Search Results (90)

Noncommunicable diseases (NCDs) like diabetes and cancer drive demand for therapeutic functional foods. This study developed freeze-dried fermented camel milk (FCM) with Ajwa date pulp (ADP), evaluating its physical and functional properties, probiotic survival, and potential benefits for diabetes and cancer. To achieve this target, six FCM formulations were prepared using ABT-5 starter culture (containing Lactobacillus acidophilus, Bifidobacterium bifidum, and Streptococcus thermophilus) with or without Lacticaseibacillus rhamnosus B-1937 and ADP (12% or 15%). The samples were freeze-dried, and their functional properties, such as water activity, dispersibility, water absorption capacity, water absorption index, water solubility index, insolubility index, and sedimentation, were assessed. Reconstitution properties such as density, flowability, air content, porosity, loose bulk density, packed bulk density, particle density, carrier index, Hausner ratio, porosity, and density were examined. In addition, color and probiotic survivability under simulated gastrointestinal conditions were analyzed. Also, antidiabetic potential was assessed via α-amylase and α-glucosidase inhibition assays, while cytotoxicity was evaluated using the MTT assay on Caco-2 cells. The results show that ADP supplementation significantly improved dispersibility (up to 72.73% in FCM15D+L). These improvements are attributed to changes in particle size distribution and increased carbohydrate and mineral content, which facilitate powder rehydration and reduce clumping. All FCM variants demonstrated low water activity (0.196–0.226), indicating good potential for shelf stability. The reconstitution properties revealed that FCM powders with ADP had higher bulk and packed densities but lower particle density and porosity than controls. Including ADP reduced interstitial air and increased occluded air within the powders, which may minimize oxidation risks and improve packaging efficiency. ADP incorporation resulted in a significant decrease in lightness (L*) and increases in redness (a*) and yellowness (b*), with greater pigment and phenolic content at higher ADP levels. These changes reflect the natural colorants and browning reactions associated with ADP, leading to a more intense and visually distinct product. Probiotic survivability was higher in ADP-fortified samples, with L. acidophilus and B. bifidum showing resilience in intestinal conditions. The FCM15D+L formulation exhibited potent antidiabetic effects, with IC₅₀ values of 111.43 μg mL⁻¹ for α-amylase and 77.21 μg mL⁻¹ for α-glucosidase activities, though lower than control FCM (8.37 and 10.74 μg mL⁻¹, respectively). Cytotoxicity against Caco-2 cells was most potent in non-ADP samples (IC₅₀: 82.22 μg mL⁻¹ for FCM), suggesting ADP and L. rhamnosus may reduce antiproliferative effects due to proteolytic activity. In conclusion, the study demonstrates that ADP-enriched FCM is a promising functional food with enhanced probiotic viability, antidiabetic potential, and desirable physical properties. This work highlights the potential of camel milk and date synergies in combating some NCDs in vitro, suggesting potential for functional food application. Full article

This paper provides a comparative study of unsupervised Deep Learning (DL) methods for anomaly detection in Flight Data Monitoring (FDM). The paper applies Long Short-Term Memory (LSTM), Gated Recurrent Units (GRUs), Convolutional Neural Network (CNN), classic Transformer architecture, and LSTM combined with a self-attention mechanism to real-world flight data and compares the results to the current state-of-the-art flight data analysis techniques applied in the industry. The paper finds that LSTM, when integrated with a self-attention mechanism, offers notable benefits over other deep learning methods as it effectively handles lengthy time series like those present in flight data, establishes a generalized model applicable across various airports and facilitates the detection of trends across the entire fleet. The results were validated by industrial experts. The paper additionally investigates a range of methods for feeding flight data (lengthy time series) to a neural network. The innovation of this paper involves utilizing Transformer architecture and LSTM with self-attention mechanism for the first time in the realm of aviation data, exploring the optimal method for inputting flight data into a model and evaluating all deep learning techniques for anomaly detection against the ground truth determined by human experts. The paper puts forth a compelling case for shifting from the existing method, which relies on examining events through threshold exceedances, to a deep learning-based approach that offers a more proactive style of data analysis. This not only enhances the generalization of the FDM process but also has the potential to improve air transport safety and optimize aviation operations. Full article

As unmanned aerial vehicle (UAV) applications expand across logistics, agriculture, and emergency response, safety and security threats are becoming increasingly complex. Addressing these evolving threats, including physical safety and network security threats, requires continued advancement by integrating traditional artificial intelligence (AI) tools such as machine learning (ML) and deep learning (DL), which contribute to significantly enhancing UAV safety and security. Large language models (LLMs), a cutting-edge trend in the AI field, are associated with strong capabilities for learning and adapting across various environments. Their emergence reflects a broader trend toward intelligent systems that may eventually demonstrate behavior comparable to human-level reasoning. This paper summarizes the typical safety and security threats affecting UAVs, reviews the progress of traditional AI technologies, as described in the literature, and identifies strategies for reducing the impact of such threats. It also highlights the limitations of traditional AI technologies and summarizes the current application status of LLMs in UAV safety and security. Finally, this paper discusses the challenges and future research directions for improving UAV safety and security with LLMs. By leveraging their advanced capabilities, LLMs offer potential benefits in critical domains such as urban air traffic management, precision agriculture, and emergency response, fostering transformative progress toward adaptive, reliable, and secure UAV systems that address modern operational complexities. Full article

As sixth-generation mobile communication (6G) advances towards ultra-high speed and global coverage, visible light communication (VLC) has emerged as a crucial complementary technology due to its ultra-high bandwidth, low power consumption, and immunity to electromagnetic interference. Modulation format recognition (MFR) plays a vital role in the dynamic optimization and adaptive transmission of VLC systems, significantly influencing communication performance in complex channel environments. This paper systematically reviews the research progress in MFR for VLC, comparing the theoretical frameworks and limitations of traditional likelihood-based (LB) and feature-based (FB) methods. It also explores the advancements brought by deep learning (DL) technology, particularly in enhancing noise robustness, classification accuracy, and cross-scenario adaptability through automatic feature extraction and nonlinear mapping. The findings indicate that DL-based MFR substantially enhances recognition performance in intricate channels via multi-dimensional feature fusion, lightweight architectures, and meta-learning paradigms. Nonetheless, challenges remain, including high model complexity and a strong reliance on labeled data. Future research should prioritize multi-domain feature fusion, interdisciplinary collaboration, and hardware–algorithm co-optimization to develop lightweight, high-precision, and real-time MFR technologies that align with the 6G vision of space–air–ground–sea integrated networks. Full article

The radiation sterilization of polymer-based drug solutions can change the characteristics that determine the efficiency of drug targeting, such as particle sizes in the solution and their surface potential. The effect of E-beam treatment at doses of 3 and 8 kGy in a Xe or air atmosphere on the hydrodynamic properties of dilute solutions of polyvinylpyrrolidone (PVP) conjugate with fullerene C₆₀ and folic acid (FA-PVP-C₆₀) was studied and compared with native PVP K30. The capillary viscometry method was used to determine the intrinsic viscosity of solutions. The particle sizes (R_h) were determined using the DLS method. The zeta potential of the particles was determined using the PALS method. The morphological features of the conjugate surface irradiated in a Xe atmosphere with a dose of 8 kGy FA-PVP-C₆₀ were studied by AFM. The functionalization of FA-PVP-C₆₀ and PVP during E-beam treatment was examined using UV- and FTIR-spectrometry. When the diluted solutions of FA-PVP-C₆₀ and PVP were irradiated in air with a dose of 3 kGy, destruction of polymer chains occurred predominantly, but when the dose was increased to 8 kGy, intermolecular cross-linking occurred, leading to an increase in the characteristic viscosity and particle size in the solution. It was shown that the average particle sizes, amounting to 3 and 8 nm for PVP and 4 and 20 nm for FA-PVP-C₆₀, did not change significantly under E-beam irradiation in a Xe atmosphere in the considered dose range. The zeta potential of the particles remained virtually unchanged for both PVP and FA-PVP-C₆₀ under all irradiation conditions. The obtained results indicate the possibility of performing radiation sterilization of FA-PVP-C₆₀ conjugate solutions in an inert gas atmosphere in the range of studied doses. Full article

In recent decades, forests have experienced an increasing trend in the number of pest outbreaks worldwide, apparently driven by strong annual variability in precipitation, higher air temperatures, and strong winds. Pest outbreaks have negative ecological, economic, and environmental impacts on forest ecosystems, such as reduced biodiversity, carbon sequestration, and overall forest health. Traditional monitoring methods of these disturbances, while accurate, are time-consuming and limited in scope. Remote sensing, particularly UAV (Unmanned Aerial Vehicle)-based technologies, offers a precise and cost effective alternative for monitoring forest health. This study evaluates the temporal and spatial progression of bark beetle damage in a fir-dominated forest in the Zao Mountains, Japan, using UAV RGB imagery and DL (Deep Learning) models (YOLO - You Only Look Ones), over a four-year period (2021–2024). Trees were classified into six health categories: Healthy, Light Damage, Medium Damage, Heavy Damage, Dead, and Fallen. The results revealed a significant decline in healthy trees, from 67.4% in 2021 to 25.6% in 2024, with a corresponding increase in damaged and dead trees. Light damage emerged as a potential early indicator of forest health decline. The DL model achieved an accuracy of 74.9% to 82.8%. The results showed the effectiveness of DL in detecting severe damage but highlighted that challenges in distinguishing between healthy and lightly damaged trees still remain. The study highlights the potential of UAV-based remote sensing and DL for monitoring forest health, providing valuable insights for targeted management interventions. However, further refinement of the classification methods is needed to improve accuracy, particularly in the precise detection of tree health categories. This approach offers a scalable solution for monitoring forest health in similar ecosystems in other subalpine areas of Japan and the world. Full article

This review explores advanced sensing technologies and deep learning (DL) methodologies for monitoring airborne particulate matter (PM), which is critical for environmental health assessments. It begins with discussing the significance of PM monitoring and introduces surface plasmon resonance (SPR) as a promising technique in environmental applications, alongside the role of DL neural networks in enhancing these technologies. This review analyzes advancements in airborne PM sensing technologies and the integration of DL methodologies for environmental monitoring. This review emphasizes the importance of PM monitoring for public health, environmental policy, and scientific research. Traditional PM sensing methods, including their principles, advantages, and limitations, are discussed, covering gravimetric techniques, continuous monitoring, optical and electrical methods, and microscopy. The integration of DL with PM sensing offers potential for enhancing monitoring accuracy, efficiency, and data interpretation. DL techniques, such as convolutional neural networks (CNNs), autoencoders, recurrent neural networks (RNNs), and their variants, are examined for applications like PM estimation from satellite data, air quality prediction, and sensor calibration. This review highlights the data acquisition and quality challenges in developing effective DL models for air quality monitoring. Techniques for handling large and noisy datasets are explored, emphasizing the importance of data quality for model performance, generalizability, and interpretability. The emergence of low-cost sensor technologies and hybrid systems for PM monitoring is discussed, acknowledging their promise while recognizing the need for addressing data quality, standardization, and integration issues. This review identifies areas for future research, including the development of robust DL models, advanced data fusion techniques, applications of deep reinforcement learning, and considerations of ethical implications. Full article

Heating, Ventilation, and Air Conditioning (HVAC) systems contribute a considerable share of total global energy consumption and carbon dioxide emissions, putting them at the heart of the issues of decarbonization and removing barriers to achieving net-zero emissions and sustainable development goals. Nevertheless, the effective implementation of artificial intelligence (AI)-based methods to optimize energy efficiency while ensuring occupant comfort in multifarious settings remains to be fully realized. This paper provides a systematic review of state-of-the-art practices (2018 and later) using AI algorithms like machine learning (ML), deep learning (DL), and other computation-based techniques that have been deployed to boost HVAC system performance. The review highlights that AI-driven control strategies can reduce energy consumption by up to 40% by dynamically adapting to environmental conditions and occupancy levels. Compared to other work that focuses on single aspects of HVAC management, this work deals with the methods of control and maintenance in a comprehensive manner. Rather than focusing on abstract applications of machine learning models, this study underlines their applicability in HVAC systems, bridging the science–practice gap. This study highlights the prospective role AI could play, on the one hand, by enhancing HVAC systems’ incorporation, energy consumption, and building technologies, while, on the other hand, also addressing the potential uses AI can have in practical applications in the future, bridging gaps and addressing challenges. Full article

The ongoing increase in urban populations has resulted in the enduring issue of traffic congestion, adversely affecting the quality of life, including commute duration, road safety, and local air quality. Consequently, recognizing and forecasting underlying traffic congestion patterns have become essential, with Traffic Congestion Prediction (TCP) emerging as an increasingly significant area of study. Advancements in Machine Learning (ML) and Artificial Intelligence (AI), as well as improvements in Internet of Things (IoT) sensor technologies have made TCP research crucial to the development of Intelligent Transportation Systems (ITSs). This review examines advanced TCP, emphasizing innovative forecasting methods and technologies and their importance for the ITS sector. This paper provides an overview of statistical, ML, Deep Learning (DL) approaches, and their ensembles that compose TCP. We examine several forecasting methods and discuss relative and absolute evaluation metrics from regression and classification perspectives. Finally, we present an overall step-by-step standard methodology that is often utilized in TCP problems. By combining these elements, this review highlights critical advancements and ongoing challenges in TCP, providing robust and detailed information for state-of-the-art ITS solutions. Full article

Avian eggs are products of consumer demand, with modern methodologies for their morphometric analysis used for improving quality, productivity and marketability. Such studies open up numerous prospects for the introduction of artificial intelligence (AI) and deep learning (DL). We first consider the state of the art of DL in the poultry industry, e.g., image recognition and applications for the detection of egg cracks, egg content and freshness. We comment on how algorithms need to be properly trained and ask what information can be gleaned from egg shape. Considering the geometry of egg profiles, we revisit the Preston–Biggins egg model, the Hügelschäffer’s model, universal egg models, principles of egg universalism and “The Main Axiom”, proposing a series of postulates to evaluate the legitimacy and practical application of various mathematical models. We stress that different models have pros and cons, and using them in combination may yield more useful results than individual use. We consider the classic egg shape index alongside other alternatives, drawing conclusions about the importance of indices in the context of applying DL going forward. Examining egg weight, volume, surface area and air cell calculations, we consider how DL might be applied, e.g., for egg storage. The value of DL in egg studies is in pre-incubation egg sorting, the optimization of storage periods and incubation regimes, and the index representation of dimensional characteristics. Each index can thus be combined to provide a synergy that is on the threshold of many scientific discoveries, technological achievements and industrial successes facilitated through AI and DL. Full article

Multifunctional nanosurfaces receive growing attention due to their versatile properties. Capillary force lithography (CFL) has emerged as a simple and economical method for fabricating these surfaces. In recent works, the authors proposed to leverage the evolution strategies (ES) to modify nanosurface characteristics with CFL to achieve specific functionalities such as frictional, optical, and bactericidal properties. For artificial intelligence (AI)-driven inverse design, earlier research integrates basic multiphysics principles such as dynamic viscosity, air diffusivity, surface tension, and electric potential with backward deep learning (DL) on the framework of ES. As a successful alternative to reinforcement learning, ES performed well for the AI-driven inverse design. However, the computational limitations of ES pose a critical technical challenge to achieving fast and efficient design. This paper addresses the challenges by proposing a parallel-computing-based ES (named parallel ES). The parallel ES demonstrated the desired speed and scalability, accelerating the AI-driven inverse design of multifunctional nanopatterned surfaces. Detailed parallel ES algorithms and cost models are presented, showing its potential as a promising tool for advancing AI-driven nanomanufacturing. Full article

Lowcost sensors are widely used for air quality monitoring due to their affordability, portability and easy maintenance. However, the performance of such sensors, such as PurpleAir Sensors (PAS), is often affected by changes in environmental (e.g., temperature and humidity) or emission conditions, and hence the resulting measurements require corrections to ensure accuracy and validity. Traditional correction methods, like those developed by the USEPA, have limitations, particularly for applications to geographically diverse settings and sensors with no collocated referenced monitoring stations available. This study introduces BaySurcls, a Bayesianoptimised surrogate model integrating deep learning (DL) algorithms to improve the PurpleAir sensor PM2.5 (PAS2.5) measurement accuracy. The framework incorporates environmental variables such as humidity and temperature alongside aerosol characteristics, to refine sensor readings. The BaySurcls model corrects the PAS2.5 data for both collocated and noncollocated monitoring scenarios. In a case study across multiple locations in New South Wales, Australia, BaySurcls demonstrated significant improvements over traditional correction methods, including the USEPA model. BaySurcls reduced root mean square error (RMSE) by an average of 20% in collocated scenarios, with reductions of up to 25% in highvariation sites. Additionally, BaySurcls achieved Nash–Sutcliffe Efficiency (NSE) scores as high as 0.88 in collocated cases, compared to scores below 0.4 for the USEPA method. In noncollocated scenarios, BaySurcls maintained NSE values between 0.60 and 0.78, outperforming standalone models. This improvement is evident across multiple locations in New South Wales, Australia, demonstrating the model’s adaptability. The findings confirm BaySurcls as a promising solution for improving the reliability of lowcost sensor data, thus facilitating its valid use in air quality research, impact assessment, and environmental management. Full article

Hydrogen is regarded as a promising energy source for the future due to its clean combustion products, remarkable efficiency and renewability. However, its characteristics of low-ignition energy, a wide flammable range from 4% to 75%, and a rapid flame speed may bring significant explosion risks. Typically, accidental release of hydrogen into confined enclosures can result in a flammable hydrogen–air mixture with concentration gradients, possibly leading to flame acceleration (FA) and deflagration-to-detonation transition (DDT). The current study focused on the evolutions of the FA and DDT of homogenous/inhomogeneous hydrogen–air mixtures, based on the open-source computational fluid dynamics (CFD) platform OpenFOAM and the modified Weller et al.’s combustion model, taking into account the Darrieus–Landau (DL) and Rayleigh–Taylor (RT) instabilities, the turbulence and the non-unity Lewis number. Numerical simulations were carried out for both homogeneous and inhomogeneous mixtures in an enclosed channel 5.4 m in length and 0.06 m in height. The predictions demonstrate good quantitative agreement with the experimental measurements in flame-tip position, speed and pressure profiles by Boeck et al. The characteristics of flame structure, wave evolution and vortex were also discussed. Full article

With the increase in climate-change-related hazardous events alongside population concentration in urban centres, it is important to provide resilient cities with tools for understanding and eventually preparing for such events. Machine learning (ML) and deep learning (DL) techniques have increasingly been employed to model susceptibility of hazardous events. This study consists of a systematic review of the ML/DL techniques applied to model the susceptibility of air pollution, urban heat islands, floods, and landslides, with the aim of providing a comprehensive source of reference both for techniques and modelling approaches. A total of 1454 articles published between 2020 and 2023 were systematically selected from the Scopus and Web of Science search engines based on search queries and selection criteria. ML/DL techniques were extracted from the selected articles and categorised using ad hoc classification. Consequently, a general approach for modelling the susceptibility of hazardous events was consolidated, covering the data preprocessing, feature selection, modelling, model interpretation, and susceptibility map validation, along with examples of related global/continental data. The most frequently employed techniques across various hazards include random forest, artificial neural networks, and support vector machines. This review also provides, per hazard, the definition, data requirements, and insights into the ML/DL techniques used, including examples of both state-of-the-art and novel modelling approaches. Full article

In this paper, a series of tetrameric surfactants (4C_nSAZs, n = 12, 14, 16) endowed with zwitterionic characteristic were synthesized by a simple and convenient method and their structures were characterized by FT-IR, ¹H NMR and elemental analysis. Their physicochemical properties were studied using the Wilhelmy plate method, fluorescence spectra and dynamic light scattering technique. 4C_nSAZs have higher surface activities and tend to adsorb at the air/water surface rather than self-assembling in aqueous solution. The thermodynamic parameters obtained from surface tension measurements show that both processes of adsorption and micellization of 4C_nSAZs are spontaneous and that the micellization processes of 4C_nSAZs are entropy-driven processes. Both adsorption and micellization of 4C_nSAZs are inclined to occur with the increase of alkyl chain length or temperature. For 4C₁₂SAZs, there are only small-size aggregates (micelles), while the large aggregates (vesicles) are observed at the alkyl length of 4C_nSAZs of 14 or 16. This shows that the alkyl chain length for oligomeric surfactants has a greater sensitivity for aggregate growth. The aggregate morphologies obtained from the calculated values of critical packing parameter (p) for 4C₁₄SAZs and 4C₁₆SAZs can be supported by the DLS measurement results. The test results obtained by the separation-water-time method show that 4C_nSAZs have good emulsification performance and that the prepared emulsions appear to exit in the form of multiple emulsions. In addition, 4C_nSAZs have good antibacterial activities against Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus). The present study reveals the unique behavior of a zwitterionic tetrameric surfactant and may give new insights into molecular design and synthesis of a high degree of surfactants with different structure characteristics for potential application in various industrial fields. Full article