Search Results (3,392)

Low-maturity shale is a prime target for in situ conversion (ICP), yet heating window selection remains largely empirical because pore evolution and hydrocarbon generation are rarely quantified in tandem. Nenjiang Formation shale from the Songliao Basin (TOC = 8.91%; R_o,max = 0.54%) was subjected to closed-system pyrolysis at 300–500 °C (20 °C h⁻¹; 72 h per step). Released oil and gas and residual chloroform-extractable bitumen (“A”) were quantified, and pore evolution was characterized using 2D low-field NMR, SEM, micro-CT, and low-pressure N₂ adsorption. Fractal dimensions (D_s and D_p) were derived from Frenkel–Halsey–Hill (FHH) fitting. Oil yield and bitumen “A” increased sharply above 350 °C and peaked at 375 °C, whereas gas generation accelerated above 400 °C and continued to increase to 500 °C. NMR indicates a temperature-dependent shift in retained hydrocarbons toward weaker confinement and higher mobility, with enhanced expulsion/mobility signals near 375 °C. At 375 °C, BJH pore volume and average pore diameter reached maxima (0.0675 cm³ g⁻¹ and 15.36 nm), while D_s and D_p reached minima (2.343 and 2.444). The coincidence of peak oil expulsion with minimum fractal complexity suggests that FHH-based fractal indices provide a quantitative metric for comparing ICP heating windows in low-maturity shale. Full article

Composting chicken manure is a source of significant ammonia (NH₃) emissions, which, because of propagation, contributes to the eutrophication of the environment and decreases in air quality. Therefore, it is reasonable to use methods to limit its emission into the atmosphere. Biofiltration, using the metabolic activity of nitrifying and heterotrophic microorganisms capable of oxidizing ammonia, is an effective method to reduce ammonia emissions. In addition, the performance of the biofiltration process depends on operational parameters such as the humidity of the medium, the temperature, the contact time of the gas with the biofiltering medium, and the chemical composition and structure of the filter material. The aim of the study was to evaluate the effectiveness of biofilter fillings in reducing ammonia emissions from composting chicken manure along with the identification of factors allowing us to determine the proposed design solution as the most advantageous in terms of efficiency. Experiments on reducing odour emissions with biofiltration were carried out in two compact composting reactors, in which a compost mixture with a C:N ratio of 10:1 was used. The mixture was prepared in a ratio of 5:1 of chicken manure to the structuring material, with wheat straw used as the structuring material. Based on the results of the research on the course of the composting process, high values of ammonia concentration were recorded. Ammonia concentrations of 886 ppm (composter 1) and 811 ppm (composter 2) were recorded, which confirms the intensive nature of this gas emissions during the process of stabilizing the chicken manure. As part of the conducted research, the effectiveness of biofiltration in reducing ammonia emissions was evaluated by analysing the influence of the aeration intensity of the biofilter (20 dm³/h and 50 dm³/h), directly determining the time of contact of the gas with the bed (EBCT—Empty Bed Contact Time). Coconut-activated carbon was used as a filter bed, which was an effective carrier for the development of microorganisms responsible for the biological removal of ammonia from waste gases generated during composting. In addition, this material showed the ability to physically adsorb ammonia, thus supporting the process of its elimination. Each of the test stations has been equipped with a biofiltration installation. To determine the effectiveness of biological removal of ammonia and to assess the legitimacy of the use of selected strains of microorganisms in the process of biological removal of ammonia, the bed of one of the biofilters (biofilter 2) was inoculated with a strain of nitrifying bacteria. During the study, the high efficiency of ammonia removal because of biofiltration was noted in each of the configurations. In the case of an aeration intensity of 20 dm³/h, a reduction in emissions of 99% was achieved; with a higher aeration value, i.e., 50 dm³/h, the efficiency was 89%. These results indicate that the intensity of aeration has a significant impact on the efficiency of the biofiltration process. The analysis of a biofilter enriched with a strain of nitrifying bacteria requires long-term testing. This is important to reliably determine the effect of inoculation on the efficiency of the biological removal of ammonia in biofilters. It has been shown that optimizing these factors allows us to achieve a reduction in ammonia emissions of up to 90%, while minimizing the formation of unpleasant odours. The use of biofiltration in composting systems for organic waste of animal origin is an effective, sustainable solution that fits into the idea of sustainable development, combining the efficiency of air purification technology with environmental protection and the responsible management of resources. This study demonstrates that biofiltration using coconut-shell-activated carbon is an effective and economical method for reducing ammonia and odour emissions from composting chicken manure. The results provide valuable theoretical and practical information on emissions management in organic waste composting processes. Data from this study could be useful in developing strategies to minimize odour emissions, including from the agricultural sector. Full article

The Internet of Things (IoT) is increasingly embedded in critical infrastructures across healthcare, energy, transportation, and industrial automation, yet its pervasiveness introduces substantial security and resilience challenges. This paper presents a comprehensive review of recent advances in IoT resilience, focusing on developments reported between 2022 and 2025. A layered taxonomy is proposed to organize resilience strategies across hardware, network, learning, application, and governance layers, addressing adversarial, environmental, and hybrid stressors. The survey systematically classifies and compares more than forty representative studies encompassing deep learning under adversarial attack, generative and ensemble intrusion detection, hardware and protocol-level defenses, federated and distributed learning, and trust and governance-based approaches. A comparative analysis shows that while adversarial training, GAN-based augmentation, and decentralized learning improve robustness, their evidence is often confined to specific datasets or attack scenarios, with limited validation in large-scale deployments. The study highlights challenges in benchmarking adaptivity, cross-layer integration, and explainable resilience, concluding with future directions for creating antifragile IoT systems that can self-heal and adapt to evolving cyber–physical threats. Full article

The use of human-edible materials like soy in animal feed raises several concerns, as it contributes to high greenhouse gas emissions and requires significant land and water use for agriculture. For this reason, research is exploring alternative ingredients rich in proteins like microalgae, which offer potential nutritional and environmental benefits. Species like Nannochloropsis are promising since their use for human consumption is very limited, making them non-competitive with human food. This article aims to formulate a poultry feed in which 30% of the crude protein from soybean meal is replaced by Nannochloropsis oceanica single-cell ingredients. Growth parameters have been evaluated in comparison with a diet based on soy protein. Additionally, the effect on meat quality was assessed by evaluating nutritional, texture, stability, and sensory parameters. Results showed that the microalgae diet caused a slight reduction in animal growth due to lower digestibility of the feed. Considering the quality parameters of the meat, no differences were found in terms of shelf life and physicochemical parameters, except for the color. The microalgae diet significantly increased the content of n-3 fatty acids and carotenoids in the meat. Finally, regarding sensory properties, the only change detected was in the amount of exudate. Full article

This review analyzes the transition from silicon to wide-bandgap (WBG) and ultrawide-bandgap (UWBG) semiconductor materials for power electronics, focusing on Silicon Carbide (SiC) and Gallium Nitride (GaN) technologies. Following a PRISMA-based systematic review methodology, we analyzed 94 peer-reviewed publications spanning device technology, converter architectures, and system applications. We employ a bottom-up approach, progressing from fundamental material properties through device architectures and converter topologies to system-level implications. We examine how intrinsic material properties enable operation at elevated temperatures, voltages, and frequencies while minimizing losses. Through analysis of Figures of Merit and system-level Key Performance Indicators, we quantify WBG benefits across automotive, industrial, renewable energy, and consumer electronics sectors, demonstrating 3–5× power density improvements and 20–40% cost reductions. The review presents emerging device technologies, including vertical GaN for medium-voltage applications and monolithic bidirectional switches (BDSs), enabling single-stage power conversion. We provide the first comprehensive topology-level comparison of emerging vertical GaN and monolithic bidirectional switches against established SiC solutions, identifying specific applications where each technology offers advantages. A comprehensive topology-by-topology comparison between SiC and GaN is provided, offering design guidelines for device selection. The review addresses practical constraints, including dynamic on-resistance degradation, threshold voltage instability, and electromagnetic interference challenges for both SiC and GaN. Finally, we examine emerging UWBG materials ($\beta$-Ga${}_2$O${}_3$, AlN, c-BN, Diamond) and their development status, manufacturing challenges, supply chain considerations, and commercialization prospects for ultra-high-voltage applications. Full article

Near-infrared (NIR) imaging, including NIR-I (800–1000 nm) and NIR-II (1000–1700 nm), has been primarily evaluated using separate cameras with different detectors, limiting comparison. We investigated whether using a single-camera system capable of both NIR-I and NIR-II acquisition, NIR-II improves spatial resolution and contrast-to-noise ratio (CNR) for nanoparticle-based imaging. Dual-mode, dual-Gd ICG (DMDG-ICG) nanoparticles were characterized for absorption and fluorescence. A custom NIR imaging system using a single InGaAs camera enabled visualizing both NIR-I and -II windows. In vitro, capillary tubes containing nanoparticles in water, in tissue-mimicking Intralipid, or covered with mouse skin were imaged, and full-width-half maximum (FWHM) and CNR were measured. In vivo, the mouse femoral artery was imaged after IV nanoparticle delivery. DMDG-ICG showed strong fluorescence at both NIR-I and NIR-II. Scatter greater at NIR-I than NIR-II increased with depth and tissue layers. FWHM was lower and CNR higher at NIR-II versus NIR-I for up to 10 mm depth (p < 0.05, n = 3) in Intralipid. In vivo, femoral artery CNR was also higher at NIR-II (p < 0.05, n = 3). Using a single-camera system allowing direct comparison, NIR-II imaging provided greater penetration, spatial resolution, and CNR compared to NIR-I. The findings support the utility of NIR-II for vascular and molecular imaging applications. Full article

N₂O emissions exacerbate the greenhouse effect, urgently demanding advances in abatement technologies. Catalytic decomposition of N₂O over cobalt-based oxides with alkali metal promoters remains challenging because these catalysts are used in pelletized form, limiting their activity to a narrow outer-shell region due to internal diffusion limitations. However, research efforts continue to focus on enhancing Co–alkali metal contact on unsupported powder samples under inert conditions, even though, under industrial conditions, catalysts are exposed to inhibitory components of waste gases and N₂O, and the powder form is unsuitable for practical application. This study aims at testing N₂O decomposition over catalysts with a Co₃O₄-Cs active phase supported on a ceramic foam. For this purpose, we characterized these catalysts by H₂ temperature-programmed reduction, H₂O and NO temperature-programmed desorption, atomic absorption spectroscopy, and X-ray diffraction and assessed their catalytic performance under an inert-gas atmosphere and with O₂, water vapor, and NO to simulate industrial conditions. Using a pseudo-homogeneous, one-dimensional model of an ideal plug flow reactor in an isothermal regime, the simulation calculations for a full-scale catalytic reactor for N₂O abatement in waste gas from HNO₃ production were performed. The Cs₂CO₃ precursor significantly enhanced catalyst reducibility and electron transferability, increasing N₂O decomposition efficiency in inert gas, but its high hygroscopicity decreased resistance to water vapor and NO, overriding its advantages under industrial conditions. Conversely, glycerol-assisted impregnation enhanced catalyst performance regardless of Cs precursor. These foam-supported catalysts offered several other advantages, including lower pressure drop and lower active phase loading with matching catalytic activity. Based on our findings, depositing Cs₂CO₃ on ceramic foam through glycerol-assisted impregnation may facilitate catalytic N₂O decomposition at the industrial level and, therefore, promote environmental sustainability by reducing N₂O emissions. Full article

Intrusion detection systems (IDS) often struggle to detect rare but high-impact attack behaviors due to severe class imbalance in real-world network traffic. This work proposes a class-specific GAN-based augmentation framework that explicitly targets sparsity in the minority-class in structured cybersecurity datasets. Unlike prior GAN-based approaches that employ global augmentation or anomaly-driven synthesis, separate Generative Adversarial Networks (GANs) are trained independently for each MITRE ATT&CK tactic using only real minority-class samples, enabling focused distribution learning without contamination from benign traffic. Using a relatively new network traffic dataset, UWF-ZeekData22, the proposed framework augments minority classes under conditions of extreme sample sparsity, where traditional classifiers and interpolation-based oversampling methods are ineffective or statistically unreliable. Five traditional classifiers—Logistic Regression, Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Decision Tree, and Random Forest—are evaluated before and after augmentation using stratified 5-fold cross-validation. Experimental results show that class-specific GAN augmentation consistently improves recall and F1-score for rare attack tactics, with the largest gains observed under extreme sparsity where pre-augmentation evaluation was infeasible. Notably, false-negative rates are substantially reduced without degrading majority-class performance, demonstrating that the proposed approach enhances minority-class separability rather than inflating evaluation metrics. These findings demonstrate that class-specific GAN-based augmentation is a practical and robust data-level strategy for improving the detection of rare MITRE ATT&CK-aligned attack behaviors in machine-learning-based IDSs. Full article

Integrating diamond with GaN provides an effective pathway to mitigate self-heating. However, the thermal boundary resistance (TBR) remains a persistent bottleneck for further heat dissipation. While carbon (C) diffusion into the SiNx interlayer is known to reduce TBR, the associated stress evolution and its impact on device performance remain underexplored. In this work, the synergistic regulation of heat transport and electrical performance induced by C diffusion was systematically investigated. Transmission electron microscopy (TEM) was employed to characterize the interfacial microstructure and the influence of C diffusion on the interface. To further assess the resulting impact on heat dissipation, transient thermoreflectance was utilized to precisely quantify the thermal transport within the heterostructures. Classical molecular dynamics (MD) simulations were then performed to analyze the underlying physical mechanisms, revealing that intensifying C diffusion increases the phonon density of states overlap and effectively reduces the TBR. Furthermore, the intrinsic stress was quantified through geometric phase analysis (GPA) based on TEM images, demonstrating that the stress induced during the diffusion process propagates to the AlGaN/GaN heterostructure. Crucially, this stress modulation enhances the piezoelectric polarization by approximately 32%, resulting in a 5% increase in the two-dimensional electron gas (2DEG) sheet density. These findings provide a comprehensive strategy for optimizing the thermal management and mechanical reliability of high-power GaN devices. Full article

The ultraviolet photodetectors based on In:Ga₂O₃/p-GaN heterojunctions were fabricated by depositing an In:Ga₂O₃ thin film on a p-GaN substrate under different oxygen pressures using the pulsed laser deposition method. The devices exhibit typical self-powered behavior and a broad-spectrum response within the wavelength range of 250–345 nm. Under low oxygen pressure, the self-powered response peak of photodetectors with negative response current is mainly located at 345 nm, corresponding to the p-GaN layer. When the oxygen pressure exceeds 5 Pa, the response peak at 250 nm related to the In:Ga₂O₃ layer becomes the predominant peak, and the response current is positive. Studies demonstrate that the response peaks at 345 nm and 250 nm of the devices could be modulated by varying the applied bias voltage. The results indicate that, as the reverse bias increases, the response peak in the near ultraviolet region gradually decreases, while the response peak in the solar blind ultraviolet region gradually increases. The tunable photoresponse mechanism is attributed to the changes in the spatial-charge region and built-in electric field caused by devices prepared under different oxygen pressures and by varying the reverse bias applied to the devices. Full article

Epilepsy is a common neurological disorder affecting over 50 million people worldwide, characterised by recurrent seizures accompanied by abnormal neuronal electrical activity. Electroencephalogram (EEG) is a technique for recording brain electrical signals, widely employed for epileptic seizure (ES) prediction due to its high temporal resolution, portability, and cost-effectiveness. However, reliable ES prediction based on EEG remains challenging, primarily owing to the limited duration of recorded pre-ictal states in publicly available datasets and the typically low signal-to-noise ratio (SNR) in non-invasive recordings. To mitigate these issues, we propose a Conditional Deep Convolutional Generative Adversarial Network (CDCGAN), which combines the representational power of Deep Convolutional Generative Adversarial Network (DCGAN) with the categorical conditioning mechanism of Conditional Generative Adversarial Network (CGAN) to generate class-specific EEG samples. By synthesising target samples, CDCGAN aims to alleviate class imbalance and enhance the quality of low-resolution spectral representations. To evaluate the practical utility of generated data, we trained a Convolutional Neural Network (CNN) on the augmented dataset and compared its performance against prior studies. Under the Leave-One-Seizure-Out cross-validation (LOSO-CV) protocol, our method achieved an average AUC of 0.876 at a 60% augmentation rate with 50 training epochs. The AUC improvement relative to corresponding control settings demonstrates that GAN-based data augmentation provides additional effective training samples for ES prediction while preserving task-relevant and discriminative pre-ictal EEG features. Full article

Chest X-ray (CXR) imaging is one of the most widely used techniques for screening and diagnosing pulmonary diseases. However, discerning subtle structural changes, such as small nodules, disordered pulmonary textures, tiny cavities, pleural thickening, or spiculation, is difficult using low-resolution images. Acquiring high-resolution CXRs typically requires higher radiation doses, posing a risk to patients. We propose a chest X-ray image super-resolution algorithm based on generative adversarial networks (GAN). Through adversarial training, our approach generates high-resolution CXRs with enhanced details and improved realism. We further incorporate a CSA hybrid attention module into the network, strengthening its ability to capture fine structures and improve texture fidelity. Moreover, we remove the pooling layer from the channel attention module to overcome limitations in super-resolution, thereby preserving spatial information more effectively. Experiments demonstrate our method’s superior performance and robustness, achieving a PSNR of 37.91 and SSIM of 0.9108 on the internal test set while consistently outperforming other methods on previously unseen external clinical datasets. After adversarial training, the method attains optimal visual performance, with LPIPS reduced to 0.0915, and the visual effect improved by 36.4% compared to low-resolution images. Ablation studies further verify the contribution of the proposed method to enhancing super-resolution capability. Overall, results indicate that the proposed method can obtain high-quality chest X-rays images from simulated low-quality inputs. Full article

The substantially lower breakdown electric field of Si compared to GaN necessitates thick buffer layers in Si-based GaN power devices for medium-voltage applications, significantly increasing cost. Recently, sapphire substrates, offering high electrical insulation and excellent mechanical strength, have emerged as a promising alternative. In this work, we demonstrate a CMOS-compatible process for sapphire-based GaN MIS-HEMTs utilizing a thin buffer layer. The fabricated devices with a W_G of 20.4 mm and an L_GD of 24 μm achieve a high off-state breakdown voltage >1650 V and a maximum on-state current > 4.1 A, with tight statistical distributions of V_TH and R_ON across the wafer. Furthermore, statistical characterization results of dynamic R_ON and leakage current under electrical stress conditions at both room temperature and 150 °C, confirm operational viability at high temperatures. Finally, long-term reliability for 650 V operation is validated by high-temperature reverse bias (HTRB) accelerated aging tests. Full article

China’s livestock farming scale rose from 54.4% in 2020 to 73.2% in 2023, increasing annual manure production to 3.8 billion tons and greenhouse gas emissions to 4–6 billion t carbon dioxide equivalent (CO₂-eq). Manure management has thus become a key barrier to agricultural pollution control and carbon reduction goals. This study analyses regional differences in manure generation, showing that East and Central China—comprising less than 40% of the national land area—bear over 48% of total manure and about 50% of N and P loads, whereas Northeast and Northwest China have surplus cropland absorption capacity. This reveals a clear spatial mismatch between manure production and land carrying capacity. By reviewing major treatment technologies (aerobic composting, anaerobic digestion) and utilisation pathways (fertiliser use, energy recovery) and integrating life cycle assessment (LCA) with geographic information system (GIS)-based spatial evaluation, this study highlights the advantages of technology coupling strategies. For example, anaerobic digestion combined with composting can reduce net climate impacts by 21%, and regional circular models cut full-cycle carbon footprints by 34.44%. The results underscore the need for GIS-supported spatial LCA to match technologies with regional conditions, providing a scientific basis for advancing livestock manure management and China’s green agricultural transition. Full article

Adversarial examples are crucial tools for assessing the robustness of deep neural networks (DNNs) and revealing potential security vulnerabilities. Adversarial example generation methods based on Generative Adversarial Networks (GANs) have made significant progress in generating image adversarial examples, but still suffer from insufficient sparsity and transferability. To address these issues, this study proposes a novel semi-white-box untargeted adversarial example generation method named Wavelet-AdvGAN, with an explicit threat model defined as follows. Specifically, the attack is strictly untargeted without predefined target categories, aiming solely to mislead DNNs into classifying adversarial examples into any category other than the original label. It adopts a semi-white-box setting where attackers are denied access to the target model’s private information. Regarding the generator’s information dependence, the training phase only utilizes public resources (i.e., the target model’s public architecture and CIFAR-10 public training data), while the test phase generates adversarial examples through one-step feedforward of clean images without interacting with the target model. The method incorporates a Frequency Sub-band Difference (FSD) module and a Wavelet Transform Local Feature (WTLF) extraction module, evaluating the differences between original and adversarial examples from the frequency domain perspective. This approach constrains the magnitude of perturbations, reinforces feature regions, and further enhances the attack effectiveness, thereby improving the sparsity and transferability of adversarial examples. Experimental results demonstrate that the Wavelet-AdvGAN method achieves an average increase of 1.26% in attack success rates under two defense strategies—data augmentation and adversarial training. Additionally, the adversarial transferability improves by an average of 2.7%. Moreover, the proposed method exhibits a lower $l_0$ norm, indicating better perturbation sparsity. Consequently, it effectively evaluates the robustness of deep neural networks. Full article