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In this paper, employing nanomechanical scrutiny of aramid fibers and rubber, novel interfacial layers of different thicknesses were designed between aramid fibers and rubber using polymerizable deep eutectic solvents. The investigation explored the correlations between interfacial layers of different thicknesses and the interfacial bonding properties, fatigue resistance and hysteresis of composites. Atomic force microscopy was used to identify the changes in the thickness and modulus of the interfacial layer before and after cyclic tensile fatigue testing. The interfacial modulus decreased rapidly at different locations after the fatigue test, and was even lower than the modulus of the rubber matrix. When the thickness of the interfacial layer was ∼1000 nm, the H pull-out force was increased by 282.3% compared with the unmodified AF, and the fatigue resistance was significantly improved and the fatigue loss was reduced from 52.39% to 14.93%. The smooth transition of modulus between aramid fibers and rubber and the appropriately thickness interfacial layer improved the mechanical properties of the composites. The microcrack extension was transferred from the surface of high modulus aramid fibers to the rubber side. In future research, this method can readily adjust the interfacial layer thickness and modulus according to different rubber types, which promotes the application of high-performance fibers in rubber.

The wear, rolling resistance, and wet grip are the most important performance of tires. The wear of tires rubber is influenced by various factors such as formula composition (such as butadiene rubber content), rubber strength, heat generation performance, etc. It is a complex problem involving mechanics and thermodynamics. Unfortunately, commonly used wear testing equipment in the tire industry, such as DIN abrasion, Akron abrasion, and Lambourn abrasion, cannot accurately characterize the wear performance of tires. The ABS control slippage during vehicle acceleration and braking provides sufficient grip in contact with the road surface, which is the most critical cause of tire wear. The wear performance of rubber at different slippage is a key factor affecting tire life. In this study, three compounds were developed, differing significantly in hardness, modulus, and the filling ratio of silica, to be manufactured as tire tread. The slippage was calculated with the results of the tire pattern model and pressure blanket tests. Subsequent wear performance tests were conducted on a fully automated wear testing machine under different slippage. Compared to the DIN and Cut-chip test results, the correlation with the taxi road test outcomes was enhanced when different slip rates were considered. This study introduces a novel method for assessing wear by calculating the tire slippage. By accounting for variables such as tire pattern and rubber rigidity, the slippage under ABS-controlled conditions was determined. Consequently, the wear performance demonstrated a stronger correlation with tire mileage as evidenced by the taxi road tests.

Natural raw rubber was prepared by flash evaporation process (VNR), its raw rubber network was compared with that of acid-coagulated rubber (STR20), and the effects of the ratios of VNR and STR20 to SSBR on the comprehensive performance of green tire composites were investigated. The results showed that compared with 80SSBR composites, the tensile strength and tensile product coefficient of 80VNR composites were improved by 23 % and 94 %, and the Payne effect, compression heat generation, and rolling resistance were reduced by 39 %, 9 %, and 5 %, respectively. Compared with 40STR20/40SSBR composites, the tensile product coefficient, wear resistance, and wet skid resistance of 40VNR/40SSBR composites are increased by 11 %, 5 %, and 5 %, respectively, and the Payne effect, compression heat generation, and rolling resistance were reduced by 46 %, 11 %, and 5 %, respectively. This study increased the amount of natural rubber in the green tire formulations, improving the performance of composites while achieving sustainability.

In the 21st-century global economy, the rapid growth of the finance industry, particularly in personal credit, fuels economic growth and market prosperity. However, the rapid expansion of personal credit business has brought explosive growth in the amount of data, which puts forward higher requirements for the risk management of financial institutions. To solve this problem, this paper constructs an intelligent evaluation model of personal credit risk under the background of big data. Firstly, based on the forest optimization feature selection algorithm, combined with initialization based on chi-square check, adaptive global seeding, and greedy search strategies, key risk factors are accurately identified from high-dimensional data. Then, the XGBoost algorithm is used to evaluate the credit risk level of customers, and the traditional Sparrow Search Algorithm is improved by using Tent chaotic mapping, sine and cosine search, reverse learning, and Cauchy mutation strategy to improve the optimization performance of algorithm parameters. Finally, using the Lending Club dataset for empirical analysis, the experiment shows that the model improves the accuracy of personal credit risk assessment and enhances the ability of risk control.

Abstract Background Cue exposure therapy is used to treat alcohol dependence. However, its effectiveness is controversial due to the limitations of the clinical treatment setting. Virtual reality technology may improve the therapeutic effect. The aim of this study is to explore whether virtual reality-based cue exposure therapy can reduce the psychological craving and physiological responses of patients with alcohol dependence. Methods Forty-four male alcohol-dependent patients were recruited and divided into the study group (n = 23) and the control group (n = 21) according to a random number table. The control group received only conventional clinical treatment for alcohol dependence. The study group received conventional clinical treatment with the addition of VR cue exposure (treatment). The primary outcome was to assess psychological craving and physiological responses to cues of patients before and after treatment. Results After virtual reality-based cue exposure therapy, the changes in VAS and heart rate before and after cue exposure in the study group were significantly lower than those in the control group (P 0.05). The changes in VAS and heart rate before and after cue exposure in the study group were significantly lower than those before treatment (P 0.05). The changes in VAS, heart rate, skin conductance and respiration before and after cue exposure in the control group were not significantly different from those before treatment (P > 0.05). Conclusion Virtual reality-based cue exposure therapy can reduce the psychological craving and part of the physiological responses of alcohol-dependent patients during cue exposure in the short term and may be helpful in the treatment of alcohol dependence. Trial registration The study protocol was registered at the China Clinical Trial Registry on 26/02/2021 ( www.chictr.org.cn ; ChiCTR ID: ChiCTR2100043680).

For floating offshore wind turbines, a significant pitch and roll motion response of the platform can affect the acceleration and power generation of the nacelle. The damping plate is considered a type of attachment that can be used to reduce rotational motion, but research on its anti-rotational effect is limited. The objective of this work is to analyze the impact of installing damping plates and varying their sizes on the pitch motion response of semi-submersible platforms, while also proposing optimization strategies for damping plate design. Firstly, a comparison between numerical simulations and experimental measurements validates the accuracy of the CFD calculations. Subsequently, different sizes of damping plates are proposed for the platforms, followed by simulations under various conditions. Finally, comprehensive data analysis is conducted. The findings suggest that installing damping plates enhances both the platform’s added moment of inertia and damping coefficients while simultaneously amplifying its motion response in regular waves. Furthermore, increasing the size of damping plates leads to an increase in both the added moment of inertia and motion response for the platform, whereas the damping coefficient exhibits an initial increasing trend followed by a subsequent decrease. Ultimately, it is found that increasing the distance between damping plates and the free surface significantly reduces wave-induced loads on the platform.

ABSTRACTChronic alcohol consumption, an important risk factor for diseases and deaths, can cause intestinal microbiota dysbiosis and increase the infection of some opportunistic pathogens. However, the current studies on the effects of alcohol-induced intestinal microbiota dysbiosis on gut colonization of Klebsiella pneumoniae are still scarce. In the present study, we established a binge-on-chronic alcohol model in mice to identify the characteristics of alcohol-induced intestinal microbiome and metabolite dysbiosis using multi-omics and explored the effects and potential mechanisms of these dysbioses on the intestinal colonization of K. pneumoniae. The results show that chronic alcohol consumption alters the diversity and composition of gut microbiota (including bacteria and fungi), decreases the complexity of the interaction between intestinal bacteria and fungi, disturbs the gut metabolites, and promotes the colonization of K. pneumoniae on the gut of mice. The relevance analyses find that alcohol-induced gut microbiome dysbiosis has a strong correlation with the alteration of secondary bile acids. In vitro results suggest that the high concentration of lithocholic acid, a secondary bile acid, could significantly inhibit the proliferation of K. pneumoniae, and the adhesion of K. pneumoniae to Caco-2 cells. Our results indicate that alcohol-induced microbiome dysbiosis contributes to decreased levels of secondary bile acids, which was one of the main reasons affecting the colonization of K. pneumoniae in mice’s intestines. Some secondary bile acids (e.g., lithocholic acid) might be a potential drug to prevent the colonization and spread of K. pneumoniae.IMPORTANCEAlcohol is one of the most commonly misused substances in our lives. However, long-term heavy drinking will increase the colonization of some opportunistic pathogens (e.g., Klebsiella pneumoniae) in the body. Here, we revealed that binge-on-chronic alcohol consumption disrupted the balance between gut bacteria and fungi, induced the gut microbiome and metabolites dysbiosis, and promoted the colonization of K. pneumoniae in the intestine of mice. In particular, alcohol-taking disrupted intestinal bile acid metabolism and reduced the lithocholic acid concentration. However, a high concentration of lithocholic acid can protect against intestinal colonization of K. pneumoniae by inhabiting the bacterial growth and adhesion to the host cell. Hence, regulating the balance of gut microbiota and intestinal bile acid metabolism may be a potential strategy for reducing the risk of K. pneumoniae infection and spread.

In response to increasingly complex social emergencies, this study realizes the optimization of logistics information flow and resource allocation by constructing the Emergency logistics information Traceability model (ELITM-CBT) based on alliance blockchain technology. Using the decentralized, data immutable and transparent characteristics of alliance blockchain technology, this research breaks through the limitations of traditional emergency logistics models and improves the accuracy and efficiency of information management. Combined with the hybrid genetic simulated Annealing algorithm (HGASA), the improved model shows significant advantages in emergency logistics scenarios, especially in terms of total transportation time, total cost, and fairness of resource allocation. The simulation results verify the high efficiency of the model in terms of timeliness of emergency response and accuracy of resource allocation, and provide innovative theoretical support and practical scheme for the field of emergency logistics. Future research will explore more efficient consensus mechanisms, and combine big data and artificial intelligence technology to further improve the performance and adaptability of emergency logistics systems.

ABSTRACTAlcohol dependence (AD) is a worldwide epidemic of psychiatric disorders. Gut microbiota dysbiosis may be involved in the development of AD. Recently, the number of studies on the relationship between gut microbiota and alcohol is increasing. However, most previous studies were focused on rodent models and gut microbiota, and the data about the alteration of the gut fungi in AD patients were scarce. Convincing evidence of the gut microbiota’s role in the development of AD is lacking. Here, we investigated the characteristics of gut microbiota (bacterial) and mycobiota (fungal) dysbioses in AD patients and assessed the role of the gut microbiome on the behaviors of rats by fecal microbiota transfer, trying to illuminate the possible microbiota mechanism in AD development. We found that AD patients developed gut microbiota dysbiosis, displayed by the fact that the bacterial genus of Ruminococcaceae significantly decreased and that of Lachnospiraceae increased and the fungal genus of Saccharomyces and Kurtzmaniella obviously increased and that of Candida decreased. Alcohol consumption disturbs the gut equilibrium between bacteria and fungi. The fungal-to-bacterial species ratio was significantly decreased, and the bacterial-fungal trans-kingdom interactions were reduced. By transplanting the gut microbiota from AD patients to rats, we confirmed that the microbiota from AD patients induced the behavioral alterations associated with alcohol dependence in rats, including increased anxiety- and depression-like behaviors, reduced exploratory and recognition memory, and higher alcohol preference. Meanwhile, the microbiota from the AD patient donor upregulated the expression levels of cholecystokinin receptors in the frontal lobe, hippocampus, and cortex in rats, which might be involved in the development of alcohol dependence. Our results demonstrate that AD patients developed gut bacterial and fungal microbiota dysbioses and the gut microbiota may be involved in the development of alcohol addiction by regulating the endogenous cholecystokinin and related receptors’ expression. Our data open new avenues for adjuvant treating AD patients via administrating the intestinal microbiota.IMPORTANCEIntestinal microbiome dysbiosis is associated with psychiatric disease through the “microbiota-gut-brain” axis. Here, we revealed that there was obvious intestinal microbiome (including bacterial and fungal) dysbiosis in alcohol-dependent patients. Alcohol consumption seriously disturbs the gut equilibrium between bacteria and fungi, reduces the interactions among bacterial-fungal trans-kingdom, and increases intestinal permeability. Gut microbiota should be considered as a whole to study the development of alcohol dependence. The gut microbiome of alcohol-dependent patients increased the anxiety- and depression-like behavior in rats. The gut microbiota dysbiosis may promote the development of alcohol dependence by regulating the endogenous cholecystokinin (CCK) and related receptors. Hence, regulating the balance of gut microbiota and the endogenous CCK may be a potential strategy for reducing the risk of relapse in alcohol addiction patients.

Exploring the influence of social network users in the blockchain environment and identifying opinion leaders can help understand the information dissemination characteristics of blockchain social networks, direct the discovery of quality content, and avoid the spread of rumors. Members of blockchain-based social networks are given new responsibilities by token awards and consensus voting, which alters how users connect to the network and engage with one another. Based on blockchain theory and the relevant theories of opinion leaders in social networks, this article combines structural information and content contributions to identify opinion leaders. Firstly, user influence indicators are defined from the perspective of network structure and behavioral characteristics of user contributions. Then, ECWM is constructed, which combines the entropy weight method and the criteria importance through intercriteria correlation (CRITIC) weighting method to address the correlation and diversity among indicators. Furthermore, an improved Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), called ECWM-TOPSIS, is proposed to identify opinion leaders in blockchain social networks. Moreover, to verify the effectiveness of the method, we conducted a comparative analysis of the proposed algorithm on the blockchain social platform Steemit by using two different methods (voting score and forwarding rate). The results show that ECWM-TOPSIS produces significantly higher performance than other methods for all selected top N opinion leaders.

BackgroundThis multicenter, three-armed, parallel, single-blind randomized controlled trial (RCT) primarily aims to compare the efficacy of virtual reality exposure therapy (VRET) with that of acceptance and commitment therapy (ACT) and treatment as usual (TAU) to depreciate the degree of alcohol craving among alcohol use disorder patients who have undergone in-patient detoxification across four timelines (t0 = baseline prior to intervention, t1 = 4 weeks after baseline, t2 = 12 weeks after baseline, and t3 = 24 weeks after baseline). The secondary aims of this RCT are to compare the efficacy of VRET with that of ACT and TAU to alleviate the severity of alcohol use disorder, dissipate comorbid depressive and anxiety symptoms, and normalize event-related potential (ERP) in electroencephalogram (EEG) monitoring across the four timelines.MethodsInitially, after 2 weeks of in-patient detoxification, 120 patients with alcohol use disorder will be randomized into three groups (VRET, ACT, and TAU control groups) via stratified permuted block randomization in a 1:1:1 ratio. Baseline assessment (t0) commences, whereby all the participants will be administered with sociodemographic, clinical, and alcohol use characteristics questionnaire, such as Alcohol Use Disorder Identification Test (AUDIT), Penn Alcohol Craving Scale (PACS), Hamilton Anxiety Rating Scale (HAM-A), and Hamilton Depression Rating Scale (HAM-D), while event-related potential (ERP) detection in electroencephalogram (EEG) will also be carried out. Then, 4 weeks of VRET, ACT, and non-therapeutic supportive activities will be conducted in the three respective groups. For the subsequent three assessment timelines (t1, t2, and t3), the alcohol use characteristic questionnaire, such as AUDIT, PACS, HAM-D, HAM-A, and ERP monitoring, will be re-administered to all participants.DiscussionAs data on the effects of non-pharmacological interventions, such as VRET and ACT, on the treatment of alcohol craving and preventing relapse in alcohol use disorder are lacking, this RCT fills the research gap by providing these important data to treating clinicians. If proven efficacious, the efficacy of VRET and ACT for the treatment of other substance use disorders should also be investigated in future.Clinical trial registrationNCT05841823 (ClinicalTrials.gov).

Pyrolytic carbon black (CBp) is the main by-product of waste tire pyrolysis. CBp has high ash content and poor surface activity which leads to the composites reinforced with CBp has lower performance than composites reinforced with commercial carbon black, making it difficult to meet the requirements of tire. In order to realize the application of CBp in tires, the effect of surfactants on the properties of rubber composites prepared by wet mixing of CBp/natural latex was investigated. In the experimental process, sodium stearate (C17H35COONa), sodium dodecylbenzene sulfonate (C18H29NaO3S), and sodium p-styrenesulfonate (C8H7NaO3S) were used to improve the surface activity and uniform dispersion of the CBp in the natural rubber matrix. The experimental results showed that the dispersity of C18H29NaO3S and C8H7NaO3S modified CBp in rubber matrix has [X] and [Y] values of 7.5 and 7.9, and 9.3 and 9.5, respectively, and the dispersion of these rubber composites is significantly better than that of C17H35COONa. The rubber composites prepared by C18H29NaO3S and C8H7NaO3S surfactants has the better physical and mechanical properties. The rubber composite prepared by C18H29NaO3S has the highest tensile strength of 25.63 MPa, which increment in tensile strength is 15.6% compare to the rubber composite prepared by C17H35COONa. Meanwhile the dynamic mechanical properties of rubber composites prepared by modifying CBp with C18H29NaO3S and C8H7NaO3S are better than CBp with C17H35COONa. The C8H7NaO3S modified CBp/rubber composite has the lowest loss factor (tanδ) value at 60°C, and the C18H29NaO3S modified CBp/rubber composite has the highest tanδ value at 0°C.

ObjectiveAlcohol dependence (AD) is a chronic recurrent mental disease caused by long-term drinking. It is one of the most prevalent public health problems. However, AD diagnosis lacks objective biomarkers. This study was aimed to shed some light on potential biomarkers of AD patients by investigating the serum metabolomics profiles of AD patients and the controls.MethodsLiquid chromatography-mass spectrometry (LC–MS) was used to detect the serum metabolites of 29 AD patients (AD) and 28 controls. Six samples were set aside as the validation set (Control: n = 3; AD group: n = 3), and the remaining were used as the training set (Control: n = 26; AD group: n = 25). Principal component analysis (PCA) and partial least squares discriminant analysis (PCA-DA) were performed to analyze the training set samples. The metabolic pathways were analyzed using the MetPA database. The signal pathways with pathway impact >0.2, value of p

Water resources are essential for agriculture. In the spatial layout of agricultural production, quantitatively identifying the spatial differences in water resource conditions, including precipitation and irrigation water supply factors, is necessary. Here, a water resource suitability index for agricultural production (WRSIA) was constructed for agricultural development using irrigation water supply convenience (IWSC) and precipitation conditions. Considering Lhasa as the study area, water resource suitability index for agricultural production was calculated on a 100 m grid scale, and the spatial distribution relationship between water resource suitability index for agricultural production and cultivated land was analyzed using geographically weighted regression (GWR). The results showed that irrigation water supply convenience severely restricted agricultural production in Lhasa, and the high water resource suitability index for agricultural production values were mainly distributed in the valleys of the Lhasa River and its tributaries. Moreover, 47.7% of the cultivated land was distributed in 5% of the area having the highest water resource suitability. According to geographically weighted regression, the cultivated land area and water resource availability were strongly correlated (R2 = 0.904). The distribution of the cultivated land was well explained by water resource suitability index for agricultural production, which could describe the differences in water resource suitability for agricultural production. Furthermore, the suitability of agricultural production was better evaluated when water resource suitability index for agricultural production was coupled with the land resource suitability index. Overall, water resource suitability index for agricultural production showed high applicability in Lhasa and other regions, thereby providing a scientific basis and technical support for the spatial layout of agricultural production.

Drone monitoring plays an irreplaceable and significant role in forest firefighting due to its characteristics of wide-range observation and real-time messaging. However, aerial images are often susceptible to different degradation problems before performing high-level visual tasks including but not limited to smoke detection, fire classification, and regional localization. Recently, the majority of image enhancement methods are centered around particular types of degradation, necessitating the memory unit to accommodate different models for distinct scenarios in practical applications. Furthermore, such a paradigm requires wasted computational and storage resources to determine the type of degradation, making it difficult to meet the real-time and lightweight requirements of real-world scenarios. In this paper, we propose an All-in-one Image Enhancement Network (AIENet) that can restore various degraded images in one network. Specifically, we design a new multi-scale receptive field image enhancement block, which can better reconstruct high-resolution details of target regions of different sizes. In particular, this plug-and-play module enables it to be embedded in any learning-based model. And it has better flexibility and generalization in practical applications. This paper takes three challenging image enhancement tasks encountered in drone monitoring as examples, whereby we conduct task-specific and all-in-one image enhancement experiments on a synthetic forest dataset. The results show that the proposed AIENet outperforms the state-of-the-art image enhancement algorithms quantitatively and qualitatively. Furthermore, extra experiments on high-level vision detection also show the promising performance of our method compared with some recent baselines.

In view of the problems of A∗ algorithm in path planning, such as collision risk, the path is not necessarily optimal, and there are numerous turning nodes. Therefore, this study proposes an improved A∗ algorithm to improve the quality of the planned path. First, the 8-neighborhood children of the parent node are generated, and the security of the planned path is improved by further investigating the properties of the neighbors of these children one by one to reasonably set virtual obstacles. Second, the heuristic function of A∗ algorithm is ameliorated to make it closer to the actual cost, so as to enhance the accuracy of the planned path; finally, the planned path is smoothed by using the cubic uniform B-spline curve to eliminate corner sharp points in the path. The simulation results show that the improved A∗ algorithm can not only ensure the safety and smoothness of the planned path but also obtain the shortest planned path in the static environment with different obstacle rates. In addition, we combine the improved A∗ algorithm with the dynamic window algorithm to enable mobile robots to realize real-time dynamic obstacle avoidance while ensuring the optimality of global path planning.

Perhydropolysilazane (PHPS) is an inorganic polymer composed of -(SiH2–NH)- repeating structural units. When used as a coating material, it has the advantages of good solubility, simple curing conditions, flexible curing process, and simple preparation method and can significantly save processing time and cost. This paper investigates the frictional behavior of the PHPS-coated metals cured at different temperatures with NR/BR/SBR composites. The effects of different curing temperatures on the PHPS coatings are characterized using infrared absorption spectroscopy. After curing, the asymmetric stretching and oscillating vibration peaks of the Si–O–Si groups are formed, which is attributed to the hydrolysis reaction of Si–N and Si–H and the subsequent cross-linking curing reaction. The denseness of PHPS coatings increases with the increase in curing temperature. When the curing temperature is 400 °C, the cross-linking curing effect of PHPS is the strongest, and the denseness of the PHPS coating is the best. Furtherly, the frictional wear of metal with NR/BR/SBR composites is analyzed from the perspective of the formulation process for the first time, and the ratio of abrasive wear and corrosion wear is calculated qualitatively. It is found that increasing the curing temperature of the PHPS coating can significantly reduce metal wear and improve corrosion resistance, which is attributed to the formation of the ethanol-water molecular film on the surface of the PHPS coating. The ethanol-water molecular film isolates the contact between the PHPS-coated metal and the NR/BR/SBR composite to a certain extent, acting as a lubricant and thus reducing friction. After rubbing the cured PHPS-coated metal with NR/BR/SBR composite, the wear amounts of the cured PHPS-coated metal at 200 °C, 300 °C, and 400 °C are reduced by 49.06%, 66.74%, and 87.33%, respectively, and the corrosion resistance are improved by 51.52%, 63.64%, and 78.79%, respectively.

The semi-transparency property of smoke integrates it highly with the background contextual information in the image, which results in great visual differences in different areas. In addition, the limited annotation of smoke images from real forest scenarios brings more challenges for model training. In this paper, we design a semi-supervised learning strategy, named smoke-aware consistency (SAC), to maintain pixel and context perceptual consistency in different backgrounds. Furthermore, we propose a smoke detection strategy with triple classification assistance for smoke and smoke-like object discrimination. Finally, we simplified the LFNet fire-smoke detection network to LFNet-v2, due to the proposed SAC and triple classification assistance that can perform the functions of some specific module. The extensive experiments validate that the proposed method significantly outperforms state-of-the-art object detection algorithms on wildfire smoke datasets and achieves satisfactory performance under challenging weather conditions.

As the basic equipment in the rubber field, the internal mixer is suitable for long-term rubber mixing. For the continuous production of the factory, the wear of the internal mixer is a problem that cannot be ignored. This article mainly studies the wear of the end face of the internal mixer. The end face of the mixer is connected to the mixing chamber. With the increase in mixing time, the end face of the mixer is inevitably worn. The abrasion of the end face of the internal mixer causes the gap between the end face of the internal mixer and the mixing chamber to increase, and the materials in the mixing chamber leak out. This affects the ratio of materials and reduces the mixing effect. And with the increase in material leakage, the abrasion of the end face of the internal mixer is intensified, which eventually leads to a decrease in the compound's performance. BF is a pure natural inorganic non-metallic material. It has good high-temperature resistance, and high compressive strength can be used in various environments and is inexpensive and cost-effective. Based on this, BF is increasingly used in the rubber field. In this paper, the effect of adding different amounts of BF on the friction and wear of the end face of the internal mixer is studied, and the proportions of varying wear forms are distinguished for the first time.The study finds that with the increase of BF in the compound, the proportion of abrasive wear first decreased and then increased, the balance of corrosion wear first increased and then decreased, and the amount of metal wear first decreased and then increased. It is found that the lowest wear of the rubber compound on the metal can be achieved when the amount of BF reaches 10 phr. When the BF amount increases, the corresponding amount of metal wear increases.

As the essential mixing equipment, long hours of work can cause wear and tear on the end face of the compactor. The wear of the end faces leads to an increase in the gap between the chamber and the end faces, which leads to leakage, reduces the mixing effect, and ultimately affects the rubber properties. Therefore, it is necessary to study the end face metal wear during the mixing process. Thermally cracked carbon black, as recycled material for scrap rubber, can be used as a substitute for other carbon blacks in some fields, vastly reducing the production cost and promoting the reuse of scrap rubber. In this paper, thermally cracked carbon black (CBp) was mixed with multi-walled carbon nanotubes (MWCNTs) and added rubber by mechanical blending to obtain CBp/MWCNTs composites. The synergistic mechanism of CBp and MWCNTs was investigated to analyze the effect of CBp/MWCNTs ratios on the frictional wear of metal on the end face of the compactor. The results showed that MWCNTs cooperated with CBp particles to form a three-dimensional “grape bunch” network structure. On the one hand, the three-dimensional “grape bunch” network structure can adsorb CBp particles to promote the dispersion of CBp particles. On the other hand, the three-dimensional “grape bunch” network structure can also hinder the overflow of aromatic oil and affect oil film formation. At the same time, the three-dimensional “grape bunch” network structure can also form a physical isolation layer between the rubber and the metal. This significantly influences the degree of CBp particle dispersion, friction coefficient, metal surface roughness, and metal wear. It seems that the synergistic effect between MWCNTs and CBp particles is maximized when the content of MWCNTs in CBp/MWCNTs composites is 4 phr, the area of CBp/MWCNTs three-dimensional mesh is maximized, the dispersion of CBp particles is the best, and the wear of CBp/MWCNTs composites on metal is the lowest.

Silica is an excellent reinforcing material and has been widely used in the rubber tire field. Silica can improve the wet grip performance and reduce rolling resistance. However, the application of silica in high-performance tires have been put forward with higher requirements due to the difficulties of dispersion, high heat generation, temperature sensitivity, and easy gel generation in the mixing process. To solve the above problems, nanocarbon-coated silica (silica@nanocarbon, SN) was proposed as a new reinforcing material in this paper. Nanocarbon coating prepared by pyrolysis of polyethylene. The hydroxyl groups on the surface of SN disappeared. Then, the new material was applied to the rubber composite, and the experimental results show that the curing time (t90) of the SN filled rubber compounds can be shortened. SN can improve the thermal conductivity of rubber compounds, and the compound has better dynamic mechanical properties. It also provides a feasible exploration for the application of modified silica in high-performance tires.

Underwater images often suffer from degradation due to scattering and absorption. With the development of artificial intelligence, fully supervised learning-based models have been widely adopted to solve this problem. However, the enhancement performance is susceptible to the quality of the reference images, which is more pronounced in underwater image enhancement tasks because the ground truths are not available. In this paper, we propose a light-enhanced pixel fusion network (LEPF-Net) to solve this problem. Specifically, we first introduce a novel light enhancement block (LEB) based on the residual block (RB) and the light enhancement curve (LE-Curve) to restore the cast color of the images. The RB is adopted to learn and obtain the feature maps from an original input image, and the LE-Curve is used to renovate the color cast of the learned images. To realize the superb detail of the repaired images, which is superior to the reference images, we develop a pixel fusion subnetwork (PF-SubNet) that adopts a pixel attention mechanism (PAM) to eliminate noise from the underwater image. The PAM adapts weight allocation to different levels of a feature map, which leads to an enhancement in the visibility of severely degraded areas. The experimental results show that the proposed LEPF-Net outperforms most of the existing underwater image enhancement methods. Furthermore, among the five classic no-reference image quality assessment (NRIQA) indicators, the enhanced images obtained by LEPF-Net are of higher quality than the ground truths from the UIEB dataset.

Recently, significant progress has been made in the field of public key encryption with keyword search (PEKS), with a focus on optimizing search methods and improving the security and efficiency of schemes. Keyword frequency analysis is a powerful tool for enhancing retrieval services in explicit databases. However, designing a PEKS scheme that integrates keyword frequency analysis while preserving privacy and security has remained challenging, as it may conflict with some of the security principles of PEKS. In this paper, we propose an innovative scheme that introduces a security deadline to query trapdoors through the use of timestamps. This means that the keywords in the query trapdoor can only be recovered after the security deadline has passed. This approach allows for keyword frequency analysis of query keywords without compromising data privacy and user privacy, while also providing protection against keyword-guessing attacks through the dual-server architecture of our scheme. Moreover, our scheme supports multi-keyword queries in multi-user scenarios and is highly scalable. Finally, we evaluate the computational and communication efficiency of our scheme, demonstrating its feasibility in practical applications.

With the prevalence of cloud computing, the outsourcing of computation has gained significant attention. Clients with limited computing power often outsource complex computing tasks to the cloud to save on computing resources and costs. In outsourcing the computation of functions, a function owner delegates a cloud server to perform the function’s computation on the input received from the user. There are three primary security concerns associated with this process: protecting function privacy for the function owner, protecting input privacy for the user and guaranteeing that the cloud server performs the computation correctly. Existing works have only addressed privately verifiable outsourcing computation with privacy or publicly verifiable outsourcing computation without input privacy or function privacy. By using the technologies of homomorphic encryption, proxy re-encryption and verifiable computation, we propose the first publicly verifiable outsourcing computation scheme that achieves both input privacy and function privacy for matrix functions, which can be extended to arbitrary multivariate polynomial functions. We additionally provide a faster privately verifiable method. Moreover, the function owner retains control over the function.

Internal mixer is an important mixing component in industrial applications. Studies show that when a mixer operates for a long time, wear may appear at the end face of the internal mixer. On the other hand, abrasion of the end face increases the gap between the mixing chamber and the end face. Then, leakage occurs, which reduces the mixing effect and affects the rubber performance. Therefore, it is of significant importance to study the wear of the end face during the mixing process. Graphene oxide (GO) and multi-walled carbon nanotubes (MWCNTs) have excellent electrical characteristics and thermal conductivity so that they have been widely used in various fields such as synthesized composite rubbers. GO and MWCNTs are mechanically blended into natural rubber to obtain GO/MWCNTs composite material. In the present study, the influence of the GO/MWCNTs ratio on the friction and wear of the end face of the internal mixer is analyzed. The obtained results show that MWCNTs and GO molecules interact to form a three-dimensional network structure, which has a greater impact on the degree of silica dispersion, silanization reaction, and metal wear. As the GO content increases, the average friction coefficient between rubber and metal, metal roughness, the proportion of abrasive wear, and the amount of metal wear gradually decrease. The lowest values can be achieved when the amount of added GO is set to 3 phr. When the GO in the mixture exceeds 3 phr, the friction coefficient between rubber and metal, and the metal roughness change, thereby affecting the proportion of abrasive wear and increasing the amount of metal wear in the mixing process. In this experiment, the amount of CNT that reduces GO aggregation is 6 phr. On the whole, when the ratio of GO/MWCNTs is 3/6, the synergistic effect of MWCNTs and GO molecules is maximized, the area of the three-dimensional network structure of GO/MWCNTs is the largest, the dispersibility of SiO2 is the best, and the amount of wear of the composite material on the metal is the lowest.

As an essential rubber mixing equipment in continuous production, an internal mixer should have a long service life while operating at high intensity. Studies show that at these severe operating conditions, end face wear of the internal mixer is inevitable. It is worth noting that the end face is an important part of the closed mixing chamber. The head of the rotor shaft is inserted into the copper ring of the sealing plate, the sealing plate and the mixing chamber are pressed together by bolts. There is a gap between the rotors and the mixing chamber. During the mixing process, the mixed rubber in the mixing chamber is in direct contact with the end face, the end face metal is frictional and abraded. Wear of the end face may result in a gap between the mixing chamber and the end face, leading to material leakage. As the leakage intensifies, the gap between the end face and the mixing chamber further increases, thereby forming negative feedback. This phenomenon not only affects the chemical composition of the rubber but also reduces the mixing efficiency and decreases the performance of the rubber compound. The end surface material is cobalt chromium molybdenum tungsten alloy. The alloy model is Stellite 6B. In the present study, the influence of different amounts of CF on the friction and wear of the end face is analyzed from the chemical composition perspective. Moreover, the ratio of corrosion wear to abrasive wear is calculated.The obtained results show that as the amount of CF in the compound increases, the proportion of the corrosive wear of the compound to the metal increases, while the proportion of abrasive wear decreases. The highest proportion of corrosive wear and the lowest amount of metal wear can be achieved for the CF amount of 9 phr. As the CF content in the composition increases, the ratio of the corrosive wear of the rubber compound to metal decreases, while the proportion of abrasive wear and the total amount of wear to metal increases.

Objective: Abnormal selective attention to drug cues and negative affect is observed in patients with substance dependence, and it is closely associated with drug addiction and relapse. Methadone maintenance is an effective replacement therapy to treat heroin addiction, which significantly reduces the relapse rate. The present study examines whether the patients with opioid use disorder on chronic methadone maintenance therapy exhibit abnormal attentional bias to drug cues and negative-affective cues. Moreover, its relation to therapeutic and neuropsychological factors is also examined.Methods: Seventy-nine patients with opioid use disorder under chronic methadone maintenance therapy and 73 age-, sex-, and education-matched healthy controls were recruited and assessed for attentional bias to drug cues and negative affect using a dot-probe detection task. Correlational analysis was used to examine the relationships between the attentional bias and the demographic, therapeutic, and neuropsychological factors.Results: No significant overall patient-control group difference is observed in drug-related or negative-affective-related attentional bias scores. In the patient group, however, a significant negative correlation is found between the attentional bias scores to negative-affective cues and the duration of methadone treatment (p = 0.027), with the patients receiving longer methadone treatment showing less attentional avoidance to negative-affective cues. A significant positive correlation is found between the negative affect-induced bias and the impulsivity score (p = 0.006), with more impulsive patients showing higher attentional avoidance to negative affective cues than less impulsive patients. Additionally, the patients detect a smaller percentage of probe stimuli following the drug (p = 0.029) or negative-affective pictures (p = 0.009) than the healthy controls.Conclusion: The results of the present study indicate that the patients under chronic methadone maintenance therapy show normalized attentional bias to drug and negative-affective cues, confirming the involuntary attention of the patients is not abnormally captured by external drug or negative-affective clues. Our findings also highlight that the attentional avoidance of negative-affective cues is modulated by the duration of methadone treatment and the impulsivity level in the patients.

The nonlinear limit-cycle oscillations induced by aileron rotational freeplay are investigated by numerical simulation and flutter wind tunnel tests. An integrated nonlinear aeroelastic analysis method and procedure based on the nonlinear system identification technology is proposed. Based on the characteristics of the structure, a low-speed nonlinear flutter wing-aileron model that considers both the linear flutter test and the nonlinear limit-cycle oscillation test with aileron rotational freeplay is designed. The wind tunnel tests are conducted with the model, and the stable limit-cycle oscillation is observed by accelerometers under the linear flutter boundary with a 0° angle of attack. Higher-order harmonic vibration is also observed with a low proportion compared to the fundamental oscillation, and the limit-cycle oscillation grows gradually with the increase of airspeed until flutter occurs. The comparison of numerical and experimental investigation shows high consistency.

Although Generative Adversarial Networks (GANs) have shown remarkable successes in various computer vision tasks, they still face challenges in image season style transfer task. In this paper, we propose a multi-season Generative Adversarial Networks (MSGANs) aimed to transfer input images into other season styles. To improve the quality of the simulated images generated by the proposed MSGAN, we propose a novel loss function to guide the optimization direction of the network. Besides, we adopt the saliency information to guide the seasonal style transformation task, so as to ensure that different image contents can have different optimization weights in MSGAN. The experimental results show that the proposed MSGAN can generate high-quality simulated images from real images, and is superior to other latest methods. Not only that, the synthetic image generated by the proposed method also be used to perform depth estimation task so that prove that the synthetic images can be well applied to other computer vision tasks.

As highly increasing of on-line fashions retail industry, automatic recognition and representation of clothing items have huge potentials. With the help of deep learning methods, many clothing attribute representation models have been proposed. However, these models are mainly suitable for coarse-grained classification which are not suitable for clothing attribute representation. To address such a problem, in this article, we propose a novel network structure named SAC, which is a combination of CNNs and Self-attention mechanism and can represent clothing attributes more fine-grained. Besides, we use Grad-CAM to visualize which part of the clothing attributes is more concerned by customers. Finally, a new labeled clothing dataset is introduced in this article, which is expected to be helpful to the researchers who are working in fashion domains for image representation.

Image dehazing on a hazy image aims to remove the haze and make the image scene clear, which attracts more and more research interests in recent years. Most existing image dehazing methods use a classic atmospheric scattering model and natural image priors to remove the image haze. In this paper, we propose an end-to-end image dehazing model termed as DRHNet (Deep Residual Haze Network), which restores the haze-free image by subtracting the learned negative residual map from the hazy image. Specifically, DRHNet proposes a context-aware feature extraction module to aggregate the contextual information effectively. Furthermore, it proposes a novel nonlinear activation function termed as RPReLU (Reverse Parametric Rectified Linear Unit) to improve its representation ability and to accelerate its convergence. Extensive experiments demonstrate that DRHNet outperforms state-of-the-art methods both quantitatively and qualitatively. In addition, experiments on image deraining task show that DRHNet can also serve for image deraining.

The large current generated by starting directly of super large capacity and high voltage induction motor would have a huge impact on the grid as well as the motor itself. The variation of the power factor and electromagnetic torque during direct start of motors with different capacity and voltage levels are obtained. Aiming at the problem that the secondary impact of auto-transformer starter is too large and the cost of magnetic control starter is too high, the auto-transformer and magnetic control soft start method of super large capacity and high voltage motor is proposed and the basic working principle is analyzed. The calculation formula of cost for magnetic control soft starter and auto-transformer and magnetic control soft starter is deduced, and specific examples are analyzed and compared. It is concluded that the choice of auto-transformer with appropriate tapping ratio can greatly reduce the cost of auto-transformer and magnetic control soft starter compared with the other one. Finally, the simulation and experiment results show that the start method can effectively avoid secondary current impact and constrain the motor starting current to less than 2.5 times the rated current.

BackgroundIn recent years, the price of pork in China continues to fluctuate at a high level. The forecast of pork price becomes more important. Single prediction models are often used for this work, but they are not accurate enough. This paper proposes a new method based on Census X12-GM(1,1) combination model.MethodsMonthly pork price data from January 2014 to December 2020 were obtained from the State Statistics Bureau(Mainland China). Census X12 model was adopted to get the long-term trend factor, business cycle change factor and seasonal factor of pork price data before September 2020. GM (1,1) model was used to fit and predict the long-term trend factor and business cycle change factor. The fitting and forecasting values of GM(1,1) were multiplied by the seasonal factor and empirical seasonal factor individually to obtain the fitting and forecasting values of the original monthly pork price series.ResultsThe expression of GM(1,1) model for fitting and forecasting long-term trend factor and and business cycle change factor was X(1)(k) = -1704.80e-0.022(k-1) + 1742.36. Empirical seasonal factor of predicted values was 1.002 Using Census X12-GM(1,1) method, the final forecast values of pork price from July 2020 to December 2020 were 34.75, 33.98, 33.23, 32.50, 31.78 and 31.08 respectively. Compared with ARIMA, GM(1,1) and Holt-Winters models, Root mean square error (RMSE), mean absolute percentage error (MAPE) and mean absolute error (MAE) of Census X12-GM(1,1) method was the lowest on forecasting part.ConclusionsCompared with other single model, Census X12-GM(1,1) method has better prediction accuracy for monthly pork price series. The monthly pork price predicted by Census X12-GM(1,1) method can be used as an important reference for stakeholders.

The atmospheric particles and aerosols from burning usually cause visual artifacts in single images captured from fire scenarios. Most existing haze removal methods exploit the atmospheric scattering model (ASM) for visual enhancement, which inevitably leads to inaccurate estimation of the atmosphere light and transmission matrix of the smoky and hazy inputs. To solve these problems, we present a novel color-dense illumination adjustment network (CIANet) for joint recovery of transmission matrix, illumination intensity, and the dominant color of aerosols from a single image. Meanwhile, to improve the visual effects of the recovered images, the proposed CIANet jointly optimizes the transmission map, atmospheric optical value, the color of aerosol, and a preliminary recovered scene. Furthermore, we designed a reformulated ASM, called the aerosol scattering model (ESM), to smooth out the enhancement results while keeping the visual effects and the semantic information of different objects. Experimental results on both the proposed RFSIE and NTIRE’20 demonstrate our superior performance favorably against state-of-the-art dehazing methods regarding PSNR, SSIM and subjective visual quality. Furthermore, when concatenating CIANet with Faster R-CNN, we witness an improvement of the objection performance with a large margin.

In recent years, with the rapid development of economy, industrial pollution problems have become more and more serious. In this paper, a differential game model is proposed for industrial pollution management, in which public participation is taken into consideration. Then, a feedback Nash equilibrium (FBNE) solution is obtained among the government, enterprises, and the public. Finally, a numerical example is given to illustrate the results. The results show that the public participation will take a positive part in forcing enterprises to reduce emissions. Furthermore, with the increase of the probability of the public reporting the illegal discharge of pollutants by enterprises, the probability of enterprises' active emission reduction will also greatly increase

The large current generated by a direct start of the super-large-capacity high-voltage induction motor would have a huge impact on the power grid as well as the motor itself. Traditional soft starters have the shortcomings of discontinuous adjustment, voltage sags, sudden torque mutation, secondary current impact and high cost. To resolve this issue, the authors propose a novel coupled autotransformer and magnetic-control (CATMC) soft-start method. The structure of the new CATMC soft starter combines the functions of the autotransformer and magnetic control reactor via an innovative electric and magnetic circuit design. In this study, the authors analyse the magnetic circuit structure and working principles of the CATMC soft starter. Then, to validate its principle and performance, the authors conduct a simulation study using ANSYS software and design and test an 18 MW/10 kV CATMC soft starter prototype. The simulation results demonstrate that the CATMC soft starter effectively avoids secondary current impact and constrains the motor starting current to

With the continuous increase in the production of electronic devices, large amounts of electronic waste (E-waste) are routinely being discarded into the environment. This causes serious environmental and ecological problems because of the non-degradable polymers, released hazardous chemicals, and toxic heavy metals. The appearance of biodegradable polymers, which can be degraded or dissolved into the surrounding environment with no pollution, is promising for effectively relieving the environmental burden. Additionally, biodegradable polymers are usually biocompatible, which enables electronics to be used in implantable biomedical applications. However, for some specific application requirements, such as flexibility, electric conductivity, dielectric property, gas and water vapor barrier, most biodegradable polymers are inadequate. Recent research has focused on the preparation of nanocomposites by incorporating nanofillers into biopolymers, so as to endow them with functional characteristics, while simultaneously maintaining effective biodegradability and biocompatibility. As such, bionanocomposites have broad application prospects in electronic devices. In this paper, emergent biodegradable and biocompatible polymers used as insulators or (semi)conductors are first reviewed, followed by biodegradable and biocompatible nanocomposites applied in electronics as substrates, (semi)conductors and dielectrics, as well as electronic packaging, which is highlighted with specific examples. To finish, future directions of the biodegradable and biocompatible nanocomposites, as well as the challenges, that must be overcome are discussed.

A novel way to measure humidity through testing the emissivity of an area radiant source is presented in this paper. The method can be applied in the environment at near room temperature (5~95 °C) across the relative humidity (RH) range of 20~90% RH. The source, with a grooved radiant surface, works in the far infrared wavelength band of 8~12 μm. The Monte-Carlo model for thermal radiation was set up to analyze the V-grooved radiant surface. Heat pipe technology is used to maintain an isothermal radiant surface. The fuzzy-PID control method was adopted to solve the problems of intense heat inertia and being easily interfered by the environment. This enabled the system to be used robustly across a large temperature range with high precision. The experimental results tested with a scanning radiant thermometer showed that the radiant source can provide a uniform thermal radiation capable of satisfying the requirements of humidity testing. The calibration method for the radiant source for humidity was explored, which is available for testing humidity.