Information Engineering Research Articles

Behaviour of rectangular concrete-filled steel tube beams under monotonic and cyclic loadings

Concrete filled steel tube (CFST) has been extensively explored for columns but limitedly explored for beams. This current study investigates the behaviour of rectangular CFST beams under monotonic and cyclic loadings. Experiments were conducted for 12 CFST beams with depth-to-thickness (D/t) ratios of 66.7–111.1, followed by theoretical analyses. The experimental results indicated that tensile yielding and compressive local buckling of steel tubes governed the plastic behaviour of the tested CFST beams. The buckling significantly degraded the strength of CFST beams. When the D/t ratio decreased from 111.1 to 66.7, the stiffness, yield load, and ultimate load increased by 33.2%, 49.4%, and 40.0%, respectively, but the ultimate deflection was reduced by ∼19.5%. The effect of cyclic loading induced a degradation of stiffness of 0.85–2.85%/cycle, depending on the increment of the peak cycle deflection. The buckling negatively affected the ultimate state, significantly reducing the ductility. Investigations into solutions to delay the buckling of steel tubes should be encouraged. Three approaches based on concepts of composite sections, fully plastic sections of steel tubes, and the proposed decoupling were applied to compute the ultimate strengths of the tested beams. The calculation results indicated that the model of fully plastic sections of steel tubes exhibited the best agreement with the test results. The fully plastic and proposed decoupling models provided reasonable results, while the composite model slightly overestimated the ultimate load of CFST beams. The results provide some technical information for structural engineers in designing rectangular CFST beams.

The Model of “Post, Course, Competition, Certificate” Integrated Education for Electronic Information Engineering Technology Major in Higher Vocational Colleges

The core education function of higher vocational colleges is to train technical talents with high quality, so as to meet the needs of talents in the development stage of our society. Under the guidance of talent training, higher vocational colleges need to pay attention to establishing an all-round and three-dimensional education model, and promote innovation of higher vocational education on the basis of this. It is also a way to promote the innovation of higher vocational education to vigorously promote the construction of “post, course, competition, certificate” mode in the construction of education mode. Through the construction of “post, course, competition, certificate” mode, the education mode of higher vocational colleges is gradually improved, so as to strengthen the effectiveness of talent training in higher vocational colleges. Therefore, in this paper, the author puts forward some suggestions to promote the construction of the integrated education mode of the electronic information engineering technology major in higher vocational colleges, so as to help improve the talent training level of higher vocational colleges.

IEEE Engineering in Medicine and Biology Society Information

IEEE Engineering in Medicine and Biology Society Information

Vertical Accuracy of Google Earth Data

Digital Elevation Models (DEMs) are an important data source used in many engineering and Geographic Information System (GIS) applications. This paper illustrates a strategy for creating a DEM by utilizing elevation data from Google Earth and evaluating the vertical positional accuracy of the generated DEM adopting a well-defined methodology. To ensure the accuracy of the elevation data obtained from Google Earth, a thorough evaluation was done in three diverse small districts of the northern shoreline in Egypt. The evaluation process involved determining the ground coordinates of reference points utilizing two surveying techniques: total station and Real-Time Kinematic (RTK) Global Positioning System (GPS) surveys. These coordinates were compared with the ones predicated by the DEM generated by putting into service Google Earth's elevation data. Furthermore, the vertical accuracy was assessed using Shuttle Radar Topographic Mission (SRTM) data of Google Earth collected at two different periods in 2015 and 2023. The vertical accuracy of the Google Earth data is detailed utilizing Mean Error (ME), Maximum Absolute Error (MAE), and Root Mean Square Error (RMSE). According to the results, Google Earth's elevation data accuracy remains consistent from 2015 to 2023, and refining SRTM data does not improve the vertical accuracy. The vertical accuracy of the total station survey surpasses the one of the RTK GPS survey, and the elevation accuracy of the RTK GPS survey decreases with increasing height difference. In addition, the vertical accuracy of DEMs was found to be sufficient for some engineering applications but not accurate enough for precise engineering studies. The accuracy achieved in small height difference terrain can be utilized to produce large-scale cadastral maps, city plans, or land use maps. Finally, the elevation data offered by Google Earth can be utilized for preliminary studies at a low cost. However, to ensure the accuracy of these data, it is recommended that users compare them with reference data before implementation.

Neuronal functional connectivity is impaired in a layer dependent manner near chronically implanted intracortical microelectrodes in C57BL6 wildtype mice

Objective. This study aims to reveal longitudinal changes in functional network connectivity within and across different brain structures near chronically implanted microelectrodes. While it is well established that the foreign-body response (FBR) contributes to the gradual decline of the signals recorded from brain implants over time, how the FBR affects the functional stability of neural circuits near implanted brain–computer interfaces (BCIs) remains unknown. This research aims to illuminate how the chronic FBR can alter local neural circuit function and the implications for BCI decoders. Approach. This study utilized single-shank, 16-channel,100 µm site-spacing Michigan-style microelectrodes (3 mm length, 703 µm2 site area) that span all cortical layers and the hippocampal CA1 region. Sex balanced C57BL6 wildtype mice (11–13 weeks old) received perpendicularly implanted microelectrode in left primary visual cortex. Electrophysiological recordings were performed during both spontaneous activity and visual sensory stimulation. Alterations in neuronal activity near the microelectrode were tested assessing cross-frequency synchronization of local field potential (LFP) and spike entrainment to LFP oscillatory activity throughout 16 weeks after microelectrode implantation. Main results. The study found that cortical layer 4, the input-receiving layer, maintained activity over the implantation time. However, layers 2/3 rapidly experienced severe impairment, leading to a loss of proper intralaminar connectivity in the downstream output layers 5/6. Furthermore, the impairment of interlaminar connectivity near the microelectrode was unidirectional, showing decreased connectivity from Layers 2/3 to Layers 5/6 but not the reverse direction. In the hippocampus, CA1 neurons gradually became unable to properly entrain to the surrounding LFP oscillations. Significance. This study provides a detailed characterization of network connectivity dysfunction over long-term microelectrode implantation periods. This new knowledge could contribute to the development of targeted therapeutic strategies aimed at improving the health of the tissue surrounding brain implants and potentially inform engineering of adaptive decoders as the FBR progresses. Our study’s understanding of the dynamic changes in the functional network over time opens the door to developing interventions for improving the long-term stability and performance of intracortical microelectrodes.

IEEE Transactions on Biomedical Engineering Information for Authors

IEEE Transactions on Biomedical Engineering Information for Authors

Evaluating Machine Learning Techniques for Web Robot Detection

A web robot, also known as a web crawler, is an automated script that systematically browses the World Wide Web in a methodical manner. Web robots are commonly used by search engines to index web pages, gather information, and update search engine databases. They follow hyperlinks from one web page to another, collecting data and information for various purposes such as indexing, data mining, and website monitoring. Web robot detection is the process of identifying and distinguishing between human users and web robots on websites, which are a major source of web traffic. This process is vital to prevent malicious web robots from having a negative effect on web servers’ traffic and their users’ privacy. Companies such as Imperva, Inc. use machine learning models to identify malicious bots and prevent them from having unauthorized access to an organization’s server. To understand the benefit that machine learning models bring to web robot detection, we used pre-published server log data to construct machine learning models on Orange (a Data Mining Software) and Python that can distinguish between malicious and benign web robots. We evaluated the performance of three well-known machine learning algorithms: kNN, neural network, and decision tree. Based on our experimental results, the neural network gains the highest precision and the lowest false-positive and false-negative percentage of web robots. However, the neural network requires more time to generate the desired output.

Learning Transformation in the Digital Era: Development of Virtual Reality-Based Learning Media to Increase Student Efficiency and Creativity

During the COVID-19 pandemic, its impact was felt in Indonesia's education world, especially when learning was online or offline. Every lecturer in educational institutions is expected to provide the latest innovations to create an effective learning process. In the post-COVID-19 pandemic era, the digitalization of education in Indonesia is experiencing rapid growth, and advances in information technology are an opportunity to increase creativity in learning media. One proposed method is the development of Virtual Reality-based learning media, which can support the quality of the teaching and learning process. The development of Virtual Reality-based learning media includes intelligent systems for the Informatics Engineering Study Program, and Agro-Industry Plantation Management for the Agro-Industry Management Study Program, especially at Campus 4 PSDKU Sidoarjo – Politeknik Negeri Jember. The results of the development of Virtual Reality-based learning media are in the form of visualization illustrations of students' understanding of the Intelligent Systems and Plantation Agro-Industry Management courses. This technology is expected to facilitate student participation with learning resources more efficiently than conventional learning media which only allows one-way communication. Based on the results of evaluations via questionnaires to students, it is known that learning efficiency reaches 82% when using Virtual Reality-based learning media.

ВИКОРИСТАННЯ ВІДДАЛЕНОГО МОНІТОРИНГУ ДЛЯ ПІДВИЩЕННЯ ЯКОСТІ ТА ЕФЕКТИВНОСТІ У ВИРОБНИЧОМУ СЕРЕДОВИЩІ

The article thoroughly examines the challenges associated with monitoring computer networks and servers in the context of rapid technological advancements and increasing complexity. The effective management and monitoring of networks and servers have become critically important due to the growing exchange of data and the need to ensure their continuous operation. The article proposes solutions based on innovative software tools such as Prometheus and Grafana, which provide real-time monitoring and data visualization capabilities to enhance understanding of the state of networks and servers. Traditional monitoring methods, their limitations, and the advantages of the new approach based on Prometheus and Grafana are discussed. Through these tools, network administrators can gain full control over the status and resource utilization of networks and servers, enabling them to promptly address any issues or deviations in their operation. The successful implementation of a monitoring system using these tools is demonstrated, along with an analysis of the prospects for further development of this approach. The methodology of the research is described in detail, including the main tasks of server and network monitoring, as well as the principles of operation of the Prometheus system. A comparison with traditional monitoring methods is conducted to highlight the advantages of the new approach and its importance in the modern information environment. The article also focuses on the possibilities of extending monitoring to other services and devices such as SSH, email, Jenkins, and Kubernetes. The architectural foundation of Prometheus and Grafana is presented, along with methods of their utilization for data visualization and system status notification. This article will serve as a valuable source of information for network administrators, system engineers, and anyone interested in the monitoring and management of information systems in industrial environments.

Enhancing Participatory Learning at SMP Negeri 2 Jaten Karanganyar through the Integration of Technology

The development of knowledge and technology significantly impacts literacy skills, essential for academic growth and school adaptation. Technology literacy is crucial for awareness and academic support, but a lack of technological knowledge can hinder education. To address this, the Indonesian government introduced the belajar.id platform, integrating Google Suite for Education (GSuite) to aid academic activities during the pandemic. Challenges like limited teacher-student interaction persist, necessitating the encouragement of electronic media and diverse educational material availability. They aimed to bridge teaching gaps, enhance technological skills, and ensure effective knowledge sharing, using participatory rural appraisal (PRA). The team of Research Group Data Information Knowledge and Engineering (RG DIKE) at the Universitas Sebelas Maret (UNS) Surakarta conducted a study on technology literacy's importance for students in SMP Negeri 2 Jaten Karanganyar. It emphasized technology's role in disaster management and prevention, striving for a strategic approach to technology-based education. Training sessions were conducted on August 15 and October 26, 2023, focused on belajar.id, GSE, and OBS integration. Teachers played a key role in guiding and updating their GSE and OBS knowledge. In summary, these sessions aimed to equip teachers and students with vital GSE and OBS skills, enhancing education quality and learning outcomes.

Scalable-produced micro-elastic triboelectric sensing ground for all-weather large-scale applications

Large-scale smart floor will be one potential form for a human–machine interface, but is also facing many difficulties and challenges in the materials science and information engineering. Here, we present a straightforward method for fabricating large-scale micro-elastic triboelectric nanogenerator (ME-TENG) arrays, which can be assembled as triboelectric sensing ground for real-time interface interaction and all-weather monitoring. ME-TENG unit of 6 by 6 arrays offers a maximum power density of 5.84 mW·m−2 under a load resistance of 70 MΩ. It displays a sensitivity of 132.48 mV/kPa in the pressure range of 0 to 180 kPa. The electrical output signals have remained stable in the durability tests over 10 days with various weather conditions. Furthermore, successfully demonstrated a regional comprehensive security monitoring (RCSM) system for enabling all-weather monitoring of the features, position, and movement trajectories of an object. Even more gratifying is that the ME-TENG unit of 30 cm by 30 cm prepared by a simple and fast “all-in-one” method, takes costs of 14.84 dollars/m2 and the production period of 2 h, compared with the most cost-effective piezoelectric sensors, the cost and duration were reduced by 98.67 % and 42.86 %, respectively. This work will greatly contribute to the development and implementation of large-scale sensing and the multidimensional security system.

Sire: An interoperability engine for prototyping algorithms and exchanging information between molecular simulation programs.

Sire is a Python/C++ library that is used both to prototype new algorithms and as an interoperability engine for exchanging information between molecular simulation programs. It provides a collection of file parsers and information converters that together make it easier to combine and leverage the functionality of many other programs and libraries. This empowers researchers to use sire to write a single script that can, for example, load a molecule from a PDBx/mmCIF file via Gemmi, perform SMARTS searches via RDKit, parameterize molecules using BioSimSpace, run GPU-accelerated molecular dynamics via OpenMM, and then display the resulting dynamics trajectory in a NGLView Jupyter notebook 3D molecular viewer. This functionality is built on by BioSimSpace, which uses sire's molecular information engine to interconvert with programs such as GROMACS, NAMD, Amber, and AmberTools for automated molecular parameterization and the running of molecular dynamics, metadynamics, and alchemical free energy workflows. Sire comes complete with a powerful molecular information search engine, plus trajectory loading and editing, analysis, and energy evaluation engines. This, when combined with an in-built computer algebra system, gives substantial flexibility to researchers to load, search for, edit, and combine molecular information from multiple sources and use that to drive novel algorithms by combining functionality from other programs. Sire is open source (GPL3) and is available via conda and at a free Jupyter notebook server at https://try.openbiosim.org. Sire is supported by the not-for-profit OpenBioSim community interest company.

INTERACTIVE PATHWAYS: EXPLORING STUDENTS’ ACCEPTANCE OF USING NEARPOD FOR ENGLISH GRAMMAR PROFICIENCY

Lately, technology-enhanced learning tools, such as Nearpod, demonstrate the efficacy of integrating learning, particularly in an English grammar class. This study aims to investigate students' reactions to technology used for teaching grammar, precisely the acceptance of using Nearpod for grammar class. This study employed a mixed-method design with questionnaire and in-depth interview. The questionnaire used in this study was based on Technology Acceptance Model (TAM) in a Likert scale. Thirty-eight undergraduate students were selected purposefully from Informatics Engineering Department who enrolled in Grammar class. The findings revealed a highly positive perception of Nearpod's utility for grammar instruction, confirming on students’ acceptance and highlighting its benefits for enhancing language skills, especially in grammar. Students agreed that Nearpod was beneficial to help understand and practice the language. The findings demonstrate that Nearpod is an effective tool for enhancing grammar instruction and suggest its implications for language educators and curriculum designers. This study contributes to our understanding of the role of technology in fostering integrative learning in grammar classes and highlights the need for further research in this area.

Accurate prediction of antibody function and structure using bio-inspired antibody language model.

In recent decades, antibodies have emerged as indispensable therapeutics for combating diseases, particularly viral infections. However, their development has been hindered by limited structural information and labor-intensive engineering processes. Fortunately, significant advancements in deep learning methods have facilitated the precise prediction of protein structure and function by leveraging co-evolution information from homologous proteins. Despite these advances, predicting the conformation of antibodies remains challenging due to their unique evolution and the high flexibility of their antigen-binding regions. Here, to address this challenge, we present the Bio-inspired Antibody Language Model (BALM). This model is trained on a vast dataset comprising 336 million 40% nonredundant unlabeled antibody sequences, capturing both unique and conserved properties specific to antibodies. Notably, BALM showcases exceptional performance across four antigen-binding prediction tasks. Moreover, we introduce BALMFold, an end-to-end method derived from BALM, capable of swiftly predicting full atomic antibody structures from individual sequences. Remarkably, BALMFold outperforms those well-established methods like AlphaFold2, IgFold, ESMFold and OmegaFold in the antibody benchmark, demonstrating significant potential to advance innovative engineering and streamline therapeutic antibody development by reducing the need for unnecessary trials. The BALMFold structure prediction server is freely available at https://beamlab-sh.com/models/BALMFold.

Design, Development, and Formative Evaluation of a Hybrid Remote Quantum Laboratory Supporting HBCU Partnerships

Quantum information science and engineering are expanding and scaling worldwide. As government and industry push to expand quantum applications around the world, education institutions scramble to train the next generation of quantum-aware engineers in the Quantum pipeline. However, as educational institutions seek to expand the pipeline, not all learners have access to Quantum training or equipment. Quantum laboratories, an expensive component of STEM education, often remain exclusively accessible to institutions with affluence and funding, thus creating systemic barriers for underrepresented learners to the Quantum pipeline. This design-based case study describes the three-year design and development of a hybrid remote online Quantum laboratory in partnership with historically black colleges and universities across the US with the goal of expanding access for underrepresented learners to Quantum training. The study describes the prototypical design, development, and formative evaluation of the prototype’s iterations and reflects on design decisions made resulting from formative feedback during the design process.

Comparative analysis of codon usage bias in chloroplast genomes of ten medicinal species of Rutaceae.

Rutaceae family comprises economically important plants due to their extensive applications in spices, food, oil, medicine, etc. The Rutaceae plants is able to better utilization through biotechnology. Modern biotechnological approaches primarily rely on the heterologous expression of functional proteins in different vectors. However, several proteins are difficult to express outside their native environment. The expression potential of functional genes in heterologous systems can be maximized by replacing the rare synonymous codons in the vector with preferred optimal codons of functional genes. Codon usage bias plays a critical role in biogenetic engineering-based research and development. In the current study, 727 coding sequences (CDSs) obtained from the chloroplast genomes of ten Rutaceae plant family members were analyzed for codon usage bias. The nucleotide composition analysis of codons showed that these codons were rich in A/T(U) bases and preferred A/T(U) endings. Analyses of neutrality plots, effective number of codons (ENC) plots, and correlations between ENC and codon adaptation index (CAI) were conducted, which revealed that natural selection is a major driving force for the Rutaceae plant family's codon usage bias, followed by base mutation. In the ENC vs. CAI plot, codon usage bias in the Rutaceae family had a negligible relationship with gene expression level. For each sample, we screened 12 codons as preferred and high-frequency codons simultaneously, of which GCU encoding Ala, UUA encoding Leu, and AGA encoding Arg were the most preferred codons. Taken together, our study unraveled the synonymous codon usage pattern in the Rutaceae family, providing valuable information for the genetic engineering of Rutaceae plant species in the future.

The Application of Computer Network Technology in Civil Aviation Electronic Information Engineering

In this information age, electronic information engineering plays a crucial role in the field of civil aviation, providing indispensable support for efficient operation and security management. This article introduces the close relationship between civil aviation electronic information engineering and computer network technology, analyzes the advantages of network technology in civil aviation electronic communication engineering, and explores in detail the specific application scenarios and roles of computer network technology in civil aviation electronic information engineering. With the continuous innovation and development of technology, network technology will continue to play a more important role in civil aviation electronic information engineering, promoting the civil aviation industry to move towards a higher level.

The Practice of Computer Internet Technology in Electronic Information Engineering

Computer Internet is an important supporting force for the development of today s society and has become an indispensable part of social life. As a wide range and fast development of electronic information engineering, its development can not be separated from the support of computer Internet technology. The introduction of computer Internet technology into the field of electronic information, for the progress of science and technology and the quality of life of the people to provide a force to support. In such a big environment, actively carrying out the practical research of computer Internet in electronic information engineering is a very necessary work to adapt to the needs of modern social and economic development, and is also worth the attention of the industry. Based on this, the article analyzes the practical application of computer Internet technology in electronic information engineering for the reference of relevant personnel.

Development of an IoT based 3D printed Mobile Robot Platform for training of Mechatronics Engineering Students

The advent of the Internet of Things (IoT) has opened up new opportunities in education, particularly in the field of mobile robotics. Mobile robotics is a fast-growing field, and with the increasing demand for skilled robotics engineers, it is essential to provide students with hands-on experience in designing and developing robots. In this regard, an IoT-based 3D printed mobile robot platform has been developed for students training. STEM (Science, Technology, Engineering, and Mathematics) education has been gaining attention as a means to improve science and engineering education. STEM education aims to foster critical thinking, problem-solving skills, creativity, and innovation among students. It involves an educational policy that aims to develop the next generation of skilled professionals by integrating the fields of Science, Technology, Engineering and Mathematics. The goal of this approach is to provide students in mechatronics, who are assigned to the robotics lab during their studies, an up to date knowledge and experience in research and development activities. Thus, this paper aims to provide education that focuses on the acquisition and application of engineering knowledge, including control engineering, mechanical engineering, electrical and electronic engineering, information engineering, among others, with the main focus on mobile robot research.

Toward less carbon-intensive, faster gas-to-market offshore development schemes a case study of using discovered resources in the Bedout Sub-basin

The Dorado and Pavo fields in the Bedout Sub-basin of the Roebuck Basin, offshore Western Australia, boast a combined 2C contingent resource of 210 MMbbls of liquid hydrocarbons and 750 bcf of gas. Their discovery represents a significant development opportunity for the Australian offshore, especially given the considerable un-risked Prospective Resources in the basin. However, Dorado faces challenges due to its high volatile oil/high condensate gas ratio gas (wet gas) accumulation and its reservoir pressure exceeding 5500 psia, necessitating a miscible gas injection strategy to maximise liquid recovery. This approach involves energy-intensive gas processing and high-pressure reinjection, leading to substantial greenhouse gas emissions. The CO2 intensity directly from Dorado compressors is estimated at 30–39 kg/bbl – on a project-level nearly double the average unit emissions of many producers. This paper explores alternative development schemes using public domain information and petroleum engineering principles to mitigate technical risks, reduce CO2 emissions, and possibly improve the economics and development timeline of the Dorado and Pavo fields. For Dorado, a simpler scheme focussing on oil production and gas delivery for domestic or liquefied natural gas (LNG) use could significantly reduce emissions. Meanwhile, Pavo might see development via a standalone FPSO, emphasising reservoir energy management through water flooding and produced water disposal, typical of conventional black oil field development. These alternatives aim for a less carbon intensive and economically viable development pathway, setting a precedent for future exploration and development in the basin.