Search Results (1,923)

In the increasingly competitive mobile ecosystem, understanding user behavior is essential to improve targeted sales and the effectiveness of advertising. With the widespread adoption of smartphones and the increasing variety of mobile applications, predicting user behavior has become more complex. This study presents a comprehensive framework for predicting mobile payment behavior by integrating demographic, situational, and behavioral factors, focusing on patterns in mobile application usage. To address the complexity of the data, we use a combination of machine-learning models, including extreme gradient boosting, light gradient boosting machine, and CatBoost, along with Shapley additive explanations (SHAP) to improve interpretability. An analysis of extensive panel data from Korean Android users reveals that incorporating application usage behavior in such models considerably improves the accuracy of mobile payment predictions. The study identifies key predictors of payment behavior, indicated by high Shapley values, such as using social networking services (e.g., KakaoTalk and Instagram), media applications (e.g., YouTube), and financial and membership applications (e.g., Toss and OK Cashbag). Moreover, the results of the SHAP force analysis reveal the individual session-level drivers of mobile purchases. These findings advance the literature on mobile payment prediction and offer practical insights for improving targeted marketing strategies by identifying key behavioral drivers of mobile transactions. Full article

This study investigates how different types of microplastics (MPs)—fibers, glitter, plastic bags, and plastic bottles—influence heavy metal uptake in lettuce (Lactuca sativa L.), a commonly consumed leafy vegetable. A controlled eight-week pot experiment was conducted in a greenhouse using contaminated loamy sand soil (polluted with Cd, Pb, Cu, and other metals) collected from a smelter-impacted area. Microplastics were added at a concentration of 70–80 mg/kg, and lettuce seedlings were grown under phytotron conditions (22 ± 2 °C, 60 ± 5% RH, 16 h light/8 h dark) without fertilizers or external contaminants. Plant roots and shoots were harvested, and heavy metals were analyzed via MP-AES and ICP-MS. The results showed that MPs altered heavy metal mobility, bioavailability, and plant uptake. Copper accumulation in leaves decreased substantially across MP treatments, from 80.84 mg/kg in the control to 26.35 mg/kg (glitter), whereas lead and cadmium concentrations increased significantly in roots under fiber and glitter exposure (Pb increased from 12.13 mg/kg to 33.57 mg/kg and Cd from 1.70 mg/kg to 2.05 mg/kg in fiber treatment). Cobalt accumulation in leaves increased under the plastic bag treatment, indicating MP-specific metal interactions. Root growth was also affected, with fibers promoting elongation and plastic bottles restricting it. Sequential extraction revealed that MPs modified metal partitioning in soil, with Pb and Ni more strongly retained in stable fractions under some treatments. Observed trends in soil pH and organic matter content were associated with changes in metal mobility, highlighting the potential role of soil properties in mediating microplastic–metal interactions. These findings highlight the role of MPs as mediators of heavy metal transport in crops and underscore the need for clear regulatory guidelines that limit microplastic contamination in agricultural soils and promote routine monitoring to safeguard food safety and crop health. Full article

Currently, the development of oil reservoirs with high water cut faces numerous challenges, including poor economic efficiency, difficulties in residual oil recovery, and a lack of effective development technologies. In light of these issues, this paper conducts research on gas drive development during the high water cut stage in middle–high permeability reservoirs and introduces an innovative technical approach for air thermal miscible flooding. In this study, the Enhanced Oil Recovery (EOR) mechanism and the dynamic characteristics of thermal miscible flooding were investigated through laboratory experiments and numerical simulations. The N₂ and CO₂ flooding experiments indicate that gas channeling is likely to occur when miscible flooding cannot be achieved, due to the smaller gas–water mobility ratio compared to the gas–oil mobility ratio during the high water cut stage. Consequently, the enhanced recovery efficiency of N₂ and CO₂ flooding is limited. The experiment on air thermal miscible flooding demonstrates that under conditions of high water content, this method can form a stable high-temperature thermal oxidation front. The high temperature, generated by the thermal oxidation front, promotes the miscibility of flue gas and crude oil, effectively inhibiting gas flow, preventing gas channeling, and significantly enhancing oil recovery. Numerical simulations indicate that the production stage of air hot miscible flooding in reservoirs with middle–high permeability and high water cut can be divided into three phases: pressurization and drainage response, high efficiency and stable production with a low air–oil ratio, and low efficiency production with a high air–oil ratio. These phases can enable efficient development during the high water cut stage in medium to high permeability reservoirs, with the theoretical EOR range expected to exceed 30%. Full article

This article discusses the tasks involved in the operational assessment of the volume of produced goods, such as tomatoes. The large-scale implementation of computer vision systems in greenhouses requires approaches that reduce costs, time and complexity, particularly in creating training data and preparing neural network models. Publicly available models like YOLO often lack the accuracy needed for specific tasks. This study proposes a method for the sequential training of detection models, incorporating Domain-Task Adaptation and Client-Specific Fine-Tuning. The model is initially trained on a large, specialized dataset for tasks like tomato detection, followed by fine-tuning with a small custom dataset reflecting real greenhouse conditions. This results in the light YOLO11n model achieving high validation accuracy (mAP50 > 0.83, Precision > 0.75, Recall > 0.73) while reducing computational resource requirements. Additionally, a custom training dataset was developed that captures the unique challenges of greenhouse environments, such as dense vegetation and occlusions. An algorithm for counting tomatoes was also created, which processes video frames to accurately count only the visible tomatoes in the front row of plants. This algorithm can be utilized in mobile video surveillance systems, enhancing monitoring efficiency in greenhouses. Full article

Visible light communication (VLC) has become one important candidate technology for beyond 5G and even 6G wireless networks, mainly thanks to its abundant unregulated light spectrum resource and the ubiquitous deployment of light-emitting diodes (LED)-based illumination infrastructures. Due to the high directivity of VLC channel propagation, relay-based cooperative techniques have been introduced and explored to enhance the transmission performance of VLC links. Nevertheless, almost all current works are limited to scenarios adopting well-known Lambertian transmitter and relay, which fail to characterize the scenarios with distinctive non-Lambertian transmitter or relay. For filling this gap, in this article, relay-assisted VLC employing diverse non-Lambertian optical beam configurations is proposed. Unlike the conventional Lambertian transmitter and relay-based research paradigm, the presented scheme employs the commercially available non-Lambertian transmitter and relay to configure the cooperative VLC links. Numerical results illustrate that up to 40.63 dB SNR could be provided by the proposed non-Lambertian relay-assisted VLC scheme, compared with about a 34.22 dB signal-to-noise ratio (SNR) of the benchmark Lambertian configuration. Full article

Spacecraft separation safety is the key characteristic of flight safety. Obtaining the velocity and distance curves of spacecraft and booster at the separation time is at the core of separation safety analysis. In order to solve the separation velocity measurement problem, this paper introduces the YOLOv8_n target detection algorithm and the circle fitting algorithm based on random sample consistency (RANSAC) to measure the separation velocity of space targets according to a space-based video obtained by a monocular camera installed on the spacecraft arrow-shaped body. Firstly, MobileNetV3 network is used to replace the backbone network of YOLOv8_n. Then, the circle fitting algorithm based on RANSAC is improved to improve the anti-interference performance and the adaptability to various light environments. Finally, by analyzing the imaging principle of the monocular camera and the results of circle feature detection, distance information is obtained, and then the measurement results of velocity are obtained. The experimental results based on a space-based video show that the YOLOv8_n target detection algorithm can detect the booster target quickly and accurately, and the improved circle fitting algorithm based on RANSAC can measure the separation speed in real time while maintaining the detection speed. The ground simulation results show that the error of this method is about 1.2%. Full article

The research aims to systematize the current scientific evidence on methodologies used to investigate the impact of the indoor built environment on well-being, focusing on indoor environmental quality (IEQ) variables such as thermal comfort, air quality, noise, and lighting. This systematic review adheres to the Joanna Briggs Institute framework and PRISMA guidelines to assess empirical studies that incorporate physiological measurements like heart rate, skin temperature, and brain activity, which are captured through various techniques in real-life contexts. The principal results reveal a significant interest in the relationship between the built environment and physiological as well as psychological states. For instance, thermal comfort was found to be the most commonly studied IEQ variable, affecting heart activity and skin temperature. The research also identifies the need for a shift towards using advanced technologies like Mobile Brain/Body Imaging (MoBI) for capturing real-time physiological data in natural settings. Major conclusions include the need for a multi-level, evidence-based approach that considers the dynamic interaction between the brain, body, and environment. This study advocates for the incorporation of multiple physiological signals to gain a comprehensive understanding of well-being in relation to the built environment. It also highlights gaps in current research, such as the absence of noise as a studied variable of IEQ and the need for standardized well-being assessment tools. By synthesizing these insights, the research aims to pave the way for future studies that can inform better design and policy decisions for indoor environments. Full article

Background/Objectives: Pseudomonas aeruginosa is one of the most significant multidrug-resistant bacteria. It poses considerable challenges in terms of treatment and causes hospital-acquired infections that lead to high morbidity and mortality. Colonization by P. aeruginosa in a patient without clinical signs of infection is a concern in hospital settings, as it is an opportunistic pathogen and can potentially be a multidrug-resistant strain. The objective of this study was to characterize and provide a detailed genomic analysis of this strain of the P. aeruginosa PSU9449 genome, an isolate obtained from a patient at Songklanagarind Hospital, Thailand. Methods: Whole-genome sequencing (WGS) and bioinformatics analysis were employed to examine the genomic features of P. aeruginosa PSU9449. We performed sequence type (ST) determination through multilocus sequence typing (MLST), identified antimicrobial resistance genes (ARGs), virulence factor genes (VFGs), and analyzed the presence of mobile genetic elements (MGEs). Additionally, we compared the PSU9449 genome with strains from neighboring countries to understand its phylogenetic relationship. Results: The P. aeruginosa PSU9449 genome contained five insertion sequences and several ARGs, including fosA, aph (3’)-IIb, bla_OXA-50, and catB7. It also harbored VFGs related to flagella (fli, fle, and flg), the type 6 secretion system (hcpA, tssA, and las), and the type 3 secretion system (exoS, exoU, and exoT). MLST identified PSU9449 as ST3777, which was reported in Thailand for the first time. Phylogenetic analysis based on core gene SNPs revealed that PSU9449 was closely related to P. aeruginosa HW001G from Malaysia and P. aeruginosa MyJU45 from Myanmar, forming a distinct clade. Conclusions: This study presents a comprehensive genomic analysis of P. aeruginosa PSU9449, shedding light on its genetic characteristics, antimicrobial resistance profile, and virulence potential. Interestingly, ST3777, the novel STs from the published genomes of P. aeruginosa in Thailand, were assigned in this study. The findings enhance valuable insights into the expanding knowledge of P. aeruginosa PSU9449 and highlight the importance of ongoing surveillance of its genetic diversity. Full article

We propose a novel ultraviolet (UV) phototransistor (PT) architecture based on an AlGaN/GaN high electron mobility transistor (HEMT) with a buried p-GaN layer. In the dark, the polarization-induced two-dimensional electron gas (2DEG) at the AlGaN/GaN heterojunction interface is depleted by the buried p-GaN and the conduction channel is closed. Under UV illumination, the depletion region shrinks to just beneath the AlGaN/GaN interface and the 2DEG recovers. The retraction distance of the depletion region during device turn-on operation is comparable to the thickness of the AlGaN barrier layer, which is an order of magnitude smaller than that in the conventional p-GaN/AlGaN/GaN PT, whose retraction distance spans the entire GaN channel layer. Consequently, the proposed device demonstrates significantly enhanced weak-light detection capability and improved switching speed. Silvaco Atlas simulations reveal that under a weak UV intensity of 100 nW/cm², the proposed device achieves a photocurrent density of 1.68 × 10⁻³ mA/mm, responsivity of 8.41 × 10⁵ A/W, photo-to-dark-current ratio of 2.0 × 10⁸, UV-to-visible rejection ratio exceeding 10⁸, detectivity above 1 × 10¹⁹ cm·Hz^1/2/W, and response time of 0.41/0.41 ns. The electron concentration distributions, conduction band variations, and 2DEG recovery behaviors in both the conventional and novel structures under dark and weak UV illumination are investigated in depth via simulations. 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 detection of fish freshness is crucial for ensuring food safety. This study addresses the limitations of traditional detection methods, which rely on laboratory equipment and complex procedures, by proposing a smartphone-based detection method, termed FreshFusionNet, that utilizes a pitaya peel pH intelligent indicator film in conjunction with multimodal deep learning. The pitaya peel indicator film, prepared using high-pressure homogenization technology, demonstrates a significant color change from dark red to yellow in response to the volatile alkaline substances released during fish spoilage. To construct a multimodal dataset, 3600 images of the indicator film were captured using a smartphone under various conditions (natural light and indoor light) and from multiple angles (0° to 120°), while simultaneously recording pH values, total volatile basic nitrogen (TVB-N), and total viable count (TVC) data. Based on the lightweight MobileNetV2 network, a Multi-scale Dilated Fusion Attention module (MDFA) was designed to enhance the robustness of color feature extraction. A Temporal Convolutional Network (TCN) was then used to model dynamic patterns in chemical indicators across spoilage stages, combined with a Context-Aware Gated Fusion (CAG-Fusion) mechanism to adaptively integrate image and chemical temporal features. Experimental results indicate that the overall classification accuracy of FreshFusionNet reaches 99.61%, with a single inference time of only 142 ± 40 milliseconds (tested on Xiaomi 14). This method eliminates the need for professional equipment and enables real-time, non-destructive detection of fish spoilage through smartphones, providing consumers and the food supply chain with a low-cost, portable quality-monitoring tool, thereby promoting the intelligent and universal development of food safety detection technology. Full article

In the context of aging populations, it has become necessary to develop new methods and devices for the daily home-based self-rehabilitation of elderly people. To this end, this paper proposes and evaluates the use of an easy-to-use single battery-powered device including a 3D accelerometer and a 3D gyroscope, where light algorithms, such as the complementary filter and the Kalman filter, are implemented to estimate the elbow joint angle. During experiments, a robotic arm and a human arm were used to obtain an error interval for each tested algorithm; the robotic arm allows for reproducible movements and reproducible results, which allows us to independently verify the impact of parameters such as the sensor’s movement speed on the algorithm precision. The experimental results show that the algorithm that uses only accelerometer data is one of the most relevant since it allows us to obtain a Root Mean Square Error between 1.83° and 5.52° at a sensor data rate of 100 Hz, which is similar to the results obtained using the data fusion algorithms tested. Nevertheless, it has a lower power consumption since it requires only 58 cycles when using an ARM Cortex-M4 processor (which is lower than that of the other data fusion algorithms tested by a factor of at least two), and it does not necessitate the additional sensor required by the other data fusion algorithms tested (such as a gyroscope or a magnetometer). The algorithm using only accelerometer data also seems to be the algorithm with the lowest power consumption and should be preferred. Moreover, its power consumption can be reduced by more than the increase in the error when reducing the rate of the data output by the sensor. In this work, a reduction in the data rate from 100 Hz to 10 Hz increased the RMSE by a factor of 1.8 but could reduce the power consumption associated with the sensor and the algorithm’s computation by a factor of 10. Finally, the experimental results show that the higher the speed of the sensor’s motion, the higher the error obtained using only accelerometer data. Nevertheless, the algorithm that uses only accelerometer data remains well suited to rehabilitation exercises or mobility evaluations since the speed of the sensor’s movement is also moderate. Full article

Localizing underwater nodes when they cannot be tethered or float on the surface presents significant challenges, primarily due to node mobility and the absence of fixed anchors with known coordinates. This paper advocates a strategy for tackling such a challenge by using visible light communication (VLC) from an airborne unit. A novel localization method is proposed where VLC transmissions are made towards the water surface; each transmission is encoded with the Global Positioning System (GPS) coordinates with the incident point of the corresponding light beam. Existing techniques deal with the problem in 2D by assuming that the underwater node has a pressure sensor to measure its depth. The proposed method avoids this limitation and utilizes the intensity of VLC signals to estimate the 3D position of the underwater node. The idea is to map the light intensity at the underwater receiver for airborne light beams and devise an error optimization formulation to estimate the 3D coordinates of the underwater node. Extensive simulations validate the effectiveness of the proposed method and capture its performance across various parameters. Full article

Cardiovascular disease remains a leading cause of morbidity and mortality worldwide, frequently arising from platelet hyperactivation and subsequent thrombus formation. Although conventional antiplatelet therapies are available, challenges, such as drug resistance and bleeding complications, require the development of novel agents. In this study, dihydrogeodin (DHG) was isolated from Fennellia flavipes and evaluated using platelets derived from Sprague–Dawley rats. Platelet aggregation induced by collagen, adenosine diphosphate, or thrombin was assessed by light transmission aggregometry; DHG significantly reduced aggregation in a dose-dependent manner. Further assays demonstrated that DHG suppressed intracellular calcium mobilization, adenosine triphosphate release, and integrin αIIbβ₃-dependent fibrinogen binding, thereby impairing clot retraction. Western blot analysis revealed that DHG reduced the phosphorylation of mitogen-activated protein kinases (ERK, JNK, p38) and PI3K/Akt, indicating inhibition across multiple platelet-signaling pathways. Additionally, SwissADME-assisted pharmacokinetics predicted favorable properties without violations of the Lipinski (Pfizer) filter, Muegge (Bayer) filter, Ghose filter, Veber filter, and Egan filter, and network pharmacology revealed inhibition of calcium and MAPK pathways. These results highlight the potential of DHG as a novel antiplatelet agent with broad-spectrum activity and promising drug-like characteristics. Further studies are warranted to assess its therapeutic window, safety profile, and potential for synergistic use with existing antiplatelet drugs. Full article

Today’s cities are facing the challenges of increasing traffic congestion, emissions, and the need to improve road safety. The solution to these problems is the use of artificial intelligence (AI) and the Internet of Things (IoT) in intelligent traffic management. The purpose of the article is to analyze and evaluate AI- and IoT-based solutions implemented in Polish cities and to identify innovative proposals that can improve traffic management. The study uses a mixed-method approach, including the analysis of crowdsourced mobility data (from GPS, smartphones, and municipal reports), GIS tools for mapping congestion, big data analytics, and machine learning algorithms, to evaluate trends and predict traffic scenarios. The evaluation focused on seven major Polish cities—Warsaw, Krakow, Wroclaw, Gdansk, Poznan, Katowice, and Lodz—where intelligent transportation systems such as dynamic traffic lights, intelligent pedestrian crossings, accident prediction systems, and parking space management have been implemented. The effectiveness of these solutions was assessed using the following six key indicators: waiting time at intersections, travel time, congestion level, CO₂ emissions, energy consumption, and number of traffic incidents. The article provides a comprehensive analysis of these solutions’ impacts on traffic flow, emissions, energy efficiency, and road safety. A key contribution of the paper is the presentation of new proposals for improvements, such as the inclusion of behavioral data in traffic modeling, integration with GPS navigation, and dynamic emergency and public transport priority management. The article also discusses further digitization and interoperability needs. The findings show that the implementation of intelligent transportation systems not only improves urban mobility and safety but also enhances environmental sustainability and residents’ quality of life. Full article