifurcation, with TAWSS consistently identifying low-shear zones (<1 Pa) across all geometries and OSI exhibiting pronounced directional oscillations in models with increased curvature and wider bifurcation angles. Conclusions: These findings demonstrate that geometric characteristics such as bifurcation angle, vessel tortuosity, and asymmetry play a critical role in shaping local haemodynamics, underscoring the utility of patient-specific CFD analysis as a diagnostic and predictive tool for atherosclerotic risk assessment and supporting more informed, personalized clinical decision-making. Full article

Sustainable spatial development requires land-use allocation that aligns with reflects the environment’s biophysical capacity. However, rapid urbanization and agricultural expansion often result to spatial mismatches between land utilization and land capability, the reby increasing environmental degradation and disaster vulnerability. East Java Province, one of Indonesia’s most densely populated regions, has experienced significant land-use transformation driven by demographic pressure and economic development. This study aims to evaluate the environmental carrying capacity by assessing the spatial compatibility among land capability, existing land use, and the Provincial Spatial Plan (RTRWP) using a Geographic Information System (GIS)-based analytical approach. Land capability was determined based on key biophysical parameters, including slope gradient, soil texture, drainage conditions, erosion susceptibility, effective soil depth, and flood hazard. Spatial overlay analysis was employed to identify areas of conformity and mismatch between land capability and both current and planned land uses. The results indicate that only approximately 52% of the provincial area is utilised in accordance with its land capability. In comparison, the remaining 48% exhibits varying degrees of spatial mismatch. Erosion is identified as the dominant limiting factor, affecting more than 43% of the region, particularly in mountainous and hilly landscapes. Furthermore, over 60% of East Java falls within Land Capability Classes III–VII, indicating moderate to severe environmental constraints on limitations intensive land use. High levels of spatial mismatch are concentrated in the southern upland districts—such as Pacitan, Trenggalek, southern Malang, and Lumajang, which are highly susceptible to landslides, as well as in the northern lowland corridor, including the Surabaya–Gresik–Sidoarjo metropolitan region, which faces a significantly flood risk. These findings suggest that land-use practices exceeding environmental carrying capacity substantially amplify disaster risk. Therefore, integrating land capability assessment into spatial planning and zoning regulations is essential and for promoting ecosystem-based disaster risk reduction and achieving sustainable spatial development in East Java Province. Full article

Background: Reliable identification remains a cornerstone of forensic investigations, particularly when encountering degraded remains or suboptimal biological evidence. This study evaluates the potential of dentine metabolomics, utilizing proton nuclear magnetic resonance (¹H-NMR) spectroscopy, to detect cancer-associated metabolic signatures in dental tissues for forensic applications. Methods: Forty-four non-carious second molars were analyzed, comprising 22 samples from deceased individuals with a documented history of cancer and 22 age- and sex-matched controls. Metabolomic profiling was conducted using ¹H-NMR spectroscopy to identify and quantify dentine metabolites. Statistical evaluation included unsupervised principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), receiver operating characteristic (ROC) curve analysis, and exploratory binary logistic regression. Results: Among the 209 identified metabolites, inosinic acid and 2-ketobutyric acid were identified as the most robust discriminative biomarkers across both multivariate and univariate frameworks. The exploration within-sample predictive model achieved a Nagelkerke R² of 0.822 and an overall classification accuracy of 90.9%, with a specificity of 95.5% and a sensitivity of 86.4%. These key metabolites are fundamentally associated with purine metabolism and oxidative stress pathways frequently dysregulated in oncogenesis. Conclusions: This pilot study suggests that dentine may retain metabolomic information associated with cancer comorbidity under heterogeneous postmortem conditions. However, the findings remain exploratory and require validation in larger cohorts with standardized postmortem variables before practical forensic implementation. Full article