Miracle-dz/UniFAR-FTUs huggingface dataset:
Detección de tendencias de homicidios en Colombia usando Machine Learning

En las últimas décadas, el número de homicidios violentos en América Latina ha crecido considerablemente debido a la ampliación y auge de grupos criminales organizados en zonas rurales y urbanas de las principales ciudades de países como México, Colombia y Venezuela. Con base en el alto índice de homicidio de estos países, consecuencia de la alta criminalidad, éstos han sido clasificados dentro de los más violentos a nivel mundial. Según datos reportados por el Observatorio del Delito de la Policía Nacional y la Fiscalía General de la Nación de Colombia, en 2019 se presentaron 1.032 asesinatos en Bogotá. Estos datos arrojan una tasa de 14,3 homicidios por cada 100.000 habitantes. A partir de esto, se estima que entre 1960 y 2019 se han generado alrededor de 226.215 homicidios, unas 3,834 muertes por año, en promedio. En este trabajo se presenta un modelo de machine learning basado en random forest, el cual permite predecir las tendencias de homicidio violento (HV) en Colombia para los próximos 5 años. El proyecto tiene como objetivo servir de instrumento para facilitar la toma de decisiones en organismos como la Fiscalía General de la Nación y la Policía Nacional. El modelo fue evaluado con un conjunto de datos generado a partir del Sistema de Información Estadístico Delincuencial, Contravencional y Operativo (SIEDCO) de la Fiscalía, el cual cuenta con 2.662. 402 registros de delitos realizados en Colombia desde el año 1960 hasta 2019.
Survey and Evaluate Uncertainty Quantification Methodologies

The Carbon Capture Simulation Initiative (CCSI) is a partnership among national laboratories, industry and academic institutions that will develop and deploy state-of-the-art computational modeling and simulation tools to accelerate the commercialization of carbon capture technologies from discovery to development, demonstration, and ultimately the widespread deployment to hundreds of power plants. The CCSI Toolset will provide end users in industry with a comprehensive, integrated suite of scientifically validated models with uncertainty quantification, optimization, risk analysis and decision making capabilities. The CCSI Toolset will incorporate commercial and open-source software currently in use by industry and will also develop new software tools as necessary to fill technology gaps identified during execution of the project. The CCSI Toolset will (1) enable promising concepts to be more quickly identified through rapid computational screening of devices and processes; (2) reduce the time to design and troubleshoot new devices and processes; (3) quantify the technical risk in taking technology from laboratory-scale to commercial-scale; and (4) stabilize deployment costs more quickly by replacing some of the physical operational tests with virtual power plant simulations. The goal of CCSI is to deliver a toolset that can simulate the scale-up of a broad set of new carbon capture technologies from laboratory scale to full commercial scale. To provide a framework around which the toolset can be developed and demonstrated, we will focus on three Industrial Challenge Problems (ICPs) related to carbon capture technologies relevant to U.S. pulverized coal (PC) power plants. Post combustion capture by solid sorbents is the technology focus of the initial ICP (referred to as ICP A). The goal of the uncertainty quantification (UQ) task (Task 6) is to provide a set of capabilities to the user community for the quantification of uncertainties associated with the carbon capture processes. As such, we will develop, as needed and beyond existing capabilities, a suite of robust and efficient computational tools for UQ to be integrated into a CCSI UQ software framework.
Ecloserie artisanale familiale de silure africain (Clarias gariepinus Burshell, 1822) et de carpe commune (Cyprinus carpio) de la région Ouest-Cameroun.

Abstract The artisanal family hatchery of the West Integrated Aquaculture Common Initiative Group(GIC-AIO), a peasant organization led by Michel Diogni, is located in Batié, the western highlands region of Cameroon (5 ° 15'- 5 ° 20'LN and 10 ° 10'-10 ° 15'LE). It was created in 1998 with the objective of producing African catfish (Clarias gariepinus) fry in order to meet local and national demand. The production of table fish is carried out on a semi-intensive basis but this tends to be reduced in favor of the production of fry. In 2009, the hatchery produced 13,000 catfish fry (C. gariepinus) and 15,000 carp fry (Cyprinus carpio) but has much greater potential. These activities, although income-generating, come up against both physical and technical limits. In particular, an improvement in the nursery phase is necessary.
Automated Foot and Mouth Disease Classification Using Transfer Learning-Based Deep Convolutional Neural Network

Foot and Mouth Disease is the most highly contagious disease around the world. Early detection of FMD is crucial for controlling this disease. Convolutional Neural Networks is the most convenient architecture in the state-of-the-art of Image Processing and Computer Vision sector. As per our knowledge, there was no method for FMD detection in the livestock sector which has been proposed by using CNN architecture. This proposed model aims that early detection of the highly infectious FMD using different types of CNN architectures with a pre-trained model like GoogleNet, AlexNet, and Residual Networks (ResNet). All the essential steps for executing the FMD with the Healthy cattle detection process are thoroughly explained in the paper, from the data assemble to the procedure and result. The proposed system is prominent to be successful, achieving results with 95% accuracy, which perhaps inhibit the human mistakes in the diagnosis process and would be conducive to recognize epidemic disease especially the FMD for veterinarians and cattle farmers.
Research on the algorithm of organ segmentation in chest X-ray images

Medical image processing is the first step in analyzing medical images, which helps to make the images more intuitive and clearer and improve the efficiency of diagnosis. Image segmentation is an important part of image processing, and it is also a difficult point. It is a bottleneck problem that restricts the application of technologies such as 3D reconstruction. In recent years, due to the rapid development of deep learning methods, image segmentation algorithms based on deep learning have made remarkable achievements in the field of medical image segmentation. This article discusses the general algorithm framework and deep learning framework for medical image segmentation and their optimization. The final part of deep learning methodology of medical image transformation of segmentation and recognition has become complicated and abundant. The related work for medical image segmentation of X-Ray images focuses on increasing the resolution of images to raise the correct rate of images segmentation. The methods using deep learning methods for medical image processing promotes the step of the methods using traditional methods with high accuracy.
Mental health analysis during COVID-19: A comparison before and during the pandemic

COVID-19 has acted as a roadblock to mental health services across the globe, and the isolation because of the lockdowns has caused various depressive problems. Through this paper, we aim to determine the effect of COVID-19 on the public's mental health. Data obtained from some specific subreddits helps us identify a pool of users whose mental condition was affected by the pandemic. Using transformer-based classification models on the selected users' Reddit activity, we found 6.4% of our user base to be free from any mental issues before the pandemic. Further experiments show that most of the users posted about their struggles due to the pandemic during the March-May period.
Design of remote monitoring system for new energy vehicles from the perspective of key technologies

The development of new energy vehicles is in a process of technology accumulation and industry formation. Vehicle enterprises and key parts enterprises are accumulating relevant information in order to improve their capabilities in the fields of technology, after-sales and service. In this paper, the remote monitoring system for electric vehicles is designed from the perspective of key technologies, which consists of two parts: the vehicle terminal and the remote monitoring platform. Each monitored electric vehicle is equipped with an on-board terminal, which CAN acquire the data of electric vehicle running status, battery voltage, current, temperature, SOC, etc. A server cluster model of data acquisition and monitoring center based on C/S and B/S hybrid architecture is proposed. This scheme can improve the real-time performance and communication reliability of the system, and has a good architecture design, which can improve the scalability and software portability of the system. It can also be used as a reference for remote monitoring systems of other devices.
SAIL-VOS 3D: A Synthetic Dataset and Baselines for Object Detection and 3D Mesh Reconstruction from Video Data

Extracting detailed 3D information of objects from video data is an important goal for holistic scene understanding. While recent methods have shown impressive results when reconstructing meshes of objects from a single image, results often remain ambiguous as part of the object is unobserved. Moreover, existing image-based datasets for mesh reconstruction don’t permit to study models which integrate temporal information. To alleviate both concerns we present SAIL-VOS 3D: a synthetic video dataset with frame-by-frame mesh annotations which extends SAIL-VOS. We also develop first baselines for reconstruction of 3D meshes from video data via temporal models. We demonstrate efficacy of the proposed baseline on SAIL-VOS 3D and Pix3D, showing that temporal information improves reconstruction quality. Resources and additional information are available at http://sailvos.web. illinois.edu.
Data Mining Technique for Prediction System of Heart Disease Using Associative Classifications

In this study, several aspects of the human body have been focused upon. This paper attempts to cast light on pre-and post-pathological conditions, man-machine interactions, human mindset, and ethics of AI. The paper emphasizes the cultural impacts of overeating, profuse drinking, and smoking habits. It uplifts the basic necessity of growing awareness schemes. Patients are seeking treatment in health care centers with the following serious pathological conditions and complications (We exclude the COVID-19 pandemic because it has been adequately publicized by media and press): Heart Attack, Stroke Cancer, Fatty liver & liver cirrhosis. Because of being the leading causes of sudden death prediction of heart attack is very important. Our main focus is to determine the best machine learning method. With optimal parameters, we evaluate the Dataset. Model Accuracy for the heart Attack Machine Learning Model was the highest for the Logistic Regression mode land it was 93.41%. On the contrary, the accuracy for Linear Regression Model was 60.10% which was the least.
Research Protocol for Exploratory Study of Entire-body PET Scans for Multiple Sclerosis (EPSMS)

Basic question: Can an entire-body positron emission tomography (PET) scanner be exploited to improve evaluation, monitoring and measurement of both peripheral and central demyelination in multiple sclerosis (MS) patients? We assume here that demyelination outside the brain may involve the spinal cord if not also possibly some of the larger peripheral nerves outside the spinal column in a manner that may be detected with the greater sensitivity and resolution of the most recent state-of-the-art PET scanners. Initial approach: Adopt a cost-effective and reduced-risk approach initially for an exploratory study by using commercially available and already FDA-approved PET amyloid imaging radiopharmaceuticals that also bind to myelin to follow radiotracer uptake in white matter, thereby tracking demyelination versus remyelination for MS patients in comparison with normal healthy subjects. This initial approach with the greater sensitivity and resolution of modern entire-body PET scanners when used for amyloid/myelin imaging should hypothetically enable monitoring of increased versus decreased activity in both the peripheral nervous system (PNS) and the central nervous system (CNS), rather than only imaging the brain as performed in most conventional imaging evaluations for MS patients. Future approach: Investigate other possible radiotracers (including those not yet FDA approved) that may be useful for monitoring demyelination, neuroinflammation and/or microglial activation in both the PNS and CNS of MS patients. In addition, PET myelin imaging by PET-CT scanners will be compared with analogous imaging by PET-MR scanners. Significance: Improved molecular imaging with new entire-body PET scanners that provide improved detection sensitivity and spatial resolution for quantitative measurement of demyelination and remyelination will support better decision-making for patient care with more robust outcome measures for monitoring therapeutic drugs evaluated in clinical trials for the treatment of multiple sclerosis.
Lemon Leaf Disease Classification Using CNN-based Architectures with Transfer Learning

Various pest-affected and citrus diseases of lemon leaf have become very severe in the temperate weather areas of southeast Asian countries. As a result, the cultivation of lemon and other citrus fruits has been badly affected. An efficient classification of these kinds of diseases can decrease the rate of loss by choosing proper pesticides in time. In this paper, we have applied some Transfer Learning-based Deep Learning models (DenseNet-201, ResNet-50, ResNet-152V2, and Xception) for a cost-effective classification of lemon leaf diseases. We have used our image dataset that is collected from the field level. Among the models we have used, Xception achieved a very higher overall accuracy of 94.34% and outperformed the other previous works.
Tips for Working with RNA and Troubleshooting Downstream Applications

High quality RNA is required for many molecular biology techniques. The isolation and use of purified RNA in the laboratory is complicated by the fact that, chemically and biologically, RNA is significantly more labile than DNA. The choice and optimization of RNA purification methods are important for successful isolation of quality RNA and consistent performance of downstream applications. In order to avoid damage or loss of RNA, caution should be exercised to avoid nuclease contamination of the RNA samples during preparation and handling.
Automatic Fetus Head Segmentation In Ultrasound Images By Attention Based Encoder Decoder Network

Fetus Head Circumference is an important biometric parameter for detecting growth abnormalities and estimating gestational age. For prenatal screening, 2D ultrasound is preferred. Because of its disadvantages like missing boundaries and low signal to noise ratio, measurement of head circumference is time consuming and challenging. So, precise automatic segmentation is needed to help sonographers in efficient head circumference measurement. We propose an encoder decoder network having dense blocks that strengthen feature propagation and channel attention in bottleneck to enhance the important features. The network performs better than UNet. It has lesser inference time, takes 7 times lesser training time, 5 times lesser parameters and storage size than UNet. It can be used in real time because of the average inference time of 0.0037 seconds per batch of 4. The proposed network achieved an average difference of ${0.77\pm 2.91{\mathrm{m}}\mathrm{m}}$ and an average absolute difference of $\mathbf{2.16}{\pm}$ 2.09 mm on the HC18 challenge test dataset.
Marine Propulsor Noise Investigations in the Hydroacoustic Water Tunnel “G.T.H.”

Since its first use in 1988, the Grand Tunnel Hydrodynamique (G.T.H.) has proved that it is a rather unique experimental facility to conduct innovative experiments to improve the design of ship propulsors specially with regard to cavitation and hydroacoustic performances. This paper presents a review of the experimental capabilities of the tunnel and of the measuring techniques used, with emphasis on the significant advance in propulsor noise investigations obtained from the model tests performed in this facility.
Identification of key genes and their microRNAs in glioma: Evidence from bioinformatic analysis

Background Glioma is one of the most common primary intracranial tumors. Although a lot of studies have been conducted to elucidate the pathogeny of glioma, the molecular mechanisms are still unclear because of its complex biological functions. Methods To identify the candidate genes in the carcinogenesis and progression of glioma, microarray datasets GSE4290, GSE122498 and GSE2223 were downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified, and performed function enrichment of DEGs by Gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. The protein-protein interaction network (PPI) was constructed using STRING and Cytoscape to find hub genes. Survival analysis and GEPIA database was conducted to screen and validate critical genes. Analysis of miRNA and genetic alteration was used to explore and predict the molecule mechanism. Results A total of 150 DEGs were identified, consisting of 54 downregulated genes and 96 upregulated genes. The enriched functions and pathways of the DEGs include regulation of transportation, synaptic transmission and SH3 domain binding. Fifteen hub genes were identified and biological process analysis revealed that these genes were mainly enriched in SH3 domain binding, neuron projection terminus, mitotic nuclear division, condensed chromosome and affected the brain development. Survival analysis showed that VAMP2, PPP3CA, DLGAP5, KIF14, REPS2, CENPU, KNTC1 and SMC4, may be involved in the carcinogenesis, invasion or recurrence of glioma. These 8 hub genes, which were related miRNAs and genetic changes were commonly involved in the development of glioma, were closely associated with tumor grade. Conclusion DEGs and hub genes identified in the present study help us understand the molecular mechanisms of carcinogenesis and progression of glioma, and provide candidate targets for diagnosis and treatment of glioma.
Collaborative techniques for achieving spatial diversity in wireless networks

Recently, there has been lots of interest in techniques that achieve spatial diversity to improve the reliability and/or the rate of the transmission in wireless networks. To achieve spatial diversity the transmitters need to be equipped with more than one antenna. To obtain maximum diversity gain, the fading among these antennas should be uncorrelated and hence the antennas should be well separated. This is usually not possible due to the cost and the small size of the wireless devices. In this case, the only way to achieve spatial diversity is a new technique which is known as cooperative diversity. In this technique, diversity is achieved through collaboration between the transmitting nodes in the network In this thesis, we develop a collaboration protocol and a practical coding strategy for the collaborative communication in a three node network with no knowledge of channel state information (CSI) at the transmitter side. Unlike most other works, we have used a variable time-fraction scheme as the basis of our protocol and will show that this protocol achieves full diversity while providing a noticeable coding gain. Then, assuming the availability of the channel state information at the transmitters via a simple feedback from the destination to the relay or to both the source and the relay, we collaboration protocols for both feedback scenarios. The performance of all of these protocols has been obtained using Monte Carlo simulations and has been compared with the outage probability of a single transmitter scenario. We have also obtained constant gain contours that demonstrate the loci of the relay that guarantees a minimum gain for all three scenarios.
ADOPTION AND IMPACTS OF NATURAL RESOURCE CONSERVATION IN ARSI ZONE, ETHIOPIA

Land degradation in Ethiopia alone accounts for 8% of the global total degradation. The most serious problem concerning country’s land resources, however, is the removal of fertile topsoil by water. Population pressure which results to intensive cultivation, overgrazing deforestation and inappropriate land use practices are the most serious cause of soil erosion in Ethiopia which is more severe in the highlands areas where, 85% of the human and 77% of livestock population are living and agriculture is intensive. To reverse the situation the government of Ethiopia designed policy and programs which are holistic and landscape wide approaches. Based on the strategies, different soil and water management programs have been implemented throughout the country and different practices were introduced to farmers as well for more than ten years. The central questions of this research were, whether farmers adopt these soil and water conservation practices; and if yes, do these soil and water conservation interventions have an impact in improving crop production in value per hectare and gross annual income of participating households? If yes, how much is the impact? Based on above objectives, the research was conducted in Arsi zone and data was collected from 202 respondents in representative districts. Both adopters and counterfactual respondents were included. Educational background of households, farming experiences, size of landholding, slop of plot, degree of vulnerability of the districts and extension contact significantly and positively affect adoption probability while land fragmentation was significant and negatively influencing adoption of soil and water conservation (SWC) activity. Though it was not statistically significant, SWC adoption has positive impact on productivity, gross production and income of the household. The average treatment effect on treated (ATT) was 6358.86 ETB of total household income which could be proxy for farm level productivity and 85.35 quintal of total annual farm production.
Tuning Biological Nutrient Removal Plants

Tuning Biological Nutrient Removal Plants increases interest in tuning to enhance both performance and capacity, to provide insight into typical plant operating characteristics, and to stimulate operators' interest in studying the behaviour of their own plants. The book focuses on understanding of plant behavioural characteristics so that optimum performance can be achieved and maintained. Tuning Biological Nutrient Removal Plants is carefully organized to cover: The approach is practical and the use of mathematics is kept to a minimum and information is supplied in graphical and tabular form. Real operating data from a wide range of plant experiences is included. The book draws on the generosity of many Australian plant owners in permitting their plant data to be incorporated. Not all process types are covered but the tuning principles expounded are universally applicable. The capacity and performance capabilities of a plant are not fixed; both are amenable to on-going enhancement through systematic and enthusiastic effort. The book will help to set new benchmarks in plant operation. Tuning Biological Nutrient Removal Plants is a valuable resource for sewage treatment operations and operations support personnel, sewage process design engineers - operating authorities, consultants, contractors, operators of industrial wastewater treatment plants and sewage treatment lecturers in chemical engineering departments and other training organisations. ISBN: 9781780404837 (eBook) ISBN: 9781780404820 (Print)