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Canine coronavirus (CCoV), canine respiratory coronavirus (CRCoV), canine adenovirus type 2 (CAV-2), and canine norovirus (CNV) are important pathogens for canine viral gastrointestinal and respiratory diseases. Especially, co-infections with these viruses exacerbate the damages of diseases. In this study, four pairs of primers and probes were designed to specifically amplify the conserved regions of the CCoV M gene, CRCoV N gene, CAV-2 hexon gene, and CNV RdRp gene. After optimizing different reaction conditions, a quadruplex RT-qPCR was established for the detection of CCoV, CRCoV, CAV-2, and CNV. The specificity, sensitivity, and repeatability of the established assay were evaluated. Then, the assay was used to test 1688 clinical samples from pet hospitals in Guangxi province of China during 2022–2024 to validate its clinical applicability. In addition, these samples were also assessed using the reported reference RT-qPCR assays, and the agreements between the developed and reference assays were determined. The results indicated that the quadruplex RT-qPCR could specifically test only CCoV, CRCoV, CAV-2, and CNV, without cross-reaction with other canine viruses. The assay had high sensitivity with limits of detection (LODs) of 1.0 × 102 copies/reaction for CCoV, CRCoV, CAV-2, and CNV. The repeatability was excellent, with intra-assay variability of 0.19–1.31% and inter-assay variability of 0.10–0.88%. The positivity rates of CCoV, CRCoV, CAV-2, and CNV using the developed assay were 8.59% (145/1688), 8.65% (146/1688), 2.84% (48/1688), and 1.30% (22/1688), respectively, while the positivity rates using the reference assays were 8.47% (143/1688), 8.53% (144/1688), 2.78% (47/1688), and 1.24% (21/1688), respectively, with agreements of more than 99.53% between two methods. In conclusion, a quadruplex RT-qPCR with high sensitivity, specificity, and repeatability was developed for rapid, and accurate detection of CCoV, CRCoV, CAV-2, and CNV.

Generally, essential genes identified using shRNA and CRISPR are not always the same, raising questions about the choice between these two screening platforms. To address this, we systematically compared the performance of CRISPR and shRNA to identify essential genes across different gene expression levels in 254 cell lines. As both platforms have a notable false positive rate, to correct this confounding factor, we first developed a graph-based unsupervised machine learning model to predict common essential genes. Furthermore, to maintain the unique characteristics of individual cell lines, we intersect essential genes derived from the biological experiment with the predicted common essential genes. Finally, we employed statistical methods to compare the ability of these two screening platforms to identify essential genes that exhibit differential expression across various cell lines. Our analysis yielded several noteworthy findings: (1) shRNA outperforms CRISPR in the identification of lowly expressed essential genes; (2) both screening methodologies demonstrate strong performance in identifying highly expressed essential genes but with limited overlap, so we suggest using a combination of these two platforms for highly expressed essential genes; (3) notably, we did not observe a single gene that becomes universally essential across all cancer cell lines.

Adversarial attacks on Graph Neural Networks (GNNs) have emerged as a significant threat to the security of graph learning. Compared with Graph Modification Attacks (GMAs), Graph Injection Attacks (GIAs) are considered more realistic attacks, in which attackers perturb GNN models by injecting a small number of fake nodes. However, most existing black-box GIA methods either require comprehensive knowledge of the dataset and the ground-truth labels or a large number of queries to execute the attack, which is often unfeasible in many scenarios. In this paper, we propose an unsupervised method for leveraging the rich knowledge contained in the graph data themselves to enhance the success rate of graph injection attacks on the initial query. Specifically, we introduce GraphContrastive Learning-based Graph Injection Attack (GCIA), which consists of a node encoder, a reward predictor, and a fake node generator. The Graph Contrastive Learning (GCL)-based node encoder transforms nodes for low-dimensional continuous embedding, the reward predictor acts as a simplified surrogate for the target model, and the fake node generator produces fake nodes and edges based on several carefully designed loss functions, utilizing the node encoder and reward predictor. Extensive results demonstrate that the proposed GCIA method achieves a first query success rate of 91.2% on the Reddit dataset and improves the success rate to over 99.7% after 10 queries.

As the penetration rate of electric vehicles (EVs) increases, how to reasonably distribute the ensuing large charging load to various charging stations is an issue that cannot be ignored. This problem can be solved by developing a suitable charging guidance strategy, the development of which needs to be based on the establishment of a realistic EV charging behaviour model and charging station queuing system. Thus, in this paper, a guidance and pricing strategy for fast charging that considers different types of EV users’ willingness to charge is proposed. Firstly, the EVs are divided into two categories: private cars and online ride-hailing cars. These categories are then used to construct charging behaviour models. Based on this, a charging decision model for EV users is constructed. At the same time, a first-come-first-served (FCFS) charging station queuing system is constructed to model the real-time charging situation in the charging station in a more practical way. Finally, a dynamic tariff updating model is used to obtain the optimal time-of-use tariff for each charging station, and then the tariffs are used to guide the fast-charging demand. By comparing the spatial and temporal distribution of charging demand loads at charging stations under different scenarios and considering whether the tariffs at each charging station play a guiding role, it is verified that the proposed strategy effectively optimises the balanced distribution of EV charging loads and alleviates the congestion at charging stations.

Canine coronavirus (CCoV) is an important gastrointestinal pathogen that causes serious harm to pet dogs worldwide. Here, 1791 clinical samples were collected from pet dogs in different pet hospitals in Guangxi Province, southern China, for the 2021–2024 period and detected for CCoV by a multiplex RT-qPCR. The results showed that 8.43% (151/1791) of samples were positive for CCoV. Sixty-five positive samples were selected to amplify, sequence, and analyze S, M, and N genes. A sequence comparison revealed that the nucleotide and amino acid similarities of the S, M, and N genes were 94.86% and 94.62%, 96.85% and 97.80%, and 96.85% and 97.80%, respectively. Phylogenetic analysis indicated that 65 CCoV strains obtained in this study belonged to the CCoV-II genotype, of which 56 CCoV strains belonged to the CCoV-IIa subtype and 9 CCoV strains belonged to the CCoV-IIb subtype. A potential recombination event analysis of S gene sequences indicated that two CCoV strains, i.e., GXBSHM0328-34 and GXYLAC0318-35, have recombination signals. A Bayesian analysis indicated that the evolutionary rates of the S, M, and N genes were 1.791 × 10−3, 6.529 × 10−4, and 4.775 × 10−4 substitutions/site/year, respectively. The population size grew slowly before 1980 and then began to shrink slowly; it then shrank rapidly in 2005 and expanded sharply in 2020, leveling off thereafter. These results indicated the CCoV strains prevalent in Guangxi Province, southern China, showed a high level of genetic diversity and maintained continuous variation among clinical epidemic strains.

Feline coronavirus (FCoV), as one of the important pathogens of feline viral gastroenteritis, has been attracting great attention. A total of 1869 rectal and nasal swabs, feces, and ascites samples were collected from eight regions in Guangxi province during 2021–2024. The multiplex RT-qPCR established in our laboratory was used to test these samples for FCoV, and 17.66% (330/1869) of the samples were positive for FCoV. The S, M, and N genes of 63 FCoV-positive samples were amplified and sequenced, and the genetic and evolutionary characteristics were analyzed. Similarity analysis showed that the nucleotide and amino acid homologies of S, M, and N genes were 81.2–99.6% and 70.2–99.5%, 89.9–100% and 91.6–100%, and 90.1–100% and 91.5–100%, respectively. Phylogenetic analysis revealed that all 63 FCoV strains, based on S gene sequences, belonged to type I FCoV (FCoV-I), and were clustered with Chinese strains and the Netherlands UU strains. Recombinant signals were detected in the S gene of strains GXLZ03-2022, GXLZ08-2022, and CCoV GD/2020/X9. The results suggest that FCoV is still prevalent in the Guangxi province of southern China, and the prevalent FCoV strains show high genetic diversity and novel epidemic characteristics.

The oxidation of benzylic alcohols is an important transformation in modern organic synthesis. A plethora of photoredox protocols have been developed to achieve the aerobic oxidation of alcohols into carbonyls. Recently, several groups described that ultraviolet (UV) or purple light can initiate the aerobic oxidation of benzylic alcohols in the absence of an external catalyst, and depicted different mechanisms involving the photoinduction of •O2− as a critical reactive oxygen species (ROS). However, based on comprehensive mechanistic investigations, including control experiments, radical quenching experiments, EPR studies, UV–vis spectroscopy, kinetics studies, and density functional theory calculations (DFT), we elucidate here that HOO•, which is released via the H2O2 elimination of α-hydroxyl peroxyl radicals [ArCR(OH)OO•], serves as the real chain carrier for the autocatalytic photooxidation of benzylic alcohols. The mechanistic ambiguities depicted in the precedent literature are clarified, in terms of the crucial ROS and its evolution, the rate-limiting step, and the primary radical cascade. This work highlights the necessity of stricter mechanistic analyses on UV-driven oxidative reactions that involve aldehydes’ (or ketones) generation.

The variant porcine epidemic diarrhea virus (PEDV) has caused considerable economic losses to the global pig industry since 2010. In this study, a total of 5859 diarrhea samples were collected from different pig farms in China’s Guangxi province during January 2020 and March 2024 and tested for PEDV using RT-qPCR. The positivity rate of PEDV was 11.90% (697/5859). Ninety-two PEDV-positive samples were selected based on sampling time, and the sampling region for amplification, sequencing, and analysis of the S1, M, and N genes. Phylogenetic analysis of the S1 gene revealed that all strains from Guangxi province were distributed in three subgroups, i.e., 81.5% (75/92) in the G2a subgroup, 4.3% (4/92) in the G2b subgroup, and 14.1% (13/92) in the G2c subgroup. The sequence analysis revealed that the S1 gene sequences from Guangxi province had higher homology with the variant strains than with the classical strains, showing as high as 99.2% with the variant strain AJ1102 and only 94.3% with the classical strain CV777. Recombination analysis revealed that the GX-BS08-2023 strain (G2c) from Guangxi province originated from inter-lineage recombination between the GX-BS09-2023 (G2a) and CH-JN547228-2011 (G1a) strains. In addition, the S1 gene of the G2a and G2b subgroup strains shared many mutations and insertions. There were common mutations of N143D and P235L in the G2a subgroup. Evolutionary analysis revealed that all Guangxi strains belonged to the G2 genotype. These strains have spread rapidly since the PEDV variant strains that emerged in 2010, weakened until 2021, and then remained stable. In conclusion, the results revealed the latest genetic evolution of circulating PEDV strains in Guangxi province in recent years, providing important information for preventing and controlling PEDV infection. Currently, the G2a subgroup strains are the predominant strains circulating in pig herds in Guangxi province, southern China.

With the increase in the number of distributed energy resources (DERs) and electric vehicles (EVs), it is particularly important to solve the problem of EV charging station siting and capacity determination under the distribution network considering a large proportion of DERs. This paper proposes a bi-level planning model for EV charging stations that takes into account the characteristics of the spatial–temporal distribution of charging loads under an uncertain environment. First, the Origin–Destination (OD) matrix analysis method and the real-time Dijkstra dynamic path search algorithm are introduced and combined with the Larin Hypercube Sampling (LHS) method to establish the EV charging load prediction model considering the spatial and temporal distribution characteristics. Second, the upper objective function with the objective of minimizing the cost of EV charging station planning and user charging behavior is constructed, while the lower objective function with the objective of minimizing the cost of distribution network operation and carbon emission cost considering the uncertainty of wind power and photovoltaics is constructed. The constraints of the lower-layer objective function are transformed into the upper-layer objective function through Karush–Kuhn–Tucker (KKT) conditions, the optimal location and capacity of charging stations are finally determined, and the model of EV charging station siting and capacity determination is established. Finally, the validity of the model was verified by planning the coupled IEEE 33-node distribution network with the traffic road map of a city in southeastern South Dakota, USA.

Tumor necrosis factor-α-induced protein 8-like 3 (TNFAIP8L3 or TIPE3) functions as a transfer protein for lipid second messengers. TIPE3 is highly upregulated in several human cancers and has been established to significantly promote tumor cell proliferation, migration, and invasion and inhibit the apoptosis of cancer cells. Thus, inhibiting the function of TIPE3 is expected to be an effective strategy against cancer. The advancement of artificial intelligence (AI)-driven drug development has recently invigorated research in anti-cancer drug development. In this work, we incorporated DFCNN, Autodock Vina docking, DeepBindBC, MD, and metadynamics to efficiently identify inhibitors of TIPE3 from a ZINC compound dataset. Six potential candidates were selected for further experimental study to validate their anti-tumor activity. Among these, three small-molecule compounds (K784-8160, E745-0011, and 7238-1516) showed significant anti-tumor activity in vitro, leading to reduced tumor cell viability, proliferation, and migration and enhanced apoptotic tumor cell death. Notably, E745-0011 and 7238-1516 exhibited selective cytotoxicity toward tumor cells with high TIPE3 expression while having little or no effect on normal human cells or tumor cells with low TIPE3 expression. A molecular docking analysis further supported their interactions with TIPE3, highlighting hydrophobic interactions and their shared interaction residues and offering insights for designing more effective inhibitors. Taken together, this work demonstrates the feasibility of incorporating deep learning and MD simulations in virtual drug screening and provides inhibitors with significant potential for anti-cancer drug development against TIPE3−.

Currently, the alteration of external factors during crude oil extraction easily disrupts the thermodynamic equilibrium of asphaltene, resulting in the continuous flocculation and deposition of asphaltene molecules in crude oil. This accumulation within the pores of reservoir rocks obstructs the pore throat, hindering the efficient extraction of oil and gas, and consequently, affecting the recovery of oil and gas resources. Therefore, it is crucial to investigate the principles of asphaltene deposition inhibition and the synthesis of asphaltene inhibitors. In recent years, the development of nanotechnology has garnered significant attention due to its unique surface and volume effects. Nanoparticles possess a large specific surface area, high adsorption capacity, and excellent suspension and catalytic abilities, exhibiting unparalleled advantages compared with traditional organic asphaltene inhibitors, such as sodium dodecyl benzene sulfonate and salicylic acid. At present, there are three primary types of nanoparticle inhibitors: metal oxide nanoparticles, organic nanoparticles, and inorganic nonmetal nanoparticles. This paper reviews the recent advancements and application challenges of nanoparticle asphaltene deposition inhibition technology based on the mechanism of asphaltene deposition and nano-inhibitors. The aim was to provide insights for ongoing research in this field and to identify potential future research directions.

Polymer flooding, one of the main methods for improving crude oil recovery using chemical flooding technology in China, is widely used for actual oil displacement. Partially hydrolyzed polyacrylamide (HPAM) is a commonly used linear polymer in polymer flooding, but it exhibits poor temperature and salt resistance due to its molecular structure. Therefore, branched polymers have been studied. This article provides a review of the specific synthetic methods and current practical applications in the petrochemical field of dendritic polymers and hyperbranched macromolecules. The advantages and disadvantages of each synthetic method for branched polymers are also elaborated. Finally, the application prospects of branched polymers are discussed. The feasibility of branched polymers in large quantities should be further verified through additional field tests, which should address concerns such as synthesis costs and reaction efficiency.

In recent years, with the rapid growth in the number of electric vehicles (EVs), the large-scale grid connection of EVs has had a profound impact on the power grid. As a flexible energy storage resource, EVs can participate in auxiliary services of the power grid via vehicle-to-grid (V2G) technology. Due to the uncertainty of EVs accessing the grid, it is difficult to accurately control their charging and charging behaviors at both the day-ahead and real-time stages. Aiming at this problem, this paper proposes a two-stage scheduling strategy framework for EVs. In the presented framework, according to historical driving data, a day-ahead scheduling model based on distributionally robust optimization (DRO) is first established to determine the total power plan. In the real-time scheduling stage, a real-time scheduling model based on model predictive control (MPC) is established to track the day-ahead power plan. It can reduce the impact of EVs’ uncertainties. This strategy can ensure the charging demand of users is under the control of the charging and discharging behaviors of EVs, which can improve the accuracy of controlling EVs. The case study shows that the scheduling strategy can achieve accurate and fast control of charging and discharging. At the same time, it can effectively contribute to the security and stability of grid operations.

The requirements for drilling bentonites are tightening due to ever-increasing demands for petroleum resources, coupled with cost and reaction technology constraints. In addition to raising the risk of drilling, bentonite’s poor performance also raises the possibility of safety incidents and significant financial losses. Organically modified bentonites effectively reduce the consumption of drilling fluids, conserve resources, and lessen environmental effects. This paper aims to provide an overview of the several organic modification methods of bentonite for drilling fluids. It also evaluates the characteristics and application impacts of bentonite. We primarily describe the three popular modification methods represented by intercalation, coupling, and grafting. Also, this review provides the effect of molecular simulation on the investigation of structure in microconfined conditions. Through microlearning, organically modified bentonite with exceptional performance is to be further developed.

In order to improve the detection performance of surface-enhanced Raman scattering (SERS), a low-cost Au@Ag nanorods (Au@Ag NRs) substrate with a good SERS enhancement effect was developed and applied to the detection of malachite green (MG) in aquaculture water and crayfish. By comparing the SERS signal enhancement effect of five kinds of Au@Ag NRs substrates with different silver layer thickness on 4-mercaptobenzoic acid (4-MBA) solution, it was found that the substrate prepared with 100 µL AgNO3 had the smallest aspect ratio (3.27) and the thickest Ag layer (4.1 nm). However, it showed a good signal enhancement effect, and achieved a detection of 4-MBA as low as 1 × 10−11 M, which was 8.7 times higher than that of the AuNRs substrate. In addition, the Au@Ag NRs substrate developed in this study was used for SRES detection of MG in crayfish; its detection limit was 1.58 × 10−9 M. The developed Au@Ag NRs sensor had the advantages of stable SERS signal, uniform size and low cost, which provided a new tool for SERS signal enhancement and highly sensitive SERS detection method development.

Intelligent polymer gel, as a popular polymer material, has been attracting much attention for its application. An intelligent polymer gel will make corresponding changes to adapt to the environment after receiving stimuli; therefore, an intelligent polymer gel can play its role in many fields. With the research on intelligent polymer gels, there is great potential for applications in the fields of drug engineering, molecular devices, and biomedicine in particular. The strength and responsiveness of the gels can be improved under different configurations in different technologies to meet the needs in these fields. There is no discussion on the application of intelligent polymer gels in these fields; therefore, this paper reviews the research progress of intelligent polymer gel, describes the important research of some intelligent polymer gel, summarizes the research progress and current situation of intelligent polymer gel in the environment of external stimulation, and discusses the performance and future development direction of intelligent polymer gel.

Transmitted HIV drug resistance in Bulgaria was first reported in 2015 using data from 1988–2011. We determined the prevalence of surveillance drug resistance mutations (SDRMs) and HIV-1 genetic diversity in Bulgaria during 2012–2020 using polymerase sequences from 1053 of 2010 (52.4%) antiretroviral therapy (ART)-naive individuals. Sequences were analyzed for DRM using the WHO HIV SDRM list implemented in the calculated population resistance tool at Stanford University. Genetic diversity was inferred using automated subtyping tools and phylogenetics. Cluster detection and characterization was performed using MicrobeTrace. The overall rate of SDRMs was 5.7% (60/1053), with 2.2% having resistance to nucleoside reverse transcriptase inhibitors (NRTIs), 1.8% to non-nucleoside reverse transcriptase inhibitors (NNRTIs), 2.1% to protease inhibitors (PIs), and 0.4% with dual-class SDRMs. We found high HIV-1 diversity, with the majority being subtype B (60.4%), followed by F1 (6.9%), CRF02_AG (5.2%), A1 (3.7%), CRF12_BF (0.8%), and other subtypes and recombinant forms (23%). Most (34/60, 56.7%) of the SDRMs were present in transmission clusters of different subtypes composed mostly of male-to-male sexual contact (MMSC), including a 14-member cluster of subtype B sequences from 12 MMSC and two males reporting heterosexual contact; 13 had the L90M PI mutation and one had the T215S NRTI SDRM. We found a low SDRM prevalence amid high HIV-1 diversity among ART-naive patients in Bulgaria during 2012–2020. The majority of SDRMs were found in transmission clusters containing MMSC, indicative of onward spread of SDRM in drug-naive individuals. Our study provides valuable information on the transmission dynamics of HIV drug resistance in the context of high genetic diversity in Bulgaria, for the development of enhanced prevention strategies to end the epidemic.

Bentonite is an important component of drilling fluid, whose quality directly affects the safety and economic benefits of water-based drilling fluid. In order to effectively cope with temperature changes, the development of temperature-sensitive modified bentonite is of great significance. In this study, a temperature-sensitive modified bentonite based on NIPAM with excellent temperature sensitivity was developed through intercalation modification. The temperature-sensitive bentonite (CMC-B-NIPAM) was prepared by grafting N-isopropyl acrylamide (NIPAM) onto the surface of calcium bentonite through the dehydration condensation of silane coupling agent KH570 after the intercalation of sodium Carboxymethyl Cellulose (CMC). The synthesis indexes of CMC-B and CMC-B-NIPAM were optimized by the single-factor method. CMC-B-NIPAM was characterized by XRD and FTIR. The temperature sensitivity, rheology, suspensibility, and expansion capacity of CMC-B-NIPAM dispersion were investigated. The results showed that CMC-B-NIPAM had good temperature sensitivity, and the rheological properties of its dispersion showed characteristics of steady flow and temperature thickening in the range of 40–70 °C. A molecular simulation model was established to observe the microsynthesis mechanism of temperature-sensitive modified bentonite based on NIPAM. The results of this study show that CMC-B-NIPAM drilling fluid has the function of ensuring the stability of drilling fluid flow patterns compared to traditional drilling fluids.

Accompanied by China’s rapid economic growth, significant urban greening has occurred in Chinese cities, in particular in the urban core areas. In contrast, rapid urbanization and economic growth also led to a high probability of vegetation degradation in urban fringe regions. However, these significant spatial differences in urban greenness associated with economic growth in Chinese cities are not well understood. This study explored the spatiotemporal characteristics of the nighttime light (NTL) and annual maximum enhanced vegetation index (EVImax) in urban areas from 2001 to 2020. A strong decoupling status between economic growth and urban greenness on the national scale was found. Overall, 49.15% of urban areas showed a decoupling status. Spatially, this percentage of urban areas with a decoupling status would significantly decrease when the long-term average NTL surpasses 51. Moreover, this significant threshold of decoupling status was found in 189 cities out of 344 (54.65%) in China. This threshold in each city showed significant spatial heterogeneity but can mostly be attributed to the gradient in the long-term average precipitation (Pmean) of each city during the period of 2001–2020. Specifically, a spatial increase in Pmean of 100 mm responded to a decrease in the threshold of 0.4 DN (p < 0.01). In contrast, there was no significant correlation between the threshold and the economic growth status of each city. Our results provide valuable insights for coordinating the development of urban greening and economic growth.

Intelligent polymers have become the focus of attention worldwide. Intelligent polymer materials through organic synthesis methods are used to make inanimate organic materials become “feeling” and “sentient”. Intelligent polymer materials have been applied in actual engineering production, and they are becoming a new research topic for scientists in various fields and countries, especially in the areas of drilling and plugging. The development of intelligent polymer materials can provide new solutions and technical means for drilling and plugging. Unlike traditional plugging materials, intelligent polymer plugging materials can cope with environmental changes. They have the characteristics of a strong target, good plugging effect, and no damage to the reservoir. However, there are currently no reviews on intelligent polymer plugging materials in the drilling field, so this paper fills that gap by reviewing the research progress of intelligent polymer plugging materials. In addition, this paper describes the mechanism and application status of intelligent polymer shape-memory polymers, intelligent polymer gels, intelligent polymer membranes, and intelligent polymer bionic materials in drilling and plugging. It is also pointed out that some intelligent polymer plugging materials still have problems, such as insufficient toughness and a poor resistance to salt and high temperature. At the same time, some suggestions for future research directions are also presented for reference.

The acidic extracellular microenvironment has become an effective target for diagnosing and treating tumors. A pH (low) insertion peptide (pHLIP) is a kind of peptide that can spontaneously fold into a transmembrane helix in an acidic microenvironment, and then insert into and cross the cell membrane for material transfer. The characteristics of the acidic tumor microenvironment provide a new method for pH-targeted molecular imaging and tumor-targeted therapy. As research has increased, the role of pHLIP as an imaging agent carrier in the field of tumor theranostics has become increasingly prominent. In this paper, we describe the current applications of pHLIP-anchored imaging agents for tumor diagnosis and treatment in terms of different molecular imaging methods, including magnetic resonance T1 imaging, magnetic resonance T2 imaging, SPECT/PET, fluorescence imaging, and photoacoustic imaging. Additionally, we discuss relevant challenges and future development prospects.

Betaine is a new surfactant with good application prospects in high-temperature and high-salinity reservoirs. The interfacial properties of two kinds of betaine mixtures with a good synergistic effect were evaluated in this paper. On this basis, the effects of temperature-resistant, salt-resistant polymers with different contents of 2-acrylamide-2-methylpropanesulfonic acid (AMPS) on dynamic interfacial tensions (IFTs) against n-alkanes and crude oil were studied. The experimental results show that the IFTs between betaine ASB and n-alkanes can be reduced to ultra-low values by compounding with anionic surfactant petroleum sulfonate (PS) and extended anionic surfactant alkoxyethylene carboxylate (AEC), respectively. ASB@AEC is very oil-soluble with nmin value ≥14, and ASB@PS is relatively water-soluble with nmin value of 10. The water solubility of both ASB@PS and ASB@AEC is enhanced by the addition of water-soluble polymers. The HLB of the ASB@AEC solution becomes better against crude oil after the addition of polymers, and the IFT decreases to an ultra-low value as a result. On the contrary, the antagonistic effect in reducing the IFT can be observed for ASB@PS in the same case. In a word, polymers affect the IFTs of surfactant solutions by regulating the HLB.

Objective: The leading treatment option for dural carotid–cavernous sinus fistula is an endovascular approach with immediate improvement. Alternatively, radiosurgery is a slow response for obliterating the fistula and poses a radiation risk to the optic apparatus and the associated cranial nerves and blood vessels. In this study, we retrieved cases from a prospective database to assess the ophthalmological outcomes and complications in treating dural carotid cavernous sinus fistula with gamma knife radiosurgery (GKRS). Material and Methods: We retrieved a total of 65 cases of carotid cavernous sinus fistula treated with GKRS with margin dose of 18–20 Gy from 2003 to 2018 and reviewed the ophthalmological records required for our assessment. Results: The mean target volume was 2 ± 1.43 cc. The onset of symptom alleviated after GKRS was 3.71 ± 7.68 months. There were two cases with residual chemosis, two with cataract, two with infarction, one with transient optic neuropathy, and four with residual cranial nerve palsy, but none with glaucoma or dry eyes. In MRA analysis, total obliteration of the fistula was noted in 64 cases with no detectable ICA stenosis nor cavernous sinus thrombosis. In the Cox regression analysis, post-GKRS residual cranial nerve palsy was highly correlated to targeted volume (p < 0.05) and age (p < 0.05). The occurrence of post-GKRS cataract was related to the initial symptom of chemosis (p < 0.05). Conclusion: GKRS for carotid cavernous sinus fistula offers a high obliteration rate and preserves the cavernous sinus vascular structure while conferring a low risk of treatment complications such as adverse radiation risk to the optic apparatus and adjacent cranial nerves.

Using cascade utilization between multiple energy sources to realize multi-energy complementarity can significantly improve the economic benefits and energy utilization of integrated energy service providers. Integrated energy service providers consider the cascade utilization of energy in the regional energy system. Through the demand response management of user power loads and different qualities of heat energy, the energy loss of the system can be reduced and the energy utilization efficiency of the system can be further improved. In this paper, we creatively establish a multi-objective optimization model with the goal of a minimum total cost and minimum exergy loss, considering cross elasticity, which is solved with the constraints of equipment operation and the energy balance in the region. The calculation example proves that the implementation of energy cascade utilization and demand response management for different users, by integrated energy service providers, can effectively reduce the system cost and improve exergy efficiency, so as to realize the optimal management of economic utilization and energy value.

Cryptocurrencies can be considered as mathematical money. As the most famous cryptocurrency, the Bitcoin price forecasting model is one of the popular mathematical models in financial technology because of its large price fluctuations and complexity. This paper proposes a novel ensemble deep learning model to predict Bitcoin’s next 30 min prices by using price data, technical indicators and sentiment indexes, which integrates two kinds of neural networks, long short-term memory (LSTM) and gate recurrent unit (GRU), with stacking ensemble technique to improve the accuracy of decision. Because of the real-time updates of comments on social media, this paper uses social media texts instead of news websites as the source data of public opinion. It is processed by linguistic statistical method to form the sentiment indexes. Meanwhile, as a financial market forecasting model, the model selects the technical indicators as input as well. Real data from September 2017 to January 2021 is used to train and evaluate the model. The experimental results show that the near-real time prediction has a better performance, with a mean absolute error (MAE) 88.74% better than the daily prediction. The purpose of this work is to explain our solution and show that the ensemble method has better performance and can better help investors in making the right investment decision than other traditional models.

The magnetic manipulation of droplets is one of the emerging magnetofluidic technologies that integrate multiple disciplines, such as electromagnetics, fluid mechanics and so on. The directly driven droplets are mainly composed of ferrofluid or liquid metal. This kind of magnetically induced droplet manipulation provides a remote, wireless and programmable approach beneficial for research and engineering applications, such as drug synthesis, biochemistry, sample preparation in life sciences, biomedicine, tissue engineering, etc. Based on the significant growth in the study of magneto droplet handling achieved over the past decades, further and more profound explorations in this field gained impetus, raising concentrations on the construction of a comprehensive working mechanism and the commercialization of this technology. Current challenges faced are not limited to the design and fabrication of the magnetic field, the material, the acquisition of precise and stable droplet performance, other constraints in processing speed and so on. The rotational devices or systems could give rise to additional issues on bulky appearance, high cost, low reliability, etc. Various magnetically introduced droplet behaviors, such as deformation, displacement, rotation, levitation, splitting and fusion, are mainly introduced in this work, involving the basic theory, functions and working principles.

Medical supply chain communication networks engender critical information and data. Notably in the COVID era, inner personal and private information is being shared between healthcare providers regarding the medical supply chain. In recent years, multiple cyber-attacks have targeted medical supply chain communication networks due to their lack of security measures. In the era where cyber-attacks are cheaper and easier due to the computational power and various algorithms available for malicious uses, security, and data privacy requires intensive and higher measures. On the other hand, Information Hiding Techniques (IHT) compromise various advanced methods to hide sensitive information from being disclosed to malicious nodes. Moreover, with the support of Blockchain, IHT can bring higher security and the required privacy levels. In this paper, we propose the implementation of Blockchain and smart contract with the information hiding technique to enhance the security and privacy of data communication in critical systems, such as smart healthcare supply chain communication networks. Results show the feasibility of the framework using Hyperledger smart contract along with the desired security level.

Temperature and precipitation are the primary regulators of vegetation phenology in temperate zones. However, the relative contributions of each factor and their underlying combined effect on vegetation phenology are much less clear, especially for the grassland of the Tibetan Plateau To quantify the contribution of each factor and the potential interactions, we conducted redundancy analysis for grasslands spring phenology on the Tibetan Plateau during 2000–2017. Generally, the individual contribution of temperature and precipitation to grasslands spring phenology (the start of growing season (SOS)) was lower, despite a higher correlation coefficient, which further implied that these factors interact to affect the SOS. The contributions of temperature and precipitation to the grasslands spring phenology varied across space on the Tibetan Plateau, and these spatial heterogeneities can be mainly explained by the spatial gradient of long-term average precipitation during spring over 2000–2017. Specifically, the SOS for meadow was dominated by the mean temperature in spring (Tspring) in the eastern wetter ecoregion, with an individual contribution of 24.16% (p < 0.05), while it was strongly negatively correlated with the accumulated precipitation in spring (Pspring) in the western drier ecoregion. Spatially, a 10 mm increase in long-term average precipitation in spring resulted in an increase in the contribution of Tspring of 2.0% (p < 0.1) for meadow, while it caused a decrease in the contribution of Pspring of −0.3% (p < 0.05). Similarly, a higher contribution of Pspring for steppe was found in drier ecoregions. A spatial decrease in precipitation of 10 mm increased the contribution of Pspring of 1.4% (p < 0.05). Considering these impacts of precipitation on the relative contribution of warming and precipitation to the SOS, projected climate change would have a stronger impact on advancing SOS in a relatively moist environment compared to that of drier areas. Hence, these quantitative interactions and contributions must be included in current ecosystem models, mostly driven by indicators with the direct and the overall effect in response to projected climate warming.

Colorectal cancer (CRC) is heterogeneous and deadly, and the exact cause of the disease is unknown. Recent progress indicated that CRC is not a single disease, but a group of diseases with significant heterogeneity. Three previous CRC subtyping systems: microsatellite instability (MSI), consensus molecular subtypes (CMS), and tumor-node-metastases (TNM) stage were evaluated for their molecular and clinical implications. Results suggested that the MSI and CMS systems are prognostic and predictive mostly in early-stage CRC. As the stage remains an influential factor for CRC subtype analysis, we developed a new subtyping system named stage supervised CRC subtypes (SSCS), in order to better stratify CRC biologically and clinically. Our subtyping system can be used to classify CRC patients into five subtypes (SSCS1-5). SSCS1 was found to have the highest frequency of MSI-H cases compared to the remaining four subtypes. SSCS2 had the most favorable prognosis, whereas the worst prognosis was seen in SSCS4. SSCS3 had cell cycle and metabolism-related gene sets upregulation, and SSCS5 subtype was enriched with amplicon-associated gene sets. Moreover, tumor-infiltrating fibroblast was found to be predictive for poor disease-free survival (DFS) only within the SSCS4 subtype. Conventional dendritic cells (cDC), on the contrary, were associated with favorable DFS in the SSCS3 subtype. Our study provides a new subtyping system SSCS, which can be used for better stratify CRC patients compared to current standards. Further exploration of the subtype-specific cell types has the potential to be novel therapies for CRC.

Distributed link-state routing protocols, including Open Shortest Path First (OSPF) and Intermediate System–Intermediate System (IS-IS), have successfully provided robust shortest path routing for IP networks. However, shortest path routing is inflexible and sometimes results in congestion on some critical links. By separating the control plane and the data plane, the centralized control of Software Defined Networking (SDN)-based approach possesses flexible routing capabilities. Fibbing is an approach that can achieve centralized control over a network running distributed routing protocols. In a Fibbing-controlled IP network, the controller cleverly generates fake protocol messages to manipulate routers to steer the flow of the desired paths. However, introducing fake nodes destroys the structure of the loop-free property of Loop-Free Alternate (LFA) that is used to achieve fast failure recovery in IP networks. This paper addresses this issue and presents a solution to provision routing paths so a Fibbing network can still apply LFA in the network. The proposed network jointly considers load-balanced and fast failure recovery. We formulate the problem as an integer linear programming problem. The numerical results reveal that the proposed method can provide 100% survivability against any single node or single link failure.

Flavors and fragrances have high commercial value in the food, cosmetic, chemical and pharmaceutical industries. It is interesting to investigate the isolation and characterization of new microorganisms with the ability to produce flavor compounds. In this study, a new strain of Klebsiella sp. O852 (accession number CCTCC M2020509) was isolated from decayed navel orange (Citrus sinensis (L.) Osbeck), which was proved to be capable of converting limonene to trans-dihydrocarvone. Besides, the optimization of various reaction parameters to enhance the trans-dihydrocarvone production in shake flask was performed for Klebsiella sp. O852. The results showed that the yield of trans-dihydrocarvone reached up to 1 058 mg/L when Klebsiella sp. O852 was incubated using LB-M medium for 4 h at 36 °C and 150 rpm, and the biotransformation process was monitored for 36 h after adding 1680 mg/L limonene/ethanol (final ethanol concentration of 0.8% (v/v)). The content of trans-dihydrocarvone increased 16 times after optimization. This study provided a basis and reference for producing trans-dihydrocarvone by biotransformation.

HIV-1 subtype CRF01_AE is the second most predominant strain in Bulgaria, yet little is known about the molecular epidemiology of its origin and transmissibility. We used a phylodynamics approach to better understand this sub-epidemic by analyzing 270 HIV-1 polymerase (pol) sequences collected from persons diagnosed with HIV/AIDS between 1995 and 2019. Using network analyses at a 1.5% genetic distance threshold (d), we found a large 154-member outbreak cluster composed mostly of persons who inject drugs (PWID) that were predominantly men. At d = 0.5%, which was used to identify more recent transmission, the large cluster dissociated into three clusters of 18, 12, and 7 members, respectively, five dyads, and 107 singletons. Phylogenetic analysis of the Bulgarian sequences with publicly available global sequences showed that CRF01_AE likely originated from multiple Asian countries, with Vietnam as the likely source of the outbreak cluster between 1988 and 1990. Our findings indicate that CRF01_AE was introduced into Bulgaria multiple times since 1988, and infections then rapidly spread among PWID locally with bridging to other risk groups and countries. CRF01_AE continues to spread in Bulgaria as evidenced by the more recent large clusters identified at d = 0.5%, highlighting the importance of public health prevention efforts in the PWID communities.

The twistronics, which is arising from the moiré superlattice of the small angle between twisted bilayers of 2D materials like graphene, has attracted much attention in the field of 2D materials and condensed matter physics. The novel physical properties in such systems, like unconventional superconductivity, come from the dispersionless flat band that appears when the twist reaches some magic angles. By tuning the filling of the fourfold degeneracy flat bands, the desired effects are induced due to the strong correlation of the degenerated Bloch electrons. In this article, we review the twistronics in twisted bi- and multi-layer graphene (TBG and TMG), which is formed both by transfer assembly of exfoliated monolayer graphene and epitaxial growth of multilayer graphene on SiC substrates. Starting from a brief history, we then introduce the theory of flat band in TBG. In the following, we focus on the major achievements in this field: (a) van Hove singularities and charge order; (b) superconductivity and Mott insulator in TBG and (c) transport properties in TBG. In the end, we give the perspective of the rising materials system of twistronics, epitaxial multilayer graphene on the SiC.

HIV-1 subtype B is the predominant strain in Bulgaria, yet little is known about the molecular epidemiology of these infections, including its origin and transmissibility. We used a phylodynamics approach by combining and analyzing 663 HIV-1 polymerase (pol) sequences collected from persons diagnosed with HIV/AIDS between 1988–2018 and associated epidemiologic data to better understand this sub-epidemic in Bulgaria. Using network analyses at a 1.5% genetic distance threshold (d) we found several large phylogenetic clusters composed mostly of men who have sex with men (MSM) and male heterosexuals (HET). However, at d = 0.5%, used to identify more recent transmission, the largest clusters dissociated to become smaller in size. The majority of female HET and persons with other transmission risks were singletons or pairs in the network. Phylogenetic analysis of the Bulgarian pol sequences with publicly available global sequences showed that subtype B was likely introduced into Bulgaria from multiple countries, including Israel and several European countries. Our findings indicate that subtype B was introduced into Bulgaria multiple times since 1988 and then infections rapidly spread among MSM and non-disclosed MSM. These high-risk behaviors continue to spread subtype B infection in Bulgaria as evidenced by the large clusters at d = 0.5%. Relatively low levels of antiretroviral drug resistance were observed in our study. Prevention strategies should continue to include increased testing and linkage to care and treatment, as well as expanded outreach to the MSM communities.

Device-to-device (D2D) communication is a direct means of communication between devices without an intermediate node, and it helps to expand cell coverage and to increase radio frequency reuse in a 5G network. Moreover, D2D communication is a core technology of 5G vehicle-to-everything (V2X) communication, which is an essential technology for autonomous driving. However, typical D2D communication in an 4G network which is typical telecommunication network has various security challenges including impersonation, eavesdropping, privacy sniffing, free-riding attack, etc. Moreover, when IoT technology emerges with 5G networks in massive machine type communication (mMTC) and ultra-reliable low latency communication (URLLC) application scenarios, these security challenges are more crucial and harder to mitigate because of the resource-constrained nature of IoT devices. To solve the security challenges in a 5G IoT environment, we need a lightweight and secure D2D communication system that can provide secure authentication, data confidentiality/integrity and anonymity. In this paper, we survey and analyze existing results about secure D2D communication systems in terms of their security considerations and limitations. Then, we lastly propose a secure D2D communication system to address the aforementioned security challenges and the limitations of the existing results. The proposed secure D2D communication was designed based on elliptic curve cryptography (ECC) and lightweight authenticated encryption with associated data (AEAD) ciphers to cover resource-constrained IoT devices.

With the refinement and intelligence of power system optimal dispatching, the widespread adoption of advanced grid applications that consider the safety and economy of power systems, and the massive access of distributed energy resources, the requirement for bus load prediction accuracy is continuously increasing. Aiming at the volatility brought about by the large-scale access of new energy sources, the adaptability to different forecasting horizons and the time series characteristics of the load, this paper proposes a phase space reconstruction (PSR) and deep belief network (DBN)-based very short-term bus load prediction model. Cross-validation is also employed to optimize the structure of the DBN. The proposed PSR-DBN very short-term bus load forecasting model is verified by applying the real measured load data of a substation. The results prove that, when compared to other alternative models, the PSR-DBN model has higher prediction accuracy and better adaptability for different forecasting horizons in the case of high distributed power penetration and large fluctuation of bus load.

Multi-district integrated energy system (IES) can make full use of the complementary characteristics of district power and thermal system, and loads in different districts. It can improve the flexibility and economy of system operation, which has a good development prospect. Firstly, based on the general energy transfer model of the district heating network (DHN), the DHN system is described by the basic equations of the heating network and nodes considering the characteristics of the transmission time delay and heat loss in pipelines. A coupling model of DHN and multi-district IES is established. Secondly, the flexible demand response (FDR) model of electric and thermal loads is established. The load characteristics of each district in IES are studied. A shiftable load model based on the electric quantity balance is constructed. Considering the flexibility of the heat demand, a thermal load adjustment model based on the comfort constraint is constructed to make the thermal load elastic and controllable in time and space. Finally, a mixed integer linear programming (MILP) model for operation optimization of multi-district IES with the DHN considering the FDR of electric and thermal loads is established based on the supply and demand sides. The result shows that the proposed model makes full use of the complementary characteristics of electric and thermal loads in different districts. It realizes the coordinated distribution of thermal energy among different districts and improves the efficiency of thermal energy utilization through the DHN. FDR effectively reduces the peak-valley difference of loads. It further reduces the total operating cost by the coordinated operation of the DHN and multi-district IES.

The integration of shiftable/curtailment distribution generators (DGs) along with quick-response storage has not only increased the transaction’s flexibility but also puzzled the bidding willingness of transmission-connected market players (TMPs). In this paper, the method of heterogeneous decomposition and coordination (HGDC) is applied to decompose the integrated transmission-distribution market framework into a bi-level problem with a transmission wholesale market master problem and several distribution retail market subproblems in a decentralized organization structure. The price-based bidding willingness of demand-side resources’ (DSRs’) aggregator is simulated considering the relation between distribution system operator’s (DSO’s) operation modes and its equivalent market transactive price. Besides the traditional mixed-integer linear programming (MILP) model, the active reconfiguration model of DSOs based on mixed-integer second-order conic programming (MI-SOCP) is proposed to rearrange its operation switch status and elaborate its operation cost according to the market transaction. Multi-period optimal operation modes could be obtained through an HGDC-based iteration process by coordinating the transmission system operator (TSO) with DSOs and considering the market energy equilibrium and reserve requirements for security considerations. Karush-Kuhn-Tucker (KKT) conditions are used to testify the optimality and convergence of the bi-level model in theory. The T5-3D33 case is employed to illustrate the efficiency of the proposed model and method.

Pool hopping attack is the result of miners leaving the pool when it offers fewer financial rewards and joining back when the rewards of mining yield higher rewards in blockchain networks. This act of leaving and rejoining the pool only during the good times results in the miner receiving more rewards than the computational power they contribute. Miners exiting the pool deprive it of its collective hash power, which leaves the pool unable to mine the block successfully. This results in its competitors mining the block before they can finish mining. Existing research shows pool hopping resistant measures and detection strategies; however, they do not offer any robust preventive solution to discourage miners from leaving the mining pool. To prevent pool hopping attacks, a smart contract-based pool hopping attack prevention model is proposed. The main objective of our research is maintaining the symmetrical relationship between the miners by requiring them all to continually contribute their computational power to successfully mine a block. We implement a ledger containing records of all miners, in the form of a miner certificate, which tracks the history of the miner’s earlier behavior. The certificate enables a pool manager to better initiate terms of the smart contract, which safeguards the interests of existing mining pool members. The model prevents frequent mine hoppers from pool hopping as they submit coins in the form of an escrow and risk losing them if they abandon the pool before completing mining of the block. The key critical factors that every pool hopping attack prevention solution must address and a study of comparative analysis with existing solutions are presented in the paper.

Recently, a large number of distributed photovoltaic (PV) power generations have been connected to the power grid, which resulted in an increased fluctuation of the net load. Therefore, load forecasting has become more difficult. Considering the characteristics of the net load, an ultrashort-term forecasting model based on phase space reconstruction and deep neural network (DNN) is proposed, which can be divided into two steps. First, the phase space reconstruction of the net load time series data is performed using the C-C method. Second, the reconstructed data is fitted by the DNN to obtain the predicted value of the net load. The performance of this model is verified using real data. The accuracy is high in forecasting the net load under high PV penetration rate and different weather conditions.

Voltage sag is one of the most serious problems in power quality. The occurrence of voltage sag will lead to a huge loss in the social economy and have a serious effect on people’s daily life. The identification of sag types is the basis for solving the problem and ensuring the safe grid operation. Therefore, with the measured data uploaded by the sag monitoring system, this paper proposes a sag type identification algorithm based on K-means-Singular Value Decomposition (K-SVD) and Least Squares Support Vector Machine (LS-SVM). Firstly; each phase of the sag sample RMS data is sparsely coded by the K-SVD algorithm and the sparse coding information of each phase data is used as the feature matrix of the sag sample. Then the LS-SVM classifier is used to identify the sag type. This method not only works without any dependence on the sag data feature extraction by artificial ways, but can also judge the short-circuit fault phase, providing more effective information for the repair of grid faults. Finally, based on a comparison with existing methods, the accuracy advantages of the proposed algorithm with be presented.

Periodic health checks of contouring errors under unloaded conditions are critical for machine performance evaluation and value-added manufacturing. Aiming at breaking the dimension, range and speed measurement limitations of the existing devices, a cost-effective knowledge-driven approach for detecting error motions of arbitrary paths using a single camera is proposed. In combination with the PNP algorithm, the three-dimensional (3D) evaluation of large-scale contouring error in relatively high feed rate conditions can be deduced from a priori geometrical knowledge. The innovations of this paper focus on improving the accuracy, efficiency and ability of the vision measurement. Firstly, a camera calibration method considering distortion partition of the depth-of-field (DOF) is presented to give an accurate description of the distortion behavior in the entire photography domain. Then, to maximize the utilization of the decimal involved in the feature encoding, new high-efficient encoding markers are designed on a cooperative target to characterize motion information of the machine. Accordingly, in the image processing, markers are automatically identified and located by the proposed decoding method based on finding the optimal start bit. Finally, with the selected imaging parameters and the precalibrated position of each marker, the 3D measurement of large-scale contouring error under relatively high dynamic conditions can be realized by comparing the curve that is measured by PNP algorithm with the nominal one. Both detection and verification experiments are conducted for two types of paths (i.e., planar and spatial trajectory), and experimental results validate the measurement accuracy and advantages of the proposed method.

Nowadays, due to the advantages of non-contact and high-speed, vision-based pose measurements have been widely used for aircraft performance testing in a wind tunnel. However, usually glass ports are used to protect cameras against the high-speed airflow influence, which will lead to a big measurement error. In this paper, to further improve the vision-based pose measurement accuracy, an imaging model which considers the refraction light of the observation window was proposed. In this method, a nonlinear camera calibration model considering the refraction brought by the wind tunnel observation window, was established first. What’s more, a new method for the linear calibration of the normal vector of the glass observation window was presented. Then, combining with the proposed matching method based on coplanarity constraint, the six pose parameters of the falling target could be calculated. Finally, the experimental setup was established to conduct the pose measurement study in the laboratory, and the results satisfied the application requirements. Besides, experiments for verifying the vision measurement accuracy were also performed, and the results indicated that the displacement and angle measurement accuracy approximately increased by 57% and 33.6%, respectively, which showed the high accuracy of the proposed method.

Voltage sag is a serious power quality phenomenon that threatens industrial manufacturing and residential electricity. A large-scale monitoring system has been established and continually improved to detect and record voltage sag events. However, the inefficient process of data sampling cannot provide valuable information early enough for governance of the system. Therefore, a novel online recognition method for voltage sags is proposed. The main contributions of this paper include: 1) The causes and waveform characters of voltage sags were analyzed; 2) according to the characters of different sag waveforms, 10 voltage sag characteristic parameters were proposed and proven to be effective; 3) a deep belief network (DBN) model was built using these parameters to complete automatic recognition of the sag event types. Experiments were conducted using voltage sag data from one month recorded by the 10 kV monitoring points in Suqian, Jiangsu Province, China. The results showed good performance of the proposed method: Recognition accuracy was 96.92%. The test results from the proposed method were compared to the results from support vector machine (SVM) recognition methods. The proposed method was shown to outperform SVM.

Cassava is becoming increasingly important as an industrial raw material in China. However, an insufficient supply of cassava raw materials and the expanding demand for cassava in downstream-processing industries restricts the development of the cassava industry in China. This paper studies how to increase the scale of cassava planting and promoting cassava production efficiency using output-oriented Data Envelopment Analysis (DEA) modeling. Overall Technical Efficiency (OTE), Pure Technical Efficiency (PTE), and Scale Efficiencies (SE) of the cassava-production system in the major cassava production areas of China are calculated using Variable Returns to Scale (VRS). Results reveal that, in addition to the Guangdong province, the OTE of Guangxi, Hainan, Fujian, Yunnan, and Jiangxi is inefficient, with an OTE of less than 1. The largest cassava-planting province, Guangxi, has the lowest SE with 0.551. The PTE of Guangxi is close to the minimum with 0.344. The OTE of Guangxi is also the lowest among the five provinces with 0.190. This study also presents ways to improve production efficiency. Results reveal that Guangxi has a large ratio of transverse adjustment on average. In the Guangxi province, 1.70% of the service cost and 1.72% of the labor cost need to be eliminated to keep the current output scale. Meanwhile, service and labor costs need to be reduced by 3164.85 and 3209.92, respectively, to achieve the best production efficiency. Further industrialization and large-scale cassava cultivation, increased yield, and strengthened cooperation with the Association of South East Asian Nations (ASEAN) and Africa are suggested as policy options to improve the cassava system in China.

This work provides the experimental and theoretical fundamentals for detecting the molecular fingerprints of six kinds of pesticides by using terahertz (THz) time-domain spectroscopy (THz-TDS). The spectra of absorption coefficient and refractive index of the pesticides, chlorpyrifos, fipronil, carbofuran, dimethoate, methomyl, and thidiazuron are obtained in frequencies of 0.1–3.5 THz. To accurately describe the THz spectral characteristics of pesticides, the wavelet threshold de-noising (WTD) method with db 5 wavelet fucntion, 5-layer decomposition, and soft-threshold de-noising was used to eliminate the spectral noise. The spectral baseline correction (SBC) method based on asymmetric least squares smoothing was used to remove the baseline drift. Spectral results show that chlorpyrifo had three characteristic absorption peaks at 1.47, 1.93, and 2.73 THz. Fipronil showed three peaks at 0.76, 1.23, and 2.31 THz. Carbofuran showed two peaks at 2.72 and 3.06 THz. Dimethoate showed three peaks at 1.05, 1.89, and 2.92 THz. Methomyl showed five peaks at 1.01, 1.65, 1.91, 2.72, and 3.20 THz. Thidiazuron showed four peaks at 0.99, 1.57, 2.17, and 2.66 THz. The density functional theory (DFT) of B3LYP/6-31G+(d,p) was applied to simulate the molecular dynamics for peak analyzing of the pesticides based on isolated molecules. The theoretical spectra are in good agreement with the experimental spectra processed by WTD + SBC, which implies the validity of WTD + SBC spectral processing methods and the accuracy of DFT spectral peak analysis. These results support that the combination of THz-TDS and DFT is an effective tool for pesticide fingerprint analysis and the molecular dynamics simulations.