Search Results (5,348)

Wine quality is closely linked to sensory attributes such as aroma, taste, and mouthfeel, all of which are influenced by grape variety, “terroir”, and vinification practices. Among these, aroma is particularly important for consumer preference, and it results from a complex interplay of numerous volatile compounds. Conventional sensory methods, such as descriptive analysis (DA) performed by trained panels, offer valuable insights but are often time-consuming, resource-intensive, and subject to individual variability. Recent advances in sensor technologies—including electronic nose (E-nose) and electronic tongue (E-tongue)—combined with chemometric techniques and machine learning algorithms, offer more efficient, objective, and predictive approaches to wine aroma profiling. These tools integrate analytical and sensory data to predict aromatic characteristics and quality traits across diverse wine styles. Complementary techniques, including gas chromatography (GC), near-infrared (NIR) spectroscopy, and quantitative structure–odor relationship (QSOR) modeling, when integrated with multivariate statistical methods such as partial least squares regression (PLSR) and neural networks, have shown high predictive accuracy in assessing wine aroma and quality. Such approaches facilitate real-time monitoring, strengthen quality control, and support informed decision-making in enology. However, aligning instrumental outputs with human sensory perception remains a challenge, highlighting the need for further refinement of hybrid models. This review highlights the emerging role of predictive modeling and sensor-based technologies in advancing wine aroma evaluation and quality management. Full article

The innate immune system protects against infection and cellular damage by recognizing conserved pathogen-associated molecular patterns (PAMPs) and damage-associated molecular patterns (DAMPs). Emerging evidence suggests that aberrant epigenetic modifications—such as altered DNA methylation and histone marks—can serve as immunogenic signals that activate pattern recognition receptor (PRR)-mediated immune surveillance. This review explores the concept that epigenetic marks may function as DAMPs or even mimic PAMPs. I highlight how unmethylated CpG motifs, which are typically suppressed using host methylation, are recognized as foreign via Toll-like receptor 9 (TLR9). I also examine how cytosolic DNA sensors, including cGAS, detect mislocalized or hypomethylated self-DNA resulting from genomic instability. In addition, I discuss how extracellular histones and nucleosomes released during cell death or stress can act as DAMPs that engage TLRs and activate inflammasomes. In the context of cancer, I review how epigenetic dysregulation can induce a “viral mimicry” state, where reactivation of endogenous retroelements produces double-stranded RNA sensed by RIG-I and MDA5, triggering type I interferon responses. Finally, I address open questions and future directions, including how immune recognition of epigenetic alterations might be leveraged for cancer immunotherapy or regulated to prevent autoimmunity. By integrating recent findings, this review underscores the emerging concept of the epigenome as a target of innate immune recognition, bridging the fields of immunology, epigenetics, and cancer biology. Full article

The emissions from biomass combustion systems have recently been the subject of increased attention. In addition to elevated concentrations of particulate matter and hydrocarbons (HCs) in the flue gas, significant levels of NO_x emissions occur depending on the used fuel, such as biogenic residues. In response to legal requirements, owners of medium-sized plants (≈100 kW) are now also forced to minimize these emissions by means of selective catalytic reduction systems (SCR). The implementation of a selective sensor is essential for the efficient dosing of the reducing agent, which is converted to ammonia (NH₃) in the flue gas. Preliminary laboratory investigations on a capacitive NH₃ sensor based on a zeolite functional film have demonstrated a high sensitivity to ammonia with minimal cross-influences from H₂O and NO_x. Further investigations concern the application of this sensor in the real flue gas of an ordinary wood-burning stove and of combustion plants for biogenic residues with an ammonia dosage. The findings demonstrate a high degree of agreement between the NH₃ concentration measured by the sensor and an FTIR spectrometer. Furthermore, the investigation of the long-term stability of the sensor and the poisoning effects of SO₂ and HCl are of particular relevance to the laboratory measurements in this study, which show promising results. Full article

In response to mounting regulatory and environmental pressures, the maritime sector must urgently improve energy efficiency and reduce greenhouse gas emissions. However, conventional operational interfaces often fail to deliver real-time, actionable insights needed for informed decision-making onboard. This work presents an innovative Augmented Reality (AR) interface integrated with an established shipboard data collection system to enhance real-time monitoring and operational decision-making on commercial vessels. The baseline data acquisition infrastructure is currently installed on over 800 vessels across various ship types, providing a robust foundation for this development. To validate the AR interface’s feasibility and performance, a field trial was conducted on a representative dry bulk carrier. Through hands-free AR smart glasses, crew members access real-time overlays of key performance indicators, such as fuel consumption, engine status, emissions levels, and energy load balancing, directly within their field of view. Field evaluations and scenario-based workshops demonstrate significant gains in energy efficiency (up to 28% faster decision-making), predictive maintenance accuracy, and emissions awareness. The system addresses human–machine interaction challenges in high-pressure maritime settings, bridging the gap between complex sensor data and crew responsiveness. By contextualizing IoT data within the physical environment, the AR-IoT platform transforms traditional workflows into proactive, data-driven practices. This study contributes to the emerging paradigm of digitally enabled sustainable operations and offers practical insights for scaling AR-IoT solutions across global fleets. Findings suggest that such convergence of AR and IoT not only enhances vessel performance but also accelerates compliance with decarbonization targets set by the International Maritime Organization (IMO). Full article

The cGAS-STING pathway initiates the core cascade of innate immune defense by recognizing pathogen-associated and self-derived abnormal nucleic acids, and key molecules (such as cGAS, STING, downstream IFN-β, IL-6, etc.) may serve as biomarkers in various diseases. The diverse mechanisms by which distinct nucleic acids activate this pathway provide novel insights for therapeutic strategies targeting infectious diseases, cancer, and autoimmune disorders. To prevent aberrant cGAS-STING pathway activation, cells employ multiple regulatory mechanisms, including restricting self-DNA recognition and terminating downstream signaling. Strategies to mitigate pathological activation involve reducing nucleic acid accumulation through nuclease degradation (e.g., of mitochondrial DNA or neutrophil extracellular traps, NETs) or directly inhibiting cGAS or STING. This review elucidates the molecular mechanism of nucleic acid-mediated regulation of cGAS-STING and its role in disease regulation. Full article

Gallium oxide (Ga₂O₃)-based memristors are gaining traction as promising candidates for next-generation electronic devices toward in-memory computing, leveraging the unique properties of Ga₂O₃, such as its wide bandgap, high thermodynamic stability, and chemical stability. This review explores the evolution of memristor theory for Ga₂O₃-based materials, emphasising capacitive memristors and their ability to integrate resistive and capacitive switching mechanisms for multifunctional performance. We discussed the state-of-the-art fabrication methods, material engineering strategies, and the current challenges of Ga₂O₃-based memristors. The review also highlights the applications of these memristors in memory technologies, neuromorphic computing, and sensors, showcasing their potential to revolutionise emerging electronics. Special focus has been placed on the use of Ga₂O₃ in capacitive memristors, where their properties enable improved switching speed, endurance, and stability. In this paper we provide a comprehensive overview of the advancements in Ga₂O₃-based memristors and outline pathways for future research in this rapidly evolving field. Full article

Power transformers are critical components in electrical power systems, where failures can cause significant outages and economic losses. Traditional maintenance strategies, typically based on offline inspections, are increasingly insufficient to meet the reliability requirements of modern digital substations. This work presents an integrated multi-sensor monitoring framework that combines online frequency response analysis (OnFRA^® 4.0), capacitive tap-based monitoring (FRACTIVE^® 4.0), dissolved gas analysis, and temperature measurements. All data streams are synchronized and managed within a SCADA system that supports real-time visualization and historical traceability. To enable automated fault diagnosis, a Random Forest classifier was trained using simulated datasets derived from laboratory experiments that emulate typical transformer and bushing degradation scenarios. Principal Component Analysis was employed for dimensionality reduction, improving model interpretability and computational efficiency. The proposed model achieved perfect classification metrics on the simulated data, demonstrating the feasibility of combining high-fidelity monitoring hardware with machine learning techniques for anomaly detection. Although no in-service failures have been recorded to date, the monitoring infrastructure is already tested and validated through laboratory conditions, enabling continuous data acquisition. Full article

Ensuring food quality and safety is a growing challenge in the food industry, where early detection of contamination or spoilage is crucial. Using gas sensors combined with Artificial Intelligence (AI) offers an innovative and effective approach to food identification, improving quality control and minimizing health risks. This study aims to evaluate food identification strategies using supervised learning techniques applied to data collected by the BME Development Kit, equipped with the BME688 sensor. The dataset includes measurements of temperature, pressure, humidity, and, particularly, gas composition, ensuring a comprehensive analysis of food characteristics. The methodology explores two strategies: a neural network model trained using Bosch BME AI-Studio software, and a more flexible, customizable approach that applies multiple predictive algorithms, including DT, LR, kNN, NB, and SVM. The experiments were conducted to analyze the effectiveness of both approaches in classifying different food samples based on gas emissions and environmental conditions. The results demonstrate that combining electronic noses (E-Noses) with machine learning (ML) provides high accuracy in food identification. While the neural network model from Bosch follows a structured and optimized learning approach, the second methodology enables a more adaptable exploration of various algorithms, offering greater interpretability and customization. Both approaches yielded high predictive performance, with strong classification accuracy across multiple food samples. However, performance variations depend on the characteristics of the dataset and the algorithm selection. A critical analysis suggests that optimizing sensor calibration, feature selection, and consideration of environmental parameters can further enhance accuracy. This study confirms the relevance of AI-driven gas analysis as a promising tool for food quality assessment. Full article

Integration of IoT and predictive modeling is critical for optimizing microclimate management in urban-agglomeration vertical farming. In this study, we present a hybrid digital twin approach that combines a physical microclimate model with a distributed IoT monitoring system to simulate and control the phytotron environment. A set of heat- and mass-balance equations governing the dynamics of temperature, humidity, and transpiration was implemented and parameterized using a genetic algorithm (GA)—an evolutionary optimization method—with real-time data collected over three intervals (72 h, 90 h, and 110 h) from LoRaWAN sensors (temperature, humidity, CO₂) and Wi-Fi-connected power meters managed by Home Assistant. The optimized model achieved mean temperature deviations ≤ 0.1 °C, relative humidity errors ≤ 2%, and overall energy consumption accuracy of 99.5% compared to measured values. The digital twin reliably tracked daily climate fluctuations and system energy use, confirming the accuracy of the hybrid approach. These results demonstrate that the proposed framework effectively integrates theoretical models with IoT-derived data to deliver precise environmental control and energy-use optimization in vertical farming, while also laying the groundwork for scalable digital twins in controlled-environment agriculture. Full article

Optimal camera placement (OCP) is a crucial task for ensuring adequate surveillance of both indoor and outdoor environments. While several solutions to this problem have been documented in the literature, there are still research gaps related to the maximization of surveillance coverage, particularly in terms of optimal placement of omnidirectional camera (OC) sensors in indoor and partially occluded environments via metaheuristic optimization algorithms (MOAs). In this paper, we present a study centered on several popular MOAs and their application to OCP for OC sensors in indoor environments. For our experiments we considered two experimental layouts consisting of both a deployment area, and visual obstructions, as well as two different omnidirectional camera models. The tested MOAs include popular algorithms such as PSO, GWO, SSO, GSA, SMS, SA, DE, GA, and CMA-ES. Experimental results suggest that the success in MOA-based OCP is strongly tied with the specific search strategy applied by the metaheuristic method, thus making certain approaches preferred over others for this kind of problem. Full article

With the development of the mining industry, safety issues in underground operations are becoming increasingly relevant. Complex gas conditions in mines, including the presence of explosive and toxic gases, pose a serious threat to the lives of miners and the stability of production processes. This paper describes the development and modeling of an integrated fire monitoring and automatic extinguishing system that combines gas collection, concentration analysis, and rapid response to emergencies. The main components of the system include the following: a gas collection module that uses an array of highly sensitive sensors to continuously measure the concentrations of methane (CH₄), carbon monoxide (CO), and hydrogen sulfide (H₂S) with an accuracy of up to 95%; a gas analysis module that uses data processing algorithms to identify gas concentration threshold exceedances (e.g., CH₄ > 5% vol. or CO > 20 ppm); and an automatic fire extinguishing module that activates nitrogen supply, ventilation, and aerosol/powder fire extinguishers when a threat is detected. Simulation results in MATLAB/Simulink showed that the system reduces the concentration of hazardous gases by 30% within the first 2 s after activation, which significantly increases safety. Additionally, scenarios with various types of fires were analyzed, confirming the effectiveness of the extinguishing modules in mines up to 500 m deep. The integrated system achieves 95% gas detection accuracy, 90 ms response latency, and 40% hazard reduction within 3 s of activation, verified in 500 m deep mine simulations. Quantitative comparison shows a 75% faster response time and 10% higher detection accuracy than conventional systems. The proposed system demonstrates high reliability in difficult conditions, reducing the risk of fires by 75% compared to traditional methods. This work opens up prospects for practical application in the coal industry, especially in regions with a high risk of spontaneous coal combustion, such as India and Germany. Full article

This Special Issue based on 15 articles/reviews focusses on low-cost sensor technology, gas sensors, chemical sensors, advanced active materials, sensing nanomaterials, sensor nodes, hardware innovation, data communication, system integration, sensor testing, functional characterization, sensor modeling, processing and correction algorithms, new sensing solutions, advanced proof of concepts, and chemical detection applications. Proper calibration techniques of chemical sensors have been explored, both in the laboratory and in field applications. Sensing solutions have been applied in the context of biochemical detection and gas monitoring. Full article

Wireless sensor networks (WSNs) are fundamental to applications in the Internet of Things, smart cities, and environmental monitoring, where coverage optimization is critical for maximizing monitoring efficacy under constrained resources. Conventional approaches often suffer from low global coverage efficiency, high computational overhead, and a tendency to converge to local optima. To address these challenges, this study proposes the fusion multi-strategy gray wolf optimizer (FMGWO), an advanced variant of the Gray Wolf Optimizer (GWO). FMGWO integrates various strategies: electrostatic field initialization for uniform population distribution, dynamic parameter adjustment with nonlinear convergence and differential evolution scaling, an elder council mechanism to preserve historical elite solutions, alpha wolf tenure inspection and rotation to maintain population vitality, and a hybrid mutation strategy combining differential evolution and Cauchy perturbations to enhance diversity and global search capability. Ablation studies validate the efficacy of each strategy, while simulation experiments demonstrate FMGWO’s superior performance in WSN coverage optimization. Compared to established algorithms such as PSO, GWO, CSA, DE, GA, FA, OGWO, DGWO1, and DGWO2, FMGWO achieves higher coverage rates with fewer nodes—up to 98.63% with 30 nodes—alongside improved convergence speed and stability. These results underscore FMGWO’s potential as an effective solution for efficient WSN deployment, offering significant implications for resource-constrained optimization in IoT and edge computing systems. Full article

The sensitive detection of n-butanol is of high scientific and practical importance for ensuring safety in industrial production. In this study, hollow Cu₂O@CuS core–shell nanocubic heterostructures were fabricated via a multistep templating method. The Cu₂O@CuS heterostructures demonstrated exceptional performance, with an ultrahigh Brunauer–Emmett–Teller specific surface area that provided abundant active sites and a unique hollow architecture that enhanced mass transport and improved gas adsorption/desorption kinetics. High-density surface oxygen vacancies on the Cu₂O@CuS nanocubic heterostructures provide a key structural basis for the preferential adsorption of n-butanol molecules on its surface. The p–p heterojunction configuration further enhanced selective sensor response by optimizing the charge carrier separation and band structure modulation. The developed sensor achieved a detection limit of 3.18 ppm while exhibiting outstanding sensitivity, stability, and response time, meeting the stringent requirements for n-butanol detection in both industrial and agricultural settings. This work provides new insights on how to design materials for gas sensors. Full article

In this study, multivariate data processing during thermal modulation of the SnO₂–PdO gas sensor was performed using the multivariate calibration (MC) method. We propose to supplement this method with a procedure that allows the solving of problems of both quantitative and qualitative analysis. The advantage of the extended method (Multivariate Calibration for Selective Analysis, MCSA) compared to other methods is its modest requirements for computing resources, which allows it to be easily implemented on standard microcontrollers. The MCSA method opens up the prospect of creating compact gas analyzers of a new generation, capable of selective gas analysis in hard-to-reach places in an autonomous mode. The implementation of the MCSA method was demonstrated using the example of selective determination of hydrogen sulfide and carbon monoxide by a sensor whose temperature periodically changed from 100 to 450 °C. The training sample data were transformed by the MCSA method, which allowed for successful qualitative and quantitative analysis of the test sample data. Full article