Search Results (534)

Rice blast caused by Magnaporthe oryzae is a major cause of yield reductions and quality deterioration in rice. Therefore, early detection of the disease is necessary for controlling the spread of rice blast. This study proposed a dual-channel feature fusion model (DCFM) to achieve effective identification of rice blast. The DCFM model extracted spectral features using successive projection algorithm (SPA), random frog (RFrog), and competitive adaptive reweighted sampling (CARS), and extracted spatial features from spectral images using MobileNetV2 combined with the convolutional block attention module (CBAM). Then, these features were fused using the feature fusion adaptive conditioning module in DCFM and input into the fully connected layer for disease identification. The results show that the model combining spectral and spatial features was superior to the classification models based on single features for rice blast detection, with OA and Kappa higher than 90% and 88%, respectively. The DCFM model based on SPA screening obtained the best results, with an OA of 96.72% and a Kappa of 95.97%. Overall, this study enables the early and accurate identification of rice blast, providing a rapid and reliable method for rice disease monitoring and management. It also offers a valuable reference for the detection of other crop diseases. Full article

This study investigates the factors influencing school trip mode choice among senior high school students in the Chiang Rai urban area, Chiang Rai, Thailand, under normal and adverse weather conditions. Utilizing data from 472 students across six extra-large urban schools, a Multinomial Logit (MNL) regression model was applied to examine the effects of socio-demographic attributes, household vehicle ownership, travel distance, and spatial variables on mode selection. The results revealed notable modal shifts during adverse weather, with motorcycle usage decreasing and private vehicle reliance increasing, while school bus usage remained stable, highlighting its role as a resilient transport option. Car ownership emerged as a strong enabler of modal flexibility, whereas students with limited access to private transport demonstrated reduced adaptability. Additionally, increased waiting and travel times during adverse conditions underscored infrastructure and service vulnerabilities, particularly for mid-distance travelers. The findings suggest an urgent need for transport policies that promote inclusive and climate-resilient mobility systems, particularly in the context of Chiang Rai, including expanded school bus services, improved first-mile connectivity, and enhanced pedestrian infrastructure. This study contributes to the literature by addressing environmental variability in school travel behavior and offers actionable insights for sustainable transport planning in secondary cities and border regions. Full article

Microgreens, having short growth cycles and efficient nutrient uptake, are ideal candidates for biofortification. This study investigated the effects of selenium (Se) and zinc (Zn) on photosynthetic performance in four hydroponically grown Brassica microgreens (broccoli, pak choi, kohlrabi, and kale), using direct and modulated chlorophyll a fluorescence and chlorophyll-to-carotenoid ratios (Chl/Car). The plants were treated with Na₂SeO₄ at 0 (control), 2, 5, and 10 mg/L or ZnSO₄ × 7H₂O at 0 (control), 5, 10, and 20 mg/L. The results showed species-specific responses with Se or Zn uptake. Selenium enhanced photosynthetic efficiency in a dose-dependent manner for most species (8–26% on average compared to controls). It increased the plant performance index (PI_tot), particularly in pak choi (+62%), by improving both primary photochemistry and inter-photosystem energy transfer. Kale and kohlrabi exhibited high PSII-PSI connectivity for efficient energy distribution, with increased cyclic electron flow around PSI and reduced Chl/Car up to 8.5%, while broccoli was the least responsive. Zinc induced variable responses, reducing PI_tot at lower doses (19–23% average decline), with partial recovery at 20 mg/L (9% average reduction). Broccoli exhibited higher susceptibility, with inhibited Q_A re-oxidation, low electron turnover due to donor-side restrictions, and increased pigment ratio (+3.6%). Kohlrabi and pak choi tolerated moderate Zn levels by redirecting electron flow, but higher Zn levels impaired PSII and PSI function. Kale showed the highest tolerance, maintaining stable photochemical parameters and total electron flow, with increased pigment ratio (+4.5%) indicating better acclimation. These results highlight the beneficial stimulant role of Se and the dual essential/toxic nature of Zn, thus emphasizing genotype and dose-specific optimizations for effective biofortification. Full article

Amman’s rapid population growth and sprawling urbanization have resulted in car-centric, fragmented neighborhoods that lack social cohesion and are vulnerable to the impacts of climate change. This study reframes walkability as a climate adaptation strategy, demonstrating how pedestrian-oriented spatial planning can reduce vehicle emissions, mitigate urban heat island effects, and enhance the resilience of green infrastructure in peri-urban contexts. Using Deir Ghbar, a rapidly developing marginal area on Amman’s western edge, as a case study, we combine objective walkability metrics (street connectivity and residential and retail density) with GIS-based spatial regression analysis to examine relationships with residents’ sense of community. Employing a quantitative, correlational research design, we assess walkability using a composite objective walkability index, calculated from the land-use mix, street connectivity, retail density, and residential density. Our results reveal that higher residential density and improved street connectivity significantly strengthen social cohesion, whereas low-density zones reinforce spatial and socioeconomic disparities. Furthermore, the findings highlight the potential of targeted green infrastructure interventions, such as continuous street tree canopies and permeable pavements, to enhance pedestrian comfort and urban ecological functions. By visualizing spatial patterns and correlating built-environment attributes with community outcomes, this research provides actionable insights for policymakers and urban planners. These strategies contribute directly to several Sustainable Development Goals (SDGs), particularly SDG 11 (Sustainable Cities and Communities) and SDG 13 (Climate Action), by fostering more inclusive, connected, and climate-resilient neighborhoods. Deir Ghbar emerges as a model for scalable, GIS-driven spatial planning in rural and marginal peri-urban areas throughout Jordan and similar regions facing accelerated urban transitions. By correlating walkability metrics with community outcomes, this study operationalizes SDGs 11 and 13, offering a replicable framework for climate-resilient urban planning in arid regions. Full article

Reverse engineering of internal vehicle communication is a crucial discipline in vehicle benchmarking. The process presents a time-consuming procedure associated with high manual effort. Car manufacturers use unique signal addresses and encodings for their internal data. Accessing this data requires either expensive tools suitable for the respective vehicles or experienced engineers who have developed individual approaches to identify specific signals. Access to the internal data enables reading the vehicle’s status, and thus, reducing the need for additional test equipment. This results in vehicles closer to their production status and does not require manipulating the vehicle under study, which prevents affecting future test results. The main focus of this approach is to reduce the cost of such analysis and design a more efficient benchmarking process. In this work, we present a methodology that identifies signals without physically manipulating the vehicle. Our equipment is connected to the vehicle via the On-Board Diagnostics (OBD)-II port and uses the Unified Diagnostics Service (UDS) protocol to communicate with the vehicle. We access, capture, and analyze the vehicle’s signals for future analysis. This is a holistic approach, which, in addition to decoding the signals, also grants access to the vehicle’s data, which allows researchers to utilize state-of-the-art methodologies to analyze their vehicles under study by greatly reducing necessary experience, time, and cost. Full article

The integration of connected autonomous vehicles (CAVs), advanced driver assistance systems (ADAS), and conventional vehicles necessitates the development of robust methodologies to enhance traffic efficiency and ensure safety across heterogeneous traffic streams. A comprehensive understanding of vehicle interactions and operating speed variability is essential to support informed decision-making in traffic management and infrastructure design. This study presents operating speed models aimed at estimating the 85th percentile speed (V₈₅) on straight road segments, utilizing floating car data (FCD) for both calibration and validation purposes. The dataset encompasses approximately 2000 km of the Italian road network, characterized by diverse geometric features. Speed observations were analyzed under three traffic conditions: general traffic, free-flow, and free-flow with dry pavement. Results indicate that free-flow conditions improve the model’s explanatory power, while dry pavement conditions introduce greater speed variability. Initial models based exclusively on geometric parameters exhibited limited predictive accuracy. However, the inclusion of posted speed limits significantly enhanced model performance. The most influential predictors identified were the V₈₅ on the preceding curve and the length of the straight segment. These findings provide empirical evidence to inform road safety evaluations and geometric design practices, offering insights into driver behavior in mixed-traffic environments. The proposed model supports the development of data-driven strategies for the seamless integration of automated and non-automated vehicles. Full article

The incorporation of Internet of Things (IoT) applications into 5G networks marks a significant step towards realizing the full potential of connected systems. 5G networks, with their ultra-low latency, high data speeds, and huge interconnection, provide a perfect foundation for IoT ecosystems to thrive. This connectivity offers a diverse set of applications, including smart cities, self-driving cars, industrial automation, healthcare monitoring, and agricultural solutions. IoT devices can improve their reliability, real-time communication, and scalability by exploiting 5G’s advanced capabilities such as network slicing, edge computing, and enhanced mobile broadband. Furthermore, the convergence of IoT with 5G fosters interoperability, allowing for smooth communication across diverse devices and networks. This study examines the fundamental technical applications, obstacles, and future perspectives for integrating IoT applications with 5G networks, emphasizing the potential benefits while also addressing essential concerns such as security, energy efficiency, and network management. The results of this review and analysis will act as a valuable resource for researchers, industry experts, and policymakers involved in the progression of 5G technologies and their incorporation with IT solutions. Full article

The automatic identification of traffic collisions is an emerging topic in modern traffic surveillance systems. The increasing number of surveillance cameras at urban intersections connected to traffic surveillance systems has created new opportunities for leveraging computer vision techniques for automatic collision detection. This study investigates the effectiveness of transfer learning utilizing pre-trained deep learning models for collision detection through dashcam images. We evaluated several state-of-the-art (SOTA) image classification models and fine-tuned them using different hyperparameter combinations to test their performance on the car collision detection problem. Our methodology systematically investigates the influence of optimizers, loss functions, schedulers, and learning rates on model generalization. A comprehensive analysis is conducted using 7 performance metrics to assess classification performance. Experiments on a large dashcam-based images dataset show that ResNet50, optimized with AdamW, a learning rate of 0.0001, CosineAnnealingLR scheduler, and Focal Loss, emerged as the top performer, achieving an accuracy of 0.9782, F1-score of 0.9617, and IoU of 0.9262, indicating a strong ability to reduce false negatives. Full article

Decades of migration and refugee influxes have driven Amman’s rapid urban growth, yet newer neighborhoods increasingly grapple with fragmented social cohesion. This study examines whether walkable design can strengthen community bonds, focusing on Deir Ghbar, a car-centric district in West Amman. Using GIS and mixed-methods analysis, we assess how walkability metrics (residential density, street connectivity, land-use mix, and retail density) correlate with sense of community. The results reveal that street connectivity and residential density enhance social cohesion, while land-use mix exhibits no significant effect. High-density, compact neighborhoods foster neighborly interactions, but major roads disrupt these connections. A critical mismatch emerges between quantitative land-use metrics and resident experiences, highlighting the need to integrate spatial data with community insights. Amman’s zoning policies, particularly the stark contrast between affluent low-density Zones A/B and underserved high-density Zones C/D, perpetuate socio-spatial segregation—a central critique of this study. We urge the Greater Amman Municipality’s 2025 Master Plan to prioritize mixed-density zoning, pedestrian retrofits (e.g., traffic calming and sidewalk upgrades), and equitable access to amenities. This study provides a replicable GIS and survey-based framework to address urban socio-spatial divides, aligning with SDG 11 for inclusive cities. It advocates for mixed-density zoning and pedestrian-first interventions in Amman’s Master Plan. By integrating a GIS with social surveys, this study offers a replicable model for addressing socio-spatial divides in cities facing displacement and inequality. Full article

To address energy management challenges for intelligent connected automated range-extended electric vehicles under vehicle-road cooperative environments, a hierarchical energy management strategy (EMS) based on speed prediction is proposed from the perspective of multi-objective optimization (MOO), with comprehensive system performance being significantly enhanced. Focusing on connected car-following scenarios, acceleration sequence prediction is performed based on Kalman filtering and preceding vehicle acceleration. A dual-layer optimization strategy is subsequently developed: in the upper layer, optimal speed curves are planned based on road network topology and preceding vehicle trajectories, while in the lower layer, coordinated multi-power source allocation is achieved through EMS_MPC-P, a Bayesian-optimized model predictive EMS based on Pontryagin’ s minimum principle (PMP). A MOO model is ultimately formulated to enhance comprehensive system performance. Simulation and bench test results demonstrate that with SoC₀ = 0.4, 7.69% and 5.13% improvement in fuel economy is achieved by EMS_MPC-P compared to the charge depleting-charge sustaining (CD-CS) method and the charge depleting-blend (CD-Blend) method. Travel time reductions of 62.2% and 58.7% are observed versus CD-CS and CD-Blend. Battery lifespan degradation is mitigated by 16.18% and 5.89% relative to CD-CS and CD-Blend, demonstrating the method’s marked advantages in improving traffic efficiency, safety, battery life maintenance, and fuel economy. This study not only establishes a technical paradigm with theoretical depth and engineering applicability for EMS, but also quantitatively reveals intrinsic mechanisms underlying long-term prediction accuracy enhancement through data analysis, providing critical guidance for future vehicle–road–cloud collaborative system development. Full article

This study aims to investigate the oxidative pyrolysis of biomass volatiles with a particular focus on the formation of liquid products. Furfural, hydroxyacetone, and 3,4-dimethoxybenzaldehyde were chosen as volatile model compounds. The impacts of the oxygen equivalence ratio (ER, 0–15%) and temperature (400–500 °C) on the product composition and distribution were examined using a two-stage quartz-tube reactor. The results showed that volatile pyrolysis was limited at the lower temperature of 400 °C even with oxygen introduction, while it could be significantly promoted at 500 °C as illustrated by the observed great decrease in the GC-MS peak areas of the volatile compounds especially under an oxidative atmosphere. For instance, the peak area of 3,4-dimethoxybenzaldehyde at 500 °C under an ER of 4% was only ~9% of that at 400 °C. Oxygen introduction enhanced the volatile decomposition with the formation of mainly permanent gases (although not given in the study) rather than liquid products, but distinct impacts were obtained for varied volatile compounds possibly due to their different chemical structures and autoignition temperatures. From the perspective of liquid product formation, furfural would undergo the cleavage of C-C/C-O bonds to form linear intermediates and subsequent aromatization to generate aromatics (benzene and benzofuran). The presence of oxygen could enhance the oxidative destruction of the C-C/C-O bonds and the removal of O from the molecules to form simple aromatics such as benzene, phenol, and toluene. Hydroxyacetone mainly underwent C-C/C-O cleavage that was further enhanced in the presence of oxygen; the resultant intermediates would recombine to generate acetoin and 2,3-pentanedione. A higher ER would directly oxidize the alcoholic hydroxyl group (-OH) into an aldehyde group (-CHO) to form methyl glyoxal, while 3,4-dimethoxybenzaldehyde mainly underwent cleavage and recombination of bonds connected with the benzene ring including aldehyde group (-CHO), C_Ar-O, C_Methoxy-O bonds, thus forming 1,2-dimethoxybenzene, toluene, and 3-hydroxybenzadehyde. This study provides more fundamental insights into the homogeneous oxidation of volatiles during the oxidative fast pyrolysis of biomass, facilitating the deployment of this technology. Full article

In the automotive industry, battery electric vehicles (BEVs) represent the future of individual mobility. To establish a long-term market presence, innovative vehicle and powertrain concepts are essential, and therefore, identifying the most promising concepts is crucial to determine where to focus research and development further. Academia plays a significant role in this identification process; however, researchers often face restricted access to data from the industry, and identifying different technological approaches is often connected to significant costs. We present a comprehensive study of the Porsche Taycan Performance Battery Plus, which integrates two technological advancements: the first series-production implementation of a two-speed transmission in an electric vehicle allowing for high acceleration while reaching high top speeds and a 800 V battery system architecture providing more efficient charging capabilities. This study details vehicle dynamics, electric powertrain efficiencies, their impact on vehicle level, and the two technological advancements. This work aims to provide researchers access to vehicle dynamometer and real-world data from one of the most advanced and innovative battery electric sports cars. This allows for further analysis of cutting-edge technologies that have yet to reach the mass market. In addition to providing researchers with this study’s results, all data utilized in this study will be made available as open-access, enabling individual use of test data for parameter identification and the development of simulation models. Full article

The automotive industry is fully shifting towards autonomous connected vehicles. By advancing vehicles’ intelligence and connectivity, the industry has enabled innovative functions such as advanced driver assistance systems (ADAS) in the direction of driverless cars. Such functions are often referred to as cyber-physical features, since almost all of them require collecting data from the physical environment to make automotive operation decisions and properly actuate in the physical world. However, increased functionalities result in increased complexity, which causes serious security vulnerabilities that are typically a result of mushrooming functionality and hence complexity. In a world where we keep seeing traditional mechanical systems shifting to x-by-wire solutions, the number of connected sensors, processing systems, and communication buses inside the car exponentially increases, raising several safety and security concerns. Because there is no safety without security, car manufacturers start struggling in making lightweight sensor and processing systems while keeping the security aspects a major priority. This article surveys the current technological challenges in securing autonomous vehicles and contributes a cross-layer analysis bridging hardware security primitives, real-world side-channel threats, and redundancy-based fault tolerance in automotive electronic control units (ECUs). It combines architectural insights with an evaluation of commercial support for TrustZone, trusted platform modules (TPMs), and lockstep platforms, offering both academic and industry audiences a grounded perspective on gaps in current hardware capabilities. Finally, it outlines future directions and presents a forward-looking vision for securing sensors and processing systems in the path toward fully safe and connected autonomous vehicles. Full article

In the past decades, both low-cost gas sensors for air quality monitoring and smartphone devices have experienced a remarkable spread in the worldwide market. Smartphone devices have become a unique tool in everyday life, whilst the use of low-cost gas sensors in air quality monitors has allowed for a better understanding of the personal exposure to air pollutants. The traditional technologies for measuring air pollutant concentrations, even though they provide accurate data, cannot assure the necessary spatio-temporal resolution for assessing personal exposure to the various air pollutants. In this respect, one of the most promising solutions appears to be the use of smartphones together with the low-cost miniaturized gas sensors, because it allows for the monitoring of the air quality characterizing the different environments frequented in everyday life by leveraging the capability to perform mobile measurements. In this research, a handheld air quality monitor based on low-cost gas sensors capable of connecting to smartphone devices via Bluetooth link has been designed and implemented to explore the different ways of its use for assessing the personal exposure to air pollutants. For this purpose, two experiments were carried out: the first one was indoor monitoring of CO and NO₂ concentrations performed in an apartment occupied by four individuals and the second one was mobile monitoring of CO and NO₂ performed in a car cabin. During the indoor measurements, the maximum value for the CO concentrations was equal to 12.3 ppm, whilst the maximum value for NO₂ concentrations was equal to 64 ppb. As concerns the mobile measurements, the maximum concentration of CO was equal to 8.3 ppm, whilst the maximum concentration of NO₂ was equal to 38 ppb. This preliminary study has shown that this system can be potentially used in all those situations where the use of traditional chemical analyzers for measuring gas concentrations in everyday life environments is hardly feasible, but also has highlighted some limits concerning the performance of such systems. Full article

Generation Z, known for its digital fluency and distinct consumer behaviors, is an increasingly influential demographic in the tourism industry. As a sustainability-focused generation, their preferences and behaviors are shaping the future of travel. This study explores the tourism experiences of Romanian Generation Z members, focusing on their travel patterns, motivations, information sources, and service preferences. A bibliometric analysis of the existing literature was conducted to identify research trends and gaps in understanding Generation Z’s tourism behaviors. Using a mixed-method approach, the study integrates survey data from 399 respondents with AI-generated insights from ChatGPT 4o mini to compare traditional research methods with AI-driven analysis. It examines how AI interprets and predicts travel behaviors, highlighting the reliability and biases inherent in AI models. Key discrepancies between the two methods were found: The survey indicated a preference for car travel and commercial accommodation, while AI predictions favored air travel and private accommodation. Additionally, AI emphasized a growing interest in eco-friendly transportation and connections to natural and cultural environments, offering a broader scope than the survey alone. Both methods revealed a trend toward digital platforms for travel planning, moving away from traditional agencies. The findings suggest that AI can complement traditional research by providing actionable insights, though its limitations emphasize the need for a balanced integration of both methods. This study offers new perspectives on Generation Z’s tourism experiences. Full article