Search Results (578)

This article deals with the analysis and exploration of the concept of integrating human knowledge (HK) and artificial intelligence (AI) in the management process. The authors point out that the implementation of advanced AI technologies into already functioning and often complex systems, such as enterprise resource planning (ERP), presents significant technical challenges and requires a well-thought-out integration strategy. The complexity arises from the need to align new solutions with existing processes, resources, and data. Using the example of a fuel distribution system, the authors present the concept of integrating human knowledge (HK) and artificial intelligence (AI) in the management process. The article presents a comprehensive analysis of the smart upgrade of fuel delivery management (FDM) architecture by incorporating an AI app to solve complex problems, such as predicting demand or traffic flows, as well as correctly detecting near-miss events. Technological convergence enables the mutual pursuit of improving the management process by developing soft skills and expanding knowledge managers. The authors’ findings show that an important factor for successful convergence is horizontal and vertical matching of the human knowledge and artificial intelligence cooperation for archive max positive synergy. Some recommendations could be useful for tank truck operators as a starting point to predict demand patterns, smart route planning, etc., where an AI app could be very successful. Full article

Microbial electrosynthesis (MES) has emerged as a promising bio-electrochemical technology for sustainable CO₂ conversion into valuable organic compounds since it uses living electroactive microbes to directly convert CO₂ into value-added products. This review synthesizes advancements in MES from 2010 to 2025, focusing on the electrode materials, microbial communities, reactor engineering, performance trends, techno-economic evaluations, and future challenges, especially on the results reported between 2020 and 2025, thus highlighting that MES technology is now a technology to be reckoned with in the spectrum of biofuel technology production. While the current productivity and scalability of microbial electrochemical systems (MESs) remain limited compared to conventional CO₂ conversion technologies, MES offers distinct advantages, including process simplicity, as it operates under ambient conditions without the need for high pressures or temperatures; modularity, allowing reactors to be stacked or scaled incrementally to match varying throughput requirements; and seamless integration with circular economy strategies, enabling the direct valorization of waste streams, wastewater, or renewable electricity into valuable multi-carbon products. These features position MES as a promising platform for sustainable and adaptable CO₂ utilization, particularly in decentralized or resource-constrained settings. Recent innovations in electrode materials, such as conductive polymers and metal–organic frameworks, have enhanced electron transfer efficiency and microbial attachment, leading to improved MES performance. The development of diverse microbial consortia has expanded the range of products achievable through MES, with studies highlighting the importance of microbial interactions and metabolic pathways in product formation. Advancements in reactor design, including continuous-flow systems and membrane-less configurations, have addressed scalability issues, enhancing mass transfer and system stability. Performance metrics, such as the current densities and product yields, have improved due to exceptionally high product selectivity and surface-area-normalized production compared to abiotic systems, demonstrating the potential of MES for industrial applications. Techno-economic analyses indicate that while MES offers promising economic prospects, challenges related to cost-effective electrode materials and system integration remain. Future research should focus on optimizing microbial communities, developing advanced electrode materials, and designing scalable reactors to overcome the existing limitations. Addressing these challenges will be crucial for the commercialization of MES as a viable technology for sustainable chemical production. Microbial electrosynthesis (MES) offers a novel route to biofuels by directly converting CO₂ and renewable electricity into energy carriers, bypassing the costly biomass feedstocks required in conventional pathways. With advances in electrode materials, reactor engineering, and microbial performance, MES could achieve cost-competitive, carbon-neutral fuels, positioning it as a critical complement to future biofuel technologies. Full article

Vehicle-to-everything (V2X) communication, a current key area of research, has a large impact on traffic safety, traffic efficiency, autonomous driving technology development, and intelligent transport. In order to achieve the low-latency performance and high transmission efficiency required for V2X communication, channel estimation for transmission channels is particularly important. In this regard, this paper proposes an improved general orthogonal match pursuit (GOMP) channel estimation algorithm based on the base extension model for an orthogonal time frequency space (OTFS) system. Firstly, the channel matrix is decomposed using the basis expansion model. Then, the strong sparsity of the basis function is exploited for channel estimation using the GOMP algorithm, while the ordinal difference restriction method and the weak selectivity principle are introduced to improve the system. The obtained improved GOMP algorithm not only shows a greater improvement in terms of normalized mean square error (NMSE) and bit error rate (BER) performance but also greatly reduces computational complexity, enabling it to better satisfy the needs of V2X communication. Full article

To achieve rapid screening and semi-quantitative analysis of pesticide residues in mobile laboratories and on-site tea testing, a novel method based on thermal-assisted plasma ionization–time-of-flight mass spectrometry (TAPI-TOF/MS) has been developed for the detection of 20 pesticide residues, including insecticides and fungicides, in tea. This method eliminates the need for liquid chromatography, or column connections. Instead, it utilizes the high temperature of the sample inlet and stage to fully volatilize and inject the sample. By integrating TAPI-TOF/MS with an automated pesticide residue pretreatment instrument, the entire sample extraction process can be performed automatically. The analysis time for each sample has been reduced to 1.5 min, allowing for the processing of 60 samples per batch. An accurate mass spectrometry database has been established for screening and confirmation purposes. The software automatically matches the mass spectrometry database by analyzing the measured ion mass deviation, ion abundance ratio, and the relative contribution weight of each ion, generating a qualitative score ranging from 0 to 100. The lowest concentration yielding a qualitative score of ≥75 was defined as the screening limit, which ranged from 0.10 to 5.00 mg/kg for the 20 pesticides. Within their respective linear ranges, the method demonstrated good linearity with correlation coefficients (R²) ranging from 0.983 to 0.999. The average recovery rates (n = 5) of the target pesticides ranged from 70.6% to 117.0% at the set standard concentrations, with relative standard deviations (RSD) ranging from 1.7% to 13.1%. Using this method, 15 tea samples purchased from the Rizhao market in China were analyzed. Ten samples were found to contain residues of metalaxyl or pyraclostrobin, yielding a detection rate of 66.7%. This technology provides technical support for the rapid detection and quality control of multiple pesticide residues in tea, meeting the requirements for high-throughput and on-site analysis. Full article

Carbon dioxide (CO₂) is a non-toxic asphyxiant gas that, once released, can pose severe risks, including suffocation, poisoning, frostbite, and even death. As a critical component of carbon capture, utilization, and storage (CCUS) technology, CO₂ pipeline transportation requires reliable leakage detection and precise localization to safeguard the environment, ensure pipeline operational safety, and support emergency response strategies. This study proposes an inversion model that integrates wireless sensor networks (WSNs) with the Gaussian plume model for CO₂ pipeline leakage monitoring. The WSN is employed to collect real-time CO₂ concentration data and environmental parameters around the pipeline, while the Gaussian plume model is used to simulate and invert the dispersion process, enabling both leak source localization and emission rate estimation. Simulation results demonstrate that the proposed model achieves a source localization error of 12.5% and an emission rate error of 3.5%. Field experiments further confirm the model’s applicability, with predicted concentrations closely matching the measurements, yielding an error range of 3.5–14.7%. These findings indicate that the model satisfies engineering accuracy requirements and provides a technical foundation for emergency response following CO₂ pipeline leakage. Full article

This study aimed to examine position-specific physical demands among elite U19 football players competing in a 4-3-3 formation, using data collected via STATSports GPS technology. A total of 23 players from a top-tier Bosnian club, FK “Sarajevo”, were monitored during 26 official matches in the 2024/2025 season. Match data included total distance, distance in six speed zones, high-speed running (HSR), sprint distance, number of sprints, maximum speed, and acceleration/deceleration events. One-way ANOVA and Bonferroni post hoc analyses revealed significant positional differences across all performance metrics (p < 0.05). Central midfielders (CMs) covered the greatest total distance and distance per minute, while side defenders (SD) and forwards (FWs) recorded the highest values in sprint distance, HSR, and sprint frequency. Central defenders (CDs) consistently demonstrated the lowest outputs in high-speed and sprint metrics. These findings highlight the distinct physical profiles required for each playing position in a 4-3-3 system and provide practical insights for designing position-specific training and load management strategies in elite youth football. Full article

This paper presents an advanced W-band bidirectional Power Amplifier–Low Noise Amplifier (PALNA) implemented using 130 nm SiGe BiCMOS technology. The proposed RF front-end eliminates the need for conventional transmit/receive (T/R) switches by employing a bidirectional architecture with a passive matching network. This approach minimizes area requirements and reduces signal losses. Post-layout simulation results demonstrate that the designed PALNA achieves a peak small-signal gain of 30 dB in Tx mode and 26 dB in Rx mode, with reverse isolation better than 40 dB. The 3 dB bandwidth spans from 94 to 106 GHz. In LNA mode, the design achieves a minimum noise figure of 6 dB at 100 GHz, remaining below 6.5 dB across the entire 3 dB bandwidth. In PA mode, the simulated saturated output power is 10.5 dBm, with a maximum power-added efficiency of 12% at 100 GHz. The chip size is 0.7 mm² including pads. It consumes 78 and 22 mW in the Tx and Rx modes, respectively. Full article

Three-dimensional printing technology is fundamentally reshaping the design and fabrication of health monitoring sensors. While it holds great promise for achieving miniaturization, multi-material integration, and personalized customization, the lack of a clear selection framework hinders the optimal matching of printing technologies to specific sensor requirements. This review presents a classification framework based on existing standards and specifically designed to address sensor-related requirements, categorizing 3D printing technologies into point-based, line-based, and area-based modalities according to their fundamental fabrication unit. This framework directly bridges the capabilities of each modality, such as nanoscale resolution, multi-material versatility, and high-throughput production, with the critical demands of modern health monitoring sensors. We systematically demonstrate how this approach guides technology selection: Point-based methods (e.g., stereolithography, inkjet) enable micron-scale features for ultra-sensitive detection; line-based techniques (e.g., Direct Ink Writing, Fused Filament Fabrication) excel in multi-material integration for creating complex functional devices such as sweat-sensing patches; and area-based approaches (e.g., Digital Light Processing) facilitate rapid production of sensor arrays and intricate structures for applications like continuous glucose monitoring. The point–line–area paradigm offers a powerful heuristic for designing and manufacturing next-generation health monitoring sensors. We also discuss strategies to overcome existing challenges, including material biocompatibility and cross-scale manufacturing, through the integration of AI-driven design and stimuli-responsive materials. This framework not only clarifies the current research landscape but also accelerates the development of intelligent, personalized, and sustainable health monitoring systems. Full article

As the global demand for clean and efficient energy continues to grow, the development of advanced energy storage technologies is becoming increasingly important. This study explores the influence of the dead volume coefficient and pulse-width modulation (PWM) control strategy on the performance of a piston expander in a micro-compressed air energy storage system. Simulation results showed that low dead volume values, combined with short air supply durations with PWM values between 0.1 and 0.2, led to improved energy utilization. This was achieved through complete piston strokes and stable power output. In contrast, high dead volume values and high PWM settings, such as 0.9, resulted in incomplete air expansion, excessive air consumption, and a significant reduction in overall system efficiency, even though peak power output may increase. Sensitivity analysis confirmed that PWM had a major impact on efficiency, with the highest value of 0.76 achieved for a dead volume coefficient of 0.05 and a PWM value of 0.2. Under these operating conditions, the expander delivered a generated power output of 970 W. Additionally, PWM enabled flexible control of power output, without requiring modifications to the system’s physical design. The study highlights the importance of adjusting the air admission strategy to match the internal volume characteristics. Full article

Non-contact measurement technology based on computer vision has been recognized as a critical approach in bridge lightweight monitoring due to its low cost and strong environmental adaptability. To address the sub-millimeter accuracy and real-time requirements of bridge displacement monitoring, this study proposes a visual monitoring method that integrates a connected-domain segmentation matching algorithm with an automatic binarization threshold adjustment mechanism. This combination significantly improves adaptability and robustness under complex lighting conditions. Moreover, the method introduces the SRCNN (Super-Resolution Convolutional Neural Network) as a lightweight super-resolution module, the method achieves a better balance between computational efficiency and measurement precision. The proposed method was validated through model testing and successfully applied to real-bridge displacement monitoring and structural damping ratio identification. These findings demonstrate the practical potential of the method as a reliable reference for static and dynamic performance evaluation and condition assessment of bridges. Full article

Traditional security methods fail to match the speed of evolving threats because Industrial Internet of Things (IIoT) technologies have become more widely adopted. A lightweight adaptive AI-based intrusion detection system (IDS) for IIoT environments is presented in this paper. The proposed system detects cyber threats in real time through an ensemble of online learning models that also adapt to changing network behavior. The system implements SHAP (SHapley Additive exPlanations) for model prediction explanations to allow human operators to verify and understand alert causes while addressing the essential need for trust and transparency. The system validation was performed using the ToN_IoT and Bot-IoT benchmark datasets. The proposed system detects threats with 96.4% accuracy while producing 2.1% false positives and requiring 35 ms on average for detection on edge devices with limited resources. Security analysts can understand model decisions through SHAP analysis because packet size and protocol type and device activity patterns strongly affect model predictions. The system underwent testing on a Raspberry Pi 5-based IIoT testbed to evaluate its deployability in real-world scenarios through emulation of practical edge environments with constrained computational resources. The research unites real-time adaptability with explainability and low-latency performance in an IDS framework specifically designed for industrial IoT security. The solution provides a scalable method to boost cyber resilience in manufacturing, together with energy and critical infrastructure sectors. By enabling fast, interpretable, and low-latency intrusion detection directly on edge devices, this solution enhances cyber resilience in critical sectors such as manufacturing, energy, and infrastructure, where timely and trustworthy threat responses are essential to maintaining operational continuity and safety. Full article

With the evolution of 6G technology toward global coverage and multidimensional integration, OTFS modulation has become a research focus due to its advantages in high-mobility scenarios. However, existing OTFS signal detection algorithms face challenges such as pilot contamination, Doppler spread degradation, and diverse interference in complex environments. This paper proposes the SPARSE-OTFS-Net algorithm, which establishes a comprehensive signal detection solution by innovatively integrating sparse random pilot design, compressive sensing-based frequency offset estimation with closed-loop cancellation, and joint denoising techniques combining an autoencoder, residual learning, and multi-scale feature fusion. The algorithm employs deep learning to dynamically generate non-uniform pilot distributions, reducing pilot contamination by 60%. Through orthogonal matching pursuit algorithms, it achieves super-resolution frequency offset estimation with tracking errors controlled within 20 Hz, effectively addressing Doppler spread degradation. The multi-stage denoising mechanism of deep neural networks suppresses various interferences while preserving time-frequency domain signal sparsity. Simulation results demonstrate: Under large frequency offset, multipath, and low SNR conditions, multi-kernel convolution technology achieves significant computational complexity reduction while exhibiting outstanding performance in tracking error and weak multipath detection. In 1000 km/h high-speed mobility scenarios, Doppler error estimation accuracy reaches ±25 Hz (approaching the Cramér-Rao bound), with BER performance of 5.0 × 10⁻⁶ (7× improvement over single-Gaussian CNN’s 3.5 × 10⁻⁵). In 1024-user interference scenarios with BER = 10⁻⁵ requirements, SNR demand decreases from 11.4 dB to 9.2 dB (2.2 dB reduction), while maintaining EVM at 6.5% under 1024-user concurrency (compared to 16.5% for conventional MMSE), effectively increasing concurrent user capacity in 6G ultra-massive connectivity scenarios. These results validate the superior performance of SPARSE-OTFS-Net in 6G ultra-massive connectivity applications and provide critical technical support for realizing integrated space–air–ground networks. Full article

Efficient and rational utilization of mineral resources significantly impacts economic and technological development. Image segmentation is a pivotal process in ore sorting, as its results directly affect the accuracy of mineral classification. Traditional segmentation methods often fail to meet the requirements for noise suppression, segmentation precision, and robustness in ore sorting. To address these issues, we propose an ore image segmentation method based on concavity matching via region retrieval, which comprises a contour approximation module, a concavity matching module, and a segmentation detection module. It introduces the concepts of single-contour, multi-contour, and segmentation regions in ore images, offering tailored segmentation approaches for varying adhesion forms and quantities. A significant contribution of this study lies in the contour approximation module, which simplifies the edge information of ore images via curve fitting, effectively removing the influence of edge noise points. The concavity matching module restricts candidate areas for matching concavity points through the construction of search regions, significantly improving matching accuracy. Finally, paired concavity points are connected to completing the segmentation process. Experimental comparisons using X-ray images of tungsten ores demonstrate that the proposed method can effectively suppress noise-induced concavity interference, achieving a noise reduction efficiency of 94.77% and a concavity region search accuracy of 93.60%, thus meeting the precision requirements for segmenting X-ray ore images. Given its high efficiency and accuracy, industrial sectors involved in mineral processing are recommended to incorporate this segmentation method into intelligent ore sorting equipment upgrading and renovation projects, enhancing the overall efficiency of mineral resource sorting and promoting the sustainable development of the mineral industry. Full article

Somatic mutations in KRAS and TP53 are among the most common genetic alterations in pancreatic ductal adenocarcinoma (PDAC). Advances in PCR-based technologies now enable the detection of these mutations in tumor tissue and cell-free DNA (cfDNA), providing a minimally invasive approach to assess tumor burden. However, in resectable PDAC, circulating tumor DNA (ctDNA) may represent less than 0.1% of total cfDNA, requiring highly sensitive detection methods. The aim of our study was to assess two PCR-based assays—competitive allele-specific PCR (castPCR) and digital PCR (dPCR)—for detecting selected somatic mutations in tumor tissue, cfDNA, and extracellular vesicle-associated DNA (EV-DNA) from plasma. Matched primary tumor and preoperative plasma samples were collected from 50 patients undergoing upfront resection for PDAC. CastPCR was used for detecting selected KRAS, TP53, SMAD4, and CDKN2A mutations in tumor DNA. Additionally, dPCR was used to analyze KRAS and TP53 mutations in tumor DNA as well as cfDNA and EV-DNA. The concordance between both platforms was 71.4% for KRAS p.G12D and 58.3% for the analysis of TP53 p.R273H mutations in tumor tissue. However, dPCR detected these mutations in an additional 28.6% and 39.6% of samples, respectively. In cfDNA, dPCR identified KRAS p.G12D in 10.2% and TP53 p.R273H in 2.0% of samples. Mutation detection in EV-DNA was limited by low DNA yield. Both platforms proved effective for tumor DNA analysis, with dPCR offering greater sensitivity. Somatic mutation detection from liquid biopsy using dPCR further supports its potential utility in the preoperative setting. Full article

Wave energy is a highly concentrated energy resource with five times higher energy density than wind and at least ten times the power density of solar energy. It is expected to make a major contribution to addressing climate change and to help end our dependency on fossil fuels. Many ingenious wave energy conversion methods have been put forward, and a large number of wave energy converters (WECs) have been developed. However, to date, wave energy conversion technology is still in the demonstration application stage. Key issues such as survivability, reliability, and efficient conversion still need to be solved. The major hurdle is the fact that ocean waves provide a slow-moving, high-magnitude force, whereas most electric generators operate at high rotary speed and low torque. Coupling the slow-moving, high-magnitude force of ocean waves normally requires conversion to a high-speed, low-magnitude force as an intermediate step before a rotary generator is applied. This, in general, tends to severely limit the overall efficiency and reliability of the converter and drives the capital cost of the converter well above an acceptable commercial target. Magnetohydrodynamic (MHD) wave energy conversion makes use of MHD generators in which a conducting fluid passes through a very strong magnetic field to produce an electric current. In contrast to alternatives, the relatively slow speed at which the fluid traverses the magnetic field makes it possible to directly couple to ocean waves with a high-magnitude, slowly moving force. The MHD generator provides an excellent match to the mechanical impedance of an ocean wave, and therefore, an MHD WEC has no rotating mechanical parts with high speeds, no complex control process, and has good response to low sea states and high efficiency under all working conditions. This review introduces the system composition, working process, and technical features of WECs based on MHD generators first. Then, the research development, key points, and issues of wave energy conversion technology based on MHD generators are presented in detail. Finally, the problems to be solved and the future research directions of wave energy conversion based on MHD generators are pointed out. Full article