Search Results (5,877)

Reliable transmission of high-quality video over wireless channels is challenged by fading and noise, which degrade visual quality and disrupt temporal continuity. To address these issues, this paper proposes a quantum communication framework that integrates quantum superposition with multi-input multi-output (MIMO) spatial diversity techniques to enhance robustness and efficiency in dynamic video transmission. The proposed method converts compressed videos into classical bitstreams, which are then channel-encoded and quantum-encoded into qubit superposition states. These states are transmitted over a $2\times 2$ MIMO system employing varied diversity schemes to mitigate the effects of multipath fading and noise. At the receiver, a quantum decoder reconstructs the classical information, followed by channel decoding to retrieve the video data, and the source decoder reconstructs the final video. Simulation results demonstrate that the quantum MIMO system significantly outperforms equivalent-bandwidth classical MIMO frameworks across diverse signal-to-noise ratio (SNR) conditions, achieving a peak signal-to-noise ratio (PSNR) up to 39.12 dB, structural similarity index (SSIM) up to 0.9471, and video multi-method assessment fusion (VMAF) up to 92.47, with improved error resilience across various group of picture (GOP) formats, highlighting the potential of quantum MIMO communication for enhancing the reliability and quality of video delivery in next-generation wireless networks. Full article

Variable-rate nitrogen (VR-N) application allows farmers to optimize nitrogen (N) input site-specifically within field boundaries, enhancing both economic efficiency and environmental sustainability. In this study, VR-N technology was applied to durum wheat in two small-scale commercial fields (3–4 ha each) located in distinct agro-climatic zones of Thessaly, central Greece. A real-time VR-N application algorithm was used to calculate N rates based on easily obtainable near-real-time data from unmanned aerial vehicle (UAV) imagery, tailored to the crop’s actual needs. VR-N implementation was carried out using conventional fertilizer spreaders equipped to read prescription maps. Results showed that VR-N reduced N input by up to 49.6% compared to the conventional uniform-rate N (UR-N) application, with no significant impact on wheat yield or grain quality. In one of the fields, the improved gain of VR-N when compared to UR-N was 7.2%, corresponding to an economic gain of EUR 163.8 ha⁻¹, while in the second field—where growing conditions were less favorable—no considerable VR-N economic gain was observed. Environmental benefits were also notable. The carbon footprint (CF) of the wheat crop was reduced by 6.4% to 22.0%, and residual soil nitrate (NO₃⁻) levels at harvest were 13.6% to 36.1% lower in VR-N zones compared to UR-N zones. These findings suggest a decreased risk of NO₃⁻ leaching and ground water contamination. Overall, the study supports the viability of VR-N as a practical and scalable approach to improve N use efficiency (NUE) and reduce the environmental impact of wheat cultivation which could be readily adopted by farmers. Full article

This study investigated the effect of different farming systems and lactation stages on the physicochemical characteristics, somatic cell count (SCC), and total bacterial count (TBC) of milk from Skopelos goats. This study was conducted over two consecutive lactation periods on two commercial farms in Greece, an extensive system on Skopelos Island and an intensive system in the Attica region, involving 237 goats of shared genetic background, thereby minimizing genetic variability and strengthening the validity of the comparisons between the production systems. Higher milk yields were observed in the extensive system (0.98 vs. 0.85 kg/day), while milk from this system also had a higher protein (3.57% vs. 3.47%; p < 0.001) and casein content (2.72% vs. 2.57%; p < 0.001), which are traits favorable for cheese production. Fat content peaked during mid-lactation (4.83%; p < 0.05) and remained unaffected by the farming system. Lactose declined from early (4.74%) to late lactation (4.42%; p < 0.001). Both SCC and TBC were significantly elevated in the extensive system (p < 0.001), possibly due to hand milking, environmental exposure, and less-controlled hygiene conditions. These findings highlight a trade-off between the nutritional advantages of extensive systems and challenges related to milk hygiene. A balanced approach, optimizing both quality and sustainability, is recommended. Full article

Generative image models have revolutionized artificial intelligence by enabling the synthesis of high-quality, realistic images. These models utilize deep learning techniques to learn complex data distributions and generate novel images that closely resemble the training dataset. Recent advancements, particularly in diffusion models, have led to remarkable improvements in image fidelity, diversity, and controllability. In this work, we investigate the application of a conditional latent diffusion model in the healthcare domain. Specifically, we trained a latent diffusion model using unlabeled histopathology images. Initially, these images were embedded into a lower-dimensional latent space using a Vector Quantized Generative Adversarial Network (VQ-GAN). Subsequently, a diffusion process was applied within this latent space, and clustering was performed on the resulting latent features. The clustering results were then used as a conditioning mechanism for the diffusion model, enabling conditional image generation. Finally, we determined the optimal number of clusters using cluster validation metrics and assessed the quality of the synthetic images through quantitative methods. To enhance the interpretability of the synthetic image generation process, expert input was incorporated into the cluster assignments. Full article

Alzheimer’s disease (AD) is a progressive, non-curable neurodegenerative disorder that poses persistent challenges for early diagnosis due to its gradual onset and the difficulty in distinguishing pathological changes from normal aging. Neuroimaging, particularly MRI and PET, plays a key role in detection; however, limitations in data availability and the complexity of early structural biomarkers constrain traditional diagnostic approaches. This review investigates the use of generative models, specifically Generative Adversarial Networks (GANs) and Diffusion Models, as emerging tools to address these challenges. These models are capable of generating high-fidelity synthetic brain images, augmenting datasets, and enhancing machine learning performance in classification tasks. The review synthesizes findings across multiple studies, revealing that GAN-based models achieved diagnostic accuracies up to 99.70%, with image quality metrics such as SSIM reaching 0.943 and PSNR up to 33.35 dB. Diffusion Models, though relatively new, demonstrated strong performance with up to 92.3% accuracy and FID scores as low as 11.43. Integrating generative models with convolutional neural networks (CNNs) and multimodal inputs further improved diagnostic reliability. Despite these advancements, challenges remain, including high computational demands, limited interpretability, and ethical concerns regarding synthetic data. This review offers a comprehensive perspective to inform future AI-driven research in early AD detection. Full article

Flow regulation and water quality maintenance are considered ecosystem services, as they provide environmental benefits with a measurable economic value to society. Distributed or semi-distributed hydrological models can help identify where land use decisions yield the greatest economic and environmental returns related to water resources. For these reasons, this study integrated simulations performed with the SWAT (Soil and Water Assessment Tool) model under varying land use conditions, aiming to balance potential benefits with the loss of ecosystem services. Among the tested parameters, those associated with surface runoff showed the highest sensitivity in simulating streamflow for the Jundiaí River Basin. Based on the statistical indicators R², Nash–Sutcliffe efficiency (NS), and Percent Bias (PBIAS), the SWAT model demonstrated a reliable performance in replicating observed streamflows on a monthly scale, even with limited spatially distributed input data. Scenario 2, which involved converting 15% of pasture/agricultural land into forest, yielded the most favorable hydrological outcomes by increasing soil water infiltration and aquifer recharge while reducing surface runoff and sediment yield. These findings highlight the value of reforestation and land use planning as effective strategies for improving watershed hydrological performance and ensuring long-term water sustainability. Full article

Based on an integrated analysis, this study summarized the current status of soil quality in Yantai apple orchards, developed a multivariate regulation model for key soil physicochemical properties, and proposed optimized fertilization strategies to improve soil quality in the region. The study analyzed the physicochemical properties of the topsoil (0–30 cm) in 19 representative apple orchards across Yantai, including indicators like pH, organic matter (OM), major nutrient ions, and salinity indicators, using standardized measurements and multivariate statistical methods, including descriptive statistics analysis, frequency distribution analysis, canonical correlation analysis, stepwise regression equation analysis, and regression fit model analysis. The results demonstrated that in apple orchards across the Yantai region, reductions in pH were significantly mitigated under the combined increased OM and exchangeable calcium (Ca). Exchangeable potassium (EK) rose in response to the joint elevation of OM and available nitrogen (AN), and AN was also positively influenced by EK, while OM also exhibited a promotive effect on Olsen phosphorus (OP). Furthermore, Ca increased with higher pH. AN and EK jointly contributed to the increases in electrical conductivity (EC) and chloride ions (Cl), while elevated exchangeable sodium (Na) and soluble salts (SS) were primarily driven by EK. Accordingly, enhancing organic and calcium source fertilizers is recommended to boost OM and Ca levels, reduce acidification, and maintain EC within optimal limits. By primarily reducing potassium’s application, followed by nitrogen and phosphorus source fertilizers, the supply of macronutrients can be optimized, and the accumulation of Na, Cl, and SS can be controlled. Collectively, the combined analysis of soil quality status and the multivariate regulation model clarified the optimized fertilization strategies, thereby establishing a solid theoretical and practical foundation for recognizing the necessity of soil testing and formula fertilization, the urgency of improving soil quality, and the scientific rationale for nutrient input management in Yantai apple orchards. Full article

This study examined the effects of both nitrogen (N) rate and form on the growth, nutrient uptake, and quality parameters of hydroponically grown purslane (Portulaca oleracea L.) during a spring cultivation cycle. Purslane was cultivated in a floating hydroponic system under either adequate or limiting N conditions. More specifically, under adequate N conditions, plants were supplied with NS where ammonium nitrogen (NH₄-N) accounted for either 7% (Nr7) or 14% (Nr14) of the total-N. The limiting N conditions were achieved through the application of either an NS where 30% of N inputs were compensated with Cl (N30), or an NS where 50% of N inputs were balanced by elevating Cl and S by 30% and 20%, respectively (N50). The results demonstrated that mild N stress enhanced the quality characteristics of purslane without significant yield losses. However, further and more severe N restrictions in the NS resulted in significant yield losses without improving product quality. The highest yield reduction (20%) occurred under high NH₄-N supply (Nr14), compared to Nr7-treated plants, which was strongly associated with impaired N assimilation and reduced biomass production. Both N-limiting treatments (N30 and N50) effectively reduced nitrate accumulation in edible tissues by 10% compared to plants grown under adequate N supply (Nr7 and Nr14); however, nitrate levels remained relatively high across all treatments, even though the environmental conditions of the experiment favored nitrate reduction. All applied N regimes and compensation strategies improved the antioxidant and flavonoid content, with the highest antioxidant activity observed in plants grown under high NH₄-N application, indirectly revealing the susceptibility of purslane to NH₄-N-rich conditions. Overall, the form and rate of N supply significantly influenced both plant performance and biochemical quality. Partial replacement of N with Cl (N30) emerged as the most promising strategy, benefiting quality traits and effectively reducing nitrate content without significantly compromising yield. Full article

This study introduces a predictive model for estimating indoor room temperatures in vernacular building using external environmental factors such as air temperature, humidity, sunshine duration, and wind speed. The dataset was sourced from the Meteorology, Climatology, and Geophysics Agency and supplemented with direct measurements collected from four rooms within a vernacular building in Aceh Province, Indonesia. A Generative Adversarial Network (GAN)-based imputation technique was implemented to address missing data during preprocessing. The prediction model adopts a hybrid framework that integrates Multiple Linear Regression (MLR) and Artificial Neural Networks (ANNs), with both models optimized using Support Vector Regression (SVR) to better capture the nonlinear dynamics between inputs and outputs. The evaluation results show that the ANN-SVR model achieved the lowest average $\overline{MAE}$ and $\overline{RMSE}$ values, at 0.164 and 0.218, respectively, and the highest average $\overline{R}$ and $\overline{R^2}$ values, at 0.785 and 0.618. Evaluation results indicate that the ANN-SVR model consistently achieved the lowest error rates and the highest correlation coefficients across all four rooms, identifying it as the most effective model for forecasting indoor thermal conditions. These results validate the combined use of ANN-SVR for prediction and GAN for preprocessing as a powerful strategy to enhance data quality and model performance. The findings offer a scientific basis for architectural planning to improve thermal comfort in vernacular buildings such as the Rumoh Aceh. Full article

Precise timing synchronization remains a fundamental requirement for modern navigation and communication systems, where the miniaturization of Loran-C infrastructure presents both technical challenges and practical significance. Conventional miniaturized loop antennas cannot simultaneously meet the requirements of the Loran-C signal for both radiation intensity and bandwidth due to inherent quality factor (Q) limitations. A sub-cubic-meter impedance matching (IM) antenna is proposed, featuring a −20 dB bandwidth of 18 kHz and over 7-fold radiation enhancement. The proposed design leverages a planar-transformer-based impedance matching network to enable efficient 100 kHz operation in a compact form factor, while a resonant coil structure is adopted at the receiver side to enhance the system’s sensitivity. The miniaturized Loran-C timing system incorporating the IM antenna achieves an extended decoding range of >100 m with merely 100 W input power, exceeding conventional loop antennas limited to 30 m operation. This design successfully achieves overall miniaturization of the Loran-C timing system while breaking through the current transmission distance limitations of compact antennas, extending the effective transmission range to the hundred-meter scale. The design provides a case for developing compact yet high-performance Loran-C systems. Full article

Artisanal and small-scale gold mining (ASGM) poses significant environmental challenges globally, particularly due to mercury (Hg) use. As an example, in Ecuador, Hg use still persists, despite its official ban in 2015. This study investigated the geogenic and anthropogenic contributions of potentially toxic elements (PTEs) in the Ponce Enriquez Mining District (PEMD), a region characterized by hydrothermally altered basaltic bedrock and Au-mineralized quartz veins. To assess local baseline values and identify PTE-bearing minerals, a comprehensive geochemical, mineralogical, and petrographic analysis was conducted on bedrock and mineralized veins. These findings reveal distinct origins for the studied PTEs, which include Hg, As, Cu, Ni, Cr, Co, Sb, Zn, and V. Specifically, Hg concentrations in stream sediments downstream (up to 50 ppm) far exceed natural bedrock levels (0.03–0.707 ppm), unequivocally indicating significant anthropogenic input from gold amalgamation. Furthermore, copper shows elevated concentration primarily linked to gold extraction. Conversely, other elements like As, Ni, Cr, Co, Sb, Zn, and V are primarily exhibited to be naturally abundant in basalts due to the presence of primary mafic minerals and to hydrothermal alterations, with elevated concentrations particularly seen in sulfides like pyrite and arsenopyrite. To distinguish natural geochemical anomalies from mining-related contamination, especially in volcanic terrains, this study utilizes Upper Continental Crust (UCC) normalization and local bedrock baselines. This multi-faceted approach effectively helped to differentiate basalt subgroups and assess natural concentrations, thereby avoiding misinterpretations of naturally elevated element concentrations as mining-related pollution. Crucially, this work establishes a robust local geochemical baseline for the PEMD area, providing a critical framework for accurate environmental risk assessments and sustainable mineral resource management, and informing national environmental quality standards and remediation efforts in Ecuador. It underscores the necessity of evaluating local geology, including inherent mineralization, when defining environmental baselines and understanding the fate of PTEs in mining-impacted environments. Full article

Despite the rapid adoption of green buildings as a sustainable development strategy, robust, data-driven approaches for assessing and predicting project risks remain limited. This study proposes an innovative hybrid framework combining the fuzzy analytic hierarchy process (FAHP), multilayer perceptron neural networks (MLPNNs), and particle swarm optimization (PSO) to quantify and forecast the impact of critical risks on green buildings’ performance. Drawing on structured input from 30 domain experts in Shenzhen, China, ten risk categories were identified and prioritized, with economic, market, and functional risks emerging as the most influential. Using these expert-derived weights, an MLP was trained to predict the effects of the top five risks on four core performance metrics—cost, time, quality, and scope. PSO was applied to optimize the model’s architecture and hyperparameters, improving its predictive accuracy. The optimized framework achieved RMSE values ranging from 0.06 to 0.09 and R² values of up to 0.95 across all outputs, demonstrating strong predictive capability. These results substantiate the framework’s effectiveness in generating actionable, quantitative risk predictions under uncertainty. Full article

Mechanical–biological treatment plants face challenges in effectively separating organic fractions from residual municipal solid waste for biological treatment. This study investigates the optimization measures carried out at the Erbenschwang MBT facility, which transitioned from solely aerobic treatment to integrated anaerobic digestion using a screw press. This study focused on evaluating the efficiency of each mechanical pretreatment step by investigating the composition of the residual waste, organic fraction recovery rate, and screw press performance in recovering organic material and biogas to press water. The results showed that 92% of the organic material from the residual waste was recovered into fine fractions after shredding and trommel screening. The pressing experiments produced high-quality press water with less than 3% inert material (0.063–4 mm size). Mass balance analysis revealed that 47% of the input fresh mass was separated into press water, corresponding to 24% of the volatile solids recovered. Biogas yield tests showed that the press water had a biogas potential of 416 m³/ton VS, recovering 38% of the total biogas potential. In simple terms, the screw press produced 32 m³ of biogas per ton of mechanically separated fine fractions and 20 m³ per ton of input residual waste. This low-pressure, single-step screw press efficiently and cost-effectively prepares anaerobic digestion feedstock, making it a promising optimization for both existing and new facilities. The operational configuration of the screw press remains an underexplored area in current research. Therefore, further studies are needed to systematically evaluate key parameters such as screw press pressure (bar), liquid-to-waste (L/ton), and feed rate (ton/h). Full article

In this study, a multi-objective optimization procedure with embedded topology optimization was presented. The procedure simultaneously optimizes the spatial arrangement and topology of bodies in a multi-body system. The multi-objective algorithm determines the locations of supports, joints, active loads, reactions, and load magnitudes, which serve as inputs for the topology optimization of each body. The multi-objective algorithm dynamically adjusts domain size, support locations, and load magnitudes during optimization. Due to repeated topology optimization calls within the genetic algorithm, the computational cost is significant. To address this, two reduction strategies are proposed: (I) using a coarser mesh and (II) reducing the number of iterations during the initial generations. As optimization progresses, Strategy I gradually refines the mesh, while Strategy II increases the maximum allowable iteration count. The effectiveness of both strategies is evaluated against a baseline (Reference) without reductions. By the 25th generation, all approaches achieve similar hypervolume values (Reference: 2.181; I: 2.112; II: 2.133). The computation time is substantially reduced (Reference: 42,226 s; I: 16,814 s; II: 21,674 s), demonstrating that both strategies effectively accelerate optimization without compromising solution quality. Full article

Large-scale models trained on extensive datasets have become the standard due to their strong generalizability across diverse tasks. In-context learning (ICL), widely used in natural language processing, leverages these models by providing task-specific prompts without modifying their parameters. This paradigm is increasingly being adapted for computer vision, where models receive an input–output image pair, known as an in-context pair, alongside a query image to illustrate the desired output. However, the success of visual ICL largely hinges on the quality of these prompts. To address this, we propose Enhanced Instruct Me More (E-InMeMo), a novel approach that incorporates learnable perturbations into in-context pairs to optimize prompting. Through extensive experiments on standard vision tasks, E-InMeMo demonstrates superior performance over existing state-of-the-art methods. Notably, it improves mIoU scores by 7.99 for foreground segmentation and by 17.04 for single object detection when compared to the baseline without learnable prompts. These results highlight E-InMeMo as a lightweight yet effective strategy for enhancing visual ICL. Full article