Mathematical Estimation Research Articles

Exploring the effects of methodological choices on the estimation and biological interpretation of life history parameters for harbour porpoises in Norway and beyond.

This study investigates effects of subtle methodological choices on the estimation and biological interpretation of age, growth and reproductive parameters for harbour porpoises. The core analyses are based on a focal Norwegian data set built on samples from 134 harbour porpoises caught incidentally in gillnet fisheries along the Norwegian coast during autumn 2016 and spring 2017. Two contrasting practices for interpretation of seasonal and ontogenetic characteristics of tooth growth layer formation resulted in significant age differences among spring samples of young porpoises and for older animals across seasons. In turn, these differences affected estimates of age at maturity and asymptotic lengths, respectively. We also found significant differences in male age at maturity between two well-documented maturity criteria and between mathematical estimators of age at maturity for both sexes. Two different criteria for corpus albicans classification furthermore resulted in different patterns of ovarian corpora accumulation, which may affect some estimates of fecundity rates and contaminant loads. Both corpora accumulation patterns were also found in reanalysed data from German and Greenlandic porpoises. Based on tabulated overviews of methodological choices made in previous harbour porpoise studies, we argue that several of the issues mentioned above have wider relevance and may affect the validity of meta-analyses as a tool for estimating harbour porpoise sensitivity to extrinsic pressures. Differences in cause of death (COD) composition between data sets can have a similar effect. We demonstrate this in a meta-analysis of published harbour porpoise pregnancy rates, showing significantly higher values for trauma-killed samples compared to samples comprising mixed COD categories. COD also affected the estimated impacts of three previously analysed extrinsic predictors as well as an added predictor for vessel noise levels. We discuss the potential contributions of methodological, biological and anthropogenic factors in shaping observed regional differences in estimates of harbour porpoise life history parameters.

On modeling the log-returns of Bitcoin and Ethereum prices against the USA Dollar

The study and investigation of the behavior of monetary phenomena is an interesting subject for actuaries and practitioners. In the recent age and development in the monetary and financial phenomena, cryptocurrency has gained much attention from actuaries. Over the past decade, several research studies have emerged on modeling and forecasting cryptocurrency exchange rates. This paper also contributes to the modeling of cryptocurrency exchange rates using a new version of the Logistic distribution, namely, a new cotangent-Logistic distribution. The mathematical properties and estimators of the new cotangent-logistic distribution's parameters are obtained. We illustrate the new cotangent-Logistic distribution using two financial data sets representing the log-returns of the Bitcoin and Ethereum prices. We compare the new cotangent-Logistic distribution with the baseline Logistic distribution and its modified version. Using the p-value and three other statistical tests, we show that the new cotangent-Logistic distribution repeatedly provides the optimal fit to cryptocurrency exchange rates.

On the structural stability for a model of mixture of porous solids

The present paper is dedicated to the structural stability of the linear model of a mixture of two porous solids. It is shown that the variation of the characteristic coefficients that describe the coupling of the various mechanical effects involved in the model in concern does not destroy its structure. This means that any small variation of these coefficients leads to small variations in the corresponding solutions of the associated initial boundary value problems. For this purpose, more mathematical estimates are presented describing precisely the continuous dependence of the solutions with respect to all external given data of the initial boundary value problem, as well as with respect to appropriate measures of the set of coupling parameters. This allows the conclusion that the model of the mixture of porous materials is consistent. In particular, it is believed that the estimates obtained in terms of structural stability are particularly meaningful with regard to the materials used in building contexts and the related decay phenomena.

Formation and Growth of Co-Culture Tumour Spheroids: New Compartment-Based Mathematical Models and Experiments.

Co-culture tumour spheroid experiments are routinely performed to investigate cancer progression and test anti-cancer therapies. Therefore, methods to quantitatively characterise and interpret co-culture spheroid growth are of great interest. However, co-culture spheroid growth is complex. Multiple biological processes occur on overlapping timescales and different cell types within the spheroid may have different characteristics, such as differing proliferation rates or responses to nutrient availability. At present there is no standard, widely-accepted mathematical model of such complex spatio-temporal growth processes. Typical approaches to analyse these experiments focus on the late-time temporal evolution of spheroid size and overlook early-time spheroid formation, spheroid structure and geometry. Here, using a range of ordinary differential equation-based mathematical models and parameter estimation, we interpret new co-culture experimental data. We provide new biological insights about spheroid formation, growth, and structure. As part of this analysis we connect Greenspan's seminal mathematical model to co-culture data for the first time. Furthermore, we generalise a class of compartment-based spheroid mathematical models that have previously been restricted to one population so they can be applied to multiple populations. As special cases of the general model, we explore multiple natural two population extensions to Greenspan's seminal model and reveal biological mechanisms that can describe the internal dynamics of growing co-culture spheroids and those that cannot. This mathematical and statistical modelling-based framework is well-suited to analyse spheroids grown with multiple different cell types and the new class of mathematical models provide opportunities for further mathematical and biological insights.

Deep Learning Equalizer Connected with Viterbi-Viterbi Algorithm for PAM D-Band Radio over Fiber Link.

D-band (110-170 GHz) has been regarded as a potential candidate for the future 6G wireless network because of its large available bandwidth. At present, the lack of electrical amplifiers operating in the high frequency band and the strong nonlinear effect, i.e., the D-band, are still important problems. Therefore, effective methods to mitigate the nonlinear issue resulting from the ROF link are indispensable, among of which machine learning is considered the most effective paradigm to model the nonlinear behavior due to its nonlinear active function and structure. In order to reduce the computation amount and burden, a novel deep learning neural network equalizer connected with typical mathematical frequency offset estimation (FOE) and carrier phase recovery (CPR) algorithms is proposed. We implement D-band 45 Gbaud PAM-4 and 20 Gbaud PAM-8 ROF transmission simulations, and the simulation results show that the real value neural network (RVNN) equalizer connected with the Viterbi-Viterbi algorithm exhibits better compensation ability for nonlinear impairment, especially when dealing with serious inter-symbol interference and nonlinear effects. In our experiment, we employ coherent detection to further improve the receiver sensitivity, so a complex baseband signal after down conversion at the receiver is inherently produced. In this scenario, the complex value neural network (CVNN) and RVNN equalizer connected with the Viterbi-Viterbi algorithm have better BER performance with an error rate lower than the HD-FEC threshold of 3.8 × 10-3.

Model predicting the onset of antiepileptic drug resistance in women of reproductive age with epilepsy: analytical study

Introduction. In attempt to find an answer regarding the possible scenarios of epilepsy evolution in women of reproductive age (e.g. worsening, remission, antiepileptic drug resistance, status epilepticus occurence), preferably - objective, based on simple, replicable, observable indicators that can be included in a mathematical probability estimation model, could significantly improve their quality of life and increase the effectiveness of prescribed treatments. Materials and methods. Bidirectional, cohort, descriptive-analytical study, conducted between 2016-2020. Primary data were collected in the Diomid Gherman Institute of Neurology and Neurosurgery, the State Hospital of Republic of Moldova and the Excellence Private Medical Institution. Out of 366 unique parameters, which were recorded in the 159 patients enrolled in the study at each visit (total, 4 documentation visits over 5 years period), 10 parameters were selected for multivariate analysis, considered relevant for predicting clinically significant outcomes. Criteria for parameter relevance were: reaching p≤0.1 in univariate analysis, easy documentation. Subsequently, testing for multicollinearity (calculation of variance inflation factor) and the contribution of each parameter in the formula was performed using the Akaike informativeness criteria. The performance of the developed predictive models was expressed by the area under the ROC curve, positive and negative prognostic power. Statistical analysis: GraphPad Prism, v. 9 trial (Graph Pad Software, Boston, USA). Results. Age at onset of the disease 14.0±6.3 years; age at first referral to specialist 24.0±7.2 years. The developed predictive model, based on 3 parameters (depressive state, annual frequency of seizures, presence of brain lesions on MRI) has a positive predictive value of 83%, negative of 62%, with an area under the ROC curve of 0.72 (95%CI = 0.56 to 0.88) and a probability of occurrence of 96%. Conclusions. Depressed patients with documented structural lesions on MRI and a high frequency of epileptic seizures have a progressive, significant risk (an OR of 5.3-24.0) of developing resistance to antiepileptic drugs.

LUMINOSITY OUTBURSTS IN INTERACTING PROTOPLANETARY SYSTEMS

FU Orionis type objects (fuors) are characterized by rapid (tens to hundreds years) episodic outbursts, during which the luminosity increases by orders of magnitude. One of the possible causes of such events is a close encounter between stars and protoplanetary disks. Numerical simulations show that the fuor-like outburst ignition requires a very close encounter ranging from a few to a few tens of au. In contrast, the observed stellar objects in fuor binaries are usually hundreds of au apart. Simple mathematical estimates show that if such a close approach took place, the binary stellar components would have an unrealistic relative velocity, at least an order of magnitude greater than the observed velocity dispersion in young stellar clusters. Thus, the bursts are either triggered with a certain delay after passage of the periastron or their ignition does not necessary require a close encounter and hence the outburst is not caused by the primordial gravitational perturbation of the protoplanetary disk. In this work, an encounter of a star surrounded by a protoplanetary disk with a diskless external stellar object was modeled using numerical hydrodynamics simulations. We showed that even fly-bys with a relatively large periastron (at least 500 au) can result in fuor-like outbursts. Moreover, the delay between the periastron passage and the burst ignition can reach several kyr. It was shown for the first time by means of numerical modeling that the perturbation of the disk caused by the external object can trigger a cascade process, which includes the development of the thermal instability in the innermost disk followed by the magneto-rotational instability ignition. Because of the sequential development of these instabilities, the rapid increase in the accretion rate occurs, resulting in the luminosity increase by more than two orders of ma-gnitude.

57 Sensitivity of functional Near-Infrared Spectroscopy in Individuals with Posterior Cortical Atrophy

Objective:Functional near infrared spectroscopy (fNIRS) is a form of non-invasive neuroimaging that uses light to measure changes in oxygenated and deoxygenated hemoglobin (Yucel et. al., 2021). Relative to fMRI, fNIRS is significantly cheaper and less susceptible to motion artifacts thereby enabling researchers to acquire data in more ecologically valid environments and has a higher temporal resolution that makes it well-suited for connectivity analyses (Tak and Ye, 2014). fNIRS is, however, uniquely limited by cortical anatomy. With a typical probe array having a penetrance depth of up to 3cm, the benefits of fNIRS may be limited by the neocortical atrophy that is characteristic in those with neurodegeneration. We present preliminary findings comparing fNIRS probe sensitivity in older adults diagnosed with posterior cortical atrophy (PCA) relative to cognitively intact older adults using Monte Carlo (MC) simulations. MC simulations offer probabilistic models that simulate photon movement through tissues of interest. We were particularly interested in fNIRS' sensitivity in the occipitoparietal cortices since these are regions characteristically affected in PCA.Participants and Methods:We acquired high resolution structural (T1) MRI on 3 cognitively intact older adults and 3 individuals who received a clinical diagnosis of PCA according to Crutch et al. (2017) criteria. Individual T1 scans were preprocessed and transformed into a twodimensional (2D) surface using FreeSurfer. This continuous 2D surface was then segmented into its main tissue priors, as well as its pial surfaces. Segmented MRIs were then imported into AtlasViewer software and registered to our full head fNIRS probe array via an affine transformation. We embedded the GPU-accelerated Monte Carlo Extreme 3D light transport simulator software (Fang and Boas, 2009) into AtlasViewer which enabled us to launch 10 million photons from each optode, compared to the 1 million that AtlasViewer is set to by default, thereby providing more accurate results (Aasted et. al., 2015). We then assessed the sensitivity profile (log units), a mathematical estimate of optical density, of the inferior and superior occipital gyri, middle occipital gyrus, the superior and inferior parietal lobules, and left and right precunei.Results:Among the regions interrogated, five channels on our fNIRS probe were markedly different between the controls and those with PCA. Specifically, sensitivity values for channels covering the right inferior (hedges g = 8.04) and left superior occipital gyrus (hedges g = 2.46), the right inferior parietal lobule (hedges g = 8.89), and the right (hedges g = 9.43) and left (hedges g = 14.83) precunei were all markedly lower in those with PCA.Conclusions:We provided preliminary evidence that the sensitivity of fNIRS appears to be markedly reduced in those with PCA. This is especially relevant for researchers using fNIRS in populations with neurodegeneration. Future work will evaluate these findings in a larger sample as well as in other neurologic conditions with the goal of helping researchers appropriately power their studies and interpret their results.

Toward the optimisation of the Kalman Filter approach in ground penetrating radar application for detection and locating buried utilities

Ground Penetrating Radar (GPR) stands as a pivotal non-destructive tool for identifying and assessing buried utilities. However, the noisy GPR radargram data requires extensive expert interpretation, making it time-consuming and subjective. While various signal processing techniques, like time-zero correction and background subtraction, help mitigate noise, mathematical estimators like the Kalman Filter (KF) and particle filter present advanced solutions. Notably, KF, known for its efficiency and computational benefits, excels in denoising and decluttering GPR radargrams. This research introduces an innovative KF-based optimisation algorithm tailored to minimise user input and pinpoint buried utilities and anomalies. Leveraging the distinctive KF parameter, Normalised Innovation Squared (NIS), the algorithm aims for enhanced target detection. A genetic algorithm-driven multi-objective optimisation model evaluates the method's efficacy, focusing on the receiver operating characteristic (ROC) and mean of innovations. Cost functions considered encompass noise covariances and optimal Fourier analysis frequency. Preliminary results showed a 23.13% rise in the area under the ROC with optimised parameters (91.4%) compared to user-selected ones (68.27%). This method not only reduces GPR data noise but also augments the detection of buried utilities. Traditional chi-squared hypothesis testing was replaced with an NIS signal function analysis, facilitating more refined anomaly detection. Collectively, this study offers a transformative approach to GPR data post-processing, emphasising efficiency and reduced user dependency.

Effect of wobble parameters on microwelding bead formation of AISI 316L stainless steel

This study examines the impact of wobble movement on a laser beam’s behavior while moving over an AISI 316L stainless steel sample of 1.2 mm thickness during welding. The laser beam oscillatory movement is superimposed on linear movement, using a 400 W fiber laser installed on an experimental bench equipped with a scanner and worktable. Mathematical modeling estimates instantaneous beam speed values, predicting thermal influence on weld bead aspects. Microwelding experiments use autogenous processing with lateral beam oscillation. Two forms of overlapping transverse wobble are tested: one with a circular path and the other describing the mathematical symbol “infinity.” Correlations are evidenced between the input parameters and results obtained in the microwelds, including penetration and width of the beads. Results show that the frequency of movement in a circle and in “infinity” for frequencies from 200 to 400 Hz has no significant influence on the result. Increasing the amplitude of the wobble movement from 0.5 to 2 mm significantly influences the width and depth of the strands generated. The wobble technique is effective in preventing discontinuities in the process, such as porosities. A bead obtained with 300 W, 50 mm/s, 0.5 mm overlapping wobble movement, and 300 Hz circular rotation frequency showed the highest relationship between width and depth.

Mathematical Estimation of Endogenous Proline as a Bioindicator to Regulate the Stress of Trivalent Chromium on Rice Plants Grown in Different Nitrogenous Conditions.

The accumulation of proline impacts the defense mechanisms of plants against the harmful effects of adverse environmental conditions; however, its concentration in plants is associated with the metabolism of N. Therefore, the effects of exogenous organic [glutamate (Glu)/arginine (Arg)] and inorganic [nitrate (NO3-)/ammonium (NH4+)] N on the accumulation of proline (Pro) in rice plants under trivalent chromium [Cr(III)] stress were studied through using the mass balance matrix model (MBMM). Application of 'NH4+' showed the largest contribution to the Pro content in rice shoots under different concentrations of Cr(III), followed by 'NO3-', 'Arg', and 'Glu' applications. On the other hand, 'Arg' application displayed the largest contribution to the Pro content in roots under Cr(III) stress, followed by 'NH4+', 'Glu', and 'NO3-' applications. The combined application of 'NH4++Arg' showed the greatest contribution to the Pro content in both roots and shoots of Cr(III)-treated rice seedlings, while the application of 'NO3-+Glu' showed the least contribution to the Pro content in rice seedlings. The current study indicated that the endogenous level of Pro in rice seedlings is quite sensitive to Cr(III) stress under different N sources, and the mathematical modeling showed a reliable result while estimating the relationship between Pro content and N source application.

Calcium-Citrate Anticoagulation during Continuous Renal Replacement Therapy in Patients with Metformin Intoxication: A Case Series, Mathematical Estimation of Citrate Accumulation, and Literature Review

Plain Language SummaryMetformin intoxication is a potentially life-threatening condition characterized by severe metabolic acidosis that could require intensive care unit admission and renal replacement therapy (RRT) for metformin removal. According to current guidelines, a citrate solution is commonly administered to generate regional anticoagulation in RRT due to its effect on chelating ionized calcium, an essential element of the coagulation pathway. Citrate metabolism can be impaired during metformin intoxication, and its administration could lead to accumulation worsening the metabolic acidosis. Citrate is not routinely measured, and its accumulation is clinically diagnosed by the ratio between total and ionized plasma calcium concentration (T/I calcium ratio) >2.5 or estimating the presence of unmeasured anions (UA), calculating the strong ion gap (SIG), i.e., the difference between the strong anions and cation, and the concentration of bicarbonate, dissociated albumin, and phosphates. We compared our results obtained from a case series of three patients with a theoretical model of citrate accumulation. Finally, we reviewed the literature to unveil clinical practice in this scenario. Our results support that diluted citrate can be safely used for RRT in patients with metformin toxicity, and the SIG is a valuable tool to estimate the presence of UA and can be used to detect and monitor citrate accumulation in this context.

Kinematic motion models based vessel state estimation to support advanced ship predictors

Advanced ship predictors can generally be considered as a vital part of the decision-making process of autonomous ships in the future, where the information on vessel maneuvering behavior can be used as the source of information to estimate current vessel motions and predict future behavior precisely. As a result, the navigation safety of autonomous vessels can be improved. In this paper, vessel maneuvering behavior consists of continuous-time system states of two kinematic motion models—the Curvilinear Motion Model (CMM) and Constant Turn Rate & Acceleration (CTRA) Model. Two state estimation algorithms—the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) are implemented on these two models with certain modifications so that they can be compatible with discrete-time measurements. Four scenarios, created by combining different models and algorithms, are implemented using simulated ship maneuvering data from a bridge simulator. These scenarios are then verified through the proposed stability and consistency tests. The simulation results show that the EKF tends to be unstable combined with the CMM. The estimates from the other three scenarios can generally be considered more stable and consistent, unless sudden actions or variations in vessel heading occurred during the simulation. The CTRA is also proven to be more robust compared to the CMM. As a result, a suitable combination of mathematical models and estimation filters can be considered to support advanced ship predictors in future ship navigation.

Estimation of Load on Control Lines of Ram Air Parachute Designed for Precise Delivery Using 9-Dof Model

Controlled Aerial Delivery System (CADS) consists of Ram Air Parachute (RAP) Airborne Guidance Unit (AGU) and useful payload. AGU pulls the control lines attached to the control surfaces of RAP and provides required maneuverability to the system. Estimation of load on control lines is essential for design and development of Airborne Guidance Unit (AGU). This paper presents the mathematical methods of the estimation of load on control lines the results of which are validated by experiments. The mathematical estimate of the load on control lines was carried out by developing a model similar to the aircraft control surface hinge moment estimation. The mathematical model was incorporated in 9DOF model to arrive at real time load estimation on control lines during inflation and subsequent steady flight condition. Experimental results are obtained during flight trials by using load cells on control lines and current sensor on power line of actuators. The mathematical results are in good agreement with the experimental results. The load on control lines are within 6 to 8% of the suspended load.

Integrated customer lifetime value models to support marketing decisions in the complementary and alternative medicine industry

PurposeCustomer lifetime value (CLV) is one of the key indicators to measure the success or health of an organization. How can an organization assess the organization's customers' lifetime value (LTV) and offer relevant strategies to retain prospective and profitable customers? This study offers an integrated view of different methods for calculating CLVs for both loyalty members and non-membership customers.Design/methodology/approachThis study outlines eleven methods for calculating CLV considering (1) the deterministic aspect of NPV (Net present value) models in both finite and infinite timespans, (2) the geometric pattern and (3) the probabilistic aspect of parameter estimates through simulation modeling along with (4) the migration models for including “the probability that customers will return in the future” as a key input for CLV calculation.FindingsThe CLV models are validated in the context of complementary and alternative medicine (CAM)in the healthcare industry. The results show that understanding CLV can help the organization develop strategies to retain valuable customers while maintaining profit margins.Originality/valueThe integrated CLV models provide an overview of the mathematical estimation of LTVs depending on the nature of the customers and the business circumstances and can be applied to other business settings.

COMPARISON OF OPTIMIZATION STRATEGIES AND ESTIMATION TECHNIQUES FOR RADIO NETWORK PLANNING AND OPTIMIZATION PROBLEMS

Background. Radio network planning is one of the main phases of the cellular network lifecycle, as it determines capital and operating costs and allows system performance evaluation at any given time. An accurate and comprehensive analysis of existing network statistics is necessary for proper cell planning during network expansion. These statistics are collected throughout the life cycle of the cellular network and usually have certain imperfections (heterogeneity of statistics, which have different densities in different parts of the search space, up to the presence of significant voids, etc.) The system describing the functioning of the radio network can be represented as a black box because its internal processes are too complex to be defined by mathematical functions. This determines the need to use appropriate tools.
 Objective. The purpose of the paper is to create a toolkit that allows finding the proper relationships between network parameters to define target values that will help to build an effective network plan in terms of performance and costs for its creation and operation. The tools should be able to work efficiently using the minimum set of available statistical data, as well as taking into account their imperfections.
 Methods. Mathematical estimation and optimization methods are used, namely Ordinary Least Squares, Ridge Regression, Lasso, Elastic-net, LARS lasso, Bayesian Ridge Regression, Automatic Relevance Determination, Stochastic gradient descent, Theil-Sen estimator, Huber Regression, Quantile regression, Polynomial regression. We consider 12 estimation methods in combination with two optimization strategies. Additionally, the method of partial analysis of the search space with different number of configurations is considered.
 Results. A software package using the Python programming language has been created, which contains a practical implementation of all the considered estimation and optimization methods, as well as tools for evaluating arbitrary configurations of the software package (benchmark) and visualizing the results. The best estimation method is Ordinary Least Squares for finding the optimal configuration of the statistical parameters of the 4G radio network to maximize the download speed. To obtain satisfactory results, it is enough to consider 25 initial and 250 estimated points - a larger number of points will not significantly increase prediction accuracy.
 Conclusions. The results indicate the possibility of using the created software package for radio network planning tasks. Further research is aimed at expanding the created software package's functionality and considering additional estimation methods and optimization strategies.
 Keywords: radio network planning; radio network optimization; cellular networks; estimation techniques; optimization strategies; big data; 2G; 3G; 4G; CQI; capacity.

Математическая оценка достоверности информации о лесных пожарах

The rapid development of digital technologies, especially methods for processing a large amount of information, offers vast opportunities for obtaining new algorithms for supporting management decisions, including the prevention of forest fires. Therefore, the requirements for data accuracy on fire hazards in forests and forest fires considerably increase. Even though the remote sensing of the Earth from space is a potential method for acquiring information independent of the human factor, it still has several technical limitations that hinder total automation. Therefore, it is important to provide а comprehensive control over the information coming from the forest fire departments. Besides, the long-term fire risk prognoses must consider retrospective statistics and cyclical weather conditions. This requires the creation of methods for evaluating the reliability of the initial data. An analysis of the records on the number of forest fires that happened in the Russian Federation from 1969 to 2020 revealed that the distribution of the values in a large sampling set is close to lognormal, which is the author’s fundamental principle. The few deviations on the right side of the distribution indirectly support the hypothesis that, in the provided information, the large forest fires in each case were presented as smaller, fragmented events. This is also consistent with the fact that such information usually occurs when the forest fire situation is complex and has many burning locations. An analysis of the records on the forest fires extinguished within one day identified a characteristic deviation, which indirectly supports the assumption that the data was probably distorted to improve recording. In such a situation, the deviation from the pattern corresponds to low combustibility and completely loses its meaning in the conditions of a severe forest fire situation with many burning areas. The authors have formed a ranking of the regions according to the validity of the archival records on the forest fires using the correspondence of the statistical data to the lognormal distribution. The proposed method can become one of the elements of a risk-oriented approach for planning control and supervisory measures in forestry policy. For citation: Kotelnikov R.V., Martynyuk A.A. Mathematical Estimation of Information Reliability Regarding Forest Fires. Lesnoy Zhurnal = Russian Forestry Journal, 2023, no. 3, pp. 21–34. (In Russ.). https://doi.org/10.37482/0536-1036-2023-3-21-34

A Novel Dynamic Approach for Determining Real-Time Interior Visual Comfort Exploiting Machine Learning Techniques

The accurate assessment of visual comfort in indoor spaces is crucial for creating environments that enhance occupant well-being, productivity, and overall satisfaction. This paper presents a groundbreaking contribution to the field of visual comfort assessment in occupied buildings, addressing the existing research gap in methods for evaluating visual comfort once a building is in use while ensuring compliance with design specifications. The primary aim of this study was to introduce a pioneering approach for estimating visual comfort in indoor environments that is non-intrusive, practical, and can deliver accurate results without compromising accuracy. By incorporating mathematical visual comfort estimation into a regression model, the proposed method was evaluated and compared using real-life scenario. The experimental results demonstrated that the suggested model surpassed the mathematical model with an impressive performance improvement of 99%, requiring fewer computational resources and exhibiting a remarkable 95% faster processing time.

Study on Estimation of Device Yield in Non-Epitaxial 4H-SiC Material Relating to Defect Densities Influencing Bipolar Degradation with XRT- Measurements

Commercially available 4H-SiC substrate quality has improved over time, and this has extensively reduced defect concentration in the active epitaxial layer, during epi growth conditions at the interface. The objective of this work is to investigate bulk crystal quality for the purpose of future vertical power device fabrication in exfoliated, non-epitaxial, undoped material layers. Mathematical estimations on the device yield fraction, that is immune to bipolar degradation for the suggested future process were calculated based on XRT measurements to detect BPD and TSD densities on donor substrates. The full wafer BPD density maps of on-axis semi-insulating wafer substrates from two vendors were compared.

Further study of eccentricity based indices for benzenoid hourglass network

Topological Indices are the mathematical estimate related to atomic graph that corresponds biological structure with several real properties and chemical activities. These indices are invariant of graph under graph isomorphism. If top(h1) and top(h2) denotes topological index h1 and h2 respectively then h1 approximately equal h2 which implies that top(h1) = top(h2). In biochemistry, chemical science, nano-medicine, biotechnology and many other science's distance based and eccentricity-connectivity(EC) based topological invariants of a network are beneficial in the study of structure-property relationships and structure-activity relationships. These indices help the chemist and pharmacist to overcome the shortage of laboratory and equipment. In this paper we calculate the formulas of eccentricity-connectivity descriptor(ECD) and their related polynomials, total eccentricity-connectivity(TEC) polynomial, augmented eccentricity-connectivity(AEC) descriptor and further the modified eccentricity-connectivity(MEC) descriptor with their related polynomials for hourglass benzenoid network.