Search Results (18,917)

Agriculture and animal feeding operations are responsible for 87% of ammonia emissions in the UK. Controlling NH₃ concentrations below 20 ppm is crucial to preserve workers’ and livestock’s well-being. Therefore, ammonia control systems are required for maintaining adequate air quality in livestock facilities. This study assessed the ammonia reduction efficiency of a novel air pollution abatement (APA) system used in a pig farm building. The monitoring duration was 11 weeks. The results were compared with the baseline from a previous pig cycle during the same time of year in 2023. A ventilation-controlled room was monitored during a two-phase campaign, and the actual ammonia concentrations were measured at different locations within the site and at the inlet/outlet of the APA system. A 98% ammonia reduction was achieved at the APA outlet through NH₃ absorption in tap water. Ion chromatography analyses of farm water samples revealed NH₃ concentrations of up to 530 ppm within 83 days of APA operation. Further scanning electron microscopy and energy-dispersive X-ray inspections revealed the presence of salts and organic/inorganic matter in the solid residues. This research can contribute to meeting current ammonia regulations (NECRs), also by reusing the process water as a potential nitrogen fertiliser in agriculture. Full article

The energy sector uses artificial intelligence (AI) as a crucial instrument to achieve environmental sustainability targets by improving resource efficiency and decreasing emissions while minimizing waste production. This paper establishes an industry-specific executive playbook that guides energy sector leaders by implementing AI technologies for sustainability management with approaches suitable for industrial needs. The playbook provides an industry-specific framework along with strategies and AI-based solutions to help organizations overcome their sustainability challenges. Predictive analytics combined with smart grid management implemented through AI applications produced 15% less energy waste and reduced carbon emissions by 20% according to industry pilot project data. AI has proven its transformative capabilities by optimizing energy consumption while detecting inefficiencies to create both operational improvements and cost savings. The real-time monitoring capabilities of AI systems help companies meet strict environmental regulations and international climate goals by optimizing resource use and waste reduction, supporting circular economy practices for sustainable operations and enduring profitability. Leaders can establish impactful technology-based sustainability initiatives through the playbook which addresses the energy sector requirements for corporate goals and regulatory standards. Full article

Reducing atmospheric carbon dioxide is a critical global priority. This study investigates the influence of Cu-Ni nanoalloy loading on the CO₂ electroreduction efficiency in the context of mesoporous carbon supports. Current methods struggle when it comes to catalyst efficiency, selectivity, and longevity. By synthesizing copper–nickel nanoparticles through chemical reduction and depositing them on porous carbon, this research aimed to optimize catalyst loading and understand the structure–activity relationships. Catalyst performance was evaluated using chronoamperometry and linear sweep voltammetry (LSV). The results showed that 12 wt% catalyst loading achieved optimal CO₂ reduction, outperforming its 36 wt% counterpart by balancing the catalyst quantity. This study reveals that 12 wt% Cu-Ni loading provides a higher CO₂ reduction current density and greater long-term stability than 36 wt% loading, owing to better nanoparticle dispersion and reduced aggregation. Unlike previous Cu-Ni/mesoporous carbon studies, this work uniquely compares different loadings to directly correlate the structure, electrochemical performance, and catalyst durability. Full article

Background/Objectives: Periodontitis is a chronic inflammation of the periodontium that induces damage in the periodontal ligaments and the surrounding alveolar bone. This study aimed to comparatively evaluate the clinical outcomes of two therapies used in the management of periodontal conditions, represented by scaling and root planing (SRP) alone and laser-assisted new attachment procedure (LANAP). Methods: Two quadrants of the oral cavity from each selected patient were randomly allocated to one of the treatment groups, SRP or LANAP. The periodontal status was documented in a periodontal chart at baseline, six weeks, and one year after treatment. SRP was performed in the first group of patients. The LANAP protocol was carried out on the patients belonging to the second group. Results: The outcomes of the study highlighted that LANAP leads to a reduction in periodontal disease signs (pocket depth, bleeding on probing, and gingival recession), contributing to the formation of new attachment tissues. LANAP shows more stability in maintaining the improvements achieved during six weeks, while SRP shows a slight deterioration in several parameters, particularly attachment loss, between six weeks and one year. The collected data at six-week and one-year follow-ups show improvements in periodontal health, thus improving oral health. Conclusions: Both minimally invasive periodontal procedures were effective, with LANAP demonstrating greater efficiency in patients with chronic periodontal disease, a greater reduction in pocket depth, and improved clinical outcomes compared to SRP alone. Full article

Evaluating the implant site immediately after implant removal is crucial for assessing its condition and ensuring morphological stability. Immediate reconstruction at the time of implant removal has been proposed as a strategy to preserve alveolar ridge width. This study aims to evaluate whether immediate alveolar bone reconstruction at the time of implant removal provides comparable or superior dimensional stability of the alveolar ridge compared to staged reconstruction approaches. The null hypothesis of this study is that there is no significant difference in alveolar bone dimensions between immediate and staged reconstructions following implant removal. This retrospective study included seven participants, consisting of six males and one female. The participants were categorized into three groups based on the treatment approach following implant removal. In Group 1, no bone grafting was performed after implant removal. In Group 2, bone grafting was conducted following implant removal, with an adequate healing period before implant placement. In Group 3, bone grafting was performed simultaneously with implant removal. Cone-beam computed tomography (CBCT) imaging was conducted before implant removal (T0), after implant removal or bone grafting (T1), and after implant placement (T2). All removed implants were successfully replaced with new ones, regardless of bone grafting. In terms of alveolar ridge width at 1 mm below the crest, Group 1 exhibited the greatest reduction (ΔT1 − T0 = −5.1 ± 3.7 mm), while Group 2 showed a mild increase (+1.1 ± 2.6 mm), and Group 3 had a moderate decrease (−1.3 ± 1.0 mm). This suggests that delayed bone grafting can better preserve or enhance bone volume during healing. A reduction in buccal ridge height between T1 and T0 (ΔT1 − T0) was observed, particularly in Group 1. In contrast, an increase in buccal ridge height was most pronounced in Group 2. Although immediate reconstruction (Group 3) did not result in statistically significant gains, it achieved successful implant placement without complications and reduced the total treatment duration, which might be beneficial from a clinical efficiency and patient satisfaction standpoint. Therefore, staged bone grafting (Group 2) appears to offer greater dimensional stability, particularly in maintaining ridge height, whereas immediate reconstruction (Group 3) remains a clinically viable alternative for stable healing in select cases, especially when shorter treatment timelines are prioritized. Full article

In recent years, heavy metal contamination in agricultural products has become a growing concern in the field of food safety. Copper (Cu) stress in crops not only leads to significant reductions in both yield and quality but also poses potential health risks to humans. This study proposes an efficient and precise non-destructive detection method for Cu stress in oilseed rape, which is based on hyperspectral false-color image construction using principal component analysis (PCA). By comprehensively capturing the spectral representation of oilseed rape plants, both the one-dimensional (1D) spectral sequence and spatial image data were utilized for multi-class classification. The classification performance of models based on 1D spectral sequences was compared from two perspectives: first, between machine learning and deep learning methods (best accuracy: 93.49% vs. 96.69%); and second, between shallow and deep convolutional neural networks (CNNs) (best accuracy: 95.15% vs. 96.69%). For spatial image data, deep residual networks were employed to evaluate the effectiveness of visible-light and false-color images. The RegNet architecture was chosen for its flexible parameterization and proven effectiveness in extracting multi-scale features from hyperspectral false-color images. This flexibility enabled RegNetX-6.4GF to achieve optimal performance on the dataset constructed from three types of false-color images, with the model reaching a Macro-Precision, Macro-Recall, Macro-F₁, and Accuracy of 98.17%, 98.15%, 98.15%, and 98.15%, respectively. Furthermore, Grad-CAM visualizations revealed that latent physiological changes in plants under heavy metal stress guided feature learning within CNNs, and demonstrated the effectiveness of false-color image construction in extracting discriminative features. Overall, the proposed technique can be integrated into portable hyperspectral imaging devices, enabling real-time and non-destructive detection of heavy metal stress in modern agricultural practices. Full article

This paper proposes a hybrid method for skin lesion classification combining deep learning features with conventional descriptors such as HOG, Gabor, SIFT, and LBP. Feature extraction was performed by extracting features of interest within the tumor area using suggested fusion methods. We tested and compared features obtained from different deep learning models coupled to HOG-based features. Dimensionality reduction and performance improvement were achieved by Principal Component Analysis, after which SVM was used for classification. The compared methods were tested on the reference database skin cancer-malignant-vs-benign. The results show a significant improvement in terms of accuracy due to complementarity between the conventional and deep learning-based methods. Specifically, the addition of HOG descriptors led to an accuracy increase of 5% for EfficientNetB0, 7% for ResNet50, 5% for ResNet101, 1% for NASNetMobile, 1% for DenseNet201, and 1% for MobileNetV2. These findings confirm that feature fusion significantly enhances performance compared to the individual application of each method. Full article

Wind farm performance monitoring has traditionally relied on deterministic models, such as power curves or machine learning approaches, which often fail to account for farm-wide behavior and the uncertainty quantification necessary for the reliable detection of underperformance. To overcome these limitations, we propose a probabilistic methodology for turbine-level active power prediction and uncertainty estimation using high-frequency SCADA data and farm-wide autoregressive information. The method leverages a Stochastic Variational Gaussian Process with a Linear Model of Coregionalization, incorporating physical models like manufacturer power curves as mean functions and enabling flexible modeling of active power and its associated variance. The approach was validated on a wind farm in the Belgian North Sea comprising over 40 turbines, using only 15 days of data for training. The results demonstrate that the proposed method improves predictive accuracy over the manufacturer’s power curve, achieving a reduction in error measurements of around 1%. Improvements of around 5% were seen in dominant wind directions (200°–300°) using 2 and 3 Latent GPs, with similar improvements observed on the test set. The model also successfully reconstructs wake effects, with Energy Ratio estimates closely matching SCADA-derived values, and provides meaningful uncertainty estimates and posterior turbine correlations. These results demonstrate that the methodology enables interpretable, data-efficient, and uncertainty-aware turbine-level power predictions, suitable for advanced wind farm monitoring and control applications, enabling a more sensitive underperformance detection. Full article

Cell-free massive multiple-input multiple-output (CF M-MIMO) is one of the most promising technologies for future wireless communication such as 5G and beyond fifth-generation (B5G) networks. It is a type of network technology that uses a massive number of distributed antennas to serve a large number of users at the same time. It has the ability to provide high spectral efficiency (SE) as well as improved coverage and interference management, compared to traditional cellular networks. However, estimating the channel with high-performance, low-cost computational methods is still a problem. Different algorithms have been developed to address these challenges in channel estimation. One of the high-performance channel estimators is a phase-aware minimum mean square error (MMSE) estimator. This channel estimator has high computational complexity. To address the shortcomings of the existing estimator, this paper proposed an efficient phase-aware element-wise minimum mean square error (PA-EW-MMSE) channel estimator with QR decomposition and a precoding matrix at the user side. The closed form uplink (UL) SE with the phase MMSE and proposed estimators are evaluated using MMSE combining. The energy efficiency and area throughput are also calculated from the SE. The simulation results show that the proposed estimator achieved the best SE, EE, and area throughput performance with a substantial reduction in the complexity of the computation. Full article

This paper presents a novel framework for optimizing multimodal large language model (MLLM) inference through task offloading and resource allocation in UAV-assisted satellite edge computing (SEC) networks. MLLMs leverage transformer architectures to integrate heterogeneous data modalities for IoT applications, particularly real-time monitoring in remote areas. However, cloud computing dependency introduces latency, bandwidth, and privacy challenges, while IoT device limitations require efficient distributed computing solutions. SEC, utilizing low-earth orbit (LEO) satellites and unmanned aerial vehicles (UAVs), extends mobile edge computing to provide ubiquitous computational resources for remote IoTDs. We formulate the joint optimization of MLLM task offloading and resource allocation as a mixed-integer nonlinear programming (MINLP) problem, minimizing latency and energy consumption while optimizing offloading decisions, power allocation, and UAV trajectories. To address the dynamic SEC environment characterized by satellite mobility, we propose an action-decoupled soft actor–critic (AD-SAC) algorithm with discrete–continuous hybrid action spaces. The simulation results demonstrate that our approach significantly outperforms conventional deep reinforcement learning methods in convergence and system cost reduction compared to baseline algorithms. Full article

Plastid division regulatory genes play a crucial role in the morphogenesis of chloroplasts and amyloplasts. Chloroplasts are the main sites for photosynthesis and metabolic reactions, while amyloplasts are the organelles responsible for forming and storing starch granules. The proper division of chloroplasts and amyloplasts is essential for plant growth and yield maintenance. Therefore, this study aimed to examine the J175 (FtsZ2-2) gene, cloned from an ethyl methanesulphonate (EMS) mutant involved in chloroplast and amyloplast division in maize, through map-based cloning. We found that J175 encodes a cell division protein, FtsZ (filamentous temperature-sensitive Z). The FtsZ family of proteins is widely distributed in plants and may be related to the division of chloroplasts and amyloplasts. The J175 protein is localized in plastids, and its gene is expressed across various tissues. From the seedling stage, the leaves of the j175 mutant exhibited white stripes, while the division of chloroplasts was inhibited, leading to a significant increase in volume and a reduction in their number. Measurement of the photosynthetic rate showed a significant decrease in the photosynthetic efficiency of j175. Additionally, the division of amyloplasts in j175 grains at different stages was impeded, resulting in irregular polygonal starch granules. RNA-seq analyses of leaves and kernels also showed that multiple genes affecting plastid division, such as FtsZ1, ARC3, ARC6, PDV1-1, PDV2, and MinE1, were significantly downregulated. This study demonstrates that the maize gene j175 is essential for maintaining the division of chloroplasts and amyloplasts and ensuring normal plant growth, and provides an important gene resource for the molecular breeding of maize. Full article

The accelerated industrialization in China has precipitated a dramatic surge in solid waste generation, causing severe land resource depletion and posing substantial environmental contamination risks. Simultaneously, the cement industry has become characterized by the intensive consumption of natural resources and high carbon emissions. This review aims to investigate the current technological advances in utilizing industrial solid waste for cement production, with a focus on promoting resource recycling, phase transformations during hydration, and environmental management. The feasibility of incorporating coal-based solid waste, metallurgical slags, tailings, industrial byproduct gypsum, and municipal solid waste incineration into active mixed material for cement is discussed. This waste is utilized by replacing conventional raw materials or serving as active mixed material due to their content of oxygenated salt minerals and oxide minerals. The results indicate that the formation of hydration products can be increased, the mechanical strength of cement can be improved, and a notable reduction in CO₂ emissions can be achieved through the appropriate selection and proportioning of mineral components in industrial solid waste. Further research is recommended to explore the synergistic effects of multi-waste combinations and to develop economically efficient pretreatment methods, with an emphasis on balancing the strength, durability, and environmental performance of cement. This study provides practical insights into the environmentally friendly and efficient recycling of industrial solid waste and supports the realization of carbon peak and carbon neutrality goals. Full article

Arteriovenous malformations (AVMs) are congenital vascular anomalies defined by abnormal direct connections between arteries and veins due to their complex structure or endovascular approaches. Pharmacological strategies targeting the underlying molecular mechanisms are thus gaining increasing attention in an effort to determine the mechanism involved in AVM regulation. In this study, we examined 30 human tissue samples, comprising 10 vascular samples, 10 human fibroblasts derived from AVM tissue, and 10 vascular samples derived from healthy individuals. The pharmacological agents thalidomide, U0126, and rapamycin were applied to the isolated endothelial cells (ECs). The pharmacological treatments reduced the proliferation of AVM ECs and downregulated miR-135b-5p, a biomarker associated with AVMs. The expression levels of angiogenesis-related genes, including VEGF, ANG2, FSTL1, and MARCKS, decreased; in comparison, CSPG4, a gene related to capillary networks, was upregulated. Following analysis of these findings, skin samples from 10 AVM patients were reprogrammed into induced pluripotent stem cells (iPSCs) to generate AVM blood vessel organoids. Treatment of these AVM blood vessel organoids with thalidomide, U0126, and rapamycin resulted in a reduction in the expression of the EC markers CD31 and α-SMA. The establishment of AVM blood vessel organoids offers a physiologically relevant in vitro model for disease characterization and drug screening. The authors of future studies should aim to refine this model using advanced techniques, such as microfluidic systems, to more efficiently replicate AVMs’ pathology and support the development of personalized therapies. Full article

Cross-docking operations in Eastern and Central European markets face increasing complexity amid persistent uncertainty and inflationary pressures. This study provides the first comprehensive comparative analysis integrating economic efficiency with sustainability indicators across strategic locations. Using mixed-methods analysis of 40 bibliographical sources and quantitative modeling of cross-docking scenarios in Bratislava, Prague, and Budapest, we integrate environmental, social, and governance frameworks with activity-based costing and artificial intelligence analysis. Optimized cross-docking achieves statistically significant cost reductions of 10.61% for Eastern and Central European inbound logistics and 3.84% for Western European outbound logistics when utilizing Budapest location (p < 0.01). Activity-based costing reveals labor (35–40%), equipment utilization (25–30%), and facility operations (20–25%) as primary cost drivers. Budapest demonstrates superior integrated performance index incorporating operational efficiency (94.2% loading efficiency), economic impact (EUR 925,000 annual savings), and environmental performance (486 tons CO₂ reduction annually). This is the first empirically validated framework integrating activity-based costing–corporate social responsibility methodologies for an emerging market cross-docking, multi-dimensional performance assessment model transcending operational-sustainability dichotomy and location-specific contingency identification for emerging market implementation. Findings support targeted infrastructure investments, harmonized regulatory frameworks, and public–private partnerships for sustainable logistics development in emerging European markets, providing actionable roadmap for EUR 142,000–EUR 187,000 artificial intelligence implementation investments achieving a 14.6-month return on investment. Full article

This paper presents a numerical investigation into the generation of 2D ordered pillar array columns for liquid chromatography columns, focusing on the development of an algorithm for the automatic creation of unit-cell morphologies and their subsequent computational fluid dynamics (CFD) simulation. The algorithm is developed to incorporate functional and operational constraints, which ensure that the generated structures are permeable and suitable for chromatographic separations. The functional constraints include the principal pathway and no dry void constraints, while the operational constraints involve symmetry and porosity thresholds. The algorithm’s efficacy is demonstrated with a reduction rate of 97.8% for order 5 matrices. CFD simulations of the generated morphologies reveal that the homogeneity of the fluid velocity profile within the unit cell is a key determinant of separation performance, suggesting that refining the resolution of discrete unit cells could enhance separation efficiency. Future work will explore the inclusion of more complex morphologies and the impact of particle shape and size on separation efficiency. Full article