Miracle-dz/UniFAR-FTUs huggingface dataset:
Can Law Save the Forest?: Lessons from Finland and Brazil

Forests are increasingly endangered by unsustainable and illegal logging practices. This book examines forest management laws and their implementation, to determine some of the causes of unsustainable forestry practices. The degree of compliance varies widely across nations. This study contrasts Brazil, a low compliance country, with Finland, a high compliance country. The question Can Law Save the Forests?  is discussed, with an exploration of the nature of regulatory approaches, market-oriented economic incentives, and other critical issues that affect compliance with forest management law. The author clearly and concisely presents the failure to implement forestry laws in Brazil, and their successful enforcement in Finland. Close examination of the Finnish model of forestry law enforcement reveals those factors that could be adapted and applied to Brazil, and to possibly to other tropical forest countries. Sustainable forest management will continue to be unattainable unless a variety of approaches are used, taking into account the interplay of the unique economic, social, and cultural factors that bear on forest management. Can Law Save the Forests? is an important empirical study which sheds light on how to ensure sustainable forestry in Brazil and in other tropical forest countries, which urgently need to improve forest law enforcement.
Fast identification of coal gangue based on FAST corner point detection Method

Computer-aided gangue recognition is of great significance to the coal industry. For the current situation of low accuracy and relatively high error rate of coal gangue in computer-aided recognition system, a coal gangue recognition model based on FAST corner point detection fusion residual network is proposed. Firstly, the coal gangue images in some data sets are selected, then the data are augmented by rotating the images by 180°, then the images are converted to single channel processing, and finally the data are divided into training set and test set (7:3), and the designed residual network (FAST-Resnet) is trained and tested. After the training, the accuracy, sensitivity and specificity of gangue recognition were 97.53%, 97.91% and 97.18%, respectively, and the calculated AUC was 0.958. By comparison, the experimental results were better than other existing methods in all indexes, and the recognition results can provide a better reference for the classification of gangue.
Indoor Fingerprint Positioning Based on WiFi Channel State Information

Since WiFi is the most widely utilized form of communication in daily life and has been installed in almost all public infrastructures, high-precision indoor positioning based on WiFi offers a lot of growth potential. The received signal strength (RSS), which is frequently employed in WiFi-based indoor positioning, is substantially impacted by the multipath effect in a complex environment, making RSS only viable for coarse-grained indoor positioning due to the high complexity of the interior environment. Therefore, in this paper, we present a fingerprint positioning method based on modified Adaptive Boosting (Adaboost) algorithm in order to accomplish high-precision positioning by designing a positioning system using WiFi channel state information (CSI) rather than RSS. In the offline phase, the CSI features for the generation of the fingerprint database are selected using the Chi-Square test and a modified Adaboost algorithm is applied to create a nonlinear mapping between CSI features and position. In the online phase, an improved probability-weighted algorithm is used to accurately estimate the position of the point to be measured. This paper evaluates and compares the placement performance of this system in the experiment to other systems. The experimental findings demonstrate a significant improvement in positioning accuracy with the positioning system suggested in this paper.
Matrix-Scaled Consensus for Second-Order Uncertain Multi-Agent Systems Under a Directed Graph

In this paper, we propose a matrix-scaled consensus algorithm for second-order inertia uncertain multi-agent systems under a directed graph without using relative velocity information in multi-dimensional space. A proper reference model is designed for each agent to track and an adaptive control method is utilized to cancel the effect of parameter uncertainties. The agents asymptotically converge to different values depending on their scaling matrices, in which the final position of each agent is accurately calculated. We also give some simulation results to support our analysis.
Supervised Machine Learning Models for the Prediction of Renal Failure in Senegal

The objective of this paper goal is to propose models to predict the diagnosis of acute, chronic or terminal chronic renal failure using supervised machine learning methods. We used medical data from the Nephrology Service of the Aristide Le Dantec Hospital in Dakar to build prediction models with supervised learning methods: Multilayer Perceptron, Support Vector Machines, Random Forest and Extreme Gradient Boosting. The model built with Extreme Gradient Boosting proved to be the best according to the performance measures we used, namely precision, recall, F1-score and accuracy. Follow, in order, the models built with Random Forest, Logistic Regression, Support Vector Machines and Multilayer Perceptron.
Oscillation Trajectory Optimization and Control Method for Two-Link Underactuated Manipulators

This paper provides an oscillation trajectory optimization and control method for two-link underactuated manipulators (UMs) in a vertical plane. The proposed method solves the problem that the UMs cannot always enter the balance region in the partitioning method. First, we establish the system dynamic model, and analyze the system couple characteristics. Then, we program an oscillation trajectory for the active link, and use the intelligent method to obtain the trajectory parameters, so ensuring the system can reach the area adjacent to the target position through tracking control. Next, we design the controller to realize the stable control at the target position. Finally, the simulation results show the effectiveness and generality of the control strategy.
Differential Sea-Ice Drift. I. Spatial and Temporal Variations in Sea-Ice Deformation

Measurements of mesoscale sea-ice deformation over a region approximately 20 km in diameter were carried out over a five-week period in the spring of 1972 at the main AIDJEX camp in the Beaufort Sea. They have been analyzed to determine non-linearities in the ice velocity field (due to the discrete small-scale nature of the ice pack), as well as a continuum mode of deformation represented by a least-squares strain-rate tensor and vorticity. The deformation-rate time series between Julian day 88 and 112 exhibited net areal changes as large as 3% and deformation rates up to 0.16% per hour. In the principal axis co-ordinate system, the strain-rate typically exhibited a much larger compression (or extension) along one axis than along the other. Persistent cycles at ≈ 12 h wavelengths were observed in the divergence rate. A comparison of the average residual error with the average strain-rate magnitude indicated that strains measured on a scale of 10 km or greater can serve as a valid measure of the continuum motion of the sea ice. This conclusion is also substantiated by a comparison between the mesoscale deformation, and macroscale deformation measured over a ≈ 100 km diameter region. Regarding pack-ice rotation, vorticity calculations indicate that at low temporal frequencies (0.02 h−1), the whole mesoscale array rotates essentially as an entity and consequently the low-frequency vorticity can be estimated accurately from the rotation of a single floe.
A Novel Pedestrian Dead Reckoning Method Based on Visual Feature Map Matching Constraints

Robust and precise pedestrian navigation in satellite denial environments is a challenging problem. Existing inertial based positioning solutions still suffer from large drifts in long-time operation. In order to achieve reliable inertial positioning without any extra infrastructure, this works proposes a novel pedestrian dead reckoning (PDR) method that constrains the inertial system drifts by exploiting visual feature map matching and graph optimization. In doing so, the cumulative error of PDR can be effectively corrected by optimizing inertial positioning results with vision poses from matching current visual observation against the given feature map. Our experimental results demonstrate that the proposed method can obtain decimeter-level pedestrian position results in several typical indoor and outdoor scenarios, and significantly improve the long-term positioning performance. The averaged positioning error is reduced by 81.3% compared with a pure PDR without our introduced map matching based visual positioning.
Exploring rhythmic visual cue impacts on gait initiation using functional EEG source connectivity

The underlying neural mechanisms of rhythmic visual cues to improve gait freezing symptoms are still remain unclear. Previous studies of rhythmic visual cues have not taken into account the fact that the brain processes information as a result of the interplay between various brain regions. This study applied the EEG source connectivity method to investigate the cortical response and to evaluate the functional integration relationship of the brain networks under the non-rhythmic and rhythmic visual cues. Results showed that there were more network connectivity under the rhythmic visual cues, which suggested that rhythmic visual cues strengthen the brain's visual processing and cognitive control functions by enhancing the interactions between brain regions. In addition, the brain functional network under rhythmic visual cues possessed stronger functional separation and functional integration. This study suggested that the modulatory mechanism of rhythmic visual cues on gait initiation may be related to the enhanced information processing efficiency of the brain functional network through visual information input. The innovative source localization of EEG signals and the combination of the analysis of functional brain networks complement the methodology of the study of rhythmic visual cues, thus filling a gap in this research.
A Two-Stage Approach for Efficient Power Sharing Within Energy Districts

The recent advances regarding the decentralization of renewable energy production, the new technologies involved in the management of smart grids, and the opening of national energy markets, enriched with the use of demand-response strategies, have led to a notable diffusion of local energy markets. A local energy market is defined as an aggregation of energy producers, consumers, and prosumers that are located in a restricted area and see an interest in joining together to form a so-called “energy district. ” In this paper, we present a two-stage approach that enables sharing renewable energy within a district and minimizes the costs and/or maximizes the revenues deriving from the provision and the sale of energy, both for single prosumers and for the district as a whole. The main novelty with respect to the state-of-the-art is the introduction in the optimization process of a second stage that, starting from the energy exchanges determined in the first stage, redistributes to the prosumers the surplus energy, i.e., the energy produced locally that exceeds the demand of the prosumers. The two-stage approach benefits have been assessed in a real-life testbed deployed on an Italian university campus.
Delivery of Agricultural Service under Decentralization by Devolution: Reality of Co-Production in Cashew Value Chain in Southern Regions of Tanzania

The research on investigating “Institutionalized co-production in delivery of agricultural services: Realities from cashew value chain in the southern regions of Tanzania” aimed in investigating realities of type, level and sources of services going in the cashew value chain in Tandahimba district council as representative of other districts growing cashew in Tanzania. The research focus arose in the contextual fact that cashew form a main cash crop that contributes to huge per centum of financial gain of district councils, for farmers and citizens of southern region of Tanzania as a whole. The main research question the research intended to answer is ‘What are realities of services offered by LGAs to agricultural sector, particularly cashew sub sectors? The subsidiary questions and issues includes who (actors) are involved in the delivery of services to cashew along value chain? What model of service delivery is used and what is relationship between those actors? More important, is the implications of all actors and their role to the development of cashew crop and livelihood of farmers in particular?
Inonotus obliquus upregulates muscle regeneration and augments function through muscle oxidative metabolism

Skeletal muscle wasting related to aging or pathological conditions is critically associated with the increased incidence and prevalence of secondary diseases including cardiovascular diseases, metabolic syndromes, and chronic inflammations. Much effort is made to develop agents to enhance muscle metabolism and function. Inonotus obliquus (I. obliquus; IO) is a mushroom popularly called chaga and has been widely employed as a folk medicine for inflammation, cardiovascular diseases, diabetes, and cancer in Eastern Europe and Asia. However, its effect on muscle health has not been explored. Here, we aimed to investigate the beneficial effect of IO extract in muscle regeneration and metabolism. The treatment of IO in C2C12 myoblasts led to increased myogenic differentiation and alleviation of dexamethasone-induced myotube atrophy. Network pharmacological analysis using the identified specific chemical constituents of IO extracts predicted protein kinase B (AKT)-dependent mechanisms to promote myogenesis and muscle regeneration. Consistently, IO treatment resulted in the activation of AKT, which suppressed muscle-specific ubiquitin E3 ligases induced by dexamethasone. IO treatment in mice improved the regeneration of cardiotoxin-injured muscles accompanied by elevated proliferation and differentiation of muscle stem cells. Furthermore, it elevated the mitochondrial content and muscle oxidative metabolism accompanied by the induction of peroxisome proliferator-activated receptor γ coactivator α (PGC-1α). Our current data suggest that IO is a promising natural agent in enhancing muscle regenerative capacity and oxidative metabolism thereby preventing muscle wasting.
Intermittent Hypoxia in Preterm Neonates and Its Effect on Neonatal Morbidity and Mortality: A Systematic Review

The goal of the present systematic review was to investigate the occurrence patterns of intermittent hypoxemia in newborns throughout the early postnatal period as well as the link between neonatal intermittent hypoxemia exposure and harmful consequences such as neonatal morbidity and death. We collected data from 2014 to 2023 using several abstracting, referencing, and indexing database libraries in the field of medical sciences. A total of 715 papers were evaluated by both authors, and only seven articles met the specified review criteria after a thorough analysis. In preterm neonates with intermittent hypoxia (IH), severe morbidities such as bronchopulmonary dysplasia (BPD), retinopathy of prematurity (ROP), motor impairment, and cognitive delay were found. Only one study that extended to 18 months noted mortality. The length and occurrence of intermittent hypoxemia and the stage of premature neonates at the time of delivery are all closely associated with these morbidities. Therefore, it becomes important to continuously measure the patterns of occurrence of intermittent hypoxemia during early postnatal life to avoid its long-term morbidity and mortality impact.
A Review of State-of-the-arts Sensing Techniques for the Ultra-early Screening of Cerebral Palsy

Cerebral palsy (CP) is characterized by neuromotor disorders. It remains a huge challenge for ultra-early screening of CP. So far, the diagnosis of CP is mainly based on a combination of neuromotor assessment methods, magnetic resonance imaging (MRI) and clinical history. However, subjective errors are highly likely to be introduced in the process of CP screening due to disparities in clinicians’ professional backgrounds, working abilities and academic levels. Cutting-edge sensing techniques have been unveiled in the field of ultra-early CP prediction which provides objective diagnosis with quantified output information by obtaining motion capture, classification, and recognition. It is necessary to contrast the strengths, weaknesses, and potential of these techniques and how to apply them in the ultra-early screening of CP. This study compares and analyzes the latest and advanced sensing methods and discusses their future applications in infantile CP screening. The work could provide technical suggestions and clinical guidance for early intervention in the field of ultra-early CP screening later on.
Robust noise control method based on deep learning optimization

The selective fixed filter active noise control (SFANC) method can achieve fast response times by way of selecting the best pre-trained control filter for different types of noise. However, since the filters are pre-trained, the system lacks adaptability and suffers from large steady-state errors for different noise signals. In contrast, the filtered-X normalized least mean square (FxNLMS) algorithm can compensate for SFANC by obtaining a lower steady-state error through adaptive optimization. Nevertheless, its slow convergence has a detrimental effect on dynamic noise attenuation. For the disadvantages of poor adaptability of SFANC and slow convergence of FxLMS algorithm, this paper presents a robust noise control method based on deep learning optimization, which uses a convolutional neural network (CNN) to automatically select the most suitable pre-trained control filter in pre-trained selective fixed filter active noise control (SFANC), while continuously updating the coefficients of the selected pre-trained control filter at the sampling rate. The experimental results show that the DeepSF-FxLMS algorithm can achieve faster response, higher noise attenuation effect and higher robustness than the SFANC method or FxLMS algorithm.
DATA PRIVACY FOR SIMPLY ANONYMIZED SOCIAL NETWORK LOGS REPRESENTED AS GRAPHS - CONSIDERATIONS FOR GRAPH ALTERATION OPERATIONS

In this paper we review the state of the art on graph privacy with special emphasis on applications to online social networks, and we consider some novel aspects which have not been greatly covered in the specialized literature on graph privacy. The following key considerations are covered: (i) choice of different operators to modify the graph; (ii) information loss based on the cost of graph operations in terms of statistical characteristics (degree, clustering coefficient and path length) in the original graph; (iii) computational cost of the operations; (iv) in the case of the aggregation of two nodes, the choice of similar adjacent nodes rather than isomorphic topologies, in order to maintain the overall structure of the graph; (v) a statistically knowledgeable attacker who is able to search for regions of a simply anonymized graph based on statistical characteristics and map those onto a given node and its immediate neighborhood.
Main Food Sources of Energy, Nutrients and Dietary Fiber, According to the Purpose and Degree of Processing, for Beneficiary Adolescents of the ‘ Bolsa Família ’ Program in Brazil

The consumption of processed foods has risen since the 1970s in developing countries such as Brazil, mainly among children and adolescents. The aim of this study was to describe the contribution of foods, classified by purpose and degree of processing and inclusion of food additives, to energy, macronutrients, dietary fiber, vitamins, and minerals intake for individuals between 10 and 18 years-old beneficiaries of the ‘Bolsa Familia’ Program (BFP) in Brazil. The data is from the personal food consumption module of the National Household Budget Survey (cross-sectional study), conducted by the Brazilian Institute of Geography and Statistics (IBGE), in 2008-2009. Food products were classified into three distinct categories: 1) fresh and minimally processed foods; 2) processed foods (containing food additives, except flavoring and coloring agents); and 3) highly processed foods (containing flavoring and coloring agents). Adolescents from the sample were grouped in two groups: BFP beneficiaries or non-beneficiaries. Average values and standard deviations of energy, dietary fiber, and nutrients ingested during one day were calculated. Beneficiaries of the BFP, in relation to the others, obtain more energy content from fresh and minimally processed foods. However, the sum of energy from processed and highly processed products provided most of the energy. Highly processed foods were highlighted as sources of trans fatty acids, but also contain considerable amounts of calcium and vitamin B1. Fresh and minimally processed foods were the main sources of dietary fiber, minerals (except for manganese), folate, vitamin A (only for boys) and vitamin E. Processed foods were major sources of vitamins of B complex, C, A (only for girls), and D vitamins. Thus, analysis about the purpose and degree of processing that food undergone may help public policy makers to design effective initiatives at improving nutrition status of population.
Reconstruction of Temporal Envelope using 1D FLCNN-LSTM Speech Enhancement model

The primary objective of speech enhancement algorithm is to enhance speech perception characteristics that have been damaged by background noise. Ensuring speech quality and intelligibility is essential in speech processing analysis for effective communication. To do this, a new temporal modulation processing speech enhancement approach is developed that reconstructs the temporal envelopes (TEVs) in the time frequency (T-F) domain using a one-dimensional feature learning convolutional neural network and long short-term memory (ID FLCNN-LSTM). This deep learning architecture is used to restore distorted envelopes. This approach is tested by utilising 2,700 words from nine different speech samples combined with babble noise and SSN (speech spectrum shaped random noise) at various SNRs. The proposed algorithm's performance is assessed using the Short time objective intelligibility (STOI) and Perceptual evaluation of speech quality (PESQ) metrics. The suggested model improved STOI and PESQ scores as 0. S2 and 2. S0 respectively. The experimental outcomes predicted for quality and intelligibility scores outperforms TEV algorithm, 1D-CNN, and other existing models in speech enhancement under noisy conditions.