Miracle-dz/UniFAR-FTUs huggingface dataset:
SIGACT News Online Algorithms

Online algorithms with advice is an area of research where one attempts to measure how much knowledge of the future is necessary to achieve a given competitive ratio. The lower bound results give robust bounds on what is possible using semi-online algorithms. On the other hand, when the advice is of an obtainable form, algorithms using advice can lead to semi-online algorithms. There are strong relationships between advice complexity and randomization, and advice complexity has led to the introduction of the ﬁrst complexity classes for online problems. This survey concerning online algorithms with advice explains the models, motivates the study in general, presents some examples of the work that has been carried out, and includes a fairly complete set of references, organized by problem studied.
Heart Attack Prediction Using Data Mining Techniques

Application of computer data processing in medical domain has been witnessing several significant revolutions in recent days. Particularly data mining has played an important role in knowing the hidden patterns of clinically relevant data sets. This technique can be employed for the diagnosis of heart attack. This is due to new information on the nature of diseases and their diagnostic criteria have been increasing at a tremendous level. Nevertheless, the data on certain illnesses always is in heterogeneous in nature. It is highly difficult to interpret such a voluminous data to arrive at a strong conclusion. Hence an organized data is mandatory. Diagnosis to carry out a suitable treatment is a difficult task for some fatal diseases. Doctor requires a precise diagnosis out of many clinical reports of the individual concerned. Therefore automation of data and mining would be advantageous for a medical professional to initiate treatment regime. Further computer machine processing neglects error and extraordinary time consumption for prediction. Data mining techniques enhance a comparative knowledge base and user friendly working environment. It helps to get the accuracy of the heart disease diagnosis. Keywords— Data mining, Clustering techniques, Heart attack prediction, Proposed algorithm, J48, Naïve, CART, KNN, NN.
http://ijere.iaescore.com Factors affecting readiness toward online learning among medical students during COVID-19 pandemic in

Info 2022 The rapid spread of transmission COVID-19 leads to the government applied a regulation to close schools and colleges for a while, as an effort to stop the spread. It also has an impact on medical student education that can be threatened by their educational system. This study identified and analyzed the factors affecting the readiness of medical students for studying online during the COVID-19 pandemic. This was an observational analytic study with a cross-sectional approach. A total of 3,331 respondents from all over Indonesia participated in this study. An online questionnaire by Indonesian Medical Student Senate Association ( Ikatan Senat Mahasiswa Kedokteran Indonesia/ ISMKI) was used and distributed from April 9th, 2020 to June 21st, 2020. A Chi-square test (alternative: Fisher exact test) and logistic regression with significance alpha=0.05 used to analyze. The university origin (p=0.031, OR=1.130 (1.012–1.263)), internet connections (p=0.001, OR=1.387 (1.134–1.696)), and learning interaction models (p<0.000, OR=0.463 (0.384–0.558)) were affected medical students’ readiness for online
Trust‐based probabilistic search with the view model of peer‐to‐peer networks

The fundamental challenge confronting peer‐to‐peer (P2P) resource‐sharing networks is to provide efficient and scalable search services in a large‐scale, open and dynamic environment. However, current search mechanisms are not efficient or scalable enough with the expansion of the systems. To improve search efficiency, this paper proposes the trust‐based probabilistic search mechanism, called the Preferential Walk (P‐Walk). Every peer adjusts its neighbors' rank continuously according to their performance in previous searches. The basic principle of the P‐Walk is that neighbors with higher ranks in the question domain have higher probabilities of being queried. Extensive simulation results show that the P‐Walk can significantly improve the success rate of the query and the response time whilst also effectively reducing traffic volume and controlling malicious behavior. Furthermore, we measure the rank distributions of the peers and find their power‐law‐like trends based on a Gnutella‐like network. Implications are drawn from the analysis of the rank distributions. The P‐Walk mechanism works effectively in a P2P view model that provides appropriate views of the P2P network for queries of different types. Copyright © 2006 John Wiley & Sons, Ltd.
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Permanent magnet (PM) synchronous motor (PMSM) is widely employed in trams, robot industries, and national defense applications. The demagnetization of the rotor magnet would cause adverse effects on the operation of PMSM. By implementing fault monitoring and severity evaluation of the rotor magnet, unscheduled maintenance and economic losses caused by demagnetization fault can be avoided. This paper introduces a rotor demagnetization fault diagnosis and severity evaluation method based on vibrating signal analysis. The vibration analysis model of rotor demagnetization fault is developed. Firstly, the fast Fourier transform is applied to analyze vibration signals. Meanwhile, frequency-domain fault features are extracted as condition indicators (CIs) for PMSM demagnetization monitoring. A statistical diagnosis method based on Chebyshev’s inequality is then presented to identify the PMSM demagnetization fault. Finally, the machine learning classification algorithm based on linear discriminant analysis (LDA) is used to identify the demagnetization degree. Experimental results show that the proposed fault diagnosis approach can provide an excellent solution for the severity evaluation of PMSM demagnetization fault. Keywords—condition monitoring, demagnetization, LDA classification, machine learning, PMSM.
Statistical Applications in Genetics and Molecular Biology Low-Order Conditional Independence Graphs for Inferring Genetic Networks

As a powerful tool for analyzing full conditional (in-)dependencies between random variables, graphical models have become increasingly popular to infer genetic networks based on gene expression data. However, full (unconstrained) conditional relationships between random variables can be only estimated accurately if the number of observations is relatively large in comparison to the number of variables, which is usually not fulfilled for high-throughput genomic data. Recently, simplified graphical modeling approaches have been proposed to determine dependencies between gene expression profiles. For sparse graphical models such as genetic networks, it is assumed that the zeroand first-order conditional independencies still reflect reasonably well the full conditional independence structure between variables. Moreover, loworder conditional independencies have the advantage that they can be accurately estimated even when having only a small number of observations. Therefore, using only zeroand first-order conditional dependencies to infer the complete graphical model can be very useful. Here, we analyze the statistical and probabilistic properties of these low-order conditional independence graphs (called 0-1 graphs). We find that for faithful graphical models, the 0-1 graph contains at least all edges of the full conditional independence graph (concentration graph). For simple structures such as Markov trees, the 0-1 graph even coincides with the concentration graph. Furthermore, we present some asymptotic results and we demonstrate in a simulation study that despite their simplicity, 0-1 graphs are generally good estimators of sparse graphical models. Finally, the biological relevance of some applications is summarized.
In-situ measurement of the vibration decay characteristics of alluvial soil deposits

An in-situ test was performed to investigate the decay of wave motions of alluvial soil deposit at Luju Industrial Park. The obtained soil layer damping parameters were used to evaluate the effects of traffic vibrations from No. 1 Provincial Road and the Kaohsiung rapid transit train on high-tech facilities. Falling weights of 200 kN and 320 kN were used to generate surface vibrations to simulate traffic vibrations of different frequencies. Vibration time histories of different distances from the source were measured and recorded within 500 m from the source. Data processing and analysis were performed to evaluate the effect of soil layers on wave propagation and to determine the vibration damping characteristics of soil deposits. The analysis results of this study showed the coefficient of material attenuation α of the alluvial soil at the testing site in directions X, Y, and Z to be 0.0088, 0.0085 and 0.0064 m, respectively, and the attenuation rates m were 1.19, 1.11, and 1.02, respectively.
Light-Weight Deep Learning Model for Accelerating the Classification of Mango-Leaf Disease

Mango leaf diseases represent a serious threat to world agriculture, necessitating prompt and accurate detection to avert catastrophic effects. In response, this study suggests a light-weight, deep learning-based method for automatically classifying mango leaf diseases. The model is based on the original DenseNet architecture, which is well known for its effectiveness in image classification tasks. Custom layers have been added over the existing layer of the original DenseNet model. The proposed model has been compared with other existing pre-trained models. Based on comparisons, the proposed model, DenseNet78, proved to be efficient even on a relatively small dataset, where the conventional model failed. The proposed model ensured generalization across regions, disease variants, and diverse datasets of mango leaves. The results demonstrate that the fine-tuned DenseNet architecture (DenseNet78), along with an ideal growth rate, modifying block size, and a number of layers, provides optimum accuracy, with 99.47% accuracy in identifying healthy mango leaves and 99.44% accuracy in detecting various mango leaf diseases. The results also demonstrate that the model is effective in accelerating the training process because of careful comparative analysis of all the available alternatives, including the most effective combination of optimizers, learning rate schedulers, and loss functions. The study's conclusion is an automated approach for diagnosing mango leaf disease using an improved and optimized DenseNet architecture (DenseNet78). Doi: 10.28991/ESJ-2024-08-01-03 Full Text: PDF
Highly efficient Rhizobium rhizogenes-mediated hairy root transformation for gene functional study in mung bean (Vigna radiata (L.) R. Wilczek)

Mung bean (Vigna radiata (L.) R. Wilczek) is one of the economically important legume crops in Asia. The demand for high quality mung bean seeds is currently increasing. Moreover, the crop can fix the atmospheric nitrogen. Those make the crop is suitable for sustainable agriculture and a major source of the plant-based protein foods. Functional genomics study in mung bean is required to comprehend the molecular mechanisms behind agronomic traits and elevate the crop trait improvement process; however, it is severely hampered due to the lacking of efficient and large-scale genetic analysis tool. Rhizobium rhizogenes-mediated hairy root transformation is a quick and efficient alternative to investigate root-specific processes and interactions in different species. In this study, we developed a protocol to generate hairy roots with low cost and high transformation efficiency that has not been reported in mung bean. Using the method, nearly 100% of the R. rhizogenes infected plants formed hairy roots and carried at least three transgenic roots harboring the desired construct per plant. It only required twenty days to complete the whole transformation process. In addition, the composite plants, that are composed transgenic roots and wild-type shoot, were used to examine with the fixing nitrogen bacteria and showed the GUS expression in nodules. In conclusion, our highly efficient R. rhizogenes-mediated transformation system provides high throughput genetic analysis assay for functional genetics study, and biotechnological application in mung bean.
Creating and Developing a High-Throughput Covert Channel via Program Execution

A covert communication channel facilitates direct data transfer between two parties through pre-communication knowledge agreements, ensuring secure and confidential transmission of information. However, the existing covert channels suffer from performance limitations, specifically in terms of throughput and speed. The encoding techniques employed in covert channels can be time-consuming and have limited data transfer capabilities. Furthermore, the ability of covert channels to handle files with different formats has not been sufficiently explored. This paper introduces a high-performance implementation of a covert channel that leverages Java exception handling during program execution. To optimize the covert channel’s performance, this research explores the use of several encoding methods, including ASCII, Byte, Hexadecimal, Base64, and Huffman coding. The proposed covert channel’s performance is evaluated and analyzed for various coding methods. To study the impact on performance, multiple file formats, including text, audio, and video, were used in the experiment. Experimental results showed that the hexadecimal coding method improves the throughput and decreases the time delay of the covert channel. This is attributed to its ability to minimize the number of tries before encountering an “Index Out of Bounds” exception. On the contrary, the Base64 method is found to be inefficient as it produces longer strings than the original inputs, resulting in increased time delays during data transfer. The best results are achieved when applying the hexadecimal method with Huffman coding. It takes 6241 milliseconds to transmit a 12.8-megabyte text file, with a throughput of 23116 bits per millisecond.
PHOSPHORUS REDUCTION IN EFFLUENT FROM SUBSURFACE FLOW CONSTRUCTED WETLANDS FILLED WITH TIRE CHIPS

Subsurface flow constructed wetlands (SFCWs) are an increasingly common method for on-site treatment of wastewater. Gravel is the most common type of wetland fill medium, but the overabundance of scrap tires has initiated alternate uses. Chipped rubber tires are less dense and less expensive than gravel medium. This study determines the treatment efficiency of SFCWs filled with gravel or tire chip media to reduce P content in domestic wastewater, both in field practices and in experimental columns. The P content in the influent and effluent of six SFCWs filled with tire chip medium and six SFCWs filled with gravel were monitored for 5 to 16 months. Additionally, five columns filled with tire chips and five filled with gravel were evaluated for 3 months for their ability to reduce P. There was a clear difference between medium types in reduction of P. Soluble P in the effluent averaged 1.6 ± 1.0 mg l-1 in the tire chip-filled wetlands and 4.8 ± 3.2 mg l-1 in the gravel-filled wetlands. Phosphorus reduction was 60 ± 13% in columns filled with tire chips and only 17 ± 15% in columns filled with gravel. Greater P reduction most likely occurred in the tire chip-filled wetlands and columns because the Fe from exposed wires in shredded steel-belted tires complexed with P to create an insoluble P compound. Tire chips may be a better fill medium for SFCWs than gravel because of higher porosity, lower cost, and greater reduction of P in effluent.
Mapping Possible Flowpaths of Contaminants through Surface and Cross-borehole Spectral Time-domain Induced Polarization

In the Capitol Region of Denmark, several sites are contaminated due to various human activities. A large fraction of these sites are in clayey moraines, where the flow of pollutants predominantly occurs in sand lenses or sandy layers. Boreholes are normally drilled in order to describe the geology, but boreholes alone do not always provide the necessary resolution to map out such sand lenses, which is why the Capitol Region initiated a project to evaluate different cross-borehole geophysical methods for mapping sand lenses/layers. A test site was established in an uncontaminated gravel pit near Hedehusene, Zeeland, Denmark (Kallerup grusgrav).
The Western Mediterranean Sea

Chapter 12 The Western Mediterranean Sea Miquel Canals,1 Isabel Cacho,1 Laurent Carozza,2 José Luis Casamor,1 Galderic Lastras1 and Anna Sànchez1 1 GRC Geociències Marines, Dept. d’Estratigrafia, Paleontologia i Geociències Marines, Universitat de Barcelona, Campus de Pedralbes, Mart́ı i Franquès s/n, 08028 Barcelona, Spain 2 UMR 5602 Géode Géographie de l’Environnement, Maison de la Recherche de l’Université du Mirail 5, Allées A. Machado, 31058 TOULOUSE Cedex 1, France
Classification of gene expression from RNA-seq data for pancreatic cancer prognosis using ensemble learning

Gene expression analysis of transcriptomic data enables us to identify changes in gene expression under some biological conditions. Ribonucleic acid (RNA) sequencing (RNA-seq) data can show genetic mutations and intricate biological process connections, which are useful in the diagnosis and treatment of cancer. The existing classical differential gene expression analysis techniques are prone to false negatives and false positives with smaller datasets. With the improvements in the field of machine learning (ML), we want to build an ensemble learning model for the classification of differentially expressed genes (DEGs) from RNA-seq data for pancreatic cancer. The gene expression data was obtained from the Cancer Genome Atlas-Pancreatic Adenocarcinoma Project database. In this paper, we are proposing a stacking classifier with cross-validation called the stacking CV classifier, which is an ensemble of K-nearest neighbor, random forest, gradient boosting, and logistic regression classifiers for effective classification of DEGs. We also made a comparative analysis between the results of our ensemble model and existing models in the literature. The results of our model were competitive (accuracy 96% and area under the curve 0.99) against the stand-alone and existing gene classification models. Our ML-based model is a promising tool for classifying DEGs based on gene expression patterns.
The uncertainty research of visual positioning in different mediums

In engineering application, there existed deviation when light spreads in different mediums. At the same time miscellaneous medium and random fluctuation may lead to inaccuracy of visual positioning. It explored visual positioning technology in condition of different mediums for binocular camera. Firstly focused on medium material and impurity in real environment, the refractive index formula is derived through binocular visual positioning principle. Then considering deviation when light spreads in different mediums, deductive process of vision positioning is realized according to calculated refractive index. Because cloud model can better describe randomness and fuzziness. It was introduced to deal with problems of random fluctuation in different mediums. Finally simulation was designed to prove the accuracy improvement in different mediums. The result showed that accuracy error had decreased by 66.7% after considering transparent organic mediums. Compared with not knowing refractive index, positioning error had decreased by 49.7%.
Effects of reactive oxygen species and mitochondrial dysfunction on reproductive aging

Mitochondria, the versatile organelles crucial for cellular and organismal viability, play a pivotal role in meeting the energy requirements of cells through the respiratory chain located in the inner mitochondrial membrane, concomitant with the generation of reactive oxygen species (ROS). A wealth of evidence derived from contemporary investigations on reproductive longevity strongly indicates that the aberrant elevation of ROS level constitutes a fundamental factor in hastening the aging process of reproductive systems which are responsible for transmission of DNA to future generations. Constant changes in redox status, with a pro-oxidant shift mainly through the mitochondrial generation of ROS, are linked to the modulation of physiological and pathological pathways in gametes and reproductive tissues. Furthermore, the quantity and quality of mitochondria essential to capacitation and fertilization are increasingly associated with reproductive aging. The article aims to provide current understanding of the contributions of ROS derived from mitochondrial respiration to the process of reproductive aging. Moreover, understanding the impact of mitochondrial dysfunction on both female and male fertility is conducive to finding therapeutic strategies to slow, prevent or reverse the process of gamete aging, and thereby increase reproductive longevity.
Sensitivity of Simulated Aerosol Properties Over Eastern North America to WRF‐Chem Parameterizations

Aerosol properties and their climatic feedbacks are characterized by high uncertainty in both global and regional model simulations. We explore sources of uncertainty in the representation of aerosol properties using an ensemble of simulations performed at 24‐km resolution with WRF‐Chem over eastern North America. The sensitivity of aerosol optical depth (AOD) and near‐surface fine particle concentrations (PM2.5) to planetary boundary layer (PBL) and aerosol schemes (modal with secondary organic aerosol versus sectional but excluding secondary organic aerosol), as well as different emission inventories (National Emission Inventory [NEI] 2005 versus 2011) is examined. We quantify the spread among ensemble members with respect to the model setup and compute statistical metrics to identify the run configuration that exhibits greatest fidelity relative to observations of aerosol and meteorological properties. Use of the Modal Aerosol Dynamics Model for Europe/Secondary Organic Aerosol Model scheme leads to highest agreement with Moderate Resolution Imaging Spectroradiometer clear‐sky AOD observations particularly when the 2005 NEI is used (with either PBL scheme). These members exhibit small negative mean fractional bias over the simulation domain (<2%) and relatively high spatial correlation in summertime mean monthly AOD (>0.5). The aerosol scheme and NEI dominate the ensemble spread in AOD. Near‐surface PM2.5 is also dependent on PBL scheme and is best reproduced in runs adopting a sectional approach and emissions for 2011. Thus, WRF‐Chem configuration associated with highest agreement with AOD observations is not the same as for PM2.5, possibly reflecting the importance of columnar water vapor in dictating AOD or other unexplored uncertainties linking surface mass concentrations to column optical properties.
THE ROLE OF REMOTE SENSING, GEOPHYSICS AND CROP MODELING IN IRRIGATION MANAGEMENT: REVIEW AND FUTURE PERSPECTIVE

The food and water security are the most critical issues across the globe owing to the continuous growing population. The agriculture sector is a largest consumer of fresh water and unwise use of water and poor irrigation systems are leading to rapid depletion of freshwater sources. Recently precision agriculture has emerged as an effective tool to improve the crop productivity while saving irrigation water. Agriculture is a complex system owing to different soil and climatic conditions, crops