Negative Numbers Research Articles

‘But For’ Test in Proving Causation in Insurance Claims in Uganda

Causation in insurance law refers to the cause-and-effect relationship between an event and the resulting loss or damage. Causation is an important concept because it determines whether an insured party is entitled to coverage under a particular policy. There are several legal principles that are used to evaluate causation in insurance disputes, including the proximate cause rule and the ‘but for’ test. The ‘but for’ test, which inquires whether the loss would not have occurred ‘but for’ the occurrence of the covered event, has been criticized for its oversimplification of the causation analysis and its failure to adequately consider the complex causal chain that often underlies loss. In addition, the ‘but for’ test tends to draw a number of false negatives while taking into account certain irrelevant considerations. In contrast, the proximate cause rule, which requires that the covered event be the primary cause of the loss or damage, offers a more nuanced and comprehensive approach to causation analysis. It considers the full range of factors that may have contributed to the loss or damage and allows for a more flexible and context-specific analysis of causation. This article argued that the ‘but for’ test is an unreliable method for proving causation in insurance law and that the proximate cause rule is more appropriate. This article concluded that the proximate cause rule is a more reliable method for proving causation in insurance law and should be adopted as the standard for determining coverage under an insurance policy.

The Effectiveness of Software Designed to Detect AI-Generated Writing: A Comparison of 16 AI Text Detectors

Abstract This study evaluates the accuracy of 16 publicly available AI text detectors in discriminating between AI-generated and human-generated writing. The evaluated documents include 42 undergraduate essays generated by ChatGPT-3.5, 42 generated by ChatGPT-4, and 42 written by students in a first-year composition course without the use of AI. Each detector’s performance was assessed with regard to its overall accuracy, its accuracy with each type of document, its decisiveness (the relative number of uncertain responses), the number of false positives (human-generated papers designated as AI by the detector), and the number of false negatives (AI-generated papers designated as human). Three detectors – Copyleaks, TurnItIn, and Originality.ai – have high accuracy with all three sets of documents. Although most of the other 13 detectors can distinguish between GPT-3.5 papers and human-generated papers with reasonably high accuracy, they are generally ineffective at distinguishing between GPT-4 papers and those written by undergraduate students. Overall, the detectors that require registration and payment are only slightly more accurate than the others.

Plasma‐Sheet Bubble Identification Using Multivariate Time Series Classification

AbstractPlasma‐sheet bubbles play a major role in the earthward transport of magnetotail particles. The most remarkable feature of bubbles is their fast bulk flow velocities, along with reduced plasma density and pressure accompanied by magnetic field dipolarization. These bubbles can be identified based on in situ observations, but subjective ambiguity necessitates human verification, due to confusion with other phenomena mostly associated with magnetic reconnection and plasma waves. In this study, we aim to employ machine learning (ML) techniques to detect bubbles automatically and to create a tool that can be utilized by individuals without specialized subject expertise. To identify bubbles, we combine three distinct techniques: MINImally RandOm Convolutional KErnel Transform (MINIROCKET), 1D convolution neural network, and Residual Network (ResNet). The imbalanced training data set consists of bubble and non‐bubble events with a ratio of 1:40 from 2007 to 2020. The results indicate that the accuracy of all three models is approximately 99%, and their precision, recall, and F2 score are all above 80% for both the validation and test datasets. The three methods are combined with the intersection set as the minimum set of predictions and the union set as the maximum set. The union set can accurately identify 66.7% of bubbles. The combined method reduces the number of false negatives significantly. In the prediction of bubbles in observations made in the year 2021 using a union set, the bubbles obtained by the model are comparable to those discovered using traditional criteria and manual inspections.

On universal realizability in the left half-plane

A list Λ = {λ1, λ2,..., λn} of complex numbers is said to be realizable if it is the spectrum of a nonnegative matrix. Λ is said to be universally realizable (UR) if it is realizable for each possible Jordan canonical form allowed by Λ. In this paper, using companion matrices and applying a procedure by Šmigoc, we provide sufficient conditions for the universal realizability of left half-plane spectra, that is, spectra Λ = {λ1,...,λn} with λ1 > 0, Re λi ≤ 0, i = 2, . . . , n. It is also shown how the effect of adding a negative real number to a not UR left half-plane list of complex numbers, makes the new list UR, and a family of left half-plane lists that are UR is characterized.

Solution of Time Independent System of First Order Ordinary Differential Equations Using Embedding Method in Fuzzy Environment

In this study, time independent system of first order fuzzy ordinary differential equations is solved using embedding method. Results for the system of FODEs are obtained and a case of non-homogeneous system of FODEs is established and used to find the Eigen values in an n x n matrix with negative real numbers R- consisting of n fuzzy numbers. Examples are constructed using the principles of existence and uniqueness. It is recommended that further study should look at the possibilities of generalizing this work to nth order system of first order ordinary differential equations using embedding method in fuzzy environment.

A NEW CONCEPT TO PROVE, √(- 1)= -1 IN BOTH GEOMETRIC AND ALGEBRAIC METHODS WITHOUT USING THE CONCEPT OF IMAGINARY NUMBERS

A particular branch of mathematics is coordinate geometry where geometry is studied with the help of algebra. According to the new concept of three types of Rectangular Bhattacharyya’s coordinate systems, plane coordinate geometry consists of four axes. In type – I, Rectangular Bhattacharyya’s coordinate system, the four axes are all neutral straight lines having no direction; in the type – II coordinate system the four axes are all count up straight lines and, in the type – III coordinate system all the axes are countdown straight lines. The author has considered all four axes to be positive in type II and type III coordinate systems. Ultimately, the author has established relations among the three types of coordinate systems and used the extended form of Pythagoras Theorem to prove √(- 1)= -1. In this paper, algebra is studied with the help of geometry. The equation, x2 + 1 = 0, means x2 = – 1 and therefore, the value of √(- 1)= -1, has been proved by the author with the help of geometry by using the new concept of the three types of coordinate systems without using the concept of the imaginary axis. Also, the author has given an alternative method of proof of √(- 1)= -1 algebraically by using the concept of the theory of dynamics numbers. The square root of any negative number can be determined in a similar way. This is the basic significance of that paper. This significance can be widely used in Mathematics, Science, and Technology and also, in Artificial Intelligence (AI), and Crypto-system

Performance of tumour microenvironment deconvolution methods in breast cancer using single-cell simulated bulk mixtures

Cells within the tumour microenvironment (TME) can impact tumour development and influence treatment response. Computational approaches have been developed to deconvolve the TME from bulk RNA-seq. Using scRNA-seq profiling from breast tumours we simulate thousands of bulk mixtures, representing tumour purities and cell lineages, to compare the performance of nine TME deconvolution methods (BayesPrism, Scaden, CIBERSORTx, MuSiC, DWLS, hspe, CPM, Bisque, and EPIC). Some methods are more robust in deconvolving mixtures with high tumour purity levels. Most methods tend to mis-predict normal epithelial for cancer epithelial as tumour purity increases, a finding that is validated in two independent datasets. The breast cancer molecular subtype influences this mis-prediction. BayesPrism and DWLS have the lowest combined numbers of false positives and false negatives, and have the best performance when deconvolving granular immune lineages. Our findings highlight the need for more single-cell characterisation of rarer cell types, and suggest that tumour cell compositions should be considered when deconvolving the TME.

The Implementation of Data Mining for Predicting XAU/USD Price Trends in the Forex Market on MetaTrader 5 using Naïve Bayes Method

The one of the trading instruments is forex or foreign exchange. The forex market provides various commodities, one of which is XAU/USD. XAU/USD dataset with daily, h4, h1 time frames obtained from the MetaTrader 5 application via the FBS broker. Predicting forex prices is difficult because there are various factors that influence it, so data mining methods are needed to predict increases or decreases. Naïve Bayes is a method and logic that can be applied in making predictions. So the research objective of this final project is to apply naïve Bayes methods and logic in predicting the price of XAU/USD on the daily, h4, h1 time frames. The application of the Naïve Bayes method uses several libraries to support research, namely pandas, jcopml, and sklearn. In naïve Bayesian logic research, this is called from the sklearn library using gaussianNB. In this study, the performance reference uses an f1 score matrix because the number of false positives and false negatives is not tight (symmetrical). This study produces values for each time frame obtained from the confusion matrix formula with f-scores of 49.99% (daily), 53.52% (h4), 55.44% (h1).

Metabolic power responses to different small sided games in football and futsal players

ABSTRACT This study was designed to examine the metabolic power (MP) responses of football and futsal players to different small sided games (SSG). Twelve football and 12 futsal players participated in this study voluntarily. Maximal oxygen consumption(VO2max) and coach scoring (CS) were used for SSG player matching. Football and futsal players participated in two bouts of 2vs2, 3vs3 and 4vs4-SSG in random order. Kinematic and MP variables during SSG were measured by using GPS. To determine the effects of branch and SSG on each dependent variable of total distance (TD), relative TD (TDRel), MPaverage, relative TD in four different MP levels, and negative and positive acceleration numbers, a main effects 2-way analysis of variance with Bonferroni post hoc was used. MPaverage was significantly higher in futsal players compared to football players (p = 0.000). TD (p = 0.000) and TDRel(p = 0.013) of futsal players was significantly higher compared to football players. Total deceleration and acceleration numbers during SSG were significantly higher for futsal players than football players (p = 0.026). MP requirements were independent of the game format (p > 0.05), except for 2vs2-SSG (p < 0.05), when the playing area per player was kept constant. The results of this study showed that external load indicators during SSG were higher in futsal players compared to football players.

Assessing Students’ Mastery and Misconceptions in the Fundamental Operations on Integers

One of the common misconceptions affecting mathematics performance of high school students involves operating integers. Evidently, students who were exposed to modular learning demonstrate poor mastery of the pre-requisite skills. This study aimed to assess the mastery level and misconceptions of the Grade 7 students involving operations on integers. A descriptive research design was used with a total of sixty-two (62) conveniently chosen Grade 7 students were twenty-nine (29) were males and thirty-three (33) were females. The Mean Percentage Scores (MPS) per operation on integers were calculated to describe the mastery level and the assessment results were analyzed to identify possible misconceptions of the students in operating integers. Similarly, data were analyzed using the Welch t-Test to determine if there is a difference in the level of mastery between the male and female students. Findings show that the overall MPS result is 50.56%, indicating that the students demonstrated Average Near Mastery (AVR) of the concepts involving operations on integers. In addition, there was no significant difference between the mastery level of male and female grade 7 students. Although the students demonstrated Average Near Mastery (AVR) in the given assessment, the item analysis showed various misconceptions and errors exhibited by the students regardless of sex and section. Students’ misconceptions include subtracting integers and dealing with negative numbers. Hence, an intervention is needed to address the misconceptions in subtracting integers and a reinforcement is proposed to enhance the students’ mastery in other operations on integers.

An Improved Design of Absolute-value Detector by Using Optimized Ripple Adder

Absolute-value detector (AVD) is one of the most basic bit-operating devices, which compares the magnitude of two inputs. However, the traditional 4-bit AVD is not only time-consuming but also has a high energy consumption. This paper uses a hybrid design to optimize the 4-bit AVD by introducing an improved ripple adder in the absolute value extraction part, which then can be less energy-consuming and requires fewer transistors. The paper divides the design of AVD into an absolute value extraction part and a comparator. The input (2’s complement) can be positive or negative numbers in the absolute value extraction part. By observing the most significant bit (MSB) of the input, the design can determine the sign of input and output magnitude. For the comparator, this paper uses a logic of step-by-step comparison from the most to the least significant bits. Then, this paper finds the minimum delay of the design by critical path analysis and gate sizing. The final delay is set at 1.5 times its minimum. Device voltage optimization is applied for calculating the energy consumption. Finally, with 1.5 times the minimum delay, the energy consumption is 40% less than the consumption when the delay is the minimum.

A STUDY ON DETECTION OF MALWARE USING MACHINE LEARNING ALGORITHMS

One of the most significant issues facing internet druggies currently is malware. Polymorphic malware is a new type of vicious software that's further adaptable than former generations of contagions. Polymorphic malware constantly modifies its hand traits to avoid being linked by traditional hand- grounded malware discovery models. Counter-attacking measures have been more effective, with antivirus companies expanding their signature database, which is routinely updated, although they are inefficient and ineffective in the case of polymorphic malware. To identify vicious pitfalls or malware, we used a number of machine literacy ways. A high discovery rate indicated that the algorithm with the stylish delicacy was named for operation in the system. As an advantage, the confusion matrix measured the number of false cons and false negatives, which handed fresh information regarding how well the system worked. In particular, it was demonstrated that detecting dangerous business on computer systems, and thereby perfecting the security of computer networks, was possible using the findings of malware analysis and discovery with machine literacy algorithms (Naive Byes, SVM,RF, and with the proposed approach) integrals. The results showed that when compared with other classifiers, DT( 99), CNN(97.76), and SVM(94.41) performed well in terms of discovery delicacy. These results are significant, as vicious software is getting decreasingly common and complex. Keywords: CNN, SVM, DT, cybersecurity, cyberattack, cyber warfare, cyber threats, suspicious activity.

Effect of Cavitator and Nozzle Parameters on the Efficiency job of the impulse Jet Generator

Studies of fluid jet flows in the presence of a ventilated cavity with a negative cavitation number, carried out at the Institute of Mechanics of Moscow State University, showed that under certain conditions, self-cavitational oscillations with a high intensity of pressure pulsations occur in such a hydraulic system. It was shown that it is possible in principle to use the self-oscillation regime to create periodic impulsed jets during outflow from a converging nozzle into the atmosphere. The influence of the parameters of the cavitator and the outlet nozzle on the intensity of the impact of the outflowing liquid on the screen located perpendicular to the outflow direction is studied. It is shown that an increase in the length of the nozzle can significantly increase the efficiency of the generator, and a smooth narrowing of the channel in front of the cavitator can increase the operating range of the generator in the direction of larger gas blows. It is shown that there is a scale effect - with increasing head pressure, the relative intensity of self-oscillations decreases, but tends to reach a horizontal asymptote.

Effect of outer-cylinder rotation on the radially heated Taylor–Couette flow

A Taylor–Couette setup with radial heating is considered where a Boussinesq fluid is sheared in the annular region between two concentric, independently rotating cylinders maintained at different temperatures. Linear stability analysis is performed to determine the Taylor number for the onset of instability. Two radius ratios corresponding to wide and thin gaps with several rotation rate ratios are considered. The rotation of the outer cylinder is found to have a general stabilizing effect on the stability threshold as compared to pure inner-cylinder rotation, with a few exceptions. The radial heating sets up an axial flow which breaks the reflection symmetry of isothermal Taylor–Couette flow in the axial coordinate. This symmetry breaking separates linear stability thresholds, and we find the fastest growing modes with both positive and negative azimuthal numbers for different parameters. Another important finding of the current study is the discovery of unstable modes in the Rayleigh-stable regime. Furthermore, closed disconnected neutral curves (CDNCs) are observed for both wide and thin gaps which can separate from or merge into open neutral–stability curves. Alternatively, CDNCs can also morph into open neutral stability curves as the rotation rate ratio is changed. CDNCs are observed to be sensitive to changes in control parameters, and their appearance/disappearance is shown to induce discontinuous jumps in the critical Taylor number. For both wide and thin gaps, the fastest-growing modes found in the pure corotation case are shown to have their origins in the instability islands at smaller values of rotation rate ratios.

Image Quality Assessment Using a Convolutional Neural Network for Clinical Skin Images

Background The quality of the images received for teledermatology evaluation is often suboptimal, with up to 50% of patients providing images that are poorly lit, off-center, or blurry. To ensure a similar level of care to in-person consultations, high-quality images are essential. Objective The aim of this study is to develop an image quality analysis tool to assess patient- and primary care physician (PCP)–derived images using a deep learning model leveraging multiple instance learning and ordinal regression for model predictions. Methods The data set used for this study was acquired from patient-derived images submitted to the Department of Dermatology, Duke University, between August 21, 2018, and December 31, 2019, and PCP-derived images between March 1, 2021, and June 30, 2022. Seven dermatology faculty members with a designation of professor, associate professor, and assistant professor evaluated 400 images each, and 2 dermatology residents evaluated 400 images, assuring that each image had 4 different quality labels. We used a pretrained model VGG16 architecture, further fine-tuned by updating weights based on the input data. The images were taken with cell phones (patients) or cameras (PCPs) in RGB scale, with the resolution being 76 pixels per inch for both height and width, and the average pixel size of the image being 2840×2793 (SD 986×983; 1471 inch2, SD 707 inch2). The optimal threshold was determined using the Youden index, which represents the best trade-off between sensitivity and specificity and balance the number of true positives and true negatives in the classification results. Once the model predicts the rank, the ordinal labels are transformed to binary labels by using a majority vote as the goal is to distinguish between 2 distinct categories (good vs bad quality) and not predict quality as a continuous variable. Results Based on the Youden index, we achieved a positive predicted value of 0.906, implying that the model will predict 90% of the good-quality images as such, while 10% of the poor-quality images are predicted as being of good quality to enhance clinical utility, with an area under the receiver operating characteristic curve (AUC) for the test set at 0.885 (95% CI 0.838-0.933) and sensitivity, specificity, and negative predictive value (NPV) of 0.829, 0.784, and 0.645, respectively. Further evaluation on independent validation consisting of 300 images from patients and 150 images from PCPs revealed AUCs of 0.864 (95% CI 0.818-0.909) and 0.902 (95% CI 0.85-0.95), respectively. The sensitivity, specificity, positive predicted value, and NPV for the 300 images were 0.827, 0.800, 0.959, and 0.450, respectively. Conclusions This study shows a practical approach to improve image quality for clinical decision-making. While patients and PCPs may have to capture additional images (due to lower NPV), this is offset by the reduced workload and improved efficiency of clinical teams due to the receipt of higher-quality images. Additional images can also be useful if all images (good or poor) are transmitted to medical records. Future studies need to focus on real-time clinical validation of our results. Conflicts of Interest None declared.

ANALISIS KESALAHAN SISWA DALAM MENYELESAIKAN SOAL PERSAMAAN KUADRAT

The observation of this study aims to find out some of the errors and the level of understanding of students in working on several types of quadratic equation problem models, because these errors must be corrected so that it is easier to solve problems in the future. By using a qualitative descriptive method, the researcher hopes to help reduce mistakes made by students when working on practice questions and exams. The researcher used subjects from class IX students of SMPI Nurul Hidayah for the 2022/2023 academic year. Data collection techniques in this observation were UH (daily tests) and per-student interviews. The conclusion from this study there are 5 types of subject errors, namely errors when determining the roots of a quadratic equation, writing set brackets, writing mathematical notation, determining the values of ????, ????, and ???? in general equations, and writing the value of the product of negative numbers.&#x0D; Keywords: error analysis, quadratic equation

A new formulation of the surface charge/surface potential relationship in electrolytes with valence less than three

AbstractSurface complexation models (SCMs) based on Gouy-Chapman theory are often used to describe adsorption of ions onto mineral surfaces. To compensate for the buildup of charge at a solid surface, the composition of the electric diffuse layer next to the surface must balance the surface charge. To calculate the diffuse layer composition, several nonlinear equations and integrals must be solved, usually with an iterative approach. Before convergence, charge balance is typically not fulfilled. One numerical difficulty is that, because of these charge balance errors, the iterative solver may attempt to take the square root of a negative number. Herein, we show that for electrolytes containing only monovalent or divalent ions (i.e., most electrolytes encountered in practice), we can greatly simplify the integrals and eliminate the appearance of complex-valued integrands; it is even possible to derive explicit analytical formulas. Furthermore, using the new method prevents converging to non-physical roots of the Grahame equation, which links surface potential to surface charge. To the best of our knowledge, the presented formulation has not been implemented in geochemical modelling software before, although similar mathematical expressions have been presented in the literature. In Gouy-Chapman theory, ions can only be distinguished by their charges, but this is not consistent with all experimental findings. We present a model that allows for the preferential accumulation of ions in the diffuse layer. The model, which is implemented mathematically by including ion exchange sites with a variable exchange capacity, is flexible and more numerically tractable than the standard models for the diffuse layer.

(Invited) Understanding Ion-Ion Correlations: From Liquid Electrolytes to Polymer Electrolytes

Mass transport in electrolytes is one of the most important design focuses of electrochemical devices such as batteries, fuel cells, and supercapacitors. Compared to the infinitely dilute solution, ion-ion correlations play a central role in determining the structure-property relationships in the concentrated solution. Therefore, disentangling ion-ion correlations and establishing their impact on transport coefficients is a fundamental and pressing issue in the field of electrolyte materials.In this talk, I will present the recent works of my group and collaborators on using molecular dynamics simulations to understand ion-ion correlations. In particular, we looked into this issue by exploring the synergy between liquid electrolytes and polymer electrolytes following the physical chemistry route started by Onsager. This has led to a number of interesting results on the relationship between the ion-pairing and the deviation from the Nernst-Einstein relation [1-3], and shed light on resolving the controversy of the negative transference number found in polymer electrolytes [4].

The Impact of E-Government on Governance in the Case of ASIA Countries in 2020

This study aims the impact of each governance indicator in 30 Asian countries through an analysis of the 2020 Worldwide Governance Indicators (WGI) and E-Government Development Index (EGDI). This study uses quantitative methods. Visual analysis of coded data using Google Data Studio with table functionality and SmartPLS version 3.0 software. samples from 30 Asian countries. The results are as follows: First, the effect of his EGDI on WGI, only one of each correlation metric had an invalid value that is voice and accountability with a value of 0.386, suggesting a potential impact on freedom of expression. Association and social media in 30 Asian countries are still low, with an original sample value (O) of 0.824, the sample mean (M) of 0.832, STDEV of 0.043, a t-statistic of 19.318, and P-value of 0.000. Second, according to the data processing results of Google Data Studio, each indicator such as COC, GE, PSA, RL, RQ, VA has a positive value, and among the 30 Asian countries affecting each indicator, Singapore scores very high. Then Pakistan, achieved an average score out of 30 Asian countries. Although this is a negative number, COC, GE, PSA, RL, RQ, and VA indicators still need improvement.

Strategic incentives when implementing Dorfman testing with assortative matching

The Dorfman pooled testing scheme is a process in which individual specimens (e.g., blood, urine, swabs, etc.) are pooled and tested together; if the merged sample tests positive for infection, each specimen from the pool is tested individually. Through this procedure, laboratories can reduce the expected number of tests required to screen a population. The literature has often advocated in favor of using ordered partitions to screen the population, i.e., of pooling subjects with similar probability of infection together, as doing so simultaneously minimizes the expected number of tests, the expected number of false negatives, and the expected number of false positive classifications, provided that certain technical conditions hold. One potential limitation of using ordered partitions, however, is that they may incentivize some subjects to misreport their types to the tester. Indeed, if subjects wish to avoid being detected as infected, ordered partitions would incentivize them to falsely claim that they have a low probability of infection (assuming that pooled testing is subject to dilution effects). These incentives would disappear if subjects were matched randomly, regardless of their probability of infection. In this article, we derive conditions under which ordered partitions outperform matching subjects randomly, despite these incentives.