Reviewerly/exHarmony huggingface dataset:
Augmented reality as a tool for teaching physics to students with special needs

Abstract Nowadays, teaching physics is becoming more and more challenging, particularly in regard to reduced number of physics lessons, lack of physics teachers and/or of the experimental equipment in the laboratories. This is becoming even more visible when students with special needs (SEN) are concerned. Consequently, physics rates among the most unpopular subjects in schools. We present a project ARphymedes plus („Augmented Reality made for student with special needs“), specifically designed to improve teaching of physics for students with special needs by applying Augmented Reality (AR). Within this project, we connect physics content with stories related to distinguished physicists, as well as to supporting experiments. For each phenomenon or physical law, examples in the form of videos, animations or 3D animated models are presented using AR. AR can be visualised in the book using tablets or smartphones, which are a daily part of students’ lives.
Q‐learning based adaptive Kalman filtering for partial model‐free dynamic systems

Summary In this article, we propose an adaptive Kalman filtering based on Q‐learning for partial model‐free dynamic systems. First, a cost function is defined to iteratively update the prior state value when the model parameters are unknown. Then, the observations in a period of time are utilized to improve the accuracy and updating speed of the prior state estimation by means of the multi‐innovation least squares. Next, considering that the weight matrix in the cost function will change due to external noise noise and model mismatch, the innovation‐based adaptive estimation algorithm is presented to adjust the weight matrix by using the covariance of the information sequence. Finally, the proposed algorithms are applied to estimate the water level of a quadruple water tank system.
Paraconsistent logics as a Way of Expressing Objective Contradictions in Science

The article focuses on the problems associated with the need to express in the language of science in a consistent way the movement and various kinds of changes taking place in the objective world by developing logics that would be tolerant of logical contradictions. The paper presents a brief history of the creation of paraconsistent (paraconsistent) logics, in which logical contradictions turned out to be quite permissible. It is noted that the priority in their development belongs to Russian scientists N. A. Vasiliev and I. E. Orlov, as well as Polish philosophers and logicians Y. Lukasevich and S. Yaskovsky; that since the seventies of the last century, the development of paraconsistent logics has assumed an international character; that interest in these logics is not abating at the present time. The possibilities of using paraconsistent logics presented in the works of foreign and domestic philosophers and logicians for the formalization of dialectics are discussed. The positive role of these logics is shown in solving many paradoxes in the foundations of classical propositional calculation and predicate logic, as well as in order to limit undesirable consequences when using IT technologies related to the processing of inconsistent or contradictory information. It is concluded that the use of paranoherent logics in the construction of models of individual fragments of dialectics contributes to its development as logic and as ontology, and indicates that the apparatus of non-classical logics as a whole is a very powerful means of studying and explaining many problems of theoretical cognition.
Digital Safeguards

The rapid integration of internet and technological advancements over the past two decades has reshaped the global landscape, leading to transformative changes in various sectors.The emergence of Industry 4.0, conceptualized in 2011, has particularly revolutionized manufacturing and logistics by advocating for systematic digitalization technology integration to enhance efficiency and reduce costs. However, this technological evolution, while fostering efficiency and process optimization, also introduces vulnerabilities to cyber threats. The logistics sector, heavily reliant on interconnected systems like internet of things, faces potential risks from cyberattacks that target sensitive data across the interconnected supply chain. This chapter aims to address the intricate relationship between digitalization and the logistics sector, emphasizing the crucial role of digital safeguards in navigating cyber dangers. Furthermore, the chapter delves into the significance of visibility in supply-chain operations and explores technologies and practices for enhancing supply-chain visibility.
Logistics Industry in the Context of the Blockchain Technology

Blockchain technology has garnered considerable interest from academics and businesses. One of the primary reasons to keep track of customer satisfaction and adoption is that customers don't use the product or service enough. Identifying the factors that influence the use and adoption of blockchain technologies will help solve the adoption problems effectively. Many significant firms, like Google, Amazon, and others, will embrace blockchain technology. It will indeed be found in the future and revolutionise logistics and transportation. Even though the advantages of blockchain technology have been well studied in the financial sector, using blockchain technology can help decrease significant logistical challenges such as border delays, product damage, errors, and multiple data entry.
Use of High-Resolution Multispectral Imagery to Assess the Role of Sand and Gravel Shoals for Flood-Infrastructure Interaction in the Yellowstone River, Montana

Gravel and sand shoals may play a significant role for infrastructure impacts during flood events. This study uses high-resolution (30-cm panchromatic resolution at nadir) pan sharpened multispectral satellite images to map sand and gravel shoals near the Cinnabar Basin Bridge in Gardiner, Montana. While the bridge was not damaged during the flood event, erosion of the riverbanks and scour were observed with significant differences noticeable between both riverbanks, which may be related to the presence or evolution of shoals during the flooding. Toward analyzing the role of the shoals on local erosion and scour patterns, the goal of this study is to classify sediments based on soil type (sand, gravel, and cobbles) and color, using unsupervised K-means clustering and spectral information divergence (SID) with images taken in October 2022 and January 2023. The soil classifications from the K-means clustering and SID analysis are then compared to the pan sharped RGB images, drone images, and on-site observations and photos to assess the results from the classifications. The resulting classifications are compared for changes in the composition of the shoals over time from the October image to the January image, but also take advantage of receding water levels over the winter. Both the K-means clustering and SID analysis yield sediment classifications matching the site photos and drone images. Results show that unsupervised K-means clustering and SID can be used to classify sediments into different groups based on soil type and color, which may enable a deeper investigation of the impacts of sand and gravel shoals on infrastructure.
A Proper Methodology to Characterize the Associated Variability of UCS Data for the Metamorphic Rocks Based on Outlier Detection Methods

This paper presents a methodology for analyzing the uncertainties of uniaxial compressive strength (UCS) test data for metamorphic rocks based on statistical and mineralogical analyses. The methodology consists of three steps: data treatment process, schistosity angle, and mineralogy. The treatment process involved different data treatment techniques to assess the variabilities of UCS data, and to determine appropriate outlier detection methods based on multiple decision-making criteria and engineering judgment. The results showed that adjusted boxplot and 2SD method provided more accurate outcomes in detecting outliers. The findings reveal that traditional boxplot may not be the best for treating UCS data. Moreover, the study examined the impact of schistosity angle and suggests removing samples within a specific angle range to decrease UCS variabilities. The mineralogical analysis examined the impact of various minerals on UCS variations, revealing a correlation between the dispersion and the variabilities of both hard and soft minerals.
ShopSculpt : Crafting Your Cart with Intelligent Ingenuity

In the present world from small companies to giant company’s product recommendation system plays a very major role, also people are very much interested in online shopping these days, so Recommendations are utilized to make the customer's job easier and faster. The majority of these recommendations are made based on their previous transaction history and association rules generated from it. Apriori Algorithm is the most widely used method for generating association rules based on frequent product sets. But it has a drawback, while generating association rules it uses the whole transactions list and frequent product sets, ignoring the seasonal transactions for generating rules. Hence seasonal transactions are not considered for mining rules, if a person is buying a cake for Christmas, it’s very likely that recommender system recommends birthday balloons, birthday caps as recommendations. But actually, it should recommend Christmas related products for recommendation. To solve this problem, we proposed a model using chi square test with improvised Apriori algorithm. This research paper introduces ShopSculpt, an innovative automated shopping cart system designed to enhance the user shopping experience by leveraging intelligent algorithms. ShopSculpt employs a multifaceted approach, considering user shopping interests, patterns, seasonal trends, and current weather conditions to provide personalized and context-aware product recommendations. The aim is to create a dynamic and responsive shopping environment that adapts to individual preferences and external factors, thereby optimizing the user's shopping journey.
A Codebook Compression Method for Vector Quantization Algorithm

This study introduces a novel approach to enhance the compression ratio of the vector quantization (VQ) algorithm by specifically targeting the compression of its codebook. The VQ algorithm typically generates an index matrix and a codebook to represent compressed images. The proposed method focuses on reducing the size of the codebook, which comprises N codewords, each with elements quantized into four levels. Each 8-bit element in a codeword is compressed to 2-bits, and the encoded codeword is accompanied by the minimum value and a threshold value in the codebook. Experimental results on benchmark color images, such as baboon, airplane, Lena, and others, demonstrate a significant reduction of 62.50% in the size of the VQ codebook.
The Information Fold

This chapter brings the folded cosmos into dialogue with contemporary theories of surveillant information capitalism. Marks suggests an “only moderately paranoid” rejoinder to the darker theorizations of information's grip, especially where information-images are concerned. This relatively cheerful view is, however, moderated in turn by an analysis of the unsustainable energy consumption of information and communication technologies. The chapter then turns to media arts of the fold and presents a protagonist for the media of our coming collapse informatics: small-file movies. Other artworks include Jonathan Monaghan's digital animation Den of Wolves and Jawa El Khash's virtual-reality work The Upper Side of the Sky.
How Do Human Resources Practices Affect the Performance of the Employees in Syria?

The purpose of this research is to analyses how HRM practices affect worker output in a high-pressure setting like the International Committee of the Red Cross (ICRC) in Syria. It contributes to literature by expanding the understanding of HRM. It provides useful insights and ideas for improving the efficiency of HRM procedures in businesses. This study highlighted the need for the ICRC to prioritize and invest in strong human resource management practices. Primary data was collected from ICRC employees through survey questionnaire. Findings show that HRM practices have a significant direct impact on the motivation and performance of ICRC employees. Meanwhile, psychological safety mediates the relationship between employee's motivation and employee's performance. On the other hand, employee's motivation partially mediates the relationship between HR Practices and employee's performance at ICRC.
The Internet of Things (IoT) Is Revolutionizing Inventory Management

Every part of the supply chain has been affected by the rise of Industry 4.0 technologies. Many businesses have been trying out this modern technology to see if it can help them make more money. IoT devices make it simple for companies to keep track of an item's specific location, which helps inventory management (IM). The time it takes to find inventory is shortened as a result. The abundance of real-time data makes it possible to offer insightful information that supports strategic and tactical business decisions. Developing a supply chain into a fully integrated supply chain has several benefits. The differences between fourth-generation technology and earlier generations make it seem like traditional ways of restocking inventory don't adapt well enough to new technologies and can't handle IoT systems. Thanks to technological advancements, supply chains are undergoing rapid transformation.
Bag-Based Feature-Class Correlation Analysis for Multi-Instance Learning Application

Multi-instance Learning (MIL) is widely applied in image classification. In MIL, an image is presented as a bag. A bag consists of multi-instance which is known as patches. Irrelevant features of the image presented to the classifier affects the classification performance. Feature selection is one of the essential phases to select relevant. However, limited studies discuss the feature selection phase in MIL. Correlation between feature-class (FC) relationship is one important criterion to analyse features’ relevance. However, it cannot be performed directly in MIL. To address this gap, this study proposed the MultiBag-FCCorr feature selection technique. It consists of three steps: transformation, evaluation and fusion. The bags of feature information are acquired from summarization from different statistical central tendency measures of trimmed mean, mode and median. In feature evaluation step, extended point biserial correlation has been used to measure FC correlation and then the FC score has been analysed. The selected features are validated via two prominent classifiers (Support Vector Machine (SVM) and K-Nearest Neighbour (KNN)) on benchmark MI image datasets: UCSB Breast Cancer, Tiger, Elephant and Fox datasets. The selected features of UCSB Breast Cancer dataset were reduced to 92% number of features from the proposed technique giving the best result of average accuracy with 86.8.% using SVM and 84.5% using KNN. The average accuracy improved 6.3% using SVM and 16.4% using KNN compared without implementing the proposed feature selection. The results proved that the selected feature set improved the performance of MI image classification.
Optimized design of smart toilet spray bar flow channel based on CFD

Abstract Currently, there are significant issues with the intelligent toilets available on the market, such as a large swing of the spray bar when spraying water column and eccentricity. This paper presents four optimization schemes for the intelligent toilet spray bar channel by analysing and redesigning a specific model. Computational Fluid Dynamics (CFD) technology was employed to analyse the water flow conditions inside the spray bar of the intelligent toilet. The calculation results indicate that for the same water inlet flow rate, the optimal structure in optimization scheme 1 is the block with a length (L) of 1.5 mm. In optimization scheme 2, the optimal structure is the block located at position (3). In optimization scheme 3, the optimal structure is a 15° rotation, which exhibits the least swirls and pressure loss while maintaining the prototype structure. When the prototype structure is modified, the optimal structure in optimization scheme 4 is the excision bend pipe. This structure exhibits the lowest swirl number and pressure loss among all the optimization schemes. The determined structure offers valuable insights for enhancing the flow characteristics of the improved product.
Adolescent Reading Literacy in View of Family Support and Reading Duration in Middle School Students

Literacy is an important skill and relates to the ability to listen, speak, write, and count. This study aims to analyze the influence of family support and duration of reading on adolescent reading literacy. This study uses a quantitative survey approach research design. The number of samples in this study were 127 students through cluster random sampling method. Data analysis was carried out in several stages, namely data coding, SPSS analysis, namely inference test, normality test, correlation test, and multiple linear regression test. The results showed that the dominant (36.2%) subjects were 14 years old. As many as (30.7%) are 15 years old. The results showed that half (50%) of the research subjects read books for entertainment several times a year. The results showed that the majority (68%) of the study subjects had moderate reading duration and the family support given to the sample was very high (93%). The results of the study also show that family support and duration of reading affect adolescent reading literacy. The result of this study have implications for parent mindset to support literacy from an early age and adolescent to read more frequently.
The Effect of the Director's Authentic Leadership that MZ Generation Early Childhood teachers acknowledge on the Constructive Voice Behavior of MZ Generation Early Childhood Teachers: Examining the Parallel Mediating Effect of Organizational commitment

The aim of this study was to examine how the MZ generation (born between 1980 and 2005) early childhood teachers perceive preschool directors' authentic leadership and how their perceptions influenced their organizational commitment and constructive voice behaviors. To this end, a survey was conducted on 242 MZ generation early childhood teachers currently employed in kindergartens and daycare centers across South Korea. Data analysis, performed using SPSS version 26.0, involved calculating frequencies, percentages, means, and standard deviations. Additionally, the PROCESS macro for SPSS version 3.5, specifically model 4, was utilized to confirm parallel mediation effects. Bootstrap analysis was employed to validate the statistical significance of the mediating effects. The findings of this study were as follows. First, the level of the director's authentic leadership recognized by MZ generation early childhood teachers, constructive voice behavior, and organizational commitment of MZ generation early childhood teachers were all reported as ranging from moderate to high. Second, parallel partial mediating effects were found through emotional and normative commitment in verifying the mediating effect of organizational commitment in the relationship between the director's authentic leadership recognized by MZ generation early childhood teachers and the constructive voice behavior of MZ generation early childhood teachers. These results suggest that the director's authentic leadership influences the constructive voice behavior of MZ generation early childhood teachers, and emotional and normative immersion can play a mediating role in this process to promote constructive voice behavior.
The Novel Mathematical Model and Methodology for Computer Simulation of Magnetic Field in a Nonlinear Medium

Introduction. Magnetic devices of various types are used in production equipment. Designing and modernizing such equipment requires a signifi cant bulk of calculations of magnetic fi elds and parameters of magnetic devices. This task is diffi cult due to large dimensionality of the system of equations and nonlinear properties of magnetic materials.Problem Statement. Due to the nonlinearity of diff erential and integral equations on which these calculations are based, they need to be solved numerically by iterative methods, the convergence of which is often uncertain. This requires powerful computing tools and considerable time. Therefore, the problem of improving mathematical mo dels and increasing the computational effi ciency of the corresponding algorithms is relevant.Purpose. To develop a mathematical model of a magnetic fi eld in a nonlinear medium in the form of a surface integral equation for a quasilinear space and a computer modeling technique with increased computational effi ciency.Material and Methods. The material of the study is the mathematical models of the magnetic fi eld in a nonlinear medium of magnetic materials and the computational properties of the corresponding algorithms. The methods of vector analysis of diff erential operators and synthesis of modifi ed formulas in the magnetic fi eld equations have been used in the work.Results. The newest mathematical model of the magnetic fi eld in which the volumetric equation for a nonlinear medium is reduced to a surface equation in quasi-linear space, which reduces the dimensionality of data arrays by one order of magnitude and the number of computational operations by two orders of magnitude, has been substantiated. On this basis, a methodology for computer modeling of fi elds with the use of a unifi ed magnetization curve has been developed.Conclusions. The applicability of this methodology to various magnetic materials and its effi ciency have been confi rmed by the example of a model problem of practical importance for improving the algorithms for calculating and analyzing magnetic fi elds in magnetic systems with nonlinear elements.
Determination of Elongated Aggregates through Computer Vision-Based Technique

The traditional measurement technique for finding elongated aggregates involves sieve analysis followed by passing each particle through an elongation gauge. In this study, machine learning has been used as a computer vision-based methodology to determine the elongation index of coarse aggregate particles. Images of aggregates were captured, and an algorithm was developed for contour detection of each particle. The particle size and the number of elongated particles were then computed in an automated form. The laboratory test results were compared with the results obtained from a machine learning-based algorithm. The results so obtained on the segregation of particles in various sizes give significant accuracy with a deviation of ±1 number of particles. The method so developed can be used by researchers and field engineers for the determination of the physical parameters of aggregates.