Systems For Applications Research Articles

Evaluation of consumer-grade wireless EEG systems for brain-computer interface applications.

With the growing popularity of consumer-grade electroencephalogram (EEG) devices for health, entertainment, and cognitive research, assessing their signal quality is essential. In this study, we evaluated four consumer-grade wireless and dry-electrode EEG systems widely used for brain-computer interface (BCI) research and applications, comparing them with a research-grade system. We designed an EEG phantom method that reproduced µV-level amplitude EEG signals and evaluated the five devices based on their spectral responses, temporal patterns of event-related potential (ERP), and spectral patterns of resting-state EEG. We discovered that the consumer-grade devices had limited bandwidth compared with the research-grade device. A late component (e.g., P300) was detectable in the consumer-grade devices, but the overall ERP temporal pattern was distorted. Only one device showed an ERP temporal pattern comparable to that of the research-grade device. On the other hand, we confirmed that the activation of the alpha rhythm was observable in all devices. The results provide valuable insights for researchers and developers when it comes to selecting suitable EEG devices for BCI research and applications.

Should I Evaluate My Augmented Reality System in an Industrial Environment? Investigating the Effects of Classroom and Shop Floor Settings on Guided Assembly.

Numerous prior studies have investigated real-time assembly instructions using Augmented Reality (AR). However, most such experiments were conducted in laboratory settings with simplistic assembly tasks, failing to represent real-world industrial conditions. To ascertain to what extent results obtained in a laboratory environment may differ from studies in actual industrial environments, we carried out a user study with 32 manufacturing apprentices. We compared assembly task execution results in two settings, a classroom and an industrial workshop environment. To facilitate the experiments, we developed AR-guided manual assembly systems for simple and more complex assets. Our findings reveal a significantly improved task performance in the industrial workshop, reflected in faster task completion times, fewer errors, and subjectively perceived higher flow. This contradicted participants' subjective ratings, as they expected to perform better in the classroom environment. Our results suggest that the actual manufacturing environment is critical in evaluating AR systems for real-world industrial applications.

Subframe-Level Synchronization in Multi-Camera System Using Time-Calibrated Video.

Achieving precise synchronization is critical for multi-camera systems in various applications. Traditional methods rely on hardware-triggered synchronization, necessitating significant manual effort to connect and adjust synchronization cables, especially with multiple cameras involved. This not only increases labor costs but also restricts scene layout and incurs high setup expenses. To address these challenges, we propose a novel subframe synchronization technique for multi-camera systems that operates without the need for additional hardware triggers. Our approach leverages a time-calibrated video featuring specific markers and a uniformly moving ball to accurately extract the temporal relationship between local and global time systems across cameras. This allows for the calculation of new timestamps and precise frame-level alignment. By employing interpolation algorithms, we further refine synchronization to the subframe level. Experimental results validate the robustness and high temporal precision of our method, demonstrating its adaptability and potential for use in demanding multi-camera setups.

An Empirical Study of the Economic Net-Zero Energy Mix in Industrial Complexes

This study examines the optimal energy mix for industrial complexes by incorporating renewable energy systems, decarbonization strategies, and sector coupling technologies. Using data from the Balan Industrial Complex in Korea, five energy scenarios were evaluated, ranging from conventional systems (Scenario 1) to advanced renewable configurations (Scenario 5). The results show that Scenario 5, which integrates sector coupling systems and decarbonization technologies, is the most cost-effective and environmentally sustainable. Scenario 5 achieves the lowest Net Present Cost (NPC), and significantly reduces CO2 emissions. Furthermore, an analysis of electricity prices and CO2 costs from Korea, the United States, and Germany highlights the critical role of regional electricity tariffs and carbon pricing in determining the economic feasibility of energy systems. While renewable setups require higher initial investments, Scenario 5 proves to be the most economically viable over time, offering both cost savings and environmental benefits. These findings provide valuable insights for policymakers and industry leaders, emphasizing the importance of customized strategies to optimize energy systems in industrial applications.

Coherent Driving of a Single Nitrogen Vacancy Center by a Resonant Magnetic Tunnel Junction.

Nitrogen vacancy (NV) centers, atomic spin defects in diamond, represent an active contender for advancing transformative quantum information science (QIS) and innovations. One of the major challenges for designing NV-based hybrid systems for QIS applications results from the difficulty of realizing local control of individual NV spin qubits in a scalable and energy-efficient way. To address this bottleneck, we introduce magnetic tunnel junction (MTJ) devices to establish coherent driving of an NV center by a resonant MTJ with voltage controlled magnetic anisotropy. We show that the oscillating magnetic stray field produced by a resonant micromagnet can be utilized to effectively modify and drive NV spin rotations when the NV frequency matches the corresponding resonance conditions of the MTJ. Our results present a new pathway to achieve all-electric control of an NV spin qubit with reduced power consumption and improved solid-state scalability for implementing cutting-edge QIS technological applications.

Study of complex instability in a Thulium-doped figure-eight fiber laser including a polarization-imbalanced NOLM with no polarization control.

This study proposes a Thulium-doped figure-eight (F8) fiber laser design to be used as a platform to explore complex dynamics in passively mode-locked fiber laser systems. The proposed scheme incorporates a polarization-imbalanced nonlinear optical loop mirror (PI-NOLM) without a polarizer in the ring section. The absence of a polarizer prevents controlling the polarization state at the PI-NOLM input, which in turn allows the PI-NOLM switching power to vary dynamically during the laser operation. This leads to an intermittent noise-like pulse (NLP) operation, which is affected by complex instability in the form of non-periodic Q-switched-like behavior. Using real-time time-domain mapping techniques, we get a deep insight into the laser intricate dynamics, which involves not only NLPs, but also solitons and quasi-continuous-wave components with their subtle interactions. This study highlights the importance of the PI-NOLM in F8 laser schemes for the study and understanding of complex dynamics in Thulium-doped fiber lasers, which have been less extensively studied compared to other rare-earth-doped lasers. Our findings will contribute to the optimization and design of advanced laser systems for various scientific and industrial applications.

Fruit and Vegetable Image Recognition Based on Multiple Tree Models: Applications of Random Forest, XGBoost and Decision Tree

The primary objective of this study is to evaluate and compare the performance of three machine learning models— Random Forest, XGBoost, and Decision Tree—in the context of fruit and vegetable image classification. This research aims to identify which model best handles the challenges associated with imbalanced datasets and complex data structures. The ultimate goal is to contribute to the development of more efficient and accurate automated systems for agricultural applications, thereby improving productivity and reducing operational costs in the industry. This study utilized a dataset of 3,825 images covering 36 fruit and vegetable classes. Images were resized, normalized, and augmented to enhance diversity. Three models—Random Forest, XGBoost, and Decision Tree—were trained on this dataset. Performance was evaluated using accuracy, precision, recall, and F1-score to assess classification effectiveness and handling of class imbalances. The evaluation revealed that XGBoost outperformed Random Forest and Decision Tree in fruit and vegetable image classification, achieving the highest accuracy of 96.66%. XGBoost demonstrated superior handling of class imbalances and complex data structures, reflected in its precision and recall scores across various classes. Random Forest also performed well, closely following XGBoost, while Decision Tree exhibited more variability in results, indicating potential overfitting in certain classes. In conclusion, this study highlights the effectiveness of ensemble methods, particularly XGBoost, in agricultural image classification tasks. These findings suggest that XGBoost is a robust model for similar applications, offering improved accuracy and reliability

Alginate-chitosan hydrogel formulations for VEGFA expressed baculovirus delivery promoting angiogenesis for wound healing and revascularization

Abstract Background Baculoviruses, engineered to express growth factors, offer a promising avenue for short-term gene therapy, such as wound dressings, due to their safety and specificity. Encapsulation in natural polymers like alginate and chitosan addresses limitations like serum inactivation and fragility, while promoting sustained delivery and shielding from immune inactivation. Wound healing involves complex processes that can be enhanced by maintaining an antimicrobial, moist environment, which promotes cell migration and angiogenesis. Vascular endothelial cell growth factor A (VEGFA) is known to be one of the most potent pro-angiogenic factors and plays a key role in wound healing. Purpose This investigation seeks to enhance wound healing and angiogenesis, support the revascularization process, and provide anti-inflammatory and anti-microbial effects. Additionally, it aims to assess the preclinical efficacy and safety of the approach. Methods Ionic cross-linking was used for virus encapsulation under aseptic conditions. The capsules were assessed for their surface morphology, particle size, zeta potential, and in vitro release profile. Additionally, their therapeutic potential was studied using cell lines. The efficacy of hydrogel delivery of VEGFA-expressing baculoviruses in promoting cell migration and angiogenesis for wound healing applications was investigated on Human Umbilical Vein Endothelial Cells (HUVECs). Hydrogel morphology, swelling, and encapsulation efficiency were evaluated, along with endothelial tube formation assays and hemocompatibility studies on HUVECs. Results Encapsulation of baculovirus expressing the VEGFA gene in alginate and chitosan–alginate hydrogels achieve a high encapsulation efficiency of 99.9%. This allows for prolonged virus delivery and an increased therapeutic window. The encapsulated baculovirus maintains activity and is released over eight days, with a transduction efficiency of over 40%. This promotes tube formation, cell proliferation, and cell migration for angiogenesis, particularly beneficial for chronic wound-healing applications. The hydrogels also prevented E. coli and C. albicans growth directly below and inhibited C. albicans growth surrounding the alginate–chitosan hydrogels. The hydrogels demonstrated no cytotoxicity and good blood compatibility. Conclusion This proof of concept underscores the potential of encapsulated baculovirus delivery systems for various gene therapy applications including cardiovascular tissue regeneration and revascularization.

Deep-Learning Empowered Customized Chiral Metasurface for Calibration-Free Biosensing.

As a 2D metamaterial, metasurfaces offer an unprecedented avenue to facilitate light-matter interactions. The current "one-by-one design" method is hindered by time-consuming, repeated testing within a confined space. However, intelligent design strategies for metasurfaces, limited by data-driven properties, have rarely been explored. To address this gap, a data iterative strategy based on deep learning, coupled with a global optimization network is proposed, to achieve the customized design of chiral metasurfaces. This methodology is applied to precisely identify different chiral molecules in a label-free manner. Fundamentally different from the traditional approach of collecting data purely through simulation, the proposed data generation strategy encompasses the entire design space, which is inaccessible by conventional methods. The dataset quality is significantly improved, with a 21-fold increase in the number of chiral structures exhibiting the desired circular dichroism (CD) response (>0.6). The method's efficacy is validated by a monolayer structure that is easily prepared, demonstrating advanced sensing abilities for enantiomer-specific analysis of bio-samples. These results demonstrate the superior capability of data-driven schemes in photonic design and the potential of chiral metasurface-based platforms for calibration-free biosensing applications. The proposed approach will accelerate the development of complex systems for rapid molecular detection, spectroscopic imaging, and other applications.

Machine Learning and IoT-Based Solutions in Industrial Applications for Smart Manufacturing: A Critical Review

The Internet of Things (IoT) has radically changed the industrial world, enabling the integration of numerous systems and devices into the industrial ecosystem. There are many areas of the manufacturing industry in which IoT has contributed, including plants’ remote monitoring and control, energy efficiency, more efficient resources management, and cost reduction, paving the way for smart manufacturing in the framework of Industry 4.0. This review article provides an up-to-date overview of IoT systems and machine learning (ML) algorithms applied to smart manufacturing (SM), analyzing four main application fields: security, predictive maintenance, process control, and additive manufacturing. In addition, the paper presents a descriptive and comparative overview of ML algorithms mainly used in smart manufacturing. Furthermore, for each discussed topic, a deep comparative analysis of the recent IoT solutions reported in the scientific literature is introduced, dwelling on the architectural aspects, sensing solutions, implemented data analysis strategies, communication tools, performance, and other characteristic parameters. This comparison highlights the strengths and weaknesses of each discussed solution. Finally, the presented work outlines the features and functionalities of future IoT-based systems for smart industry applications.

Offshore wind farms interfacing using HVAC-HVDC schemes: A review

Offshore wind farms (OWF) have emerged as a pivotal component in the transition towards renewable energy, offering substantial potential for reducing carbon emissions and enhancing energy security. Integration these offshore installations into the existing power grid present opportunities and challenges, particularly in terms of efficiency, stability, and cost-effectiveness. This review explores the interfacing of OWF with the power grid through High Voltage Alternating Current (HVAC) and High Voltage Direct Current (HVDC) schemes. This research offers a thorough examination of the latest technologies, and comparing the benefits and drawbacks of HVAC and HVDC systems for offshore wind applications. The review delves into various aspects, including technological advancements, economic considerations, and environmental impacts of HVAC and HVDC technologies. The review also addresses recent developments and contrasting viewpoints in the field, highlighting innovations and ongoing debates related to integrating OWF. By synthesizing recent studies and industry reports, this paper offers a balanced overview of the strengths and weaknesses associated with HVAC and HVDC schemes. It identifies critical areas for future research and provides recommendations for optimizing offshore wind farm integration. This review seeks to enhance understanding of current technologies and support decision-making in the development and deployment of offshore wind energy infrastructure.

Enhanced Diabetes Detection and Blood Glucose Prediction Using TinyML-Integrated E-Nose and Breath Analysis: A Novel Approach Combining Synthetic and Real-World Data

Diabetes mellitus, a chronic condition affecting millions worldwide, necessitates continuous monitoring of blood glucose level (BGL). The increasing prevalence of diabetes has driven the development of non-invasive methods, such as electronic noses (e-noses), for analyzing exhaled breath and detecting biomarkers in volatile organic compounds (VOCs). Effective machine learning models require extensive patient data to ensure accurate BGL predictions, but previous studies have been limited by small sample sizes. This study addresses this limitation by employing conditional generative adversarial networks (CTGAN) to generate synthetic data from real-world tests involving 29 healthy and 29 diabetic participants, resulting in over 14,000 new synthetic samples. These data were used to validate machine learning models for diabetes detection and BGL prediction, integrated into a Tiny Machine Learning (TinyML) e-nose system for real-time analysis. The proposed models achieved an 86% accuracy in BGL identification using LightGBM (Light Gradient Boosting Machine) and a 94.14% accuracy in diabetes detection using Random Forest. These results demonstrate the efficacy of enhancing machine learning models with both real and synthetic data, particularly in non-invasive systems integrating e-noses with TinyML. This study signifies a major advancement in non-invasive diabetes monitoring, underscoring the transformative potential of TinyML-powered e-nose systems in healthcare applications.

AI-Assisted Automated Identification of Human Activities Using Ficus religiosa Leaf-Based Triboelectric Nanogenerator.

Movement monitoring and effective identification of different actions are the keys that help in fitness services, health status, clinical studies, etc. In this technological era, Internet of Things (IoT) technologies, including smart wireless devices and sensors, are very effectively used for monitoring human activities, but the demand for sustainable and green power sources is a crucial issue with these devices. Triboelectric nanogenerators (TENG) are proven to be promising applications in these devices because they harvest energy from the surrounding environment and eliminate the use of batteries as power sources. As a green energy source, this study emphasizes the fabrication of biodegradable materials-based TENGs, which are eco-friendly and are related to clean and green energy as per the UN's sustainable development goals SDG 7 (affordable and clean energy). In the present work, a natural Ficus religiosa leaf (FRL) of the F. religiosa tree is used in designing and fabricating a TENG (FRL-TENG). Also, an approach is discussed to compare the performance of FRL-TENG with TENGs fabricated from other waste biodegradable materials such as garlic tunic, onion tunic, and eggshell membrane (ESM). During the experimental study, it is observed that the FRL-based TENG produced maximum voltage in comparison to other material combinations selected in this study. The generated electric output from these TENG combinations is also used to power an array of tens of green-light-emitting diodes (LEDs). Furthermore, this paper also proposes the use of FRL-TENG as a wearable sensor to collect information and monitor the physical activities of the user, viz., walk, jump, and run. To recognize the movement status, the FRL-TENG sensor is integrated with an extra randomized tree-based machine learning model for accurately distinguishing the user's three activities with an accuracy of 96%. The work showcases an innovative approach to encourage customized uses of TENG sensors in human motion monitoring and permits the development of intelligent, self-powered systems for new applications.

Flexural Behavior of 3D-Printed Carbon Fiber-Reinforced Nylon Lattice Beams.

This study investigates the flexural behavior of 3D-printed multi-topology lattice beams, with a specific emphasis on octet and cube lattice geometries created through fused deposition modeling (FDM). The mechanical properties of these beams were evaluated through quasi-static three-point bending tests. A comparative analysis of load-carrying capacity, energy absorption, and specific energy absorption (SEA) indicates that octet lattice beams exhibit superior performance to cube lattice beams. The octet lattice beam in the triple-layer double-column (TL-DC) arrangement absorbed 14.99 J of energy, representing a 38% increase compared to the 10.86 J absorbed by the cube lattice beam in the same design. The specific energy absorption (SEA) of the octet beam was measured at 0.39 J/g, which exceeds the 0.29 J/g recorded for the cube beam. Two distinct types of deformations were identified for the struts and the beam layers. Octet struts exhibit enhanced performance in stretch-dominated zones, whereas the cube system demonstrates superior efficacy in compressive-dominated regions. The results highlight the enhanced efficacy of octet lattice structures in energy absorption and mechanical stability maintenance. The investigation of sandwich lattice topologies integrating octet and cube structures indicates that while hybrid designs may exhibit efficiency, uniform octet structures yield superior performance. This study provides valuable insights into the structural design and optimization of lattice systems for applications requiring high-energy absorption and mechanical robustness.

Mapping Data to Concepts: Enhancing Quantum Neural Network Transparency with Concept-Driven Quantum Neural Networks

We introduce the concept-driven quantum neural network (CD-QNN), an innovative architecture designed to enhance the interpretability of quantum neural networks (QNNs). CD-QNN merges the representational capabilities of QNNs with the transparency of self-explanatory models by mapping input data into a human-understandable concept space and making decisions based on these concepts. The algorithmic design of CD-QNN is comprehensively analyzed, detailing the roles of the concept generator, feature extractor, and feature integrator in improving and balancing model expressivity and interpretability. Experimental results demonstrate that CD-QNN maintains high predictive accuracy while offering clear and meaningful explanations of its decision-making process. This paradigm shift in QNN design underscores the growing importance of interpretability in quantum artificial intelligence, positioning CD-QNN and its derivative technologies as pivotal in advancing reliable and interpretable quantum intelligent systems for future research and applications.

No two clones are alike: characterization of heterologous subpopulations in a transgenic cell line of the model diatom Phaeodactylum tricornutum

BackgroundConjugation-based episome delivery is a highly efficient method used to transfer DNA into the diatom Phaeodactylum tricornutum, facilitating the production of recombinant proteins and high-value metabolites. However, previous reports have indicated phenotypic heterogeneity among individual cells from clonally propagated exconjugant cell lines, potentially affecting the stability of recombinant protein production in the diatom.ResultsHere, we characterized the differences between subpopulations with distinct fluorescence intensity phenotypes derived from a single exconjugant colony of P. tricornutum expressing the enhanced green fluorescent protein (eGFP). We analyzed the expression cassette sequence integrity, plasmid copy number, and global gene expression. Our findings reveal that lower copy numbers and the deletion of the expression cassette in part of the population contributed to low transgene expression. Gene co-expression analysis identified a set of genes with similar expression pattern to eGFP including a gene encoding a putative Flp recombinase, which may be related to variations in fluorescence intensity. These genes thus present themselves as potential candidates for increasing recombinant proteins production in P. tricornutum episomal expression system.ConclusionsOverall, our study elucidates genetic and transcriptomic differences between distinct subpopulations in a clonally propagated culture, contributes to a better understanding of heterogeneity in diatom expression systems for synthetic biology applications.

Iontophoresis and electroporation-assisted microneedles: advancements and therapeutic potentials in transdermal drug delivery.

Transdermal drug delivery (TDD) using electrically assisted microneedle (MN) systems has emerged as a promising alternative to traditional drug administration routes. This review explores recent advancements in this technology across various therapeutic applications. Integrating iontophoresis (IP) and electroporation (EP) with MN technology has shown significant potential in improving treatment outcomes for various conditions. Studies demonstrate their effectiveness in enhancing vaccine and DNA delivery, improving diabetes management, and increasing efficacy in dermatological applications. The technology has also exhibited promise in delivering nonsteroidal anti-inflammatory drugs (NSAIDs), treating multiple sclerosis, and advancing obesity and cancer therapy. These systems offer improved drug permeation, targeted delivery, and enhanced therapeutic effects. While challenges remain, including safety concerns and technological limitations, ongoing research focuses on optimizing these systems for broader clinical applications. The future of electrically assisted MN technologies in TDD appears promising, with potential advancements in personalized medicine, smart monitoring systems, and expanded therapeutic applications.

E-waste driven eco-friendly highly sensitive wearable capacitive sensors for physical activity monitoring and sign language recognition

Owing to the continual and rapid advancement in technology, there has been a significant surge in electronic waste (e-waste) in recent decades, resulting in a harmful impact on our environment. Due to the growing demand for battery-powered devices, non-rechargeable batteries are a significant source of e-waste. This study describes fabricating a paper-based graphite strain sensor that is eco-friendly, inexpensive, biodegradable, and sensitive to the applied strain. Readily accessible materials are employed to design and fabricate MIDC strain sensors. The Modified Interdigital Capacitor (MIDC) structure is formed on basic printing paper, and the conductive substance made with the ink of a gel pen and graphite from a drained dry cell is painted on the paper and used as electrode material. The suggested sensor has a high gauge factor value of 1998.578 and fast response and recovery time of 112 ms and 100 ms, respectively. The proposed prototype has also been utilized to inspect fitness exercises and physical activities on the human body, inspiring the potential for its integration into wearable systems for various applications. Reliable detection and differentiation of finger positions are achieved, highlighting the sensor's potential for more advanced sign language applications. It’s a good option to integrate the suggested strain sensor in wearable systems, such as telemedicine systems, electromechanical detection, healthcare, and physical activity monitoring of humans.

Fabrication of rGO-Bridged TiO2/g-C3N4 Z-Scheme Nanocomposites via Pulsed Laser Ablation for Efficient Photocatalytic CO2 Reduction.

Highly efficient photocatalysts can be fabricated using favorable charge transfer nanocomposite channel structures. This study adopted pulsed laser ablation in liquid (PLAL) to obtain rGO-bridged TiO2/g-C3N4 (rGO-TiO2/g-C3N4) photocatalytic Z-scheme without the need for noble metals. In addition to evaluating the resulting nanocomposite (comprising rGO nanosheets, TiO2 nanotubes, and g-C3N4 nanosheets) CO2 reduction effectiveness, its chemical, morphological, structural, and optical characteristics were examined using various analytical techniques. The findings revealed a synergistic interaction between g-C3N4 and TiO2, suggesting the presence of unique interfacial bonding, as well as enhanced visible light absorption. Notably, the ternary rGO-TiO2/g-C3N4 Z-scheme exhibits an excellent photocatalytic performance by photocatalytically converting CO2 into CO and CH4, with 81 % selectivity towards the CO and 1.91 % apparent quantum efficiency at 420 nm. Thus, the findings can pave the way for various Z-scheme systems in wide photocatalytic applications.

Potentiostatic electrodeposition of copper, indium, and cadmium sulfides for CO2 electroreduction: A path toward sustainable hydrogen generation

This study focuses on the potentiostatic electrodeposition of copper indium and cadmium sulfide (CuS, In2S3, and CdS) thin films on copper substrates, aiming to develop efficient electrocatalysts for the electrochemical reduction of CO2 and sustainable hydrogen generation. Utilizing Cu Kα radiation for X-ray diffraction (XRD) analysis, the crystal structures of the electrodeposited films were confirmed, revealing well-defined crystalline phases. The films exhibited average particle sizes of 55.99 nm for CuS, 50.37 nm for In2S3, and 63.51 nm for CdS. Cyclic voltammetry and chronoamperometry were employed to optimize the deposition parameters, ensuring efficient nucleation and growth of the metal sulfide films. The electrochemical performance of the synthesized electrocatalysts was rigorously tested, demonstrating their potential for CO2 reduction under optimized conditions with the highest efficiency for CdS electrodes. Additionally, the electrodeposited CuS and CdS are of the p-type, and In2S3 is of the n-type semiconductor were studied and verified by the Mott–Schottky test. CuS, In2S3, and CdS were found to have donor and acceptor concentrations (ND) of 1.68 × 106, 1.44 × 105, and 8.9 × 105 cm3, respectively. The photoelectrochemical measurements of the electrodeposited metal sulfides were studied at ambient temperature and under illumination, providing higher photocurrent responses for CdS and demonstrating its strong potential as a photoelectrode material for CO2 reduction. This work underscores the significance of facile electrochemical synthesis methods in developing cost-effective and scalable electrocatalysts, paving the way for their integration into photoelectrochemical systems for green energy applications.