The widespread use of antibiotics is rapidly increasing the prevalence of antibiotic-resistant bacteria (ARB) and their associated antibiotic-resistant genes (ARGs), posing a significant environmental challenge and high health risk to the global population. Wastewater treatment plant (WWTP) effluents containing untreated antibiotics are key hotspots for the generation and spread of ARGs and ARBs, with serious consequences for human and animal health, as well as threats to ecological security. This paper reviews the occurrence and risk of ARGs in wastewater and enumerates the main pathways and potential impacts of ARGs transmission. Statistics on the reduction techniques of ARGs by bibliometric means are also presented to provide important insights for effective control of ARGs. In addition, treatment processes for removing ARGs from wastewater are critically discussed. Finally, the topical issues of ARGs prompted by composite pollution are summarized to provide future research directions and solutions for composite pollution.

In recent years, the advantages and potential of microalgae in deep sewage purification and renewable energy production have become increasingly prominent. However, the traditional microalgae cultivation and harvesting technology has some limitations in practical application because of its high energy consumption, high cost and potential biological pollution. Hydrogel technology provides a new microalgae harvesting method, which has the characteristics of high efficiency, fast and controllable, and provides a new idea and method for the efficient harvesting and utilization of microalgae. In this paper, the action mode and mechanism of superabsorptive gel technology in microalgae harvesting were discussed, and the optimization methods and research progress were made to solve the problems in the optimization of hydrogel itself, interaction with microalgae and environmental friendliness. Finally, the development prospect of hydrogel technology in microalgae harvesting was put forward, which provided some theoretical and methodological support for the development and application of hydrogel technology in microalgae harvesting.

Membrane fouling poses a limitation to the application of forward osmosis (FO), and reverse solute flux (RSF) from the draw solutes further reduces the permeability of the membrane. Therefore, this study investigated the dynamic relationship between reverse solute flux and organic membrane fouling using typical inorganic draw solutions (DSs)(NaCl,NaHCO₃,NaH₂PO₄,NH₄Cl,CaCl₂). On one hand, the reverse solute flux directly interacted with the membrane fouling layer, affecting its configuration. For example, the reverse flux of Ca²⁺ from CaCl₂ draw solute readily crosslinked with sodium alginate (SA), forming a three-dimensional network structure that exacerbated membrane fouling. On the other hand, the formation of membrane fouling reduced the reverse flux of Ca²⁺ from CaCl₂ draw solute (5.8±1.6 mmol·m^-2·h^-1) while increasing the reverse diffusion of NH⁺₄ from NH₄Cl draw solution (129.2±12.8 mmol·m^-2·h^-1). Consequently, the water recovery using NH₄Cl as the draw solute (151.4±10.6 g) was lower than that using CaCl₂ (246.4±124.7 g). Furthermore, reverse solute flux altered the properties of the feed solute, leading to the dominance of different filtration mechanisms in the formation of membrane fouling, which subsequently impacts the degree of fouling.

To achieve the fractional precipitation and persistent fixation of multiple heavy metals during the treatment of acid mine drainage (AMD) using sulfate-reducing bacteria (SRB), a five-compartment anaerobic baffled reactor (ABR) was used to treat the simulated AMD. The effects of AMD on system operation efficiency, physical and chemical properties, biological activity and microbial community structure of granular sludge were observed. The characteristics of heavy metal fractional precipitation and precipitated biomore formation in AMD were investigated. The results indicated that the sulfate reduction system of ABR was capable of precipitating cadmium (Cd), zinc (Zn), and iron (Fe) from AMD in a graded manner, with Cd and Zn primarily removed in the first compartment, and Fe predominantly eliminated in the second and third compartments, all with removal rates exceeding 99%. Chemical speciation analysis of granular sludge and SEM observations revealed that the removed heavy metals mainly deposit in sulfide-bound forms in the sludge, subsequently transforming into lattice states and forming irregular particles (0.3~0.7 μm) on the sludge surface. XRD analysis showed that the main phases in the first compartment were sphalerite, wurtzite, and greenockite, while magnetite and pyrite were predominant in compartments two to five. Analysis of microbial community structure demonstrated the crucial roles of Lactobacillus and Desulfovibrio in heavy metal precipitation and mineral formation processes within the reactor. This study provides theoretical basis for the resourceful treatment and mineralization of heavy metals in AMD.

Algae blooms in lake and reservoir caused by eutrophication has a potential impact on the drinking water treatment process. In order to accurately grasp the latest research and development trend in this field, CiteSpace bibliometrics software was used to analyze the literature on algal coagulation in drinking water from 1999 to 2023 in the core database of the China National Knowledge Infrastructure (CNKI) and Web of Science(WOS) in detail. The results showed that: ① Within the statistical interval, the total number and average number of published papers on algae coagulation in drinking water in the CNKI database were higher than those in the WOS database. Additionally, the number of published papers in both databases was observed to be stable and gradually increasing over time. ② The publication volume of high-yield authors in the CNKI database was significantly lower compared with high-yield authors in the WOS database. Additionally, the CNKI database have not established a high-yield core author group. ③ Publishing institutions in the WOS database primarily consist of universities and research institutes, with limited connections to local enterprises. On the other hand, the cooperation relationships among CNKI database publishing institutions showed clear regional affiliations, but their ability for cross-regional collaboration was weak, which may hinder the advancement of future research. ④ The research content of the two databases mainly includes drinking water, drinking water treatment, eutrophication, pre-oxidation, and disinfection by-products, with a different focus. The WOS database primarily concentrates on molecular-level research of cell metabolism, covering topics such as cell integrity, dissolved organic carbon, and moderate pre-oxidation. While the CNKI database focuses on physical and chemical algae removal technologies like membrane fouling, enhanced coagulation, chlorine dioxide, coagulation, and pre-oxidation. Combining the findings from both databases can enhance the theoretical framework in the field of drinking water coagulation and algae removal, thereby enriching the depth and breadth of the research. Although there was a wealth of practical experience and theoretical basis in the field of algae coagulation in drinking water, there are still shortcomings in the depth of research and cooperation. And integration, environmental protection and intelligence are the trend of future development.

With the rapid development of the new technologies about Internet of Things (IoT) and automatic driving, an advanced research target has been injected into the optimization of eco-driving and energy management of hybrid vehicles based on the connected driving environment. Aiming at the fuel cell hybrid vehicles driving on multi-signalized urban roads, this paper proposes a hierarchical multi-objective optimization method combined deep deterministic policy gradient and dynamic planning (DDPG-DP) for speed planning and energy management. The DDPG algorithm is used in the upper layer of energy-saving speed planning, while the multi-objective reward value function and the priority experience replay mechanism are designed to carry out the multi-objective speed planning for energy saving, driving comfort, and passage efficiency on the basis of improving the algorithm’s speed and stability, and the dynamic planning algorithm is used in the lower layer of energy management to achieve the optimal energy-saving of the hybrid system with the goal of minimizing the hydrogen consumption. In scenarios 1 and 2, the results show that the DDPG-DP algorithm improves the traveling efficiency by 15.25% and 20.18% than the IDM-DP algorithm, and reduces the hydrogen fuel consumption by 25.66% and 17.86%, respectively. Meanwhile, there is a gap of only about 5 s in the passing time of the DDPG-DP algorithm compared with the global optimal algorithm (DP-DP) in Scenarios 1 and 2, and the hydrogen fuel consumption is lower than the optimal algorithm. Meanwhile, there is only a difference of about 5 s between the DDPG-DP algorithm and the global optimal algorithm (DP-DP) in traveling time, and there is only a difference of 2.84% and 4.7% in the hydrogen fuel consumption compared with the DP-DP algorithm. In field of driving smoothness, the DDPG-DP algorithm has less speed fluctuation than the other two algorithms (IDM-DP and DP-DP) and doesn’t have large acceleration/deceleration. It will provide greater energy-saving potential for daily driving of hybrid vehicles and support the further research for multi-objective eco-driving optimization of connected fuel cell hybrid vehicles.

In order to improve the efficiency of single intersection signal control, aiming at the problems of inaccurate traffic state description and low sampling efficiency of experience pool in Deep reinforcement learning algorithm, an improved DQN signal control algorithm is proposed. Considering the vehicle length, the distance between cell and stop line and the number of detectors, the state space with non-uniform division of cell length is constructed to accurately characterize the traffic state. The dynamic greedy strategy is proposed to optimize the iterative process to improve the learning efficiency of the algorithm. Based on SUMO modeling, the experimental results show that the improved DQN algorithm can obtain better signal control effect. Compared with the traditional DQN algorithm, the cumulative delay and average queue length of vehicles in off-peak hours are reduced by 83.63% and 83.48% respectively, and the two indexes in peak hours are reduced by 94.88% and 94.87% respectively.

In order to improve the transient assessment model’s ability to extract electrical dynamic features and its generalization ability when facing changes in system topology, this paper proposes an online assessment model with a temporal and spatial dual-channel parallel structure. Firstly, the dynamic information of the transient time series data of the model is captured by the Gated Recurrent Unit (GRU). The nonlinear fitting relationship between power system topology and transient stability state is constructed based on the Graph Attention Network (GAT). And the spatial and temporal features of the two channels are fused through the attention mechanism to obtain more reliable evaluation results. Then, when the topology of the original system changes, the model is combined with transfer learning technology to update the network parameters of the model and realize the online update of the model. Finally, through the simulation and verification of IEEE 39-node system and IEEE 300-node system, the model evaluation accuracy reaches 98.62% and 98.51%, respectively. The results show that the proposed method can realize efficient transient stability evaluation and has strong robustness.

Accurate and efficient heat load forecasting is very important to ensure the stable operation of thermal system and rational planning of thermal resources. In order to improve the accuracy of heat load forecasting, a TimesNet short-term heat load forecasting model based on successive variational mode decomposition (SVMD) and an improved beluga optimization algorithm (IBWO) is proposed. Firstly, SVMD is used to decompose the original heat load data, and several stable and regular modal components are obtained after removing noise. Secondly, according to the characteristics of each modal component, the appropriate feature is selected as the input. Then, three strategies are introduced to improve the Beluga optimization algorithm, and the IBWO-TimesNet prediction model is established. Finally, the prediction performance of the model is evaluated in detail by an example. The results show that MAE, RMSE and R² of SVMD-IBWO-TimesNet model are 0.647, 1.190 and 99.1%, respectively. Compared with other mainstream prediction models, this model has higher prediction accuracy. At the same time, the SVMD-IBWO-TimesNet model can still effectively predict the heat load and has strong generalization ability when the training samples are reduced. Therefore, the validity of the proposed model is verified, and the reference is provided for the precise regulation and control of the heating load of the thermal system.

There are many types of sensors, and optical fiber sensors stand out from many sensors due to their miniaturization and excellent performance. Therefore, a D-type double-groove single-channel photonic crystal fiber high refractive index sensor is designed and studied by using the finite element method to calculate the surface plasmon resonance theory as the support point. The results show that the sensor has a good sensing effect under the optimal structural parameters, with a maximum sensitivity of 16 200 nm/RIU and a maximum figure of merit of 255.92 /RIU in a wide detection range of 1.32-1.41. It can be seen that the sensor designed in this paper meets the needs of modern development and is expected to achieve application value in medical detection, bio-sensing and other aspects.

This paper proposes a Dual-branch Feature Fusion (DFF) method to enhance the robustness of head posture in facial expression recognition, addressing the issue of low recognition performance caused by head posture interference. Firstly, in the expression branch, high-dimensional rough semantic features are extracted using the ResNet18 backbone network. Then, the Spatial Feature Enhancement (SFE) module is employed to facilitate information interaction among high-level semantic features at the spatial level, thereby improving the expression feature extraction capability. Meanwhile, in the head pose branch, head pose features are extracted using the Head Pose Feature Extraction (HPFE),which is pre-trained on the head pose dataset 300W_LP with fixed weights. Finally, the expression features in the expression branch and the head pose features in the head pose branch are fused element-by-element to attain complementary information and establish a pose-robust emotional representation. The proposed method is evaluated on two widely-used datasets: RAF-DB dataset and FERPlus dataset. On the Pose Variation test set, the recognition accuracy of the two head poses (Pose>30° and Pose>45°) is 89.98% and 89.96% on the RAF-DB dataset, and 89.20% and 87.94% on FERPlus dataset, respectively. The experimental results show that the method proposed in this paper improves the accuracy of facial expression recognition in images under head posture interference, which is of great significance for research on facial expression recognition in natural environments.

Image segmentation technology is an important branch in the field of medical image research, and this technology helps doctors diagnose and treat cancer. In order to further improve the accuracy of image segmentation, a multi-scale axial attention model MAU-Net (multi-scale axial attention U-Net) is proposed in this paper for organ segmentation. Firstly, the model uses a deep residual network to extract image features in the encoder stage to improve the model’s generalization ability. Secondly, a pixel fusion module (PFM) is added to the decoder to enhance the ability to extract feature position information by re-encoding and linearly enhancing the feature information of the encoder. Finally, a multi-branch axial attention module (MAM) is added between the decoders to capture contextual information and enhance the ability to identify key feature information. Experimental results on multiple multi-organ image data sets such as Synapse, ACDC, and SegTHOR show that MAU-Net can achieve better results in both organ recognition and edge prediction.

The defect handling work of power equipment mainly depends on the knowledge reserve and experience of the handling personnel. However, due to the lack of assistance of a perfect historical defect knowledge base, people with relatively insufficient experience and knowledge cannot effectively learn from the experience of the predecessors, and it is inevitable that there will be mistakes in decision-making. This situation, which in turn affects the elimination of power equipment. A question answering system implementation method based on power equipment defect knowledge graph is proposed to address the above issues. Firstly, the requirements of the equipment defect question answering system are analyzed, and the system architecture is designed. Then, the question entity recognition model, question intent recognition model, and query sentence generation are established respectively. The model parses the question sentence semantically. Finally, a power equipment defect question answering system is based on the power equipment defect knowledge graph. The results of question entity recognition and question intention recognition show that the indicators of the improved algorithm have been greatly improved. In the aspect of question entity recognition, the precision rate, recall rate and F₁ reach 92.34%, 97.65% and 95.36%, respectively. In the aspect of question intention recognition, the accuracy rate, precision rate, recall rate and F₁ reach 82.17%, 85.38%, 82.36% and 80.56%, respectively. The function test of the question answering system also shows that the system can be well applied to the defect elimination process of power equipment. And the system can quickly improve the accuracy of defect, repair strategy formulation and the efficiency of defect elimination of defective equipment, and promote the safe and stable operation of the power system.

The first-order logic automated theorem proving is the core foundation of artificial intelligence. Heuristic strategies have attracted much attention in improving first-order logic automated theorem provers, and selecting effective clauses based on clause properties to participate in deduction is an important research topic. Based on the standard contradiction separation rule, the literals in clauses are divided into two parts, which are constructing standard contradictory text and constructing contradictory separation text. By analyzing the relationship and differences between variables, functions, and constants, a stability based on clause measurement method is proposed, and its core idea is to evaluate the stability of clause participating in deduction through the components of contained term. A multi-clause dynamic deduction algorithm (CFA, clause stability algorithm) is proposed based on clause stability, aiming to search for optimal paths during the current deduction process. This newly-built CFA algorithm is applied to the internationally well-known prover Prover9 and top prover Eprover2.6, and two new provers CFA_P and CFA_E are formed, respectively. The international competition problems of CASC-26 (FOF division) is tested on CFA_P and CFA_E. CFA_P can solve 119 theorems more than the original Prover9, and CFA_E outperforms Eprover2.6, solving 11 theorems more than the original Eprover2.6, and the average proof time of theorems of CFA_P and CFA_E is reduced by 14.76 s and 2.54 s, respectively with the same total number of solved theorems. Focusing on the 94 theorems unsolved by Eprover2.6, CFA_E solves 27 theorems which accounts for 28.7% in the total. The experimental results demonstrate the effectiveness of the CFA algorithm, which has good performance in optimizing deduction paths and can enhance the performance of first-order logic automated theorem prover.

A class of parametric boundary value problems with two-term fractional derivatives and non-zero boundary values is investigated in this paper. Firstly, Green’s function is constructed by Laplace transform, and the boundary value problem is transformed into the equivalent second kind of Fredholm integral equation. Secondly, by using the properties of Green’s function, Guo-Krasnoselskii fixed point theorem and Leggett-Williams fixed point theorem, sufficient conditions for the existence, nonexistence and multiplicity of positive solutions for boundary value problems of fractional differential equations are obtained. Thirdly, the existence of positive solutions for boundary value problems of usual fractional differential equations is extended to boundary value problems with two fractional derivatives. Finally, an example is given to illustrate the feasibility of the obtained results.

The forms and properties of Bayes estimates of the Burr distribution parameters are investigated using the Bayes parameter estimation method under the weighted p, q symmetric loss function. The general form of the Bayes estimation of the parameters and the exact form under the uninformative prior distribution are obtained, and the obtained estimates are proved to be admissible and have the property of minimum maximum, and the multilayer Bayes estimation of the parameters is further obtained. Numerial Simulation with R software combined with MCMC algorithm on the obtained estimates shows that the Bayes estimate under the conjugate prior distribution is more accurate than the Bayes estimate under the uninformative prior distribution.

The iterative algorithm for fixed points is a hot topic in nonlinear functional analysis research. The viscosity algorithm for constructing a new generalized three-step implicit double midpoint rule for non-expanding mappings with fixed points in a uniformly smooth Banach spaces is proposed. Under appropriate conditions, the dual mapping definition and Banach limit definition and techniques are used to prove that the iterative sequence generated by this algorithm strongly converges to the common element of the common fixed point set of three non-expanding mappings, and the inference in special cases is given. The results improve and generalize the relevant results in recent literature.

Predicting the range of suitable areas for plants under different climatic conditions is beneficial for understanding the geographical distribution characteristics of this plants and its response strategies to climate change. The potential distribution area of Solanum torvum in China under present and three future Representative Concentration Pathways scenarios (RCP2.6, RCP4.5 and RCP8.5) in 2050s and 2070s were simulated by the MaxEnt model (3.4.4), and then using ArcGIS (10.8) for visualization and analysis of its potential spatial patterns and environmental influencing factors in China. The AUC value for the reconstructed MaxEnt was 0.962, indicating excellent prediction accuracy of the model. Temperature annual range (bio7), mean temperature of the coldest quarter (bio11), annual precipitation (bio12), and precipitation of the driest quarter (bio17) were the dominant environmental factors that affected the distribution of S. torvum. The potentially suitable areas for the current distribution of S. torvum cover 79.14×10⁴ km², with a high suitability area 17.86×10⁴ km². The high suitability areas were mainly located in Guangxi and Guangdong provinces. There were significant differences in the suitable area of S. torvum between different periods, and its suitable area generally showed an expanding trend in future climate scenario. However, compared with the potential suitable area in the current climate, its suitable areas in Taiwan decreased 0.05×10⁴ km² under the 2050s RCP4.5 period. Therefore, climate warming was beneficial for the geographical expansion of S. torvum.

Pseudotsuga sinensis is an endemic plant in China, which is not only a valuable economic tree but also an important component of the mountain ecosystem, and is National Level II Key Protected Species in China. However, in recent years, due to logging activities, its number has declined sharply, so it is urgent to carry out related research on its protection. In this study, MaxEnt model was used to simulate the potential distribution and spatial pattern changes of the suitable areas of P. sinensis under the two extreme greenhouse gas emission scenarios of SSP126 and SSP585 in the future, to explore the main environmental factors influencing its distribution and its dynamic changes, and to evaluate the future survival prospects of P. sinensis combined with the age structure of the main distribution areas so as to provide a theoretical basis for the protection and rational utilization of P. sinensis germplasm resources under the background of climate change in the future. The results indicated that: 1) Precipitation in the driest month, annual precipitation, elevation and the lowest temperature in the coldest month were the key factors affecting the distribution of P. sinensis, and their contribution rates were 40.8%, 24.7%, 15.8% and 7.2%, respectively; 2) At present, the suitable habitat areas of P. sinensis were mainly distributed in Taiwan and southwest China. The core distribution areas were concentrated in the alpine area of Yunnan-Guizhou Plateau; 3) In the future, under different scenarios and at different periods, the distribution of suitable areas of P. sinensis showed a trend of northward migration, and the area of suitable areas increasd slightly, but the distribution strip of the core high suitable area was fractured, showing a certain degree of fragmentation distribution. In addition, the suitable areas of most key protected populations remained unchanged, but the suitable areas of some populations were reduced. These populations are at potential risk of extinction and should be prioritized for protection.

Myodes rufocanus, Apodemus peninsulae and Apodemus agrarius are the three main rodent pests in the Northeast region of China. The gut microbiota of animals, along with their gastrointestinal tract, form a complex microbial ecosystem and play an important role in the host’s intestine. To investigate the composition and phylogenetic relationships of gut viruses in these three rodent species and the impact of their habitats, this study conducted metagenomic sequencing on 17 samples of Korean field mice, 17 samples of brown-backed voles from the same habitat, and 9 samples of striped field mice from a different habitat. After screening for viral information, the gut viral composition and differences among the three rodent species were compared and analyzed. The results showed that Artverviricota and Uroviricota were dominant viral phyla shared by the three rodent species, while Betaretrovirus and Gammaretrovirus were dominant viral genera shared by the three rodent species. Furthermore, significant differences were observed among the gut viral communities of the three rodent species, with nine phyla and 18 genera of viruses enriched in different samples. Among the top ten viruses in terms of relative abundance in the gut, Korean field mice and striped field mice exhibited significant differences in five phyla and five genera, while brown-backed voles and Korean field mice exhibited significant differences in three phylum and six genera. At the phylum level, the gut viral composition of brown-backed voles was more similar to that of Korean field mice, whereas at the genus level, the gut viral composition of striped field mice was more similar to that of Korean field mice. Based on these findings, it can be inferred that habitat had a greater influence on gut viral composition at the phylum level, while factors such as diet, phylogenetic relationship had a greater impact.

In order to study the sequence characteristics and tissue expression of KLF6 gene in Siniperca chuatsi and its influence on the expression of branchable amino acid metabolizing enzyme gene, bioinformatics analysis of KLF6 gene was conducted through online tools and software. Real-time fluorescence quantitative PCR (RT-qPCR) was used to detect the expression abundance of KLF6 gene in 7 different tissues of the Sinfinfish and the mRNA expression levels of KLF6 gene and BCAAs metabolizing enzyme related genes after siRNA interference. The results showed that the total length of the coding region of the KLF6 gene of S. chuatsi was 3 791 bp (entry number: XM_044176561.1), in which the 5′ end and 3′ end were 271 bp and 2 659 bp, respectively, and the open reading frame was 860 bp, encoding a total of 286 amino acids. It was most closely related to the KLF6 gene (98.6%) and more distant to the KLF6 gene (82.3%) of carp. KLF6 has one KLF6-N type domain (2-204 aa), one COG5048 domain (194-268 aa) and three Znf-C2H2 domains, which were located between 203-227, 233-257 and 263-285 amino acids, respectively. The structure of the S. chuatsi KLF6 protein was composed of random curl (56.99%), α-helix (26.22%), extension chain (10.84%) and β-angle (5.94%). The results of RT-qPCR showed that the relative expression level of KLF6 gene in S. chuatsi tissues was in the following order from high to low: heart, spleen, brain, kidney, intestine, muscle and liver, and the expression level in the center was significantly higher than that in liver (P<0.01) and muscle (P<0.05), while the expression level in spleen, brain, kidney and intestine was higher than that in liver (P<0.05). The mRNA expression of BCAAs metabolizing enzymes (BCKDHb, ACADM, MCCC1, MCCC2, MUT, IVD) was significantly increased after the mRNA expression of KLF6 gene was inhibited (P<0.05). This study provides a basis for the study of the regulatory mechanism of KLF6 gene in muscle BCAAs metabolism of S. chuatsi.

In this study, male C57BL/6 mice were used as experimental animals to establish muscle injury model by cardiotoxin (CTX) injection. Structural alterations of skeletal muscle were observed by the HE staining, and the expressions profiling of related genes of skeletal muscle growth and differentiation were detected by real-time quantitative PCR (RT-qPCR), the untreated mice were used as the control. The results revealed that there was significant muscle histologic alteration and tibialis anterior muscle weight-to-length ratio was decreased by the single injection of 50 μL CTX (1 g/L) into the mouse tibialis anterior muscle after 4 days. The expression of related genes for muscle differentiation, MyOD1, MyOG, MYH1, MYH2, AMPK, and mTOR signaling pathway marker genes (PRKAA1, NOTCH1, AKT, PI3K, mTOR) in the tibialis anterior muscle decreased significantly compared with those of the untreated group. This study successfully established the mouse muscle injury model after intramuscular Cardiotoxin injection by morphological examination and detection of related genes expression. The research provided a theoretical basis for molecular regulatory mechanisms for muscle regeneration and treatment methods for muscle injury.