Miracle-dz/UniFAR-FTUs huggingface dataset:
Interactive online brain shape visualization

Urbanization presents unique environmental challenges to human commensal species. The Afrotropical Anopheles gambiae complex contains a number of synanthropic mosquito species that are major vectors of malaria. To examine ongoing cryptic diversification within the complex, we performed reduced representation sequencing on 941 mosquitoes collected across four ecogeographic zones in Cameroon. We find evidence for clear subdivision within An. coluzzii and An. gambiae s.s. - the two most significant malaria vectors in the region. Importantly, in both species rural and urban populations of mosquitoes were genetically differentiated. Genome scans of cryptic subgroups reveal pervasive signatures of selection centered on genes involved in xenobiotic resistance. Notably, a selective sweep containing eight detoxification enzymes is unique to urban mosquitoes that exploit polluted breeding sites. Overall, our study reveals that anthropogenic environmental modification is driving population differentiation and local adaptation in African malaria mosquitoes with potentially significant consequences for malaria epidemiology.
An UML Statechart Diagram-Based MM-Path Generation Approach for Object-Oriented Integration Testing

describes the dynamic interactions between methods in object-oriented systems. This paper discusses the classifications of MM-Path, based on the characteristics of object-oriented software. We categorize it according to the generation reasons, the effect scope and the composition of MM-Path. A formalized representation of MM-Path is also proposed, which has considered the influence of state on response method sequences of messages. Moreover, an automatic MM-Path generation approach based on UML Statechart diagram has been presented, and the difficulties in identifying and generating MM-Path can be solved. As a result, it provides a solid foundation for further research on test cases generation based on MM-Path.
Classification of Dataset Using Deep Belief Networks Clustering Method

Dataset in large collection involves considerable handling in its analysis especially when it is being employed in classification problems that involve big data. Due to the technology development, the manner and approach in which this dataset is being manipulated for classification purposes differ not only in one respect but in many respects with different uncorrelated results which sometimes make prediction inaccurate. By definition, classification is the act of arranging objects into classes or categories of the same type; these objects can be huge or otherwise, and to manually classify them will be a herculean task. The basic reason for classification is to punctiliously predict the class for each case in the dataset using class label. Notable classification, clustering and regression methods are support vector machines, neural networks, random forest, k-nearest neighbor and decision trees. The conventional clustering method that is widely employed to classification problems cannot handle the weight associated problem which characterized the transmission of neurons from layer to layer within the network. Employed in this work for the classification and clustering resolution is deep belief networks clustering method. The neural network architecture and loss function popularly employ in deep learning are considered for transforming the input data to clustering-friendly feature representation.
Poisson matting

In this paper, we formulate the problem of natural image matting as one of solving Poisson equations with the matte gradient field. Our approach, which we call Poisson matting, has the following advantages. First, the matte is directly reconstructed from a continuous matte gradient field by solving Poisson equations using boundary information from a user-supplied trimap. Second, by interactively manipulating the matte gradient field using a number of filtering tools, the user can further improve Poisson matting results locally until he or she is satisfied. The modified local result is seamlessly integrated into the final result. Experiments on many complex natural images demonstrate that Poisson matting can generate good matting results that are not possible using existing matting techniques.
Petrographic Observations of the Building Stones of the Great Pyramid of Giza

This article gives the readers an opportunity to look at the well-known archaeological monument in a different way. The Great Pyramid at Giza has in its four walls triangles that are made of different types of limestone compared the other parts. These triangles were made during the construction of the pyramid. On the top of the northern triangle used to be an entrance to the pyramid. These facts are presented and conscientiously proven in this article. A total of 12 samples from various rocks were taken from the Great Pyramid at Giza to conduct this research, and 13 samples were taken from the bedrock upon which the triangles had been built. This number includes also the samples from the rock in the quarry.
Fake Review Detection Using Machine Learning Techniques

Online reviews play a very important role in today's e-commerce for decision-making. Large part of the population i.e. customers read reviews of products or stores before making the decision of what or from where to buy and whether to buy or not. As writing fake/fraudulent reviews comes with monetary gain, there has been a huge increase in deceptive opinion spam on online review websites. Basically fake review or fraudulent review or opinion spam is an untruthful review. Positive reviews of a target object may attract more customers and increase sales; negative review of a target object may lead to lesser demand and decrease in sales. These fake/fraudulent reviews are deliberately written to trick potential customers in order to promote/hype them or defame their reputations. Our work is aimed at identifying whether a review is fake or truthful one. Index Terms – Naïve Bayes (NB), Support Vector Machine (SVM), K-Nearest Neighbors (KNN-IBK), KStar (K*) and Decision Tree(DT)
in Computer

— With the use of digital technologies and distribution of digital contents likes images, videos, audios and reports over the internet then digital right management has become critical issue. The need of copyrights, intellectual property protection and authentication of the original data over the internet or communication channel is required. The solution of this problem is digital watermarking. Digital watermarking is new and highly multidisciplinary research area for data protection over internet. Digital watermarking is the process of inserting a digital signal or pattern into digital content. Digital watermarking system can be view as communication system consist of three main blocks an embedder, a communication channel which is optional and a detector. This paper is provides complete review of digital watermarking techniques in spatial, transform and sparse domain for different applications like copyright protection, tracking of original data, broadcast monitoring, authentication and medical safety. Now day’s digital watermarking techniques are used for security and authenticity of biometric templates and provide more accuracy in authentication system. Also paper gives all evolution parameters for digital watermarking techniques.
in Computer

— It is a well known widely accepted fact that the static spectrum allocation has led to the under exploitation of the available frequency band. This problem can be mitigated by cognitive radio technology. It is a concept which with its learning ability is aware about its environment and intelligently adapts to it with objectives like reliable communication, efficient utilization of spectrum and minimal or no interference among users. Various techniques have been suggested in the research works to access the spectrum dynamically in a cognitive radio environment. These techniques basically use the concepts of Artificial Intelligence. Game theory, Markov chain models, Auctioning, Neural Networks, Multi Agent Systems and Fuzzy Logic approaches are some of the AI techniques for DSA and are compared in this paper.
Bled Econference Eintegration in Action Comparing Two Business Model Ontologies for Designing E-business Models and Value Constellations

Business models have been an important topic in various disciplines and particularly e-business. Yet, little research has tempted to compare and integrate the different business model approaches. This paper compares two business model ontologies, the Business Model Ontology BMO and the e3value ontology, for the design of business models and value constellations. For that purpose it introduces a framework that allows the comparison of different conceptual approaches to business models. The two ontologies are illustrated through a case study in the domain of rights music management. The outcome of the analysis is twofold. Firstly, it permits a better understanding of business model research. Secondly, it highlights the possible paths to integrate the two ontologies in order to improve the representation, design, and analysis of business models.
Simulating the Impacts of Irrigation Levels on Soybean Production in Texas High Plains to Manage Diminishing Groundwater Levels

There is an increasing need to strategize and plan irrigation systems under varied climatic conditions to support efficient irrigation practices while maintaining and improving the sustainability of groundwater systems. This study was undertaken to simulate the growth and production of soybean [Glycine max (L.)] under different irrigation scenarios. The objectives of this study were to calibrate and validate the CROPGRO‐Soybean model under Texas High Plains’ (THP) climatic conditions and to apply the calibrated model to simulate the impacts of different irrigation levels and triggers on soybean production. The methodology involved combining short‐term experimental data with long‐term historical weather data (1951–2012), and use of mechanistic crop growth simulation algorithms to determine optimum irrigation management strategies. Irrigation was scheduled based on five different plant extractable water levels (irrigation threshold [ITHR]) set at 20%, 35%, 50%, 65%, and 80%. The calibrated model was able to satisfactorily reproduce measured leaf area index, biomass, and evapotranspiration for soybean, indicating it can be used for investigating different strategies for irrigating soybean in the THP. Calculations of crop water productivity for biomass and yield along with irrigation water use efficiency indicated soybean can be irrigated at ITHR set at 50% or 65% with minimal yield loss as compared to 80% ITHR, thus conserving water and contributing toward lower groundwater withdrawals. Editor's note: This paper is part of the featured series on Optimizing Ogallala Aquifer Water Use to Sustain Food Systems. See the February 2019 issue for the introduction and background to the series.
Machine Learning Classification of Event-Related Brain Potentials during a Visual Go/NoGo Task.

Machine learning (ML) methods are increasingly being applied to analyze biological signals. For example, ML methods have been successfully applied to the human electroencephalogram (EEG) to classify neural signals as pathological or non-pathological and to predict working memory performance in healthy and psychiatric patients. ML approaches can quickly process large volumes of data to reveal patterns that may be missed by humans. This study investigated the accuracy of ML methods at classifying the brain's electrical activity to cognitive events, i.e., event-related brain potentials (ERPs). ERPs are extracted from the ongoing EEG and represent electrical potentials in response to specific events. ERPs were evoked during a visual Go/NoGo task. The Go/NoGo task requires a button press on Go trials and response withholding on NoGo trials. NoGo trials elicit neural activity associated with inhibitory control processes. We compared the accuracy of six ML algorithms at classifying the ERPs associated with each trial type. The raw electrical signals were fed to all ML algorithms to build predictive models. The same raw data were then truncated in length and fitted to multiple dynamic state space models of order nx using a continuous-time subspace-based system identification algorithm. The 4nx numerator and denominator parameters of the transfer function of the state space model were then used as substitutes for the data. Dimensionality reduction simplifies classification, reduces noise, and may ultimately improve the predictive power of ML models. Our findings revealed that all ML methods correctly classified the electrical signal associated with each trial type with a high degree of accuracy, and accuracy remained high after parameterization was applied. We discuss the models and the usefulness of the parameterization.
Reducing Noise-Induced Hearing Loss in Longwall Coal Mine Workers

The National Institute for Occupational Safety and Health (NIOSH) is conducting research with the objective of preventing hearing loss in the mining industry. Longwall mining is one sector that has received considerable attention. Studies involving the monitoring of worker noise exposures, characterizing equipment sound levels and dominant noise sources, evaluating engineering noise controls, analyzing hearing protection device (HPD) effectiveness, and testing of improved sound level monitoring techniques specifically for longwall mining systems, are being conducted. The research efforts revealed that stageloader operators are the most exposed longwall workers, with the discharge end being the noisiest location along the stageloader. In addition, the studies have shown that using stationary dosimeters, coupled with observational data of the cutting cycle and worker habits, has the potential for improved monitoring of the effectiveness of engineering noise controls. To address the ineffective use of soft foam earplugs, a simplified 3-step method was developed for inserting soft foam earplugs. Results indicate a 9 dB greater attenuation after training subjects on the 3-step method. Finally, muff type HPDs are being evaluated for effects of speech intelligibility in noise. The combined results of these studies illustrates that much can and has been done to prevent noise-induced hearing loss (NIHL) in the mining industry, especially with respect to longwall mining workers. This paper presents an overview of ongoing studies, a progress report of results, and the implications for reducing worker noise exposures and preventing NIHL in longwall mining workers.
The Impact of Facebook Social Media Usage on the Social Relationships of Teenagers in Makassar City

This study aims to investigate the influence of Facebook social media on the social relationships of teenagers in Makassar City, along with the government's endeavors to mitigate the adverse effects of Facebook usage in this demographic. Employing a qualitative approach, the research utilizes various data collection methods, including in-depth interviews with teenagers aged 18-22, discussions with the City's Communication and Information Office (Diskominfo), observation of teenage Facebook users, and document analysis related to the study's focus. The research findings undergo thorough analysis through stages of data reduction, presentation, and conclusion drawing. The results reveal that Facebook usage has both positive and negative impacts on teenagers in Makassar City. Positively, the platform facilitates the establishment and regulation of friendships, promoting positive communication through content sharing and constructive comments. Conversely, negative consequences arise as users feel offended by statuses, comments, and uploaded media touching on personal matters. Moreover, these negative impacts extend to teenagers displaying apathy towards social relationships, leading to procrastination and forgetfulness. Teenagers in Makassar predominantly use Facebook for gaming, entertainment, watching, and online selling. The government, as a key policymaker, encounters challenges in significantly minimizing the negative effects of Facebook usage among teenagers due to privacy concerns. Current governmental initiatives are confined to socialization campaigns and encouraging responsible and positive social media use among teenagers. Despite these efforts, more attention is required to effectively address the identified negative impacts.
Model Multivariate Adaptive Regression Spline (MARS) Pada Persamaan Regresi Nonparametrik

Economic growth is one of the benchmarks for the success of development or increasing welfare in the government of a region in the economic sector as measured by the Gross Regional Domestic Product (GRDP). It has time series data that often fluctuates so that the appropriate method is nonparametric regression. This study also aims to determine the most influential factors for economic growth in North Sumatra in 2019-2021 using the MARS model, using secondary data published by BPS for 2019-2021. The MARS model is obtained by obtaining a combination of BF, MI, and MO values that have a minimum Generalized Cross Validation (GCV) value. The results of this study indicate that the best MARS model is a combination of BF=28, MI=1, and MO=1 with a GCV value of 8.42E+06. Therefore there are five of the seven variables that have a significant effect on economic growth in North Sumatra, namely population (X_7 ) with an interest rate of 100%, domestic investment (X_5) of 76.86%, local revenue (X_1) of 31.14%, allocated funds special (X_3) of 28.89%, general allocation funds (X_2) of 23.14%.
Determination of Methamphetamine by High-Performance Liquid Chromatography in Odor-Adsorbent Material Used for Training Drug-Detection Animals

The objective of the present report was to develop and validate a simple, sensitive, and selective analytical method for the determination of methamphetamine in an odor-adsorbent material (gauze) which was used to improve and standardize the training method used for drug-detection animals. High-performance liquid chromatography (HPLC) was performed using a Spherisorb ODS2 C18 column (200 mm × 4.6 mm, 5 μm), with a mobile phase consisting of a 0.25% methanol/triethylamine aqueous solution (V:V = 20:80), the pH of which was adjusted to 3.1 using glacial acetic acid, at a flow rate of 1.0 mL/min. The column temperature was 25 °C, and the detection of the analytes was performed at a wavelength of 260 nm. Methamphetamine showed good linearity (R2 = 0.9999) in the range of 4.2~83.2 mg/mL. The stability of the test material was good over 24 h. The precision of the method was good, with an average spiked recovery of 86.2% and an RSD of 2.9%. The methamphetamine content in the gauze sample was determined to be 7.8 ± 2.2 μg/sample. A high-performance liquid chromatography (HPLC) method was optimized and validated for the determination of methamphetamine in adsorbent materials (gauze). Validation data in terms of specificity, linearity, the limit of detection and the limit of quantification, reproducibility, precision, stability, and recovery indicated that the method is suitable for the routine analysis of methamphetamine in adsorbent materials (gauze) and provided a basis for training drug-detection animals.
Research on Lifecycle-based Financial Strategy on New Energy Enterprises: Take CATL as an Example

The emergence of energy security issues reflects the current global turmoil and division. The two energy crises and regional conflicts have put the global energy system in a severe winter. Energy security is not only the latest requirement for the high-quality development of countries under the "carbon neutrality" background but also an aspect of overall global progress. This study selects the leading enterprise in the new energy industry, Contemporary Amperex Technology Co., Limited (CATL), as the research object. Based on the life cycle theory and financial strategy theory, the paper analyzes the company's financing, investment, and profit distribution strategies and constructs a financial strategy matrix to study the problems existing in the company's current financial strategy. The research results show that there are issues such as unreasonable financing structure, insignificant investment benefits, and significant fluctuations in dividend distribution policies. Finally, targeted optimization suggestions are proposed, hoping to provide a reference value for peers.
Frost Destruction of Concrete – A Study of the Validity of Different Mechanisms

Abstract Well-known frost destruction mechanisms applicable to concrete not exposed to salt are, (1) hydraulic pressure during freezing, (2) growth of ice-bodies in capillaries during freezing. Theories behind these mechanisms are presented. A third mechanism, ice expansion during heating of frozen concrete, is put forward. The validity of a certain mechanisms is discussed by analyzing its ability to explain experimental observations.
Research on Antecedent Configuration of Smart City Governance Path: Qualitative Comparative Analysis of Fuzzy Sets Based on 30 Cities in China

This study focuses on 30 typical cases of smart cities in China, constructing an analytical framework that includes seven explanatory factors: information infrastructure, administrative pressure, government attention, public service level, financial support, technical talent, and government-enterprise cooperation. Utilizing the fuzzy set qualitative comparative analysis method, the study conducts configuration analysis of the influencing factors for the efficient construction of smart cities. The research reveals that government-enterprise cooperation and the public service level are crucial factors for achieving high construction efficiency in smart cities. These are collectively categorized into three construction models: "service innovation type driven by pressure," "multi-factor-induced type driven by technology," and "proactive follow-up type under fiscal incentives. " The results of the study provide reference points for the construction of smart cities in different regions of China. Urban governments should undertake strategic and combinational construction actions based on their unique characteristics and resource endowments.
Recovery of secular deformation field of Mojave Shear Zone in Southern California from historical terrestrial and GPS measurements

The 1992 Mw 7.3 Landers and 1999 Mw 7.1 Hector Mine earthquakes struck the Eastern California Shear Zone (ECSZ) in the Mojave Desert, Southern California. Coseismic and postseismic deformation from these events affect efforts to use Global Positioning System (GPS) observations collected since these events to establish a secular surface velocity field, especially in the near field of the coseismic ruptures. We devise block motion models constrained by both historical pre‐Landers triangulation and trilateration observations and post‐Landers GPS measurements to recover the secular deformation field and differentiate the postseismic transients in the Mojave region. Postseismic transients are found to remain in the Southern California Earthquake Center Crustal Motion Map Version 4, Plate Boundary Observatory, and Scripps Orbit and Permanent Array Center GPS velocity solutions in the form of 2–3 mm/yr excess right‐lateral shear across the Landers and Hector Mine coseismic ruptures. The cumulative deformation rate across the Mojave ECSZ is 13.2–14.4 mm/yr, at least twice the geologic rate since the late Pleistocene (≤6.2 ± 1.9 mm/yr). Postseismic GPS time series based on our secular velocity field reveal enduring late‐stage transient motions in the near field of the coseismic ruptures that provide new constraints on the rheological structure of the lower crust and upper mantle.