Agricultural marketing increasingly integrates Agriculture 4.0 technologies—Blockchain, AI/ML, IoT, and recommendation systems—yet systematic evaluations of computational maturity and deployment readiness remain limited. This Systematic L...
Human pose estimation (HPE) has become a core technology for human-centered visual understanding and is widely used in sports analysis, rehabilitation assessment, human–computer interaction, autonomous driving, and industrial safety monitoring....
Over the past few decades, industrial process monitoring has been greatly advanced by machine learning and data analytics, with sophisticated models continuing to be developed and validated on benchmark processes such as the Tennessee Eastman simulat...
Road authorities increasingly use machine-learning models to rank high-risk locations and to allocate scarce safety resources. These models are almost always judged by their predictive accuracy on a random hold-out sample. This study shows that pract...
Reducing building energy consumption is central to EU climate-neutrality targets and to sustainable development goals: buildings account for around 40% of EU final energy consumption, placing Building Energy Management Systems (BEMS) at the intersect...
For many years, carbon capture and storage (CCS) has been discussed as a technology that may make a significant contribution to achieving major reductions in greenhouse gas emissions. At present, however, only two large-scale power plants capture a t...
Accurate determination of the liquid crystal (LC) cell gap after filling is essential for ensuring device performance in LC-based optical applications. However, the introduction of birefringent materials significantly distorts the transmission spectr...
Freezing of gait (FoG) affects 38–65% of advanced Parkinson’s disease patients, yet automated detection algorithms are often validated solely on laboratory datasets. This study quantifies the critical performance gap between laboratory an...
Road traffic crashes remain a global public health burden and a persistent resource allocation problem that undermines progress toward the sustainable development of safe, equitable mobility systems. Saudi Arabia’s Vision 2030 targets fewer tha...
This work proposes the design of Environmental Sensor Networks (ESN) through balancing robustness and redundancy. An Evolutionary Algorithm (EA) is employed to find the optimal placement of sensor nodes in the Region of Interest (RoI). Data quality i...
Artificial intelligence is increasingly applied to crab research, yet no prior review has mapped this work or its progress toward real-world use. This scoping review maps artificial intelligence applications in true crabs (Brachyura) across aquacultu...
According to the World Economic Forum, countries and regions should steer their energy systems toward cheaper, safer, and more sustainable energy sources, and move away from their reliance on traditional energy sources. With this trend, it is signifi...
Shellfish aquaculture is considered a major pillar of the seafood industry for its high market value, which increases the value for global food security and sustainability, often constrained in terms of conventional, labor-intensive practices. This r...
Microplastics are now recognized as a persistent and heterogeneous class of environmental contaminants, whose reliable monitoring requires analytical strategies capable of handling large numbers of particles across complex matrices. Although Fourier-...
Financial institutions must decide, within the milliseconds of a payment authorisation window and on evidence that is almost entirely unlabelled, whether a transaction is fraudulent: positives account for less than 0.2% of records, confirmatory label...
Underwater sensor deployment in military applications requires high precision, yet existing robotic solutions often lack the maneuverability and adaptability required for complex aquatic environments. To address this gap, this study proposes a bio-in...
Renewable energy sources have become one of the important roles for sustainable energy development. One of the promising mechanisms to deploy renewable energy is through a Virtual Power Plant (VPP), which can integrate various distributed renewable e...
The opaque nature of black-box deep learning (DL) models poses significant challenges for intrusion detection systems (IDSs) in Internet of Things (IoT) networks, where transparency, trust, and operational reliability are critical. Although explainab...
Highway vehicle–road collaborative systems rely on the effective deployment of roadside equipment (RSE) to support both traffic sensing and communication. In mixed connected and automated vehicle (CAV) and human-driven vehicle (HDV) traffic env...
Hydrogen-based zero-emission transport technologies have reached a level of technical maturity that enables their operational deployment; however, their large-scale uptake remains constrained by the limited availability of refuelling infrastructure....
Modern Industrial Internet of Things (IIoT) integrates machine learning models for monitoring and control. However, they remain vulnerable to adversarial machine learning (AML) attacks, where an adversary adds small changes to the input data. These s...
In this paper, we address the Wireless Sensor Network (WSN) deployment issue. We assume that the observed area is characterized by the geographical irregularity of the sensed events. Formally, we consider that each point in the deployment area is ass...
Artificial Emotional Intelligence (AEI) has gained significant attention for enabling machines to recognize and interpret human affective states through modalities such as speech. While deep learning-based speech emotion recognition (SER) models have...
Robotic systems increasingly operate in complex and distributed environments, where software deployment and orchestration pose major challenges. This paper presents a model-driven approach that automates the containerized deployment of robotic system...
The objective of this work is to develop a framework related to energy storage systems implementation. The work focuses on a Brazilian scenario and applies information regarding demographic changes, economic, governmental and energy resources studies...
The automation of material receiving processes is central to ensuring traceability, process control, and data consistency in warehouse and logistics environments. This paper presents an integrated automation framework combining on-device optical char...
There is a large gap between the vast solar resources and the magnitude of solar energy deployment in Argentina. In the case of photovoltaics, the country only reached the 1000 GWh electricity generated yearly landmark in 2020. Solar thermal technolo...
Drone deployment in Qatar has been lagging behind that in other countries due to a wide range of reported challenges. This study developed a framework to address these operational gaps and serve as a roadmap for different stakeholders to enable drone...
To address the deployment accuracy issues of multi-frequency band reflector antennas, this study takes a hexagonal prism modular deployable antenna as an example and proposes an accuracy design method. This paper proposes a screw-theory-based sub-cha...
Existing deep-well multi-node observation instrument hoisting and lowering systems suffer from poor compartment coupling adaptability, fuzzy anti-jamming judgment criteria, non-standard operation procedures, and lack of classified hoisting control st...
Static and mobile radiation detectors can be deployed in urban environments for a range of nuclear security applications, including radiological source search-and-tracking scenarios. Modeling detector performance for such applications is challenging,...
Offshore wind energy is emerging as a vital component of the global transition to renewable energy, leveraging consistent wind conditions and higher power density compared to onshore systems. Integrating variable offshore wind power with hydrogen pro...
Spiking Neural Networks (SNNs) executed on neuromorphic hardware promise energyefficient, low-latency inference well-suited to edge deployment in size, weight, and powerconstrained environments such as autonomous vehicles, wearable devices, and unman...
Accountability is a value often mentioned in the debate on intelligent systems and their increased pervasiveness in our society. When focusing specifically on autonomous systems, a critical gap emerges: although there is much work on governance and a...
Countries considering high-speed rail (HSR) developments face enormous challenges because of their high deployment cost, environmental obstacles, political opposition, and their potentially adverse effects on society. Nevertheless, HSR services are i...
A deployable reflectarray antenna (RA) using a three-times expansion structure working at the V-band for 12U CubeSat is presented in this paper. Double-circle ring unit cells with excellent dispersion characteristics are used to constitute the layout...
This article proposes a systematic review of the recent literature on the impacts of the deployment of autonomous vehicles through the lens of lifestyle changes that will modify our mobility practices. It discusses the main findings of the studies, a...
The advent of satellite-borne machine learning hardware accelerators has enabled the onboard processing of payload data using machine learning techniques such as convolutional neural networks (CNNs). A notable example is using a CNN to detect the pre...
Disaster robotics has become a research area in its own right, with several reported cases of successful robot deployment in actual disaster scenarios. Most of these disaster deployments use aerial, ground, or underwater robotic platforms. However, t...
High-throughput plant phenotyping (HTPP) is increasingly limited by the mismatch between the need for field-relevant, fine-grained phenotypic information and the restricted capability of conventional observation platforms under complex agricultural c...
Distributed navigation interference is a critical technique for degrading the performance of multi-element adaptive nulling antennas, where optimal spatial deployment of interference sources is pivotal to maximizing overall interference effectiveness...
LLM privacy risks arise across different lifecycle stages and architectural boundaries, and existing protection mechanisms provide only partial coverage. This paper analyzes the main families of privacy-preserving approaches for LLM systems through a...
With the rapid growth of multimodal remote sensing (RS) data, there is an increasing demand for intelligent onboard computing to alleviate the transmission and latency bottlenecks of traditional orbit-to-ground downlinking workflows. While many light...
Video Anomaly Detection (VAD) has emerged as a promising technology for automated surveillance and industrial monitoring. However, selecting an appropriate VAD architecture for real-world deployment remains challenging because benchmark-oriented eval...
In this article we present the Intelligent Industrial Internet (I3) Mote, an open hardware platform targeting industrial connectivity and sensing deployments. The I3Mote features the most advanced low-power components to tackle sensing, on-board comp...
Digital twin technology is a highly valued asset in the manufacturing sector, with its unique capability to bridge the gap between the physical and virtual parts. The impact of the rapid increase in this technology is based on the collection of real-...
Floating solar photovoltaic (FPV) systems have become an increasingly attractive application of photovoltaics (PV) because of land-use constraints, the cost of land and site preparation, and the perceived energy and environmental co-benefits. Despite...
Improving the efficiency and range of hydrogen-powered electric vehicles (HPEVs) is essential for their global adoption, especially in developing countries with limited resources. This study systematically evaluates regenerative braking and suspensio...
Data-driven battery state estimation has been widely studied in recent years to enable safe, reliable, and lifetime-aware battery management in e-mobility applications, where accurate estimation is essential under highly dynamic operating conditions....
As 6G networks become increasingly complex and heterogeneous, effective classification of network slicing is essential for optimizing resources and managing quality of service. While recent advances demonstrate high accuracy under controlled laborato...
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