Department of Mathematical Sciences

Manufacturing operations that emphasize environmental responsibility, product quality and end-of-life (EOL) management are increasingly adopted by firms. These steps are necessary due to increasing environmental legislation and a growing consumer demand for sustainable products. An advantageous approach to effectively addressing returned items within this framework is remanufacturing. The findings of this research explore the dynamic processes involved in the remanufacturing stages, namely the collection, sorting, and testing phase, as well as the development phase, which remanufactures the product, and how they affect the entire supply chain (SC) performance. The current research offers a unified strategy to thoroughly evaluate model responses across a wide range of variables by analysing the impact of key variables within these phases and employing various distribution functions to model their behaviour. A two-stage Data Envelopment Analysis (DEA), an efficient analytical method in Multi-Criteria Decision Making (MCDM), is employed to evaluate the efficiency of the developed models. Research findings indicate that the initial phases of collection, sorting and testing are more efficient than the development phase. Furthermore, the findings show that the cooperative method yields more efficient outcomes than the leader-follower approach in the non-cooperative setting.

The distinguishing number is an important invariant used to measure the extent to which symmetries of graphs and permutation group actions can be broken by vertex labelings. In this paper, we investigate distinguishing labelings arising from permutation group actions with particular emphasis on Cartesian power constructions and wreath product actions. We establish structural bounds for distinguishing numbers in terms of orbit structure, stabilizers, and base size of permutation groups. Furthermore, we analyze the behavior of distinguishing numbers under Cartesian powers of sets and derive bounds for wreath product actions of the form G ≀ Sm acting on Xm.

We focused on researching multi-target tracking algorithms based on improved DeepSORT. The basic principles of multi-target tracking algorithms are introduced, followed by the basic architecture of DeepSORT. CIOU loss function, PP LCNet backbone, and SE attention mechanism are introduced to address the problems of previous algorithms, and an end-to-end tracking algorithm framework based on improved DeepSORT is constructed. This paper tells on the experimental index, criteria, and KITTI dataset. Through ablation experiments, the performance of individual modules is verified and improved. The obtained model is compared with baseline algorithms such as ByteTrack and BotSORT, and the improvement in accuracy, efficiency, and robustness of the improved model is very quantified.

We present a geometric model in which each particle is associated with its own private spacetime manifold—a world-block—constructed from Fermi–Walker coordinates. The intrinsic spatial metric on each proper-time slice is treated as a dynamical field with a universal stiffness constant. This single assumption leads to a consistent model where self-gravity is repulsive and mutual gravity is attractive and exactly Newtonian. Newton’s constant emerges from the fundamental stiffness. The model provides a geometric derivation of the inverse-square law and suggests a connection to cosmology: the constant part of the strain field on large scales can be interpreted as a cosmological constant whose magnitude is set by the Hubble radius.

One of the most important knowledge gaps for nature management is the species diversity in an area. In order to know the spatial distribution of richness of different species groups, observations of species are needed. This is challenging, because for many species groups it is resource-intensive to systematically map the occurrence of species over large areas. Instead, citizen science data has become more important, but as it is mostly collected opportunistically , there is a high geographical bias in the collection. If this is not taken into account, one gets the impression that the most species-rich areas are where there are the most people. This bias can be estimated and corrected if citizen science data is correctly combined with survey data, e.g. from national monitoring programs.Integrated species distribution models (iSDM) are relatively new statistical tools that combine opportunistically collected data with systematically collected data with information about collection intensity. These models have great potential to benefit from the strengths of different types of data, while at the same time being able to take into account some of the challenges, but the models have not been used to a large extent in large-scale modeling of species diversity. Large-scale data presents both opportunities and problems. It allows us to leverage information across species, but the size of the data makes it infeasible to model every species together.Here we will present our approach to this large-scale modelling, using iSDMs across species to produce fine-scale maps of communities over Norway, along with their associated uncertainties and biases. The model is based on a state space, using ideas from point processes, which has proved flexible for different data types and for which there are efficient and flexible fitting methods. We will outline the model and the methods we have used to overcome the problems of large data and the requirement for fine spatial scales. We will also outline how this is being extended to include time and topologies such as river networks.

This study is devoted to the analysis of symbolic 2-plithogenic limits and the development of systematic procedures for their computation. In particular, the plithogenic factorization approach and the plithogenic rationalization approach are examined as effective techniques for evaluating such limits. The accuracy of the obtained expressions is further confirmed through the application of L’Hôpital’s rule. Additionally, several special stats of symbolic 2-plithogenic limits, including trigonometric forms, are established and analyzed. The theoretical findings are supported and clarified by solving carefully selected numerical examples.

This paper presents a semi-analytical solution for one-dimensional advection-diffusion equation coupled with a linear partial differential equation with constant coefficients. The mathematical model describes a grain-fumigant-air system during fumigation processes, where fumigant gas transports through a storage silo. The coupled system considers both diffusion and advection mechanisms with constant velocity and diffusivity parameters. The solution methodology employs the Laplace transformation technique to convert the partial differential equations into ordinary differential equations in the Laplace domain. The Stehfest numerical algorithm is subsequently applied to invert the Laplace transforms and obtain the time-domain solution. Numerical computations are performed using MATLAB software to simulate the fumigant concentration distributions. Graphical results illustrate the fumigant gas concentration in air versus vertical height within the silo for different time intervals. Additional plots demonstrate the fumigant concentration absorbed by grain particles over time. The analysis examines effects of varying initial gas concentration and flow velocity on the transport process. Results indicate that higher initial concentrations and increased velocities accelerate the fumigation process, requiring less time to fill the silo completely. The proposed solution provides a mathematical framework for optimizing fumigation parameters in agricultural storage applications.

Concerns about climate change and its possible impacts on human activities have increased the ‎awareness that climatic conditions are dynamic. West Africa, of which Ghana is part is one of the ‎areas in the world that had experienced major climatic anomalies in the past century. The purpose ‎of this research is to model rainfall data using block maxima approach of extreme value theorem. ‎Monthly rainfall data covering the period 1960 – 2022 were obtained from the Ghana ‎Meteorological Agency, Accra Airport substation. Statistical properties of the data indicated data ‎was stationary, however it was not normally distributed. Time series analysis of the data indicated ‎consistent increase in rainfall values with both downward and upward spikes indicating ‎fluctuations in the rainfall values. The Generalized Extreme value distribution was used to fit the ‎model of rainfall values. The rainfall values were fitted using the Generalized Extreme value ‎distribution. The Fréchet distribution was found to be the most appropriate model for the monthly ‎rainfall data. Additionally, it was discovered that the amplitude of the extreme values grows with ‎return periods, with higher return levels predicted to become more uncommon but severe over ‎time‎.

Binder-free (BF) homogeneous Molybdenum disulfide (MoS2) nanosheets showing an average crystallite size of 34.59 nm, are deposited on nickel foam (NiF) by chemical vapor deposition process. The MoS2/NiF electrode exhibited a surface area of 109 m2/g, specific capacitance of 2726 F/g at 5 mV/s, charge transfer resistance of 2.36 Ω, and diffusion resistance of 1.16 for 10,000 cycles. The electrode is presented cyclic stability and Coulombic efficiency of 83% and 95% after 10,000 cycles. The capacitive contributions are remained lower than diffusion across scan rates 5-140 mV/s. The assembled device delivers capacitance of 191 F/g at 1.5 A/g, with maximum energy density of 68Wh/kg at minimum power density of 1200 W/kg. The theoretical model simulations is confirmed that the synthesized electrode showed excellent electrochemical performance due to combined current contributions of capacitive and diffusion controlled processes. Therefore, the MoS2/NiF electrodes are suitable for hybrid supercapacitive performance.