Seeding plays a crucial role in agricultural production. The traditional mechanized seeding suffers from inefficiencies, low precision, and lack of control, which makes it inadequate for the high demands of the modern precision agriculture, such as the high speed, high precision, and real-time control. Therefore, this study proposes a precision seeding scheme based on multi-sensor information fusion

Multimodal Crop Electronic Medical Records (CEMRs) contain complex information, including disease symptoms, crop conditions, environmental factors, and diagnostic prescriptions, making them crucial for intelligent prescription recommendations. However, effectively integrating complementary features from different CEMRs modalities has remained a key challenge. Current CEMRs research primarily focuses

Millions of individuals across the world use automatic speech recognition (ASR) systems every day to dictate messages, operate gadgets, begin searches, and enable data entry in tiny devices. The engagement in these circumstances is determined by the accuracy of the voice transcriptions and the system's response. A second barrier to natural engagement for multilingual users is the monolingual nature

This paper presents a human-centric mixed reality (MR) collaborative training platform that employs a kinesthetic learning technique in industrial robotic training, specifically focusing on robot pick–and–place (RPP) operations. Collaborating with ABB Robotics Vietnam, we conducted a user study to investigate the user experiences and practical perceptions of university students and novice trainees

The building sector remains a major contributor to increasing energy consumption and emissions. Meanwhile, the energy system is becoming more complex due to the transition to clean energy sources. Current tools and policies struggle to manage this complexity, as the existing infrastructure was not designed for such large dynamic distributed energy resources. This creates an urgent need to adopt emerging

Production planning and control (PPC) is essential in industrial manufacturing, ensuring efficient resource allocation and process management. Industry 4.0 introduces advanced technologies like cyber physical systems (CPS), artificial intelligence (AI), and internet of things (IoT) to effectively manage and monitor manufacturing operations. However, integrating these technologies into existing machinery

Advanced simulation tools allow the optimization of processes prior to production implementation. Our study aims to integrate industrial information and data into a digital model based on artificial intelligence (AI) to simulate acoustic and vibration behavior during the production preparation phase. This model integrates real manufacturing conditions with generated vibrations and acoustic waves, creating

Deep eutectic solvents (DESs) are recently proposed as green materials to remove nitric oxide (NO) from released streams into the atmosphere. The mathematical aspect of this process attracted less attention than it deserved. A straightforward approach in this field will help engineer DES chemistry and optimize the equilibrium conditions to maximize the amount of removed NO. This study covers this gap

With the rapid digital transformation of manufacturing, vast amounts of data are being generated and analyzed to uncover valuable patterns in areas such as energy efficiency, predictive maintenance, production scheduling etc. However, much of this data and the intelligence derived from it remain isolated within individual companies. This is strongly influenced by companies reluctance to share data

An important issue in coopetitive supply chains is ensuring business confidentiality when sharing sensitive information among partner actors. This challenge becomes even more complex in blockchain-based supply chains due to inherent transparency, conflicting with businesses’ need to safeguard sensitive information and posing risks to proprietary data. In this paper, we propose an approach based on

This paper deals with the problem of sensor faults isolation from overlapping un-stealthy attacks based on coding sensor outputs for industrial systems represented by Lipschitz affine nonlinear models. A novel structure of a coding scheme, a network of three groups of interlinked observers, and an adaptive threshold technique is developed. To detect the abnormal behaviors, the first group is developed

With the rapid development of Internet of Things (IoT) devices, the volume of data generate across various fields, such as smart healthcare, smart home, smart transportation has significantly increased. This surge raises serious concerns about the secure storage of sensitive data for e.g., biometric information (e.g., fingerprints and facial recognition) and medical records etc. The centralized cloud

Unfortunately, within the framework of blockchain contracting, a significant gap exists in comprehending contractual behavior, and the feasibility of predictive contracts has largely remained unexplored. A principal obstacle stems from the absence of a seamless integration between predictive concepts and blockchain technology. This deficiency is attributed to a failure to consider the inherent characteristics

The prediction of stock prices is a complex task due to the influence of various factors, high noise, and nonlinearity. This paper focuses on addressing the challenges of low prediction accuracy and poor stability, which have been a key area of interest in academic research. We proposed an optimized ensemble model that combines an LSTM-based attention mechanism and a cyclic multidimensional gray model

Recently, research at the intersection of quantum mechanics and machine learning has gained attention. This interdisciplinary field aims to tackle the computational efficiency of machine learning by leveraging quantum computing and to derive novel machine learning algorithms inspired by quantum principles. Despite substantial progress in quantum science research, several challenges persist, including

This study investigates the impact of Industry 4.0 (I4.0) enabling technologies in enhancing the resilience of supply chain systems and the barriers to adopting Industry 4.0 in the supply chains. We use the novel grey influence analysis (GINA) to examine the influence relations among supply chain resilience enhancers and barriers. The study has identified seven enhancers and nine barriers to adopting

The most essential criteria to improve digital transformation in renewable energy projects should be identified. This situation helps the companies to use limited financial budgets and human resources in the most efficient way. Therefore, a new study is needed to analyze the performance indicators of the digital transformation process in renewable energy projects. Accordingly, this study aims to identify

Semantic Building Information Modeling (BIM) consists in translating data expressed using BIM formats (namely IFC) into Semantic Web files using RDF serializations (e.g., Turtle). This enables the inference of new knowledge and constraint checking, among other advantages. While several software tools for translating BIM models into Semantic Web languages have been proposed in the literature, they differ

Industrial automation is an essential part of modern industries, including manufacturing and utilities, driven by the need to enhance productivity, precision and efficiency. This paper provides a comprehensive review of recent advances in industrial automation, focusing on the role of 6G wireless communication as a key enabler. We explore various categorizations and reference use cases within industrial

Despite a small overfitting risk, the deep forest model and its variants cannot automatically match data features; they rely on manual experience and comparative experiments for forest learner selection. This study proposes an automated deep forest model (ATDF) to enhance deep forest automation by automatically determining forest learners’ types and numbers based on training data. The model introduces

Industrial information integration plays a crucial role in modern supply chains by ensuring the smooth flow of data across all stages, including recovery, recycling, and disposal, which is essential for the successful implementation of a closed-loop supply chain (CLSC) model. Building on this, our paper addresses a global CLSC problem by incorporating International Commercial Terms (Incoterms) and

System engineering has been shifting from document-centric to model-based approaches, where assets are becoming more and more digital. Although digitisation conveys several benefits, it also brings several concerns (e.g., storage and access) and opportunities. In the context of Cyber-Physical Systems (CPS), we have experts from various domains executing complex workflows and manipulating models in

Accurately assessing the difficulty of material excavation is crucial for reducing excavator energy consumption, ensuring operational safety, and optimizing excavator efficiency. Addressing the challenges of uncertain and difficult-to-judge excavation conditions for underground materials, this paper proposes a Bayesian deep learning-based method that integrates excavation process data to identify excavation

The increasing adoption of commercial-off-the-shelf infrastructure components and the rising integration of sensors into assets have led to a notable proliferation of operational data in industrial systems. As a result, a significant portion of investment and risk management decisions now heavily rely on the provenance and quality of heterogeneous data, sourced both internally and externally from specific

In the chemical pipelining industry, owing to the high-pressure transportation process, an accurate hydraulic transient simulation tool plays a central role in preventing the slack line flow and overpressure from causing pipeline operation treacherous. Nevertheless, the current model-driven method often faces challenges in balancing computational efficiency with accuracy, and the existing data-driven

In the complex process of manufacturing storage chips, the photo-etching and acid-etching stages play a crucial role, significantly affecting energy consumption and environmental impact. This paper introduces a novel Bi-Level Programming Model for Storage Chip Manufacturing (BLPM-SCM) aimed at optimizing the coordination between these two stages. The upper-level model focuses on minimizing the time

Decreasing energy consumption in networks with limited resources, such as the Internet of Things, has always been one of the main challenges in guaranteeing network performance. In this article, cooperative game theory is employed to improve the cooperation patterns of fog computing resources. The EDLIoT method consists of two main steps: “Topology Construction” and “Determining Optimal Fog Computing

The integration of cybernetic principles into space technology has led to a significant shift in spacecraft guidance systems and space station operations. This scholarly work provides a comprehensive analysis of the profound impact that cybernetics has had on space technology, particularly focusing on the development and implementation of closed-loop control systems. Building on foundational contributions

Assembly instructions are process documents in detail describing the operation steps, materials, tools, fixtures, and assembly sequences in assembly procedures. Due to assembly instructions including numerous contents, and the content being easy for workers to understand, process designers need to spend lots of time thinking and authoring assembly instructions to ensure that workers can complete the

Blockchain technology (BT) is a digitally decentralized, distributed and public ledger, which considers a secure and viable solution for storing and accessing the record transactions in a public or private peer-to-peer network and assures a smart world of automation of complex services. This paper aims to evaluate the factors persuading the BT adoption and also to assess the possible application areas

Municipal solid waste (MSW) management is a critical aspect of urban planning and public health. As societies strive towards environmental sustainability and socio-economic development, robust techniques to transform waste into energy become paramount. Assessment of waste-to-energy (WTE) techniques is based on a spectrum of criteria that are often vague and imprecise. The current study addresses this

As the complexity of modern engineering systems increases, traditional fault detection models face growing challenges in achieving accuracy and reliability. This paper presents a novel Digital Twin-assisted fault diagnosis framework specifically designed for hydraulic systems. The framework utilizes a virtual model, constructed using Modelica, which is integrated with real-time system data through

A smart production system can be made energy-efficient using renewable energy and is considered to maintain the extended material requirement planning under a logistics system by using radio frequency identification. The tracking technology provides information about products with real-time notification. This study investigates renewable energy usage within a smart production system as renewable energy

Industry 4.0/5.0 has brought together technologies like Internet of Things (IoTs), Industrial IoT (IIoT), Cyber–Physical Systems (CPS), Edge Computing, big data analytics, communication technologies (4G/5G/6G) and Digital Twins (DTs), aiming for more intelligent, interconnected systems. However, their real-time efficiency hinges on how well these components integrate and interact. This paper examines

Accurate soil type categorization is very important for resource management in space exploration. Using a complete system including a space station, rovers, and a deep learning framework, this study proposes an advanced deep learning model for soil type categorization in space informatics. Gathering and preprocessing multispectral and hyperspectral soil data, the rovers send it to the space station

Automated guided vehicle (AGV), which was initially designed for indoor operations in industry, has been increasingly applied in outdoor heavy-duty logistics tasks. In typical navigation tasks, such as the autonomous tracking of a designated object or a person, relative angle and relative distance between AGV and the target is required. To obtain the necessary information, various on-board sensors

In 2021, United Nations released the "Creating Resilient Cities 2030 Project", which aims to strengthen urban resilience in developing and implementing disaster reduction strategies. Resilient cities are a new type of urban development model that emphasizes the ability of cities to resist natural disasters and social pressures, reduce losses, and allocate resources reasonably to quickly recover from

Integrated Pest Management (IPM) techniques have been widely used in agriculture to manage pest damage in the most economical way and to minimise harm to people, property and the environment. However, current research and products on the market cannot consolidate this process. Most existing solutions either require experts to visually identify pests or cannot automatically assess pest levels and make

Cyber–physical systems (CPSs) are being increasingly adopted for industrial applications, yet they involve a dynamic threat landscape that requires CPSs to adapt to emerging threats during their operation. Recently, digital twin (DT) technology (which refers to a virtual representation of a product, process, or environment) has emerged as a suitable candidate to address the security challenges faced

Accurate fault diagnosis of rotating machinery is critical to avoid catastrophic accidents. However, insufficient fault data seriously limit the performance of fault diagnosis in industrial applications. In this paper, a novel domain adaptive and adversarial network (DAAN) is proposed for data-driven fault diagnosis of the rotating machinery, which consists of a deep feature extractor, a domain classifier

Recently, China and the United States have achieved remarkable success in aerospace science and technology over the years. Space has become another field of competition in the technological advancement of various countries. Through space missions, space tourism, moon and Mars exploration, China and the United States can demonstrate the sophistication of their technologies to the public and audiences

Despite recent significant advancements in gas sensor array technology, accurately identifying gases in mixed environments remains challenging. This difficulty is primarily due to the rapid and competing processes of gas molecules attaching to (adsorption) and detaching from (desorption) the sensor. In this study, we present a simple method to fabricate a 2 × 4 SMO-based gas sensor array, coupled with

Human-centered smart manufacturing is an essential direction for the future development of manufacturing. Safe and reliable smart human-robot collaboration is the foundation for realizing human-centered smart manufacturing. Digital twin-based human-robot collaboration has been proposed as a new manufacturing paradigm to devise collaborative strategies, simulate collaborative processes, and ensure worker

With the rapid transition of Industry 4.0 to 5.0, modern industrial physio-chemical processes are characterized by two critical challenges: process safety and the quality of the final product. Traditional industrial monitoring methods have low reliability in accuracy and robustness, and they are inefficiently providing satisfactory results. This paper introduces a novel integration technique that employs

Position-controlled servomechanisms are the core elements of flexible manufacturing plants, primarily utilized to actuate robotic systems and automated machines. To match specific torque and costs requirements, typical servomechanism arrangements comprise precision reducers, which introduce motion errors that heavily limit the final performance achievable. Such errors are complex to model and depend

Edge computing empowered Deep Learning (DL) solutions have risen as the foremost facilitators of automation in a multitude of smart manufacturing applications. These models are implemented on edge devices with frozen learning capabilities to execute DL inference task(s). Nevertheless, the data they process is susceptible to intermittent alterations amidst the ever-changing landscape of dynamic smart

This paper aims to develop a smart multiphysics approach for wind turbine design utilizing Industry 5.0. A new blade profile is developed and optimized by non-dominated sorting genetic algorithm II (NSGA-II) for shape design, and a 3D modeling of wind turbines is proposed. The aerodynamic modeling of a horizontal axis wind turbine (HAWT) is an important step in the design of wind turbines. The blade

The integration of Health IoT (H-IoT) and blockchain technologies are being heavily exploited and used in many domains, especially for e-healthcare to collect the data i.e electronic health record (EHR) from the patient. The H-IoT devices have the ability to provide real-time sensory data from patients to be processed and analyzed, and distributed. Blockchain is providing decentralized computation

As drilling technology advances and operations extend into more complex geological environments, evaluating drilling risks has become increasingly complex, challenging the effectiveness of traditional methods. The novel physics-guided spatial-temporal data mining method that integrates external and internal causal attention mechanisms for drilling risk evaluation is proposed to address this issue.

Recent global disruptions, such as the COVID-19 pandemic and the ongoing geopolitical conflicts, have profoundly exposed vulnerabilities in traditional supply chains, requiring exploration of more resilient alternatives. Among various solution offerings, Autonomous supply chains (ASCs) have emerged as key enablers of increased integration and visibility, enhancing flexibility and resilience in turbulent

Decision making approaches depending on the assessments of individual decision makers produce inaccurate results due to the existence of multiple uncertainties. To model intrapersonal uncertainty, interpersonal uncertainty and randomness in decision making assessments, this research study proposes a novel approach by integrating linear and non-linear type of fuzzy numbers with cloud model theory using

Space information integration can better obtain the environmental information, crop information, climate information and other key factors of the farmland, which is more helpful for crop management and harvesting. In the traditional crop management and harvesting process, crop management and harvesting are two relatively independent processes, lacking a complete full-process scheme. In this regard

Anode and cathode active materials from spent lithium–ion batteries may be recovered and potentially used in new batteries to promote recycling and resource circulation. Froth flotation was applied to pristine active materials and the black mass obtained from pretreated spent batteries. Flotation kinetics was simulated with the use of computational fluid dynamics and surface chemistry. Bubble surface

With the widespread use of Mobile Edge Computing (MEC) in smart manufacturing systems in Industrial Internet of Things (IIoT) and 5G networks, determining how to efficiently offload computing tasks has become a hot research area. The Role-Based Collaboration (RBC) Environments-Classes, Agents, Roles, Groups, and Objects (E-CARGO) model is introduced to comprehensively manage MEC servers and user computation

In the context of the burgeoning low-altitude economy, low-altitude intelligent transportation (LAIT) has emerged as the focal point of research. This study comprehensively explores the current state, challenges, and future development prospects of LAIT from three key aspects: system architecture, infrastructure, and critical technologies. First, we propose a future LAIT system framework based on a

Human-Machine Interaction (HMI) and Brain-Computer Interface (BCI) are evolving technologies that show the great potentials to extract and utilize humans’ intents in controlling smart machines. However, existing HMI and BCI technologies are limited in terms of (1) the number of Degrees- of-Freedom (DoF) to be controlled and (2) the ways the performance of BCI-enabled control systems are verified and

The main objective of this article is to provide a framework for intelligent capture-acquisition analysis of geometric information from geological outcrops. By combining deep learning methods with photogrammetric data from unmanned aerial vehicles (UAVs), FPV drones, and terrestrial cameras acquired by a hybrid vision-photogrammetric system (HVPS), intelligent fracture detection and geometric information

Engineering-to-order (ETO) companies satisfy a very demanding market, where each client specifies the type of product they require and actively participate in the design, selection of materials, and other activities. This converts the production processes of ETO companies into one-of-a-kind processes (OKP) type, where production planning and control (PPC) activities are extremely complex. The cause

We look back at seven years of work to reduce test cost in complex aerospace manufacturing organizations using a bill-of-materials visualization, describing the evolution from a data integration team to a data analytics team, and several of the innovations we developed to be successful. We illustrate a maturity path for digital transformation using open-source tools that visualize over a hundred products