Multidimensional Integrals Research Articles

An ULS reliability-based design method for mooring lines using an efficient full long-term approach

In the long-term scenario, the environmental actions to which floating offshore structures are subjected to, such as waves, wind and current, are non-stationary stochastic processes. However, this long-term behavior is usually modeled as a series of short-term stationary conditions. In a full long-term analysis approach, an estimate of the N-year response can be obtained through a multi-dimensional integration over expected short-term environmental conditions. An innovative and more efficient long-term integration approach based on the Importance Sampling Monte Carlo Simulation (ISMCS) method is presented, where the uniform distribution over an environmental contour is used as the sampling function. In parallel, a multi-dimensional joint environmental model that statistically describes all relevant environmental parameters is employed, contemplating linear and directional variables, and thoroughly accounting for the occurrences of wind waves and swell. The methodology is applied to two FPSOs systems installed in Brazilian ultradeep waters. Ultimately, a design-oriented procedure based on the developed methodologies is provided, using an Ultimate Limit State (ULS) reliability-based design with calibrated safety factors in an LRFD (Load and Resistance Factors Design) format. It is shown that the developed procedures can be powerful tools to account for the simultaneous occurrence of wind sea and swell waves in offshore system response evaluations required in the design and life extension analyses.

Focal volume optics for composite structuring in transparent solids

Abstract Achieving high-level integration of composite micro-nano structures with different structural characteristics through a minimalist and universal process has long been the goal pursued by advanced manufacturing research but is rarely explored due to the absence of instructive mechanisms. Here, we revealed a controllable ultrafast laser-induced focal volume light field and experimentally succeeded in highly efficient one-step composite structuring in multiple transparent solids. A pair of spatially coupled twin periodic structures reflecting light distribution in the focal volume are simultaneously created and independently tuned by engineering ultrafast laser-matter interaction. We demonstrated that the generated composite micro-nano structures are applicable to multi-dimensional information integration, nonlinear diffractive elements, and multi-functional optical modulation. This work presents the experimental verification of highly universal all-optical fabrication of composite micro-nano structures with independent controllability in multiple degrees of freedom, expands the current cognition of ultrafast laser-based material modification in transparent solids, and establishes a new scientific aspect of strong-field optics, namely, focal volume optics for composite structuring transparent solids.

Mott Memristors for Neuromorphics

AbstractNeuromorphic computing has emerged as a key solution for overcoming the challenge of von Neumann bottleneck, offering a pathway to more efficient and biologically inspired computing systems. A crucial advancement in this field is the utilization of Mott insulators, where the metal‐insulator transition (MIT) elicits substantial alterations in material properties, infusing renewed vigor into the progression of neuromorphic systems. This review begins by explaining the MIT mechanisms and the preparation processes of Mott insulators, followed by an introduction of Mott memristors and memristor arrays, showing different types of multidimensional integration styles. The applications of Mott memristor in neuromorphic computing are then discussed, which include artificial synapse designs and various artificial neuron architectures for sensory recognition and logic calculation. Finally, facing challenges and potential future directions are outlined for utilizing Mott memristors in the advancement of neuromorphic computing. This review aims to provide a thorough understanding of the latest advancements in Mott memristors and their applications, offering a comprehensive reference for further research in related areas, and contributing to bridging the gap between traditional silicon‐based electronics and future brain‐inspired architectures.

Assessing water quality environmental grades using hyperspectral images and a deep learning model: A case study in Jiangsu, China

Water quality assessment is essential for effective environmental management, yet traditional methods such as chemical sampling are often labor-intensive and inefficient for large-scale, continual monitoring. This study addresses these limitations by leveraging hyperspectral images (HSIs) analysis and introducing a capsule network (CapsNet) model enhanced with a multidimensional integration attention (MDIA) mechanism. The model is specifically designed to integrate both channel and spatial information, enabling precise water quality grade assessment by detecting subtle features within HSIs data. To validate the performance of the model, spectral data from 5 water quality regions are collected and processed via a UAV-carried spectrometer, with 4503 water quality data samples. Rigorous classification experiments demonstrated that the model achieves 98.73 % accuracy, with an average improvement of 4.89 % compared with the other models. This approach significantly improves decision support systems for water resource management, facilitating the sustainable use of water resources.

Multiple Regulatory Mechanisms Synergistically Control the Soluble Expression of CsCE for Enhanced Enzymatic Productivity of Lactulose in E. coli.

The limited expression of cellobiose 2-epimerase poses a significant constraint on the industrial enzymatic production of lactulose. Extensive modifications to the expression cassette offer a means to enhance the yield of recombinant proteins. In this study, an integrated strategy, combining individual and collaborative approaches, is proposed to fine-tune each stage of the CsCE overexpression program. This strategy involves the multidimensional integration of standardized genetic elements at various levels, including transcription, translation, folding, and three-dimensional structure. The volumetric activity of the final recombinant strain was markedly increased by 12-fold compared to the wild-type strain, reaching 2260.62 U/L. The protein expression in the newly developed high-yield recombinant strain exhibited a significant enhancement, with a higher proportion of soluble protein compared to that of inclusion bodies. Our findings offer insights into the multifaceted synergistic regulation of protein expression processes, holding promising implications for the production of heterologous recombinant proteins.

On a numerical estimation method with a given accuracy of a quantile criterion in the case of a piece-linear loss function and a gaussian probability density

The solution of many practical problems leads to the calculation of the values of probabilistic criteria, the most common of which are the quantile and probability functionals. It is known that, under fairly general assumptions, methods suitable for solving problems of finding the values of a probabilistic criterion can be used to solve the problem of quantile analysis. The proposed method for solving the problem of quantile analysis is based on the use of the method of numerical multidimensional integration described in the previous works of the author. One of the important properties of this integration method is universality (when using it, we can set an arbitrary number of variables n and an arbitrary number of linear constraints r). The only limitation is the case of an unacceptably long solution time. Thus, the indicated universality is transferred to the solution of the considered problem of quantile analysis.

A Comparative Study of Single-Chain and Multi-Chain MCMC Algorithms for Bayesian Model Updating-Based Structural Damage Detection

Bayesian model updating has received considerable attention and has been extensively used in structural damage detection. It provides a rigorous statistical framework for realizing structural system identification and characterizing uncertainties associated with modeling and measurements. The Markov Chain Monte Carlo (MCMC) is a promising tool for inferring the posterior distribution of model parameters to avoid the intractable evaluation of multi-dimensional integration. However, the efficacy of most MCMC techniques suffers from the curse of parameter dimension, which restricts the application of Bayesian model updating to the damage detection of large-scale systems. In addition, there are several MCMC techniques that require users to properly choose application-specific models, based on the understanding of algorithm mechanisms and limitations. As seen in the literature, there is a lack of comprehensive work that investigates the performances of various MCMC algorithms in their application of structural damage detection. In this study, the Differential Evolutionary Adaptive Metropolis (DREAM), a multi-chain MCMC, is explored and adapted to Bayesian model updating. This paper illustrates how DREAM is used for model updating with many uncertainty parameters (i.e., 40 parameters). Furthermore, the study provides a tutorial to users who may be less experienced with Bayesian model updating and MCMC. Two advanced single-chain MCMC algorithms, namely, the Delayed Rejection Adaptive Metropolis (DRAM) and Transitional Markov Chain Monte Carlo (TMCMC), and DREAM are elaborately introduced to allow practitioners to understand better the concepts and practical implementations. Their performances in model updating and damage detection are compared through three different engineering applications with increased complexity, e.g., a forty-story shear building, a two-span continuous steel beam, and a large-scale steel pedestrian bridge.

Entanglement in Bound States of Two Particles with a Localized Center-of-Mass Wave-Function

Abstract We explore some entanglement-related features of systems consisting of two particles coupled through a central potential. We consider two types of systems, constituted by particles in one spatial dimension interacting through an attractive Dirac Delta potential, or through a harmonic quadratic potential. The degree of freedom corresponding to the system’s center of mass is either regarded as confined within a one dimensional box, or described by a localized Gaussian wave-packet. As a quantitative indicator of the degree of entanglement between the particles, we use the linear entropy of the one-particle marginal density matrix. In general, this quantity cannot be calculated analytically (except in some special cases related to the Harmonic interaction). Consequently, the linear entropy has to be evaluated numerically. Since the concomitant numerical problem consist in evaluating a multi-dimensional integral in four dimensions, the Monte Carlo integration method is an appropriate one for this task. We explore numerically how the system’s entanglement depends on the different interaction potentials, on the different types of confinement for the system’s center of mass, and on the associated parameters describing the size and geometry of the system.

Fast empirical scenarios

We seek to extract a small number of representative scenarios from large panel data that are consistent with sample moments. Among two novel algorithms, the first identifies scenarios that have not been observed before, and comes with a scenario-based representation of covariance matrices. The second proposal selects important data points from states of the world that have already realized, and are consistent with higher-order sample moment information. Both algorithms are efficient to compute and lend themselves to consistent scenario-based modeling and multi-dimensional numerical integration that can be used for interpretable decision-making under uncertainty. Extensive numerical benchmarking studies and an application in portfolio optimization favor the proposed algorithms.

Descriptive analysis of diseases, non-battle injuries and climate among deployed Swedish military personnel

IntroductionHistorically, diseases and non-battle injuries (DNBI) typically stand for 70%‒95% of all medical events during military missions. There is, however, no comprehensive compilation of medical statistics for Swedish soldiers during...

A Filon-Clenshaw-Curtis-Smolyak rule for multi-dimensional oscillatory integrals with application to a UQ problem for the Helmholtz equation

A Filon-Clenshaw-Curtis-Smolyak rule for multi-dimensional oscillatory integrals with application to a UQ problem for the Helmholtz equation

Illumination Intelligent Adaptation and Analysis Framework: A comprehensive solution for enhancing nighttime driving fatigue monitoring.

Nighttime driving presents a critical challenge to road safety due to insufficient lighting and increased risk of driver fatigue. Existing methods for monitoring driver fatigue, mainly focusing on behavioral analysis and biometric monitoring, face significant challenges under low-light conditions. Their effectiveness, especially in dynamic lighting environments, is limited by their dependency on specific environmental conditions and active driver participation, leading to reduced accuracy and practicality in real-world scenarios. This study introduces a novel 'Illumination Intelligent Adaptation and Analysis Framework (IIAAF)', aimed at addressing these limitations and enhancing the accuracy and practicality of driver fatigue monitoring under nighttime low-light conditions. The IIAAF framework employs a multidimensional technology integration, including comprehensive body posture analysis and facial fatigue feature detection, per-pixel dynamic illumination adjustment technology, and a light variation feature learning system based on Convolutional Neural Networks (CNN) and time-series analysis. Through this integrated approach, the framework is capable of accurately capturing subtle fatigue signals in nighttime driving environments and adapting in real-time to rapid changes in lighting conditions. Experimental results on two independent datasets indicate that the IIAAF framework significantly improves the accuracy of fatigue detection under nighttime low-light conditions. This breakthrough not only enhances the effectiveness of driving assistance systems but also provides reliable scientific support for reducing the risk of accidents caused by fatigued driving. These research findings have significant theoretical and practical implications for advancing intelligent driving assistance technology and improving nighttime road safety.

(Invited) Biofabricated Carbon Nanotube Electronics: Toward Energy-Efficient Switches in Multi-Dimensional Integration

High-performance Si devices are approaching their physical boundaries during the continuous miniaturization following Moore's Law. New semiconductor materials are in urgent necessity, among which, carbon nanotubes（CNTs）are suggested by the International Technology Roadmap for Semiconductors（ITRS）as potential candidate to continue Moore's Law due to its quasi-one-dimensional structure and excellent electrical properties.To unleash the advantages of CNTs in the post-Moore era, optimizing materials morphology, interfaces composition, and integrated structure proves to be pivotal, which constitutes the basic idea of our research. Regarding material morphology, we have fabricated evenly spaced parallel CNT materials with a spacing down to 10nm, by employing the DNA-directed high-precision assembly method. At CNT interface, we have realized fast on/off switching high-performance switches by engineering the interfacial compositions. Individual CNT displays 1G0 conductance with subthreshold swing superior to conventional Si transistors. Furthermore, we built the preliminary foundation to integrate CNT transistors into multi-dimensional circuits. Based on the control of material, interface, and integration structure, our work elucidates the potential of constructing energy-efficient computing circuit using carbon nanotube electronics.

Digital twin of wooden heritage through multidimensional model construction and integration

The concept of digital twin (DT) has gained significant attention in preventive conservation for prolonging the lifespan of cultural heritage. Focusing on preventive conservation, this study employs the integration of multidimensional modeling and multiple approaches to meticulously construct a DT for wooden cultural heritage. The constructed DT model integrates digital image correlation with iterative closest point, finite element model updating methods, moisture diffusion model, moisture-strain model, accumulated damage model, and the matter-element extension method research to acquire accurate data and risk assessment for real-time monitoring-based model, providing a versatile framework applicable to wooden cultural heritage. Illustrated through the case study of Quanzhou ships, the research exemplifies the generalizability of the construction and integration methods to diverse wooden cultural heritages. A DT system platform is devised for the structural monitoring, prediction and risk assessment of Quanzhou ships, encompassing geometry, physics, behavior, rule models, and services model based on monitoring results. The developed system platform integrates prediction, monitoring, alarm, and warning functions, enabling timely interventions and proactive preservation strategies to safeguard wooden cultural heritage. In conclusion, this study contributes a robust framework and methodology for the DT and preservation of wooden cultural heritages. The development of a DT integration and interaction system platform enhances the capabilities of preventive conservation efforts and provides practical insights into the application of digital twinning methodologies for preserving wooden heritages.

LANGUAGE SKILLS AND SUCCESSFUL MISSION OF UN PEACEKEEPERS: A CASE STUDY OF ACEHNESE POLICEWOMEN

UN peacekeeping operations rely on international cooperation to constitute competitive female peacekeepers. Meanwhile, Indonesia is an active contributor to global peace, yet its women peacekeepers dealt with linguistic challenges to participate in peacekeeping missions in French-speaking countries. This article investigates the exhaustive intellectual and physical performances of a policewoman from Banda Aceh to take part in the MINUSCA ( United Nations Multidimensional Integrated Stabilization Mission in the Central African Republic) 2020-2021. This paper aims to illustrate the theory of feminism by using a descriptive qualitative analysis. In UN peacekeeping missions, having female peacekeepers is crucial for ensuring gender-balanced representation. This study describes how the determinism of the female gender through personal commitment, self-engagement, and motivation is as valuable as the male gender. The data are collected via recorded Zoom meetings. The findings revealed the components of French language proficiency for MINUSCA, learning activities, the Indonesian policewoman’s experiences in learning French, and French language teaching andlearning issues that needed to be addressed in Banda Aceh. In addition, this article underlines factual issues on the absence of an official French language academy in Banda Aceh. Addressing those issues will contribute to maximizing Indonesian human resources for global peace.

Renewable energy transition and regional integration: Energizing the pathway to sustainable development

This study examines the influence of renewable energy transition on sustainable development, with multi-dimensional regional integration as a moderator. Using various regression techniques, a balanced panel dataset was analyzed for 64 countries participating in the Belt and Road Initiative (BRI) and three sub-regions (Asia, Europe, and Middle East-Africa) from 2005 to 2020. The findings reveal a positive relationship between renewable energy transition and sustainable development across the full panel and sub-regions of the BRI. Multi-dimensional regional integration also positively influences sustainable development. The interaction of regional integration with renewable energy transitions positively moderates sustainable development in Asia and Europe but negatively in the Middle East-African economies. Macroeconomic conditions, institutional quality, and population size positively affect sustainable development for the full panel, with mixed effects in sub-regions. Conversely, government consumption negatively impacts sustainable development for both the full and regional panels of the BRI. This study underscores important policy implications for promoting a balanced transition from conventional energy sources to renewables, thereby fostering green economic growth, environmental sustainability, and social inclusion.

Women, Peace and Security Agenda in Africa

The complex opportunities and challenges of the Women, Peace and Security (WPS) Agenda articulated in the UN Security Council Resolution 1325 of 2000, and other successive resolutions, subject themselves to the proverbial interpretation of either a cup half-empty or a cup half-full. The WPS is a progressive agenda for gender programmes in Africa. The downside is that the WPS agenda appears not known beyond policy and activist circles in the continent. This study sought to interrogate the progress made by the WPS agenda in Africa since the UNSCR 1325 was adopted using secondary research and content analysis (CA) of current literature. It was revealed that despite some challenges faced the gender perspective is not deficient in peacekeeping operations (PKOs) except in peace agreements, but that gender 1325 commitments are mirrored in the mandates of PKOs. The African Union-United Hybrid Operation in Darfur (UNAMID), showed the least WPS agenda consideration but the United Nations Multidimensional Integrated Stabilisation Mission in the Central African Republic (MINUSCA) significantly reflected this. For the rest of Africa, it remains unclear if the agenda is stagnating or going forward. There is a host of structural challenges rooted in masculinity issues and patriarchal mind-sets among others.

Digital peacekeeping, cyborg soldiers and militarised masculinities: a posthuman critique

ABSTRACT This article contends that digital technologies are profoundly altering peacekeeping practices as well as peacekeeper consciousness and modes of being. It is proposed that combining postcolonial humanist and posthumanist ontological perspectives when undertaking ethnographic research enrichens investigations into global race and gender power relations in peacekeeping. Drawing on posthumanism and Bourdieusian practice theory, the article examines how 16 British infantry soldiers articulate reconnaissance and civilian protection experiences and construct their militarised masculinities prior to and after deployment to United Nations Multidimensional Integrated Stabilisation Mission in Mali (MINUSMA) in 2020–2021. Findings show that deployment to a high-risk, stressful mission and functioning as ‘information processing devices’ in an increasingly centralised UN peacekeeping system disempowers and emasculates the men. The peacekeepers respond by engendering and racialising the British Army’s ‘gender-neutral’ cyborg soldier figuration and draw on virtual gaming syntax and framings to create relational distance between themselves, female colleagues and local populations.

On the long-term fatigue analysis of marine structures

This paper investigates the performance of several procedures available in literature used to evaluate the multi-dimensional integral associated with long-term fatigue assessment of marine structures. Direct integration methods, Monte Carlo-based approaches and Dimension-Reduction methods are investigated. The focus is on solving the four-dimensional integral for wave-induced fatigue cases when wind-sea and swell waves are simultaneously considered in the analysis. Although the procedures investigated can be used either for time-domain or frequency-domain fatigue approaches, all fatigue analyses performed in this work are restricted to the frequency-domain and are based on idealized stress transfer functions that attempt to mimic the complex behavior of some marine structures. To evaluate the performance of these methodologies, different transfer functions of unimodal and bimodal types are studied. The long-term fatigue damages are calculated using all the investigated methods, and their accuracies and efficiencies are compared. It is found that the efficiency of Monte Carlo Simulation-based methods is problem-dependent and may require many numerical simulations to achieve good accuracy for some cases. Additionally, the Dimension-Reduction methods can lead to highly suitable results with a very low number of integration points and, among all methods investigated, the BDRM (Bivariate Dimension-Reduction Method) stands out due to its excellent performance.

Accelerating analysis of Boltzmann equations using Gaussian mixture models: Application to quantum Bose-Fermi mixtures

The Boltzmann equation is a powerful theoretical tool for modeling the collective dynamics of quantum many-body systems subject to external perturbations. Analysis of the equation gives access to linear response properties including collective modes and transport coefficients, but often proves intractable due to computational costs associated with multidimensional integrals describing collision processes. Here, we present a method to resolve this bottleneck, enabling the study of a broad class of many-body systems that appear in fundamental science contexts and technological applications. Specifically, we demonstrate that a Gaussian mixture model can accurately represent equilibrium distribution functions, thereby allowing efficient evaluation of collision integrals. Inspired by cold atom experiments, we apply this method to investigate the collective behavior of a quantum Bose-Fermi mixture of cold atoms in a cigar-shaped trap, a system that is particularly challenging to analyze. We focus on monopole and quadrupole collective modes above the Bose-Einstein transition temperature, and find a rich phenomenology that spans interference effects between bosonic and fermionic collective modes, dampening of these modes, and the emergence of hydrodynamics in various parameter regimes. These effects are readily verifiable experimentally. Published by the American Physical Society 2024