Step In Construction Research Articles

Impact of the K and Fe insertion methods in KFeCeZr catalysts on the CO2 hydrogenation to C2/C3 olefins at room pressure

Light olefins, traditionally sourced from petroleum, are currently being investigated through alternative routes, such as CO2 hydrogenation, aligning with CCUS policies. Alkali-promoted Fe-based catalysts have shown promise in coupling reverse water–gas shift and Fischer-Tropsch processes to produce light olefins from CO2 directly. The in-situ generated Fe5C2 phase is commonly associated with the high activity of these catalysts. However, the preparation methods employed can lead to distinct catalyst properties, which considerably impact the performance during the reaction. Here, we investigate different strategies to introduce K and Fe in the KFeCeZr system and correlate catalyst properties with catalytic activity during CO2 hydrogenation at ambient pressure. The catalyst prepared by one-pot precipitation of Fe, Ce, and Zr followed by K impregnation showed the most promising results in terms of conversion and productivity of light olefins. The effective incorporation of Fe in the CeZrOx structure during synthesis was essential for maintaining the high dispersion of metallic Fe particles after reduction, improving reactant chemisorption. Under reaction conditions, this catalyst showed the most remarkable propensity to form iron carbides (FexCy), mainly composed of Fe5C2 phase. In general, this work highlights the impact that the construction steps of a multifunctional catalyst have on the physicochemical properties and, consequently, on the catalytic activity.

Social Intelligence of Fifth-Grade Primary School Pupils

The aim of the research is to build a social intelligence scale for fifth-grade primary school pupils in science course. The research followed the descriptive-analytical approach and the scale was built according to established construction steps. Various types of validity, including face validity, construct validity, and discriminant validity, were confirmed, and the scale's reliability was verified. It was applied to a sample of 500 fifth-grade primary school pupils, and appropriate statistical methods were used. The results indicated a moderate level of social intelligence among the study sample, with differences between pupils in social intelligence attributed to gender, favoring females. Several recommendations were made to improve the social intelligence level among the research sample.

Attribute expansion relation extraction approach for smart engineering decision‐making in edge environments

SummaryIn sedimentology, the integration of intelligent engineering decision‐making with edge computing environments aims to furnish engineers and decision‐makers with precise, real‐time insights into sediment‐related issues. This approach markedly reduces data transfer time and response latency by harnessing the computational power of edge computing, thereby bolstering the decision‐making process. Concurrently, the establishment of a sediment knowledge graph serves as a pivotal conduit for disseminating sediment‐related knowledge in the realm of intelligent engineering decision‐making. Moreover, it facilitates a comprehensive exploration of the intricate evolutionary and transformative processes inherent in sediment materials. By unveiling the evolutionary trajectory of life on Earth, the sediment knowledge graph catalyzes a deeper understanding of our planet's history and dynamics. Relationship extraction, as a key step in knowledge graph construction, implements automatic extraction and establishment of associations between entities from a large amount of sedimentary literature data. However, sedimentological literature presents multi‐source heterogeneous features, which leads to a weak representation of hidden relationships, thus decreasing the accuracy of relationship extraction. In this article, we propose an attribute‐extended relation extraction approach (AERE), which is specifically designed for sedimentary relation extraction scenarios. First, context statements containing sediment entities are obtained from the literature. Then, a cohesive hierarchical clustering algorithm is used to extend the relationship attributes between sediments. Finally, mine the relationships between entities based on AERE. The experimental results show that the proposed model can effectively extract the hidden relations and exhibits strong robustness in dealing with redundant noise before and after sentences, which in turn improves the completeness of the relations between deposits. After the relationship extraction, a proprietary sediment knowledge graph is constructed with the extracted triads.

BAD2matrix: Phylogenomic matrix concatenation, indel coding, and more

AbstractPremiseCommon steps in phylogenomic matrix production include biological sequence concatenation, morphological data concatenation, insertion/deletion (indel) coding, gene content (presence/absence) coding, removing uninformative characters for parsimony analysis, recording with reduced amino acid alphabets, and occupancy filtering. Existing software does not accomplish these tasks on a phylogenomic scale using a single program.Methods and ResultsBAD2matrix is a Python script that performs the above‐mentioned steps in phylogenomic matrix construction for DNA or amino acid sequences as well as morphological data. The script works in UNIX‐like environments (e.g., LINUX, MacOS, Windows Subsystem for LINUX).ConclusionsBAD2matrix helps simplify phylogenomic pipelines and can be downloaded from https://github.com/dpl10/BAD2matrix/tree/master under a GNU General Public License v2.

How to improve “construct, merge, solve and adapt"? Use reinforcement learning!

AbstractIn this work, we propose a new variant of construct, merge, solve, and adapt (CMSA), which is a recently introduced hybrid metaheuristic for combinatorial optimization. Our newly proposed variant, named reinforcement learning CMSA (RL-CMSA), makes use of a reinforcement learning (RL) mechanism trained online with data gathered during the search process. In addition to generally outperforming standard CMSA, this new variant proves to be more flexible as it does not require a greedy function for the evaluation of solution components at each solution construction step. We present RL-CMSA as a general framework for enhancing CMSA by leveraging a simple RL learning process. Moreover, we study a range of specific designs for the employed learning mechanism. The advantages of the introduced CMSA variant are demonstrated in the context of the far from most string and minimum dominating set problems, showing the improvement in performance and simplicity with respect to standard CMSA. In particular, the best performing RL-CMSA variant proposed is statistically significantly better than the standard algorithm for both problems, obtaining 1.28% and 0.69% better results on average respectively.

Cosmic ray test of shashlik electromagnetic calorimeter modules for NICA-MPD

The electromagnetic calorimeter (ECal) plays a critical role in high-energy particle colliders, by measuring the energy of particles that interact primarily via the electromagnetic interaction such as electrons, positrons and photons. Fudan University has undertaken part of the mass production of ECal modules for the Multi-Purpose Detector (MPD) project of the Nuclotron-based Ion Collider fAcility (NICA). Preliminary tests on the ECal modules is an essential step in detector construction. This article introduces the cosmic ray testing of the ECal modules produced by Fudan University. We tested the cosmic ray under the conditions of horizontal and vertical placement of the module, calculated the average number of generated photons, and estimated the module’s energy resolution. Moreover we analyzed the time resolution performance of the device. The test results indicate that when cosmic rays vertically traverse the entire ECal, the number of photons produced can reach 834, with a potential energy resolution less than 5%/E. The time resolution is 189ps. Based on the comprehensive test results, the production of the ECal meets the design requirements.

The [formula omitted]-RWELL technology for tracking apparatus in High Energy Physics

The micro-Resitive WELL (μ-RWELL) is a Micro Pattern Gas Detector (MPGD) that inherits some of the best characteristics of existing MPGDs, like GEMs and MicroMegas, while simplifying the detector construction. A significant progress towards large-scale applications has been achieved through the consolidation and industrial cost-effective manufacturing of this technology. The μ-RWELL, showing excellent spatial performance, good time resolution and stability under irradiation, is proposed for several tracking apparatus for future experiments at future accelerators such as FCC (CERN), CEPC (China) and EIC (Brookhaven National Laboratory). The reduced impact in terms of material budget makes this technology suitable for the development of tracking devices in the muon spectrometer. In addition, the flexibility of the μ-RWELL base material makes this device suitable for the development of very light, fully cylindrical inner trackers at future high luminosity tau-charm factories, SCTF (China). We report in this paper some results in the development of 2D readouts for IDEA apparatus and the construction steps of a cylindrical μ-RWELL.

Digital twin method for intelligent lifting of cable structures under multi-collaborative mode

In order to ensure the high efficiency and quality consistency of cable structure in each construction stage, this study proposes a digital twin method for intelligent lifting of cable structure under multi-collaborative mode. Firstly, a construction and operation methods of the digital twin logic model are formed. In the logical model, a relationship between the transmission and iteration of construction information in the collaborative process is defined. In order to realize the adaptability of working conditions, the coupling relationship between construction step and structural elevation is established. A cable structure lifting analysis model driven by an improved long short-term memory neural network is proposed, which effectively solves the problem of poor convergence of the traditional finite element method. An automatic time window selection method is formed for the neural network. An adaptive moment estimation method is used for neural network parameter optimization, which improves the efficiency and accuracy of the analysis. Taking the lifting process of a cable truss structure test model as an example, a digital twin collaborative construction platform is established to verify the effectiveness and feasibility of the research method. The results show that the digital twin logic model realizes the process collaboration, scene collaboration and data collaboration, and significantly improves the stability of construction quality and the linkage of construction process. Through the improved long short-term memory neural network, the coupling relationship between the construction step and the structural elevation is established, which accurately guides the whole construction process.

How to Construct a Carbon Asset Management System for Chinese Power Enterprises: A Survey-Based Approach

The greenhouse effect of atmospheric pollution is globally concerning. China is transitioning to market-driven emission reduction from policy-driven efforts. In 2021, key power industry emitters were included in the national carbon trading market. However, many companies lack willingness and understanding of carbon assets, hindering progress. Research on power companies in Beijing, a political and carbon market pilot region, is valuable. This study obtained data on the participation of Beijing’s power generation companies in the carbon market and the construction of their carbon management systems during the first compliance period through the distribution of surveys. The findings revealed that the implementation and preparation of carbon inventory, Chinese Certified Emission Reduction (CCER) development, the allocation of carbon management personnel, and training are key factors influencing the actual effectiveness of carbon management within companies. Based on the survey results and the impact pathways, this study outlines the preparatory work, system content, and construction steps for power companies to build a carbon management system. It summarizes five key areas of work for power companies in managing carbon assets: carbon inventory, carbon management personnel and mechanisms, carbon trading, carbon emission reduction, and carbon finance. This provides guidance to help power companies fulfill their obligations smoothly, add value to their carbon assets, and achieve low-carbon development goals. Additionally, it offers a reference for other industry enterprises that are about to enter carbon trading.

Practice and Reflection on the Construction of a “Communities of Learners” in Normal Students’ Linguistics Courses: An Innovative Exploration of the Modern Chinese Course

By virtue of educational practice, this thesis delves into the construction strategies and steps of the “communities of learners” model in the linguistics course of normal students. In addition, three effective cases of combining it with the content of modern Chinese courses are summarized. Running on the problems of students’ motivation and participation, teachers’ roles and guidance methods, learning resources and environmental support in the implementation process, this thesis reflects on the problems of students’ learning motivation and participation, and puts forward effective solutions, such as teacher-student interaction, group cooperation, multiple technical means, digital learning platform, and shaping a “cooperative colleague relationship” among teachers.

Instance type completion in equipment knowledge graph based on translation model

Knowledge graph completion is one of the steps of knowledge graph construction. It aims to complete the incomplete triple data in the initially constructed knowledge graph and make the data in the knowledge graph more abundant and complete. At present, there are few studies on knowledge graph completion in the equipment field, and the attribute and relationship samples in the equipment knowledge graph are prone to uneven distribution, while the traditional knowledge graph completion method is difficult to solve the problem of uneven distribution of attribute and relationship samples. Therefore, this paper proposes an internal instance type completion method of equipment knowledge graph based on EP2TP-TRT. First, the TransE model is used to embed the relationship and attributes of the equipment instance, respectively. Then, the EP2TP model is used to map the instance attributes, and the TRT model is used to map the type relationship. Finally, the scores of the EP2TP and TRT models are integrated by designing different weights, and the training prediction is carried out to enhance the representation ability of instance information and type information. Compared with the mainstream advanced models, this method improves the MRR and HITS @ 1 indicators by about 0.89% and 2.1%, respectively.

Artificial Intelligence-Powered Molecular Docking and Steered Molecular Dynamics for Accurate scFv Selection of Anti-CD30 Chimeric Antigen Receptors.

Chimeric antigen receptor (CAR) T cells represent a revolutionary immunotherapy that allows specific tumor recognition by a unique single-chain fragment variable (scFv) derived from monoclonal antibodies (mAbs). scFv selection is consequently a fundamental step for CAR construction, to ensure accurate and effective CAR signaling toward tumor antigen binding. However, conventional in vitro and in vivo biological approaches to compare different scFv-derived CARs are expensive and labor-intensive. With the aim to predict the finest scFv binding before CAR-T cell engineering, we performed artificial intelligence (AI)-guided molecular docking and steered molecular dynamics analysis of different anti-CD30 mAb clones. Virtual computational scFv screening showed comparable results to surface plasmon resonance (SPR) and functional CAR-T cell in vitro and in vivo assays, respectively, in terms of binding capacity and anti-tumor efficacy. The proposed fast and low-cost in silico analysis has the potential to advance the development of novel CAR constructs, with a substantial impact on reducing time, costs, and the need for laboratory animal use.

Lap Length of Steel Bars Between Novel Prefabricated Wall and Foundation Floor Structures: Full-Scale Experiment

In the context of promoting environmentally friendly practices, prefabricated components are increasingly utilized in civil engineering design and construction. Prefabricated shear walls, in particular, have become prevalent in contemporary residential and office buildings. Following an economic analysis of the construction steps for prefabricated walls, Shanghai Hengxu Energy Conservation Company developed a novel type of prefabricated wall for use in building basements. This innovative wall consists of two thin plates joined by distributed tie pieces, with an internal steel bar cage positioned between them. The core concrete is cast in place between the plates. The wall is designed to withstand water and soil pressures from underground and connects to the basement floor through overlapping steel bars. The effectiveness of the wall's integration with the foundation floor depends on the lap length of the steel bars, a critical factor in structural design and construction. To optimize the lap length, three full-scale specimens were tested, differentiated by varying steel bar lap lengths: 0.8 lae, 1.0 lae, and 1.2 lae, where lae represents the basic anchorage length. The specimens were subjected to horizontal loading to assess failure modes, crack propagation, load-displacement behaviors, and stress-strain responses of the reinforcements. The experimental results revealed that increasing the lap length of the steel bars beyond 0.8 lae does not significantly enhance the bearing capacity or deformation potential of the specimens.

Low-cost desktop learning factory to support the teaching of artificial intelligence

The following document details low-cost hardware and open-source available software tools that can be combined to support active teaching methodologies like Problem-Based Learning (PBL) and incorporate work-oriented technological skills in students. This proposal presents a prototype of Open Educational Resources (OER) that integrates software and hardware tools for the specific purpose of facilitating instruction in Artificial Intelligence. The hardware consists of affordable electronic devices, including an Arduino board, servo motors, sensors, a relay and a motor, all integrated into a scaled conveyor belt. On the other hand, open software was used to implement an image classification program with different features (shape, color, size, among others). The exact construction steps, circuits, and code are presented in detail and should encourage other scientists to replicate the experimental setup, especially if they are looking for experimental teaching of artificial intelligence, since the system allows object classification using the machine learning paradigm to facilitate the teaching of artificial intelligence concepts with computer vision concepts.

Distributionally robust chance-constrained optimization with Gaussian mixture ambiguity set

Conventional chance-constrained programming methods suffer from the inexactness of the estimated probability distribution of the underlying uncertainty from data. To this end, a distributionally robust approach to the problem allows for a level of ambiguity considered around a reference distribution. In this work, we propose a novel formulation for the distributionally robust chance-constrained programming problem using an ambiguity set constructed from a variant of optimal transport distance that was developed for Gaussian Mixture Models. We show that for multimodal process uncertainty, our proposed method provides an effective way to incorporate statistical moment information into the ambiguity set construction step, thus leading to improved optimal solutions. We illustrate the performance of our method on a numerical example as well as a chemical process case study. We show that our proposed methodology leverages the multimodal characteristics from the uncertainty data to give superior performance over the traditional Wasserstein distance-based method.

BDCore: Bidirectional Decoding with Co-graph Representation for Joint Entity and Relation Extraction

Relation extraction has become a crucial step for the automatic construction of Knowledge Graph (KG). Recently, researchers leverage Sequence-to-Sequence (Seq2Seq) models for Joint Entity and Relation Extraction (JERE). Nevertheless, traditional decoding methods entail the generation of the target sequence incrementally from left to right by the decoder, without the ability to revise earlier predictions when errors occur. This limitation becomes evident when decoding errors manifest prior to the current decoding step. Furthermore, the interrelations among triplets originating from the same sentence exhibit a robust correlation, which has been overlooked. In this paper, we propose Bidirectional Decoding with Co-graph representation (BDCore) to address the issues mentioned above. Specifically, we first introduce a backward decoder to decode the target sequence in a reverse order. Then, the forward decoder introduces two attention mechanisms to simultaneously considering the hidden states of the encoder and the backward decoder. Thus, the backward decoding information helps to alleviate the negative impact of the forward decoding errors. Besides, we construct a relation co-occurrence graph (Co-graph) and exploit Graph Convolutional Network (GCN) to capture the relation correlation. The extensive experiments demonstrate the benefits of the proposed bidirectional decoding and co-graph representation for relation extraction. Compared to the previous methods, our approach significantly outperforms the baselines on the NYT benchmark.

Assessment of pDNA isoforms using microfluidic electrophoresis.

The recent rise in nucleic acid-based vaccines and therapies has resulted in an increased demand for plasmid DNA (pDNA). As a result, there is added pressure to streamline the manufacturing of these vectors, particularly their design and construction, which is currently considered a bottleneck. A significant challenge in optimizing pDNA production is the lack of high-throughput and rapid analytical methods to support the numerous samples produced during the iterative plasmid construction step and for batch-to-batch purity monitoring. pDNA is generally present as one of three isoforms: supercoiled, linear, or open circular. Depending on the ultimate use, the desired isoform may be supercoiled in the initial stages for cell transfection or linear in the case of mRNA synthesis. Here, we present a high-throughput microfluidic electrophoresis method capable of detecting the three pDNA isoforms and determining the size and concentration of the predominant supercoiled and linear isoforms from 2 to 7kb. The limit of detection of the method is 0.1ng/µL for the supercoiled and linear isoforms and 0.5ng/µL for the open circular isoform, with a maximum loading capacity of 10-15ng/µL. The turnaround time is 1min/sample, and the volume requirement is 10µL, making the method suitable for process optimization and batch-to-batch analysis. The results presented in this study will enhance the understanding of electrophoretic transport in microscale systems dependent on molecular conformations and potentially aid technological advances in diverse areas relevant to microfluidic devices.

Improvement and Application of Directional Reaming While Willing Bit in Tahe Oilfield

Unlike conventional sidetracking technology, the technology of reaming while directional drilling makes it possible for us to conduct directional drilling and reaming simultaneously using dual core bit, which simplifies the construction steps and can greatly shorten the drilling cycle and reduce drilling costs. With the increasing of the tasks of sidetrack horizontal well in the old wells of Tahe Oilfield, the technology of reaming while directional drilling has become an important means for sidetrack of old wells in Tahe Oilfield. However the substratum of Bachu formation has high content of gravel with large diameter and strong consolidation, which cause severe abrasion of dual core bit, short drill life and low ROP. With the problems in the substratum of Bachu Formation, Tahe Oilfield continuously improves dual core bit based on its effect of reaming while drilling, and breaks the national records of the maxim setting depth, inclination and continuous expansion length during the operations of well TH12124CH. This paper shows the improvements of dual core bit and its application effect both in well TH10233CH and well TH12124CH, and put forward a further scheme to improve the dual core bit for the substratum of Bachu Formation of Tahe Oilfield.

HybridOctree_Hex: Hybrid octree-based adaptive all-hexahedral mesh generation with Jacobian control

We present a new software package, “HybridOctree_Hex,” for adaptive all-hexahedral mesh generation based on hybrid octree and quality improvement with Jacobian control. The proposed HybridOctree_Hex begins by detecting curvatures and narrow regions of the input boundary to identify key surface features and initialize an octree structure. Subsequently, a strongly balanced octree is constructed using the balancing and pairing rules. Inspired by our earlier preliminary hybrid octree-based work, templates are designed to guarantee an all-hexahedral dual mesh generation directly from the strongly balanced octree. With these pre-defined templates, the sophisticated hybrid octree construction step is skipped to achieve an efficient implementation. After that, elements outside and around the boundary are removed to create a core mesh. The boundary points of the core mesh are connected to their corresponding closest points on the surface to fill the buffer zone and build the final mesh. Coupled with smart Laplacian smoothing, HybridOctree_Hex takes advantage of a delicate optimization-based quality improvement method considering geometric fitting, Jacobian and scaled Jacobian, to achieve a minimum scaled Jacobian that is higher than 0.5. We empirically verify the robustness and efficiency of our method by running the HybridOctree_Hex software on dozens of complex 3D models without any manual intervention or parameter adjustment. We provide the HybridOctree_Hex source code, along with comprehensive results encompassing the input and output files and statistical data in the following repository: https://github.com/CMU-CBML/HybridOctree_Hex.

Hardware Sequence Combinators

Recent advances in formal methods for constructing parsers have employed the notion of combinators: primitive elemental parsers with well-defined methods for combining them in sequences or through choice. This paper explores the subtleties associated with leveraging sequence combinators to produce compact, custom hardware traffic validators. This involves a fully automated process that takes as input a formal grammar specifying message formats and produces a parsing circuit capable of validating traffic headers and payload content. The resulting circuit is deployed through network guard appliances that employ Field Programmable Gate Array (FPGA) devices, or alternatively, within the on-chip FPGA associated with System-on-Chip (SoC) devices, such as the Xilinx UltraScale MPSoC. Each guard appliance acts as a hidden “bump-in-the-wire” that either forwards or drops individual packets based on the message parsing outcome, thereby hardening network segments against zero-day attacks and persistent implants. Guards may operate on a wide variety traffic protocols and formats including TCP/IP, CAN/J1939, or MIL-STD-1553. The central step in parser construction is to build a collection of standard shift/reduce parsing tables that can be employed by a push-down automata to check each byte in a message. Typically, these tables are sparse, resulting in excessive use of FPGA circuit resources to represent them. By leveraging sequence combinators, along with other optimizations, we have been able to produce highly compact representations that can reduce table size by up to 95% for non-trivial grammars. Depending on the grammar, this translates directly into FPGA resource reductions. The reductions now make it viable to implement complex parsers on small, inexpensive FPGA’s, or alternatively combine parsers with encryption and encapsulation to enhance guard capabilities.