Cost Optimization of Steel Beam-to-Column Connections using AVOA

The joint-bolt-African Vulture optimization algorithm(AVOA)model is proposed for the design of building connections to improve the stability of steel beam-to-column connections.For this algorithm,the type of steel is first determined,and the number of bolts needed by the corresponding steel type is referenced in Eurocode 3.Then,the bearing capacity of the joint can be calculated.The joint-bolt-AVOA model is established by substituting the bolt number required by the steel into the algorithm to obtain the optimal bolt number required while ensuring joint stability.The results show that the number of bolts required by the joint-bolt-AVOA model based on the stability of steel is lower than that calculated by Eurocode 3.Therefore,AVOA can effectively optimize the number of bolts needed in building connections and save resources.

改进RRT-Connect与DWA算法的巡检机器人路径规划研究

针对传统RRT-Connect算法在密集复杂环境中路径规划效率低、动态避障效果差等问题,提出一种改进RRT-Connect与DWA融合算法。该算法通过改进采样策略、动态步长优化和碰撞检测引导随机树生长;在随机树中采用贪心策略和角度约束优化路径。基于巡检机器人建立运动学模型,通过速度采样空间生成轨迹簇;建立模糊逻辑系统自适应调整DWA算法评价函数的权重系数,将全局最优路径点融入DWA算法中实现全局最优路径和实时避障。仿真结果表明,在油气站场密集复杂环境中,改进RRT-Connect算法较传统算法路径缩短约27.09%,平滑度提高约84.6%,碰撞距离提高约18.75%;改进融合算法路径减少约2.97%,平滑度提高约78.8%,碰撞距离提高约30.6%,验证了提出算法的有效性。

Age-related hearing loss accelerates the decline in fast speech comprehension and the decompensation of cortical network connections

Patients with age-related hearing loss face hearing difficulties in daily life.The causes of age-related hearing loss are complex and include changes in peripheral hearing,central processing,and cognitive-related abilities.Furthermore,the factors by which aging relates to hearing loss via changes in audito ry processing ability are still unclear.In this cross-sectional study,we evaluated 27 older adults(over 60 years old) with age-related hearing loss,21 older adults(over 60years old) with normal hearing,and 30 younger subjects(18-30 years old) with normal hearing.We used the outcome of the uppe r-threshold test,including the time-compressed thres h old and the speech recognition threshold in noisy conditions,as a behavioral indicator of auditory processing ability.We also used electroencephalogra p hy to identify presbycusis-related abnormalities in the brain while the participants were in a spontaneous resting state.The timecompressed threshold and speech recognition threshold data indicated significant diffe rences among the groups.In patients with age-related hearing loss,information masking(babble noise) had a greater effect than energy masking(speech-shaped noise) on processing difficulties.In terms of resting-state electroencephalography signals,we observed enhanced fro ntal lobe(Brodmann’s area,BA11) activation in the older adults with normal hearing compared with the younger participants with normal hearing,and greater activation in the parietal(BA7) and occipital(BA19) lobes in the individuals with age-related hearing loss compared with the younger adults.Our functional connection analysis suggested that compared with younger people,the older adults with normal hearing exhibited enhanced connections among networks,including the default mode network,sensorimotor network,cingulo-opercular network,occipital network,and frontoparietal network.These results suggest that both normal aging and the development of age-related hearing loss have a negative effect on advanced audito ry processing capabilities and that hearing loss accele rates the decline in speech comprehension,especially in speech competition situations.Older adults with normal hearing may have increased compensatory attentional resource recruitment represented by the to p-down active listening mechanism,while those with age-related hearing loss exhibit decompensation of network connections involving multisensory integration.

基于改进RRT-Connect算法的全局路径规划

针对RRT-Connect算法在复杂环境内的路径规划中存在探索性弱、收敛速度慢、冗余节点多、搜索路径较长等问题,提出一种改进的RRT-Connect算法。通过引入高质量随机点和动态步长的方法,提高了生成随机树的质量并减少了冗余节点数量;采用正向寻优和逆向贪婪的方式,改善了搜索路径较长的问题。实验结果表明,改进RRT-Connect算法平均路径规划时间缩短26.41%,平均路径规划长度缩短19.05%,平均路径规划节点个数减少41.91%,证明了改进RRT-Connect算法相比于原算法规划效率更高,规划时间更少,规划路径质量更优。

Analytical Investigation into the Rotational Performance of Glulam Bolted Beam-Column Connections under Coupled Bending Moment and Shear Force

Considering the glulam beam-column connection form and the number of bolts,monotonic loading test and finite element analysis was carried out on 9 connection specimens in 3 groups to study the rotational performance and failure mode of the connection.The test results revealed that compared with U-shaped connectors,T-shaped connectors can effectively improve the ductility of connections,and the increase in the number of bolts can reduce the initial stiffness and ductility of connections.By theoretical analysis,formulas for calculating the initial stiffness and ultimate moment of connections were deduced.Subsequently,the moment-rotation theoretical model of connections was established based on the formulas,which were validated according to the test data and simulation results.The proposed model can not only improve the current theoretical system of heavy-duty glulam beam-column structure but also provide a theoretical basis for calculating the mechanical properties of the glulam beam-column connection.

Shear Behaviors of Steel-Plate Connections for Timber-Concrete Composite Beams with Prefabricated Concrete Slabs

To promote the development of timber-concrete composite(TCC)structures,it is necessary to propose the assembly-type connections with high assembly efficiency and shear performances.This article presented the experimental results of the innovative steel-plate connections for TCC beams using prefabricated concrete slabs.The steel-plate connections consisted of the screws and the steel-plates.The steel-plates were partly embedded in the concrete slabs.The concrete slabs and the timber beams were connected by screws through the steel-plates.The parameters researched in this article included screw number,angle steel as the reinforcement for anchoring,and shallow notches on the timber surface to restrict the slip of the steel-plates.Experimental results were discussed in terms of failure modes,ultimate bearing capacities,and slip moduli.It was found that increasing the number of screws could lead to the obvious improvement on the ultimate bearing capacities and the slip moduli at the ultimate state;and the angle steel as the reinforcement showed the slight influence on the ultimate bearing capacities and the slip moduli.The application of the shallow notch can greatly improve the ultimate bearing capacities and the slip moduli.The calculation models for the ultimate bearing capacities and the slip moduli of the steel-plate connections with and without shallow notches were proposed,which showed good accuracy compared with the experimental results.

The impact of political connections on firm pollution: New evidence based on heterogeneous environmental regulation

Increasing attention has been focused on the extent to which corporate political connections influence the growing pollution due to the rapid growth in the popularity of firm pollution in developing countries.We adopt a static threshold regression model to investigate the effects of heterogeneous environmental regulation on political connections and firm pollution based on the panel data from China’s A-share listed companies from 2012 to 2019.The empirical results show a non-linear relationship between the degree of political connection of listed company executives and the level of firm pollution.And the relationship between the two roughly presents a U-shaped relationship under the action of the marketincentive environmental regulation threshold.However,it roughly presents an inverted N-shaped relationship under the action of the command-control environmental regulation threshold.Additionally,the group test results show that the existence of regional and ownership heterogeneity causes certain differences in the environmental behaviour of politically connected enterprises.These findings indicate that diverse environmental regulations are needed to promote sustainable green development and to further expand the theoretical and practical exploration of political connections on firm pollution.

The Effect of Self-Investment on Hoarding Tendency of Chinese College Students:Role of Psychological Connections

Because of factors such as energy and time one invests in an object,the stronger the connection,value,and reluc-tance to lose said object individual will have.Hoarding behavior arises when individuals incorporate a strong attachment with themselves to an object.The purpose of this study is to examine the effect of self-investment on hoarding tendency and the roles of possession-self link and liking level in this connection.A hypothetical model of the relationship between self-investment,possession-self link,liking level,and hoarding tendency was tested.A convenience sampling method was used to survey 450 college students in Yunnan Province on either a paper-based or online self-report scale.The data were collected using self-investment,possession-self link,and liking level questionnaires,as well as the Saving Inventory Revised.Results showed positive relationship between the study variables,ranging from 0.37 to 0.87.College students’self-investment had a direct positive pre-dictive effect on hoarding tendency;self-investment,in turn,indirectly predicted hoarding tendency through the mediating effect of possession-self link;and individual liking level of items had a moderating effect for self-invest-ment on the possession-self link.This study shows how self-investment affects the hoarding tendency of college students,and the results of this study also help demonstrate the value of self-investment and possession-self link in optimizing students’hoarding tendency and thus promoting good psychological status.

Experimental Study of Moso Bamboo to-Steel Connections with Embedded Grouting Materials

Moso bamboos have attracted excessive attention as a renewable green building material to the concept of sustainable development.In this paper,the 20 bolted Moso bamboo connection specimens with embedded steel plates and grouting materials were designed according to connection configurations with different bolt diameters and end distance of bolt holes,and their bearing capacities and failure modes were analyzed by static tension tests.According to the test results of all connectors,the failure modes of the specimens are divided into four categories,and the effects of bolt diameter and bolt hole end distance on the connection bearing capacity and failure mode are analyzed.The test results show that the deformation and failure process can be divided into four stages.The main influence factor of connector bearing capacity is bolt diameter.Connectors can be divided into four failure modes,and brittle failure can be avoided by adopting certain structural measures.Filling with grouting material can improve the bearing capacity of joints.Due to the large variability of bamboo,further experiments are needed.

An Improved Time Feedforward Connections Recurrent Neural Networks

Recurrent Neural Networks(RNNs)have been widely applied to deal with temporal problems,such as flood forecasting and financial data processing.On the one hand,traditional RNNs models amplify the gradient issue due to the strict time serial dependency,making it difficult to realize a long-term memory function.On the other hand,RNNs cells are highly complex,which will signifi-cantly increase computational complexity and cause waste of computational resources during model training.In this paper,an improved Time Feedforward Connections Recurrent Neural Networks(TFC-RNNs)model was first proposed to address the gradient issue.A parallel branch was introduced for the hidden state at time t−2 to be directly transferred to time t without the nonlinear transforma-tion at time t−1.This is effective in improving the long-term dependence of RNNs.Then,a novel cell structure named Single Gate Recurrent Unit(SGRU)was presented.This cell structure can reduce the number of parameters for RNNs cell,consequently reducing the computational complexity.Next,applying SGRU to TFC-RNNs as a new TFC-SGRU model solves the above two difficulties.Finally,the performance of our proposed TFC-SGRU was verified through sev-eral experiments in terms of long-term memory and anti-interference capabilities.Experimental results demonstrated that our proposed TFC-SGRU model can cap-ture helpful information with time step 1500 and effectively filter out the noise.The TFC-SGRU model accuracy is better than the LSTM and GRU models regarding language processing ability.

Lightweight Technologies in Sustainable Architecture:The Importance of Connections in Disassembly

The aim of this paper is to investigate the role of lightweight structures and connections in the DfD(design for disassembly)framework.The construction sector is facing pressure to reduce its environmental impact,which has led to heightened interest in DfD as a strategy for transitioning from a linear“Cradle to Grave”economic model to a circular“Cradle to Cradle”model.At the social level,DfD’s technological and spatial flexibility provides opportunities for self-build and self-maintenance processes,which can decrease land consumption and reduce costs for both owners and tenants.In this context,lightweight structures and connections are crucial for enabling these processes.The methodology used for analysis involves breaking down three technological elements chosen from three different projects to evaluate ease of disassembly,flexibility,potential for reuse,and recyclability.As a result,this paper aims to promote the development of an abacus of existing technological solutions,to provide designers with a tool that can help them pursue DfD strategies.

A divergent pattern in functional connectivity: a transdiagnostic perspective

Functional magnetic resonance imaging(fMRI)is a popular tool used to investigate not only how the brain responds to specific stimuli during sensorimotor or cognitive tasks,but also brain activity at rest.The physics beyond this approach is based on the analysis of the blood oxygenation level-dependent signal.

Research on Quantitative Identification of Three-Dimensional Connectivity of Fractured-Vuggy Reservoirs

The fractured-vuggy carbonate oil resources in the western basin of China are extremely rich.The connectivity of carbonate reservoirs is complex,and there is still a lack of clear understanding of the development and topological structure of the pore space in fractured-vuggy reservoirs.Thus,effective prediction of fractured-vuggy reservoirs is difficult.In view of this,this work employs adaptive point cloud technology to reproduce the shape and capture the characteristics of a fractured-vuggy reservoir.To identify the complex connectivity among pores,fractures,and vugs,a simplified one-dimensional connectivity model is established by using the meshless connection element method(CEM).Considering that different types of connection units have different flow characteristics,a sequential coupling calculation method that can efficiently calculate reservoir pressure and saturation is developed.By automatic history matching,the dynamic production data is fitted in real-time,and the characteristic parameters of the connection unit are inverted.Simulation results show that the three-dimensional connectivity model of the fractured-vuggy reservoir built in this work is as close as 90%of the fine grid model,while the dynamic simulation efficiency is much higher with good accuracy.

Structural and functional connectivity of the whole brain and subnetworks in individuals with mild traumatic brain injury:predictors of patient prognosis

Patients with mild traumatic brain injury have a diverse clinical presentation,and the underlying pathophysiology remains poorly understood.Magnetic resonance imaging is a non-invasive technique that has been widely utilized to investigate neuro biological markers after mild traumatic brain injury.This approach has emerged as a promising tool for investigating the pathogenesis of mild traumatic brain injury.G raph theory is a quantitative method of analyzing complex networks that has been widely used to study changes in brain structure and function.However,most previous mild traumatic brain injury studies using graph theory have focused on specific populations,with limited exploration of simultaneous abnormalities in structural and functional connectivity.Given that mild traumatic brain injury is the most common type of traumatic brain injury encounte red in clinical practice,further investigation of the patient characteristics and evolution of structural and functional connectivity is critical.In the present study,we explored whether abnormal structural and functional connectivity in the acute phase could serve as indicators of longitudinal changes in imaging data and cognitive function in patients with mild traumatic brain injury.In this longitudinal study,we enrolled 46 patients with mild traumatic brain injury who were assessed within 2 wee ks of injury,as well as 36 healthy controls.Resting-state functional magnetic resonance imaging and diffusion-weighted imaging data were acquired for graph theoretical network analysis.In the acute phase,patients with mild traumatic brain injury demonstrated reduced structural connectivity in the dorsal attention network.More than 3 months of followup data revealed signs of recovery in structural and functional connectivity,as well as cognitive function,in 22 out of the 46 patients.Furthermore,better cognitive function was associated with more efficient networks.Finally,our data indicated that small-worldness in the acute stage could serve as a predictor of longitudinal changes in connectivity in patients with mild traumatic brain injury.These findings highlight the importance of integrating structural and functional connectivity in unde rstanding the occurrence and evolution of mild traumatic brain injury.Additionally,exploratory analysis based on subnetworks could serve a predictive function in the prognosis of patients with mild traumatic brain injury.

MSADCN:Multi-Scale Attentional Densely Connected Network for Automated Bone Age Assessment

Bone age assessment(BAA)helps doctors determine how a child’s bones grow and develop in clinical medicine.Traditional BAA methods rely on clinician expertise,leading to time-consuming predictions and inaccurate results.Most deep learning-based BAA methods feed the extracted critical points of images into the network by providing additional annotations.This operation is costly and subjective.To address these problems,we propose a multi-scale attentional densely connected network(MSADCN)in this paper.MSADCN constructs a multi-scale dense connectivity mechanism,which can avoid overfitting,obtain the local features effectively and prevent gradient vanishing even in limited training data.First,MSADCN designs multi-scale structures in the densely connected network to extract fine-grained features at different scales.Then,coordinate attention is embedded to focus on critical features and automatically locate the regions of interest(ROI)without additional annotation.In addition,to improve the model’s generalization,transfer learning is applied to train the proposed MSADCN on the public dataset IMDB-WIKI,and the obtained pre-trained weights are loaded onto the Radiological Society of North America(RSNA)dataset.Finally,label distribution learning(LDL)and expectation regression techniques are introduced into our model to exploit the correlation between hand bone images of different ages,which can obtain stable age estimates.Extensive experiments confirm that our model can converge more efficiently and obtain a mean absolute error(MAE)of 4.64 months,outperforming some state-of-the-art BAA methods.

Dynamic interwell connectivity analysis of multi-layer waterflooding reservoirs based on an improved graph neural network

The analysis of interwell connectivity plays an important role in the formulation of oilfield development plans and the description of residual oil distribution. In fact, sandstone reservoirs in China's onshore oilfields generally have the characteristics of thin and many layers, so multi-layer joint production is usually adopted. It remains a challenge to ensure the accuracy of splitting and dynamic connectivity in each layer of the injection-production wells with limited field data. The three-dimensional well pattern of multi-layer reservoir and the relationship between injection-production wells can be equivalent to a directional heterogeneous graph. In this paper, an improved graph neural network is proposed to construct an interacting process mimics the real interwell flow regularity. In detail, this method is used to split injection and production rates by combining permeability, porosity and effective thickness, and to invert the dynamic connectivity in each layer of the injection-production wells by attention mechanism.Based on the material balance and physical information, the overall connectivity from the injection wells,through the water injection layers to the production layers and the output of final production wells is established. Meanwhile, the change of well pattern caused by perforation, plugging and switching of wells at different times is achieved by updated graph structure in spatial and temporal ways. The effectiveness of the method is verified by a combination of reservoir numerical simulation examples and field example. The method corresponds to the actual situation of the reservoir, has wide adaptability and low cost, has good practical value, and provides a reference for adjusting the injection-production relationship of the reservoir and the development of the remaining oil.

Profile of Autoantibodies and Clinical Symptoms in Guinean Patients with Connective Tissue Diseases

Connective tissue diseases (CTDs) are Autoimmune diseases (AIDs) characterized by the appearance of autoantibodies, which are diagnostic markers. Investigations of these autoantibodies play a major role in the management of several autoimmune diseases. The objective of this study was to describe the profile of anti-ENA antibodies according to the clinical symptoms of mixed CTDs in Conakry teaching Hospital. We performed a cross-sectional study during six months. A total of 20 patients was recruited and we measured antibodies using the ELISA technique. The mean age of our patients was 36.5 years, with a predominance of females. Cutaneous and rheumatological signs were the main clinical manifestations. SLP was the most frequent CTDs;the threshold of ENA antibodies positivity was higher in scleroderma with and SLP. Anti-ENA identification reveals the frequency of anti-SSA (83.33%), anti-U1RNP (66.66%) and anti-histone (50%) antibodies. Antinuclear antibodies (ANA) react with various components of the cell nucleus. Their detection is of major interest in the diagnosis of CTDs. Our results highlight the importance of determining the specificity of these antibodies to guide differential diagnosis.

Ensuring Secure Platooning of Constrained Intelligent and Connected Vehicles Against Byzantine Attacks:A Distributed MPC Framework

This study investigates resilient platoon control for constrained intelligent and connected vehicles(ICVs)against F-local Byzantine attacks.We introduce a resilient distributed model-predictive platooning control framework for such ICVs.This framework seamlessly integrates the predesigned optimal control with distributed model predictive control(DMPC)optimization and introduces a unique distributed attack detector to ensure the reliability of the transmitted information among vehicles.Notably,our strategy uses previously broadcasted information and a specialized convex set,termed the“resilience set”,to identify unreliable data.This approach significantly eases graph robustness prerequisites,requiring only an(F+1)-robust graph,in contrast to the established mean sequence reduced algorithms,which require a minimum(2F+1)-robust graph.Additionally,we introduce a verification algorithm to restore trust in vehicles under minor attacks,further reducing communication network robustness.Our analysis demonstrates the recursive feasibility of the DMPC optimization.Furthermore,the proposed method achieves exceptional control performance by minimizing the discrepancies between the DMPC control inputs and predesigned platoon control inputs,while ensuring constraint compliance and cybersecurity.Simulation results verify the effectiveness of our theoretical findings.

The Gut Brain Connection

The gut-brain connection is a bidirectional communication system that links the gut microbiome to the central nervous system (CNS). The gut-brain axis communicates through a variety of mechanisms, including the release of hormones, neurotransmitters, and cytokines. These signaling molecules can travel from the gut to the brain and vice versa, influencing various physiological and cognitive functions. Emerging therapeutic strategies targeting the gut-brain connection include probiotics, prebiotics, and faecal microbiota transplantation (FMT). Probiotics are live microorganisms that are similar to the beneficial bacteria that are naturally found in the gut. Prebiotics are non-digestible fibers that feed the beneficial bacteria in the gut. FMT is a procedure in which faecal matter from a healthy donor is transplanted into the gut of a person with a diseased microbiome. Probiotics, prebiotics, and FMT have been shown to be effective in treating a variety of gastrointestinal disorders, and there is growing evidence that they may also be effective in treating neurological and psychiatric disorders. This review explores the emerging field of the gut-brain connection, focusing on the communication pathways between the gut microbiome and the central nervous system. We summarize the potential roles of gut dysbiosis in various neurological and psychiatric disorders. Additionally, we discuss potential therapeutic strategies, research limitations, and future directions in this exciting area of research. More research is needed to fully understand the mechanisms underlying the gut-brain connection and to develop safe and effective therapies that target this pathway. However, the findings to date are promising, and there is the potential to revolutionize the way we diagnose and treat a variety of neurological and psychiatric disorders.

Brain mechanism of acupuncture for children with anisometropic amblyopia: a resting functional magnetic resonance imaging study based on voxel-mirrored homotopic connectivity

AIM:To explore the brain mechanism of acupuncture for children with anisometropic amblyopia using the voxelmirror homotopic connectivity(VMHC)analysis method of resting functional magnetic resonance imaging(rs-fMRI)technology based on clinical effectiveness.METHODS:Eighty children with anisometropic monocular amblyopia were randomly divided into two groups:control(40 cases,1 case of shedding)and acupuncture(40 cases,1 case of shedding)groups.The control group was treated with glasses,red flash,grating,and visual stimulations,with each procedure conducted for 5min per time.Based on routine treatment,the acupuncture group underwent acupuncture of“regulating qi and unblocking meridians to bright eyes”,Jingming(BL1),Cuanzhu(BL2),Guangming(GB37),Fengchi(GB20)acupoints were taken on both sides,with the needle kept for 30min each time.Both groups were treated once every other day,three times per week,for a total of 4wk.After the treatment,the overall curative effect of the two groups and the latency and amplitude changes of P100 wave of pattern visual-evoked potential were counted.At the same time,nine children with left eye amblyopia were randomly selected from the two groups and were scanned with rsfMRI before and after treatment.The differences in the brain regions between the two groups were compared and analyzed with VMHC.RESULTS:Chi-square test showed a notable difference in the total efficiency rate between the acupuncture(94.87%)and control groups(79.49%).Regarding the P100 wave latency and amplitude,the acupuncture group had significantly shorter latency and higher amplitude of P100 wave than the control group.Moreover,the VMHC values of the bilateral temporal lobe,superior temporal gyrus,and middle temporal gyrus were notably increased in the acupuncture group after treatment.CONCLUSION:Acupuncture combined with conventional treatment can significantly improve the corrected visual acuity and optic nerve conduction in children with anisometropic amblyopia.Compared with the conventional treatment,the regulation of acupuncture on the functional activities of the relevant brain areas in the anterior cerebellum may be an effective acupuncture mechanism for anisometropic amblyopia.