Search Results (912)

The soil phytomonitoring and phytostabilization potential of Syringa vulgaris and Ficus carica was evaluated regarding 16 priority polycyclic aromatic hydrocarbons (PAHs) using a chemometric approach and the calculation of bioconcentration factors (BCFs) for each individual PAH in plants’ roots from each selected location in the Bor region. PAHs in roots and the corresponding soils were analyzed using the QuEChERS (Quick, Effective, Cheap, Easy, Rugged, Safe) method with some new modifications, gas chromatography/mass spectrometry, Pearson’s correlation study, hierarchical cluster analysis, and BCFs. Several central conclusions are as follows: Each plant species developed its own specific capability for PAH management, and root concentrations ranged from not detected (for several compounds) to 5592 μg/kg (for fluorene in S. vulgaris). In some cases, especially regarding benzo(a)pyrene and chrysene, both plants had a similar tactic—the total avoidance of assimilation (probably due to their high toxicity). Both plants retained significant quantities of different PAHs in their roots (many calculated BCFs were higher than 1 or were even extremely high), which recommends them for PAH phytostabilization (especially fluorene, benzo(b)fluoranthene, and benzo(k)fluoranthene). In soil monitoring, neither of the plants are helpful because their roots do not reflect the actual situation found in soil. Finally, the analysis of the corresponding soils provided useful monitoring information. Full article

Strawberry planting requires different amounts of soil water-holding capacity and fertilizer at different growth stages. Determining the stages of strawberry growth has important guiding significance for irrigation, fertilization, and picking. Quick and accurate identification of strawberry plants at different stages can provide important information for automated strawberry planting management. We propose an improved multistage identification model for strawberry based on the YOLOv9 algorithm—the ASHM-YOLOv9 model. The original YOLOv9 showed limitations in detecting strawberries at different growth stages, particularly lower precision in identifying occluded fruits and immature stages. We enhanced the YOLOv9 model by introducing the Alterable Kernel Convolution (AKConv) to improve the recognition efficiency while ensuring precision. The squeeze-and-excitation (SE) network was added to increase the network’s capacity for characteristic derivation and its ability to fuse features. Haar wavelet downsampling (HWD) was applied to optimize the Adaptive Downsampling module (Adown) of the initial model, thereby increasing the precision of object detection. Finally, the CIoU function was replaced by the Minimum Point Distance based IoU (MPDIoU) loss function to effectively solve the problem of low precision in identifying bounding boxes. The experimental results demonstrate that, under identical conditions, the improved model achieves a precision of 97.7%, a recall of 97.2%, mAP50 of 99.1%, and mAP50-95 of 90.7%, which are 0.6%, 3.0%, 0.7%, and 7.4% greater than those of the original model, respectively. The parameters, model size, and floating-point calculations were reduced by 3.7%, 5.6% and 3.8%, respectively, which significantly boosted the performance of the original model and outperformed that of the other models. Experiments revealed that the model could provide technical support for the multistage identification of strawberry planting. Full article

The cognitive cycle has been studied via cognitive architectures and by analyzing cognitive experiments. An emerging theoretical approach suggests that several automatic cognitive processes retrieve information, making it available to an internal agent, which in turn decides which information to access. Derived from this view, four phases of the cognitive cycle can be formulated and reproduced within a cognitive monitoring system. This exploratory work presents a new theory, Attention as Internal Action, and proposes a hypothesis about the relationship between an iteration of the cognitive cycle and a conscious motor action. The design of a continuous reaction time task is presented as a tool for quick cognitive evaluation. Via continuously provided user responses, the computational system behind the task adapts triggering stimuli based on the suggested hypothesis. Its software implementation was employed to assess whether a previously conducted simulation of the cognitive cycle’s time range aligned with empirical data. A control group was assigned to perform a separate simple reaction time task in a sequence of five days. The analysis showed that the experimental cognitive monitoring system produced results more closely aligned with the established understanding of the timing of the cognitive cycle than the control task did. Full article

The Tomographic Synthetic Aperture Radar (TomoSAR) technique is widely used for monitoring urban infrastructures, as it enables the mapping of individual scatterers across additional dimensions such as height (3D), thermal dilation (4D), and deformation velocity (5D). Retrieving this information is crucial for building management and maintenance. Nevertheless, accurately extracting it from TomoSAR data poses several challenges, particularly the presence of outliers due to uneven and limited baseline distributions. One way to address these issues is through statistical detection approaches such as the Generalized Likelihood Ratio Test, which ensures a Constant False Alarm Rate. While effective, these methods face two primary limitations: high computational complexity and the off-grid problem caused by the discretization of the search space. To overcome these drawbacks, we propose an approach that combines a quick initialization process using Fast-Sup GLRT with local descent optimization. This method operates directly in the continuous domain, bypassing the limitations of grid-based search while significantly reducing computational costs. Experiments conducted on both simulated and real datasets acquired with the TerraSAR-X satellite over the Spanish city of Barcelona demonstrate the ability of the proposed approach to maintain computational efficiency while improving scatterer localization accuracy in the third and fourth dimensions. Full article

Barcode technology is widely used as an automated identification system that enables rapid and efficient data capture, particularly in retail environments. Despite its practicality, barcode-based systems are increasingly vulnerable to security threats—most notably, barcode substitution fraud. To address these challenges, this paper presents an innovative system for the secure generation and verification of Quick Response (QR) codes using a digital watermarking technique. The proposed method embeds tamper-resistant information within QR codes, enhancing their integrity and making unauthorized modification more difficult. Additionally, a neural network-based authentication model was developed to verify the legitimacy of scanned QR codes. The system was evaluated through experimental testing on a dataset of 5000 QR samples. The results demonstrated high accuracy in distinguishing between genuine and fraudulent QR codes, confirming the system’s effectiveness in supporting fraud prevention in real-world applications. Full article

Construction 4.0 is propelling the construction sector towards a digital, automated, and sustainable framework. This paper reviews advancements in automation and digitalization within the steel construction industry, framed by the principles of Construction 4.0. An analysis of the existing literature indicates that previous review studies have explored the technologies and concepts associated with Construction 4.0. However, none have consolidated these technologies and concepts (T&C) specifically within the context of the steel construction industry to evaluate their impact on steel manufacturing and assembly processes which was the main criterion for article selection. Therefore, this paper aims to consolidate various Construction 4.0 technologies and concepts to explore their integration into the steel construction industry. Based on data from the Web of Science and Scopus, a thorough screening process identified 56 out of 161 articles for analysis regarding their applicability to the steel construction industry. The evolution of various technologies in the steel construction industry has been examined over the years, starting with the initial references to each technology. In addition to discussing the advancements of these technologies and their influence on contemporary digitalization and automation within the steel sector, the authors seek to identify which T&C are most commonly utilized in manufacturing and assembly processes. The graphical results of this review indicate that each type of T&C can serve as a tool for quality control throughout the manufacturing and assembly processes. However, it is noteworthy that most research remains concentrated on enhancing material tracking and identification during these stages of production. Full article

(1) Background: Side-channel attacks (SCAs) exploit unintended information leakage to compromise cryptographic security. In cyber-physical systems (CPSs), embedded systems are inherently constrained by limited resources, restricting the implementation of complex countermeasures. Traditional countermeasures, such as hiding techniques, attempt to obscure power consumption patterns; however, their effectiveness has been increasingly challenged. This study evaluates the vulnerability of dummy power traces against deep learning-based SCAs (DL-SCAs). (2) Methods: A power trace dataset was generated using a simulation environment based on Quick Emulator (QEMU) and GNU Debugger (GDB), integrating dummy traces to obfuscate execution signatures. DL models, including a Recurrent Neural Network (RNN), a Bidirectional RNN (Bi-RNN), and a Multi-Layer Perceptron (MLP), were used to evaluate classification performance. (3) Results: The models trained with dummy traces achieved high classification accuracy, with the MLP model reaching 97.81% accuracy and an F1-score of 97.77%. Despite the added complexity, DL models effectively distinguished real and dummy traces, highlighting limitations in existing hiding techniques. (4) Conclusions: These findings highlight the need for adaptive countermeasures against DL-SCAs. Future research should explore dynamic obfuscation techniques, adversarial training, and comprehensive evaluations of broader cryptographic algorithms. This study underscores the urgency of evolving security paradigms to defend against artificial intelligence-powered attacks. Full article

The COVID-19 pandemic has caused a profound interruption in the way people travel and has had a very negative impact on tourism and economics throughout the world, especially on the coastal fishing communities in South Korea. These previously problematic areas, having suffered a decrease in the local population as well as stood in the midst of the economic downturn, experienced a great cut in the number of tourists coming from far away, which additionally caused their collapse of resilience and sustainability. This research investigates the recovery trends of 45 seashore-fishing districts in South Korea and how the change in travel distance and the number of visitors before and after the pandemic have affected these trends. Through the utilization of big data from the Korea Tourism Data Lab (2019–2023) and Geographic Information System (GIS) analysis, we observe the changes in visitor flows, use the indices of resilience as an indicator to measure them, and investigate how proximity affects travel recovery. The survey results indicate that the regions neighboring metropolitan zones were not only the ones that suffered the most from travel distance during the pandemic but also experienced quick recovery after the pandemic. The new promotional campaigns, in tandem with an improved network of transportation, contributed to the swift recovery of these areas. The remote areas, on the other hand, persist in fighting the problems of regionalized tourism and have only limited accessibility. The proposition of “distance-dependent resilience” theory as well as the Blue Urbanism framework is offered in order to bring up the ideas of sustainable tourism and population stabilization. The study is expected to serve as a cornerstone for the practice of adaptive governance and strategic planning in the matter of the coastal areas after the pandemic. Full article

Global crop yield is stagnant due to quick release of N from fertilizers and its lower availability at critical growth stages of crops. Urea impregnation of aluminosilicate minerals, especially zeolites, holds NH₄⁺ and delays its release for later crop stages. limited information is available in the literature about the effect of zeolite-based urea fertilizer on soil nitrogen dynamics. Zeolitic urea formulation was prepared and tested in a field experiment under a wheat–rice cropping system. Sources of N were urea and zeolitic urea formulation. N was reduced up to 50% in the treatments from zeolitic urea. Soil parameters (NH₄-N, NO₃-N, available N and total N) and plant parameters were recorded by following the standard procedures. Zeolitic urea retained the highest contents of NH₄-N and NO₃-N at critical growth stages (booting and maturity) of wheat crop. Zeolitic urea retained the highest NO₃ in 0–30 cm soil depth, while these were highest at 60–90 cm depth with urea. Grain yield of wheat crops with N_100%U were similar to that of with N_75%ZU, where 25% less N was applied and nitrogen use efficiency was improved to 25.82% by zeolitic urea. Zeolite-based N fertilizer retains N in soil for an extended period of time and maintains crop yield even with less applied N as compared with urea. Full article

Background/Objectives: In recent years, interest has grown in the clinical applications of artificial intelligence (AI)-based chatbots powered by large language models (LLMs) in oral and maxillofacial surgery (OMFS). However, there are conflicting opinions regarding the accuracy and reliability of the information they provide, raising questions about their potential role as support tools for both clinicians and patients. This systematic review aims to analyze the current literature on the use of conversational agents powered by LLMs in the field of OMFS. Methods: The review was conducted following PRISMA guidelines and the Cochrane Handbook for Systematic Reviews of Interventions. Original studies published between 2023 and 2024 in peer-reviewed English-language journals were included. Sources were identified through major electronic databases, including PubMed, Scopus, Google Scholar, and Web of Science. The risk of bias in the included studies was assessed using the ROBINS-I tool, which evaluates potential bias in study design and conduct. Results: A total of 49 articles were identified, of which 4 met the inclusion criteria. One study showed that ChatGPT provided the most accurate responses compared to Microsoft Copilot (ex-Bing) and Google Gemini (ex-Bard) for questions related to OMFS. Other studies highlighted that ChatGPT-4 can assist surgeons with quick and relevant information, though responses may vary depending on the quality of the questions. Conclusions: Chatbots powered by LLMs can enhance efficiency and decision-making in OMFS routine clinical cases. However, based on the limited number of studies included in this review (four), their performance remains constrained in complex clinical scenarios and in managing emotionally sensitive patient interactions. Further research on clinical validation, prompt formulation, and ethical oversight is essential to safely integrating LLM technologies into OMFS practices. Full article

Monitoring infectious diseases is essential for safeguarding equine health and ensuring the sustainability of the horse industry. In 2019, the Royal Veterinary Association of the Netherlands (KNMvD) and Royal GD (GD Animal Health) launched SEIN (Surveillance of Equine Infectious diseases in the Netherlands), a voluntary surveillance system for laboratory-confirmed outbreaks of equid alphaherpesvirus 1 (EHV-1), equid alphaherpesvirus 4 (EHV-4), equine influenza virus (EIV), and Streptococcus equi subsp. equi. This retrospective study analyzed 364 confirmed outbreaks reported through SEIN between June 2019 and April 2023. S. equi was the most commonly reported pathogen overall (64%). Among outbreaks involving respiratory disease, S. equi accounted for 74% of cases, followed by EHV-4 (16%), EIV (6%), and EHV-1 (4%). The geographical distribution of outbreaks covered 80 of the 90 postal code regions (89%), and approximately half of all participating practices generated at least 1 alert. Vaccination data revealed low coverage against EHV-1/4, EIV, and S. equi among both affected horses and premises. Clinical signs overlapped between pathogens, but some were more pathogen-specific, e.g., coughing in EIV, and abscessation in S. equi. The SEIN system provided spatiotemporal information on confirmed outbreaks. These results underscore the importance of quick diagnostics and structured surveillance systems in guiding prevention strategies. Full article

Glu-Phe-Asp (GFD) proteinoids represent a class of synthetic polypeptides capable of self-assembling into microspheres, fibres, or combinations thereof, with morphology dramatically influencing their electrical properties. Extended recordings and detailed waveforms demonstrate that microspheres generate rapid, nerve-like spikes, while fibres exhibit consistent and gradual variations in voltage. Mixed networks integrate multiple components to achieve a balanced output. Electrochemical measurements show clear differences. Microspheres have a low capacitance of $1.926\pm 5.735$$\mathsf{\mu }$F. They show high impedance at $6646.282\pm 178.664$ Ohm. Their resistance is low, measuring 15,830.739 ± 652.514 m$\mathsf{\Omega }$. This structure allows for quick ionic transport, leading to spiking behaviour. Fibres show high capacitance ($9.912\pm 0.171$$\mathsf{\mu }$F) and low impedance ($209.400\pm 0.286$ Ohm). They also have high resistance (163,067.613 ± 9253.064 m$\mathsf{\Omega }$). This combination helps with charge storage and slow potential changes. The 50:50 mixture shows middle values for all parameters. This confirms that hybrid electrical properties have emerged. The differences come from basic structural changes. Microspheres trap ions in small, round spaces. This allows for quick release. In contrast, fibers spread ions along their length. This leads to slower wave propagation. In mixed systems, diverse voltage zones emerge, suggesting cooperative dynamics between morphologies. This electrical polymorphism in simple proteinoid systems may explain complexity in biological systems. This study shows that structural polymorphism in GFD proteinoids affects their electrical properties. This finding is significant for biomimetic computing and sheds light on prebiotic information-processing systems. Full article

A thorough understanding of forest resources and development trends is based on quick and accurate forest inventories. Because of its flexibility and localized independence, mobile laser scanning (MLS) based on simultaneous localization and mapping (SLAM) is the best option for forest inventories. The gap in the review studies in this field is filled by this study, which offers the first comprehensive review of SLAM-based MLS in forest inventory. This synthesis includes methods, research progress, challenges, and future perspectives of SLAM-based MLS in forest inventory. The precision and efficiency of SLAM-based MLS in forest inventories have benefited from improvements in data collection techniques and the ongoing development of algorithms, especially the application of deep learning. Based on evaluating the research progress of SLAM-based MLS in forest inventory, this paper provides new insights into the development of automation in this field. The main challenges of the current research are complex forest environments, localized bias, and limitations of the algorithms. To achieve accurate, real-time, and applicable forest inventories, researchers should develop SLAM technology dedicated to forest environments in the future so as to perform path planning, localization, autonomous navigation, obstacle avoidance, and point cloud recognition. In addition, researchers should develop algorithms specialized for different forest environments and improve the information processing capability of the algorithms to generate forest maps capable of extracting tree attributes automatically and in real time. Full article

Data from community-based research offer crucial insights into community needs, challenges, and strengths, informing effective decision making for development strategies. To ensure efficient analysis, accessible and user-friendly tools are necessary for quick and accurate results. While successful tools and programming languages exist, many social science researchers struggle with complex analytical tools due to limited exposure during their education, as such tools are often not required. Developing an automated, user-friendly tool for community research can support students, researchers, and data centers by bridging gaps in analysis capabilities and enhancing the accessibility of valuable insights. We developed a new automated tool using the Shiny framework in R designed primarily for analyzing data in community research, which often involves pre- and post-analysis tests. While the tool is specifically tailored for pre- and post-survey data, it can also be easily adapted to provide other statistical information. The findings presented in this paper highlight the efficiency of using this tool for community-based research and emphasize the need for further development to address its shortcomings. Furthermore, this paper is considered the groundwork for developing more accessible, user-friendly, and free tools in the future, especially in an era of advanced and complex technologies. Full article

The cultivation of Moringa oleifera Lam. (M. oleifera) has steadily increased over the past few decades, and interest in the crop continues to rise due to its unique multi-purpose properties. However, knowledge pertaining to its water use to guide decision-making in relation to the growth and management of this crop remains fairly limited. Since acquiring such information can be challenging using traditional in situ or remote sensing-based methods, particularly in resource-poor regions, this study aims to explore the potential of using the cloud-based Earth Engine Evapotranspiration Flux (EEFlux) model to quantify the water use of M. oleifera in a semi-arid region of South Africa. For this purpose, EEFlux estimates were acquired and compared with eddy covariance measurements between November 2022 and May 2023. The results of these comparisons demonstrated that EEFlux unsatisfactorily estimated ET, producing root mean square error, mean absolute error, and R² values of 2.03 mm d⁻¹, 1.63 mm d⁻¹, and 0.24, respectively. The poor performance of this model can be attributed to several factors such as the quantity and quality of the in situ data as well as inherent model limitations. While these results are less than satisfactory, EEFlux affords users a quick and convenient approach to extracting crucial ET and ancillary data. Subsequently, with further refinement and testing, EEFlux can potentially serve to provide a wide variety of users with an invaluable tool to guide and inform decision-making with regards to agricultural water use management, particularly those in resource-constrained environments. Full article