Multidimensional Integrals Research Articles

Pikoe: A computer program for distorted-wave impulse approximation calculation for proton induced nucleon knockout reactions

pikoe, Proton-Induced KnockOut reaction calculation for Exclusive processes, is a Fortran 90 program that calculates triple- and quadruple-differential cross sections, vector analyzing powers, and momentum distributions of reaction residues, for proton-induced nucleon knockout reactions in normal and inverse kinematics. The distorted-wave impulse approximation with the factorization approximation to the nucleon-nucleon transition matrix is adopted, and the distorted waves of the incoming proton and the outgoing two nucleons are calculated quantum mechanically. Kinematics of the reaction particles are treated in a relativistic manner, which gives the proper asymptotics of the three-body scattering wave in the plane-wave limit. Program summaryProgram Title: pikoeCPC Library link to program files:https://doi.org/10.17632/m594h58kck.1Licensing provisions: MITProgramming language: Fortran 90Supplementary material: See https://www.rcnp.osaka-u.ac.jp/~kazuyuki/pikoe/index.phpNature of problem: Calculation of triple- and quadruple-differential cross sections, vector analyzing powers, and momentum distributions of reaction residues, for nucleon knockout reactions with proton beams/targets in normal/inverse kinematics.Solution method: Numerov method for solving single-channel second-order differential equations; multi dimensional integration with the Gauss-Legendre quadrature rule.Additional comments including restrictions and unusual features: The global optical potential by Koning and Deraloche [1] for nucleons at energies from 1 keV to 200 MeV, for (near-)spherical nuclei with mass number between 24 and 209, is implemented in the program. Tables of nucleon-nucleon (NN) elastic differential cross sections and NN transition amplitudes calculated with the NN effective interaction by Franey and Love [2] are provided as external files for pikoe (included in the package). Users are encouraged to prepare tables for the optical potentials and the cross sections of the elementary process according to the reaction system of interest by themselves. With external files thus prepared, pikoe can be applied to various kinds of knockout processes such as α or deuteron knockout by proton, α-induced knockout reactions, etc. Calculation of spin observables, the vector analyzing power in fact, however, is restricted to nucleon-induced nucleon knockout processes with the coplanar kinematics.

Bayesian Risk Assessment Technique for Economic Stress-Strength Models

This paper explores two areas of risk assessment modelling in economics and business: the Stress-Strength model and Bayesian techniques. The model assumes that the probability of stress exceeding strength is a measure of risk. The interpretation of stress and strength largely depends on the particular event or phenomenon being modelled. The use of the Stress-Strength model is demonstrated through the Gaussian assumption of probability distributions for random model parameters, particularly in assessing the risk of not achieving a required margin value. The concept of the capability function, representing the difference between strength and stress, is introduced in the modelling process. The probability distribution for the capability function is initially determined based on the Gaussian distribution of the random variables used in the model, allowing for evaluating the risk metric. The Bayesian approach is then applied to generalise the problem statement when dealing with unknown parameters of probability distributions for the Stress and Strength models. The uncertainty of these parameters is modelled through uniform probability distributions, and equations for calculating prior and posterior estimates are consistently obtained. Since multidimensional integrals are involved in these calculations, and solutions cannot be obtained in closed analytical form, Monte Carlo simulation is used to solve this computation problem.

Air Pollution Modeling of UNI-DEM Model by using Innovative Stochastic Approaches

In this paper we develop advanced multidimensional sensitivity analysis, based on some innovative stochastic approaches for performing air pollution modelling on a large-scale model of long-range transport of air pollutants. The Unified Danish Eulerian Model (UNI-DEM) is a very important mathematical model, and can be applied in many different studies related to damaging effects caused by high air pollution levels. We shall use it in this paper to get a reliable answer to a some very important questions regrading environmental protection. We develop some advanced Monte Carlo and quasi-Monte Carlo methods, based on special lattice and digital sequences. In this paper we will improve the digital ecosystem modeling by improving the existence stochastic approaches. The computational efficiency (in terms of relative error and computational time) of the advanced Monte Carlo algorithms for multidimensional numerical integration has been studied to analyze the sensitivity of UNI-DEM model output to variation of input emissions of the anthropogenic pollutants and of rates of several chemical reactions. The algorithms will be applied to compute global Sobol sensitivity measures corresponding to the influence of several input parameters on the concentrations of important air pollutants. The study will be done for the areas of several European cities with different geographical locations.

MULTIDIMENSIONAL CUBATURES WITH SUPER-POWER CONVERGENCE

In many applications, multidimensional integrals over the unit hypercube arise, which are calculated using Monte Carlo methods. The convergence of the best of them turns out to be quite slow. In this paper, fundamentally new cubatures with super-power convergence based on the improved Korobov grids and special variable substitution are proposed. A posteriori error estimates are constructed, which are practically indistinguishable from the actual accuracy. Examples of calculations illustrating the advantages of the proposed methods are given.

OUIS and MINUSCA in the CAR: The Effectiveness of Realist and Liberal Peacekeeping Paradigms

The conflict in the Central African Republic (CAR) has attracted international attention since the early 2000s. The African Union, the United Nations (UN) and the French Republic have all been involved in trying to help resolve the conflict. Since 2018, Russia has been actively involved in resolving the conflict in the Central African Republic. The government and people of the CAR have repeatedly expressed their dissatisfaction with the functioning of the United Nations Multidimensional Integrated Stabilization Mission in the Central African Republic (MINUSCA) contingent and, conversely, expressed their appreciation for the Russian military specialists stationed in the CAR. The author attempts to compare the effectiveness of the UN peacekeeping contingent (MINUSCA) and Russian military specialists operating under the auspices of the Officers Union for International Security (OUIS), based on the essential unity of purpose of these forces. Based on an analysis of data on state control in the CAR and violent incidents from 2014 to 2022, the author concludes that the Russian contingent is more effective in providing security and strengthening the state in the CAR (tasks also outlined in the MINUSCA mandate). The author also analyzes the problems in the functioning of the UN peacekeeping contingents, highlighting their systemic and structural shortcomings. The main shortcoming, however, in his view, is the UN’s commitment to a liberal approach that challenges the sovereignty of the host state. The Russian realist approach, by contrast, focuses on sovereignty and its power component. Thus, the actions of the Russians in the CAR can be studied within the paradigm of “illiberal” peacekeeping.

An integral representation for quantum amplitudes

The central impediment to reducing multidimensional integrals of transition amplitudes to analytic form, or at least to a fewer number of integral dimensions, is the presence of magnitudes of coordinate vector differences (square roots of polynomials) in disjoint products of functions. Fourier transforms circumvent this by introducing a three-dimensional momentum integral for each of those products, followed in many cases by another set of integral representations to move all of the resulting denominators into a single quadratic form in one denominator whose square my be completed. Gaussian transforms introduce a one-dimensional integral for each such function while squaring the square roots of coordinate vector differences and moving them into a common exponential. Addition theorems may also be used for extracting the angular variables, and sometimes direct integration is even possible. Each method has its strengths and weaknesses. An integral representation is derived herein that stands as an alternative to these four approaches. A number of consequent integrals of Macdonald functions, hypergeometric functions, and Meijer G-functions with complicated arguments are given.

First time around: Local conditions and multi-dimensional integration of refugees

We study the effect of local unemployment and attitudes towards immigrants at the time of arrival on refugees’ multi-dimensional integration outcomes. We leverage a centralized allocation policy in Germany where refugees were centrally assigned to live in specific counties. To measure sentiments of native residents towards immigrants, we use geo-coded Twitter data, which provides our “negative sentiment index”. Our results show that attitudes towards immigrants are as important as local unemployment rates in shaping refugees’ integration outcomes. A one standard deviation increase in unemployment or in the negative sentiment index predicts five percentage points lower probability of refugees being employed in 2016 to 2018. In additional robustness check, we present an analysis that uses far-right vote share as an alternative measure of sentiments of native residents.

Fast and flexible inference for joint models of multivariate longitudinal and survival data using integrated nested Laplace approximations.

Modeling longitudinal and survival data jointly offers many advantages such as addressing measurement error and missing data in the longitudinal processes, understanding and quantifying the association between the longitudinal markers and the survival events, and predicting the risk of events based on the longitudinal markers. A joint model involves multiple submodels (one for each longitudinal/survival outcome) usually linked together through correlated or shared random effects. Their estimation is computationally expensive (particularly due to a multidimensional integration of the likelihood over the random effects distribution) so that inference methods become rapidly intractable, and restricts applications of joint models to a small number of longitudinal markers and/or random effects. We introduce a Bayesian approximation based on the integrated nested Laplace approximation algorithm implemented in the R package R-INLA to alleviate the computational burden and allow the estimation of multivariate joint models with fewer restrictions. Our simulation studies show that R-INLA substantially reduces the computation time and the variability of the parameter estimates compared with alternative estimation strategies. We further apply the methodology to analyze five longitudinal markers (3 continuous, 1 count, 1 binary, and 16 random effects) and competing risks of death and transplantation in a clinical trial on primary biliary cholangitis. R-INLA provides a fast and reliable inference technique for applying joint models to the complex multivariate data encountered in health research.

Machine learning Post-Minkowskian integrals

We study a neural network framework for the numerical evaluation of Feynman loop integrals that are fundamental building blocks for perturbative computations of physical observables in gauge and gravity theories. We show that such a machine learning approach improves the convergence of the Monte Carlo algorithm for high-precision evaluation of multi-dimensional integrals compared to traditional algorithms. In particular, we use a neural network to improve the importance sampling. For a set of representative integrals appearing in the computation of the conservative dynamics for a compact binary system in General Relativity, we perform a quantitative comparison between the Monte Carlo integrators VEGAS and i-flow, an integrator based on neural network sampling.

The UN - African Union Partnership on the Women, Peace and Security Agenda: Frameworks, Policies and Strategies

A strong partnership between the United Nations (UN) and the African Union (AU) is essential to achieve strategic convergence, coherence and effective solutions to Africa’s complex peace and security challenges. This article analyzes the key challenges in the UN - AU partnership for peacekeeping, including the UN - AU frameworks, policies and strategies in implementing the Women, Peace and Security (WPS) agenda to establish equal, full and constructive participation of women in the peacekeeping process. The authors identify key challenges in the UN - AU partnership for peacekeeping and assess the effectiveness of the mechanisms and tools of this partnership in the field of maintaining peace and security. The implementation of the WPS agenda in Africa in 2003-2022 is discussed in detail. The empirical basis for the research was drawn from the UN peacekeeping website database, official AU documents, the United Nations General Assembly (UNGA) and United Nations Security Council (UNSC) resolutions, and AU and UN statistics on the gender composition of key UN peacekeeping missions during the period under review. This study employs quantitative methods of assessment and comparative analysis of UN and the AU statistics on women’s participation in African peacekeeping from 2003 to 2022. It focuses on the United Nations Multidimensional Integrated Stabilization Mission in Mali (MINUSMA) as a case study. The article concludes that there is a direct link between the deteriorating security situation in Africa, the effective implementation of the WPS agenda, and the level of strategic partnership between the UN and the AU.

#3953 TOXIC MICROBIOME AND CHRONIC KIDNEY DISEASE: INSIGHTS FROM THE CKD-REIN COHORT STUDY

Abstract Background and Aims Many uremic toxins (UTs) originate from gut microbiome, and contribute to chronic kidney disease (CKD) progression and cardiovascular morbidity. In order to reduce uremic symptoms and CKD progression, patients have several dietary restrictions, which may influence gut microbiome composition, and impact UTs production. An altered microbiome may contribute to UTs increase in those patients. However, the role of key bacterial taxa in producing UTs and the impact of diet on UTs variance in non-dialyzed patients are not well known. The objectives of this study were, first, to compare microbial features between CKD patients and healthy controls, and, second, to investigate the relation of gut microbiome with uremic toxicity, as well as the potential impact of diet on such relationship. Method Characterization of gut metagenomes, 10 UTs and 3 precursors’ serum concentrations by LC-MS/MS, host characteristics and diet were obtained from 240 non-dialysis CKD patients from the CKD-REIN cohort (mean ± SD): age: 68 ± 11 years, 71% male, estimated glomerular filtration rate (eGFR): 33.2 ± 12.7 ml/min/1.73m². First, to identify microbial biomarkers characterizing the gut microbiome-related toxicity in CKD, we compared microbiome features between 78 CKD patients and 78 age-, sex-, and BMI-matched healthy controls from the Milieu Interieur (MI) cohort: age: 58 ± 10 years, 60% male, eGFR: 89 ± 13. Second, we performed a multiomics’ data integration analysis via a supervised modelling to investigate cross-sectionally the association between host characteristics, gut microbiome, UTs, and diet-related features according to CKD severity (eGFR<30, n = 110 vs eGFR ≥30 mL/min/1.73m², n = 130). Results Compared to healthy controls, CKD patients had a significant reduced gut microbiome health index. Several Metagenomic Species Pan-genomes (MSPs) were significantly contrasted between MI and CKD cohorts: 43 species were enriched in CKD patients vs 24 in controls. Species most enriched in CKD patients included several UTs producers such as Lachnospiraceae spp, Dysosmobacter – Oscillibacter spp, Butyricimonas faecihominis, Victivallis vadensis and Hungatella spp, some of which were positively correlated with the following UTs: 3-Carboxy-4-methyl-5-propyl-2-furanpropionate (CMPF), trimethylamine-N-oxide (TMAO), and indole-3-acetic acid (3-IAA). Moreover, species belonging to Enterocloster and Hungatella genera (both members of Lachnospiraceae family) were found to be negatively correlated with eGFR. Among species associated with CKD severity, species carrying genes for UTs production were observed such as Desuflovibiro fairfieldensis, Bacteroides clarus and Blautia obeum along with increasing alcohol and hot drinks consumption, CRP and several UTs (kynurenic acid, indoxyl sulfate and Phenylacetylglutamine) levels. In contrast, some taxa like Faecalibacterium prausnitzii and Dysosmobacter welbionis were associated with legume intake but not with UTs. Conclusion Our study highlights an alteration of gut microbiome in CKD patients compared to healthy controls, with increased abundance of UTs producer species. The results of the multidimensional data integration modelling suggest a strong interplay between food intake, gut microbiome modifications, UTs accumulation and clinical features. These findings might open to promising therapeutic strategies to reduce microbiome-related toxicity.

Adapting a new formula to generalize multidimensional transforms

In this article, we present a new general formula of a multidimensional general integral transform; this new formula covers mostly all types of multi‐integral transforms of the Laplace class. Moreover, single, double, and triple integral transforms of the Laplace kind can be derived from the proposed formula. So, the obtained results in this study can be utilized to reduce computations when using these transforms of same category in other studies. Fundamental properties of the new transform, including linearity, uniqueness, and the inverse, are introduced. In addition, we establish the multi‐convolution theorem and some important results related to partial derivatives. To show the applicability of new multidimensional transform, we employ the new proposed transform to investigate the solutions of some partial differential equations and partial integro‐differential equations.

A robust experimental design for conceptual model discrimination based on information theory

This study introduces firm information gain for model discrimination based on Shannon entropy and worst-case scenario experimental design. Firm information gain is the minimal additional information gained by an experimental design with respect to existing information. Robust experimental design aims to maximize the firm information gain by searching for the least number of new pumping wells and observation wells. Robust experimental design includes a Bayes factor threshold to ensure that new data provide strong evidence for model discrimination. To maximize the firm information gain, a framework is proposed that combines the parallel-sequential genetic algorithm (GA) for parallel computing and the nested quadrature rule for efficiently solving multidimensional integrals. The numerical experiment involves the true model for the purpose of verification. The results show that using a full covariance matrix is imperative to avoid exaggerating firm information gain. Collecting new groundwater data is prioritized over exploring additional pumping wells. Maximizing firm information gain is able to identify the same and true model.

Four critical engineering and technical problems in manufacturing measurement of traditional Chinese medicine

Focusing on the development and quality improvement strategy of the traditional Chinese medicine(TCM) industry, the scientific and technological innovation of the new engineering of TCM should be paid attention to solve the "stuck neck" dilemma. Under the background of the ecological and industrial revolution of the scientific and technological innovation system, the super-scale information interaction and multi-dimensional integration will inevitably lead to profound changes in the manufacturing mode of TCM. Manufacturing measurement of TCM is formed on the basis of the reliability engineering theory of process control of TCM production. It is the development extension of system theory and system science ideas and a cross-fertilization discipline that combines theory with practice and adheres to the "four-oriented" re-epistemology improvement of the TCM discipline. In response to the problems of complex raw material sources, coarse process technology, unclear material basis, and poor applicability of equipment and technology in the manufacture of TCM, the transformation research mode of "aiming at the integration of pharmaceutical industry-developing intelligent production line-enabling industrial transformation" has been developed. This paper proposed the four key engineering technical problems, i.e., the identification of critical quality attributes(CQA) in the manufacture of TCM, the quality by design(QbD) and product development of the manufacturing process of TCM, the quality transfer principle and multivariate process capability index of TCM manufacturing, and the development of measurement technology and equipment of the manufacturing measurement of TCM, to achieve the systematization of quality control indicators, real-time process control, digitalization of manufacturing process, transparency of quality transfer, and intelligent whole-process control. In this paper, the new concepts, new theories, and new technologies provide a reference for the industrialization of TCM.

QIPs Financing of the United Nations Multidimensional Integrated Stabilization Mission in Mali (MINUSMA) and Milk Production:Case of Bazi Haoussa in the Ansongo Circle in Mali

QIPs Financing of the United Nations Multidimensional Integrated Stabilization Mission in Mali (MINUSMA) and Milk Production:Case of Bazi Haoussa in the Ansongo Circle in Mali

QIPs Financing of the United Nations Multidimensional Integrated Stabilization Mission in Mali (MINUSMA) and Agriculture: Case of the Commune of Bourem Sidi Amar in the Timbuktu Region of Mali

QIPs Financing of the United Nations Multidimensional Integrated Stabilization Mission in Mali (MINUSMA) and Agriculture: Case of the Commune of Bourem Sidi Amar in the Timbuktu Region of Mali

Bayesian optimal sensor placement for parameter estimation under modeling and input uncertainties

A Bayesian optimal sensor placement (OSP) framework for parameter estimation in nonlinear structural dynamics models is proposed, based on maximizing a utility function built from appropriate measures of information contained in the input–output response time history data. The information gain is quantified using Kullback–Leibler divergence (KL-div) between the prior and posterior distribution of the model parameters. The design variables may include the type and location of sensors. Asymptotic approximations, valid for large number of data, provide valuable insight into the measure of information. Robustness to uncertainties in nuisance (non-updatable) parameters associated with modeling and excitation uncertainties is considered by maximizing the expected information gain over all possible values of the nuisance parameters. In particular, the framework handles the case where the excitation time history is measured by installed sensors but remains unknown at the experimental design phase. Introducing stochastic excitation models, the expected information gain is taken over the large number of uncertain parameters used to model the random variability in the input time histories. Monte Carlo or sparse grid methods estimate the multidimensional probability integrals arising in the formulation. Heuristic algorithms are used to solve the optimization problem. The effectiveness of the method is demonstrated for a multi-degree of freedom (DOF) spring–mass chain system with restoring elements that exhibit hysteretic nonlinearities.

A Bibliometric Analysis of Current Knowledge Structure and Research Progress Related to Sustainable Furniture Design Systems

Sustainable furniture design has emerged as a prominent area of research, focusing on the interrelationships among people, furniture, and the environment. Despite being in its nascent stage, this field has garnered attention from scholars across disciplinary boundaries, seeking to explore new opportunities for industrial development and innovative perspectives. The objective of this paper is to clarify the extent of the current research literature, present the current significant themes, and elaborate further on the developed and potential future research directions in sustainable furniture design. In this research, a systematic analysis of bibliometric performance and scientific mapping of literature on sustainable furniture design was carried out by examining 1578 published papers from 2002 to 2023 selected from the Web of Science database, utilizing BibliometrixR package, VOSviewer, and CiteSpace tools for the analysis. The findings indicate a marked increase in research output in this area since 2012, with China being the primary contributor, although not achieving the highest citation rates. Furthermore, the study identifies 713 instances of international collaborations among researchers from 81 countries, with China and the United States being the most frequent partners. The research direction of highly cited literature, journals with the most published papers on sustainable furniture design, and authors with high total citation rates indicate a prominent connection between sustainable furniture design and fields such as product technical science, environmental science, ecology, and material research. The analysis of co-citation and keywords reveals the evolution of research themes, these key issues include furniture-human body scale match, furniture structure and performance, lifecycle, carbon emissions reduction, furniture industry chain, artificial intelligence, new development, school furniture safety and quality, and furniture strength and load capacity, with the focus shifting from the ontology of furniture and the human body to the external scope, emphasizing the connection with life, the environment, and the future. The latest research highlights industry chains, artificial intelligence, and innovative development, reflecting the trends of future research topics as “multi-dimensional integration”, “health and intelligence”, and “environment and system” will be the direction for the next stage.

Integrated Disaster Risk Management (IDRM): Elements to Advance Its Study and Assessment

This study analyzed the international key literature on integrated disaster risk management (IDRM), considering it a dynamic sociocultural process subjected to the historical process of social formation, offering a closer look at the concept while exploring conceptual elements and ideas to advance IDRM in both national and international contexts. Methodologically, the study adopted a literature review strategy, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach, combined with qualitative content analysis. This article examines the history of IDRM, discusses current challenges for implementation, looks at some experiences, and proposes avenues for further research. Some findings point out the lack of an overarching IDRM approach, which is characterized by a rather disperse set of ideas and experiences concerning what IDRM is and how it should be operationalized, thereby revealing the need for a more comprehensive theory and methodologies to further advance it. Other findings highlight that IDRM encompasses different kinds and levels of “integrations” that go from internal (that is, disaster risk reduction and management domains) to external (that is, all societal processes such as sustainable development), including temporal and spatial integrations. Hence, we are talking about a multidimensional integration of disaster risk management. In this regard, the article proposes four dimensions for integration: sectoral, spatial/hierarchical, temporal, and externally with other cross-cutting societal challenges, especially climate change and sustainable development. These dimensions cover 29 ideas for indicators or “proto-indicators” to guide the discussion, exploration, and analysis of IDRM in specific contexts.

Focus on Multi-Dimensional Integration of Wuxi Modern Industrial Cluster Innovation and Development Model

Wuxi, located in the center of the Yangtze River Delta, is an important part of the Suzhou-wuxi-changzhou metropolitan area and a typical representative of the southern Jiangsu model of China's regional economy, wuxi, which is in the transition period of economic structure, can rank the top of the per capita GDP of the central cities in China, and has its own unique modern industrial cluster development model. Based on a large number of on-the-spot investigation and research, this paper analyzes the basic advantages, main characteristics, industrial structure transformation and development layout of modern industrial cluster in Wuxi, this paper makes a systematic analysis and induction of the important supporting and ensuring factors that constitute the modern industrial system with Wuxi characteristics and the layout of innovative industrial structure, it is intended to reveal the development process, the reality, the basic characteristics and the advanced experience of the southern Jiangsu model in order to obtain the necessary enlightenment and reference. At the same time, put forward the existing problems and suggestions.