Recent droughts increase the risk of wildfires in the Mediterranean region, which in turn produce greenhouse gas (GHG) emissions from biomass burning, further exacerbating climate change. Accurate mapping of forest fires is urgently required to avoid underestimating GHG emissions in national GHG inventories and for fire management and prevention. In this study, we test a cumulative sum change detection algorithm on both Sentinel-1 and Sentinel-2 data in two test areas in the Sardinia region, Italy. The objective is to improve the mapping of two small-scale fires (less than 50 ha) undetected by the European Forest Fire Information System (EFFIS). Through the integration of multi-sensor information, the resulting maps are able to match more than 50% of the fieldmeasured burnt area. The maps also provide the exact timing of the fire events, matching the records from the Forest Service. Given its versatility and mapping potential, this methodology shows promise for application in other Mediterranean regions.

Semantic communication has gained much attention in recent years thanks to its ability to tailor information transmission to achieve specific tasks. This magazine article provides an overview of the potential applications of semantic communication in autonomous vehicular networks. First, the article discusses the existing literature on the topic, highlighting the main research directions. Next, we illustrate the merits of semantic communication within the context vehicular networks and how connected and autonomous vehicles (CAVs) can perform road feature extraction and transmission. After that, the article elaborates on satellite-to-autonomous vehicles' transmission of traffic signs via semantic auto-encoders. The results depict that semantic auto-encoders significantly boost the traffic sign classification and quality of reconstruction in case of impaired visibility conditions under bandwidth limitations. Furthermore, we illustrate a collaborative semantic transmission setup in vehicular platoons that highly improves the classification accuracy of traffic signs. Open research directions are then discussed, followed by a summarized conclusion. The disucssion in this article offers persuasive evidence of semantic communication capability to optimize autonomous vehicular communication, balancing high performance with efficient bandwidth usage.

Cyber-Physical Systems (CPS) seamlessly integrate computers, networks, and physical devices, enabling machines to communicate, process data, and respond to real-world conditions in real-time. By bridging the digital and physical worlds, CPS ensures operations that are efficient, safe, innovative, and controllable. As smart cities and autonomous machines become more prevalent, understanding CPS is crucial for driving future progress. Recent advancements in edge computing, AI-driven vision, and collaborative systems have significantly enhanced CPS capabilities. synchronization, optimization, and adaptation are intricate processes that impact CPS performance across different domains. Therefore, identifying emerging trends and uncovering research gaps is essential to highlight areas that require further investigation and improvement. This systematic review and analysis aims to offer a unique point to researcher and facilitates this process by allowing researchers to benchmark and compare various techniques, evaluate their effectiveness, and establish best practices. It provides evidence-based insights into optimal strategies for implementation while addressing potential trade-offs in performance, resource usage, and reliability. Additionally, such reviews help identify widely accepted standards and frameworks, contributing to the development of standardized approaches.

Predicting signal phase and timing (SPaT) most likely switching times and confidence level is needed to enhance green light optimal speed advisory (GLOSA) systems. This study proposes an architecture based on transformer encoders to improve prediction performance. This architecture is combined with different deep learning methods, including Multilayer perceptrons (MLP), long-short-term memory neural networks (LSTM), and Convolutional long-short term memory neural networks (CNNLSTM) to form an ensemble of predictors. The ensemble is used to make data-driven predictions of SPaT information for six different intersections along the Gallows Road corridor in Virginia. The study outlines three primary tasks. Task one is predicting whether a phase would change within 20 seconds. Task two is predicting the exact change time within 20 seconds. Task three is assigning a confidence level to that prediction. The experiments show that the proposed transformer-based architecture outperforms all the previously used deep learning methods for the first two prediction tasks. For the first task, the transformer encoder model provides an average accuracy of 96%. For task two, the transformer encoder models provided an average mean absolute error (MAE) of 1.49 seconds, compared to 1.63 seconds for other models. Consensus between models is shown to be a good leading indicator of confidence in ensemble predictions. The ensemble predictions with the highest level of consensus are within one second of the true value for 90.2% of the time as opposed to those with the lowest confidence level, which are within one second for only 68.4% of the time.

Cyberbullying has become a major problem nowadays especially on social networking sites. Training a system to detect the negative comments to remove them from the sites can solve this problem. Analyzing the attitude of the comments or posts on social media using word embeddings can identify it. word embeddings with a simple dimensionality reduction operation called Hellinger PCA (Principal Component Analysis) is used in this paper. The dataset is created by collecting comments from popular pages of Facebook using Graph API. On detecting cyberbullying and negative comments, accuracy of 72% is obtained for our corpus of 10500 with this approach. The performance is improving for larger corpus.

The design of a rural hospital discussed in this article places emphasis on the integration of telehealth services aimed at elevating the standard of healthcare provision in the marginalized areas that lack basic and advanced medical facilities and services. The project seeks to support the population of the island nation of Sudan as they encounter barriers with regard to accessing health services especially due to distance and inadequate facilities. In terms of research methodology, close attention is paid to secondary data, including field studies: assessing health needs in the predefined target population and allocating space in the hospital for such purposes as emergency and outpatient departments and even outpatient diagnostic services. This paper also considers possible issues which can limit the effectiveness of the operations of the hospital in the rural area including staffing limitations, communication issues, and suggests approaches to tackle such issues through training and development of infrastructure. This paper highlights the significance of the project in narrowing the health discrepancies between residents of cities and those in rural regions, providing a sustainable model that can be replicated in more such regions so as to elevate the standards of healthcare and enhance fairness in the distribution of health services.

Big Data has transformed industries worldwide by empowering organizations to utilize massive data volumes for actionable insights that drive strategic and operational benefits. This whitepaper investigates Big Data adoption trends across a range of industries, examining crucial drivers, organizational and cultural considerations, and strategic methods to implement successful implementation. With an emphasis on early adopters, emerging industries, and diverse use cases, this whitepaper offers a practical roadmap to guide organizations considering Big Data transformation.

The importance of continuously emerging new distribution is a mandate to understand the world and environment surrounding us. In this paper, the author will discuss a new distribution defined on the interval (0,1) as regards the methodology of deducing its PDF, some of its properties and related functions. A simulation and real data analysis will be highlighted.

This study investigates the mechanical properties of three common infill patterns-grid, gyroid, and cubic-constructed using fused filament fabrication. Test specimens were printed with polylactic acid, and stiffness, strength, and specific energy absorption were evaluated under compression across varying relative densities. The results demonstrate the superior performance of the grid pattern in terms of stiffness and energy absorption, although this advantage is direction-dependent. The gyroid pattern exhibited competitive energy absorption with improved isotropy, while the cubic pattern showed the lowest mechanical properties across all metrics. These findings provide actionable insights for optimizing 3D-printed components for load-bearing applications by balancing material efficiency and mechanical performance.

This paper presents an extensive review of the recent advancements in gait analysis using machine learning (ML) techniques, focusing on its application for detecting and treating genu valgum, a condition characterized by inward angling of the knees. The integration of various technologies such as wearable sensors, motion capture systems, and sophisticated ML algorithms has significantly enhanced the classification and prediction of abnormal gait patterns, aiding clinicians in assessing genu valgum and developing personalized treatment plans. However, challenges persist regarding model interpretability, dataset diversity, and the variability of realworld conditions. This paper highlights future directions for research, emphasizing the need for advanced algorithms, larger datasets, and improved integration of wearable sensors for realtime gait analysis.

Access to suitable learning platforms is crucial for motivating learners and making Science, Technology, Engineering, and Mathematics (STEM) an attractive field of study. This study analyze the potential benefit of integrating Microcontrollers into STEM learning in the schools. The responses of participants in the STEM Outreach program is analyzed to evaluate the potential impact in encouraging the rural youths in taking up the STEM fields of study in higher education. Coding on two microcontrollers, namely the runlinc E32W controller and Arduino, was introduced in ten schools, involving 447 students, of which 211 were girls and 236 were boys. The focus was on making them understand and helping them project the larger scope of STEM through developing basic applications on automation, the Internet of Things (IoT), and robotics, particularly on solving local problems through technology interventions. Pre-and post-program empirical data were collected to examine how access to and hands-on learning could influence young learners' decision to pursue STEM as their chosen area of study. To gauge the impact of the hands-on learning experience, the researchers employed a Dependent Sample T-Test, ANOVA, Pearson correlation, and linear regression analyses to predict impact. All the tests indicated a significant positive impact on the influence of appropriate learning tools in STEM interest among the students. Further, the SPSS ANN algorithm predicted the significant impact on students' perception of STEM and its future scopes.

This paper introduces a new portable impedance measurement system for Electrical Impedance Spectroscopy (EIS) and continuous impedance monitoring of various flexible-wearable electrodes and sensors. The system comprises a low-power, highly sensitive Electro-impedance Front End (EFE) and a Bluetooth Low Energy (BLE) microcontroller. Impedance measurements are conducted onchip and transmitted wirelessly via BLE to a nearby mobile device or PC application. As a case study, the implemented prototype interfaces flexible sensors designed for measuring changes in impedance. Preliminary results demonstrate the sensors ability to perform EIS and detect impedance variations caused by sensor stretching.

This study evaluates the effectiveness of a 6-hour Calculus for Engineering preparation course designed for high school seniors (Group A) and incoming first-year engineering students (Group B), utilizing advanced educational technologies. Unlike the traditional 9-15 hour approach at the Engineering Faculty of Rajamangala University of Technology Krungthep (RMUTK), which begins with foundational topics such as numbers, function values, limits, and continuity, our course focuses directly on differentiation using curve slopes and symbolic mathematics. The course employs GeoGebra and SymPy Live web applications to facilitate learning. Paired sample t-tests revealed significant improvements in both groups: Group A (n = 35) showed a mean increase of 4.97 (t(34) =-10.62, p < 0.001), and Group B (n = 27) showed a mean increase of 2.93 (t(26) =-4.05, p < 0.001). An independent samples t-test indicated a significant difference in improvement between the groups (t(60) = 2.47, p = 0.016). The results suggest that the 6-hour calculus preparation course effectively enhanced the calculus competency of both high school seniors and incoming first-year engineering students, with Group A showing greater improvement than Group B. This provides a more efficient alternative to the traditional methods used at RMUTK.

In recent years, increasing model complexity and improvements in self-attention mechanisms have enabled language models to perform remarkably well on tasks involving human language understanding and generation. However, limitations in preserving context over long sequences continue to present challenges, as models often struggle to retain coherence across extended discourse. The introduction of the Trans-Contextual Embedding (TCE) Mechanism provides a novel approach to addressing these challenges through a context-aware embedding layer integrated into transformer architectures, uniquely designed to maintain semantic depth and logical continuity. Through incorporating TCE into the initial embedding layer, the model achieves improved contextual coherence by dynamically weighting contextual relevance, leading to more accurate representation of complex, interdependent text elements. A series of experiments evaluated the TCE Mechanism on various opensource models, with results indicating a marked improvement in context continuity, semantic density, and reduced error rates, suggesting the applicability of TCE in a range of complex language tasks. Furthermore, the model achieved these improvements with minimal increase in computational overhead, presenting TCE as a scalable option for enhancing LLM capabilities in real-world applications. These findings lay foundational insights into context preservation mechanisms, positioning TCE as a significant advancement for the development of sophisticated and contextually robust language models.

In this paper, a new method for temperature prediction combining Random Forest (RF) and Long Short-Term Memory Network (LSTM) is proposed to address the challenges in the field of weather prediction. The method combines the advantages of RF and LSTM, which considers the temporal correlation of meteorological data and captures the intrinsic correlation among meteorological elements. While LSTM mines the temporal patterns in the historical weather data, RF provides more informative input data, which improves the timeliness and accuracy of the prediction. Compared with the LSTM model, the RF-LSTM model has improved the RMSE, MAE, and MAPE values by 0.27, 0.22, and 0.07, respectively, and the RF-LSTM model performs significantly in the temperature prediction in Nanjing with high prediction accuracy and stability. The method provides a new idea for meteorological prediction, which not only improves the overall level of prediction, but also provides valuable guidance for people's production and life.

This article provides a research viewpoint on the training procedure of a Bidirectional Encoder Representations from Transformers (BERT) model starting from the beginning, employing the Tensor Processing Unit (TPU), with a particular emphasis on the Spanish language. This article focuses on the techniques and strategies, such as the whole word masking (WWM) technique, that are essential for achieving successful BERT training in multiple languages, including English. The goal is to provide efficient development of natural language processing applications for understanding the Spanish language. The BEThiz model exhibits exceptional proficiency in comprehending contextual cues and producing accurate responses for masked inputs, surpassing other models within the same category and language. This characteristic sets our model apart and solidifies its role as a particularly potent tool in the field of natural language processing.

It is commonly known that gray matter, a tissue type with high concentrations of neuronal cell bodies, is indicative of brain health. Nonetheless, there has not been a lot of research on the relationship between gray matter volume and incidental memory. In my study, I analyzed the anatomical images of the participants of the Magic, Memory, and Curiosity research project, a study dedicated to the influence of magic tricks on a subject's curiosity and incidental memory. Using the provided masks, I utilized FSL to calculate the ratio of gray matter volume to total brain volume. After doing so, I calculated the p-value and r-value for the correlation between the volume ratio and the eight different memory scores. The recollection and recall scores turned out to be a moderate combination of positive and negative correlation coefficients, and an appreciable number turned out to be statistically significant. It follows that there is a correlation between gray matter volume and recall as well as recognition memory. In the future, scientists could measure the volume ratio of a brain region specifically associated with memory, such as the hippocampus or amygdala. Perhaps, measuring the gray matter thickness or using another set of measures would have also yielded more significant results.

The diverse range of Internet of Things (IoT) devices available today offers great potential for home automation and Smart Home (SH) development, whether partial or complete. However, for research in this area to advance, it is essential to overcome the scarcity of data, as data collection in SHs faces significant challenges, such as the complexity of devices and environments, privacy and security concerns, and restrictions imposed by proprietary devices. Furthermore, freely available datasets often have limitations, such as incomplete annotations, restricted device usage, and insufficient spatial and temporal coverage. To address this limitation, we developed a novel SH simulator that supports multi-state devices and captures detailed information on device usage and inhabitant activities, including metrics like lamp brightness and air conditioning settings (temperature, mode, etc.). The primary goal is to provide researchers in SHs, the SH, and related fields with a tool for generating datasets that effectively represent an inhabitant or group of inhabitant activity patterns in simulated environments. Home Environment Simulator Targeting Inhabitant Activities (HESTIA) resolves the inconsistency in message exchanges between devices, which often stems from differences in device manufacturers, while also eliminating the time-consuming and costly process of collecting real-world data by generating synthetic datasets. The simulator HESTIA offers rich, dynamic information that can be tailored to specific user profiles. To validate our method, we collected data over two weeks from a real environment using four devices and compared it to a synthetic version of the same setup generated by HESTIA. The results show that the simulator accurately replicates real-world environments, even for devices with inconsistent status changes. Additionally, we evaluated the computational complexity of this simulator in terms of time.