Partial Connection Research Articles

Experimental study of the static and hysteretic performance of grouted steel beam-column inter-connections for modular integrated construction

Modular Integrated Construction (MiC) has gained prominence as a construction method, and the integrity of its structural connections plays a critical role in ensuring the overall stability and safety of buildings. This paper presents an experimental investigation into the static and hysteretic performance of grouted steel beam-column inter-connections designed for MiC. The study examines eight full-scale specimens subjected to both monotonic and cyclic loading conditions, with a focus on failure characteristics, deformation behavior, loading-displacement curves, loading-strain curves, skeleton curves of hysteretic loops, stiffness degradation, and energy dissipation. Key findings of the study include the observation of weld cracking between beam flanges and columns in all specimens, highlighting the necessity of welding quality control to prevent such issues. Grouted connections exhibited superior integrity, with no gap opening or misalignment between upper and lower columns. Additionally, it was found that grouting could enhance the stiffness of certain connections. Removal of diagonal stiffeners significantly decreased peak bearing capacity, emphasizing their importance. The composite action brought about by bolts connecting ceiling and floor beams also proved influential. According to Eurocode 3, all grouted specimens fall under the classification of semi-rigid connections, with a distinction made for partial and full-strength connections. This research contributes valuable insights into the static behavior and hysteretic performance of grouted steel beam-column inter-connections for MiC, providing essential knowledge for the structural design and safety of buildings utilizing this innovative construction method.

An Integrated Risk Assessment Methodology of In-Service Hydrogen Storage Tanks Based on Connection Coefficient Algorithms and Quintuple Subtraction Set Pair Potential

At present, there have been a number of hydrogen storage tank explosions in hydrogen filling stations, causing casualties and property losses, and having a bad social impact. This has made people realize that the risk assessment and preventive maintenance of hydrogen storage tanks are crucial. Therefore, this paper innovatively proposes a comprehensive risk assessment model based on connection coefficient algorithms and quintuple subtractive set pair potential. First of all, the constructed index system contains five aspects of corrosion factors, material factors, environmental factors, institutional factors and human factors. Secondly, a combined weighting analysis method based on FAHP and CRITIC is proposed to determine the weight of each indicator. The basic indicators influencing hydrogen storage tanks are analyzed via the quintuple subtraction set pair potential and full partial connection coefficient. Finally, the risk level and development trend of hydrogen storage tanks in hydrogen filling stations are determined by a combination of the three-category connection coefficient algorithms and the risk level eigenvalue method. The results of our case analysis show that the proposed risk assessment model can identify the main weak indicators affecting the safety of hydrogen storage tanks, including installation quality, misoperation and material quality. At the same time, it is found that the risk of high-pressure hydrogen storage tanks is at the basic safety level, and the development trend of safety conditions holds a critical value. The evaluation results can help establish targeted countermeasures for the prevention and maintenance of hydrogen storage tanks.

Strength calculation of composite slabs by the partial shear connection method considering the friction

This paper aims to evaluate the behavior and longitudinal shear strength of the steel-concrete composite slab using the Steel Deck P75 through the partial shear connection method considering the contribution of the friction acting in the interface of the steel deck with the concrete at the region of supports. The method proposed by EN 1994-1-1:2004 for the design of composite slab systems is based in the analytical model similar to that of composite beams which allows to determine the degree shear connection between the steel sheeting and the concrete. This method allows to analyse the influence of friction at the support, in addition to the contribution of stud bolts on the mechanical resistance of composite slab. The composite slab was tested in the Federal University of Minas Gerais structure laboratory which used the semi-empirical "m-k" method to obtain its resistance. The behavior of the composite slab system was analysed through of the graphics deflections, end slips and strains, allowing the determination of its failure mode. Finally, the results obtained from the longitudinal shear resistance considering the friction at the support were applied in one practical example.

Detection of coconut leaf diseases using enhanced deep learning techniques

Coconut farming is a significant agricultural activity in South India, but the coconut trees face challenges due to adverse weather conditions and environmental factors. These challenges include various leaf diseases and pest infestations. Identifying and locating these issues can be difficult because of the large foliage and shading provided by the coconut trees. Recent research has shown that Computer Vision algorithms are becoming increasingly important for solving problems related to object identification and detection. So, in this work, the YOLOv4 algorithm was employed to detect and pinpoint diseases and infections in coconut leaves from images. The YOLOv4 model incorporates advanced features such as cross-stage partial connections, spatial pyramid pooling, contextual feature selection, and path-based aggregation. These features enhance the model’s ability to efficiently identify issues such as yellowing and drying of leaves, pest infections, and leaf flaccidity in coconut leaf images taken in various environmental conditions. Furthermore, the model’s predictive accuracy was enhanced through multi-scale feature detection, PANet feature learning, and adaptive bounding boxes. These improvements resulted in an impressive 88% F1-Score and an 85% Mean Average Precision. The model demonstrates its effectiveness and robustness even when dealing with medium-resolution images, offering improved accuracy and speed in disease and pest detection on coconut leaves.

Improved Differentiable Architecture Search With Multi-Stage Progressive Partial Channel Connections

Improved Differentiable Architecture Search With Multi-Stage Progressive Partial Channel Connections

Video assisted thorascopic assisted correction of left partial anomalous pulmonary venous connection: one case report

IntroductionThe left partial anomalous pulmonary vein connection is a rare congenital heart disease, especially with intact atrial septum. Now we reported a case of the left superior pulmonary vein drainage to left innominate vein through a vertical vein, and corrected with video assisted thoracoscopy.Case presentationA-59-years old man diagnosed left anomalous partial pulmonary vein connection with presentation of short breathiness and palpation, and diagnosed with computer tomography pulmonary angiography. The operation was carried out under video assisted thoracoscopy with one manipulation incision and one observational incision, the vertical vein was dissected and anastomosis with left atrial appendage. The patients recovered smoothly and postoperative CTPA showed anastomosis ostium was unobstructed.ConclusionThe left lateral thoracotomy and video assisted thoracoscopic surgery is a feasible for correction of left PAPVC with intact interatrial septum without using CPB.

Three-Dimensional Tolerance Analysis and Allocation for Valve Gap of Engine

The engine is a typical complex machine with many mechanisms and high precision. The tolerance of the valve gap is an accumulative result of component tolerances on the valve mechanism, which is very critical to engine performance. The current tolerance analysis is carried out using the traditional linear dimension chain, and it is difficult to consider a large number of geometric tolerances. The analysis result is not consistent with reality. Based on an innovative three-dimensional tolerance model, i.e., the unified Jacobian-Torsor model, tolerance analysis and allocation for valve gap were presented in this paper, where large geometric and partial parallel connections were taken into consideration. For the special conical structure in the valve mechanism, the expression and variation of the conical torsor were deduced. At last, a real engine was selected as an example of the analysis process. Sufficient comparison and discussion were made for the results, which demonstrated the effectiveness of the method.

Effect of Stud Connector Diameter on the Behavior of Steel–Lightweight Self-Compacting Concrete Composite Beams with Partial shear Connection

This study investigates the efficacy of using lightweight self-compacting concrete (LWSCC) in steel-concrete composite beams, focusing on the role of headed stud connectors in shear connection. The research aimed to evaluate the behavior and shear strength of M16 and M20 headed stud connectors within LWSCC. To this end, six push-out test samples and six simply supported steel-LWSCC composite beams were fabricated and tested, with variables including the degree of shear connection (DSC) and stud diameter. The results demonstrated that an increase in DSC significantly enhanced the ultimate load capacity, service load, and stiffness of the composite beams, with marked improvements observed in the beams with M16 connectors (94%, 95%, and 122%, respectively) and those with M20 connectors (43%, 43%, and 20%, respectively). Furthermore, increasing the stud diameter from 16mm to 20mm resulted in a 38% increase in shear strength of the connectors and notably improved the mechanical characteristics of the beams. The study also found that while an increased DSC reduces deflection at ultimate and service loads due to heightened stiffness, a larger stud diameter has only a marginal effect on deflection. This research highlights the potential of LWSCC in enhancing the structural performance of steel-concrete composite beams, particularly with optimized shear connections, offering valuable insights for advancements in construction materials and methods. Highlights : Enhanced Load Capacity: Increase in the degree of shear connection significantly boosts ultimate load capacity and stiffness in composite beams. Stud Diameter Impact: A 38% rise in shear strength is achieved by increasing stud diameter from 16mm to 20mm. Deflection Reduction: Higher degree of shear connection effectively minimizes deflection, though stud diameter has minimal impact. Keywords : Lightweight Self-Compacting Concrete, Steel-Concrete Composite Beams, Shear Connection, Headed Stud Connectors, Structural Performance.

Long-Term Prediction of Sea Surface Temperature by Temporal Embedding Transformer With Attention Distilling and Partial Stacked Connection

Long-Term Prediction of Sea Surface Temperature by Temporal Embedding Transformer With Attention Distilling and Partial Stacked Connection

Partial remote synchronization in star-like networks with partial connections among leaf nodes

Partial remote synchronization in star-like networks with partial connections among leaf nodes

A hybrid one-vertex model for susceptible–infected–susceptible diseases on networks with partial connection information

We analyze the propagation of the susceptible–infected–susceptible model on the network, where the partial connection information is known. In order to analyze the spread of network epidemics, the quenched mean-field method requires complete connection information, while the heterogeneous mean-field method requires complete topology information. When only partial connection information is available, the quenched mean-field method may be disabled. In view of this situation, a hybrid model consisting of connection and topology information is proposed. The theoretical results are in good agreement with stochastic simulations on scale-free networks. In addition, as the proportion of connection information in the network increases, model predictions about final epidemic size become more accurate when the effective transmission rate is slightly larger than the simulation threshold.

Sensory Co-laboring

Demining has not been an exclusively human affair. Mine detection dogs have been indispensable in the work of detection and in the slow but essential effort to regain trust in mine-suspected landscapes. Famously renowned for their extraordinary sensory perception, physical strength, and mental traits, they are part of human-nonhuman units training and working together to perceive explosives’ odors. This article considers the role of these units, known in Colombia as binomios caninos, in the strenuous task of mine clearance. Drawing on eighteen months of ethnographic engagement with global and local humanitarian demining efforts in Colombia, it examines detection choreographies and daily interactions, proposing to think of their joint work in terms of sensory co-laboring. Bringing anthropological work on collaboration between worlds, sensory labor, and animal work into dialogue, this composite term foregrounds detection as labor and as a result of human-nonhuman cooperation. It also highlights the asymmetrical field in which these collaborators converge and the divergent desires, affects, and attachments that mobilize their participation in demining. Mine detection is conceptualized as a sensory task through which dogs and humans intra-act, both together and apart. Recognizing this partial connection allows us to rethink how humans and other creatures are ontologically reconstituted and how overlapping histories of warfare and humanitarianism, legacies of animal behavioral practice, and instrumental-affective interactions shape these reconstitutions.

Branched Latent Neural Maps

We introduce Branched Latent Neural Maps (BLNMs) to learn finite dimensional input–output maps encoding complex physical processes. A BLNM is defined by a simple and compact feedforward partially-connected neural network that structurally disentangles inputs with different intrinsic roles, such as the time variable from model parameters of a differential equation, while transferring them into a generic field of interest. BLNMs leverage latent outputs to enhance the learned dynamics and break the curse of dimensionality by showing excellent in-distribution generalization properties with small training datasets and short training times on a single processor. Indeed, their in-distribution generalization error remains comparable regardless of the adopted discretization during the testing phase. Moreover, the partial connections, in place of a fully-connected structure, significantly reduce the number of tunable parameters. We show the capabilities of BLNMs in a challenging test case involving biophysically detailed electrophysiology simulations in a biventricular cardiac model of a pediatric patient with hypoplastic left heart syndrome. The model includes a 1D Purkinje network for fast conduction and a 3D heart-torso geometry. Specifically, we trained BLNMs on 150 in silico generated 12-lead electrocardiograms (ECGs) while spanning 7 model parameters, covering cell-scale, organ-level and electrical dyssynchrony. Although the 12-lead ECGs manifest very fast dynamics with sharp gradients, after automatic hyperparameter tuning the optimal BLNM, trained in less than 3 h on a single CPU, retains just 7 hidden layers and 19 neurons per layer. The resulting mean square error is on the order of 10−4 on an independent test dataset comprised of 50 additional electrophysiology simulations. In the online phase, the BLNM allows for 5000x faster real-time simulations of cardiac electrophysiology on a single core standard computer and can be employed to solve inverse problems via global optimization in a few seconds of computational time. This paper provides a novel computational tool to build reliable and efficient reduced-order models for digital twinning in engineering applications. The Julia implementation is publicly available under MIT License at https://github.com/StanfordCBCL/BLNM.jl.

Experimental investigation on the seismic behavior of composite steel plate shear wall restrained by ECC panels

The composite structures using engineered cementitious composites (ECC) and steel are promising to achieve good seismic performance due to their excellent deformability and energy dissipation. This paper proposed a novel composite steel plate shear wall restrained by ECC panels. The shear wall system consists of boundary frame, steel plate and precast ECC panels. The steel plate is sandwiched between two precast ECC panels by bolts. Then, the sandwich panel is bolted to the boundary frame by angle steels. Experimental investigation was conducted on the proposed composite shear walls to evaluate their seismic performance. Five one-story one-span specimens with ECC panels considering the effects of panel-frame connection types, reinforcement ratio in ECC panels and aspect ratio were tested, as well as a control specimen with concrete panels. Test results revealed that compared with ordinary buckling-restrained steel plate shear wall, the proposed composite shear walls displayed sufficient ductility, high initial stiffness and satisfactory energy dissipated capacity. Severe cracking and spalling of concrete panels were detected, while the ECC panels still maintained integrity and had high residual strength in the later stage of loading. ECC panels made a considerable contribution to shear resistance in addition to effectively restraining the buckling of the steel plate. In comparison with the specimen using partial four-side connection, the shear capacity, initial lateral stiffness and accumulated dissipated energy of the specimen using four-side connection were notably enhanced by 103%, 22.8% and 108%, respectively. Moreover, using two-side connection weakened the shear capacity and initial lateral stiffness of the specimen by 29.4% and 29.8%, because it could not exhibit complete shear and tension field actions as the specimen using four-side connection.

Cold-formed Steel-Concrete Composite Beams with Back-to-Back Channel Sections in Bending

Steel-concrete composite structures are very attractive because of their characteristics, which can be emphasised by using cold-formed steel instead of hot-rolled ones. This paper presents possible analytical approaches and a parametric finite element study of cold-formed steel-concrete composite beams in bending. Analysed beams are formed of back-to-back cold-formed steel channels and concrete slabs connected by demountable shear connectors. A solid concrete slab on a profiled metal sheet analysed. Also, the study investigates the influence of corrugated web between the back-to-back channels of different thicknesses. In the case of a corrugated web, the distance between the shear connectors is increased. Furthermore, different degrees of shear connection, shear connector quality, and their arrangements are considered. An analytical study is based on full and partial shear connection assumptions and non-linear bending resistance. It is shown that the steel channel thickness and degree of shear connection significantly influence the beam bending capacity as well as concrete slab configurations. Conversely, a discrete connection between steel elements has a minor effect. A comparison of the maximum obtained bending capacities in FE analyses is in good agreement with analytical approaches for full and partial shear connections. Doi: 10.28991/CEJ-2023-09-10-01 Full Text: PDF

Heterogeneous Reinforcement Learning Network for Aspect-Based Sentiment Classification With External Knowledge

Aspect-based sentiment classification aims to automatically predict the sentiment polarity of the specific aspect in a text. However, it is challenging to confirm the mapping between the aspect and the core context since a number of existing methods concentrate on building the global relations of the full context rather than the partial connections based on the aspects. Motivated by the fundamental insights of reinforcement learning, we propose a novel <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>H</b></u> eterogeneous <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>R</b></u> einforcement <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>L</b></u> earning <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>N</b></u> etwork for aspect-based sentiment analysis (HRLN) to alleviate these issues, which contains two primary components, a heterogeneous network module, and a knowledge graph-based reinforcement learning module consistent with common-sense knowledge and emotional knowledge. To evaluate the effectiveness of HRLN, we conduct extensive experiments on five benchmark datasets, which indicate that HRLN achieves competitive performance and yields state-of-the-art results on all datasets. Additionally, we present an intuitive comprehension of why our HRLN model is more robust for aspect-based sentiment classification via case studies.

State-of-the-Art: Integrating Fastener Technology and Design Guidelines for Enhanced Performance of Cold-Formed Steel Sections

Cold-formed steel (CFS) elements have gained significant attention in the field of structural engineering due to their numerous advantages, including high strength-to-weight ratio, cost-effectiveness, and ease of assembly and prefabrication. This review paper presents a comprehensive state-of-the-art analysis of the design and analysis of CFS structures, with a specific focus on columns and beams. The primary objectives and aims of this review paper are to provide a detailed assessment of the factors influencing the behavior and performance of CFS elements, including partial composite action, fastener spacing, bolt arrangement, web aperture, stiffeners, and connection spacing, to propose and present various formulas and methodologies that accurately estimate critical buckling loads, strength, and moment resistance for CFS members, and to emphasize the significance of proper screw and bolt placement in preventing premature failure and enhancing the overall load-carrying capacity of CFS structures. Additionally, the impact of temperature on the mechanical properties and performance of CFS members is discussed. The review paper proposes different formulas and methodologies to accurately estimate critical buckling loads, strength, and moment resistance for CFS members. Moreover, the paper highlights the importance of proper screw and bolt placement to prevent early failure and improve the overall load-carrying capacity of CFS structures. The discussion also emphasizes the need for revisions in existing standards and codes to provide more practical guidelines for designers and engineers. Overall, this state-of-the-art review paper provides valuable insights and recommendations for researchers and practitioners involved in the design and analysis of CFS elements.

Evaluation of industrial green development based on set pair analysis

Industrial green development is an important part of the global sustainable development goals, and it is the only way for China to achieve the "dual carbon goals". This paper aims to explore the methods and indicators for the assessment of the level of industrial green development, and emphasizes its applicability in the "city area" scale. According to the characteristics of industrial green development, an evaluation system including 4 first-level indicators of economic benefits and structure, technological innovation and investment, environmental impact and governance, resource consumption and reuse, and a total of 16 s-level indicators were established, each indicator was determined using the entropy value theory The weight value is based on the conditions of 15 sub-provincial cities across the country to determine the standard value of each indicator. Through the set pair analysis of the five-element connection number and partial connection number, the comprehensive connection degree and the full partial connection number are obtained, so as to evaluate the level of industrial green development in Chengdu static evaluation and dynamic analysis. The results show that the constructed index system and evaluation method are applicable to the evaluation of industrial green development at the "city" scale. The current status of Chengdu's industrial green development is at a qualified level, and the industrial process will have a more active and green development trend in the future. The government management department should formulate appropriate management policies and strengthen comprehensive evaluation and research, so as to take the green industrial development process of Chengdu to a new level.

Research Method of Discontinuous-Gait Image Recognition Based on Human Skeleton Keypoint Extraction.

As a biological characteristic, gait uses the posture characteristics of human walking for identification, which has the advantages of a long recognition distance and no requirement for the cooperation of subjects. This paper proposes a research method for recognising gait images at the frame level, even in cases of discontinuity, based on human keypoint extraction. In order to reduce the dependence of the network on the temporal characteristics of the image sequence during the training process, a discontinuous frame screening module is added to the front end of the gait feature extraction network, to restrict the image information input to the network. Gait feature extraction adds a cross-stage partial connection (CSP) structure to the spatial-temporal graph convolutional networks' bottleneck structure in the ResGCN network, to effectively filter interference information. It also inserts XBNBlock, on the basis of the CSP structure, to reduce estimation caused by network layer deepening and small-batch-size training. The experimental results of our model on the gait dataset CASIA-B achieve an average recognition accuracy of 79.5%. The proposed method can also achieve 78.1% accuracy on the CASIA-B sample, after training with a limited number of image frames, which means that the model is more robust.

Construction and Application of a Water Resources Spatial Equilibrium Model: A Case Study in the Yangtze River Economic Belt

The Yangtze River Economic Belt, as crucial component of China’s “T-shaped” strategy for territorial development and economic layout, has been challenged by the unbalanced spatial distribution of water resources, which has seriously affected high-quality development in harmony with the social economy and ecological environmental protection. In this study, we aim to enhance the conceptual definition of water resource spatial equilibrium. Additionally, we propose a water resource spatial equilibrium evaluation model based on a variable set and partial connection number. This model effectively addresses the limitations of traditional methods by incorporating fuzzy indices and dynamic information, which have previously been overlooked. The spatiotemporal characteristics and future evolutionary trend of water resource spatial equilibrium were analyzed in 11 provinces and 110 cities in the Yangtze River Economic Belt from 1999 to 2018. The results showed that the conceptual definition of water resource spatial equilibrium involves the water resource endowment, water resource development, water resource utilization, water resource supply and demand, water resource matching, and water resource protection. The water resource spatial equilibrium in the 11 provinces gradually improved following a temporal trend; in terms of the spatial trend, the south was better than the north and the west was better than the east. These provinces were sorted as follows: Yunnan &gt; Sichuan &gt; Zhejiang &gt; Jiangxi &gt; Hunan Province &gt; Guizhou &gt; Hubei &gt; Chongqing &gt; Anhui &gt; Jiangsu &gt; Shanghai. The evolutionary trend increased except in Yunnan. The water resource spatial equilibrium of the 110 cities showed that the spatial trends of the three major urban agglomerations were much better than in the other regions, and the temporal trend steadily improved. The 11 provinces and 110 cities could be divided into three and five categories, respectively, according to their spatiotemporal trends. City-scale research on water resource spatial equilibrium can effectively identify and optimize the control area compared with using a provincial scale. When the control targets were set to 20%, 40%, 60%, and 80%, the proportion of the administrative area based on the city scale decreased by 1.20%, 4.99%, 10.52%, and 19.05%, respectively.