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Optimal inventory management is one of the most critical components for companies to thrive in the competitive market while meeting their customers' demands, reducing costs, and developing their operations. In this paper, the utilization of different technologies and instruments ranging from the most modern ones to mathematical ones was analyzed to demonstrate how the system can function successfully. It is expected that Neutrosophic fuzzy logic is one of the most complicated approaches that allow for proper uncertainty management, forecasting, and inventory control improvements. Fundamentally, the process could be that much more insightful due to the availability of mathematical modelling and on-the-go support systems. Through the use of dynamic programming with the help of Python tools to process these models, Full optimization under fuzzy demand is possible to achieve. Therefore, one could conclude that companies have many opportunities to develop their operations, reduce costs, and keep their customers happy even in a highly dynamic and uncertain business environment. [ABSTRACT FROM AUTHOR]

Detecting small targets and handling target occlusion and overlap are critical challenges in weld defect detection. In this paper, we propose the S-YOLO model, a novel weld defect detection method based on the YOLOv8-nano model and several mathematical techniques, specifically tailored to address these issues. Our approach includes several key contributions. Firstly, we introduce omni-dimensional dynamic convolution, which is sensitive to small targets, for improved feature extraction. Secondly, the NAM attention mechanism enhances feature representation in the region of interest. NAM computes the channel-wise and spatial-wise attention weights by matrix multiplications and element-wise operations, and then applies them to the feature maps. Additionally, we replace the SPPF module with a context augmentation module to improve feature map resolution and quality. To minimize information loss, we utilize Carafe upsampling instead of the conventional upsampling operations. Furthermore, we use a loss function that combines IoU, binary cross-entropy, and focal loss to improve bounding box regression and object classification. We use stochastic gradient descent (SGD) with momentum and weight decay to update the parameters of our model. Through rigorous experimental validation, our S-YOLO model demonstrates outstanding accuracy and efficiency in weld defect detection. It effectively tackles the challenges of small target detection, target occlusion, and target overlap. Notably, the proposed model achieves an impressive 8.9% improvement in mean Average Precision (mAP) compared to the native model. [ABSTRACT FROM AUTHOR]

Notwithstanding the impact that the Coronavirus pandemic has had on the physical and psychological wellness of people, it has also caused a change in the psychological conditions of many employees, particularly among organizations and privately owned businesses, which confronted numerous limitations because of the unique states of the pandemic. Accordingly, the current review expected to implement an RBF neural network to dissect the connection between demographic variables, resilience, Coronavirus, and burnout in start-ups. The examination technique was quantitative. The statistical populace of the review is directors and representatives of start-ups. In view of the statistical sample size of the limitless community, 384 of them were investigated. For information gathering, standard polls incorporating MBI-GS and BRCS and specialist-made surveys of pressure brought about by Coronavirus were utilized. The validity of the polls was affirmed by a board of specialists and their reliability was affirmed by Cronbach's alpha coefficient. The designed network structure had ten neurons in the input layer, forty neurons in the hidden layer, and one neuron in the output layer. The amount of training and test data were 70% and 30%, respectively. The output of the neural network and the collected results were compared with each other, and the designed network was able to classify all the data correctly. Using the method presented in this research can greatly help the sustainability of companies. [ABSTRACT FROM AUTHOR]

This article investigates the physical features of static axial sources that produce complexity within the matter configuration within the perspective of f (R ,   T) theory, where R is the curvature invariant and T identifies the trace of matter energy tensor. In this case, the contracted Bianchi identities of effective as well as normal matter are used to develop the conservation equations. We split the curvature tensor to compute structure scalars, involving the physical aspects of the source in the influence of modified factors. We explore the evolving source and compute the complexity of the system. Three complexity factors are determined by using structure scalars; after that, the corresponding propagation equations are explored to investigate the intense gravitational consequences. Finally, the outcomes of irregular anisotropic spheroids are presented using the criterion of vanishing complexity. The f (R ,   T) corrections are shown to be an additional source of complexity for the axial anisotropic configuration. [ABSTRACT FROM AUTHOR]

Organisations are attempting to make their supply chain eco-friendly due to rising carbon emissions and unsustainable use of natural resources. In this context, this study seeks to give an up-to-date literature review on green supply chain management (GSCM) from 2011 to December 2021. Initially, 375 articles were collected from the Web of Science (WoS) database for metadata analysis. In metadata analysis, the descriptive statistics of research trends of GSCM; most contributing authors, countries/regions and institutions; and most prominent journals, keywords and subject areas are discussed in detail. Later, 50 scholarly publications were selected according to their citations for content analysis. Based on their contents, the papers were classified into four categories: GSCM practices and performances, mathematical techniques, drivers and barriers of GSCM, and general articles related to GSCM. According to the in-depth analysis, most of the publications are theoretical works that contribute to the theory-building of GSCM. Likewise, mathematical techniques are gaining appeal among researchers, whereas research on drivers and barriers is limited. In articles regarding GSCM practices and performance, the structural equation modelling methodology was often employed. The results and future research directions presented may assist beginners in exploring new GSCM research domains. [ABSTRACT FROM AUTHOR]

This scientific article explores the use of artificial intelligence (AI) as an effective tool in teaching mathematical techniques applied to the fields of biology, mining and environment. It examines the potential of AI to improve the learning of complex mathematical concepts and their application in real-world scenarios. Various AI approaches, such as machine learning and neural networks, which have shown promise in optimizing and automating mathematical tasks, are discussed. Concrete examples of AI applications in solving mathematical problems related to biology, mining and the environment are presented. Finally, the advantages and limitations of these techniques are highlighted and the future perspectives of the integration of AI in the teaching of mathematical techniques in these fields are discussed. [ABSTRACT FROM AUTHOR]

The core of this manuscript is to conduct a broad investigation into the features of static matter configurations with hyperbolical symmetry, which might possibly serve as formation of corresponding spacetime within the limits of f(R,T,Q) gravity, where (Q ≡ R α σ T α σ ). We recognize that such matter distributions can be anisotropic in pressure, with just two primary stresses unequal and a negative energy density. Usually, negative matter densities are suggested in extreme cosmological and astrophysical situations, particularly with regard to quantum occurrences that might occur within the horizon. Eventually, we construct a generic formalism that allows every static hyperbolically symmetric (HS) fluid solution to be expressed with respect to two generating functions (GFs). [ABSTRACT FROM AUTHOR]

Detecting small targets and handling target occlusion and overlap are critical challenges in weld defect detection. In this paper, we propose the S-YOLO model, a novel weld defect detection method based on the YOLOv8-nano model and several mathematical techniques, specifically tailored to address these issues. Our approach includes several key contributions. Firstly, we introduce omni-dimensional dynamic convolution, which is sensitive to small targets, for improved feature extraction. Secondly, the NAM attention mechanism enhances feature representation in the region of interest. NAM computes the channel-wise and spatial-wise attention weights by matrix multiplications and element-wise operations, and then applies them to the feature maps. Additionally, we replace the SPPF module with a context augmentation module to improve feature map resolution and quality. To minimize information loss, we utilize Carafe upsampling instead of the conventional upsampling operations. Furthermore, we use a loss function that combines IoU, binary cross-entropy, and focal loss to improve bounding box regression and object classification. We use stochastic gradient descent (SGD) with momentum and weight decay to update the parameters of our model. Through rigorous experimental validation, our S-YOLO model demonstrates outstanding accuracy and efficiency in weld defect detection. It effectively tackles the challenges of small target detection, target occlusion, and target overlap. Notably, the proposed model achieves an impressive 8.9% improvement in mean Average Precision (mAP) compared to the native model.

Abstract In the previous works, the limiting case for the motion of a rigid body about a fixed point in a Newtonian force field, which comes from a gravity center lies on Z-axis, is solved. The authors apply the small parameter technique which is achieved giving the body a sufficiently large angular velocity component r o about the fixed z-axis of the body. The periodic solutions of motion are obtained in neighborhood r o tends to $$\infty$$ ∞ . In our work, we aim to find periodic solutions to the problem of motion in the neighborhood of r 0 tends to $$0$$ 0 . So, we give a new assumption that: r o is sufficiently small. Under this assumption, we must achieve a large parameter and search for another technique for solving this problem. This technique is named; a large parameter technique instead of the small one well known previously. We see the advantage of the new technique which appears in saving high energy used to begin the motion and give the solution of the problem in another domain. The obtained solutions by the new technique depend on r o. We consider that the center of mass of this body does not necessarily coincide with the fixed point O. We reduce the six nonlinear differential equations of the body and their three first integrals to a quasilinear autonomous system of two degrees of freedom and one first integral. We solve the rational case when the frequencies of the generating system are rational except $$(\,\omega = \,1,\,2,1/2,3,1/3, \ldots )$$ ( ω = 1 , 2 , 1 / 2 , 3 , 1 / 3 , … ) under the condition $$\gamma^{\prime\prime}_{0} = \cos \theta_{o} \approx 0$$ γ 0 ″ = cos θ o ≈ 0 . We use the fourth-order Runge–Kutta method to find the periodic solutions in the closed interval of the time t and to compare the analytical method with the numerical one.

This article investigates the physical features of static axial sources that produce complexity within the matter configuration within the perspective of f(R, T) theory, where R is the curvature invariant and T identifies the trace of matter energy tensor. In this case, the contracted Bianchi identities of effective as well as normal matter are used to develop the conservation equations. We split the curvature tensor to compute structure scalars, involving the physical aspects of the source in the influence of modified factors. We explore the evolving source and compute the complexity of the system. Three complexity factors are determined by using structure scalars; after that, the corresponding propagation equations are explored to investigate the intense gravitational consequences. Finally, the outcomes of irregular anisotropic spheroids are presented using the criterion of vanishing complexity. The f(R, T) corrections are shown to be an additional source of complexity for the axial anisotropic configuration.

The core of this manuscript is to conduct a broad investigation into the features of static matter configurations with hyperbolical symmetry, which might possibly serve as formation of corresponding spacetime within the limits of f(R,T,Q) gravity, where (Q ≡ RασTασ). We recognize that such matter distributions can be anisotropic in pressure, with just two primary stresses unequal and a negative energy density. Usually, negative matter densities are suggested in extreme cosmological and astrophysical situations, particularly with regard to quantum occurrences that might occur within the horizon. Eventually, we construct a generic formalism that allows every static hyperbolically symmetric (HS) fluid solution to be expressed with respect to two generating functions (GFs).

Data envelopment analysis (DEA) is one of the best mathematical techniques to compute the overall performance of units with some inputs and outputs. The original DEA methods are developed to tackle the information based on the crisp number but no ability to handle the indeterminacy, impreciseness, vagueness, inconsistent, and incompleteness information such as triangular neutrosophic numbers (TNNs). This study attempts to establish a new model of DEA, where the information on decision-making units is TNNs. Initially, the concept and features of a conventional DEA model and the comparative TNNs are discussed. Besides, some new ranking functions of TNNs are presented. Furthermore, based on the mentioned ranking functions, an algorithm for solving the new model has been established. A comparison of the new model with an existing method and other kinds of uncertainty tools has been provided. In comparison with the existing methods, the significant characteristic of the new model is that it can handle the triangular neutrosophic information simply and effectively. Finally, the implementation of this strategy for an example has been applied for various models of DEA.

In the previous works, the limiting case for the motion of a rigid body about a fixed point in a Newtonian force field, which comes from a gravity center lies on Z-axis, is solved. The authors apply the small parameter technique which is achieved giving the body a sufficiently large angular velocity component ro about the fixed z-axis of the body. The periodic solutions of motion are obtained in neighborhood ro tends to ∞ . In our work, we aim to find periodic solutions to the problem of motion in the neighborhood of r0 tends to 0 . So, we give a new assumption that: ro is sufficiently small. Under this assumption, we must achieve a large parameter and search for another technique for solving this problem. This technique is named; a large parameter technique instead of the small one well known previously. We see the advantage of the new technique which appears in saving high energy used to begin the motion and give the solution of the problem in another domain. The obtained solutions by the new technique depend on ro. We consider that the center of mass of this body does not necessarily coincide with the fixed point O. We reduce the six nonlinear differential equations of the body and their three first integrals to a quasilinear autonomous system of two degrees of freedom and one first integral. We solve the rational case when the frequencies of the generating system are rational except (ω = 1 , 2 , 1 / 2 , 3 , 1 / 3 , ...) under the condition γ 0 ″ = cos θ o ≈ 0 . We use the fourth-order Runge–Kutta method to find the periodic solutions in the closed interval of the time t and to compare the analytical method with the numerical one. [ABSTRACT FROM AUTHOR]

This paper is devoted to understanding a few characteristics of static irrotational matter content that assumes hyperbolical symmetry. For this purpose, we use metric f(R) gravity to carry out our analysis. It is noticed that the matter distribution cannot fill the region close to the center of symmetry, thereby implying the existence of an empty core. Moreover, the evaluation of the effective energy density reveals that it is inevitably negative, which could have utmost relevance in understanding various quantum field events. To derive the structure scalars, we perform the orthogonal splitting of the Riemann tensor in this modified gravity. Few relationships among matter variables and both Tolman and Misner Sharp are determined. Through two generating functions, some hyperbolically symmetric cosmological models, as well as their physical interpretations, are studied. To delve deeply into the role of f(R) terms, the model of the less-complex relativistic system of Einstein gravity is presented.

Accurate quantification of the terrestrial water cycle relies on combinations of multisource datasets. This analysis uses data from remotely sensed, in-situ, and reanalysis records to quantify the terrestrial water budget/balance and component uncertainties in the upper Chao Phraya River Basin from May 2002 to April 2020. Three closure techniques are applied to merge independent records of water budget components, creating up to 72 probabilistic realizations of the monthly water budget for the upper Chao Phraya River Basin. An artificial neural network (ANN) model is used to gap-fill data in and between GRACE and GRACE-FO-based terrestrial water storage anomalies. The ANN model performed well with r ≥ 0.95, NRMSE = 0.24 − 0.37, and NSE ≥ 0.89 during the calibration and validation phases. The cumulative residual error in the water budget ensemble mean accounts for ~15% of the ensemble mean for both the precipitation and evapotranspiration. An increasing trend of 0.03 mm month−1 in the residual errors may be partially attributable to increases in human activity and the relative redistribution of biases among other water budget variables. All three closure techniques show similar directions of constraints (i.e., wet or dry bias) in water budget variables with slightly different magnitudes. Our quantification of water budget residual errors may help benchmark regional hydroclimate models for understanding the past, present, and future status of water budget components and effectively manage regional water resources, especially during hydroclimate extremes.

This article has reviewed international research, up to the first half of 2021, focused on sustainability, big data and the mathematical techniques used for its analysis. In addition, a study of the spatial component (city, region, nation and beyond) of the works has been carried out and an analysis has been made of which Sustainable Development Goals (SDGs) have received the most attention. A bibliometric analysis and a fractal cluster analysis were performed on the papers published in the Web of Science. The results show a continuous increase in the number of published articles and citations over the whole period, demonstrating a growing interest in this topic. China, the United States and India are the most productive countries and there are more papers at the regional level. It has been found that the environmental dimension is the most studied and the least studied is the social dimension. The mathematical techniques used in the empirical work are mainly regression analysis, neural networks and multi-criteria decision methods. SDG9 and SDG11 are the most worked on. The trend shows a convergence in recent years towards big data applied to supply chains, Industry 4.0 and the achievement of sustainable cities.

This paper expounds the necessity and significance of the construction of matliematics experimental teaching platform from three aspects: the complex problem solving， the cultivation of creative ability and the reform of mathematics teaching. Three problems that should be noticed in the construction of the mathematical technology experiment platform are pointed out. They are the construction of the experimental soft environment，the construction of experimental teaching team，and construction of quality assurance system for experimental teaching. Combined with the experience of the teaching of mathematics experiment，the paper introduces the practice and experience of the construction of the teaching platform of mathematics experiment in South China University of Technology that include practice course system construction， teaching concept and mode innovation， theoretical teaching and experimental fusion. [ABSTRACT FROM AUTHOR]

This paper takes a new look at the mechanisms underlying the double-edge pulse-width modulation (PWM) process. It presents a novel way of deriving equations for the spectrum of double-edge PWM using basic mathematical techniques. In the process the underlying nonlinearities that generate the PWM sidebands are identified. Unlike the classical double Fourier series approach, the proposed method of deriving the PWM spectrum does not require the construction of the so-called unit cell. The interaction between this new model of the pulse-width modulator and the regular sampling process is studied, and generalized equations for the Fourier transforms of regularly sampled PWM waveforms are derived. A general solution to the important question of what happens to the PWM spectrum when the PWM reference consists of a summation of signals is presented. It is shown that the addition of reference signals in the time domain results in a double convolution of the PWM sidebands in the frequency domain. As an application of this result, it is shown how new analytic equations for the harmonics of third-harmonic injection PWM and space vector modulation can easily be derived. Finally, the new theoretical results are benchmarked against results from the well-established double Fourier series method. [ABSTRACT FROM AUTHOR]

Highlights: [•] Robust analysis of faecal egg count reductions requires complex statistical methods. [•] We have developed an R package for such analysis. [•] For the non-specialist, a user friendly web interface has been developed. [•] The use of these tools is illustrated with three data sets. [Copyright &y& Elsevier]

The American Cancer Society (ACS) recommends women aged 40 and above to have a mammogram every year and calls it a gold standard for breast cancer detection. Early detection of breast cancer can improve survival rates to a great extent. Inter-observer and intra-observer errors occur frequently in analysis of medical images, given the high variability between interpretations of different radiologists. Also, the sensitivity of mammographic screening varies with image quality and expertise of the radiologist. So, there is no golden standard for the screening process. To offset this variability and to standardize the diagnostic procedures, efforts are being made to develop automated techniques for diagnosis and grading of breast cancer images. A few papers have documented the general trend of computer-aided diagnosis of breast cancer, making a broad study of the several techniques involved. But, there is no definitive documentation focusing on the mathematical techniques used in breast cancer detection. This review aims at providing an overview about recent advances and developments in the field of Computer-Aided Diagnosis (CAD) of breast cancer using mammograms, specifically focusing on the mathematical aspects of the same, aiming to act as a mathematical primer for intermediates and experts in the field. [ABSTRACT FROM PUBLISHER]

We explore the applicability of the programming method of Feijen and van Gasteren to the domain of security protocols. This method addresses the derivation of concurrent programs from a formal specification, and it is based on common notions like invariants and pre- and post-conditions. We show that fundamental security concepts like secrecy and authentication can nicely be specified in this way. Using some small extensions, the style of formal reasoning from this method can be applied to the security domain. To demonstrate our approach, we discuss an authentication protocol and a public-key distribution protocol, and we deal with their composition. By focussing on a general setting where agents run the protocols multiple times, the nonce concept turns out to pop-up naturally. Although this work does not contain any new protocols, it does offer a new view on reasoning about security protocols. [ABSTRACT FROM AUTHOR]

This paper is devoted to understanding a few characteristics of static irrotational matter content that assumes hyperbolical symmetry. For this purpose, we use metric f (R) gravity to carry out our analysis. It is noticed that the matter distribution cannot fill the region close to the center of symmetry, thereby implying the existence of an empty core. Moreover, the evaluation of the effective energy density reveals that it is inevitably negative, which could have utmost relevance in understanding various quantum field events. To derive the structure scalars, we perform the orthogonal splitting of the Riemann tensor in this modified gravity. Few relationships among matter variables and both Tolman and Misner Sharp are determined. Through two generating functions, some hyperbolically symmetric cosmological models, as well as their physical interpretations, are studied. To delve deeply into the role of f (R) terms, the model of the less-complex relativistic system of Einstein gravity is presented. [ABSTRACT FROM AUTHOR]

Abstract: Mathematical techniques are presented which allow for analytical solutions of the catalyst layer transport and electrochemical problem in PEM fuel cells. These techniques transform the volumetric reaction terms to boundary flux terms, thereby eliminating the need for computational solving of the catalyst layer problem. The result is a semi-analytical fuel cell model—a computational model that entails analytical rather than computational catalyst layer solutions. This helps to alleviate the meshing difficulties inherent in the catalyst layers caused by large geometric aspect ratios, and hence reduce the computational requirements for fuel cell models. These analytical solutions are implemented in a 3D PEM fuel cell model, and the results of the semi-analytical model match well with the full computational model in terms of the polarization performance and species concentration distribution. In addition, these analytical solutions were able to reduce the required computational memory by a factor of approximately 3, and the computational time by a factor of approximately 4. [Copyright &y& Elsevier]

Accurate quantification of the terrestrial water cycle relies on combinations of multisource datasets. This analysis uses data from remotely sensed, in-situ, and reanalysis records to quantify the terrestrial water budget/balance and component uncertainties in the upper Chao Phraya River Basin from May 2002 to April 2020. Three closure techniques are applied to merge independent records of water budget components, creating up to 72 probabilistic realizations of the monthly water budget for the upper Chao Phraya River Basin. An artificial neural network (ANN) model is used to gap-fill data in and between GRACE and GRACE-FO-based terrestrial water storage anomalies. The ANN model performed well with r ≥ 0.95, NRMSE = 0.24 − 0.37, and NSE ≥ 0.89 during the calibration and validation phases. The cumulative residual error in the water budget ensemble mean accounts for ~15% of the ensemble mean for both the precipitation and evapotranspiration. An increasing trend of 0.03 mm month−1 in the residual errors may be partially attributable to increases in human activity and the relative redistribution of biases among other water budget variables. All three closure techniques show similar directions of constraints (i.e., wet or dry bias) in water budget variables with slightly different magnitudes. Our quantification of water budget residual errors may help benchmark regional hydroclimate models for understanding the past, present, and future status of water budget components and effectively manage regional water resources, especially during hydroclimate extremes. [ABSTRACT FROM AUTHOR]

Dengue fever is a mosquito-borne disease that has rapidly spread throughout the last few decades. Most preventive mechanisms to deal with the disease focus on the eradication of the vector mosquito and vaccination campaigns. However, appropriate mechanisms of response are indispensable to face the consequent events when an outbreak takes place. This study applied single and multiple objective linear programming models to optimize the allocation of patients and additional resources during an epidemic dengue fever outbreak, minimizing the summation of the distance travelled by all patients. An empirical study was set in Ciudad del Este, Paraguay. Data provided by a privately run health insurance cooperative was used to verify the applicability of the models in this study. The results can be used by analysts and decision makers to solve patient allocation problems for providing essential medical care during an epidemic dengue fever outbreak. [ABSTRACT FROM AUTHOR]

This article has reviewed international research, up to the first half of 2021, focused on sustainability, big data and the mathematical techniques used for its analysis. In addition, a study of the spatial component (city, region, nation and beyond) of the works has been carried out and an analysis has been made of which Sustainable Development Goals (SDGs) have received the most attention. A bibliometric analysis and a fractal cluster analysis were performed on the papers published in the Web of Science. The results show a continuous increase in the number of published articles and citations over the whole period, demonstrating a growing interest in this topic. China, the United States and India are the most productive countries and there are more papers at the regional level. It has been found that the environmental dimension is the most studied and the least studied is the social dimension. The mathematical techniques used in the empirical work are mainly regression analysis, neural networks and multi-criteria decision methods. SDG9 and SDG11 are the most worked on. The trend shows a convergence in recent years towards big data applied to supply chains, Industry 4.0 and the achievement of sustainable cities. [ABSTRACT FROM AUTHOR]