Statistical Nature Research Articles

Controllable Human Trajectory Generation Using Profile-Guided Latent Diffusion

Trajectory generation is a vital element in AI applications. Firstly, it enables simulation such as traffic simulation and epidemic spreading modeling. Secondly, it can provide synthetic privacy-preserving data for training AI models. Notably, trajectory generation featuring controllable user profiles holds substantial value in generating customized mobility trajectories tailored to diverse requirements. However, relevant work is still lacking. On the one hand, traditional deep generative models fall short in guiding controllable trajectory generation due to the statistical nature of human mobility patterns and the corresponding insufficient control mechanisms. On the other hand, though the diffusion model has demonstrated strong generative capabilities in many fields, to achieve controllable generation on discrete trajectory data, we still need to redesign the structure of the continuous diffusion model. In this paper, we introduce a controllable trajectory generation framework that leverages a continuous diffusion model and classifier guidance for more robust condition control. Our proposed framework comprises two modules: a latent trajectory diffusion model and a trajectory classifier for profile guidance. Experiments on two real-world mobility datasets consistently demonstrate its capability of generating trajectories matching given user profiles and conforming to human mobility patterns. Our source code and trained models are released at https://github.com/tsinghua-fib-lab/User-Profile-Guided-Latent-Diffusion .

An insight for the interaction of Aceclofenac with hemoglobin using spectroscopic, electrochemical and in silico approaches

The interactions between protein and ligand are crucial to practically every biological function in the body, and investigations of these interactions are critical from the perspective of logical medication development. In this work, several biophysical methods like UV–Visible spectroscopy, fluorescence spectroscopy, and in-silico studies have been explored to examine the interaction between aceclofenac and haemoglobin. Spectrofluorimetric data confirmed the static nature of the protein quenching mechanism caused by the medication. ΔG for the formation of the complex via the interaction was found to be − 12.65 kJ/mol. Molecular docking showed a strong interaction between aceclofenac and hemoglobin with a binding energy of − 31.79 kJ/mol. Further, molecular dynamics simulations using Gromacs were run to investigate the stability of the protein with and without aceclofenac, and the ΔG for the formation of the complex (hemoglobin-aceclofenac) is − 105.09 kJ/mol with an error of 13.51 kJ/mol. Spectroscopic and in-silico calculations indicate the feasible binding of the aceclofenac with hemoglobin. Further, the interaction of haemoglobin with aceclofenac was studied by cyclic voltammetry.Graphical

Administration management in patient safety culture and barriers to adverse event reporting in the nursing profession

Patient safety is very important in the health field, and for this, parameters such as having quality in the service provided and guaranteeing primary care in an effective manner must be considered. The objective of this article is to know the aspects of the patient safety culture and the safety climate perceived by the nursing staff who work in the participating hospital. The methodology used is a descriptive, cross-sectional observational type; It will be observational because it is a specific type of study that is defined by having a statistical or demographic nature, where 46 health professionals were surveyed. In this investigation, it was identified that there is disagreement with the actions that are implemented, lack of supervision, lack of monitoring, strategies, reports, and high frequency of communication between professionals where the purpose is to provide patient safety and demonstrate all the notifications of adverse events and incidents.

Phenomenology of a Rydberg impurity in an ideal Bose-Einstein condensate

We investigate the absorption spectrum of a Rydberg impurity immersed in and interacting with an ideal Bose-Einstein condensate. Here, the impurity-bath interaction range can greatly exceed the mean interparticle distance; this discrepancy in length scales challenges the assumptions underlying the universal aspects of impurity atoms in dilute bosonic environments. Our analysis finds three distinct parameter regimes, each characterized by a unique spectral response. In the low-density regime, we find that the Rydberg impurity is dressed by the surrounding bath similarly to the known Bose polaron. Transitioning to intermediate densities, the impurity response, given by sharp quasiparticle peaks, fragments into an intricate pattern bearing the hallmarks of a diverse molecular structure. Finally, at high density, a universal Gaussian response emerges as the statistical nature of the bath dominates its quantum dynamics. We complement this analysis with a study of an ionic impurity, which behaves equivalently. Our exploration offers insights into the interplay between interaction range, density, and many-body behavior in impurity systems. Published by the American Physical Society 2024

A universal formulation of uncertainty relation for errors

We present a universal formulation of uncertainty relation valid for any conceivable quantum measurement of statistical nature. Owing to its simplicity and operational tangibility, our general relation is also experimentally verifiable. Our relation violates the traditional naïve bound ħ/2 for the position-momentum measurement while respecting Heisenberg's original philosophy of the uncertainty principle. Our relation entails, among others, the Arthurs–Kelly–Goodman and Ozawa relations as corollaries to its special cases, and also seamlessly reduces itself to the standard Kennard–Robertson (Schrödinger) relation when the measurement is non-informative.

Evaluating Spatial Interpolation Maps of the Age Structure of the Population of Thi Qar Governorate Using Geographic Information Systems (GIS) Technologies

The research aims to harness spatial interpolation techniques to produce maps with a high level of perceptual accuracy in representing the population data of the study area. This is achieved after exploring the statistical and spatial nature of the databases used, analyzing them, and determining their distribution using a variety of spatial data exploration tools available within the GIS (Geographic Information Systems) environment. These tools contribute to evaluating the characteristics, distribution, and analysis of data, including testing data distribution, identifying its direction, and uncovering its spatial correlations. This highlights the importance of the study and understanding the distribution pattern of the population data in the study area, thereby facilitating the preparation of future that serve spatial organization by building spatial models for the numerical distribution of the data for age groups: young, middle-aged, and elderly, at the level of administrative units of the study area by relying on the inductive approach and the quantitative analysis approach. This is done using Geostatistical Analysis methods within the concept of spatial interpolation in GIS. The research also seeks to predict the population maps for Thi Qar Governorate by comparing spatial interpolation methods, namely Inverse Distance Weighting (IDW) and Simple Kriging (SK). The study concluded, after verifying the accuracy of the results using the Cross-Validation curve, that the Simple Kriging (SK) method is the most accurate model in spatial prediction of population data for representing the data of the young age group in the study area, as it had the lowest Root Mean Square Error (RMSE) of 10.012. It was followed by the Inverse Distance Weighting (IDW) method for representing the data of the elderly age group with an RMSE of 350.48. This indicates that the use of statistical criteria improves the accuracy of spatial interpolation and that the spatial prediction of the population data for the study area using the Simple Kriging (SK) method was more accurate than the Inverse Distance Weighting (IDW) method based on statistical indicators.

An adaptive multistage intrusion detection and prevention system in software defined networking environment

The advancements made in Software-Defined Networking (SDN) technology seem quite promising, with potential wide application in managing and controlling the latest network infrastructures. SDN technology decouples the control plane from the data plane, enabling effective and flexible network management. However, this dynamic phenomenon brings new security challenges. With the increasing dynamism and programmable nature of networks, conventional security protocols may not sufficient to protect against advanced and sophisticated attacks. Although Intrusion Detection Systems (IDSs) have been extensively applied for identifying and preventing security threats in traditional network environments, IDS models designed specifically for traditional network requirements may not be adequate for SDN environments. These issues may stem from the static nature of conventional networks, contrasting with the dynamicity of advanced SDN networks, and the traditional IDS’s inability to adapt to the dynamic nature of SDN. To address these challenges, the current research proposes a novel Deep Hybrid IDS model to enhance network security in SDN environments and prevent attacks using Scapy. The proposed model detects signature-based attacks by integrating Gated Recurrent Units (GRU) and Long Short-Term Memory (LSTM) for real-time simulated datasets, achieving an accuracy of 97.8%, which is comparatively better than existing models.

Evolving Adversarial Training (EAT) for AI-Powered Intrusion Detection Systems (IDS)

Intrusion Detection Systems (IDS) are crucial components of network security, yet traditional IDS models often fail to cope with rapidly evolving adversarial attacks that exploit their static nature. This study proposes a novel approach, Evolving Adversarial Training (EAT), to enhance the adaptability and robustness of AI-powered IDS against dynamic threats. The EAT framework integrates continuous model evolution with advanced adversarial training techniques, enabling the IDS to dynamically adjust to new attack patterns. Experimental results demonstrate that the EAT framework significantly enhances IDS performance, leading to increased detection accuracy and reduced false positive rates compared to conventional methods. These findings emphasize the potential of EAT in fortifying network defenses against evolving cyber threats, offering a promising avenue for future research in scalable and adaptive IDS solutions that can effectively combat the complexities of modern cyber adversaries. The research explores three key objectives: dynamic adaptation and adversarial training, continuous learning and enhanced threat detection, and robustness and generalization. By focusing on these objectives, the study aims to develop AI-powered IDS that can effectively navigate the ever-changing cyber threat landscape. The research methodology includes data collection, model architecture design, training and evaluation, continuous learning, simulation, and real-world testing, all aimed at enhancing the resilience of AI-powered IDS against adversarial attacks. By systematically following this framework, the study intends to enhance the security system of IDS through the effective implementation of EAT.

Modeling Approach of Cloud 4D Printing Service Composition Optimization Based on Non-Dominated Sorting Genetic Algorithm III

The manufacturing industry is currently experiencing a paradigm shift from traditional centralized systems to distributed, personalized, and cloud-based intelligent manufacturing ecosystems. The advent of 4-dimensional (4D) printing technology introduces dynamic characteristics to manufacturing design and functionality, necessitating the effective management of these emergent 4D printing services. This study aims to bridge the gap between the static nature of existing cloud manufacturing services and the dynamic requirements imposed by 4D printing technology. We propose a comprehensive multiobjective optimization model for cloud-based 4D printing service portfolios, incorporating the intricate complexities of 4D printing services and assessing the efficacy of the Non-Dominated Sorting Genetic Algorithm III (NSGA III) in optimizing these service portfolios to meet dynamic demands. In this research, the NSGA III algorithm is employed to develop a multiobjective optimization framework for 4D printing service portfolios, addressing critical issues such as service cost, time, quality, adaptability, and overall service optimization amidst fluctuating demand and service availability. The findings indicate that the NSGA III algorithm demonstrates superior performance in terms of generational distance (GD) and inverted generational distance (IGD), particularly excelling in convergence and diversity for high-dimensional optimization problems when compared to the comparison algorithms. The study concludes that the NSGA III algorithm exhibits significant potential in optimizing the orchestration of cloud-based 4D printing service portfolios, underscoring its effectiveness in managing the complexities associated with these services. This research provides valuable insights for the advancement of intelligent cloud-based 4D printing systems, paving the way for future developments in this field.

A closed form expression of stress intensity factor for an arbitrarily shaped planar crack in 3-D under tensile loading

A closed-form equation was developed in this work to quantify the stress intensity factor (SIF) along an arbitrarily shaped planar crack under mode-I loading by substituting the ellipse-related parameters in Irwin’s solution with geometry-related parameters fi of the crack. This allowed incorporation of the local crack curvature effect on SIF at each discrete point along the crack front and its relative weighted contribution to SIF of the neighboring crack point i, where either stress concentration or shielding took place, into computation of the SIF distribution along the crack front. Good agreement was obtained between the KI values derived in the current work and previous numerical simulation-based studies. The current method could capture the KI distributions for surface and embedded cracks having the same shape, with their maximum values being consistent with Murakami’s area method. This solution of KI allows for highly efficient calculation of ΔKI for quantification of the 3D growth behaviors of a fatigue crack with a complex shape, paving the way for studying the statistical nature of short fatigue crack growth.

The role of the socio-professional characteristics of university professors in the implementation of sustainability

Purpose In a context of growing concern about the environmental and social crisis, education for sustainability has emerged as a key educational approach to address these challenges. University education plays a crucial role in training professionals who are aware of and committed to sustainability. This study aims to examine the impact of the socio-professional characteristics of university professors on the awareness and implementation of sustainability in teaching and research. Design/methodology/approach This study uses the academics’ statements through a survey to describe the degree of awareness and implementation of sustainability at the University of the Basque Country (UPV/EHU) and evaluates the influence that certain socio-professional variables can have on their commitment to sustainability. For this purpose, the paper proposes the use of descriptive and inferential statistical techniques. Findings The research reveals that UPV/EHU is in the first phases of implementation of sustainability, indicating a low overall implementation. Therefore, it is proposed to promote internal awareness about sustainability, promote teacher training, integrate sustainability competencies in academic programs and promote research in areas with less scientific production related to sustainability, with emphasis on interdisciplinary collaboration and gender equality to achieve greater integration of sustainability in the university. In the case of the UPV/EHU, the sustainable development champions fulfill the profile of a female professor, with full-time dedication and whose disciplinary area is a minority. Research limitations/implications This research has an individual framework and a static nature, and the results should be interpreted only in their context. Future research should have a national framework, a longitudinal nature and a broader range of university institutional agents. Another limitation of this methodology is the question coding process (Likert scales), which opens a wide range of subjectivity for the researcher. Originality/value To the best of the authors’ knowledge, this is the first study that measures the degree of implementation of sustainability at the UPV/EHU with the aim of specifying its efforts in the implementation and promotion of sustainability.

A review on free space optical communication links: 5G applications

Abstract Free space optical (FSO) communications has garnered significant attention as a potential substitute for radio frequency (RF) wireless communication in short and long range transmissions. Free-space optical (FSO) communication is a method where an optical beam is sent across an unguided channel, gaining favour in high-speed wireless networks due to its large optical bandwidth, fast data transmission, lack of licencing requirements, secure nature, and easy setup. Despite several inherent advantages that FSO has over existing radio frequency (RF) communication technologies, its link performance is severely hampered by atmospheric fog and turbulence, which not only reduce optical irradiance but also cause the modulated optical beam to deviate from its intended path, impacting the quality of the received signal. This review paper discusses FSO technology, channel impairments under varied atmospheric attenuation conditions, advantages, and applications. Various channel models in terms of probability density functions are described, which characterise the statistical nature of the irradiance fluctuations of optical transmission through scattering and atmospheric turbulence. Key objectives that need to be addressed in the near future, particularly in fifth-generation (5G) and beyond, includes enhancing capacity, boosting data rates, minimising latency, and enhancing service quality. Last but not the least, several uses of FSO that facilitates 5G and future technologies have also been covered in this study.

Visual disability in spatio-temporal assemblages: conceptualizing reference points from a non-pointillist perspective

ABSTRACT Reference points provide crucial spatial information for people with visual disabilities, and existing literature categorizes and describes their functions. However, these studies still adhere to a geographical pointillist logic that perceives points as fixed and stable entities and overlooks corporeal processes through which reference points are created. Utilizing concepts from Deleuze-Guattarian philosophy and drawing from sit-down and go-along interviews with visually disabled people, this article answers the question of how to conceptualize reference points from a non-pointillist perspective. By elucidating how reference points need to be actualized through affirmative bodily acts of complex spatio-temporal assemblages, it dismantles their seemingly disembodied and static nature. Consequently, the text explores reference points interwoven with diverse assemblages and examines scenarios in which suitable assemblages could not be assembled, nor could reference points, therefore, be actualized. Thus, the article departs from conceiving of points solely as nouns and presents them in verbal terms, effectively reconciling stability with dynamism.

Simplifying IP multimedia systems by introducing next-generation networks with scalable architectures

The IP Multimedia Subsystem (IMS) is essential to providing multimedia services over IP networks. However, maintaining its scalability in the face of increasing demand and changing next-generation networks is still a significant concern. In light of growing loads and service diversity, scalable solutions are required, as this article explores the shortcomings of the IMS designs in use today. To present an innovative, scalable architecture for IMS that integrates cutting-edge computer science techniques, enabling effective service delivery in next-generation networks. By bridging the gap between the static nature of previous IMS designs and the dynamic demands of contemporary telecommunications, this article hopes to deliver results. A mixed-method approach was utilized, integrating evaluations of architectural flexibility with network loads and service needs analysis. In order to evaluate the suggested designs' performance to current benchmarks, the study used simulations to predict the architectures' performance under various scenarios. Compared to conventional IMS frameworks, the suggested design showed outstanding scalability, enabling a tenfold increase in simultaneous connections and services. It notably improved fault tolerance and service delay and increased flexibility for network modifications and service diversification. Next-generation network demands are met by IMS designs that use modern computer science methodologies to improve scalability and flexibility significantly. More practical, scalable, and reliable service delivery methods will be possible thanks to the foundation our work creates for future research into dynamic, resilient telecommunications infrastructures.

The effect of chemical short-range order on incipient plastic behavior in FCC structured high entropy alloys

Chemical short-range order (CSRO) was assumed as one of the most important structure feature of high entropy alloys and the influence of CSRO on mechanical properties is a fundamental issue yet to be fully understood. In this work, we performed extensively nanoindentation experiments on CoCrFeNiAlx alloys to study the effect of CSRO on the incipient nanomechanical properties. The statistical nature of strengths at the first pop-in event was analyzed to gain insight of deformation mechanisms. All samples examined here exhibit bimodal distribution which indicate non-unique dislocation nucleation mechanisms. The bimodal distributions can be decomposed into two Gaussian distributions and the activation volumes can be obtained in the range of 0.73–1.38b3. The peaks shift to higher stress level after the development of CSRO. The heterogeneous dislocation nucleation plays a dominant role at low indentation stress with the aid of pre-existing crystalline defects. The homogeneous dislocation nucleation mechanism prevails when indentation stress close to theoretical values. The transmission electron microscopy characterization indicates the presence of chemical ordering in the aged samples. Both the degree of chemical ordering and lattice distortion are much higher in the Al containing HEAs due to the distinctive difference of properties in Al and other transition element atoms.

Collective model description of parity-doublet bands in odd mass nuclei

The yrast parity doublet rotational bands in odd medium mass and heavy nuclei are described by a quadrupole–octupole axially symmetric collective model within the strong coupling assumption regarding the odd nucleon. Energy levels of the same spin and opposite parity are interpreted as symmetric and antisymmetric quantum states of the quadrupole–octupole shape fluctuation. The energy spectrum and the associated electromagnetic transitions of the parity doublet bands observed in 21 selected nuclei, are reproduced with a very good accuracy. Predictions are performed for energies of unobserved states and electromagnetic properties. The model parameters are used to establish the static or dynamic nature of the octupole correlations in the ground state and their evolution with spin. A general systematization of the octupole effects as a function of nucleon numbers is realized in conjunction with results on even–even nuclei.

Numerical simulations of equilibrium, kinetics, and chain length distribution in linear polycondensation proceeding in nano-scale small volumes: An apparent violation of the law of mass action

A simple polycondensation process Mi + Mj ⇄ Mi+j + Z, proceeding in dispersion of nano-scale droplets was described by analytical solutions and differential equations giving insight into equilibrium and kinetics of the process. Stochastic and deterministic simulations show that the statistical nature of polycondensation, both irreversible and reversible one, affects the way the macromolecules of different lengths are formed and distributed among droplets. The droplet distributions in respect to the number of reacting chains and the chain length distributions depend, for the given conversion, on the polycondensation equilibrium constant Kcond and the initial conditions: monomer concentration and the number of its molecules in a droplet. The apparent equilibrium constants of condensation K¯ij=[Mi+j]¯e[Z]¯e/[Mi]¯e[Mj]¯e depend on oligomer/polymer sizes as well as on the initial number of monomer molecules in a droplet. This apparent violation of the law of mass action was explained by stochasticity of involved processes. Moreover, when the polycondensation byproduct Z is being removed reversibly from the system, K¯ij depends also on average equilibrium concentration [Z]¯e. The indicated effects are observed not only for ideally dispersed systems (all droplets contain initially the same number of monomer molecules), but also when the initial numbers of monomer molecules conform the Poisson distribution. However, the chain length distribution as well as kinetics and K¯ij differ here from those observed for systems with uniform distribution.

Bibliometric Analysis on Precision Agriculture Technology

This study employs a bibliometric analysis using VOSviewer to visualize the authorship network within the domain of precision agriculture, identifying key researchers and their collaborative relationships. By mapping the connections based on publications from a specified time period, the analysis highlights the central figures like Erickson, B, who play pivotal roles in the network and reveals the interlinkages among various contributors. The study provides insights into the structural dynamics of research collaborations and elucidates the influence patterns among the scholars. Despite the inherent limitations such as database selection bias and the static nature of the bibliometric snapshot, the results offer valuable implications for enhancing research collaboration, academic planning, and strategic positioning within the scholarly community. This approach not only aids in recognizing influential entities and emerging talents but also assists institutions and funders in making informed decisions that could drive impactful research in precision agriculture.

Molecular dynamics simulations of neutron induced collision cascades in Zr — Statistical modelling of irradiation damage and potential applications

Understanding irradiation damage involves a multi-scale and multi-physics approach, integrating data from experiments, simulations and phenomenological models. This paper focuses on its early stages, specifically neutron-induced collision cascades in zirconium, as nuclear-grade zirconium alloys are widely used in fuel assemblies. We have gathered and analysed a significant sample of results from high-fidelity, large-scale molecular dynamics (MD) simulations, employing existing interatomic potentials and the two-temperature model to account for electron–phonon coupling. Our data can be directly applied to higher-scale methods.Furthermore, we carried out a comprehensive statistical analysis of the features associated with the defect production, including the number of defects, their distribution and size of the affected area. As a result, we developed a generative model of collision cascades that is parametric, hierarchical and stochastic, i.e. it takes into account a statistical nature of the phenomenon, is interpretable and shareable. This model has been developed with three primary objectives: to provide a sufficient descriptor of a cascade, to interpolate data obtained from high-fidelity simulations, and to demonstrate that the statistical model can produce representative distributions of primary irradiation defects. The results can be used to generate synthetic inputs for models at longer length and time scales, to provide fast approximations that take into account the morphology of introduced defects, and in general to serve as a powerful analytical tool.

The rise of electrically tunable metasurfaces.

Metasurfaces, which offer a diverse range of functionalities in a remarkably compact size, have captured the interest of both scientific and industrial sectors. However, their inherent static nature limits their adaptability for their further applications. Reconfigurable metasurfaces have emerged as a solution to this challenge, expanding the potential for diverse applications. Among the series of tunable devices, electrically controllable devices have garnered particular attention owing to their seamless integration with existing electronic equipment. This review presents recent progress reported with respect to electrically tunable devices, providing an overview of their technological development trajectory and current state of the art. In particular, we analyze the major tuning strategies and discuss the applications in spatial light modulators, tunable optical waveguides, and adaptable emissivity regulators. Furthermore, the challenges and opportunities associated with their implementation are explored, thereby highlighting their potential to bridge the gap between electronics and photonics to enable the development of groundbreaking optical systems.