Negative Numbers Research Articles

Combined multiplex panel test results are a poor estimate of disease prevalence without adjustment for test error.

Multiplex panel tests identify many individual pathogens at once, using a set of component tests. In some panels the number of components can be large. If the panel is detecting causative pathogens for a single syndrome or disease then we might estimate the burden of that disease by combining the results of the panel, for example determining the prevalence of pneumococcal pneumonia as caused by many individual pneumococcal serotypes. When we are dealing with multiplex test panels with many components, test error in the individual components of a panel, even when present at very low levels, can cause significant overall error. Uncertainty in the sensitivity and specificity of the individual tests, and statistical fluctuations in the numbers of false positives and false negatives, will cause large uncertainty in the combined estimates of disease prevalence. In many cases this can be a source of significant bias. In this paper we develop a mathematical framework to characterise this issue, we determine expressions for the sensitivity and specificity of panel tests. In this we identify a counter-intuitive relationship between panel test sensitivity and disease prevalence that means panel tests become more sensitive as prevalence increases. We present novel statistical methods that adjust for bias and quantify uncertainty in prevalence estimates from panel tests, and use simulations to test these methods. As multiplex testing becomes more commonly used for screening in routine clinical practice, accumulation of test error due to the combination of large numbers of test results needs to be identified and corrected for.

Improved rain event detection in commercial microwave link time series via combination with MSG SEVIRI data

Abstract. The most reliable areal precipitation estimation is usually generated via combinations of different measurements. Path-averaged rainfall rates can be derived from commercial microwave links (CMLs), where attenuation of the emitted radiation is strongly related to rainfall rate. CMLs can be combined with data from other rainfall measurements or can be used individually. They are available almost worldwide and often represent the only opportunity for ground-based measurement in data-scarce regions. However, deriving rainfall estimates from CML data requires extensive data processing. The separation of the attenuation time series into rainy and dry periods (rain event detection) is the most important step in this processing and has a high impact on the resulting rainfall estimates. In this study, we investigate the suitability of Meteosat Second Generation Spinning Enhanced Visible and InfraRed Imager (MSG SEVIRI) satellite data as an auxiliary-data-based (ADB) rain event detection method. We compare this method with two time-series-based (TSB) rain event detection methods. We used data from 3748 CMLs in Germany for 4 months in the summer of 2021 and data from the two SEVIRI-derived products PC and PC-Ph. We analyzed all rain event detection methods for different rainfall intensities, differences between day and night, and their influence on the performance of rainfall estimates from individual CMLs. The radar product RADKLIM-YW was used for validation. The results showed that both SEVIRI products are promising candidates for ADB rainfall detection, yielding only slightly worse results than the TSB methods, with the main advantage that the ADB method does not rely on extensive validation for different CML datasets. The main uncertainty of all methods was found for light rain. Slightly better results were obtained during the day than at night due to the reduced availability of SEVIRI channels at night. In general, the ADB methods led to improvements for CMLs performing comparatively weakly using TSB methods. Based on these results, combinations of ADB and TSB methods were developed by emphasizing their specific advantages. Compared to basic and advanced TSB methods, these combinations improved the Matthews correlation coefficient of the rain event detection from 0.49 (or 0.51) to 0.59 during the day and from 0.41 (or 0.50) to 0.55 during the night. Additionally, these combinations increased the number of true-positive classifications, especially for light rainfall compared to the TSB methods, and reduced the number of false negatives while only leading to a slight increase in false-positive classifications. Our results show that utilizing MSG SEVIRI data in CML data processing significantly increases the quality of the rain event detection step, in particular for CMLs which are challenging to process with TSB methods. While the improvement is useful even for applications in Germany, we see the main potential of using ADB methods in data-scarce regions like West Africa where extensive validation is not possible.

Charge fluctuations on the dust grains in the presence of energetic electrons

Abstract Dust charging is an arbitrary process occurring at random times which results in fluctuations of dust charge around its equilibrium value. To have a better insight into the mechanism of charge fluctuations, a numerical simulation of the statistical nature of the dust charging process is investigated. Here, a multicomponent non-Maxwellian hydrogen plasma comprising two electron groups, positive ions, and dust grains is modelled. An increase in the overall negative dust charge number is reported in the presence of non-Maxwellian electrons. Additionally, the study emphasizes the role of electron distribution and hot electron temperature and density on the charge fluctuations of the dust grains.

Evaluating the machine learning based Efficacy of Decision Tree and Support Vector Machines in Smart Irrigation Systems for Precise Irrigation Status Classification for Optimizing Water Management in Agriculture

The research paper deals with the improvement of water management practices in agriculture through the use of machine learning in smart irrigation systems. The efficiency of the Decision Tree and the Support Vector Machine models with real-time sensor data was analyzed. The Internet of Things system was developed to analyze the data collected at the field with the help of various sensors, including temperature, humidity, and soil moisture sensors. The layout and choice of sensors were based on the official parameters required for most average plants and corps. After data collection and development of classifiers, the Decision Tree and Support Vector Machine models were used and analyzed regarding their efficiency for making irrigation-related decisions. As the result, it was found that both models showed a good performance, but the SVM model was slightly better due to a smaller number of false positives and false negatives, which allow it to divide the data into the corresponding categories more accurately. At the same time, both models use history data, and the more recent data are better for decision as it was also noted during the development when the use of models based on history data for irrigation decision result in regular irrigation, which in long term would cause the loss of productivity of plans, due to the issue that ancient and no crops are watered too. As the final results, it can be stated that the models show good performance, and the algorithms that were tested can be stated viable for the use at practice. At the same time, the SVM model shows slightly better performance than DT.

Balance Board Math: Exploring the Sense of Balance as a Basis for Functions and Graphing and Number Line Concepts

AbstractCan math concepts be experienced through the sensory modality of balance? Balance Board Math (BBM) is a set of pedagogical math activities designed to instantiate mathematical concepts through stimulation to the vestibular sense: an organ in the inner ear that detects our bodily balance and orientation. BBM establishes the different ways children spontaneously rock and move as the basis for inclusively exploring mathematical concepts together across diverse sensory profiles. I describe two activity sets where students explore focal concepts by shifting their balance on rockable balance boards: “the Balance Number Line,” using analog materials to foster understandings of the number line and negative numbers, and “Balance Graphing,” using sensors and a digital display to foster exploration of functions and graphing concepts, including the parameters of trigonometric functions and function addition. I outline proposed ways that engaging with concepts through balance-activating movement can change learners’ mathematical thinking and learning.

Effect of trapping of electrons and positrons on the evolution of shock wave in magnetized plasma: A complex trapped K-dV burgers’ equation

Our research demonstrates that positive and negative ions, along with trapped electrons and positrons, propagate as small-amplitude electrostatic shock waves in a magnetized collisions-less plasma. The trapped Korteweg-de Vries Burger's (T-KdVB) equation was derived by the widely recognized standard reductive perturbation technique (RPT), employing two separate scales of standard coordinates. The study examines the impact of temperature ratio on the shock wave characteristics of positive and negative ions, as well as positrons with electrons. The analysis and discussion focus on the impacts of negative ion number densities, as well as the number densities of electrons and positrons. The T-KdVB equations are responsible for governing rarefactive shock waves. Additionally, it is important to acknowledge that nonlinearity and dissipation play a key role in controlling the amplitude of the shock.

An efficient and robust shape optimization framework for gridshell designs based on node shifting method

In this paper, a novel and efficient shape optimization framework for freeform gridshell designs, especially for cantilevering structures, is proposed based on the node shifting method. The nodal coordinates are directly chosen as the design variables without any extra parameterization, and are iteratively updated to minimize the structural compliance. The distributed load filter is introduced for a smoother and more efficient shape updating scheme, where the negative sensitivity numbers are applied as the virtual external load for the shape updating analysis to guarantee both the smoothness of surfaces and the sustained reduction of structural compliance at each step. The penalized volume adjustment subroutine is then applied to satisfy the volume constraint and to improve the uniformity of grids. Compared to the conventional nodal shifting methods, it is observed that a much more stable and faster convergence is achieved. A variety of examples are presented to demonstrate the improved effectiveness and the efficiency of the proposed algorithm.

A hybrid approach for optimizing software defect prediction using a gray wolf optimization and multilayer perceptron

PurposeSoftware defect prediction (SDP) is a critical aspect of software quality assurance, aiming to identify and manage potential defects in software systems. In this paper, we have proposed a novel hybrid approach that combines Gray Wolf Optimization with Feature Selection (GWOFS) and multilayer perceptron (MLP) for SDP. The GWOFS-MLP hybrid model is designed to optimize feature selection, ultimately enhancing the accuracy and efficiency of SDP. Gray Wolf Optimization, inspired by the social hierarchy and hunting behavior of gray wolves, is employed to select a subset of relevant features from an extensive pool of potential predictors. This study investigates the key challenges that traditional SDP approaches encounter and proposes promising solutions to overcome time complexity and the curse of the dimensionality reduction problem.Design/methodology/approachThe integration of GWOFS and MLP results in a robust hybrid model that can adapt to diverse software datasets. This feature selection process harnesses the cooperative hunting behavior of wolves, allowing for the exploration of critical feature combinations. The selected features are then fed into an MLP, a powerful artificial neural network (ANN) known for its capability to learn intricate patterns within software metrics. MLP serves as the predictive engine, utilizing the curated feature set to model and classify software defects accurately.FindingsThe performance evaluation of the GWOFS-MLP hybrid model on a real-world software defect dataset demonstrates its effectiveness. The model achieves a remarkable training accuracy of 97.69% and a testing accuracy of 97.99%. Additionally, the receiver operating characteristic area under the curve (ROC-AUC) score of 0.89 highlights the model’s ability to discriminate between defective and defect-free software components.Originality/valueExperimental implementations using machine learning-based techniques with feature reduction are conducted to validate the proposed solutions. The goal is to enhance SDP’s accuracy, relevance and efficiency, ultimately improving software quality assurance processes. The confusion matrix further illustrates the model’s performance, with only a small number of false positives and false negatives.

Efficient 3’s Complement Circuit for Ternary-ALU

Carrying more information makes the ternary-computation effective in order to reduce the interconnect complexity and hence, ternary computer can be the future alternative to conventional (binary) counterpart. As a consequence the ternary arithmetic has become the centre of choice among circuit/system researcher in recent time. Ternary adder/subtractor is the integral part of Ternary Arithmetic Logic Unit (TALU) and the 3’s complement is used to represent negative ternary number in TALU. This work proposes a new two-step low hardware-cost strategy to converts ternary input into its 3’s complement output. Novel hardware optimization using normal process Enhancement-type Metal Oxide Semiconductor (E-MOS)-transistor is explored and exploited to design proposed 4-trit 3’s complement generator on 32nm standard CMOS technology with 0.9V supply-rail at 27°C temperature using typical MOS-transistor. Unbalanced ternary digit “0”, “1” and “2” are denoted with ground, supply/2 and supply respectively. The T-Spice transient simulations with all possible test patterns validate the working of proposed circuit and the corresponding speed-power characteristic is compared with most recent counterpart. The circuit performance is also evaluated with different load condition. The 4-trit 3’s complement circuit is extended next to propose 16-trit 3’s complement generator and the impact of Process and Environmental variation on the proposed circuit is studied.

On the Dependence of the Critical Success Index (CSI) on Prevalence.

The critical success index (CSI) is an established metric used in meteorology to verify the accuracy of weather forecasts. It is defined as the ratio of hits to the sum of hits, false alarms, and misses. Translationally, CSI has gained popularity as a unitary outcome measure in various clinical situations where large numbers of true negatives may influence the interpretation of other, more traditional, outcome measures, such as specificity (Spec) and negative predictive value (NPV), or when unified interpretation of positive predictive value (PPV) and sensitivity (Sens) is needed. The derivation of CSI from measures including PPV has prompted questions as to whether and how CSI values may vary with disease prevalence (P), just as PPV estimates are dependent on P, and hence whether CSI values are generalizable between studies with differing prevalences. As no detailed study of the relation of CSI to prevalence has been undertaken hitherto, the dataset of a previously published test accuracy study of a cognitive screening instrument was interrogated to address this question. Three different methods were used to examine the change in CSI across a range of prevalences, using both the Bayes formula and equations directly relating CSI to Sens, PPV, P, and the test threshold (Q). These approaches showed that, as expected, CSI does vary with prevalence, but the dependence differs according to the method of calculation that is adopted. Bayesian rescaling of both Sens and PPV generates a concave curve, suggesting that CSI will be maximal at a particular prevalence, which may vary according to the particular dataset.

Automatically Identifying CVE Affected Versions With Patches and Developer Logs

While vulnerability databases are important sources of information for software security, it is known that information in these databases is inconsistent. How to rectify these incorrect data is a challenging issue. In this paper, we employ developer logs and patches to automatically identify vulnerable source code versions that each CVE really affects. Our tool organizes all versions of a piece of software into a version tree, and identifies the first vulnerable version, and the last vulnerable versions in the version tree trunk and branches. For evaluation, we took Linux Kernel as the case study and quantified the error rate of the vulnerable versions reported by the NVD. The total number of vulnerable Linux Kernel versions reported by the NVD was 43,727 (as of September 2020), of which the total number of false positives reached 2,497 and the total number of false negatives reached 9,330, accounting for 5.7% and 21.34%, respectively. In addition, we compare our tool with two vulnerability detection tools and show that our tool could achieve high detection accuracy.

Ion correlations in quaternary ionic liquids electrolytes

Lithium-ion batteries are currently the most popular and widely used energy storage devices, almost omnipresent within modern society in portable devices, electrical vehicles, energy storage stations, and so on. The demand for more efficient, more durable, and more sustainable batteries is rapidly growing. The electrolyte is a key element to improve the performance of lithium-ion batteries. In this work, we focus on quaternary ionic liquid electrolyte (ILE), which uses a four-component ionic liquid as the solvent. Quaternary ILE has found wide applications in energy storage systems, but the ion transport in the electrolyte has not been fully characterized to provide the best strategy for performance optimisation. In this work, we systematically analyse the ion transport in the quaternary ILE and uncover how the correlations between various ions affect the conductivity of the electrolyte. We have found that lithium ions are transported in charge clusters, leading to a negative effective transference number of lithium ions. Furthermore, we identify the stable cluster conformations in ILE by cluster analysis and quantum chemical computing. This work highlights the necessity of considering ion correlations in multi-component electrolyte systems.

Associations between Verbal Fluency and Asymmetry of White Matter Integrity in the Superior Longitudinal Fasciculus in At-Risk Mental States for Psychosis.

Verbal fluency is one of the most severely impaired components of cognitive function in schizophrenia and is also impaired in at-risk mental states (ARMSs) for psychosis. The aim of this study was to explore the markers of disease progression in subjects with ARMSs by comparing the association between the white matter integrity of the superior longitudinal fasciculus (SLF) and verbal fluency in subjects with ARMSs and healthy control (HC) subjects. The correlations of the fractional anisotropy (FA) values on diffusion tensor imaging (DTI) and the laterality index (LI) values of SLF branches I, II, and III with the verbal fluency performance were analyzed in right-handed subjects with ARMSs (ARMS group; n = 18) and HC subjects (HC group; n = 34) aged 18 to 40 years old. In the HC group compared with the ARMS group, the LI values suggested right lateralization of the SLF II and III. Letter fluency was significantly correlated with the LI of the SLF III in both the ARMS and HC groups. The regression coefficient (β) of this correlation was calculated using the least squares method and yielded a positive number (73.857) in the ARMS group and a negative number (-125.304) in the HC group. The association of the rightward asymmetry of the SLF III with the verbal fluency performance observed in the HC group appeared to be lost in the ARMS group, and this could serve as one of the markers of the pathological progression to psychosis in patients with schizophrenia.

The impact of early childhood education and medical care on children

Causal inference is a statistical approach that aims to understand and quantify the causal relationship between variables, allowing us to determine the impact of one variable on another while accounting for potential confounding factors. In this study, we chose the Infant Health and Development Program (IHDP) dataset to test whether high-quality early childhood education and medical care will enhance their cognitive and academic ability. We use some common and classic algorithms to achieve this study by calculating the CATE (Conditional Average Treatment Effect) of the dataset; if the result is a negative number, that means early treatment doesn’t improve children’s further ability; if the result is a positive number, that means treatment improves it. After fitting the targeted models, BART(Bayesian Additive Regression Trees) and Metalearners, into this dataset, we found out that all the models will get positive CATE, which means that these early treatments positively affect children’s future development. Also, we can judge which model gives us a more accurate result according to the standard variance of TE(treatment effect). The result of this study will provide us with an insightful idea about whether we should give children some treatments regarding education and medical care and which model is more suitable for this casual inference test.

Evaluating the coding accuracy of type 2 diabetes mellitus among patients with non-alcoholic fatty liver disease

BackgroundNon-alcoholic fatty liver disease (NAFLD) describes a spectrum of chronic fattening of liver that can lead to fibrosis and cirrhosis. Diabetes has been identified as a major comorbidity that contributes to NAFLD progression. Health systems around the world make use of administrative data to conduct population-based prevalence studies. To that end, we sought to assess the accuracy of diabetes International Classification of Diseases (ICD) coding in administrative databases among a cohort of confirmed NAFLD patients in Calgary, Alberta, Canada.MethodsThe Calgary NAFLD Pathway Database was linked to the following databases: Physician Claims, Discharge Database, National Ambulatory Care Reporting System, Pharmaceutical Information Network database, Laboratory, and Electronic Medical Records. Hemoglobin A1c and diabetes medication details were used to classify diabetes groups into absent, prediabetes, meeting glycemic targets, and not meeting glycemic targets. The performance of ICD codes among these groups was compared to this standard. Within each group, the total numbers of true positives, false positives, false negatives, and true negatives were calculated. Descriptive statistics and bivariate analysis were conducted on identified covariates, including demographics and types of interacted physicians.ResultsA total of 12,012 NAFLD patients were registered through the Calgary NAFLD Pathway Database and 100% were successfully linked to the administrative databases. Overall, diabetes coding showed a sensitivity of 0.81 and a positive predictive value of 0.87. False negative rates in the absent and not meeting glycemic control groups were 4.5% and 6.4%, respectively, whereas the meeting glycemic control group had a 42.2% coding error. Visits to primary and outpatient services were associated with most encounters.ConclusionDiabetes ICD coding in administrative databases can accurately detect true diabetic cases. However, patients with diabetes who meets glycemic control targets are less likely to be coded in administrative databases. A detailed understanding of the clinical context will require additional data linkage from primary care settings.

Errors and Misconceptions in Linear Inequalities Among Senior High Students in Mfantseman Municipality

This study aimed to investigate the different errors and misconceptions made by students when dealing with linear inequalities. The goal was to uncover the nature and causes of these errors and misconceptions among students in Senior High Schools within the Mfantseman Municipality in the Central Region of Ghana. The research employed an explanatory sequential mixed methods design and was conducted in two public Senior High Schools selected from the Municipality. A total of 180 Senior High School students and teachers participated in the study, including 10 teachers. The sample was selected using a random sampling technique, which yielded 170 students from the two chosen public Senior High Schools. Data collection encompassed results from students' tests on linear inequalities, interviews with students, and questionnaires given to mathematics teachers. The collected data was coded and analyzed using descriptive statistics. The study's findings revealed common errors made by students, such as multiplying/dividing by a negative number, substituting inequality symbols with "equal to" symbols, performing operations on only one side or different numbers on the two sides of a compound inequality, as well as errors in algebraic operations, simplification, and arithmetic. Misconceptions observed included confusion between equality and inequality, misconceptions when dividing or multiplying through an inequality by a negative number, and struggles with compound inequalities. Students' difficulties arose from an inadequate understanding of basic inequality concepts, overgeneralization, limited mastery of inequality rules, and insufficient exposure to compound inequalities. Translating word problems into algebraic symbols posed a significant challenge. The study also highlighted that mathematics teachers were aware of the errors made by students. Consequently, teachers made efforts to address these errors during linear inequality classes. The findings suggest that teachers not only need assistance in identifying errors but also in understanding how errors can emerge during the learning process. One of the recommendations is to enhance teacher education by emphasizing diverse teacher-student interactions that thoroughly consider students' mathematical ideas. This approach aims to support teachers in effectively utilizing students' experiences in the learning process.

A112 THE EDMONTON PANCREATICOBILIARY INFLAMMATION AND CANCER (EPIC) PROGRAM – A NEW MULTIDISCIPLINARY COORDINATION OF CARE INITIATIVE

Abstract Background Pancreaticobiliary (PB) cancer is associated with a poor prognosis. A timely diagnosis and a coordinated, multidisciplinary effort may improve outcomes as patients navigate the healthcare system for investigations and treatment. Aims To establish and pilot a multidisciplinary (gastroenterology [GI], hepatopancreatic biliary [HPB] surgery, radiology, oncology, palliative medicine, and nutrition) collaborative care pathway (CCP) to coordinate timely referral and access to management for patients with PB cancer. Methods With multidisciplinary expert input, a CCP for PB cancer patients was developed. A donation to our hospital foundation facilitated the hiring of a nurse navigator (NN) to coordinate this process. The EPIC program was initiated as a 6-month pilot project on 01/07/23 for all consecutive patients referred to the University of Alberta Hospital with suspected PB cancer. Referrals were triaged by GI and HPB surgery into resectable, borderline resectable, or unresectable arms of the CCP. EUS biopsy and/or ERCP for stent were performed as needed. A referral to medical oncology was made after a diagnosis was established. Descriptive statistics were completed. Results At 3 months, 64 patients (39 M, 25 F), mean age 67±12 years (range 19-95 years), were referred to EPIC. Presenting symptoms were abdominal pain (73%), weight loss (40%), jaundice (27%), nausea/vomiting (21%), itching (8%), and new-onset diabetes (5%). Site of cancer was pancreas (77%, 49/64), bile duct (20%, 13/64), and ampulla (3%, 2/64). Timelines to care access were compared with a similar pre-CCP patient cohort as shown in Table. Conclusions The role of the NN is to reduce variability and coordinate timely referral, scheduling, and follow-up based on the care pathway, and to serve as a point of contact for ongoing patient issues. The EPIC program pilot is on track to meet the timelines set forth in the care pathway. Primary care outreach to increase awareness of symptoms and for prompt investigation may further improve access to care in these patients. Note: negative numbers indicate days where the GI/HPB review occurred before the CT/MRI, or intervention before GI/HPB review EPIC COLLABORATIVE CARE PATHWAY Funding Agencies None

Self-reporting of hearing loss and tinnitus in the diagnosis of ototoxicity by meglumine antimoniate in patients treated for American tegumentary Leishmaniasis.

American Tegumentary Leishmaniasis (ATL) treatment is based on pentavalent antimonials (Sb5+), but these drugs have been associated to several adverse effects. Hearing loss and tinnitus during treatment with meglumine antimoniate (MA) have already been reported. This study aimed to describe the usefulness of self-reporting of hearing loss and tinnitus in diagnosing MA-induced ototoxicity. A prospective longitudinal study was conducted with 102 patients with parasitological diagnosis of ATL, treated with different MA schemes. The presence of clinical auditory toxicity was defined as the emergence or worsening of self-reporting hearing loss and/or tinnitus during monitoring. Measures of sensitivity, specificity, and the positive and negative predictive value of the patient's self-reporting of hearing loss and tinnitus in relation to the result of the audiometric test (considered the gold standard) were calculated. The age of the evaluated patients ranged from 15 to 81 years, with a median of 41 years, and most were male (73.5%). Seventy-five patients (73.5%) had cutaneous leishmaniasis and 27 (26.5%) mucosal leishmaniasis. Eighty-six patients (84.3%) received intramuscular (IM) treatment and 16 (15.7%) were treated with intralesional MA. During treatment, 18 (17,6%) had tinnitus and 7 (6,9%) had complaint of hearing loss. 53 (52%) patients had cochlear toxicity confirmed by tone threshold audiometry and high frequency audiometry, from which 60% received a dose of 20 mg Sb5+/kg/day (p = 0.015) and 96.2% were treated with IM MA (p = 0.001). Tinnitus has greater specificity and positive predictive value than hearing loss, with a low number of false positives, but with a high false negative value. Although the large number of false negatives suggests that self-report of hearing loss or tinnitus cannot be considered a good screening test for referring the patient to an audiometry, the low number of false positives suggests the need to value the patient's complaint for referral. Otherwise, this study reinforces the importance of audiological monitoring during treatment with MA, especially in those patients with self-reporting of hearing loss or tinnitus when treated with 20 mg Sb5+/kg/day via IM.

Analysis of deck house height and center of gravity in anti-capsize patrol boat

The design of patrol boats, especially in Indonesian waters which have extreme sea conditions, requires fairly good stability capabilities and special self-righting capabilities. Designing a self-righting ship will be closely related to the ship stability because the center of gravity (G) is affected by the load and the height of the deckhouse of a ship, which has implications for the ship’s self-righting. Present study was carried out with experimental study for patrol boat design that has capability self-righting moment. The patroal boat has 4 deckhouse height variations with 2.01 m, 2.11 m, 2.21 m, and 2.31 m, respectively. There are 4 variations of the load conditions, i.e., the condition with 50% of the maximum amount of cargo, the condition 50% the passengers and cargo, full load condition without passengers, and the last one is the condition of the passenger baggage is only 50%. The results showed that a deckhouse with a height of 2.01 m has the worst GZ curve analysis, where in some conditions the results touch a negative number before heeling to 180°. The results showed by trial and error that the minimum deckhouse height is 2.07 m to have a self-righting moment in all conditions.

Use of information redundancy to increase the reliability of devices for storing, processing and transmitting information

In modern information transmission systems, a special place is occupied by telecommunication systems (TS), which include digital data transmission systems (DSTS) containing specialized computers (SEVMs). The systems under consideration must ensure fast and reliable transmission of discrete data over communication lines. In this case, the loss of even one transmitted character is unacceptable; therefore, the main indicator of the reliability of a computer is the probability of failure-free operation. To increase the probability of failure-free operation of a storage device (MS), corrective codes and duplication are used, and to increase the probability of failure-free operation of the arithmetic-logical unit (ALU) of a computer, majority redundancy is used. The disadvantage of the majority method is the high hardware costs for redundancy, which reduces the efficiency of its use. The disadvantages of using correction codes include: as a rule, they are used for separate redundancy (detection and correction of errors that occur in storage devices; majority redundancy is used to detect and correct errors in the ALU); not all arithmetic and logical operations can be controlled based on existing correcting codes; a sharp increase in hardware costs when using algebraic linear codes to detect and correct multiple errors. Detection of errors in the ALU of a computer processor can be achieved on the basis of duplication in a loaded mode with replacement, however, this is associated with solving the main problem of computer duplication: - selection of monitoring tools for determining a failed channel (detection of errors in storage and information processing devices). The purpose of the research is to develop a scientific and methodological apparatus for computer backup using the duplication method with error detection in backup channels based on the use of an algebraic linear code, which makes it possible to detect errors not only in storage devices, but also adapted for detecting errors when performing arithmetic and logical operations. Results. An analysis of operation and selection of reliability indicators for specialized computers (SEVMs) of telecommunication systems was carried out. Requirements for computer backup methods are formulated. A comparative assessment of the detecting ability and hardware costs was carried out when implementing the majority redundancy method, the duplication method and the use of correcting codes. The expediency of using the duplication method is substantiated to increase the probability of failure-free operation and survivability of self-healing digital computers, using algebraic linear codes to determine the faulty channel, which allows to significantly reduce hardware costs for constructing monitoring equipment and use 10-30% of backup equipment for these purposes. It is proposed to use an algebraic linear code, in which, unlike well-known codes, the values of the check bits correspond to the direct and inverse values of the information bits, which makes it possible to detect errors when reading information from the inverse outputs of the memory, correct single errors, detect double errors and control the logical inversion operation , necessary to represent a negative number in two's complement code, which makes it possible to adapt the code to control arithmetic and logical operations of the computer processor. An assessment was made of the probability of failure-free operation of a duplicated computer, with its general redundancy, with detection and correction of single errors in the backup memory channels and detection of errors in the backup channels of the processor ALU based on the proposed code, and an assessment of the probability of failure-free operation of the computer, with its separate redundancy, with error detection in the backup channels of the duplicated memory based on the Hamming code and error correction in the backup channels of the processor ALU based on the majority method. A comparative assessment of the probabilities of failure-free operation allows us to conclude that the general redundancy of the computer based on the proposed code, in comparison with the separate redundancy of the memory and ALU of the computer processor, allows for a gain in the probability of failure-free operation of the computer and its functional devices throughout the entire period of operation. The practical significance lies in the fact that the proposed scientific and methodological apparatus provides: reduction of hardware costs for identifying a faulty channel when organizing duplication; detection of errors in the memory and ALU of the computer processor with time costs not exceeding the time of error detection when using standard methods of monitoring the computer system.