Miracle-dz/UniFAR-FTUs huggingface dataset:
Object-based machine learning approach for soybean mapping using temporal sentinel-1/sentinel-2 data

Abstract Soybean mapping in Indian context is challenging owing to its short growing period coinciding with the monsoon clouds, inter-cropping and smallholders’ land. This study proposes an approach for mapping soybean by integrating object-based image analysis with machine learning (ML) based classification using temporal Sentinel-1 SAR (S-1) and Sentinel-2 optical (S-2) data. Field objects were delineated with scale-optimized multi-resolution segmentation using historical S-2 data. Object-based temporal VH-backscatter and NDVI were extracted for training, validation and testing of the three ML models. Validation results showed the outperformance of Extreme gradient boosting (OB-XGBoost) over Random Forest (OB-RF) and Support vector machine (OB-SVM) with an overall accuracy (OA) of 92.50, 91.08 and 90.1, respectively. Testing of OB-XGBoost model resulted in OA, kappa statistics, and F-score (soybean) of 86.12%, 0.82, and 87.23%, respectively. The soybean map produced by the proposed methodology has shown better representation in terms of homogeneity and uniformity than the pixel-based classification.
Action and coping plans related to the behavior of adherence to oral anti-diabetic medication

Study Design: Study Design: This mixed-methods study. Objective: To describe action plans related to the behavior of adherence to oral antidiabetic medications produced by people with type 2 diabetes mellitus and identify the challenges and coping strategies for the establishment of this behavior. Methods: The study followed-up 44 people with type 2 diabetes mellitus who used oral antidiabetic medications from southeast Brazil. Adults were invited to build action and coping plans based on the Implementation Intention Theory. The plans were quantitative and qualitatively analyzed. Results: Action plans included three major themes: (1) binding oral antidiabetic medication to time markers or the sleep/wake cycle; (2) in specific environments; (3) associated with daily life activities. The motivation for coping with the perceived barriers focused on placing the pills in visible places, asking for help from family members, establishing a routine and feeding properly. Conclusions: The most effective way to manage satisfactory oral antidiabetic medication adherence seems to be recognizing the perceived barriers by patients. The implementation of specific and individualized action and coping plans to overcome perceived barriers was the stark difference.
Magnetic resonance T1w/T2w ratio in multiple system atrophy: A combined study with voxel-based morphometry

Diagnosis of multiple system atrophy (MSA) may be improved by using multimodal imaging approaches. We investigated the use of T1-weighted/T2-weighted (T1w/T2w) images ratio combined with voxel-based morphometry to evaluate brain tissue integrity in MSA compared to Parkinson’s disease (PD) and healthy controls (HC). Twenty-six patients with MSA, 43 patients with PD and 56 HC were enrolled. Whole brain voxel-based and local regional analyses were performed to evaluate gray and white matter (GM and WM) tissue integrity and mean regional values were used for patients classification using logistic regression. Increased mean regional values of T1w/T2w in bilateral putamen were detected in MSA-P compared to PD and HC. The combined use of regional GM and T1w/T2w values in the right and left putamen showed the highest accuracy in discriminating MSA-P from PD and good accuracy in discriminating MSA from PD and HC. A good accuracy was also found in discriminating MSA from PD and HC by either combining regional GM and T1w/T2w values in the cerebellum or regional WM and T1w/T2w in the cerebellum and brainstem. The T1w/T2w image ratio alone or combined with validated MRI parameters can be further considered as a potential candidate biomarker for differential diagnosis of MSA.
Acidophilic bioleaching: A Review on the Process and Effect of Organic–inorganic Reagents and Materials on its Efficiency

ABSTRACT Bio-hydrometallurgical applications have been receiving more attention globally for processing of several metal bearing wastes. Developments in bioleaching enable this technique to be utilized as an alternative process for extraction of metals (ex-copper, gold, etc.) from complex ores or even concentrates, consequently solving several environmental issues. Organic and inorganic reagents or additives used during the process have been seen to affect the leaching efficiency of microbes for metal dissolution. The present review discusses the influence of certain organic–inorganic reagents and other materials or additives on the growth, oxidation activity, and bioleaching efficiency of acidophiles on metal dissolution.
The Study of Clinical Pharmacist Participating in the Pain Management of Outpatients with Cancer in A Prescription Period

Purpose. The management of outpatients with cancer painis very important for medication safety. Medical interviews by CP is conducive to pain management. Our objective was to describe the contribution of the clinical pharmacist (CP) to outpatients in pain-relief clinic.Methods. This was a prospective, case-by-case self-control study. One clinical pharmacist conducted three questionnaire surveys. The first happened in the clinic face to face, the second by telephone one week later after the visit and the third by telephone two weeks later. The interventions by CP and comprehensive pain assessment (scored by NRS, sleep, mood and general activities) were both recorded. Analgesic-related knowledge were delivered by CP and were measured both before and after therapy. 1.Department of Pharmacy ,National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China E-mail: erxia100@163.com2. Department of Comprehensive Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China E-mail: doccong@vip.163. comResults. 51 patients were enrolled. The top 5 interventions in the first telephone interview(second survey) was suggestions for treating adverse drug reactions(47%), correcting the wrong usage(35%), medication education(26%), recommendations for dosage adjustment(18%) and suggestions for further treatment(15%). While in the second telephone interview (third survey), the rankings were totally inverted. Comprehensive pain assessment showed better in the first interview and then worse in the second. Analgesic-related knowledge increased significantly after the education by CP.Conclusions. CP made a totally different interventions at the different stages of medication in the outpatients with cancer pain,which may be related to the pain control. Patients’ knowledge regarding analgesics significantly increased after education by CP.
Target Tracking Using Radial Distance and Lucas Cluster-Based Ridge Regression in Wireless Sensor Network

Target tracking is one of the most significant concerns in Wireless Sensor Networks (WSNs). In a densely deployed WSN, spatial trajectories in future mission monitoring can provide comprehensive communication but with dynamically changing network, targets were lost, compromising stability. Some approaches pursue minimum overload and energy consumption occurring during tracking at the cost of tracking accuracy, while some other mechanisms aim to achieve the best tracking accuracy without consideration of energy consumption. In this paper, we present a new method called, Radial Distance and Lucas Cluster-based Ridge Regression (RD-LCRR) to detect and track the target in WSN considering both energy efficiency and accuracy. Initially, we sense the object via Radial Distance Edge Propagation Object Sensing model based on the cluster formation and then identify moving objects through additive value. Next, the object to be tracked is obtained by applying Lucas Kanade Cluster-based Ridge Regression model. Here, target detection is first initialized via Lucas Kanade and finally, the tracking of the actual target is performed through Ridge Regression. The performance of the RD-LCRR method is considered for different scenarios and experiments are performed in a simulation environment against state-of-the-art methods. Results of the experiments show that the presented target detection and tracking method enables a significant reduction in the amount of energy consumption, target tracking time with higher accuracy rate.
Deblurring of Images using Novel Deconvolutional Neural Network (DNN) Algorithm to Enhance the Accuracy and Comparing with Richardson-Lucy Deconvolution Algorithm (RLD)

Aim- Machine learning techniques are rapidly used in the area of digital image processing research due to its impressive results in deconvolution and deblurring of images. The objective of this study is to evaluate the performance of Novel DNN algorithm in deblurring of images by comparing it with the RLD algorithm. Materials and Methods - Novel Deconvolutional Neural Network (DNN) and Richardson-Lucy Deconvolution (RLD) algorithms were implemented to deblur the input images upto 256 pixels range. These algorithms were implemented to enhance the accuracy rate of deblurred images using MATLAB Software and analyzed by collecting the dataset of 40 samples with 80% of pretest power. Results - From the MATLAB simulation result, DNN achieves image deblurring rate with 98% accuracy and RLD method achieves image deblurring rate with 92% accuracy. The significance value obtained as (P < 0.002). Conclusion - Novel DNN classifier appears to have better accuracy compared to RLD Classifier.
Extraction of the Major Features of Brain Signals using Intelligent Networks

The brain-computer interface is considered one of the main tools for implementing and designing smart medical software. The analysis of brain signal data, called EEG, is one of the main tasks of smart medical diagnostic systems. While EEG signals have many components, one of the most important brain activities pursued is the P300 component. Detection of this component can help detect abnormalities and visualize the movement of organs of the body. In this research, a new method for processing EEG signals is proposed with the aim of detecting the P300 component. Major features were extracted from the BCI Competition IV EEG data set in a number of steps, i.e. normalization with the purpose of noise reduction using a median filter, feature extraction using a recurrent neural network, and classification using Twin Support Vector Machine. Then, a series of evaluation criteria were used to validate the proposed approach and compare it with similar methods. The results showed that the proposed approach has high accuracy.
Optimal observer‐based output feedback controller for traffic congestion with bottleneck

This article designs an optimal observer‐based output feedback control for traffic breakdown to dissolve traffic congestion using the backstepping method and optimization. The linearized Aw–Rascle–Zhang model is used to represent the congested traffic dynamics resulting from traffic breakdown. Based on the factors leading to traffic breakdown, we take into account the boundary conditions consisting of a boundary with a constant density and a speed drop at the upstream inlet of a bottleneck, and a boundary with a disturbance of inflow (high traffic demand) at the inlet of the road segment under consideration. To dissolve traffic congestion, a dynamic feedback controller is designed at the upstream boundary. By using the backstepping approach, an observer‐based output feedback controller is computed to guarantee the integral input‐to‐state stability of the closed‐loop system. By establishing an optimization problem and solving it, the optimal value of the considered class controller is obtained. The performance of the output feedback controller is also validated by numerical simulations.
LuxGeo: Efficient and Security-Enhanced Geometric Range Queries

As location-based applications flourishing, we will witness soon the transferring of a prodigious amount of data from the local to a public cloud. The rising demand for outsourced data is moving toward a wider geographical area with arbitrary distribution (i.e., dense or sparse) and query scope (i.e., limited or vast). The outsourced individual data should be preserved when being queried as facing cloud risks, especially for location information. Geometric range queries are one of the most fundamental search functions. However, the existed works of secure geometric queries are far from practical usage on efficiency and security simultaneously. In this paper, we propose a novel scheme, LuxGeo. Our scheme reaches a <italic>constant</italic> navigation and a <italic>linear</italic> sweep, which is tailored for secure and efficient location-lookup. Our experiments over three real-world spatial datasets have shown its practical efficiency. For example, it only takes <inline-formula><tex-math notation="LaTeX">$\mathbf {10.01s}$</tex-math><alternatives><mml:math><mml:mrow><mml:mn mathvariant="bold">10</mml:mn><mml:mo>. </mml:mo><mml:mn mathvariant="bold">01</mml:mn><mml:mi mathvariant="bold">s</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="guo-ieq1-3093909.gif"/></alternatives></inline-formula> with <inline-formula><tex-math notation="LaTeX">$\mathbf {728}$</tex-math><alternatives><mml:math><mml:mn mathvariant="bold">728</mml:mn></mml:math><inline-graphic xlink:href="guo-ieq2-3093909.gif"/></alternatives></inline-formula> tuples retrieved over <inline-formula><tex-math notation="LaTeX">$\mathbf {63,369}$</tex-math><alternatives><mml:math><mml:mrow><mml:mn mathvariant="bold">63</mml:mn><mml:mo>,</mml:mo><mml:mn mathvariant="bold">369</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href="guo-ieq3-3093909.gif"/></alternatives></inline-formula> ciphertext dataset for a single query. LuxGeo has better performance than the existed solutions for a GSE problem on efficiency and security.
Beslisregels voor langetermijnwaardecreatie

Toekomstgerichte bedrijven sturen op langetermijnwaardecreatie (LTW) in plaats van alleen op aandeelhouderswaarde. LTW gaat om het beheren en balanceren van verschillende ‘waardedimensies’: financieel, sociaal en ecologisch. Dat vergt beslisregels voor de investeringen van bedrijven. Dit artikel leidt dergelijke beslisregels af door te analyseren wat nodig is voor LTW; en hoe LTW verschilt van aandeelhouderswaardemaximalisatie. Ook schetst het artikel transitietrajecten voor bedrijven die momenteel waardevernietigend zijn op één van de dimensies. Ten slotte introduceert het eenvoudige modellen die het mogelijk maken om specifieke soorten waarde te prioriteren, in overeenstemming met de missie van de onderneming.
TrackInFactory: A Tight Coupling Particle Filter for Industrial Vehicle Tracking in Indoor Environments

Localization and tracking of industrial vehicles have a key role in increasing productivity and improving the logistics processes of factories. Due to the demanding requirements of industrial vehicle tracking and navigation, existing systems explore technologies, such as LiDAR or ultra wide-band to achieve low positioning errors. In this article we propose TrackInFactory, a system that combines Wi-Fi with motion sensors, achieving submeter accuracy and a low maximum error. A tight coupling approach is explored in sensor fusion with a particle filter (PF). Information regarding the vehicle’s initial position and heading is not required. This approach uses the similarity of Wi-Fi samples to update the particles’ weights as they move according to motion sensor data. The PF dynamically adjusts its parameters based on a metric for estimating the confidence in position estimates, allowing to improve positioning performance. A series of simulations were performed to tune the PF. Then the approach was validated in real-world experiments with an industrial tow tractor, achieving a mean error of 0.81 m. In comparison to a loose coupling approach, this method reduced the maximum error by more than 60% and improved the overall mean error by more than 20%.
Control Strategy Based on Arm-Level Control for Output and Circulating Current of MMC in Stationary Reference Frame

This paper proposed a control method for output and circulating currents of modular multilevel converter (MMC). The output and circulating current are controlled with the help of arm currents, which contain DC, fundamental frequency, and double frequency components. The arm current is transformed into a stationary reference frame (SRF) to isolate the DC and AC components. The AC component is controlled with a conventional proportional resonant (PR) controller, while the DC component is controlled by a proportional controller. The effective control of the upper arm and lower arm ultimately controls the output current so that it delivers the required power to the grid and circulating current in such a way that the second harmonic component is completely vanished leaving behind only the DC component. Comparative results of leg-level control based on PR controller are included in the paper to show the effectiveness of the proposed control scheme. A three-phase, five-level MMC is developed in MATLAB/Simulink to verify the effectiveness of the proposed control method.
Observation of Gravity Waves at the Tropical Tropopause Using Superpressure Balloons

Tropical gravity wave activity is investigated using measurements of momentum fluxes gathered during Strateole‐2 superpressure balloon flights. The data set consists of eight balloon flights performed in the deep tropics from November 2019 to February 2020. The flights lasted for 2–3 months each, and in‐situ meteorological data were collected every 30 s. The relation between gravity waves and deep convection is investigated using geostationary satellite data from the NOAA/NCEP GPM_MERGIR satellite data product, at 1 h resolution. The amplitude of gravity wave momentum fluxes shows a clear dependence on the distance to the nearest convective system, with a strong decay as distance to convection increases. The largest momentum‐flux values (> 5 mPa) are only found less than 200 km away from deep convection. The sensitivity of the wave flux to distance from convection is stronger for high frequency gravity waves (periods shorter than 60 min). Lower frequency waves tend to a non‐zero, background value away from convection, supporting some background value in gravity‐wave drag parameterizations. On the other hand, the wide range of momentum flux values observed close to the convection emphasizes the intermittent nature of the gravity‐wave source. The large scale variation of gravity‐wave intermittency within the equatorial belt is also studied. The results highlight spatial variations of gravity wave activity, with the highest momentum flux recorded over land.
Implementation of Augmented Reality Learning Media Technology at the Primary and Secondary Education Levels in Cimahi City, West Java

Currently the development of technology is progressing very rapidly and its development cannot be stopped. With so many advanced technologies that have emerged, various kinds of human work can be done in more innovative ways. One example is in the field of education. Currently, there are various kinds of technology-based learning media, one of which is learning media using Augmented Reality technology or often shortened to AR. Application Development as Learning Media to Build Space for Elementary/Junior High School Level Using Marker Augmented Reality Method is one form of utilizing AR technology in the field of education, especially as a learning medium. With the creation of this Augmented Reality-based learning media for building space, It is hoped that students can understand learning materials easily, and students' interest in learning can increase because the objects in the space are designed to be attractive, colorful, and interactive to avoid boredom. Based on this, this research is one of the new breakthroughs in the world of education that utilizes technological advances and helps teachers provide more interesting learning media. Meanwhile, the results of this study are in the form of an AR application and a mathematical book (module). this research is one of the new breakthroughs in the world of education that utilizes technological advances and helps teachers provide more interesting learning media. Meanwhile, the results of this study are in the form of an AR application and a mathematical book (module). this research is one of the new breakthroughs in the world of education that utilizes technological advances and helps teachers provide more interesting learning media. Meanwhile, the results of this study are in the form of an AR application and a mathematical book (module).
Financial Education: an experience with 3rd Grade students

Financial education is a subject that has been developing in schools with younger pupils, as they become active consumers, even at an early age. For this reason, they must gain knowledge and develop financial skills, which will enable them to acquire financial competencies. With the theme of financial education, it is intended that citizen be trained to be able to act consciously and responsibly in the financial sphere and, consequently, maintain a healthy and balanced relationship with money. Thus, it is essential to provide individuals, all of them, with knowledge and skills that allow them to respond effectively to everyday financial situations, even the simplest and most routine ones.
Converting raw transcripts into an annotated and turn-aligned TEI-XML corpus: the example of the Corpus of Serbian Forms of Address

This paper describes the procedure of building a TEI-XML corpus of spoken Serbian starting from raw transcripts. The corpus consists of semi–structured interviews, which were gathered with the aim of investigating forms of address in Serbian. The interviews were thoroughly transcribed according to GAT transcribing conventions. However, the transcription was carried out without tools that would control the validity of the GAT syntax, or align the transcript with the audio records. In order to offer this resource to a broader audience, we resolved the inconsistencies in the original transcripts, normalised the semi-orthographic transcriptions and converted the corpus into a TEI-format for transcriptions of speech. Further, we enriched the corpus by tagging and lemmatising the data. Lastly, we aligned the corpus turns to the corresponding audio segments by using a force-alignment tool. In addition to presenting the main steps involved in converting the corpus to the XML-format, this paper also discusses current challenges in the processing of spoken data, and the implications of data re-use regarding transcriptions of speech. This corpus can be used for studying Serbian from the perspective of interactional linguistics, for investigating morphosyntax, grammar, lexicon and phonetics of spoken Serbian, for studying disfluencies, as well as for testing models for automatic speech recognition and forced alignment. The corpus is freely available for research purposes.