Miracle-dz/UniFAR-FTUs huggingface dataset:
Easy-Learning Website

— The aim is to describe the design and development of an e-learning website that offers an enhanced learning experience. The study identifies the key features and functionality required in an e-learning platform and how these can be integrated into the design and development of a website. Thepaper also highlights the benefits of e-learning platforms for both learners and instructors and the challenges faced in developing such websites. An easy learning website is a platform that provides educational resources and tools designed to help learners acquire knowledge in a simple, efficient, and engaging manner. The website's primary goal is to offer learners a variety of educational materials, including videos, audio recordings, articles, interactive quizzes, and games, to facilitate learning and retention. The website's content is carefully crafted to cater to learners of all ages and levels, and the website's interface is user-friendly and intuitive, making it easy for learners to navigate and find the information they need. With its vast array of educational resources, an easy learning website is an excellent tool for anyone looking to expand their knowledge and acquire new skills
Adsorption of phosphate in aqueous solution by ash from the fruit peel of Caryocar Coriaceum WITTM: Characteristics and adsorption behavior

francisco José De Paula Filho (  francisco. lho@ufca.edu.br ) Federal University of Cariri: Universidade Federal do Cariri https://orcid.org/0000-0002-6253-461X Yago Neco Teixeira Federal University of Cariri: Universidade Federal do Cariri Vinícius Pereira Bacurau Universidade Federal de São Carlos: Universidade Federal de Sao Carlos Anderson Zhong Fan Universidade Federal de São Carlos: Universidade Federal de Sao Carlos Jorge Marcell Coelho Menezes Federal University of Cariri: Universidade Federal do Cariri Thiago Mielle Brito Ferreira Oliveira Federal University of Cariri: Universidade Federal do Cariri Raimundo Nonato Pereira Teixeira Regional University of Cariri: Universidade Regional do Cariri Henrique Douglas Melo Coutinho Regional University of Cariri: Universidade Regional do Cariri Ronaldo Ferreira do Nascimento Universidade Federal do Ceara Centro de Ciencias
Modeling and control of an unstable system using probabilistic fuzzy inference system

Summary: A new type Fuzzy Inference System is proposed, a Probabilistic Fuzzy Inference system which model and minimizes the eﬀects of statistical uncertainties. The blend of two diﬀerent concepts, degree of truth and probability of truth in a unique framework leads to this new concept. This combination is carried out both in Fuzzy sets and Fuzzy rules, which gives rise to Probabilistic Fuzzy Sets and Probabilistic Fuzzy Rules. Introducing these probabilistic elements, a distinctive probabilistic fuzzy inference system is developed and this involves fuzziﬁcation, inference and output processing. This integrated approach accounts for all of the uncertainty like rule uncertainties and measurement uncertainties present in the systems and has led to the design which performs optimally after training. In this paper a Probabilistic Fuzzy Inference System is applied for modeling and control of a highly nonlinear, unstable system and also proved its eﬀectiveness.
Rural Economic Development through ‘Basuduai’: A Portrait of Business Model Canvas on Cattle Breeding Partnership in West Sumatera

The concept of ‘Basuduai’ on cattle breeding investment has been popularly implemented in West Sumatra for decades. Currently, this partnership modelling is still implemented in several villages and rural areas. This study aims to describe ‘basuduai’ as a socio -entrepreneurship approach in a simple descriptive method using the Business Model Canvas (BMC). Perhaps this model could also be implemented in other rural areas as an economic development approach. The data was collected through ten in-depth interviews and two focus group discussions (FGD) among the local partners in Payakumbuh, West Sumatra. Content and context analysis is conducted to describe the categorical theme for results analysis. The finding shows that cattle breeding investment as the 'Basuduai' system is an indigenous approach in the local community in West Sumatra. However, based on the business model canvas, the role of community leader should be prioritized to balance the system. Hence, the need for a formal socio-entrepreneurship entity as the middleman should be developed. Perhaps, this study could contribute to the development of rural economic programs and could be replicated in other rural areas in Indonesia and also in other countries. Discussion and limitations are also stated in the study.
Secure And Transparent Kyc for Banking System Using Interplanetary File System (Ipfs) And Blockchain Technology

: The know your customer or know your client (KYC) is a guideline for the banking system to validate a customer using identity, appropriateness, risk assessment in establishing a banking relationship. With the growing concern of security, the KYC process is complex and involves a high cost for completing for a single customer. In this work, we propose an economical, swift, secure, and transparent platform for KYC document veriﬁcation for the Banking system through InterPlanetary File System (IPFS) and blockchain technology. The proposed system allows a customer to open an account at one Bank, complete the KYC process there, and generate a hash value using the IPFS network and share it using the blockchain technique. Upon receiving the private key, any Bank/ﬁnancial organization can retrieve, store customer data (i.e., KYC) securely using IPFS network if the customer wishes to open another account in that Bank/ﬁnancial organization. The proposed system can save time, money, and repetitive work during the KYC process when someone tries to open an account at multiple Banks.
Complexity and Approximability of Hyperplane Covering Problems

The well known N.Megiddo complexity result for Point Cover Problem on the plane is extended onto d-dimensional space (for any fixed d > 1). It is proved that MindPC problem isL-reducible to Min(d + 1)PC problem, therefore for any fixedd > 1 there is no PTAS for MindPC problem, unless P = NP.
pyESDv1.0.1: an open-source Python framework for empirical-statistical downscaling of climate information

Abstract. The nature and severity of climate change impacts vary significantly from region to region. Consequently, high-resolution climate information is needed for meaningful impact assessments and the design of mitigation strategies. This demand has led to an increase in the application of empirical-statistical downscaling (ESD) models to general circulation model (GCM) simulations of future climate. In contrast to dynamical downscaling, the perfect prognosis ESD (PP-ESD) approach has several benefits, including low computation costs, the prevention of the propagation of GCM-specific errors, and high compatibility with different GCMs. Despite their advantages, the use of ESD models and the resulting data products is hampered by (1) the lack of accessible and user-friendly downscaling software packages that implement the entire downscaling cycle, (2) difficulties reproducing existing data products and assessing their credibility, and (3) difficulties reconciling different ESD-based predictions for the same region. We address these issues with a new open-source Python PP-ESD modeling framework called pyESD. pyESD implements the entire downscaling cycle, i.e., routines for data preparation, predictor selection and construction, model selection and training, evaluation, utility tools for relevant statistical tests, visualization, and more. The package includes a collection of well-established machine learning algorithms and allows the user to choose a variety of estimators, cross-validation schemes, objective function measures, and hyperparameter optimization in relatively few lines of code. The package is well-documented, highly modular, and flexible. It allows quick and reproducible downscaling of any climate information, such as precipitation, temperature, wind speed, or even short-term glacier length and mass changes. We demonstrate the use and effectiveness of the new PP-ESD framework by generating weather-station-based downscaling products for precipitation and temperature in complex mountainous terrain in southwestern Germany. The application example covers all important steps of the downscaling cycle and different levels of experimental complexity. All scripts and datasets used in the case study are publicly available to (1) ensure the reproducibility and replicability of the modeled results and (2) simplify learning to use the software package.
"The Nexus of AI and Cybersecurity: An In-depth Analysis of Machine Learning and Deep Learning Techniques in Anomaly Detection"

— This scholarly exploration delves into the burgeoning domain of Artificial Intelligence (AI), with particular focus on its application in anomaly detection, a pivotal component of modern cybersecurity measures. The objective of this discourse is to meticulously analyze the interplay of AI's two primary subfields - Machine Learning (ML) and Deep Learning (DL) - in the realm of anomaly detection, with an emphasis on their efficacy in forestalling fraudulent activities and cyber-attacks. The research methodology adopted is a rigorous synthesis of both quantitative and qualitative approaches, with an overarching emphasis on empirical data analysis. This approach allowed for a comprehensive exploration of the myriad facets of AI, ML, and DL, and their implications for anomaly detection. The key findings suggest that the integration of advanced AI techniques, particularly ML and DL, can significantly bolster the potency of anomaly detection systems. This, in turn, can create a fortified bulwark against cyber threats, thereby curbing potential financial losses and ensuring regulatory compliance. Nevertheless, the research also unveils certain challenges, notably the need for high-quality data, the complexity of developing and tuning models, and the ongoing requirement for human oversight to manage false positives and negatives. This research, therefore, holds profound implications for both industry and academia. From an industrial perspective, it elucidates a path towards enhanced cybersecurity systems, whilst from an academic viewpoint, it enriches the existing compendium of knowledge in this rapidly evolving field. (Abstract)
Retrieval Algorithm for Aerosol Effective Height from the 1 Geostationary Environment Monitoring Spectrometer (GEMS)

22 An algorithm for aerosol effective height (AEH) was developed for operational use 23 with observations from the Geostationary Environment Monitoring Spectrometer 24 (GEMS). The retrieval technique uses the slant column density of the oxygen dimer 25 (O 2 -O 2 ) at 477 nm, which is converted into AEH after retrieval of aerosol and surface 26 optical properties from GEMS operational algorithms. The AEH retrieval results show 27 significant AEH values and continuously monitor aerosol vertical height information in 28 severe dust plumes over East Asia, and the collection of plume height information for 29 anthropogenic aerosol pollutants over India. Compared to the AEH retrieved from 30 Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), the retrieval results show 31 insignificant bias with a standard deviation of 1.4 km for the AEH difference over the 32 GEMS observation domain from January to June 2021 due to uncertainty in input 33 parameters for aerosol and surface. The AEH difference depends on aerosol optical 34 properties and surface albedo. Compared to the aerosol layer height obtained from the 35 tropospheric monitoring instrument (TROPOMI), differences of 0.78 ± 0.81 and 1.16 ± 36 0.92 km were obtained for pixels with single scattering albedo (SSA) < 0.90 and 0.90 < 37 SSA < 0.95, respectively, with significant dependence on aerosol type. 38
A fault tolerant tracking control for a quadrotor UAV subject to simultaneous actuator faults and exogenous disturbances

ABSTRACT An observer-based robust adaptive Fault Tolerant Control approach is proposed in this paper to tackle the problem of trajectory tracking for a quadrotor unmanned aerial vehicle (UAV) suffering simultaneous actuator faults, exogenous disturbances and actuator saturation limits. An adaptive fuzzy state observer is proposed to estimate the immeasurable states by using fuzzy logic systems to approximate the unknown nonlinear functions of the uncertain system model. Based on the estimates of the fuzzy observer, an Integral Terminal Sliding Mode Controller that guarantees finite time convergence of the states to a small neighbourhood of zero, even under impaired conditions, is developed. Stability analysis was carried over using the Lyapunov method. The proposed approach was implemented to a quadrotor UAV and its performance was assessed under nominal conditions, and by subjecting the quadrotor to disturbances, simultaneously occurring actuator faults and input saturation limits. Excellent tracking performance and robustness even under worst-case scenarios are among the positive features of the proposed approach.
RoGeR v3.0.3 – a process-based hydrological toolbox model in Python

Although water availability and water quality are equally important for an effective water resources management, to date, a combined representation of soil water balance components and water quality components in Python is not available. The new RoGeR toolbox contains models that can be used for the quantification of hydrological processes, fluxes and stores, but also solute transport processes based on StorAge selection. This study presents the code structure and functionalities of RoGeR developed as a scientific model toolbox following defined open-source software guidelines. RoGeR uses five 10 different computational back ends covering just-in-time compilation, parallelism and graphical-processing units that might be used for optimizing computational performance. We show that graphical-processing unit computing have the greatest potential to improve computation time and energy usage especially for large modelling experiments. A simple modelling experiment highlights the capabilities of the new RoGeR model toolbox. We simulated the soil water balance, stable water isotope ( 18 O) transport and corresponding travel time distributions through the soil of a grassland plot for a three-year period. 15 Further development of RoGeR as a scientific software is possible and also needed due to the current limitations for a variety of process components and easily possible due to the open software architecture.
Central Schur rings over the projective special linear groups

ABSTRACT In [1], Bannai presents a fusion condition and uses this to consider central Schur rings (S-rings) over the simple groups PSL(2,q) where q is a prime power. In this paper, we concretely describe all such S-rings in terms of symmetric S-rings over cyclic groups. The final section discusses counting these.
Realtime stereo matching using maximally stable extremal regions

In this paper we present a novel stereo matching algorithm Cyclops. The algorithm uses Maximally stable extremal regions (MSER) for localizing parts of the image in rectiﬁed stereo image pair. Each detected MSER is normalized to a ﬁxed size image. The normalized MSER regions are matched in the stereo image pair taking into account the epipolar line constraints. Matched MSER pairs are used to calculate the disparity image using the semi-global matching technique. Cyclops algorithm reduces stereo matching computation time and memory usage. The proposed algorithm is therefore useful for real time stereo vision.
DETACTION AND CLASSIFICATION OF LUNG ABNORMALITIES BY USE OF CNN

-Automatic defects detection in CT images is very important in many diagnostic and therapeutic applications. Because of high quantity data in CT images and blurred boundaries, tumor segmentation and classification is very hard. This work has introduced one automatic lung cancer detection method to increase the accuracy and yield and decrease the diagnosis time. The goal is classifying the tissues to three classes of normal, benign and malignant. In MR images, the amount of data is too much for manual interpretation and analysis. During past few years, lung cancer detection in CT has become an emergent research area in the field of medical imaging system. Accurate detection of size and location of lung cancer plays a vital role in the diagnosis of lung cancer. The diagnosis method consists of four stages, pre-processing of CT images, feature, extraction, and classification, the features are extracted based on DTCWT and PNN. In the last stage, PNN employed to classify the Normal and abnormal
The effect of atmospherically relevant aminium salts on water uptake

Abstract. Atmospheric new particle formation is initiated by clustering of gaseous precursors, such as small acids and bases. The hygroscopic properties of those precursors therefore affect the hygroscopic properties of aerosol particles. In this work, the water uptake of different salts consisting of atmospheric small acids and amines was studied computationally using the conductor-like screening model for real solvents (COSMO-RS). This method allows for the prediction of water activities in atmospherically relevant salts that have not been included in other thermodynamics models. Water activities are reported here for binary aqueous salt solutions, as well as ternary solutions containing proxies for organic aerosol constituents. The order of the studied cation species regarding water activities is similar in sulfate, iodate, and methylsulfonate, as well as in bisulfate and nitrate. Predicted water uptake strengths (in mole fraction) conform to the following orders: tertiary > secondary > primary amines and guanidinos > amino acids. The addition of water-soluble organic to the studied salts generally leads to weaker water uptake compared to pure salts. On the other hand, water-insoluble organic likely phase separates with aqueous salt solutions, leading to minimal effects on water uptake.
Feelings and Religiosity Interlacing in the Face of Breast Cancer Diagnosis: A Netnographic Study

- Globally, female breast cancer exceeded lung cancer as the most commonly diagnosed cancer, with an estimated 2.3 million new yearly cases. This pathology produces profound changes in women's lives. Religiosity and spirituality are essential resources at diagnosis and when making therapeutic decisions. We aimed to analyze women’s feelings in the face of breast cancer diagnosis and faith as an ally in confronting the disease. Netnographic research was conducted in February 2023 on the "Oncoguia" website from testimonials in the "Patient Space" section. We analyzed testimonials of women diagnosed with (healed or in treatment) breast cancer who mentioned in writing words about faith, religiosity, or spirituality. Two researchers read and analyzed the reports, organizing them into two themes: “Diagnosis and explosion of feelings” and "Faith: the greatest ally in the fight against cancer”. We observed that multiple feelings emerge at diagnosis, including despair and uncertainties. In this process, faith is vital for deponents to favor the quality of life and confront breast cancer.
Overview and statistical analysis of boundary layer clouds and precipitation over the western North Atlantic Ocean

Abstract. Due to their fast evolution and large natural variability in macro- and microphysical properties, the accurate representation of boundary layer clouds in current climate models remains a challenge. One of the regions with large intermodel spread in the Coupled Model Intercomparison Project Phase 6 ensemble is the western North Atlantic Ocean. Here, statistically representative in situ measurements can help to develop and constrain the parameterization of clouds in global models. To this end, we performed comprehensive measurements of boundary layer clouds, aerosol, trace gases, and radiation in the western North Atlantic Ocean during the NASA Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE) mission. In total, 174 research flights with 574 flight hours for cloud and precipitation measurements were performed with the HU-25 Falcon during three winter (February–March 2020, January–April 2021, and November 2021–March 2022) and three summer seasons (August–September 2020, May–June 2021, and May–June 2022). Here we present a statistical evaluation of 16 140 individual cloud events probed by the fast cloud droplet probe and the two-dimensional stereo cloud probe during 155 research flights in a representative and repetitive flight strategy allowing for robust statistical data analyses. We show that the vertical profiles of distributions of the liquid water content and the cloud droplet effective diameter (ED) increase with altitude in the marine boundary layer. Due to higher updraft speeds, higher cloud droplet number concentrations (Nliquid) were measured in winter compared to summer despite lower cloud condensation nucleus abundance. Flight cloud cover derived from statistical analysis of in situ data is reduced in summer and shows large variability. This seasonal contrast in cloud coverage is consistent with a dominance of a synoptic pattern in winter that favors conditions for the formation of stratiform clouds at the western edge of cyclones (post-cyclonic). In contrast, a dominant summer anticyclone is concomitant with the occurrence of shallow cumulus clouds and lower cloud coverage. The evaluation of boundary layer clouds and precipitation in the Nliquid ED phase space sheds light on liquid, mixed-phase, and ice cloud properties and helps to categorize the cloud data. Ice and liquid precipitation, often masked in cloud statistics by a high abundance of liquid clouds, is often observed throughout the cloud. The ACTIVATE in situ cloud measurements provide a wealth of cloud information useful for assessing airborne and satellite remote-sensing products, for global climate and weather model evaluations, and for dedicated process studies that address precipitation and aerosol–cloud interactions.