Order Fluctuations Research Articles

Investigating the structure of gluon fluctuations in the proton with incoherent diffraction at HERA

Impact parameter dependent dipole models are ideal tools for investigating the spatial structure of the proton. We investigate the incoherent ep cross section in exclusive J/psi photoproduction as measured by HERA, and find that as |t| increases, the models need several levels of substructure of gluon density fluctuations in order to describe the measured data well. In lieu of a perturbative description, we add this substructure by hand. This substructure is modelled as hotspots within hotspots. This enables us to describe measurements for |t|> 1 GeV^2, which is necessary for describing any observable which integrate over the t-spectrum, such as the rapidity or W_{gamma p}. We find that three levels of proton substructure are adequate for a good description of all available ep data up to |t|=30 GeV^2. We note that the gluonic density fluctuation structure follows a scaling behaviour, such that the logarithms of the number of hotspots and their size fall on a line, effectively reducing the available parameter space of the model. Our findings systematically constrains and provides a benchmark for the development of a perturbative model of spatial gluon fluctuations in nucleons.

Multifractal Characteristics of China’s Stock Market and Slump’s Fractal Prediction

It is necessary to quantitatively describe or illustrate the characteristics of abnormal stock price fluctuations in order to prevent and control financial risks. This paper studies the fractal structure of China’s stock market by calculating the fractal dimension and scaling behavior on the timeline of its eight big slumps, the results show that the slumps have multifractal characteristics, which are correlated with the policy intervention, institutional arrangements, and investors’ rationality. The empirical findings are a perfect match with the anomalous features of the stock prices. The fractal dimensions of the eight stock collapses are between 0.84 and 0.98. The fractal dimension distribution of the slumps is sensitive to market conditions and the active degree of speculative trading. The more mature market conditions and the more risk-averse investors correspond to the higher fractal dimension and the fall which is less deep. Therefore, the fractal characteristics could reflect the evolution characteristics of the stock market and investment philosophy. The parameter set calculated in this paper could be used as an effective tool to foresee the slumps on the horizon.

Penentuan Harga Pokok Produksi Usaha Mikro, Kecil, dan Menengah Charu Dhatri Madiun

Micro, Small and Medium Enterprises (MSMEs) Charu Dhatri explained that there were difficulties in formulating the Cost of Production (HPP) because there were on-off workers who depended on the volume of orders. The number of workers is determined by fluctuations in orders. Therefore, workers’ wages are difficult to include in the calculation of HPP. In response, community service activities were carried out to overcome it. The solution offered to solve the partner’s problems was by providing training and assistance in calculating the cost of production (HPP). The results show that MSME production activities can run smoothly according to the plan, while partners actively participate in activities, and there is an increase in knowledge of 26.19% after training and mentoring. After that, the partner’s satisfaction through the programs and activities reached a very good level. The next hope is that Charu Dhatri’s MSME business activities will run well, healthy, and sustainably.

A forecasting analytics model for assessing forecast error in e-fulfilment performance

PurposeDemand forecast methodologies have been studied extensively to improve operations in e-commerce. However, every forecast inevitably contains errors, and this may result in a disproportionate impact on operations, particularly in the dynamic nature of fulfilling orders in e-commerce. This paper aims to quantify the impact that forecast error in order demand has on order picking, the most costly and complex operations in e-order fulfilment, in order to enhance the application of the demand forecast in an e-fulfilment centre.Design/methodology/approachThe paper presents a Gaussian regression based mathematical method that translates the error of forecast accuracy in order demand to the performance fluctuations in e-order fulfilment. In addition, the impact under distinct order picking methodologies, namely order batching and wave picking. As described.FindingsA structured model is developed to evaluate the impact of demand forecast error in order picking performance. The findings in terms of global results and local distribution have important implications for organizational decision-making in both long-term strategic planning and short-term daily workforce planning.Originality/valueEarlier research examined demand forecasting methodologies in warehouse operations. And order picking and examining the impact of error in demand forecasting on order picking operations has been identified as a research gap. This paper contributes to closing this research gap by presenting a mathematical model that quantifies impact of demand forecast error into fluctuations in order picking performance.

Integrated Impacts of Non-Ideal Factors on the Vibration Characteristics of Permanent Magnet Synchronous Motors for Electric Vehicles

The nonlinear electromagnetic vibration of the motor is a major factor that deteriorates the noise, vibration, and hardness (NVH) performance of a vehicle’s electric drive system. Considering the nonlinear characteristics of the inverter, the nonsinusoidal distribution of the air-gap magnetic field, the cogging torque, and the current measurement error, a mathematical model of a permanent magnet synchronous motor of an electric vehicle was established, and its dynamic and electromagnetic vibration characteristics under different speed–load conditions were simulated and analyzed. The results show that the nonlinear characteristics of the inverter and nonsinusoidal distribution of the air-gap magnetic field cause the odd current harmonics, such as the 5th, 7th, 11th, and 13th, which lead to the 6th and its integer multiple order fluctuations of the electromagnetic torque. Moreover, the vibration amplitude is intensified under the coupling action of the nonlinear characteristics of the inverter and the nonsinusoidal distribution of the air-gap magnetic field. The current measurement error produces the 1st and 2nd harmonics of the d- and q-axes currents, which result in the 1st and 2nd order fluctuations of the electromagnetic torque. The cogging torque mainly leads to a 12th order torque ripple of the electromagnetic torque. In addition, the non-ideal factors cause a sharp deterioration in the system vibration state under high-speed and heavy-load conditions. This study provides a theoretical reference for the mathematical modeling and electromagnetic vibration research of permanent magnet synchronous motors, considering non-ideal factors comprehensively.

Evaluation of DNA extraction protocols and real-time PCR-based methods for efficient investigation of pig traces in foods

Industrially processed foods are composed of a complex mixture of molecules combined under specific chemical and physical conditions. Besides their native interactions, most of the ingredients included in processed foods are highly transformed through extreme heat variations, grinding, freezing, pH, and pressure fluctuations in order to reach the desired final product. Due to their complex structure and high level of degradation, processed foods are difficult to analyse. Undeclared components are often detected in processed foods, and accurate diagnostic testing is required to protect those with health, cultural, and religious restrictions. Molecular biology techniques involving PCR are most frequently used for determining the authenticity of foods containing derivatives of living organisms. In the present work, we investigated four different DNA extraction protocols of three commercial kits, two different quantitative PCR (qPCR) techniques, and six different primer pairs. We analysed 96 extracts (12 samples from each of the eight products) by SYBR Green-based qPCR using the two most specific and sensitive primer pairs, and compared these results to those obtained with standard commercial kits that use dual dye-labelled probes. Adopting high-efficiency DNA extraction protocols, our findings highlighted the importance of targeting several small regions of the mitochondrial genome to effectively detect small traces of porcine products, and reduce the risk of false-negative results. Adopting these will ensure that consumers can make accurate and informed choices.

Revealing the role of liquid preordering in crystallisation of supercooled liquids

The recent discovery of non-classical crystal nucleation pathways has revealed the role of fluctuations in the liquid structural order, not considered in classical nucleation theory. On the other hand, classical crystal growth theory states that crystal growth is independent of interfacial energy, but this is questionable. Here we elucidate the role of liquid structural ordering in crystal nucleation and growth using computer simulations of supercooled liquids. We find that suppressing the crystal-like structural order in the supercooled liquid through a new order-killing strategy can reduce the crystallisation rate by several orders of magnitude. This indicates that crystal-like liquid preordering and the associated interfacial energy reduction play an essential role in nucleation and growth processes, forcing critical modifications of the classical crystal growth theory. Furthermore, we evaluate the importance of this additional factor for different types of liquids. These findings shed new light on the fundamental understanding of crystal growth kinetics.

Smart Hydropower Water Distribution Networks, Use of Artificial Intelligence Methods and Metaheuristic Algorithms to Generate Energy from Existing Water Supply Networks

In this paper, the possibility of installing small hydraulic turbines in existing water-supply networks, which exploit the daily pressure fluctuations in order to produce energy, is examined. For this purpose, a network of five pressure sensors is developed, which is connected to an artificial intelligence system in order to predict the daily pressure values of all nodes of the network. The sensors are placed at the critical nodes of the network. The locations of the critical nodes are implemented by applying graph theory algorithms to the water distribution network. EPANET software is used to generate the artificial intelligence training data with an appropriate external call from a Python script. Then, an improvement model is implemented using the Harmony Search Algorithm in order to calculate the daily pressure program, which can be allocated to the turbines and, consequently, the maximum energy production. The proposed methodology is applied to a benchmark water supply network and the results are presented.

How Did Covid 19 Affect Strategic Goods? A Study Using the Scenario Method

Based on a review of literature dealing with the impact of the Covid-19 pandemic on international transactions, it should be noted that this pandemic in the world has led to a radical change in several areas. In this article, we have chosen to focus on the sectors that are strongly affected by this pandemic, namely the oil sector and not forgetting to study fluctuations related to the metals sector (gold). Indeed, the oil sector is a market that is well affected by this pandemic, which has caused significant price fluctuations. That is why this study is trying to identify the impact of this pandemic on this sector. Similarly, due to the lack of stability in the prices of metals (particularly gold), this leads us to analyze and process these fluctuations in order to determine the effect caused by the pandemic. Finally, to clarify our research questions, a study based on the scenario model is retained to identify the impact of Covid-19 on each of these variables, at the beginning and to verify the effect between all these variables, thereafter. The obtained results demonstrate that the pandemic affects negatively Oil prices. On the contrary, the high number of infected people leads to the rise in gold price during the forecast period.

Kentsel Su Yönetiminde Basınç Kontrolü İçin Fayda Maliyet Analizi Hesaplama Aracının Geliştirilmesi

Kentsel su yönetiminde arıza yoğunluğuna bağlı olarak işletme koşulları bozulmaktadır. Arızaların oluşmasında en önemli faktör sistemdeki yüksek basınç ve basınçtaki düzensizliktir. Bu nedenle yeni arıza oluşumunun azaltılması için basıncın kontrol altına alınması ve dalgalanmaların azaltılması gerekmektedir. Ancak basınç kontrolü oldukça kapsamlı saha çalışması gerektiren, maliyetli bir süreçtir. Bu nedenle basınç yönetimi uygulanmadan önce oluşacak maliyetler (ekipman, saha çalışmaları, kurulum) ve faydalar (sisteme kazandırılacak potansiyel su hacmi, arızadaki potansiyel azalmalar) analiz edilmelidir. Bu çalışmada bir izole bölgede basınç yönetimi uygulanması durumunda ortaya çıkan maliyetlerin ve faydaların analiz edilmesi için hesaplama aracının geliştirilmesi amaçlanmıştır. Bu amaçla saha verilerine göre maliyet bileşenleri tanımlanmıştır. Ayrıca literatürde önerilen yaklaşımlar esas alınarak basınç kontrolüne bağlı olarak sızıntı ve arıza oranındaki azalmalar analiz edilmektedir. Bu fayda ve maliyet bileşenleri geliştirilen hesaplama aracına tanımlanmıştır. Geliştirilen bu hesaplama aracının özellikle uygulayıcılar için referans teşkil edeceği düşünülmektedir. Bu hesaplama aracında izole bölge çalışmaları için de fayda maliyet bileşenleri tanımlanmıştır.

Abstract 1700: Serial monitoring of single-cell circulating tumor cell genomics in metastatic lobular breast cancer to identify precision and immuno-oncology biomarkers with therapeutic implications

Abstract Clinical decisions for precision and immuno-oncology therapies are based on predictive biomarkers commonly obtained from a single metastatic biopsy, or from archived primary tumor material. Circulating genomic biomarkers present a minimally invasive way to monitor the intra-patient tumor heterogeneity and its fluctuations in order to provide a real-time evaluation of the changing clonal architecture with potential therapeutic implications. Single-cell DNA next generation sequencing (scNGS) of circulating tumor cells (CTC) is a particularly well-suited method of unraveling and monitoring that heterogeneity to complement biomarker information obtained from tissue and cell-free circulating tumor DNA (ctDNA). In this proof-of-concept study we analyzed 123 CTC, 15 white blood cells (WBC), and ctDNA from 15 CTC-positive lobular breast cancer patients, five of whom had CTC available at both metastatic baseline and after progression on a variety of therapies chosen at their physician’s discretion. CTC were enriched with the CellSearch® system and isolated as single cells with the DEPArray™ system. Whole genome amplified CTC DNA underwent scNGS with the Oncomine Comprehensive Assay covering ~500 genes and 1.1Mb of genomic space to detect mutations, copy number alterations, tumor mutation burden (TMB) and microsatellite instability (MSI). 99.1% of cells were informative, with a mean sequencing depth of 664x. Using our previously developed, CTC-based precision medicine reporting platform, MI-CTCSeq, multiple CTC in seven of 15 patients (47%) had mutations that were actionable by FDA-approved targeted therapies including in the oncogenes PIK3CA (alpelisib) and FGFR2 (erdafitinib). 13 patients (87%) displayed intra-patient, inter-CTC genomic heterogeneity of putative driver mutations. Two of five (40%) patients with CTC at both baseline and progression displayed fluctuations in their CTC subclonal makeup between timepoints. One of the two harbored a baseline ESR1 (estrogen receptor α) p.D538G activating mutation that largely disappeared at progression and was replaced by a CTC subclone with a different ESR1 activating mutation, p.Y537S. Intriguingly, this patient’s CTC also harbored an FGFR2 p.K659M mutation in an actionable “hotspot” at progression, which was absent at baseline, suggesting potential utility of serial monitoring by CTC scNGS. TMB scores and MSI status in CTC were highly concordant with those measured in clinical tissue biopsies. Taken together, these data suggest the non-invasive interrogation of the CTC genomic landscape and its serial monitoring to inform precision and immuno-oncology treatments in real time. Citation Format: Andi K. Cani, Emily M. Dolce, Elizabeth P. Darga, Kevin Hu, Chia-Jen Liu, James M. Rae, Daffyd G. Thomas, Scott A. Tomlins, Arul M. Chinnaiyan, Aaron M. Udager, Costanza Paoletti, Erin F. Cobain, Daniel F. Hayes. Serial monitoring of single-cell circulating tumor cell genomics in metastatic lobular breast cancer to identify precision and immuno-oncology biomarkers with therapeutic implications [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1700.

On the second order fluctuations for minimal difference partitions

The class of minimal difference partitions MDP(q) is defined by the condition that successive parts in an integer partition differ from one another by at least q≥0. As an extension, Bogachev and Yakubovich (2020) introduced a variable MDP-type condition encoded by an integer sequence q→=qi and found the limit shape. Based on their work, we establish a central limit theorem for the fluctuations of parts around the limit shape near the edge.

Demand Forecasting Application with Regression and IoT Based Inventory Management System: A Case Study of a Semiconductor Manufacturing Company

The accuracy of demand forecasting has a significant impact on the supply chain system's performance, which in turn has a major effect on company performance. Accurate forecasting will allow the organization to make the best use of its resources. The synchronization of customer orders to support production is critical for on-time order fulfillment. However, In fact many organizations report that their forecasting method is not working as effectively as they had hoped because orders regularly alter due to client demands. The purpose of this paper is to present an Internet of Things (IoT)-based inventory management system (IMS) that combines a causal method of multiple linear regressions (MLR) with genetic algorithms (GA) to improve the accuracy of demand forecasting in the future period by the customer as closely as feasible and enable smart inventory for Industry 4.0. Based on the data gathered from a semiconductor company that specializes in low-volume, high-mix contract manufacturing equipment and services integration, the suggested IoT-based IMS indicates that inventory productivity and efficiency could be enhanced, and it is resilient to order fluctuation.

Fluctuations of the number of excursion sets of planar Gaussian fields

For a smooth, stationary, planar Gaussian field, we consider the number of connected components of its excursion set (or level set) contained in a large square of area $R^2$. The mean number of components is known to be of order $R^2$ for generic fields and all levels. We show that for certain fields with positive spectral density near the origin (including the Bargmann-Fock field), and for certain levels $\ell$, these random variables have fluctuations of order at least $R$, and hence variance of order at least $R^2$. In particular, this holds for excursion sets when $\ell$ is in some neighbourhood of zero, and it holds for excursion/level sets when $\ell$ is sufficiently large. We prove stronger fluctuation lower bounds of order $R^\alpha$, $\alpha \in [1,2]$, in the case that the spectral density has a singularity at the origin. Finally, we show that the number of excursion/level sets for the Random Plane Wave at certain levels has fluctuations of order at least $R^{3/2}$, and hence variance of order at least~$R^3$. We expect that these bounds are of the correct order, at least for generic levels.

Cerebello-thalamofrontal dysconnectivity in paroxysmal kinesigenic dyskinesia: A resting-state fMRI study

IntroductionThe pathophysiology of paroxysmal kinesigenic dyskinesia (PKD) remains elusive to date; however, several lines of evidence from neuroimaging studies suggest involvement of the basal ganglia-thalamocortical network in PKD. We combined fractional amplitude of low-frequency fluctuation (fALFF) and seed-based functional connectivity (FC) analyses in order to comprehensively investigate intrinsic brain activity alterations and their relationships with disease severity in patients with idiopathic PKD. MethodsResting-state functional MRI data were obtained and processed in 34 PKD patients and 34 matched controls. fALFF and seed-based FC maps were computed and compared between patients and controls. Linear regression analysis was further performed between regional fALFF values or FC strengths and clinical parameters in patients. ResultsPKD patients had a significant increase in fALFF in bilateral thalamus and cerebellum compared with controls. FC analysis seeding at the thalamic clusters revealed significant FC increases in motor cortex and supplementary motor area in PKD patients relative to controls. Longer disease duration was associated with increasing FC strength between the thalamus and motor cortex. ConclusionWe have provided evidence for abnormal intrinsic activity in the cerebello-thalamic circuit and increased thalamofrontal FC in PKD patients, implicating interictal cerebello-thalamofrontal dysconnectivity in the pathophysiology of PKD. Given the increasing FC strength in proportion to disease duration, the thalamofrontal hyperconnectivity might reflect either a consequence of recurrent dyskinesias on the brain or an innate pathology causing dyskinesias in PKD.

Weakly first-order transition in an athermal lattice gas

We investigate the phase behavior of a two-dimensional athermal lattice gas in which every hard-core particle can have two or fewer nearest neighboring occupied sites on the square lattice. The ground state and close packing density are determined and it is found that at large chemical potential the model undergoes an ordering phase transition with preferential sublattice occupation. Although near the transition point the particle density and entropy exhibit an apparent discontinuity, we find that the order parameter and fluctuations of thermodynamic quantities do not scale with the system volume. These paradoxical results are reconciled by analyzing the size-dependent flow of the thermal exponent by phenomenological renormalization and the curve-crossing method, which lead to a weakly first-order phase transition scenario.

Применение нейронных сетей в системе распознавания промысловых гидробионтов в условиях повышенной флуктуации

The article focuses on using artificial neural networks for the recognition of commercial hydrobionts in conditions of increased fluctuation. Research has not been conducted in the field of recognition of aquaculture objects for reasons of solving the applied problems and achieving the required indicators. There is described the development of software for recognition of aquaculture objects based on the YOLOv5 model, as well as the proper methodology for the semi-automatic formation of effective visual training samples. There has been analyzed the model operation scheme and substantiated the importance of the training sample and its influence on the quality of pattern recognition by a neural network. Effective identification of aquatic organisms requires the methods for dividing it into zones according to various distinctive features and separating the required objects not only from the background, but also taking into account fluctuations above the water surface. There has been developed a method for eliminating fluctuations in order to increase the efficiency of recognition of aquatic organisms by a neural network. There is described the process of testing software for the detection of commercial hydrobionts trained on a sample by the proposed method. 
 It has been inferred that the recognition rate directly depends not only on the quality of the training sample and the volume of images, but also on the technical characteristics of the electronic computer

Dissipation of energy and higher-order fluctuations of the largest fragment charge in projectile fragmentation

Abstract The higher-order fluctuations of the largest fragment charge were found to exhibit the signatures of a second-order phase transition in experimental data and simulations of the statistical multifragmentation model recently (Pietrzak et al 2020 Phys. Lett. B 809 135763). This work is devoted to study the dissipation of energy and higher-order fluctuations of the largest fragment charge in projectile fragmentation. The isospin-dependent quantum molecular dynamics (IQMD) model is used to study the non-equilibrium thermalization and fragmentation. The statistical code GEMINI is applied to simulate the second decay of the pre-fragments. To reveal how the incident energy dissipates into the excitation energy of the projectile-like system, the time evolution of the density, collective velocity, and random kinetic energy are displayed. It is found that the competition between the heat conduction and heat flux affects the excitation energy of the projectile-like system, which is the key variable in the fragmenting process. Displaying the mean multiplicity of the intermediate mass fragments and the higher-order fluctuations of the largest fragment charge, it is found that the decay mechanism of the projectile-like system transfers from the multi-fragmentation at mid-peripheral collision into the nucleon-evaporation at peripheral collision. The pseudo-critical point of this transition can be indicated by the zero of third order fluctuation together with the minimum of fourth order fluctuation. The calculations of the pseudo-critical point by the IQMD + GEMINI model agree with that extracted from the experimental data. Finally, the influences of the pseudo-critical point by the second decay, the mass number of the colliding system, and the incident energy are studied.

Connections between structural characteristics and crystal nucleation of Al–Sm glasses near glass transition temperature

A systematic investigation on connections between structural characteristics and crystal nucleation during isothermal devitrification of Al–Sm glasses has been carried out by utilizing molecular dynamics simulations combined with an effective data-driven approach named principal component analysis. The results suggest that icosahedral order dominants the local orders in Al–Sm liquids and glasses, whereas the development of crystal-like clusters has been greatly suppressed. Doping of Sm not only increases the population of icosahedral order in Al–Sm alloys, but also promotes the tendency of formation of icosahedral medium range order. Based on the calculation of local density and bond-orientational order parameters, a distinct decoupling in spatial distributions of fluctuations in local density and bond-orientational order is observed, and an obvious lagging of densification behind structural ordering is unveiled. These facts unambiguously evidence that the bond-orientational order instead of local density is the determinant factor for the origin of crystal nucleation in Al–Sm glass.

Semiclassical and quantum features of the Bianchi I cosmology in the polymer representation

We analyze the Bianchi I cosmology in the presence of a massless scalar field and describe its dynamics via a semiclassical and quantum polymer approach by adopting three different sets of configurational variables: the natural Ashtekar connections, a set of anisotropic volumelike coordinates and the Universe volume plus two anisotropy coordinates (the latter two sets of variables would coincide in the case of an isotropic Universe). In the semiclassical analysis we demonstrate that the big bounce clearly emerges in all the sets of variables. Moreover, when adopting the proper volume variables (i.e. when defining the Universe volume itself on the polymer lattice) we also derive the exact polymer-modified Friedmann equation for the Bianchi I model. This way, the expression of the critical energy density that includes the anisotropic energylike contribution is obtained, demonstrating that in this set of variables the big bounce has a universal nature (i.e. its critical energy density has a maximum value fixed by fundamental constants and the Immirzi parameter). Finally, a proposal is made to recover the equivalence between the dynamics in the Ashtekar set and in the anisotropic volumelike one, showing that this request is satisfied when a polymer parameter depending on the configurational coordinates is considered. Then, we apply the Arnowitt-Deser-Misner reduction of the variational principle and we quantize the system. We study the resulting Schr\"odinger-like dynamics only in the Ashtekar variables and in the proper volume ones, stressing that the behavior of the Universe wave packet over time singles out common features with the semiclassical trajectories. However, we show that the standard deviation for the Universe volume operator grows with time. This is a signal of the spreading of the Bianchi I wave packet and hence of the importance of the quantum fluctuations in order to properly study the big bounce picture.