Hasil untuk "Applied mathematics. Quantitative methods"

Vaibhav Kumar, Munawar A. Shaik

Biopharmaceuticals are therapeutic drugs engineered to target specific sites within the body. Their manufacturing process comprises two primary stages: upstream processing (USP) and downstream processing (DSP). USP primarily involves cell culture and growth, whereas DSP focuses on purifying and packaging the final product. The recent literature only reports a few studies addressing production planning and scheduling in biopharmaceutical manufacturing. In this work, we address a long-term scheduling and midterm planning problem incorporating on-time or late delivery of final products with unknown finite delivery rates. Early delivery is prohibited, and late delivery incurs a penalty cost. Published models and evolutionary algorithms exhibit key limitations in areas such as shelf-life modeling, inventory management, and product delivery. To overcome these shortcomings, we propose a revised mixed-integer linear programming (MILP) model implemented using the General Algebraic Modeling System (GAMS). When applied to two illustrative examples, the model reduces optimum event counts by two to three, improving computational efficiency through fewer binary variables, continuous variables, and constraints. Furthermore, it achieves up to 7% improvement over two published benchmarks, underscoring its potential to enhance scheduling strategies for multiproduct biopharmaceutical facilities.

José-Joel González-Barbosa, Israel Cruz Rangel, Alfonso Ramírez-Pedraza et al.

Bats are ecologically vital mammals, serving as pollinators, seed dispersers, and bioindicators of ecosystem health. Many species inhabit natural caves, which offer optimal conditions for survival but present challenges for direct ecological monitoring due to their dark, complex, and inaccessible environments. Traditional monitoring methods, such as mist-netting, are invasive and limited in scope, highlighting the need for non-intrusive alternatives. In this work, we present a portable multisensor platform designed to operate in underground habitats. The system captures multimodal data, including near-infrared (NIR) imagery, ultrasonic audio, 3D structural data, and RGB video. Focusing on NIR imagery, we evaluate the effectiveness of the YOLO object detection framework for automated bat detection and counting. Experiments were conducted using a dataset of NIR images collected in natural shelters. Three YOLO variants (v10, v11, and v12) were trained and tested on this dataset. The models achieved high detection accuracy, with YOLO v12m reaching a mean average precision (mAP) of 0.981. These results demonstrate that combining NIR imaging with deep learning enables accurate and non-invasive monitoring of bats in challenging environments. The proposed approach offers a scalable tool for ecological research and conservation, supporting population assessment and behavioral studies without disturbing bat colonies.

Vladimir Gonzalez-Gamboa, Jan Barkmann, Rainer Marggraf

Abstract The adoption of agroforestry species offers a sustainable solution for regions devoid of forest cover by mitigating soil degradation, enhancing biodiversity, and boosting household income. Understanding how information about these practices disseminates through social networks is critical for promoting their adoption, particularly in regions like southern Ecuador. This study examines information-sharing networks among two ethnic groups—Saraguro and Mestizo-colonos—that differ in their engagement with agroforestry practices. Using an Exponential-Family Random Graph Model (ERGM), we uncover distinct network patterns in information exchange. Results reveal that information on less commercially valuable species is predominantly shared within homophilic (same-ethnicity) ties, while knowledge of commercially valuable species is centralized around a single actor from the Mestizo-colono group. Transitivity within ethnic groups highlights localized knowledge-sharing dynamics, whereas intergroup exchanges reflect divergent priorities and expertise. These findings underscore the role of social network structures in shaping the dissemination of agroforestry innovations and provide methodological insights into applying ERGM for understanding complex information flows in rural contexts.

Roxane Cohen, Robin David, Florian Yger et al.

Abstract Protecting sensitive program content is a critical concern in various situations, ranging from legitimate use cases to unethical contexts. Obfuscation is one of the most used techniques to ensure such a protection. Consequently, attackers must first detect and characterize obfuscation before launching any attack against it. This paper investigates the problem of function-level obfuscation detection using graph-based approaches, comparing algorithms, from classical baselines to advanced techniques like Graph Neural Networks (GNN), on different feature choices. We consider various obfuscation types and obfuscators, resulting in two complex datasets. Our findings demonstrate that GNNs need meaningful features that capture aspects of function semantics to outperform baselines. Our approach shows satisfactory results, especially in a challenging 11-class classification task and in two practical binary analysis examples. It highlights how much obfuscation and optimization are intertwined in binary code and that a better comprehension of these two principles are fundamental in order to obtain better detection results.

Radosław Wolniak, Kinga Stecuła

The paper presents the results of research on the quality of e-learning in Polish higher education. The authors used an internet questionnaire for the study. The research sample was 621 students. Firstly, the researchers determined 14 variables that are important for the quality of e-learning. Then the students evaluated these variables with scores from 1 to 5. The students agreed the most with the following statements: “using the e-learning platform is convenient” (average: 4.20 and median 5.00), and “logging in to the e-learning platform is easy” (average 4.38, median 5.00). Moreover, the authors studied the relation between the quality of e-learning in Polish universities and the following variables: the ease with which the student can acquire content in traditional teaching and e-learning, the student’s knowledge of information technology and their possession of the resources necessary for e-learning, and the student’s assessment of the innovation of e-learning solutions used by the university where the student studies.

Prabir Panja

A three-species model of the interactions of crop, pest, and predator has been constructed in this work. In the absence of pests and harvesting of crops, the rate of crop growth is considered as logistic. It is considered that pests consume crops whereas predators consume pests. Harvesting of the crops has been considered here. Holling type II functional response has been considered for crop and pest consumption. It is also assumed that the predators may consume pests via Holling type II functional response. Positivity and boundedness of the solution of the model have been investigated. The various equilibrium points of the model are evaluated, and the local and global stability are then investigated. Studies on the Hopf bifurcation existence criterion in connection to a crucial model parameter have been conducted. The optimal crop harvesting has then been determined using an objective function that has been defined. The Pontryagin’s maximum theory is used to calculate the ideal crop harvesting rate. The consumption of pests by predators has been proven to help keep the system stable up to a point, but a higher rate of pest consumption by predators can potentially cause instability. According to observation, the model may become unstable as the mortality rate of predators of pests rises. It is found that the increase in optimal harvesting of crops may lead the model towards stability. It has been noted that increased rate of crops consumption by pests may result in lower agricultural yields.

Gladys T., G.V. Ramana Reddy

The present study is focused on the contributions of variable viscosity and thermal conductivity on the dynamics of non-Newtonian nanofluids flow with nonlinear buoyancy force. A constant magnetic field is applied transversely to the flow direction. The nonlinear couples partial differential equations (PDEs) and the boundary conditions were reduced into system of ordinary differential equations by using appropriate transformation. The transformed equations were solved by utilizing spectral homotopy analysis method (SHAM) and spectral relaxation method (SRM). A higher value of Casson parameter was noticed to decrease the velocity profile. The outcome of this paper was compared with previously published works and was in good agreement.

Juliano Espezim Soares Faria, Claudia Glavam Duarte

Neste artigo apresentamos parte dos resultados de uma tese que, em linhas gerais, problematizou as relações entre a educação matemática e o teatro, obtidas a partir de trabalhos acadêmicos relacionados a artigos de eventos, revistas e dissertações compreendidos entre os anos de 2007 e 2018. Com base na análise do discurso foucaultiana, damos visibilidade a enunciações que buscam entrelaçar ao teatro a educação matemática com base em dois momentos: a definição de uma legitimidade para tal relação e as implicações sobre a matemática escolar. Como resultados, identificamos, em um primeiro momento, que enunciações que criam a legitimação do uso do teatro ocorrem especialmente por uma caracterização negativa da Matemática, seja pelo próprio conhecimento, seja pelos métodos de ensino. Sugerimos, assim, que o teatro funciona como um catalisador de outros discursos que atravessam a Educação Matemática, como a necessidade de um ensino lúdico, interdisciplinar e alicerçado na realidade do aluno.

SYAFIQAH HANIS MOHD FAUZI, Norazaliza Mohd Jamil

The wastewater treatment process is aimed to reduce pollution to the appropriate level. An oxidation pond system can treat contaminants in wastewater. Oxidation ponds are use sunlight, bacteria, and algae to treat wastewater. This study developed an improved mathematical model and a graphical user interface (GUI), called oxi-P GUI to predict the wastewater treatment process in an oxidation pond. The correlation between dissolved oxygen (DO), chemical oxygen demand (COD), coliform bacteria, as well as concentrations of phototrophic bacteria (PSB) were examined. In MATLAB software, a revised model consisting of ordinary differential equations (ODEs) set of integrating the Monod equation was numerically solved utilising the fourth order Runge-Kutta method. The current model's root mean square error (RMSE) values were compared to the suggested model's RMSE values for model validation. The model offered a more accurate estimate than the existing model of changes in the amount of concentration in oxidation pond, which was necessary to produce acceptable water quality. A wastewater management personnel may use GUI to track water quality and determine the most effective wastewater treatment mechanism. Additionally, this user-friendly GUI will give a better understanding about the treatment process, especially to people with less programming skills. ARTICLE HISTORY Received: 21/01/2022 Revised: 27/03/2022 Accepted: 31/03/2022 Published: 31/03/2022

Ali Baheri

This paper presents a safe reinforcement learning system for automated driving that benefits from multimodal future trajectory predictions. We propose a safety system that consists of two safety components: a rule-based and a multimodal learning-based safety system. The rule-based module is based on common driving rules. On the other hand, the multi-modal learning-based safety module is a data-driven safety rule that learns safety patterns from historical driving data. Specifically, it utilizes mixture density recurrent neural networks (MD-RNN) for multimodal future trajectory predictions to mimic the potential behaviors of an autonomous agent and consequently accelerate the learning process. Our simulation results demonstrate that the proposed safety system outperforms previously reported results in terms of average reward and collision frequency.

Jeanette Schmidt, Stefan Irnich

For a given graph with a vertex set that is partitioned into clusters, the generalized traveling salesman problem (GTSP) is the problem of finding a cost-minimal cycle that contains exactly one vertex of every cluster. We introduce three new GTSP neighborhoods that allow the simultaneous permutation of the sequence of the clusters and the selection of vertices from each cluster. The three neighborhoods and some known neighborhoods from the literature are combined into an effective iterated local search (ILS) for the GTSP. The ILS performs a straightforward random neighborhood selection within the local search and applies an ordinary record-to-record ILS acceptance criterion. The computational experiments on four symmetric standard GTSP libraries show that, with some purposeful refinements, the ILS can compete with state-of-the-art GTSP algorithms.

A. Noori, B. Pradhan, Q. Ajaj

Abstract In the last few decades, the northern region of Iraq has been severely impacted by climate change, which has created long-term drought, water shortage, and casual flood events. Dam construction in suitable sites is a primary water management strategy to solve flood and drought. Dam site selection depends on a set of qualitative and quantitative criteria, such as geology, soil type, and altitude. In this study, appropriate areas of dam site selection for water management were identified using remote sensing, geographic information system (GIS), and multi-criteria decision making techniques. In addition, the proposed method of site suitability was evaluated by comparing it with the traditional analytic hierarchy process (AHP). A new validation method to evaluate the accuracy of AHP and fuzzy logic according to the preselected location of a dam was previously suggested in the study area (Bekhme dam) by the local government. Remote sensing data (Landsat imageries and ASTER DEM) and field/reference maps were used to derive 12 conditioning factors needed to produce a suitability map for construction. The criteria considered were geological formation, soil type, fault line, tectonic line, altitude, slope, rainfall data (2000–2011), water discharge, land use/cover, road network, and material used for dam construction. Two mathematical models, namely, AHP and fuzzy logic, were applied in this study. The best model was selected by using location and cluster-based accuracy assessment depending on the proposed dam site (i.e., Bekhme dam). Results showed that fuzzy logic is more accurate than AHP. In terms of the surface area of suitable lands identified, the AHP model determined 13,446,900 m2 as a highly suitable area, whereas the fuzzy model identified 3,409,200 m2 from the total area of 69,347,700 m2. Finally, this study identified four sites for intermediate and large dams in the study area. The total water capacities of proposed Dam 1, Dam 2, Dam 3, and Dam 4 are about 81.5, 239.3, 374.6, and 646.5 million m3, respectively, when the dam height is 60 m. The proposed methods can be easily replicated to other areas for similar applications. The finding reveals that AHP model is distributed and scattered in the whole study area, whereas the fuzzy model is more suitable for clustered areas.

I. Spassiani, G. Sebastiani, G. Palù

(1) Background: A better understanding of COVID-19 dynamics in terms of interactions among individuals would be of paramount importance to increase the effectiveness of containment measures. Despite this, the research lacks spatiotemporal statistical and mathematical analysis based on large datasets. We describe a novel methodology to extract useful spatiotemporal information from COVID-19 pandemic data. (2) Methods: We perform specific analyses based on mathematical and statistical tools, like mathematical morphology, hierarchical clustering, parametric data modeling and non-parametric statistics. These analyses are here applied to the large dataset consisting of about 19,000 COVID-19 patients in the Veneto region (Italy) during the entire Italian national lockdown. (3) Results: We estimate the COVID-19 cumulative incidence spatial distribution, significantly reducing image noise. We identify four clusters of connected provinces based on the temporal evolution of the incidence. Surprisingly, while one cluster consists of three neighboring provinces, another one contains two provinces more than 210 km apart by highway. The survival function of the local spatial incidence values is modeled here by a tapered Pareto model, also used in other applied fields like seismology and economy in connection to networks. Model’s parameters could be relevant to describe quantitatively the epidemic. (4) Conclusion: The proposed methodology can be applied to a general situation, potentially helping to adopt strategic decisions such as the restriction of mobility and gatherings.

D. N. Munahefi, Mulyono, M. Z. Zahid et al.

The purpose of this study was to analyze the mathematical creative thinking ability test instrument in open-ended problems with ethnomathematic nuances. The aspects that must be possessed in a mathematical creative thinking ability test instrument consist of: fluency, flexibility, originality, and elaboration. These four aspects are contained in open-ended questions. Open-ended questions are questions that can be worked on with several answers or various settlement strategies. Ethnomatematics applied to open-ended questions is batik. Batik that has various styles should be able to explore the mathematical creative thinking ability. The method used to collect data on mathematical creative thinking ability is the essay test method which consists of 5 items. The data analysis technique used is qualitative and quantitative data analysis. Qualitative analysis was carried out by examining the validity of the content. Meanwhile, the quantitative analysis was carried out by using the classical test theory approach which the calculations were assisted by the Microsift Excell program.

Sunisa Lutfi, Rahma Faelasofi Sunanto, Nihayati Nihayati

The problem in this research is the low student learning outcomes that are thought to be due to the use of teaching methods, where teachers still dominate the teaching and learning process. Sampling was done by using the cluster random sampling technique. The research instrument used was a test consisting of 5 items in the form of a description. The data analysis technique used the t-test because the population research data were normally distributed and homogeneous. The difference in the average of mathematics learning outcomes of students between the experimental class and the control class is shown by the results and the average of mathematics learning outcomes of students in the experimental class is 77.93 higher than the average mathematics learning outcomes of students in the control class 55.72 which means that there is the influence of the Wahdah method on mathematics learning outcomes of 22.21.

Alex Zhao, Kavin Kumaravel, Emanuele Massaro et al.

Abstract Group testing has recently become a matter of vital importance for efficiently and rapidly identifying the spread of Covid-19. In particular, we focus on college towns due to their density, observability, and significance for school reopenings. We propose a novel group testing strategy which requires only local information about the underlying transmission network. By using cellphone data from over 190,000 agents, we construct a mobility network and run extensive data-driven simulations to evaluate the efficacy of four different testing strategies. Our results demonstrate that our group testing method is more effective than three other baseline strategies for reducing disease spread with fewer tests.

Shaival H. Nagarsheth, Shambhu N. Sharma

In this paper, we extend the sensitivity integrals for linear feedback systems via a non-analytic sensitivity function in Poisson–Jensen’s terminology. First, we sketch a generalized sensitivity integral accounting for the NMP zeros and the RHP poles of the sensitivity function. Interestingly, the sensitivity integral accounting for the NMP zeros becomes a special case of the generalized integral. Then the concerning sensitivity inequality and the associated lower bound are achieved. We also sketch a sensitivity integral that exploits a modified sensitivity function-based approach and stabilizes the linear feedback system. Their concerning sensitivity inequality and the associated lower bound are achieved as well. An illustrative case study example is employed to demonstrate the idea of the ‘sensitivity lower bound’ to choose the ability of controllers for the given linear feedback systems. The rearranged sensitivity lower bound offers simplified analysis involving fewer computation efforts.

Mirna Charif, Lech Zielinski

We investigate eigenvalue perturbations for a class of infinite tridiagonal matrices which define unbounded self-adjoint operators with discrete spectrum. In particular we obtain explicit estimates for the convergence radius of the perturbation series and error estimates for the Quantum Rabi Model including the resonance case. We also give expressions for coefficients near resonance in order to evaluate the quality of the rotating wave approximation due to Jaynes and Cummings.

Ehsan Shirazi, Mohammad Zolghadr

This paper studies a design of a puzzle-based storage system. We developed an item retrieval algorithm for our system which has three advantages over the previous counterparts in the literature: (i) we can retrieve items from all sides of our storage system; (ii) the existence of only one empty cell in our system is sufficient to retrieve an item; and (iii) our algorithm never ends in deadlocks. The main feature of our algorithm is to prefer three moves to five moves in the process of moving the seized empty cell toward the optimal side of the requested item. The conventional view in the literature assumes that increasing the number of empty cells always reduces the number of movements required for retrieving items; however, our simulation results show that depending on the size of the puzzle and the number of the requested items, increasing empty cells might make the retrieval process more complicated.

Destiana Dian Arfiani, H. Ulya, S. Wanabuliandari

This study aims to analyze (1) the average mathematical problem solving ability of students who can achieve the KKM, (2) the proportion of students who can complete classical completeness of 75%, (3) differences in students' mathematical problem solving abilities before and after the REACT model assisted by fable-math book media is applied, (4) increasing mathematical problem solving abilities. The research method used is quantitative research with a pre-experimental design trial design in the form of one group pretest posttest. Sampling was done by means of purposive sample. The sample in this study were IV grade students in academic year 2019/2020. Collecting data using interview techniques, documentation, and tests. The data that has been collected is then analyzed using statistical analysis in the form of one sample t-test, z-proportion test, paired sample t-test, and n-gain test. After applying the REACT model assisted by the fable-math book media the results of the study showed that the average score of the student's problem-solving ability test got more than 70, the proportion of students who completed the test could achieve classical completeness above 75%, there were significant differences in the pretest and posttest results, and there is an increase in students' mathematical problem solving abilities with the medium category.