Precise Information Research Articles

A comparative analysis of PlanetScope 4-band and 8-band imageries for land use land cover classification

Earth-observing satellites have become essential in comprehending human impacts on the landscape. Satellite-based imagery is indispensable for mapping Earth's features, managing resources, and studying environmental changes. Readily available remote sensing data with improved radiometric, spectral, spatial, and temporal resolution presents opportunities for advanced data analysis. Precise and accurate land use land cover (LULC) information is essential for the surveillance of environmental conditions and the effective management of natural resources. This research assesses the performance of PlanetScope product SuperDove sensor (PSB.SD), having two different band combinations, including 4-band (Red, Blue, Green and Near-Infrared (NIR)) and 8-band (Blue, Green II, Red, NIR, Coastal Blue, Green, Yellow, Red-Edge) imagery in ArcGIS Pro for the month of July 2021. Four different supervised classifiers, including support vector machine (SVM), k-nearest neighbours (KNN), random forest (RF), and maximum likelihood (ML) classifiers. This study was carried out for the three major areas, i.e., City of Summerside, City of Charlottetown, and Town of Three Rivers in Prince Edward Island (PEI), Canada and LULC classification scheme consist of six major classes which includes Agriculture, Forest, Vegetation, Bare Land, Urban and Water bodies. For accuracy assessment, overall accuracy as well as kappa coefficient were estimated to identify the most accurate combination of LULC classifier and different band combination imagery from PlanetScope. Results show that the highest overall accuracy of 0.94 for Town of Three Rivers and 0.93 for City of Summerside and City of Charlottetown were observed using 8-band imagery with SVM classifier. The lowest overall accuracy of 0.78 for Town of Three Rivers, 0.83 for City of Charlottetown, and 0.82 for City of Summerside was observed using 4-band imagery using ML classifier. Further, the SVM classifier performs well in accuracy with 8-band imagery of PlanetScope, showcasing its potential in LULC classification compared to previous PlanetScope 4-band imagery.

Acoustic Tunable Battery‐Free Implants Based on Sustainable Triboelectric Nanogenerators With Metal‐Polymer Intermixing Layers

AbstractUltrasound‐driven triboelectric nanogenerators (US‐TENGs) offer an innovative solution for transcutaneous power transfer, with the potential to enable battery‐free, permanently implantable electronics. However, research to date has primarily demonstrated only fragmentary functionalities for these applications. This work presents the simultaneous transmission of acoustic power and precise acoustic information using a double‐electrode US‐TENG, enabling a battery‐free implant controlled via ultrasound. High and sustained output from a US‐TENG is crucial for operating the versatile system; therefore, a novel triboelectric membrane with a top electrode incorporating a gold‐polymer intermixing layer has been designed. Reversible micro‐cracks form in the intermixing layer, ensuring electrical connectivity under high‐frequency strain. In vivo experiments confirm that the system is biocompatible and can be reliably operated inside living rats. These achievements represent a significant step toward realizing multifunctional implantable electronics that can be reliably powered and controlled by ultrasound.

Exploring winter diet, gut microbiota and parasitism in caribou using multi-marker metabarcoding of fecal DNA.

In conservation strategies, getting precise and repeatable information on the species' diet and health without relying on invasive or laborious methods is challenging. Here, we developed an efficient and non-invasive workflow for the sequencing and analysis of four taxonomic markers from fecal DNA to characterize the gut microbiota, parasites, and plants and lichens composing the winter diet of caribou (Rangifer tarandus), Canada's most iconic endangered species. Sequencing of the 18S rRNA gene of eukaryotes from seven locations in Manitoba and Saskatchewan, Canada, allowed for the detection of five genera of parasites in caribou feces (including Nematodirella and Parelaphostrongylus) with variable frequency of occurrence depending on sampling location and sex. Our workflow also revealed a rich winter plant and lichen diet in caribou, with respectively 29 and 18 genera identified across all samples through plant and fungal ITS2 sequencing. Relationships between the gut microbiota and both the diet and parasite richness were also identified. Of note, the Central Saskatchewan sampling location was characterized by a clearly distinct gut microbiota which could be linked to an epiphytic lichen-rich diet. Overall, our results showed the potential of this multi-marker DNA metabarcoding workflow as an efficient tool to provide insights into the species biology and ecology.

Accounting for bias due to outcome data missing not at random: comparison and illustration of two approaches to probabilistic bias analysis: a simulation study.

Bias from data missing not at random (MNAR) is a persistent concern in health-related research. A bias analysis quantitatively assesses how conclusions change under different assumptions about missingness using bias parameters that govern the magnitude and direction of the bias. Probabilistic bias analysis specifies a prior distribution for these parameters, explicitly incorporating available information and uncertainty about their true values. A Bayesian bias analysis combines the prior distribution with the data's likelihood function whilst a Monte Carlo bias analysis samples the bias parameters directly from the prior distribution. No study has compared a Monte Carlo bias analysis to a Bayesian bias analysis in the context of MNAR missingness. We illustrate an accessible probabilistic bias analysis using the Monte Carlo bias analysis approach and a well-known imputation method. We designed a simulation study based on a motivating example from the UK Biobank study, where a large proportion of the outcome was missing and missingness was suspected to be MNAR. We compared the performance of our Monte Carlo bias analysis to a principled Bayesian bias analysis, complete case analysis (CCA) and multiple imputation (MI) assuming missing at random. As expected, given the simulation study design, CCA and MI estimates were substantially biased, with 95% confidence interval coverages of 7-48%. Including auxiliary variables (i.e., variables not included in the substantive analysis that are predictive of missingness and the missing data) in MI's imputation model amplified the bias due to assuming missing at random. With reasonably accurate and precise information about the bias parameter, the Monte Carlo bias analysis performed as well as the Bayesian bias analysis. However, when very limited information was provided about the bias parameter, only the Bayesian bias analysis was able to eliminate most of the bias due to MNAR whilst the Monte Carlo bias analysis performed no better than the CCA and MI. The Monte Carlo bias analysis we describe is easy to implement in standard software and, in the setting we explored, is a viable alternative to a Bayesian bias analysis. We caution careful consideration of choice of auxiliary variables when applying imputation where data may be MNAR.

Burned Area Detection with Sentinel-2A Data: Using Deep Learning Techniques with eXplainable Artificial Intelligence

Abstract. Annually, a considerable quantity of forest is burned on a global scale. Therefore, it is essential to obtain precise and fast information regarding the size of burned regions in order to effectively monitor the adverse consequences of wildfires. The objective of this investigation is to indicate the effectiveness and usefulness of a deep learning (DL) architecture, such as Convolutional Neural Networks (CNNs), in the mapping of areas affected by fire, employing an eXplainable artificial intelligence (XAI) algorithm known as SHapley Additive exPlanations (SHAP) with accuracy evaluation criteria. Furthermore, this paper presents the evaluation of the Çanakkale-Kizilkeçili village wildfire. The research investigated the impacts of a variety of spectral indices, including the normalized burn index (NBR), differentiated normalized burn index (dNBR), Green-Red Vegetation Index (GRVI), simple ratio vegetation index (RVI), and normalized difference vegetation index (NDVI). At the end of the training process, the model achieved a training accuracy of approximately 0.99, with model loss values converging to approximately 0.1. The findings of the burned area identification analysis indicate that by incorporating spectral indices as supplementary information, the CNN model achieved a high level of accuracy, with an overall accuracy of 98.88% and a Kappa Coefficient of 0.98. Additionally, the SHAP technique was employed to gain insights into the output of the models. The feature importances of the spectral bands were determined through the SHAP analysis of the CNN model. Hence, the significance of the auxiliary data generated by the NBR, dNBR, and NDVI indices was identified as being the highest among the original bands and auxiliary data employed in this investigation.

Unmanned Aerial Vehicle Photogrammetry for Monitoring the Geometric Changes of Reclaimed Landfills

Monitoring reclaimed landfills is essential for ensuring their stability and monitoring the regularity of facility settlement. Insufficient recognition of the magnitude and directions of these changes can lead to serious damage to the body of the landfill (landslides, sinkholes) and, consequently, threaten the environment and the life and health of people near landfills. This study focuses on using UAV photogrammetry to monitor geometric changes in reclaimed landfills. This approach highlights the advantages of UAVs in expanding the monitoring and providing precise information critical for decision-making in the reclamation process. This study presents the result of annual photogrammetry measurements at the Słabomierz–Krzyżówka reclaimed landfill, located in the central part of Poland. The Multiscale Model to Model Cloud Comparison (M3C2) algorithm was used to determine deformation at the landfill. The results were simultaneously compared with the landfill’s reference (angular–linear) measurements. The mean vertical displacement error determined by the photogrammetric method was ±2.3 cm. The results showed that, with an appropriate measurement methodology, it is possible to decide on changes in geometry reliably. The collected 3D data also gives the possibility to improve the decision-making process related to repairing damage or determining the reclamation direction of the landfill, as well as preparing further development plans.

MonoFG: Monocular 3D Object Detection with Knowledge Distillation for Human-Centric Autonomous Driving Systems

Monocular 3D object detection is essential for identifying objects in road images, thus offering valuable environmental perception data that are crucial for human-centric autonomous driving systems. However, due to the inherent limitations of camera imaging, obtaining precise depth information from images alone is challenging, which hampers the accuracy of within-scene object localization. In this paper, we introduce a monocular 3D object detection method called MonoFG that uses knowledge distillation with separated foreground and background components to improve the accuracy of object localization. First, detached foreground and background distillation processes can strategically leverage the distinct positional information acquired from each location to optimize the produced global distillation effects. This step serves as the foundation for the subsequent feature and response distillation process, which focuses on the distilled foreground and background rather than isolated object distillation. Second, triple attention mechanism-based feature distillation intensifies the feature imitation and feature representation capabilities of the student network. Spatial and channel attention mechanisms encourage the student network to capture crucial pixels and channels from the teacher network, whereas a self-attention mechanism globally transfers the learned relationships between pixels. Third, localization error-based response distillation facilitates a clearer transfer of positional information from the teacher network to the student network. Only when the positioning ability of the teacher network exceeds that of the student network can knowledge be comprehensively distilled across both the foreground and background. Therefore, the distillation process is constrained to specific content, which is delineated by positioning errors that serve as the boundaries. Finally, experiments conducted on the KITTI benchmark dataset demonstrate that our method outperforms many well-known baseline methods in several representative evaluation tasks (e.g., 3D object detection and bird's-eye view (BEV) detection) involving human-centric autonomous driving systems.

An Integrative Framework for Healthcare Recommendation Systems: Leveraging the Linear Discriminant Wolf–Convolutional Neural Network (LDW-CNN) Model

In the evolving healthcare landscape, recommender systems have gained significant importance due to their role in predicting and anticipating a wide range of health-related data for both patients and healthcare professionals. These systems are crucial for delivering precise information while adhering to high standards of quality, reliability, and authentication. Objectives: The primary objective of this research is to address the challenge of class imbalance in healthcare recommendation systems. This is achieved by improving the prediction and diagnostic capabilities of these systems through a novel approach that integrates linear discriminant wolf (LDW) with convolutional neural networks (CNNs), forming the LDW-CNN model. Methods: The LDW-CNN model incorporates the grey wolf optimizer with linear discriminant analysis to enhance prediction accuracy. The model’s performance is evaluated using multi-disease datasets, covering heart, liver, and kidney diseases. Established error metrics are used to compare the effectiveness of the LDW-CNN model against conventional methods, such as CNNs and multi-level support vector machines (MSVMs). Results: The proposed LDW-CNN system demonstrates remarkable accuracy, achieving a rate of 98.1%, which surpasses existing deep learning approaches. In addition, the model improves specificity to 99.18% and sensitivity to 99.008%, outperforming traditional CNN and MSVM techniques in terms of predictive performance. Conclusions: The LDW-CNN model emerges as a robust solution for multidisciplinary disease prediction and recommendation, offering superior performance in healthcare recommender systems. Its high accuracy, alongside its improved specificity and sensitivity, positions it as a valuable tool for enhancing prediction and diagnosis across multiple disease domains.

A review of the literature on sedentary behavior

Sedentary behavior (such as sitting, watching TV, and driving) is characterized by little physical movement and low energy expenditure (≤1.5 metabolic equivalents). Previous studies have shown associations between sedentary behavior and adverse outcomes, including cancer, cardiovascular disease, and death. This study is a narrative review that aims to summarize the assessment tools for sedentary behavior as well as the association between sedentary behavior and adverse health outcomes. Additionally, guidelines for future research on sedentary behavior are discussed.Sedentary behavior is assessed using subjective methods such as questionnaire responses or objective methods such as accelerometer readings. Although questionnaire methods have low validity, they allow researchers to collect information regarding the purpose or occasion of physical activity at a low cost. Accelerometers are expensive but collect precise information on the duration of sedentary behavior. Sedentary behavior is a risk factor for the incidence of cardiovascular diseases and cancer and associated mortality. The association between sedentary behavior and some diseases such as cancer type have been reported. Previous studies have found a dose-response relationship between sedentary time and adverse outcomes. Research on the associations between sedentary behavior and the subtypes of stroke, heart disease, and orthopedic diseases is insufficient; further studies are needed to clarify these associations. Recently, sedentary behavior has been the focus of guidelines in some countries because of increasing evidence on its adverse consequences. Intervention studies have shown that taking breaks and replacing sedentary behavior with vigorous or high-intensity physical activity benefit the health of participants. Future studies are needed to examine the cutoff values of sitting time that reduce the risk of morbidity for each disease and the health effects of interventions that shorten sitting time.

A review of deep learning-based stereo vision techniques for phenotype feature and behavioral analysis of fish in aquaculture

The industrialization, high-density, and greener aquaculture requires a more precise and intelligent aquaculture management. Phenotypic and behavioral information of fish, which can reflect fish growth and welfare status, play a crucial role in aquaculture management. Stereo vision technology, which simulates parallax perception of the human eye, can obtain the three-dimensional phenotypic characteristics and movement trajectories of fish through different types of sensors. It can overcome the limitations in dealing with fish deformation, frequent occlusions and understanding three-dimension scenes compared to the traditional two-dimensional computer vision techniques. With the deep learning development and application in aquaculture, stereo vision has become a super computer vision technology that can provide more precise and interpretable information for intelligent aquaculture management, such as size estimation, counting and behavioral analysis of fish. Hence, it is very beneficial for researchers, managers, and entrepreneurs to possess a thorough comprehension about the fast-developing stereo vision technology for modern aquaculture. This study provides a critical review of relevant topics, including the four-layer application structure of stereo vision technology in aquaculture, various deep learning-based technologies used, and specific application scenarios. The review contributes to research development by identifying the current challenges and provide valuable suggestions for future research directions. This review can serve as a useful resource for developing future studies and applications of stereo vision technology in smart aquaculture, focusing on phenotype feature extraction and behavioral analysis of fish.

Critical infrastructure resilience: a guide for building indicator systems based on a multi-criteria framework with a focus on implementable actions

Abstract. Criteria and indicators are frequently used for assessing the resilience of critical infrastructure (CI). Moreover, to generate precise information on conditions, the assessment designed for CI resilience could rely on indicator systems. However, few practical tools exist for guiding CI managers to build specific indicator systems for considering real cases. Therefore, the main objective of this study is to develop a step-by-step guide that contains guidance on operational steps and required resources for criteria and indicator setting, reference definition, and data collection. This guide enables CI managers to build systems of indicators tailored to different real cases. This guide could assist CI managers in their decision-making process, as it is structured based on a multi-criteria framework that takes into account the cost–benefit analysis and side effects of implementable actions. This guide could furthermore advance the application of indicator-based CI resilience assessment in practical management. In addition, this study provides an example to demonstrate how to use this guide. This example is based on specific circumstances for the Nantes Ring Road (NRR) network: when the ring road is flooded and closed, the road network manager suggests alternative roads to the public. An indicator system consisting of 4 criteria, 7 sub-criteria, and 11 indicators was built for these circumstances using the guide developed in this paper. This example relates to criteria and indicators in the technical, social, and environmental dimensions and involves 62 676 data points.

Integrating augmented reality and LLM for enhanced cognitive support in critical audio communications

Operation and Maintenance (O&M) missions are often time-sensitive and accuracy-dependent, requiring rapid and precise information processing in noisy, chaotic environments where oral communication can lead to cognitive overload and impaired decision-making. Augmented Reality (AR) and Large Language Models (LLMs) offer potential for enhancing situational awareness and lowering cognitive load by integrating digital visualizations with the physical world and improving dialogue management. However, synthesizing these technologies into a real-time system that effectively aids operators remains a challenge. This study explores the integration of AR and GPT-4, an advanced LLM, in time-sensitive O&M tasks, aiming to enhance situational awareness and manage cognitive load during oral communications. A customized AR system, incorporating the Microsoft HoloLens2 for cognitive monitoring and GPT-4 for decision making assistance, was tested in a human subject experiment with 30 participants. The 2×2 factorial experiment evaluated the effects of AR and LLM assistance on task performance and cognitive load. Results demonstrated significant improvements in task accuracy and reductions in cognitive load, highlighting the effectiveness of AR and LLM integration in supporting O&M missions. These findings emphasize the need for further research to optimize operational strategies in mission critical environments.

Absolute Train Localization Based on the Identification of Surrounding Structures Using 1D LiDAR Sensor

ABSTRACTTrain localization is an essential technology for effective train control. Currently, train localization primarily relies on using track circuits and balises, which are placed along the track to provide precise location information. However, balises have to be placed at intervals of a few kilometers. This increases maintenance costs and makes them vulnerable to being damaged by ice blocks falling from moving trains. Therefore, in this study, we propose a method for absolute train localization based on structure detection and identification using a 1D light detection and ranging (LiDAR) sensor to reduce the number of balises. Structure identification is achieved using scan matching. In the experiments using a car, the proposed method achieved an identification success rate of over 90%. We also considered the effect of raindrops by filtering the measurement data. By testing and analyzing the identification results, we successfully reduced all cases of misidentification.

Modeling the interplay between disease spread, behaviors, and disease perception with a data-driven approach

Individuals’ perceptions of disease influence their adherence to preventive measures, shaping the dynamics of disease spread. Despite extensive research on the interaction between disease spread, human behaviors, and interventions, few models have incorporated real-world behavioral data on disease perception, limiting their applicability. In this study, we propose an approach to integrate survey data on contact patterns and disease perception into a data-driven compartmental model, by hypothesizing that perceived severity is a determinant of behavioral change. We explore scenarios involving a competition between a COVID-19 wave and a vaccination campaign, where individuals’ behaviors vary based on their perceived severity of the disease. Results indicate that behavioral heterogeneities influenced by perceived severity affect epidemic dynamics, in a way depending on the interplay between two contrasting effects. On the one hand, longer adherence to protective measures by groups with high perceived severity provides greater protection to vulnerable individuals, while premature relaxation of behaviors by low perceived severity groups facilitates virus spread. Differences in behavior across different population groups may impact strongly the epidemiological curves, with a transition from a scenario with two successive epidemic peaks to one with only one (higher) peak and overall more numerous severe outcomes and deaths. The specific modeling choices for how perceived severity modulates behavior parameters do not strongly impact the model’s outcomes. Moreover, the study of several simplified models indicate that the observed phenomenology depends on the combination of data describing age-stratified contact patterns and of the feedback loop between disease perception and behavior, while it is robust with respect to the lack of precise information on the distribution of perceived severity in the population. Sensitivity analyses confirm the robustness of our findings, emphasizing the consistent impact of behavioral heterogeneities across various scenarios. Our study underscores the importance of integrating risk perception into infectious disease transmission models and gives hints on the type of data that further extensive data collection should target to enhance model accuracy and relevance.

3D morphometry of Martian craters from HRSC DEMs using a multi-scale semantic segmentation network and morphological analysis

The morphology of impact craters reveals the structure and composition of the Martian surface, especially the subsurface conditions and Martian geological history, which have increasing importance in Mars exploration missions. This work presents a 3D morphometric method for detecting and delineating Martian craters, and a 3D morphological analysis was conducted. Specifically, this work first focused on the segmentation of Martian craters. Based on the segmentation results, clustering of crater instances was then carried out. Finally, with the individual craters that were extracted, a morphological analysis involving the measurements of their diameter, depth, area, RMS height, rim height, circularity, and the statistics thereof was performed. Unlike previous studies, which have mainly used optical images and object detection approaches, this work regards crater extraction as a semantic segmentation task instead of an object detection task to better delineate the precise shape and boundary information. Digital elevation model (DEM) was utilized as primary data to directly obtain 3D information, which was converted into 3D point cloud format and fed to a multi-scale semantic segmentation network. The semantic segmentation results achieved an overall accuracy of 0.932 and mIOU of 0.871 on the test data. We automatically counted 63 craters in Noachis Terra and 40 craters in Terra Cimmeria. The 3D morphological measurements showed that 66% of the impact craters in the first region were larger than 10 km in diameter, while 50% of the impact craters in the second region were larger than 10 km. In both areas, craters could reach a maximum depth of 2000 m. With the proposed method, we can automatically conduct 3D morphological measurements of Martian craters with high efficiency that is improved by 15 times compared with that of manual crater analysis tools. The achieved 3D morphometric results can provide a reference and support for future research on Martian landforms.

Shape variability and morphological discrimination of haptoral attachment structures in Urocleidoides spp. Mizelle et Price, 1964 (Monogenea: Dactylogyridae) parasitizing fish from neotropical rivers in southern Mexico

The morphology of attachment organs in Monogenea is an essential source of evidence for taxonomic assessments and understanding of ecology and evolutionary history. It has been demonstrated that geometric morphometry analysis of the haptoral attachment organs provides rich and precise information about monogeneans morphology. In this study, we consider three monogenean species of Urocleidoides, U. simonae, U. vaginoclaustrum, U. vaginoclaustroides, and the incertae sedis species U. strombicirrus that parasitize fish of the Poeciliidae, Profundulidae and Characidae in neotropical rivers in southern Mexico. We aimed to identify new morphological characteristics that can be used to describe these populations with greater certainty and to test whether these characteristics present geographic variation. Anchor shape data (n = 69) were generated using geometric morphometric methods. We discriminated species using principal components and canonical variate analysis of shape data. Our results showed that there was discernible interspecific variation. The four species contributed four new morphological characteristics: anchor point length, short versus long anchor roots, elongated versus wide anchor shape, and the relative length of inner versus outer roots. Intraspecific variation was relatively low compared to interspecific anchor shape variation in Urocleidoides spp. These new morphological characteristics may also be useful for delimiting other similar species in Monogenea.

Evaluation of the Usability of UAV LiDAR for Analysis of Karst (Doline) Terrain Morphology.

Traditional terrain analysis has relied on Digital Topographic Maps produced by national agencies and Digital Elevation Models (DEMs) created using Airborne LiDAR. However, these methods have significant drawbacks, including the difficulty in acquiring data at the desired time and precision, as well as high costs. Recently, advancements and miniaturization in LiDAR technology have enabled its integration with Unmanned Aerial Vehicles (UAVs), allowing for the collection of highly precise terrain data. This approach combines the advantages of conventional UAV photogrammetry with the flexibility of obtaining data at specific times and locations, facilitating a wider range of studies. Despite these advancements, the application of UAV LiDAR in terrain analysis remains underexplored. This study aims to assess the utility of UAV LiDAR for terrain analysis by focusing on the doline features within karst landscapes. In this study, we analyzed doline terrain using three types of data: 1:5000 scale digital topographic maps provided by the National Geographic Information Institute (NGII) of Korea, Digital Surface Models (DSMs) obtained through UAV photogrammetry, and DEMs acquired via UAV LiDAR surveys. The analysis results indicated that UAV LiDAR provided the most precise three-dimensional spatial information for the entire study site, yielding the most detailed analysis outcomes. These findings suggest that UAV LiDAR can be utilized to represent terrain features with greater precision in the future; this is expected to be highly useful not only for generating contours but also for conducting more detailed topographic analyses, such as calculating the area and slope of the study sites.

Interpretable Multi-Horizon Time Series Forecasting of Cryptocurrencies by Leverage Temporal Fusion Transformer

This research delves into the obstacles and difficulties associated with predicting cryptocurrency movements in the volatile global financial market. This study develops and evaluates an advanced Deep Learning-Enhanced Temporal Fusion Transformer (ADE-TFT) model to estimate Bitcoin values more accurately. This research employs cutting-edge artificial intelligence (AI) and machine learning (ML) techniques to comprehensively examine various aspects of cryptocurrency forecasting, including geopolitical implications, market sentiment analysis, and pattern detection in transactional datasets. The study demonstrates that the ADE-TFT model outperforms its lower-layer counterparts in terms of forecasting accuracy, with reduced Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), and Root Mean Square Error (RMSE) values, particularly when using a higher hidden layer configuration (h=8). The study emphasizes the importance of experimenting with different normalization strategies and utilizing various market-related data to enhance the model's performance. The results suggest that improving forecasting accuracy may require addressing these limitations and incorporating additional factors, such as market sentiment. By providing investors with more precise market predictions, the techniques and information presented in this research have the potential to significantly increase investor power in an unpredictable digital currency market, enabling wise investment choices.

Robot skill acquisition for precision assembly of flexible flat cable with force control

Abstract The flexible flat cable (FFC) assembly task is a prime challenge in electronic manufacturing. Its characteristics of being prone to deformation under external force, tiny assembly tolerance, and fragility impede the application of robotic assembly in this field. To achieve reliable and stable robotic automation assembly of FFC, an efficient assembly skill acquisition strategy is presented by combining a parallel robot skill learning algorithm with adaptive impedance control. The parallel robot skill learning algorithm is proposed to enhance the efficiency of FFC assembly skill acquisition, which reduces the risk of damaging FFC and tackles the uncertain influence resulting from deformation during the assembly process. Moreover, FFC assembly is also a complex contact-rich manipulation task. An adaptive impedance controller is designed to implement force tracking during the assembly process without precise environment information, and the stability is also analyzed based on the Lyapunov function. Experiments of FFC assembly are conducted to illustrate the efficiency of the proposed method. The experimental results demonstrate that the proposed method is robust and efficient.

AN ASSESSMENT OF LAND USE AND LAND COVER CHANGE IN MANGA GRASSLAND OF YOBE STATE, NIGERIA

Human activities and natural events continually reshape land use patterns, underscoring the critical need for accurate land use information in various applications such as natural resource management and environmental monitoring. Land use change has become pivotal in current strategies for these purposes globally. The rapid advancements in land use mapping have spurred increased studies worldwide, aiming to assess and monitor the extent and health of forests, grasslands, and agricultural lands. Remotely sensed data, including aerial photographs and satellite imagery, are indispensable for extracting precise land use change information. This study focusing on Manga grassland (Machina, Yusufari, and Yunusari LGAs), satellite images from Landsat TM (1988), Landsat ETM (1998), Landsat ETM+ (2008), and Landsat 8 (2018) for image classification. The study employed supervised classification methods to analyse land use changes, revealing significant alterations in vegetation cover over the study period. In conclusion, it was observed that observed that vegetation cover increases from north toward the south follow the trend of rain fall, over-cutting, over grazing, and other climatic factors, which determines species distribution. Vegetation degradation is detected more in north boundary Niger republic. This may be due to southern flow of the Sahara Desert into the northern part of Nigeria around the study area. Data analysis shows that land use land cover had been changing through the study zones and this change had negative impact on natural resource development. Finally, it was recommended that the development of suitable and adequate mean of awareness for local community on the importance of species...