## Keynote & Invited Speakers

### Keynote Speaker

**Keynote Speaker I**

**Associate Professor Dr Nuanpan Lawson**

King Mongkut's University of Technology North Bangkok, Thailand

Title: **Adjusting For Nonresponse in the Analysis of Sample Survey Data**

Abstract: Sample surveys are often subject to non-response, and exhibit cluster-level association as well. In this research, we study how to fit a linear regression model at the cluster-level when non-response occurs on the study variable. We consider the relationship between response rates and the survey variables at the cluster-level. We propose an alternative approach to find the estimated values of regression coefficients under two underlying models for non-response, while the usual regression coefficients are estimated by ignoring non-response. An example is provided to illustrate the properties of these estimators: a simulation study from a survey of employees that includes both non-response and clusters consisting of workplaces.

**Keynote Speaker II**

**Associate Professor Dr Adibah Shuib**

President of the Management Science/Operations Research Society of Malaysia (MSORSM)

Deputy Director (Research & Education)

Malaysia Institute of Transport (MITRANS)

Universiti Teknologi MARA (UiTM), Shah Alam, Malaysia

Title: **An Optimization Model for Airport Aeronautical Revenues**

Abstract: Maximising aeronautical revenues is one of the greatest challenges for airports, particularly regional airports. Daily operational factors may have different influences towards aeronautical revenues generated for the airport. This paper discusses the development of the Aeronautical Revenue Optimisation Model (AROM) with input data concerning mode of operations, traffic types, flights details, fleet types and type of flights in order to determine the composition of flight operations of the airport that produces optimum aeronautical revenues that could be achieved. A baseline matrix, established using the Bayesian Network (BN) method, analyses the impact of particular action related to factors identified and associated uncertainties which could provide valuable information to airport planner or manager in determining strategies based on the probability calculated for potential aeronautical revenues that could be generated. Among the main elements that generate aeronautical revenues are the aircraft landing charges and airport taxes. AROM is formulated as a mathematical programming model with two objective functions, to maximize the aeronautical revenues from the arriving and departing flights. Results obtained show that the maximum aeronautical revenue achieved depends on certain flights composition, which specify the flight types with higher frequency in contrast to the current practise of offering small number of mixed flight types. The proposed model offers flexibility to decision makers in setting the bounds of the flights’ constraints. The model can be extended to include more variants of the airside operations factors and generalised to consider domestic or international traffics or both. The traffic types can also be adjusted to include shorter or longer list of flight types or different types of aircraft.

**Keynote Speaker III**

**Associate Professor Dr Suhartono**

Department of Statistics

Institut Teknologi Sepuluh Nopember

Indonesia

Title: **MGSTAR: An Extension of the Generalized Space-Time Autoregressive Model**

Abstract: Up to now, Generalized Space-Time Autoregressive (GSTAR) models are mostly focused only for univariate spatial-temporal data. This research proposes an extension of GSTAR for multivariate spatial-temporal data, known as Multivariate GSTAR or MGSTAR. Three studies were conducted in this research, i.e., theoretical, simulation, and applied studies. These studies were initially developed based on bivariate spatial-temporal data. A theoretical study was done by developing MGSTAR based on the framework of Vector Autoregressive (VAR) models. In this proposed MGSTAR model, the parameter estimation was obtained by implementing Ordinary Least Square (OLS) method. The simulation study showed that OLS method yielded unbiased estimator. Furthermore, the MGSTAR models have applied for forecasting CO and PM10 at three stations in Surabaya City. The results showed that MGSTAR model could explain well the dynamic relationship between variables and locations. However, based on Root Mean Square Error Prediction (RMSEP), the results showed that MGSTAR model yielded less accurate forecast than univariate ARIMA model due to MGSTAR employed simpler order of Autoregressive. Further research is needed to expand the MGSTAR model with a higher order of Autoregressive, particularly to handle trend and seasonal order.

### Invited Speaker

**Invited Speaker I**

**Assistant Professor Dr Daisuke Sasaki**

International Research Institute of Disaster Science (IRIDeS)

Tohoku University, Japan

Title: **Possibility of Utilizing Disaster Statistics**

Abstract: At the Third UN World Conference on Disaster Risk Reduction (UNWCDRR) held in March 2015 in Sendai City, Japan, the Sendai Framework for Disaster Risk Reduction 2015–2030 (SFDRR) containing seven global targets was adopted by 187 UN member states. Monitoring and reporting on the progress in achieving these global targets is mandatory, thus, more scientific and statistical analysis has been needed than ever. The Global Centre for Disaster Statistics (GCDS) in Tohoku University was established in April 2015 to support the SFDRR in the monitoring and evaluation of progress by providing support at country level for capacity building in developing national statistics on disaster damage and by establishing an improved global database for such statistics. The study focuses on the possibility of utilizing disaster statistics for implementing the SFDRR effectively and efficiently and how the GCDS could contribute to the literature of disaster statistics.

**Invited Speaker II**

**Professor Chia Gek Ling**

Department of Mathematical and Actuarial Sciences

Universiti Tunku Abdul Rahman

Malaysia

Title: **How to Square A Square?**

Abstract: "Can a square be cut into smaller squares no two of which have the same size?"; This problem, which dates back to around 1925 has resisted the efforts of many who attempted to solve it until four students from Cambridge University made an attack on it (in the years 1936 - 8) by translating it into an electrical-network problem (equipped with graph theory). In this talk, we give a brief account on how these students finally squared the square.

**Invited Speaker (Parallel Session)**

**Professor Adebayo Agunbiade**

Department of Mathematical Sciences

Olabisi Onabanjo University

Nigeria

Title: **An Integer-Valued GARCH Model to Predict New Cases of COVID-19**

Abstract: In recent times, a lot of studies have been conducted on modelling COVID-19 in the confine of Mathematical Sciences. The area considered includes prediction and detection using various techniques such as Non-linear differential equation, Bayesian techniques, Machine learning, to mention but a few. This study considered modelling and predicting new cases of COVID-19 using integer-valued generalised autoregressive conditional heteroscedasticity (INGARCH) process to predict the future daily cases of COVID-19 using daily data of Nigeria from 30th March 2020 to 20th September 2020. Poisson and Negative Binomial distribution based on INGARCH were used, and Negative Binomial distribution was found to outperform the Poisson model. Therefore, the result for Negative Binomial distribution was considered conditional distribution and parametric bootstrap method; and as such was used to forecast for future new cases which could be used to determine period to flatten the curve of coronavirus in Nigeria.

**Invited Speaker (Parallel Session)**

**Professor Ahmad R. Soltani**

Department of Statistics and Operations Research

Faculty of Science

Kuwait University

Kuwait

Title: **A Class of Cumulative Reward Processes: Actuarial Application**

Abstract: In this article, we introduce a class of cumulative processes for Poisson processes that are closely related to random weights sample averages whenever the sample size follows a Poisson distribution, and to the B-spline densities with random knots. The cumulative processes formulated and introduced in this article are based on marked Poisson processes, and indeed are Poisson piecewise linear cumulative reward processes with random rewards. We provide the limiting distribution of the process as time increases to infinity. We demonstrate how this cumulative model can be used to successfully explain random phenomenon where the magnitude, time, and number of events in question are all random. An actuarial application in explaining the Swiss car insurance data is provided.

**Invited Speaker (Parallel Session)**

**Professor Cihan ÖZGÜR**

Department of Mathematics

Balikesir University

Turkey

Title: **On C-Parallel Curves on Some Riemannian Manifolds**

Abstract: This talk is a survey of the papers [1], [2] and [4]. We consider C-parallel curves in trans-Sasakian manifolds, non-Sasakian contact metric manifolds and S-manifolds. In trans-Sasakian manifolds, we find the curvature characterizations of slant curves with C-parallel and C-proper mean curvature vector fields in the tangent and normal bundles, respectively. The same problems are also considered for Legendre curves in (2n+1)-dimensional non-Sasakian contact metric manifolds and slant curves in S-manifolds. Moreover, we present some examples of these kinds of curves which satisfy the conditions of our theorems.

**References**

[1] Ş. Güvenç and C. Özgür, On slant curves in trans-Sasakian manifolds, Rev. Un. Mat. Argentina 55 (2014), no. 2, 81-100.

[2] Ş. Güvenç and C. Özgür, On slant curves in S-manifolds, Commun. Korean Math. Soc. 33 (2018), no. 1, 293-303.

[3] J-E. Lee, Y. J. Suh and H. Lee, C-parallel mean curvature vector fields along slant curves in Sasakian 3-manifolds, Kyungpook Math. J. 52 (2012), no. 1, 49-59.

[4] C. Özgür, On C-parallel Legendre curves in non-Sasakian contact metric manifolds, Filomat 33 (2019), no. 14, 4481-4492.

**Invited Speaker (Parallel Session)**

**Professor Pradyumn Kumar Sahoo**

Department of Mathematics

Birla Institute of Technology and Science-Pilani

Hyderabad, India

Title: **Wormholes in exponential f(R, T) gravity**

Abstract: Alternative gravity is nowadays an extremely important tool to address some persistent observational issues, such as the dark sector of the universe. They can also be applied to stellar astrophysics, leading to outcomes one step ahead of those obtained through General Relativity. In the present article we test a novel *f*(*R*, *T*) gravity model within the physics and geometry of wormholes. The *f*(*R*, *T*) gravity is a reputed alternative gravity theory in which the Ricci scalar *R* in the Einstein-Hilbert gravitational lagrangian is replaced by a general function of *R* and *T*, namely *f*(*R*, *T*) with *T* representing the trace of the energy-momentum tensor. We propose, for the first time in the literature, an exponential form for the dependence of the theory on *T*. We derive the field equations as well as the non-continuity equation and solve those to wormhole metric and energy-momentum tensor. The importance of applying alternative gravity to wormholes is that through these theories it might be possible to obtain wormhole solutions satisfying the energy conditions, departing from General Relativity well-known outcomes. In this article, we indeed show that it is possible to obtain wormhole solutions satisfying the energy conditions in the exponential *f*(*R*, *T*) gravity. Naturally, there is still a lot to do with this model, as cosmological, galactical and stellar astrophysics applications, and the reader is strongly encouraged to do so, but, anyhow, one can see the present outcomes as a good indicative for the theory.

**Invited Speaker (Parallel Session)**

**Professor Sandip Banerjee**

Department of Mathematics

Indian Institute of Technology Roorkee

India

Title: **A Mathematical Model to Elucidate Brain Tumor Abrogation by Immunotherapy with T11 Target Structure**

Abstract: T11 Target structure (T11TS), a membrane glycoprotein isolated from sheep erythrocytes, reverses the immune suppressed state of brain tumor induced animals by boosting the functional status of the immune cells. This study aims at aiding in the design of more efficacious brain tumor therapies with T11 target structure. We propose a mathematical model for brain tumor (glioma) and the immune system interactions, which aims in designing efficacious brain tumor therapy. The model encompasses considerations of the interactive dynamics of glioma cells, macrophages, cytotoxic T-lymphocytes (CD8+ T-cells), TGF-β, IFN-γ and the T11TS. The system undergoes sensitivity analysis, that determines which state variables are sensitive to the given parameters and the parameters are estimated from the published data. Computer simulations were used for model verification and validation, which highlight the importance of T11 target structure in brain tumor therapy.

**Invited Speaker (Parallel Session)**

**Assistant Professor Dr Changhwa Woo**

Department of Applied Mathematics

Pukyong National University

Republic of Korea

Title: **Energy Bening Slant Curves in the 4-dimensional Complex Projective Plane with Complex Curvature Functions**

Abstract: We study closed slant curves in complex projective space *CP*^{2}(4) which are critical points of the elastic energy. Let *CP*^{2}(4) be the 2-dimensional complex projective space of constant holomorphic sectional curvature 4, endowed with complex structure J, Fubini-Study metric <, > and Levi-Civita connection ∇. Given a curve γ(*t*), γ: [0, 1] → *CP*^{2}(4) smoothly immersed in *CP*^{2}(4). If γ is a geodesic, then it is critical as we know. If rank(γ) =1, then it can be shown that γ lies, as an extremal of *F _{a}*, in either a totally geodesic complex

*S*

^{2}(4)or in a totally geodesic totally real

*RP*

^{2}. These cases have been basically studied in the previous section. By using complex Frenet frame along the curve, we classify closed elastic proper slant curves in

*CP*

^{2}(4) and show that they form a one-parameter family of helices.

**Invited Speaker (Parallel Session)**

**Assistant Professor Dr Supriya Pan**

Department of Mathematics

Presidency University

India

Title: **Tension in the Dark**

Abstract: According to a series of potential observational data, the expansion of our Universe is currently accelerating. Observational evidences further indicate that nearly 96% of the total energy budget of the Universe is comprised of some dark energy fluid with negative pressure (~68% of the total energy budget of the Universe) driving this accelerating expansion and the pressureless or cold dark matter (~28% of the total energy budget of the Universe) which is responsible for the observed structure formation of our Universe. Within the context of General Relativity, this observed universe is nicely portrayed by the Λ-Cold Dark Matter (ΛCDM) cosmological model where the positive cosmological constant (Λ) acts as the dark energy fluid. Based on the available observational records, although ΛCDM model fits most of the observational data in an excellent way, but recently, Hubble constant (*H*_{0}) tension has become a very serious issue for modern cosmology which signals for a new physics beyond the standard ΛCDM cosmology. In this talk, I shall elaborately discuss the Hubble constant tension and show how one can alleviate/solve this *H*_{0} tension in alternative cosmological models.