This study investigates the prevalence of resistance and virulence genes in Klebsiella isolates from surface waters in Northern Portugal, within the broader context of freshwater quality challenges in Southern Europe. The aim of this research is to explain how Klebsiella dynamics, antibiotic resistance, and biofilm formation interact in surface waters. Antimicrobial susceptibility was examined using the Kirby–Bauer disk diffusion method against 11 antibiotics and screening for Extended-Spectrum Beta-Lactamase (ESBL) production using the double-disk synergy. PCR was employed to detect resistance and virulence genes, while biofilm production was assessed using the microplate method. Out of 77 water isolates, 33 Klebsiella (14 Klebsiella spp. and 19 K. pneumoniae strains) were isolated. ESBL production was observed in 36.8% of K. pneumoniae and 28.6% of Klebsiella spp. High resistance rates to bla_CTX-U were observed in both. The papC gene was prevalent, signifying potential environmental risks. Biofilm production averaged 81.3% for K. pneumoniae and 86.9% for Klebsiella spp. These findings underscore the intricate interplay between Klebsiella’s dynamics and freshwater quality, with ESBL’s prevalence raising concerns about waterborne dissemination and public health implications. This work supports the need for vigilance of Klebsiella in surface waters in Southern Europe. Full article

This study investigates the combined effect of microplastics and cadmium on the decomposition of litter, the structure of fungal communities, and the feeding behavior of invertebrates in an aquatic ecosystem. Through a series of microcosm experiments, we demonstrate that exposure to MPs and Cd significantly reduced the decomposition of leaf litter. Notably, the cumulative impact of combined MP and Cd exposure was found to be greater than their individual effects. During this process, the carbon–nitrogen ratio of the litter increased, while dehydrogenase activity and fungal biomass were inhibited. Additionally, the relative abundance of Ascomycota and Basidiomycota fungi decreased, weakening their role in the decomposition of leaf litter. Conversely, MPs and Cd reduced the relative content of leaf litter lignin, improving its quality as food, thereby leading to an increase in the feeding rate of invertebrates. This dual effect indicates that micropollutants suppress the decomposition of litter by regulating microbial metabolic activity and fungal community structure but promote invertebrate feeding. Our findings provide crucial insights into the adverse effects of MPs and Cd on the structure and diversity of aquatic fungal communities, which could have long-term impacts on the food webs and nutrient cycling progress of aquatic ecosystems. Full article

As critical equipment in nuclear power systems, the stability of circulating water pumps (CWP) directly impacts the efficiency of power plants. To investigate the impact mechanisms of the unsteady flow characteristics and flow-induced forces on the rotation system, numerical simulation methods were employed to calculate the internal flow of a volute mixed-flow CWP under different flow rates (0.8Q_d, 1.0Q_d, 1.2Q_d). The flow field results indicate that, under the part-load condition, the flow within the volute is chaotic with high energy losses, while under the over-load condition, there is a significant velocity gradient within the impeller, leading to relatively severe flow losses. Additionally, the rotor–stator interface is a major factor in flow-induced pulsations, and the asymmetric pressure distribution within the volute results in radial force imbalance. The finite element method (FEM) results indicate that the position of maximum stress on the pump shaft is closely related to the ratio of radial and axial force. Increasing the flow rate appropriately has been noted to be advantageous in reducing flow-induced forces and their amplitude, consequently diminishing the forces on the rotation system and improving the long-term operational stability of the CWP. Full article

Intellectual disability (ID), which affects around 2% to 3% of the population, accounts for 0.63% of the overall prevalence of neurodevelopmental disorders (NDD). ID is characterized by limitations in a person’s intellectual and adaptive functioning, and is caused by pathogenic variants in more than 1000 genes. Here, we report a rare missense variant (c.350T>C; p.(Leu117Ser)) in HACE1 segregating with NDD syndrome with clinical features including ID, epilepsy, spasticity, global developmental delay, and psychomotor impairment in two siblings of a consanguineous Pakistani kindred. HACE1 encodes a HECT domain and ankyrin repeat containing E3 ubiquitin protein ligase 1 (HACE1), which is involved in protein ubiquitination, localization, and cell division. HACE1 is also predicted to interact with several proteins that have been previously implicated in the ID phenotype in humans. The p.(Leu117Ser) variant replaces an evolutionarily conserved residue of HACE1 and is predicted to be deleterious by various in silico algorithms. Previously, eleven protein truncating variants of HACE1 have been reported in individuals with NDD. However, to our knowledge, p.(Leu117Ser) is the second missense variant in HACE1 found in an individual with NDD. Full article

This study investigates the interaction between static and fatigue strength and the rheological properties of acrylonitrile–butadiene–styrene (ABS) polymer reinforced with graphene nanoplatelets (GNPs) in both filament and 3D-printed forms. Specifically focusing on the effects of 1.0 wt.% GNPs, the study examines their influence on static/fatigue responses. The rheological behaviour of pure ABS polymer and ABS/GNPs nanocomposite samples, fabricated through material extrusion, is evaluated. The results indicated that the addition of 1.0 wt.% GNPs to the ABS matrix improved the elastic modulus of the nanocomposite filaments by up to about 34%, while reducing their ductility by approximately 60%. Observations revealed that the static and fatigue responses of the composite filament materials and 3D-printed parts were not solely attributed to differences in mechanical properties, but were also influenced by extrusion-related process parameters. The shark-skin effect, directly related to the material’s rheological properties, had a major impact on static strength and fatigue life. The proposed method involved adjusting the temperature of the heating zones of the extruder during filament production to enhance the static response of the filament and using a higher nozzle temperature (270 °C) to improve the fatigue life of the 3D-printed samples. The findings reveal that the proposed parameter optimisation led to filaments with minimised shark-skin effects, resulting in an improvement in ultimate tensile strength compared to pure ABS. Moreover, the 3D-printed samples produced with a higher nozzle temperature exhibited increased fatigue lives compared to those manufactured under identical conditions as pure ABS. Full article

The present work aimed to prepare novel bio-based composites by adding fillers coming from agro-wastes to an acrylate epoxidized soybean oil (AESO) resin, using liquid crystal display (LCD) 3D printing. Different photocurable formulations were prepared by varying the reactive diluents, iso-bornyl methacrylate (IBOMA) and tetrahydrofurfuryl acrylate (THFA). Then, two fillers derived from different industrial wastes, corn (GTF) and wine (WPL-CF) by-products, were added to the AESO-based formulations to develop polymer composites with improved properties. The printability by LCD of the photocurable formulations was widely studied. Bio-based objects with different geometries were realized, showing printing accuracy, layer adhesion, and accurate details. The thermo-mechanical and mechanical properties of the 3D-printed composites were tested by TGA, DMA, and tensile tests. The results revealed that the agro-wastes’ addition led to a remarkable increase in the elastic modulus, tensile strength, and glass transition temperature in the glassy state for the systems containing IBOMA and for flexible structures in the rubbery region for systems containing THFA. AESO-based polymers demonstrated tunable properties, varying from rigid to flexible, in the presence of different diluents and biofillers. This finding paves the way for the use of this kind of composite in applications, such as biomedical for the realization of prostheses. Full article

Developing high-performance and low-cost protein purification materials is of great importance to meet the demands for highly purified proteins in biotechnological industries. Herein, a facile strategy was developed to design and construct high-efficiency protein absorption and separation media by combining aerogels’ molding techniques and impregnation processes. Poly (ethylene-co-vinyl alcohol) (EVOH) nanofibrous aerogels (NFAs) were modified by grafting butane tetracarboxylic acid (BTCA) over them in situ. This modification was carried out using polyphosphoric acid as a catalyst. The resulting EVOH/BTCA NFAs exhibited favorable comprehensive properties. Benefiting from the highly interconnected porous structure, good underwater compressive properties, and abundant absorption ligands, the obtained EVOH/BTCA NFAs possessed a high static absorption capacity of 1082.13 mg/g to lysozyme and a short absorption equilibrium time of about 6 h. A high saturated dynamic absorption capacity for lysozyme (716.85 mg/g) was also realized solely by gravity. Furthermore, EVOH/BTCA NFAs displayed excellent reusability, good acid and alkaline resistance, and unique absorption selectivity performance. The successful synthesis of such aerogels can provide a potential candidate for next-generation protein absorbents for bio-separation and purification engineering. Full article

A proton exchange membrane fuel cell (PEMFC) is an efficient and environmentally friendly power production technology that uses hydrogen energy. The cathodic oxygen reduction electrode is a critical component in the development of PEMFC. Most techniques deposit catalyst nanoparticles in areas that are inaccessible for catalytic processes, reducing platinum utilization. The substrate used in this study was carbon paper (CP) with a self-supporting structure. First, electrochemical acidification technology was employed to modify the CP’s surface, followed by nanoparticle manufacturing and fixation on the CP in a single step by electrodeposition. The Pt/C_0.5V_2.24CP catalyst electrode demonstrated high-quality activity in the oxygen reduction reaction (ORR), with a homogeneous particle dispersion and particle size of around 50 nm. The mass activity and electrochemical active surface area (ECSA) of the Pt/C_0.5V_2.24CP catalyst electrode were 1.74 and 3.98 times higher than those of the Pt/C/CP-1 electrodes made with commercial catalysts, respectively. After 5000 cycles of accelerated durability testing (ADT), the mass activity and ECSA were 1.28 times and 6.16 times more than Pt/C/CP-1. This paper successfully proved the viability of electrodepositing Pt nanoparticles on CP following acidification, and that the electrochemical acidification methods have a positive influence on improving electrode ORR activity. Full article

Freeze–thaw (FT) erosion intensity may exhibit a future increasing trend with climate warming, humidification, and permafrost degradation in the Qinghai–Tibet Plateau (QTP). The present study provides a reference for the prevention and control of FT erosion in the QTP, as well as for the protection and restoration of the regional ecological environment. FT erosion is the third major type of soil erosion after water and wind erosion. Although FT erosion is one of the major soil erosion types in cold regions, it has been studied relatively little in the past because of the complexity of several influencing factors and the involvement of shallow surface layers at certain depths. The QTP is an important ecological barrier area in China. However, this area is characterized by harsh climatic and fragile environmental conditions, as well as by frequent FT erosion events, making it necessary to conduct research on FT erosion. In this paper, a total of 11 meteorological, vegetation, topographic, geomorphological, and geological factors were selected and assigned analytic hierarchy process (AHP)-based weights to evaluate the FT erosion intensity in the QTP using a comprehensive evaluation index method. In addition, the single effects of the selected influencing factors on the FT erosion intensity were further evaluated in this study. According to the obtained results, the total FT erosion area covered 1.61 × 10⁶ km², accounting for 61.33% of the total area of the QTP. The moderate and strong FT erosion intensity classes covered 6.19 × 10⁵ km², accounting for 38.37% of the total FT erosion area in the QTP. The results revealed substantial variations in the spatial distribution of the FT erosion intensity in the QTP. Indeed, the moderate and strong erosion areas were mainly located in the high mountain areas and the hilly part of the Hoh Xil frozen soil region. Full article

Vegetation coverage is a crucial parameter in agriculture, as it offers essential insight into crop growth and health conditions. The spatial resolution of spaceborne sensors is limited, hindering the precise measurement of vegetation coverage. Consequently, fine-resolution ground observation data are indispensable for establishing correlations between remotely sensed reflectance and plant coverage. We estimated rice plant coverage per pixel using time-series Sentinel-2 Multispectral Instrument (MSI) data, enabling the monitoring of rice growth conditions over a wide area. Coverage was calculated using unmanned aerial vehicle (UAV) data with a spatial resolution of 3 cm with the spectral unmixing method. Coverage maps were generated every 2–3 weeks throughout the rice-growing season. Subsequently, crop growth was estimated at 10 m resolution through multiple linear regression utilizing Sentinel-2 MSI reflectance data and coverage maps. In this process, a geometric registration of MSI and UAV data was conducted to improve their spatial agreement. The coefficients of determination (R²) of the multiple linear regression models were 0.92 and 0.94 for the Level-1C and Level-2A products of Sentinel-2 MSI, respectively. The root mean square errors of estimated rice plant coverage were 10.77% and 9.34%, respectively. This study highlights the promise of satellite time-series models for accurate estimation of rice plant coverage. Full article

Wildland fires cause economic and ecological damage with devastating consequences, including loss of life. To reduce these risks, numerous fire detection and recognition systems using deep learning techniques have been developed. However, the limited availability of annotated datasets has decelerated the development of reliable deep learning techniques for detecting and monitoring fires. For such, a novel dataset, namely, SWIFT, is presented in this paper for detecting and recognizing wildland smoke and fires. SWIFT includes a large number of synthetic images and videos of smoke and wildfire with their corresponding annotations, as well as environmental data, including temperature, humidity, wind direction, and speed. It represents various wildland fire scenarios collected from multiple viewpoints, covering forest interior views, views near active fires, ground views, and aerial views. In addition, three deep learning models, namely, BoucaNet, DC-Fire, and CT-Fire, are adopted to recognize forest fires and address their related challenges. These models are trained using the SWIFT dataset and tested using real fire images. BoucaNet performed well in recognizing wildland fires and overcoming challenging limitations, including the complexity of the background, the variation in smoke and wildfire features, and the detection of small wildland fire areas. This shows the potential of sim-to-real deep learning in wildland fires. Full article

Object detection in UAV aerial images faces domain-adaptive challenges, such as changes in shooting height, viewing angle, and weather. These changes constitute a large number of fine-grained domains that place greater demands on the network’s generalizability. To tackle these challenges, we initially decompose image features into domain-invariant and domain-specific features using practical imaging condition parameters. The composite feature can improve domain generalization and single-domain accuracy compared to the conventional fine-grained domain-detection method. Then, to solve the problem of the overfitting of high-frequency imaging condition parameters, we mixed images from different imaging conditions in a balanced sampling manner as input for the training of the detection network. The data-augmentation method improves the robustness of training and reduces the overfitting of high-frequency imaging parameters. The proposed algorithm is compared with state-of-the-art fine-grained domain detectors on the UAVDT and VisDrone datasets. The results show that it achieves an average detection precision improvement of 5.7 and 2.4, respectively. The airborne experiments validate that the algorithm achieves a 20 Hz processing performance for 720P images on an onboard computer with Nvidia Jetson Xavier NX. Full article

Soil moisture plays a crucial role in determining the yield of winter wheat. The Huang-Huai-Hai (HHH) Plain is the main growing area of winter wheat in China, and frequent occurrence of drought seriously restricts regional agricultural development. Hence, a daily-scale Non-stationary Standardized Precipitation Evapotranspiration Index (NSPEI), based on winter wheat crop coefficient (Kc), was developed in the present study to evaluate the impact of drought characteristics on winter wheat in different growth stages. Results showed that the water demand for winter wheat decreased with the increase in latitude, and the water shortage was affected by effective precipitation, showing a decreasing trend from the middle to both sides in the HHH Plain. Water demand and water shortage showed an increasing trend at the jointing stage and heading stage, while other growth stages showed a decreasing trend. The spatial distributions of drought duration and intensity were consistent, which were higher in the northern region than in the southern region. Moreover, the water shortage and drought intensity at the jointing stage and heading stage showed an increasing trend. The drought had the greatest impact on winter wheat yield at the tillering stage, jointing stage, and heading stage, and the proportions of drought risk vulnerability in these three stages accounted for 0.25, 0.21, and 0.19, respectively. The high-value areas of winter wheat loss due to drought were mainly distributed in the northeast and south-central regions. Full article

Interactions between carbon (C), nitrogen (N), and phosphorus (P), the vital indicators of ecological restoration, play an important role in signaling the health of ecosystems. Rapidly and accurately mapping foliar C, N, and P is essential for interpreting community structure, nutrient limitation, and primary production during ecosystem recovery. However, research on how to rapidly map C, N, and P in restored areas with mixed plant communities is limited. This study employed laser imaging, detection, and ranging (LiDAR) and hyperspectral data to extract spectral, textural, and height features of vegetation as well as vegetation indices and structural parameters. Causal band, multiple linear regression, and random forest models were developed and tested in a restored area in northern China. Important parameters were identified including (1), for C, red-edge bands, canopy height, and vegetation structure; for N, textural features, height percentile of 40–95%, and vegetation structure; for P, spectral features, height percentile of 80%, and 1 m foliage height diversity. (2) R² was used to compare the accuracy of the three models as follows: R² values for C were 0.07, 0.42, and 0.56, for N they were 0.20, 0.48, and 0.53, and for P they were 0.32, 0.39, and 0.44; the random forest model demonstrated the highest accuracy. (3) The accuracy of the concentration estimates could be ranked as C > N > P. (4) The inclusion of LiDAR features significantly improved the accuracy of the C concentration estimation, with increases of 22.20% and 47.30% in the multiple linear regression and random forest models, respectively, although the inclusion of LiDAR features did not notably enhance the accuracy of the N and P concentration estimates. Therefore, LiDAR and hyperspectral data can be used to effectively map C, N, and P concentrations in a mixed plant community in a restored area, revealing their heterogeneity in terms of species and spatial distribution. Future efforts should involve the use of hyperspectral data with additional bands and a more detailed classification of plant communities. The application of this information will be useful for analyzing C, N, and P limitations, and for planning for the maintenance of restored plant communities. Full article

Electroplated diamond wire sawing is widely used as a processing method to cut hard and brittle difficult-to-machine materials. Currently, obtaining the sawing capability of diamond wire saw through the wire bow is still difficult. In this paper, a method for calculating the sawing capability of diamond wire saw in real-time based on the wire bow is proposed. The influence of the renewed length per round trip, crystal orientation of sapphire, wire speed, and feed rate on the wire sawing capability has been revealed via slicing experiments. The results indicate that renewing the diamond wire saw, and reducing the wire speed and feed rate can delay the reduction in sawing capability. Furthermore, controlling the value of renewed length per round trip can make the diamond wire saw enter a stable cutting state, in which the capability of the wire saw no longer decreases. The sawing capability of diamond wire saw cutting in the A-plane of the sapphire is smaller than that of the C-plane, and a suitable feed rate or wire speed within the range of sawing parameters studied in this study can avoid a rapid decrease in the sawing capability of the wire saw during the cutting process. The knowledge obtained in this study provides a theoretical basis for monitoring the performance of the wire saw, and guidance for the wire cutting process in semiconductor manufacturing. In the future, it may even be possible to provide real-time performance parameters of diamond wire saw for the digital twin model of wire sawing. Full article

This paper addresses the following problem: given m potential observations to determine n parameters, $m>n$, what is the best choice of n observations. The problem can be formulated as finding the $n\times n$ submatrix of the complete $m\times n$ observation matrix that has maximum determinant. An algorithm by Gu and Eisenstat for a determining a strongly rank-revealing QR factorisation of a matrix can be adapted to address this latter formulation. The algorithm starts with an initial selection of n rows of the observation matrix and then performs a sequence of row interchanges, with the determinant of the current submatrix strictly increasing at each step until no further improvement can be made. The algorithm implements rank-one updating strategies, which leads to a compact and efficient algorithm. The algorithm does not necessarily determine the global optimum but provides a practical approach to designing an effective measurement strategy. In this paper, we describe how the Gu–Eisenstat algorithm can be adapted to address the problem of optimal experimental design and used with the QR algorithm with column pivoting to provide effective designs. We also describe implementations of sequential algorithms to add further measurements that optimise the information gain at each step. We illustrate performance on several metrology examples. Full article

Root respiration is a critical physiological trait that significantly influences root system activity. Recent studies have associated root respiration with the economic functioning of roots; however, research on root respiration in tropical plants remains limited. This study examined the fine root respiration and the relationship between root respiration and root chemical and morphological traits in 16 tropical plant species, including both evergreen and deciduous species. Findings revealed that deciduous species exhibited higher root respiration compared to evergreen species. Root respiration positively correlated with root nitrogen concentration and specific root length and correlated negatively with root diameter and root tissue density across all species. The root respiration patterns in evergreen species aligned with those seen in all tree species, while deciduous species showed a distinct negative correlation with root tissue density and no significant correlations with other root traits. Principal component analysis revealed that the patterns of root variation in both evergreen and deciduous trees were multidimensional, with deciduous trees exhibiting acquisitive traits and evergreen trees displaying conservative traits. Random forest and multiple regression analysis showed that specific root length exerted the most significant influence on root respiration in both evergreen and deciduous trees. These findings are ecologically significant, enhancing our understanding of root respiration in tropical plants and its impact on ecosystem functions. They contribute valuable insights and support the conservation and management of tropical vegetation. Full article

Electric ship propulsion is considered one of the most promising alternatives to conventional combustion systems. Its goal is to reduce the carbon footprint and increase a ship’s maneuverability, operational safety, and reliability. The high requirements for ship propulsion make permanent magnet synchronous motors (PMSMs) an attractive solution due to their characteristics. This paper discusses the control problem of a PMSM based on the input–output feedback linearization method combined with the optimal and adaptive control techniques. The method presented here integrates the parameter tuning process with the optimal design of the baseline controller. Since the load disturbances are treated as an additional unknown parameter, there is no need to introduce an integral action to deal with the resulting steady-state error. An important feature of the designed controller is the so-called energetic optimization of the system; i.e., in addition to the aforementioned adaptive and optimal controller, it has a feature of ensuring zero reactive power consumed by the system. The performed simulations of the machine speed stabilization process confirmed the high efficiency of the proposed controller despite the assumed uncertainty of the system parameters and environmental (load) disturbances. Besides achieving high-quality control, an essential feature of this approach is the elimination of the tuning problem. Full article

Increasing clean energy access for the rural population of developing countries is a priority to meet the United Nation’s Sustainable Development Goals-Zero hunger and affordable modern/clean energy for all. Similarly, to meet this goal, Ethiopia moved towards the development of renewable energy. However, there is a limited knowledge on the biomass energy potential for biogas technology adoption at the local/district level. Thus, this study aimed at assessing the biomass energy potential for biogas technology adoption and its determinant factors among rural households in Limmu Kossa district, Ethiopia. Data was collected from 411 households from 13–24 June 2021. The quantitative data was analyzed using Statistical software Package for Social Science (SPSS) version 23 and Microsoft Word-Excel. The qualitative data was analyzed using content analysis. The study showed that over 96% of households rely on the traditional use of biomass energy for cooking. Nevertheless, on average, about 1 m³ of biogas energy can be potentially available from livestock dung and human excreta per household per day. However, the huge potential of biomass energy did not contribute to improved energy technologies such as biogas. The adoption of biogas is hampered by the non-functionality of the installed biogas, a lack of awareness, the availability of firewood, and the socio-economic characteristics of the households. Thus, improving the awareness of the community, arranging financial access, and training biogas technicians, especially from the local community, would increase the adoption of the technology. However, meeting the digester water demand with the water collected from the walking distances of 15–20 min can be challenging. Community-based biogas digesters or biogas involving income generation with a water supply around the digester would be a better and more sustainable option for biogas energy adoption and use. Full article

The computational demand of neutron Monte Carlo transport simulations can increase rapidly with the spatial and energy resolution of tallied physical quantities. Convolutional neural networks have been used to increase the resolution of Monte Carlo simulations of light water reactor assemblies while preserving accuracy with negligible additional computational cost. Here, we show that a convolutional neural network can also be used to upsample tally results from Monte Carlo simulations of sodium-cooled fast reactor assemblies, thereby extending the applicability beyond thermal systems. The convolutional neural network model is trained using neutron flux tallies from 300 procedurally generated nuclear reactor assemblies simulated using OpenMC. Validation and test datasets included 16 simulations of procedurally generated assemblies, and a realistic simulation of a European sodium-cooled fast reactor assembly was included in the test dataset. We show the residuals between the high-resolution flux tallies predicted by the neural network and high-resolution Monte Carlo tallies on relative and absolute bases. The network can upsample tallies from simulations of fast reactor assemblies with diverse and heterogeneous materials and geometries by a factor of two in each spatial and energy dimension. The network’s predictions are within the statistical uncertainty of the Monte Carlo tallies in almost all cases. This includes test assemblies for which burnup values and geometric parameters were well outside the ranges of those in assemblies used to train the network. Full article

Icing on the blades of wind turbines during winter seasons causes a reduction in power and revenue losses. The prediction of icing before it occurs has the potential to enable mitigating actions to reduce ice accumulation. This paper presents a framework for the prediction of icing on wind turbines based on Supervisory Control and Data Acquisition (SCADA) data without requiring the installation of any additional icing sensors on the turbines. A Temporal Convolutional Network is considered as the model to predict icing from the SCADA data time series. All aspects of the icing prediction framework are described, including the necessary data preprocessing, the labeling of SCADA data for icing conditions, the selection of informative icing features or variables in SCADA data, and the design of a Temporal Convolutional Network as the prediction model. Two performance metrics to evaluate the prediction outcome are presented. Using SCADA data from an actual wind turbine, the model achieves an average prediction accuracy of $77.6\%$ for future times of up to 48 h. Full article

Studying the hydro-liquefaction kinetics of lignite contributes to optimizing the mild liquefaction process for lignite. In this paper, the direct liquefaction performance of Shengli lignite (SL) was investigated using a H₂/THN system with 4 MPa of initial pressure, and reaction kinetic models were established for the heating-up stage and the isothermal stage. The result showed that the liquefaction performance of the SL was excellent, with a conversion of 62.18% and an oil and gas (O + G) yield of 29.88% at 698.15 K. After one hour of reaction, the conversion and O + G yield were 94.61% and 76.78%, respectively. During the heating-up stage, the easily reactive part of the SL was 50.07%, and it was converted directly into oil, gas, asphaltene (AS), and preasphaltene (PA) simultaneously. There was no significant secondary hydrogenation conversion of the AS and PA products. During the isothermal stage, the hard-to-react part was predominantly converted into AS and PA, while the remaining easily reactive part continue to react completely. The conversion of AS and PA into oil and gas was a rate-controlling step during this stage. The amount of unreacted coal estimated using the model calculated in the isothermal stage was 2.98%, which was significantly consistent with the experimental value of 2.81%. Full article