Paratemporal methods based on tree expansion have proven to be effective in efficiently generating trajectories of stochastic systems. However, combinatorial explosion of branching arising from multiple choice points presents a major hurdle that must be overcome to implement such techniques. In this paper we tackle this scalability problem by developing a systems theory-based framework covering both conventional and proposed tree expansion algorithms for speeding up discrete event system stochastic simulations while preserving desired accuracy. An example is discussed to illustrate the tree expansion framework in which a discrete event system specification (DEVS) Markov stochastic model takes the form of a tree isomorphic to a free monoid over the branching alphabet. We derive the computation times for baseline, non-merging, and merging tree expansion algorithms to compute the distribution of output values at any given depth. The results show the remarkable reduction from exponential to polynomial dependence on depth effectuated by node merging. We relate these results to the similarly reduced computation time of binomial coefficients underlying Pascal’s triangle. Finally, we discuss application of tree expansion to estimating temporal distributions in stochastic simulations involving serial and parallel compositions with potential real world use cases.

Several factors influence traffic congestion and overall traffic dynamics. Simulation modeling has been utilized to understand the traffic performance parameters during traffic congestions. This paper focuses on driver behavior of route selection by differentiating three distinguishable decisions, which are shortest distance routing, shortest time routing and less crowded road routing. This research generated 864 different scenarios to capture various traffic dynamics under collective driving behavior of route selection. Factors such as vehicle arrival rate, behaviors at system boundary and traffic light phasing were considered. The simulation results revealed that shortest time routing scenario offered the best solution considering all forms of interactions among the factors. Overall, this routing behavior reduces traffic wait time and total time (by 69.5% and 65.72%) compared to shortest distance routing.

This paper deals with the service performance analysis and improvement using discrete event simulation has been used. The simulation of the heath care has been done by arena master development 14-version software. The performance measurement for this study are patients output, service rate, service efficiency and it is directly related to waiting time of patients in each service station, work in progress, resource utilization.Simulation model was building for Bahir Dar clinic and then, prepared the proposed model for the system. Based on the simulation model run result, the output of the existing healthcare service system is low due to presence of bottlenecks on the service system. Moreover, the station with the largest queue and high resource utilization are identified as a bottleneck. The bottlenecks, which have identified are reduced by using reassigning the existing resources and add new resources and merging the similar services, which has under low resource utilization (nurses). Finally, the researchers have proposed a developed model from different scenarios. Moreover, the best scenario is developed by combining scenario 2 and 3. And then, service efficiency of the healthcare has increased by 9.86 percent, the work in progress (WIP) are reduced by 3 patients from the system and the service capacity of the system is increased 34 to 40 patients per day due to the reduction of bottleneck stations.

Today, maritime transport is responsible for moving approximately 80 percent of the volume of world trade, which has favored Colombia, which, due to its geographical position, has managed to become one of the most competitive and dynamic economies in the world. South America. For this reason, this investigation is carried out in a port of their country, with the aim of identifying and analyzing restrictions or critical points that may cause delays in the unloading of goods, these would also cause delays in the loading of vehicles. To do this, it began with a diagnosis of the current situation through interviews with the operational personnel of the port, then a simulated model was designed in the ARENA software, in which it was observed that the weighing activities are where the queues were formed, this will cause a delay in the download. As possible solutions, it is recommended to senior management that a proper verification of the operation of the weighing equipment be carried out in order to unload goods more quickly. In addition, a replacement weighing equipment must be purchased. With this change, it would be possible to reduce inefficiencies or cost overruns caused by download delays, which are reducing the fluidity of the goods and the competitiveness of the organization

To assure the opacity, an optimal problem is to retain the occurring events sequences as many as possible. Contrary to such problem, the other optimal problem is to preserve the minimal occurring events sequences. In the paper, based on the choosing cost, an optimal opacity-enforcing model is presented to minimize the sum of discount choosing cost, which is not only to preserve the opacity, but also to retain the secret to the maximum. To solve the model, two scenarios on opacity are considered. For the two scenarios, some algorithms are presented to achieve the optimal solution for the model by using the method of dynamic programming. And then, the solutions produced by the algorithms are proved correctly by theoretical proof. Finally, some illustrations for the algorithms are given.

This paper presents an optimized energy management strategy for Li-ion power batteries used on electric vehicles (EVs) at low temperatures. Under low-temperature environments, EVs suffer a sharp driving range loss resulted from the energy and power capability reduction of the battery. Simultaneously, because of Li plating, battery degradation becomes an increasing concern as temperature drops. All these factors could greatly increase the total vehicle operation cost. Prior to battery charging and vehicle operating, preheating battery to a battery-friendly temperature is an approach to promote energy utilization and reduce total cost. Based on the proposed LiFePO4 battery model, the total vehicle operation cost under certain driving cycles is quantified in the present paper. Then given a certain ambient temperature, a target temperature of preheating is optimized under the principle of minimizing total cost. As for the preheating method, a liquid heating system is also implemented on an electric bus. Simulation results show that the preheating process becomes increasingly necessary with a decreasing ambient temperature; however, the preheating demand declines as driving range grows. Vehicle tests verify that the preheating management strategy proposed in this paper is able to save total vehicle operation cost.

The advanced industrial systems are characterized by complex event-driven processes. The specification and computer analysis are illustrated by the common problem of a servo drive with the PWM controller. A mathematical model from the class of Cauchy problems with non-liner control logic is presented to resolve the task discussed. The designed model is performed in two stages: the mathematical model specification (modeling) and implementation (simulation). Two approaches to make a model specification are considered, the first one is the traditional structural technique, and the advanced method is based on the finite state machine paradigm. Structural and finite state software models are built in progressive simulation tools. Computer implementation of the software models of hybrid systems is performed using platform built-in libraries with numerical methods and algorithms for correct event detection. The results of computational experiments for transient processes of phase variables are qualitatively identical, that proves the adequacy of the developed models. The algorithm for asymptotic event detection is considered in detail. This technique demonstrated the best results by the one-sidedness criterion, which is explained by using the author’s algorithm for the integration step determining and the event function dynamics monitoring in the vicinity of the event limits.

Bus operators around the world are facing the transformation of their fleets from fossil-fuelled to electric buses. Two technologies prevail: Depot charging and opportunity charging at terminal stops. Total cost of ownership (TCO) is an important metric for the decision between the two technologies, however, most TCO studies for electric bus systems rely on generalised route data and simplifying assumptions that may not reflect local conditions. In particular, the need to re-schedule vehicle operations to satisfy electric buses’ range and charging time constraints is commonly disregarded. We present a simulation tool based on discrete-event simulation to determine the vehicle, charging infrastructure, energy and staff demand required to electrify real-world bus networks. These results are then passed to a TCO model. A greedy scheduling algorithm is developed to plan vehicle schedules suitable for electric buses. Scheduling and simulation are coupled with a genetic algorithm to determine cost-optimised charging locations for opportunity charging. A case study is carried out in which we analyse the electrification of a metropolitan bus network consisting of 39 lines with 4748 passenger trips per day. The results generally favour opportunity charging over depot charging in terms of TCO, however, under some circumstances, the technologies are on par. This emphasises the need for detailed analysis of the local bus network in order to make an informed procurement decision.

: Tunnel instability and lining integrity are intimately tied to the creep properties of the surrounding rock. The acquisition of rock mass creep parameters is critical in ascertaining the appropriate timing for lining construction. Nevertheless, creep tests on rock specimens conducted in a controlled setting cannot be straightforwardly extrapolated to rock mass creep analysis. This study performs laboratory-based creep tests on mudstone samples and establishes the corresponding creep constitutive model. Integration of a Mohr-Coulomb element with the Burgers model in series serves to characterise the yield creep behaviour of the rock mass. Additionally, the research outlines a series of 100 orthogonal experiments utilising randomised creep parameters, and formulates a GA-BP neural network inversion model. Employing long-term deformation measurements from the crown and sidewalls of the project site, this study deduces the creep parameters of the mudstone in the investigated tunnel section and examines the prolonged deformation traits of the surrounding rock. Drawing on the deformation traits of the surrounding rock and the forces impacting the primary support and lining structures, this paper evaluates the earliest and latest viable lining casting periods and pinpoints an optimal timing interval for lining implementation. The methodologies employed herein can serve as a benchmark for analogous endeavors internationally.

Visual object tracking is an important technology in camera based sensor networks, which has a wide range of practicability in auto drive system. A transformer is a deep learning model that adopts the mechanism of self-attention, and it differentially weights the significance of each part of the input data. It has been widely applied in the field of visual tracking. Unfortunately, the security of the transformer model is unclear. It makes such transformer-based applications be exposed to security threats. In this work, the security of the transformer model is investigated with the important component of autonomous driving, visual tracking. Such deep-learning-based visual tracking is vulnerable to adversarial attacks, so adversarial attacks are implemented as the security threats to conduct the investigation. First, adversarial examples are generated on top of video sequences to degrade tracking performance, and the frame-by-frame temporal motion is taken into consideration when generating perturbations over the predicted tracking results. Then, the influence of perturbations on performance is sequentially investigated and analyzed. Finally, numerous experiments on OTB100, VOT2018, and GOT-10k data sets demonstrate that the executed adversarial examples are effective on the performance drops of the transformer-based visual tracking.

To investigate the variation patterns of spectral characteristics of main branches in fruit trees due to uneven distribution of growth, pruning, and fruit quality, as well as to determine the optimal fruit vi-bration harvesting, a vibration differential model of tree branches was constructed using the concen-trated mass method. A three-dimensional model of the fruit tree was built using finite element software. The spectral characteristics of each branch were obtained using the linear sweep frequency method. The method of changing the mass of each branch was employed to simulate the variation in branch mass. The frequency of occurrence, mean acceleration, and coefficient of variation of the inherent frequencies were used as evaluation indicators. The results revealed that when the vibration frequency of the fruit tree was at the peak frequency in the tree trunk spectrum, the frequency of occurrence and mean accel-eration were optimal. And the coefficient of variation of branch acceleration was less than 1, indicating a moderate dispersion. Additionally, reducing the branch mass resulted in an increase in the peak fre-quency of the tree trunk spectrum. Therefore, the growth, pruning, and uneven distribution of the branches in fruit trees can alter the spectral characteristics. When harvesting fruit by vibration, selecting the peak frequency in the trunk vibration spectrum as the excitation force frequency yields the best fruit detachment.

Large-scale hydrological modeling in China is challenging given the sparse meteorological stations and large uncertainties associated with atmospheric forcing data.Here we introduce the development and use of the China Meteorological Assimilation Driving Datasets for the SWAT model (CMADS) in the Heihe River Basin(HRB) for improving hydrologic modeling, by leveraging the datasets from the China Meteorological Administration Land Data Assimilation System (CLDAS)(including climate data from nearly 40000 area encryption stations, 2700 national automatic weather stations, FengYun (FY) 2 satellite and radar stations). CMADS uses the Space Time Multiscale Analysis System (STMAS) to fuse data based on ECWMF ambient field and ensure data accuracy. In addition, compared with CLDAS, CMADS includes relative humidity and climate data of varied resolutions to drive hydrological models such as the Soil and Water Assessment Tool (SWAT) model. Here, we compared climate data from CMADS, Climate Forecast System Reanalysis (CFSR) and traditional weather station (TWS) climate forcing data and evaluatedtheir applicability for driving large scale hydrologic modeling with SWAT. In general, CMADS has higher accuracy than CFRS when evaluated against observations at TWS; CMADS also provides spatially continuous climate field to drive distributed hydrologic models, which is an important advantage over TWS climate data, particular in regions with sparse weather stations. Therefore, SWAT model simulations driven with CMADS and TWS achieved similar performances in terms of monthly and daily stream flow simulations, and both of them outperformed CFRS. For example, for the three hydrological stations (Ying Luoxia, Qilian Mountain, and ZhaMasheke) in the HRB at the monthly and daily Nash-Sutcliffe efficiency ranges of 0.75-0.95 and 0.58-0.78, respectively, which are much higher than corresponding efficiency statistics achieved with CFSR (monthly: 0.32-0.49 and daily: 0.26 – 0.45). The CMADS dataset is available free of charge and is expected to a valuable addition to the existing climate reanalysis datasets for deriving distributed hydrologic modeling in China and other countries in East Asia.

In this note, I analyze the data generated by M. Fodje's (2013) simulation programs "epr-simple" and "epr-clocked". They were written in Python and published on Github. Inspection of the program descriptions shows that they make use of the detection-loophole and the coincidence-loophole respectively. I evaluate them with appropriate modified Bell-CHSH type inequalities: the Larsson detection-loophole adjusted CHSH, and the Larsson-Gill coincidence-loophole adjusted CHSH (NB: its correctness is conjecture, we do not have proof). The experimental efficiencies turn out to be approximately eta = 81% (close to optimal) and gamma = 55% (far from optimal). The observed values of CHSH are, as they should be, within the appropriately adjusted bounds. Fodjes' detection-loophole model turns out to be very, very close to Pearle's famous 1970 model, so the efficiency is close to optimal. The model has the same defect as Pearle's: the joint detection rates exhibit signaling. Fodje's coincidence-loophole model is actually a clever modification of his detection-loophole model. Because of this, however, it cannot lead to optimal efficiency.

The proximal part of the colon offers opportunities to prolong the absorption window following oral administration of a drug. In this work, we used computer simulations to understand how the hydrodynamics in the proximal colon might affect the release from dosage forms designed to target the colon. For this purpose, we developed and compared three different models: a completely-filled colon, a partially-filled colon and a partially-filled colon with a gaseous phase present (gas-liquid model).The highest velocities of the liquid were found in the completely-filled model, which also shows the best mixing profile, defined by the distribution of tracking particles over time. No significant differences with regard to the mixing and velocity profiles were found between the partially-filled model and the gas-liquid model. The fastest transit time of an undissolved tablet was found in the completely-filled model. The velocities of the liquid in the gas-liquid model are slightly higher along the colon than in the partially-filled model. The filling level has an impact on the exsisting shear forces and shear rates, which are decisive factors in the development of new drugs and formulations.

Crop production in Afghanistan suffers from limited phosphorus (P) availability, which severely hinders national agriculture sustainability. This present study hypothesized that deep fertilizer placement could significantly enhance uptake of immobile P and thus tissue P accumulation and crop yield. A two-year pot experiment growing two maize (Zea mays) hybrid cultivars (Xida-789 and Xida-211) was therefore conducted to test these hypotheses under three contrasting fertilizer placement methods (broadcast, side band, and deep band). In doing so, P concentrations in both maize tissues and soils were compared at 45, 60, and 115 days after sowing (DAS) under nine combinations of nitrogen (N) and P fertilizer rates (kg ha-1: N112P45, N112P60, N112P75, N150P45, N150P60, N150P75, N187P45, N187P60, N187P75). Results have shown that deep band placement significantly increased P uptake efficiency, leading to greater P concentration and accumulation in maize tissues compared to other two fertilization methods. This improved P uptake was attributed to several factors associated with deep placement, including reduced P fixation, enhanced root access to P, and moisture availability for P uptake. Additionally, deep band placement combined with higher N application rates (N187 and N150) further enhanced plant P uptake by promoting P availability and utilization mechanisms. Deep band placement also resulted in significantly higher total soil P, Olsen-P, and P use efficiency than broadcast and side band methods, indicating a more efficient P fertilization strategy for maize that can improve growth and yield. This present study also found positive correlations between P concentration in plant organs and soil Olsen-P, highlighting the importance of adequate soil P levels for optimal plant growth. Overall, our results have showed that deep band fertilizer placement emerged as a superior strategy for enhancing P uptake efficiency, utilization, and maize productivity compared to broadcast and side band placement. This approach can potentially optimize P fertilizer utilization, improve maize growth and yield, while minimizing environmental P losses associated with broadcast applications.

All types of sports are potential application scenarios for automatic and real-time visual object and event detection. In rink hockey, the popular roller quad skate variant of hockey team sports, it is of great interest to automatically track player’s movements and positions, player’s sticks and, also, making other judgments, such as being able to locate the ball. In this work, we introduce a real-time pipeline composed by an object detection model, created specifically for rink hockey games, followed by a knowledge-based event detection module. Even in the presence of occlusions and quick motions, our deep learning object detection model effectively identifies and tracks, in real-time, important visual elements such as: ball; players; sticks; referees; crowd; goalkeeper; and goal. Using a curated dataset composed by a collection of videos of rink hockey, comprising 2525 annotated frames, we trained and evaluated the algorithm performance and compare it to state of the art object detection techniques. Our object detection model, based on YOLOv7, presents a global accuracy of 80%, and presents a good performance in terms of accuracy and speed, according to our results, making it a good choice for rink hockey applications. In our initial tests, the event detection module successfully detected one important event type in rink hockey games, the occurrence of penalties.

Conditions under which compositions of component systems form a well-defined system-of-systems here are formulated at a fundamental level. Statement of what defines a well-defined composition and sufficient conditions guaranteeing such a result offers insight into exemplars that can be found in special cases such as differential equation and discrete event systems. For any given global state of a composition, two requirements can be stated informally as: 1) the system can leave this state, i.e., there is at least one trajectory defined that starts from the state, and 2) the trajectory evolves over time without getting stuck at a point in time. Considered for every global state, these conditions determine whether the resultant is a well-defined system and if so, whether it is non-deterministic or deterministic. We formulate these questions within the framework of iterative specifications for mathematical system models that are shown to be behaviorally equivalent to the Discrete Event System Specification (DEVS) formalism. This formalization supports definitions and proofs of the afore-mentioned conditions. Implications are drawn at the fundamental level of existence where the emergence of a system from an assemblage of components can be characterized. We focus on systems with feedback coupling where existence and uniqueness of solutions is problematic.

The article presents a new approach to the analysis of the stability of automatic systems with discrete links.In almost all modern automatic control systems, there are links that break signals in time. These are power controlled switches - transistors or thyristors operating in a pulsed mode and digital links in regulators.Time discretization significantly affects the stability of processes in the automatic control system. The theoretical analysis of such systems is rather complicated and requires a significant change in engineering approaches to analysis. In connection with the improvement of digital controllers and a significant increase in their performance, in recent years this problem has practically not been remembered. However, its mathematical "content" has not changed since the 80s of the 20th century, when discreteness began to play a major role among the problems hindering progress in automatic control systems, in terms of the transition to digital systems.In this paper, a new approach is proposed, which consists in interpreting the sampling operation by a link with the proposed frequency characteristic, which determines the suppression of input high-frequency signals. This link greatly simplifies engineering calculations and demonstrates the new capabilities of sampling systems. These possibilities include the rational distribution of digitalization resources - the number of bits and the sampling interval between the regulator channels, depending on the frequency range of the efficiency of these channels. Theoretical statements have been verified and confirmed by simulation. It is shown how this approach makes it possible to formulate new principles of construction of seemingly well-known controllers - PID controllers and variable structure systems (VSS).

Medical image segmentation is a critical step in various healthcare applications, aiding in diagnosis, treatment planning, and disease monitoring. In this study, we investigate the efficacy of a segmentation approach based on the 2D wavelet transform. Leveraging a dataset comprising 10 diverse medical images, we evaluate the performance of our segmentation method using three key metrics: accuracy, precision, and recall. Our findings demonstrate that the proposed approach enhances segmentation accuracy, offering promising results compared to existing methods. By harnessing the multi-resolution feature extraction capabilities of the 2D wavelet transform, our method achieves improved delineation of medical image structures, paving the way for more accurate and efficient healthcare interventions.

The paper presents an error model of a measurement chain containing link performing discrete wavelet transform algorithm which is most often the last stage of measurement signal processing. The goal is to determine the uncertainty budget of the input quantities of the wavelet transform algorithm. The error model takes into account parts of analog, analog-to-digital and digital processing, describing the properties of subsequent fragments of the chain using their transmittance and processing functions. The proposed model enables the description of both deterministic and non-deterministic properties of signal errors. The proposed model was validated using an example measurement chain created for this purpose.

The accompanying mechanical behavior during the flow of ore-rock dispersion is an important factor leading to the instability and failure of the orepass in the ore storage section. It is of great significance to accurately understand the instability characteristics of the well wall in the ore section, during the ore drawing process by understanding the flow characteristics and internal mechanical transfer mechanism of ore-rock dispersion. Using the discrete element method to analyze the flow characteristics, contact compactness, stress distribution characteristics and contact force probability distribution of the ore-rock dispersion, it realizes the quantitative characterization of the damage degree of ore-rock flow, and reveals the damage mechanism of mine storage wall. The results show that: (1) The friction between ore-rock particles, between ore-rock and well wall is an important reason for the change of the macroscopic flow form of ore-rock from “one” to “V” type. (2) The compactness and stress concentration of ores are increasing with the increase of storage depth, which is shown that with the increase of storage depth, the greater the probability of well wall instability. (3) The probability distribution of contact force strength between particles decreases exponentially in the whole process of ore drawing, in which the strong chain plays a major role in the stability of the dispersion system. (4) The number of overpressure and the overpressure coefficient can be used to quantitatively characterize the wall damage degree under the action of ore-rock flow. The dynamic side pressure decreases periodically in the form of exponential fluctuations, and the dynamic load formed by the ore-rock flow mainly played a role in the lower area of the ore storage section.

Cloudburst is one of the most devastating and frequently occurring natural hazardous events in Indian Himalayan region. Localized deep cumulus convective clouds have a capability of giving enormous amount of rainfall over a limited horizontal area, within a short span of time. Whenever, such events occur, lead to flash floods causing landslides, house collapses, dislocation of traffic, and human casualties on a large scale. Therefore, it is necessary to predict the cloudburst inundation zones accurately to avoid damage associated with them. For this, high resolution Digital Elevation Model generated from CartoSat-1 (Stereo pair) were integrated in MIKE 11 Hydrodynamic 1D model to generate longitudinal profile of the study area and to find water level, peak discharge, flow velocity, flow width at different reaches along the Asi ganga and Bhagirathi river, to know the Cloudburst flood inundation scenario. On 3^rd August 2012 one of the major Cloudburst event occurred in Asi Ganga Valley in Indian Himalayan region which was considered for simulation of hydrodynamic model. For a Cloudburst event, 100 mm/hr rainfall was considered for the simulation of the hydrodynamic model. It is observed that the discharge rise from 50 m³/s to 549.164 m³/s (an abrupt increase of about 10 times) within 1 hr at Sangamchetty in Asiganga river and at Joshiyara area rise from 600 m³/s to 3378.69 m³/s (an abrupt increase of about 5 times) within 4 hr in Bhagirathi river. Similarly the water level rises around 3 m and 6m in Asi Ganga and Bhagirathi rivers respectively. Flash Flood inundation areas due to Cloudburst on 3^rd August 2012 were demarcated from the simulation results in GIS environment.

This paper proposed an optimal tracking control scheme through adaptive dynamic programming(ADP) for a class of partially unknown discrete-time nonlinear systems based on radial basis function neural network(RBF-NN). In order to acquire the unknown system dynamics, we use two RBF-NNs, the one is used to construct the identifier, and the another is used to directly approximate the steady-state control input, where a novel adaptive law is proposed to update neural network weights. While the optimal feedback control and the cost function are derived via feedforward neural networks approximating, it is proposed to regulate the tracking error, the critic network and the actor network are then trained online to obtain the solution of the associated Hamilton–Jacobi–Bellman (HJB) equation being built under the ADP framework. Simulations verify the effectiveness of the optimal tracking control technique using the neural networks.

This article presents a concise survey of basic discrete and semi-discrete nonlinear models which produce two- and three-dimensional (2D and 3D) solitons, and a summary of main theoretical and experimental results obtained for such solitons. The models are based on the discrete nonlinear Schrödinger (DNLS) equations and their generalizations, such as a system of discrete Gross-Pitaevskii (GP) equations with the Lee-Huang-Yang corrections, the 2D Salerno model (SM), DNLS equations with long-range dipole-dipole and quadrupole-quadrupole interactions, a system of coupled discrete equations for the second-harmonic generation with the quadratic (χ⁽²⁾) nonlinearity, a 2D DNLS equation with a superlattice modulation opening mini-gaps, a discretized NLS equation with rotation, a DNLS coupler and its PT-symmetric version, a system of DNLS equations for the spin-orbit-coupled (SOC) binary Bose-Einstein condensate, and others. The article presents a review of basic species of multidimensional discrete modes, including fundamental and vortex solitons, their bound states, gap solitons populating mini-gaps, symmetric and asymmetric solitons in the conservative and PT-symmetric couplers, cuspons in the 2D SM, discrete SOC solitons of the semi-vortex and mixed-mode types, 3D discrete skyrmions, and some others.

This paper represents the first investigation into the consensus problem of linear time-varying multi-agent systems utilizing an event-triggered communication scheme. First, a general event-triggered consensus control scheme is proposed for a general category of linear time-varying multi-agent systems. Under some suitable assumptions, it is demonstrated that all agents’ states will converge exponentially, with Zeno behaviour being ruled out. Second, the consensus problem in a network of linear time-varying multi-agent systems with a spanning tree is investigated using the proposed control strategy. It demonstrates that the consensus issue for the specified system can be reformulated as a stabilization problem for an error system through a time-varying linear transformation. Then, the event-triggered consensus problem is just a special instance of the general event-triggered consensus problem mentioned above. Finally, to illustrate the efficacy of the event-triggered method proposed in this study, simulation results are shown.

Aiming at the problems of high cost of manual pulling, low reliability of existing pulling devices, high breaking rate and high leakage rate in the process of cotton stalk reuse after leaving the field in Xinjiang cotton area, a loosening soil and root-cutting cotton stalk pulling the setting machine has been researched and designed, established the root-cutting force model, calculated and optimized the key parameters of the V-shaped root-cutting knife, and determined the range of slide cutting angle, cutting edge angle and soil entry angle. The shoveling process simulation of V-shaped root-cutting knife and root-soil complex was constructed, and the working mechanism of V-shaped root-cutting knife was clarified. In order to verify the reliability and operation performance of the V-shaped root-cutting knife, the slide cutting angle, cutting edge angle and soil entry angle were used as the test factors, the response surface test of three-factors and three-levels was carried out with root-breaking force and mean value of the cutting resistance as the test index. The test results were analyzed by variance analysis, the significant of influencing factors on the root-breaking force are in a descending order as: slide cutting angle, cutting edge angle, soil entry angle; the degree of influence on the mean value of the cutting resistance are as follows: slide cutting angle, soil entry angle, cutting edge angle. In order to make the V-shaped root-cutting knife achieve the optimal working state, the parameters of the test index were optimized, and the optimal design parameters of the V-shaped root-cutting knife were set as follows: the slide cutting angle was 48.3°, cutting edge angle was 43.4°, and soil entry angle was 26.2°. The field uprooting test showed that the average pass rate of root breakage was 94.8% and the average pull-out rate of the cotton stalk was 93.2%. This study provides theoretical guidance for the development of root breaking mechanism of cotton straw harvester.

LCK, a member of the Scr kinase family, is a non-receptor tyrosine kinase involved in immune cell activation, antigen recognition, tumor growth, and cytotoxic response. The enzyme has usually been linked to T lymphocyte activation upon antigen recognition. Lck activation is central to CD4, CD8, and NK activation. However, recently, it has become clearer that activating the enzyme in CD8 cells can be independent of antigen presentation and enhance cytotoxic response. The role of Lck in NK cytotoxic function has been controversial in a similar fashion as the role of the enzyme in CAR T cells. Inhibition of Lck has been mainly to treat hematologic malignancies with a high rate of success. They may be useful in treating other tumor types, and they may be useful to prevent cell exhaustion. New, more selective inhibitors have been documented, and they have shown inter-esting activities not only in tumor growth but in the treatment of autoimmune diseases, asthma, and graft vs host disease. Drug repurposing and bioinformatics can aid in solving several unsolved is-sues on the role of Lck in cancer. In summary, the role of Lck in immune response and tumor growth is not a simple event and requires more research.

In this study, regression analyses were used to find a relationship between the rain gauge rainfall rate R and radar reflectivity Z for the urban catchment of the Służewiecki Stream in Warsaw, Poland. Rainfall totals for 18 events which were measured at two rainfall stations were used for these analyses. Various methods for determining calculational values of radar reflectivity in reference to specific rainfall cells with 1-km resolution within an event duration were applied. The influence of each of these methods on the Z-R relationship was analyzed. The correction coefficient for data from the SRI (Surface Rainfall Intensity) product was established, in which the values of rainfall rate are calculated based on parameters a and b determined by Marshall and Palmer. Relatively good agreement between measured and estimated rainfall totals for the analyzed events was obtained using the Z-R relationships as well as the correction coefficient determined in this study. Rainfall depths estimated from radar data for two selected events were used to simulate flow hydrographs in the catchment using the SWMM (Storm Water Management Model) hydrodynamic model. Different scenarios were applied to investigate the stream response to changes in rainfall depths, in which the data both for 2 existing as well as 64 virtual rain gauges assigned to appropriate rainfall cells in the catchment were included.

Estimating ground surface PM2.5 with fine spatiotemporal resolution is a critical technique for exposure assessments in epidemiological studies of its health risks. Previous studies have utilized monitoring, satellite remote sensing or air quality modeling data to evaluate the spatiotemporal variations of PM2.5 concentrations, but such studies rarely combined these data simultaneously. We develop a three-stage model to fuse PM2.5 monitoring data, satellite-derived aerosol optical depth (AOD) and community multi-scale air quality (CMAQ) simulations together and apply it to estimate daily PM2.5 at a spatial resolution of 0.1˚ over China. Performance of the three-stage model is evaluated using a cross-validation (CV) method step by step. CV results show that the finally fused estimator of PM2.5 is in good agreement with the observational data (RMSE = 23.00 μg/m^3 and R2 = 0.72) and outperforms either AOD-retrieved PM2.5 (R2 = 0.62) or CMAQ simulations (R2 = 0.51). According to step-specific CVs, in data fusion, AOD-retrieved PM2.5 plays a key role to reduce mean bias, whereas CMAQ provides all-spacetime-covered predictions, which avoids sampling bias caused by non-random incompleteness in satellite-derived AOD. Our fused products are more capable than either CMAQ simulations or AOD-based estimates in characterizing the polluting procedure during haze episodes and thus can support both chronic and acute exposure assessments of ambient PM2.5. Based on the products, averaged concentration of annual exposure to PM2.5 was 55.75 μg/m3, while averaged count of polluted days (PM2.5 > 75 μg/m3) was 81, across China during 2014. Fused estimates will be publicly available for future health-related studies.

Using numerical simulation method, combined with the pre-branch material characteristics of the measurement results, based on the LS-DYNA software to simulate the branch crushing process, the analysis found that not only the existence of the cutter cutting in the branch destruction process, there is also the cutting of the opposing surface of the bending fracture, with the increase in the speed of the knife rollers, the cutter cutting resistance increases, the cutting resistance reaches 2690 N when the cutter at 2500 r/min, the cutter cutting resistance is the smallest in the cutting simulation at the blade angle of 55°, the blade angle is more suitable for the cutting of the branch. In the cutting simulation under different cutting edge angles, the cutting resistance of the tool is the smallest when the edge angle is 55°, which is 1860 N. This edge angle is more suitable for branch crushing and cutting. Using Fluent software to analyse the airflow characteristics of the crushing device, it was found that with the increase of the knife roller speed, the inlet flow and negative pressure of the crushing chamber are increasing, the speed of 2500 r/min, the inlet flow and negative pressure were 1.92 kg/s and 37.16 Pa, which will be conducive to the feeding of branches, but the speed is too high and it will also lead to the enhancement of the vortex of part of the area in the crushing device, which will affect the feeding of branches and the throwing of the crushed material. affect the feeding of branches and the throwing out of crushed materials, and finally determined the speed range of the crushing knife roller is 1800 ~ 2220 r/min. Based on ANSYS/Model module modal analysis of the crushing knife roller, the knife roller of the first six orders of the intrinsic frequency and vibration pattern, crushing knife roller of the lowest order of the modal intrinsic frequency of 137.42 Hz, much larger than the crushing knife roller operating frequency of 37 Hz, the machine will not resonate during operation.

This paper aims to address the exponential stability and stabilization problems for a class of delayed nonlinear Markov jump systems under randomly occurring Denial-of-Service (DoS) attack and packet loss. Firstly, the stochastic characteristics of DoS attack and packet loss are depicted by the attack success rate and packet loss rate. Secondly, a Period Observation Window (POW) method and a hybrid-input strategy are proposed to compensate for the impact of DoS attack and packet loss on the system. Thirdly, A Dynamic Event-triggered Mechanism (DETM) is introduced to save more network resources and ensure the security and reliability of systems. Then, by constructing a general common Lyapunov functional and combining with the DETM and other inequality analysis techniques, the less conservative stability and stabilization criteria for the underlying systems are derived. In the end, the effectiveness of our result is verified through a numerical example.

To conduct high-precision and high-resolution numerical simulation of complex flow structures in turbomachinery, a high-order finite volume weighted essentially non-oscillatory (WENO) scheme for the large eddy simulation (LES) is improved and embedded into the three-dimensional viscous unsteady CFD solver NUAA-Turbo. Firstly, the spectral characteristics and unsteady convergence of the improved WENO scheme are studied. Compared with the classical WENO scheme, the improved WENO scheme has better dissipation and dispersion characteristics and faster convergence speed. Then, the coefficient CW of the Wall-Adapting Local Eddy-viscosity (WALE) model is calibrated by decaying homogeneous isotropic turbulence (DHIT) test case. Finally, the scheme is applied to conduct high-precision LES calculations of turbulent boundary layer flow, supersonic compression corner flow, and low-pressure turbine cascade separation flow. The numerical simulation results show that this WENO scheme has excellent shock wave capture ability and turbulence resolution and can capture more flow field details with less mesh size, greatly reducing the computational cost and laying a foundation for large-scale engineering application of LES.

The Impact of Event Scale-Revised (IES-R) is the most popular measure of post-traumatic stress disorder (PTSD), which has been recently validated in Arabic. This instrumental study aimed to determine optimal cutoff scores of the IES-R and its subscales in Arab samples of psychiatric patients (N = 168, 70.8% females) and healthy adults (N = 992, 62.7% females) from Saudi Arabia during the COVID-19 pandemic as an ongoing collective traumatic event. Based on a cutoff score of 14 of the Depression Anxiety Stress Scale 8-items (DASS-8), receiver operator curve (ROC) analysis revealed two optimal points of 39.5 and 30.5 for the IES-R in the samples (area under the curve (AUC) = 0.86 & 0.91, p values = 0.001, 95% CI: 0.80-0.92 & 0.87 to 0.94, sensitivity = 0.85 & 0.87, specificity = 0.73 & 0.83, Youden index = 0.58 & 0.70, respectively). Different cutoffs were detected for the six subscales of the IES-R, with numbing and avoidance expressing the lowest predictivity for distress. Meanwhile, hyperarousal followed by the irritability expressed stronger predictive capacity for distress than all subscales in both samples. In path analysis, pandemic-related irritability resulted from direct and indirect effects of key PTSD symptoms (intrusion, hyperarousal, and numbing). Irritability contributed to traumatic symptoms of sleep disturbance in both samples while the opposite was not true. The findings suggest usefulness of the IES-R at a score of 30.5 for detecting adults prone to trauma related distress, with higher scores needed for screening in psychiatric patients. Various PTSD symptoms may induce dysphoric mood, which represents a considerable burden that may induce circadian misalignment and more noxious psychiatric problems/ co-morbidities (sleep disturbance) in both healthy and diseased groups.

Aiming at the APF algorithm in the collision-free path planning and LQR algorithm tracking process, there are problems such as unreachable target points, local optimization, low safety coefficient of autonomous lane change collision avoidance tracking control and poor stability, this paper proposes an optimized APF method, which solves the defects of the traditional artificial potential field method by adding the distance adjustment factor, the safety distance protection model and the sub-target virtual potential field, and at the same time generates the autonomous driving car collision-free path. The path tracking control system is designed based on feed-forward control and DLQR controller, in which the vehicle dynamics state space model is used as the basis, the model is linearized and discretized, and the corresponding constraint variables are set to minimize the control command objective cost function under the constraints, and the objective of the DLQR controller.The objective is to accurately determine the steering angle in order to achieve path tracking and eliminate the lateral error between the reference path and the actual path caused by external interference.Finally, the simulink model and CarSim parameter configuration joint simulation platform are built, and different obstacle scenarios and different speeds are selected for collision-free path planning and tracking motion simulation research and analysis, which verifies the accuracy and practicability of the algorithm, and shows more obvious advantages in obstacle avoidance ability, path smoothing degree, and collision avoidance safety stability. The ideas proposed in this paper are expected to be further extended and popularized to actual path planning problems, which has important practical significance and engineering value.

The development of virtual models has recently been generalized in the field of complex mechatronic product design. Virtual models have various advantages such as: the systemic design approach, most often parametric, the implementation of extreme, expensive, and perhaps risky scenarios for the operation of the designed product, the testing of a large number of variants, etc. The main contribution of the paper consists of the development of an interface family called DeSeRoI (Dedicated Serial Robot Interface). Each member of the interface family integrates Simulink libraries and is created by the programmer in accordance with user requests. It follows that the interfaces composing the DeSeRoI family can have different levels of complexity depending on user needs and constraints. We are implementing a MATLAB-based application with user-provided configuration for serial robots in order to automatically visualize the kinematics data and simulation. Within MATLAB, two scripts are created responsible for generating Simulink models that compute the kinematics problem. One of the scripts (Main) saves information about the configuration of the robot and the other one (Generates_Simulink_Models) computes the kinematics problem. Different types of functions (software module) have been developed. Using these functions, the Simulink models result as modular entities. The automated approach eases user’s interaction and understanding of serial robots’ kinematics by performing the simulation procedure in a shorter amount of time and with higher efficiency, assuring a precise outcome despite the chosen parameters.

This study examines geothermal energy extraction potential from the basement rock within the Denver-Julesburg (DJ) Basin, focusing on flow performance and heat extraction efficiency of different geothermal well configurations. It specifically compares U-Shaped, V-Shaped, Inclined V-Shaped, and Pipe-in-Pipe configurations against Enhanced Geothermal Systems (EGS) setups. Through numerical modeling, we evaluated the thermal behavior of these systems under various operational scenarios and fracture conditions. Results suggest that while closed-loop systems offer moderate temperature increases, EGS configurations show substantial potential for high-temperature extraction. This underscores the importance of evaluating well configurations in complex geological settings like the DJ Basin. The insights from this study aid in strategic geothermal energy planning and development, marking significant advancements in geothermal technology and setting a foundation for future explorations and optimizations.

Driven by the growing interest of the scientific community and the proliferation of research in this field, cranial implants have seen significant advancements in recent years regarding design techniques, structural optimisation, appropriate material selection and fixation system method. Custom implants not only enhance aesthetics and functionality but are also crucial for achieving proper biological integration and optimal blood irrigation, critical aspects in bone regeneration and tissue health. This research aims to optimize the properties of implants designed from triply periodic minimal surface structures. The gyroid architecture is employed for its balance between mechanical and biological properties. Experimental samples were designed varying three parameters of the surface model: cell size, isovalue and shape factor. Computational simulation tools were used for determining the relationship between those parameters and the response variables: the surface area, permeability, porosity and Young modulus. These tools include computer aided design, finite element method and computational fluid dynamics. With the simulated values, the corresponding regression models were fitted. Using the NSGA-II, a multi-objective optimisation was carried out, finding the Pareto set which includes surface area and permeability as targets, and fulfil the constraints related with the porosity and Young modulus. From these non-dominated solutions, the most convenient for a given application was chosen, and an optimal implant was designed, from a patient computed tomography scan. An implant prototype was additively manufactured for validating the proposed approach.

This study examines the feasibility of using event-related potentials (ERPs) obtained from electroencephalographic (EEG) recordings as biomarkers for long-term memory item classification. Previous studies have identified old/new effects in memory paradigms associated with explicit long-term memory and familiarity. Recent advancements in convolutional neural networks (CNNs) have enabled the classification of ERP trials under different conditions and identification of features related to neural processes at the single-trial level. We employed this approach to compare three CNN models with distinct architectures using experimental data. Participants (N = 25) performed an association memory task while recording ERPs that were used for training and validation of the CNN models. The EEGNET-based model achieved the most reliable performance in terms of precision, recall, and specificity compared with the shallow and deep convolutional approaches. The classification accuracy of this model reached 70% for known items and 62% for unknown items. Good overall accuracy requires a trade-off between recall and specificity and depends on the architecture of the model and the dataset size. These results suggest the possibility of integrating ERP and CNN into online learning tools and identifying the underlying processes related to long-term memorization.

In this paper, we first discuss the research status and application progress of the finite element method and the smoothed particle method. By analyzing the advantages of the smoothed particle method and the finite element method, a new coupling algorithm, namely FEM-SPH algorithm, is proposed. By the method of comparison, it shows that finite element method and SPH method in the simulation of large deformation problems each have advantages and disadvantages, the finite element method smoothed particle coupling algorithm is effective to achieve the performance of high computational efficiency and can naturally simulate large deformation problems across. In the process of calculation, the large deformation unit can be freely into an algorithm to facilitate the calculation accuracy and efficiency of three methods of numerical simulation. Through the study found, FEM-SPH algorithm not only overcome the defect of smooth particle tensile instability, but also overcomes the problem of low efficiency of finite element computation. To further test the FEM-SPH algorithm has advantages in the practical engineering, we have carried out the actual test to the example of the super high speed collision, concluded that, since the target of most of the computational domain is always finite element, smoothed particle focused only in contact with the projectile and target of local area, particle number is not much, the whole calculation process just ten minutes, computational efficiency has been greatly improved, at the same time in the simulation of large deformation, the advantage is very obvious .This provides a criterion for the actual project, depending on the specific material deformation mode and choose a more appropriate conversion algorithm.

This study addresses the pressing need for flood extent and exposure information in data-scarce and vulnerable regions, with a specific focus on West Africa, particularly Senegal. Leveraging the Google Earth Engine (GEE) platform and integrating data from Sentinel-1 SAR, Global Surface Water, HydroSHEDS, Global Human Layer Settlement, and MODIS Land Cover, our primary ob-jective is to delineate flood extents and compare them with centennial flood-prone areas, offering a comprehensive assessment of exposure during the period from July to October 2022 across Sen-egal's 14 regions. The findings underscore a total inundation area of 2 951 square kilometers, impacting 297 142 people, 175 square kilometers of urban and 16 square kilometers of crops. Notably, August wit-nessed the largest flooded areas, reaching 780 square kilometers, constituting 0.40% of the coun-try's surface. Subsequent regions, including Saint-Louis, Ziguinchor, Fatick, and Matam, experi-enced varying extents of flooding, with August data showing a 1.34% overall overlap compared to centennial flood-prone areas derived from hydrological and hydraulic modeling. This low per-centage reveals the distinct purposes and natures of the two approaches (remote sensing and modeling), as well as their complementarity. Turning to flood exposure, August emerges as the most critical month, affecting 76 595 people (0.43% of the total population). Dakar, Diourbel, Thiès, and Saint-Louis regions bore substantial impacts, affecting 100 707, 57 648, 31 579, and 26 581 people, respectively. These findings emphasize the imperative for comprehensive disaster preparedness and mitigation efforts. The study provides a crucial national-scale perspective to guide Senegal's authorities in formulating effective flood management, intervention, and adaptation strategies.

This paper studies the duration patterns of unemployment spells for immigrants and the determinants of unemployment’s completion into one of a number of studied labour market states in Finland. We estimate a duration model for unemployment with competing risks of its terminating into employment, labour market training or economic inactivity. Taking into account the wide period of observation and opportunities to analyse processes of labour market integration during various periods of economic development in Finland, in combination with the individualistic character of the labour careers of immigrants, this research is beneficial owing to the many various findings concerning labour market integration of immigrants. The approach undertaken in this research has a dualistic “descriptive-dynamic” character under which integration is understood as a never-ending process, which is conditioned by a time period of long-term existence and a context of solitary action. We find that transitions out of unemployment spells have a cyclical character; after every new “cycle” in unemployment, the probability of terminating unemployment decreases further. We also find that ascriptive factors make sense in the process of job-placement of immigrants from unemployment. Therefore, the gender, education and age of immigrants, as well as the effect of the period in which first unemployment occurred, potentially predict transitions out of unemployment and further labour market integration of immigrants.

Different variants of thinning for discrete random variables are studied. The thinning procedure allows to introduce an analog of scale parameter for positive integer-valued random variables. Sufficient and necessary conditions for the existence of such a scale are given.

The majority of existing numerical simulations of the effect of bridge piers on water movement are based on a limited number of bridge piers at a laboratory scale. Furthermore, some 2D numerical simulations for actual bridge projects have deficiencies, including the use of overly large mesh sizes and an inadequate treatment of bridge piers. In this study, we compare three methods of structural mesh encryption, suspension mesh, and nonstructural mesh based on Delft3D, and apply the optimization scheme to a real bridge project. It is demonstrated that optimal results can be achieved by utilising a grid size comparable to the pier diameter (Dp) in the region away from the piers. In the vicinity of the piers, the grid cell size should be no larger than 1/9 Dp. The suspended grid technique (DD Boundary) can yield results consistent with those obtained using a full-area high-resolution grid, provided that the total number of grids can be reduced and the computational time is considerably reduced. In this study, the unstructured mesh (Deflt3D Flexible Mesh) scheme was unable to capture the oscillations of the wake flow behind the bridge piers. However, the application of the optimized scheme in bridge engineering has demonstrated its practical value. The findings of this study on mesh resolution and suspension mesh scheme can be applied to the Delft3D software and are also useful for other numerical simulation work.

In this paper we report a new development on the numerical model for aluminum-steam combustion. This model is based on diffusion flame of continuum regime and the thermal equilibrium between the particle and the flow field, which can be used to calculate the aluminum particle combustion model for two phase calculation conditions. The model prediction is in good agreement with the experimental data. A new type of vortex combustor was proposed for the combustion of aluminum and steam, and the mathematical model of the two phase reacting flow with in this combustor was established. The turbulence effects are modeled using the Reynolds Stress Model (RSM) with Linear Pressure-Strain approach, and the Eddy-Dissipation model is used to simulate the gas phase combustion. Aluminum particles are injected into the vortex combustor and form a swirling flow around the chamber and their trajectories are traced using the Discrete Phase Model (DPM). The simulation results show that the vortex combustor can achieve high efficient combustion of aluminum and steam. The influencing factors, such as the eccentric distance of the inlet of aluminum particles, particle size and steam inlet diameter, etc., are studied. The work described in this paper represents an attempt to the design of a vortex combustor in order to increase aluminum combustion efficiency.

The control of the fine particle emission from the coal-fired power plants has been a big challenge in china. Either the conventional electrostatic precipitator or the fabric filter has some shortages. The electrostatic precipitator is hard to keep high efficiency for the submicron particles. The fabric filter could keep high efficiency but with very high pressure drop. One solution is to combine them together. Several types of hybrid particle collectors (HPC) combining the electrostatic precipitator and the fabric filter together have been developed and applied in a number of projects in China. In recent years, numerical simulation technology has been widely used in the design of the collectors. In this paper, an unsteady numerical model is used to investigate the particle deposition characteristics in a scale hybrid collector. The fluid field is modeled by using incompressible Navier-Stokes equations with the RNG turbulence equations. The corona discharge is solved by using a finite volume method. The particle charging is modeled by using a filed-diffusing combined model. And an unsteady cake formation model is used to simulate the filtration process.

Frontal Polymerization (FP) is recognized as a polymer synthesis method capable of generating a highly localized and self-propagating exothermic front, considered a more rapid and energy-efficient manufacturing scheme for polymer-based fiber-reinforced composite materials. This study conducts a numerical investigation based on the Finite Element Method into the initiation and propagation of frontal polymerization of acrylamide. Utilizing Differential Scanning Calorimetry (DSC) experiments, the study phenomenologically tests the curing kinetics reactions of acrylamide (AM) in deep eutectic solvent (DES)-based polymerizable systems, thereby establishing a curing kinetics model for the autocatalytic reaction of polyacrylamide. By coupling the heat conduction diffusion equation of the frontal polymerization reaction, the Finite Element Method is employed to solve the reaction-diffusion model, aiming to explore the evolution of temperature and degree of curing during the manufacturing process. The results demonstrate the reliability and accuracy of the nth-order autocatalytic model in the study of curing kinetics for DES-based synthesis, indicating that the front propagation speed of the AM-based FP reaction is influenced by the activation temperature, duration of activation temperature, and initial temperature of the solution. Experimental validation of the polyacrylamide manufacturing through frontal polymerization corroborates the accuracy and reliability of the model predictions, providing a reference for the numerical model study and temperature control in the synthesis of acrylamide composite hydrogels via FP.

Originally developed to image the shadow region of the central black hole in Sagittarius A* and in the nearby galaxy M87, the Event Horizon Telescope (EHT) provides deep, very high angular resolution data on other AGN sources too. The challenges of working with EHT data have spurred the development of new image reconstruction algorithms. This work briefly reviews the status of the EHT and its utility for observing AGN sources, with emphasis on novel imaging techniques that offer the promise of better reconstructions at 1.3 mm and other wavelengths.

With the rapid development of intelligent logistics and network environment, it has become an urgent problem to efficiently and accurately handle and analyse the huge amount of uncertain decision information. Traditional decision making methods often fail to make the best use of complex and incomplete information, especially in the field of cyber defence. To address these problems, this paper introduces a new mathematical tool, discrete multidimensional Z-numbers (MZs), for expressing and dealing with uncertainty and reliability in network defence decisions. In this paper, we first introduce the synthesis method of discrete multidimensional Z-numbers, which allows us to consider the uncertainty and reliability of multi-source information in network defence strategy. Then, the hidden probability of MZs is calculated by using the hidden probability model, and the multidimensional Z+−number is proposed, which improves the expressiveness of the model in dealing with information uncertainty. Based on MZs, we define a variety of utility functions and build a multi-attribute group decision framework for network defence around these functions. The MZs provides a novel perspective for analysing and designing response strategies, especially against highly adaptive and covert attacks. The method in this paper is verified by a case of network security assessment of an intelligent logistics company. The results show that this method can significantly improve the accuracy and efficiency of decision making, demonstrating its unique advantages and wide application potential in network defence. This integrated discrete multidimensional Z-number group decision method is not only suitable for intelligent logistics decision making, but also provides an efficient decision support tool for network defence, which helps to improve the intelligence and adaptability of network security defence.

Urban rail transit hub platform is the most important area for passenger flow distribution. In order to calculate passenger flow volume in platform and evaluate platform service level during rush hours, this paper presents a method for modeling and simulation of passenger flow distribution in platform. Passenger flow distribution model (PFDM) is proposed based on the basic analysis and the superposition principle of passenger flow. Simulation design for PFDM is proposed by Anylogic, which contains simulation process and simulation model. Experiment results show that PFDM and simulation design are effective and accordant with the reality scenario, and the simulation precision is comparatively ideal. This research could provide a beneficial reference for train scheduling and operation management under the viewpoint of traffic safety and service level.

This article presents a new methodology for modeling and analyzing discrete dynamic systems through the construction of inverse algebraic models, giving rise to the Theory of Inverse Discrete Dynamical Systems. Key concepts such as inverse modeling, structural analysis in inverse algebraic trees, and the establishment of topological equivalences for the analytical transfer of properties between the canonical system and its inverted counterpart are introduced. Central theorems on homeomorphic invariance and topological transport are demonstrated, consolidating the validity of transferring cardinal attributes between both equivalent dynamic representations. As a concrete application, an alternative proof of the historic Collatz Conjecture is presented through the rigorous construction of the associated inverse model and the analytical transfer of properties exhibited in the inverted tree structure. Furthermore, evidence has been presented on Conway's Conjecture in the Game of Life through meticulous inverse modeling in bounded cases, overcoming initial limitations of the theory in combinatorial explosions. Although a complete proof of the conjecture is not achieved, this positively expands the modeling and analysis capacity of the methodology. The impact of properly applying this novel theory to expand understanding and solve open problems in discrete dynamical systems seems vast and profound.

Hasselmann and coauthors proposed the discrete interaction approximation (DIA) as the best tool replacing the nonlinear evolution term in a numerical wind-wave model. Much later, Polnikov and Farina radically improved the original DIA by means of location all the interacting four wave vectors, used in the DIA configuration, exactly at the nodes of the numerical frequency-angular grid. This provides nearly two-times enhancing the speed of numerical calculation for the nonlinear evolution term in a wind-wave model. For this reason, the proposed version of the DIA was called as the fast DIA (FDIA). In this paper we demonstrate all details of the FDIA concept for several frequency-angular numerical grids of high resolution, with the aim of active implementation the FDIA in modern versions of world-wide used wind-wave models.

We formulate the statistics of the discrete multicomponent fragmentation event using a methodology borrowed from statistical mechanics. We generate the ensemble of all feasible distributions that can be formed when a single integer multicomponent mass is broken into fixed number of fragments and calculate the combinatorial multiplicity of all distributions in the set. We define random fragmentation by the condition that the probability of distribution be proportional to its multiplicity and obtain the partition function and the mean distribution in closed form. We then introduce a functional that biases the probability of distribution to produce in a systematic manner fragment distributions that deviate to any arbitrary degree from the random case. We corroborate the results of the theory by Monte Carlo simulation and demonstrate examples in which components in sieve cuts of the fragment distribution undergo preferential mixing or segregation relative to the parent particle.

This algorithm is designed to perform Discrete Fourier Transforms (DFT) to convert temporal data into spectral data. This algorithm obtains the Fourier Transforms by studying the Coefficient of Determination of a series of artificial sinusoidal functions with the temporal data, and normalizing the variance data into a high-resolution spectral representation of the time-domain data with a finite sampling rate. What is especially beneficial about this DFT algorithm is that it can produce spectral data at any user-defined resolution.

Shear dynamics simulation under extreme pressure was used to optimise the composition of ester-based lubricant base oil and extreme pressure additives. As a result, the ester-based lubricant in the weak-gel drilling fluid achieves reduction and increases the efficiency in drilling construction, and the field operation efficiency improves. The results of base oil lubrication simulation show that three base oils, namely, methyl oleate, ethyl oleate and butyl oleate, can be stably adsorbed on the surface layer of the shear surface. Methyl oleate has the lowest system temperature after shear stabilisation and has a high load-bearing capacity. The shear stress of methyl oleate decreases by changing the initial shear temperature and speed. It increases by 6.36 MPa when the speed increases from 10 m/s to 30 m/s. At this time, the shear stress of butyl oleate increases by 10.11 MPa. The results of the four-ball friction experiments show that the radius of the abrasion spot of methyl oleate is 179.83 μm, which is smaller than that of the other two base oils. Moreover, the friction coefficient of methyl oleate is 0.0018, which is approximately 1/3 of that of butyl oleate. Methyl oleate has better lubrication efficiency. The shear simulation results are consistent with the experimental results. The simulation can be used for the molecular design of lubricant materials. The oleic acid amide–containing amine group and the glyceride oleic–containing hydroxyl group were selected as the extreme pressure functional components for shear simulation. Results show that the shear stress of oleic acid amide is much lower than that of oleic acid glyceride when the initial shear temperature and speed change. The extreme pressure lubrication component–containing amine group is better than the extreme pressure lubrication material–containing hydroxyl group. The effects of different oleamide contents in the two-component mixtures on the lubricating properties were investigated through shear simulation, with methyl oleate as the base oil and oleamide as the extreme pressure functional component. The simulation results show that the lubricant has the optimal shear stress when 10% oleamide is added to the lubricant. The base properties of the lubricant were tested using a mixture of 90% methyl oleate and 10% oleamide, formulated as a lubricant. Results show that the friction coefficient can be reduced to 0.03 when 0.5% of the lubricant is added to the base mud. Moreover, the lubricant film formed shows high strength. Then, 1% lubricant was added to the weak-gel drilling fluid to evaluate the effect of the lubricant on the gel structure of the drilling fluid. Results show that adding lubricant does not affect the rheological properties of the drilling fluid. The gel structure of the system also remains stable even after 7 days of continuous ageing. The results of the field experiments in Hua3-01 well show that adding 1% ester-based lubricant to the field drilling fluid reduces the drilling friction by 33%. Thus, the efficiency of directional side drilling can be effectively improved, complicated accidents, such as poor starting and stopping of drilling and stuck jamming, can be avoided, and the goals of reducing quantity and increasing efficiency can be completed.

This study presents the design and comprehensive 3D multiphysics simulation of a novel microfluidic immunosensor for real-time, non-invasive monitoring of pro-inflammatory biomarkers in human sweat. The patch-like device, designed using COMSOL Multiphysics, integrates magnetofluidic manipulation with direct-field capacitive sensing. The sensor comprises two distinct units: an immunocomplex enhancement unit, employing a series of microcoils to optimize the binding efficiency, reaction kinetics, and homogeneity of biomarker-magnetic nanoparticle (MNP) interactions, thereby enhancing the sensor's specificity; and a layered capacitive sensing unit, designed to concentrate and detect biomarker-tagged MNPs, thus amplifying sensitivity. Simulations of the capacitive sensing unit revealed a substantial sensitivity increase of up to 42.48\% at an 85\% MNP concentration within the detection zone. These findings highlight the potential of the proposed immunosensor for efficient and precise real-time biomarker monitoring, which may facilitate early disease diagnosis and enable personalized healthcare interventions.

Two coupled climate models which participated the CMIP6 project (Coupled Model Intercomparison Project Phase 6), the Earth System Model of Chinese Academy of Sciences version 2 (CAS-ESM2-0) and the Nanjing University of Information Science and Technology Earth System Model version 3 (NESM3), were assessed in terms of the impact of four new sea ice parameterization schemes. These four new schemes are related to air-sea heat flux, radiation penetration and absorption, melt ponds, ice-ocean flux, respectively. To evaluate the effectiveness of these schemes, key sea ice variables with and without these new schemes, such as sea ice concentration (SIC) and sea ice thickness (SIT), were compared against observation and reanalysis product from 1980 to 2014. The simulations followed the design of historical experiments within the CMIP6 framework. The results revealed that both models demonstrated improvements in simulating Arctic SIC and SIT when the new parameterization schemes were implemented. The model bias of SIC in some marginal sea ice zones of Arctic was reduced, especially during March. The SIT was increased and the transpolar gradient of SIT was reproduced. The changes of spatial pattern of SIC and SIT after adding new schemes bear similarities between the two coupled models. This suggests that the new schemes have potential for broad application in climate models, especially for those with underestimated SIT.

Electrical contacts are important circuit components with diverse industrial applications, and their failure can lead to multiple unwanted effects. Hence, the behavior of electrical contacts is a widely studied topic in scientific literature based on various approaches, tools, and techniques. The present study proposes a numerical modeling and simulation approach based on the Holm contact theory to study the dependence between the electric potential and the temperature within an electrical contact. Structured in 5 sections, the research was conducted using COMSOL Multiphysics software and its solid-state mechanics, electric current, and heat transfer modules in order to highlight contact behavior from mechanical, electrical and thermal points of view: the von Mises stress, contact force, the electric field amplitude, the variation of the electrical potential along the current path, the temperature gradient, and dependence of temperature along the contact elements edges were obtained by simulation and graphically represented. The results are in line with the expectations and with past research that was studied in the literature review.

Advanced Finite Element (FE) modeling and simulations were performed on vehicular crashes into a Three-bar Metal Bridge Rail (TMBR). The FE models of a sedan, a pickup truck, and a TMBR section were adopted in the crash simulations subject to Manual for Assessing Safety Hardware (MASH) Test Level 2 (TL-2) and Test Level 3 (TL-3) requirements. The test vehicle models were first validated using full-scale physical crash tests conducted on a two-bar metal bridge using a sedan and a pickup truck with similar overall physical properties and sizes to their respective vehicles used in the simulations. The validated vehicular models were then used to evaluate the crash performance of the TMBR using MASH Evaluation Criteria for structural adequacy, occupant risk, and post-impact trajectory. The TMBR met all MASH TL-2 requirements but failed to meet the MASH TL-3 Criteria H and N requirements when impacted by the sedan. The TMBR was also evaluated under in-service conditions (behind a 1.52-m wide sidewalk) and impacted by the sedan under MASH TL-3 conditions. The simulation results showed that the TMBR behind a sidewalk met all safety requirements except for the occupant impact velocity in the longitudinal direction, which exceeded the MASH limit by 3.93%.

Addressing the significant challenge in high-speed cotton dibbling operations in Xinjiang—specifically, the duck bill’s ("Duck bill" is a type of duck-billed bunch planting mechanism or component). shortened opening duration leading to seed throwing and floating—a new crawler-type delayed hole-forming mechanism has been designed. This mechanism extends the duck bill horizontally, thereby prolonging its opening time, enabling cotton sowing speeds of up to 6 km/h. Following agronomic principles for cotton planting, the overall layout of the crawler-type hole-forming mechanism, the structural parameters of the key components and the trajectory of the hole-forming are determined. We use the Discrete Element Method (DEM) and multi-body dynamics coupling method to simulate and analyze the motion process of the device. The fixed the tilt angle of the duck bill α(A), the depth of the duck bill hole-forming into the soil h(B), and the angle of rotation of the moving duck bill β(C) were taken as the experimental factors, with the longitudinal length of cotton seed hole (Y1) and the hole-forming depth of cotton seed hole (Y2) as the response values. The parameter range of each factor and the preliminary influence on the response value were determined through pre-experiments and single-factor simulation test. Based on RecurDyn-EDEM coupling, a quadratic regression orthogonal rotation combination simulation test was carried out. The results show that the order of the primary and secondary factors affecting the Y1 of the time-lapse hole-forming device was: A > C > B. The order of primary and secondary factors affecting the Y2 of the delayed hole forming device was: B > C > A. When the A was 2.4 °, the B was 42.4 mm, and the C was 30.5 °, the performance of the hole-forming was the best, and the Y1 was 26.4 mm, and the Y2 was 25.0 mm. The bench verification test results show that under the optimized combination of operating parameters, the average Y1 was 29.1 mm, and the average Y2 was 23.2 mm. Compared with the results of the previous coupled simulation model, the average relative errors of the two are 7.23 % and 4.36 %, respectively, indicating the accuracy and effectiveness of the established DEM simulation model and virtual test analysis. The hole-forming area and the opening time of the duck bill after the stable operation of the machine were recorded and measured. The calculated hole dislocation rate Hc was 4.33 %, which did not exceed the national standard requirement of 6 %. The qualified rate of hole distance Hx was 94.33 %, which was much higher than the national standard requirement of 80 %. The average opening time of the duck bill at a speed of 6 km/h is 0.45 s, which is much longer than the time required for cotton seeds to fall from the duck bill 0.11 s. It meets the requirements of high-speed cotton planting in China and facilitating advancements in future high-speed planter technology.

A power factor correction (PFC) rectifier is compulsory for any electronic circuitry connected to the electrical grid. The majority of PFCs that are utilized are based on a step-up converter that supplies high DC voltage at its output terminal. Electric vehicle battery voltage varies from a few hundred to almost a thousand volts. Thus, an additional conversion step is obligatory when designing an all-brand universal charger. This paper presents a new type of front-end versatile step up / down three voltage level PFC. The rectifier can operate in continuous conduction mode or discontinuous conduction mode. First, the rectifier’s principle of operation is described, and then an innovative rectifier is analyzed for continuous and discontinuous conduction modes. Finally, the proposed theory is experimentally validated in a multiplier-less dual loop mode at discontinuous conduction modes. It was shown that although there is no multiplier use in the control circuitry, the power factor is near unity. It was revealed that the rectifier could swing the output voltage from 50 V to 900 V while the input voltage was 230 Vrms. The innovative topology suits any standard PFC rectifier, three-level voltage supplement, and dual-stage low-voltage power supply.

Differences are introduced as outputs of linear systems called differencers, being considered two classes: shift and scale-invariant. Several types are presented, namely: nabla and delta, bilateral, tempered, bilinear, stretching, and shrinking. Both continuous and discrete-time are described. ARMA type systems based on differencers are introduced and exemplified. In passing, the incorrectness of the usual delta difference is shown.

The basis of the discrete-impulse energy supply (DIES) concept is the efficient use of supplied energy. The references describe in detail the general principles of DIES, examine the energy and thermodynamic aspects and the main mechanisms of intensification that can be initiated on the basis of this principle. DIES mechanisms conveniently can be divided into hard and soft ones. The former should be used to stimulate hydromechanical processes, and the latter to accelerate the processes of phase heat and mass transfer, or for the purpose of intensive mixing of multicomponent media. The authors have studied the possibility of using DIES to intensify the hydromechanical processes, in particular emulsification of milk fat (homogenization of milk, preparation of spreads), processing of cream cheese masses. Objects of research were whole non-homogenized milk, fat emulsions, cream cheese mass. In order to evaluate the efficiency of milk homogenization the homogenization coefficient change was studied, which was determined by centrifugation method as the most affordable and accurate one. Emulsions were evaluated according to the degree of destabilization, resistance and dispersion of the fat phase. The rheological characteristics of cheese masses were evaluated by the effective viscosity change.

In this paper we show how to efficiently implement parallel discrete simulations on Multi-Core and GPU architectures through a real example of application: a cellular automata model of laser dynamics. We describe the techniques employed to build and optimize the implementations using OpenMP and CUDA frameworks. We have evaluated the performance on two different hardware platforms that represent different target market segments: high-end platforms for scientific computing, using an Intel Xeon Platinum 8259CL server with48cores and also an NVIDIA Tesla6V100 GPU, both running on Amazon Web Server (AWS) Cloud, and on a consumer-oriented platform, using an Intel Core i9 9900k CPU and an NVIDIA GeForce GTX 1050 TI GPU. Performance results are compared and analysed in detail. We show that excellent performance and scalability can be obtained in both platforms, and we extract some important issues that imply a performance degradation for them. We also found that current Multi-Core CPUs with large core numbers can bring a performance very near to that of GPUs, even similar in some cases.

We investigate the impact of aging on exchangeable inter-arrival times in mixed renewal processes, exploring its implications for reliability and survival analysis. In this study, first, we revisit the definition of renewal point processes, where inter-event time intervals are considered as exchangeable non-negative random variables. Then, we define the concept of statistical aging as latency in the observational process of event counting. Latency affects event detection but preserves exchangeability. However, it may alter the statistical properties of inter-event time intervals. Our analytical and numerical assessments highlight the significance of aging in exchangeable lifetimes, offering insights into key metrics such as the failure survival function, renewal function, and hazard rate function. Through archetypal examples and empirical findings, we illustrate the implications of aging on renewal processes. In particular, employing a Bayesian perspective, we analyze high-frequency currency exchange rate data to assess the impact of aging on volatility risk evaluation. This study contributes novel insights to the literature of renewal theory and survival analysis, emphasizing the role of latency aging in stochastic point processes under exchangeability assumption.

The catastrophic failure of two dams in Libya in the night of 10th and 11th September 2023, resulted in a significant amount of water and debris destroying a large part of the city of Derna, which is located downstream of the two dams. The flood caused more than 6000 fatalities and the dis-placement of thousands of people from the city. The dams failed due to heavy rainfall caused by the Medicane Daniel, resulting in intense precipitation and the dams overspilling after reaching full capacity. This paper analyses the consequences of the dam breach by modelling the flow discharge and the flooding of the city of Derna. The analysis compares the computed quantities with the findings of the post-event satellite image analysis from UNOSAT and Copernicus, the field reports, and information gathered from the social media through AI detection. The results allows to better understand the dynamics of the dam breach, its initial parameters and an assessment of the accu-racy of the results.

The fractional advection-dispersion equation FADE plays an important role, especially in explaining the process of solute-transport in natural porous media with different degrees of heterogeneity. However, studies of the flow and transport of contaminants in the underground environment are based on the knowledge of many functional parameters of the subsoil. The delay factor caused by nonlinear absorption is one of these parameters inheriting significant ambiguities in its estimation in heterogeneous media. In the study, a fractional advection-dispersion FADE model was developed by the implicit Euler method, based on a Grunwald and Caputo-Liousville approximation to explicitly evaluate and simulate the transport mechanism of contaminants in media with unsaturated porous materials subjected to a nonlinear sorption model. Better solute mass retention was observed for fractional advection-dispersion FADE model. Moreover, the absorption parameter of the retardation coefficient has a better impact on the motion of the solute in a saturated porous medium. In other words, it has a significant effect on the dispersion of pollutants in aquifers. This study highlights the need to characterize the aquifer to predict solute transport in porous media.

Tinnitus is a common phantom auditory percept, believed to be related to plastic changes in the brain due to hearing loss. However, tinnitus can also occur in absence of any clinical hearing loss. Then, since there is no hearing loss, the mechanisms that drive plastic changes remain largely enigmatic. Previous studies showed subtle differences in sound-evoked brain activity associated with tinnitus in subjects with tinnitus and otherwise, normal hearing, but the results are not consistent across studies. Here, we aim to investigate those differences using monaural rather than binaural stimuli. Sound-evoked responses were measured using functional magnetic resonance imaging (MRI) in participants with and without tinnitus. All participants had clinically normal audiograms. The stimuli were pure tones with frequencies between 353 and 8000 Hz, presented monaurally. A Principal Component Analysis (PCA) of the response in the auditory cortex revealed no difference in tonotopic organization, which confirmed earlier studies. A GLM analysis showed hyperactivity in the lateral areas of the bilateral auditory cortex. Consistent with the tonotopic map, this hyperactivity mainly occurred in response to low stimulus frequencies. This may be related to hyperacusis, which was associated with tinnitus found in the parahippocampus. This may reflect an interference between tinnitus and spatial orientation.

This paper proposed a three-layer distributed simulation network architecture: 1. Distributed virtual simulation network, 2. Perception and control sub-network, and 3. Collaborative communication service network. This simulation architecture runs on a distributed platform. The platform can provide a unique virtual scenario and several simulation services to verify the individual robot system's basic perception, control, and planning algorithms and the distributed collaborative algorithms of heterogeneous multi-robot systems. Furthermore, we designed a simulation experiment scenario for classical heterogeneous robot systems such as unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs). At last, We gave a rough estimation model of the bandwidth.

Discrete Double Hilbert Exponential sums along polynomials. This is preprint version (not peer reviewed) And we now check the proof of (ii) of Lemma 3.2 whether it is really true.

In this paper, we introduce two discrete degenerate random variables, namely the degenerate binomial and degenerate Poisson random variables. We deduce the expectations of the degenerate binomial random variables. We compute the generating function of the moments of the degenerate Poisson random variables, which leads us to define the new type degenerate Bell polynomials, and hence obtain explicit expressions for the moments of those random variables in terms of such polynomials. We also get the variances of the degenerate Poisson random variables. Finally, we illustrate two examples of the degenerate Poisson random variables.

Let be a power function $f_{r,M}(s)$ defined for every $s$ within the finite set $M$ as follows $$f_{r,M}(s)= \begin{cases} s^r, \ &s\in M,\\ 0, \ &\mathrm{otherwise}. \end{cases} $$ Let a discrete convolution of $f_{r,M}(s)$ be denoted as follows $\mathrm{Conv}_{r,M}[n]=(f_{r,M}*f_{r,M})[n]$. Let a real coefficients $A_{m,j}$ be given by the following recurrence $$ A_{m,j} = \begin{cases} 0, & \mathrm{if } \ j<0 \ \mathrm{or } \ j>m, \\ (2j+1)\binom{2j}{j} \sum_{d=2j+1}^{m} A_{m,d} \binom{d}{2j+1} \frac{(-1)^{d-1}}{d-j} B_{2d-2j}, & \mathrm{if } \ 0 \leq j < m, \\ (2j+1)\binom{2j}{j}, & \mathrm{if } \ j=m. \end{cases} $$ In this paper we show that for every $n>0$ the following odd-power identities involving coefficients $A_{m,j}$ and convolution transform $\mathrm{Conv}_{r,M}[n]$ hold $$ \begin{split} n^{2m+1}+1&=\sum_{r=0}^{m}A_{m,r}\mathrm{Conv}_{r,\mathbb{N}}[n],\\ n^{2m+1}-1&=\sum_{r=0}^{m}A_{m,r}\mathrm{Conv}_{r,\mathbb{Z}_{>0}}[n],\\ n^{2m+1}&=\sum_{r=0}^{m}A_{m,r}\sum_{k=1}^{n} k^r(n-k)^r\\ &=\sum_{r=0}^{m}A_{m,r}\sum_{k=0}^{n-1} k^r(n-k)^r. \end{split} $$

We study certain function algebras and their operator algebra completions on r-discrete abelian groupoids, the corresponding conditional expectations, maximal abelian subalgebras (masa) and eigen-functionals.

This paper describes a stochastic clustering architecture that is used in the paper for making predictions over energy data. The design is discrete, localised optimisations based on similarity, followed by a global aggregating layer, which can be compared with the recent random neural network designs, for example. The topic relates to the IDEAS Smart Home Energy Project, where a client-side Artificial Intelligence component can predict energy consumption for appliances. The proposed data model is essentially a look-up table of the key energy bands that each appliance would use. Each band represents a level of consumption by the appliance. This table can replace disaggregation from more complicated methods, usually constructed from probability theory, for example. Results show that the table can accurately disaggregate a single source to a set of appliances, because each appliance has quite a unique energy footprint. As part of predicting energy consumption, the model could possibly reduce costs by 50% and more than that if the proposed schedules are also included. The hyper-grid has been changed to consider rows as single units, making it more tractable. A second case study considers wind power patterns, where the grid optimises over the dataset columns in a self-similar way to the rows, allowing for some level of feature analysis.

The data from social networks like Twitter is a valuable source for research but full of redundancy, making it hard to provide large-scale, self-contained, and small datasets. The data recording is a common problem in social media-based studies and could be standardized. Sadly, this is hardly done. This paper reports on lessons learned from a long-term evaluation study recording the complete public sample of the German and English Twitter stream. It presents a recording solution proposal that merely chunks a linear stream of events to reduce redundancy. If events are observed multiple times within the time-span of a chunk, only the latest observation is written to the chunk. A 10 Gigabyte Twitter raw dataset covering 1,2 Million Tweets of 120.000 users recorded between June and September 2017 was used to analyze expectable compression rates. It turned out that resulting datasets need only between 10\% and 20\% of the original data size without losing any event, metadata or the relationships between single events. This kind of redundancy reduction recording makes it possible to curate large-scale (even nation-wide), self-contained, and small datasets of social networks for research in a standardized and reproducible manner.

The shear stress generated by wall turbulence is the main cause of wall friction resistance in turbulent flow through pipes. This paper investigates the impact of inserting rest periods (regions of constant Reynolds number) within the pulsating operating cycle of velocity on the fully turbulent flow at large time-averaged Reynolds numbers, using the Large Eddy Simulation (LES) method. The study aims to explore the effect of increasing rest periods within pulsations on drag resistance. The dimensionless shear stress and drag reduction rates during different time periods of rest were analyzed. Numerical simulation results indicate that the pulsating velocity operation mode does not necessarily lead to drag reduction; it may even result in increased resistance. Inserting rest periods within the pulsation cycle can achieve drag reduction effects, with the maximum drag reduction rate reaching 21.8\%. Comparisons with experimental data and Direct Numerical Simulation (DNS) from the literature validate the feasibility of using the LES method for pipe pulsating operation modes.

Although, promotion of urbanization culture in recent decades has made inevitable development of cities in the world, however, the development can be guided in a direction that leave, to the extent possible, minimum socioeconomic and environmental impacts. For this, it is required to first forecast auto-spreading orientation of cities and suburbs in rural areas over time and then avoid shapeless growth of cities. This paper is an attempt to develop a dynamic hybrid model based on logistic regression (LR), Markov chain (MC), and cellular automata (CA) for prediction of future urban sprawl in fast-growing cities. The model was developed using 12 widely-used urban development criteria, whose significant coefficient was determined by logistic regression, and validated by relative operating characteristic (ROC) analysis. The validated model was run in Guilan, a tourist province in northern Iran with a very high rate of urban development. For this, changes in the area of urban land use were detected over the period of 1989 to 2013 and then, future sprawl of the province was forecasted by the years 2025 and 2037. The analysis results revealed that the area of urban land use was increased by more than 1.7 % from 36012.5 ha in 1989 to 59754.8 ha in 2013, and the area of Caspian Hyrcanian forestland was reduced by 31628 ha. The results also predicted an alarming increase in the rate of urban development in the province by the years 2025 and 2037, during which urban land use is predicted to develop 0.9 % and 1.38 %, respectively. The development pattern is expected to be uneven and scattered, without following any particular direction. The development will occur close to the existing or newly-formed urban basements as well as around major roads and commercial areas. This development, if not controlled, will lead to the loss of 13863 ha of Hyrcanian forests and if the trend continues, 21013 ha of Hyrcanian forests and 20208 ha of Barren/open lands are expected to be destroyed by the year 2037. In general, the proposed model is an efficient tool for the support of urban planning decisions and facilitates the process of sustainable development of cities by providing decision-makers with an overview on future development of cities where the growth rate is very fast.

In the recent years, tubular nanostructures have been related with immense advances in various fields of science and technology. Considerable research efforts have been centered on the theoretical prediction and manufacturing of non-carbon nanotubes (NTs), which meet modern requirements for the developing of novel devices and systems. In this context, diatomic inorganic nanotubes formed by atoms of elements from the 13th group of the periodic table (B, Al, Ga, In, Tl) and nitrogen (N), have received much research attention. In the present work, the elastic properties of single-walled boron nitride, aluminium nitride, gallium nitride, indium nitride and thallium nitride nanotubes were numerically evaluated using the nanoscale continuum modelling approach (also called molecular structural mechanics). The elastic properties (rigidities, surface Young´s and shear moduli, and Poisson´s ratio) of nitride nanotubes are discussed with respect to the bond length of the corresponding diatomic hexagonal lattice. The results obtained contribute to a better understanding of the mechanical response of nitride compound-based nanotubes, covering a broad range from the well-studied boron nitride NTs to the hypothetical thallium nitride NTs.

In this work, two plasmonic structures with two different periodic nanostructures (nanodisk and nanostrip arrays) separated from the gold layer by an ultrathin dielectric Al2O3 layer are proposed, and their optical properties are simulated as 3-dimensional models using COMSOL Multiphysics software tool. The optical responses, such as the amplification of the electric field and the scattering, absorption and extinction cross sections of the two structures, are investigated and compared. The results show that both structures show strong field enhancement in their gap layer, and the structure with the periodic array of metal nanodisks shows stronger field enhancement and larger absorption, extinction, and scattering cross sections in its dipole resonance wavelength, which is defined as a bright plasmonic mode. In addition, some weaker resonant modes are observed for both structures and the reason for their generation is dis-cussed. The analysis of different possible modes in such structures is crucial for their application in various fields.

Gas and power networks are tightly coupled and interact with each other due to physically interconnected facilities. In an integrated gas and power network, a contingency observed in one system may cause iterative cascading failures, resulting in network wide disruptions. Therefore, understanding the impacts of the interactions in both systems is crucial for governments, system operators, regulators and operational planners, particularly, to ensure security of supply for the overall energy system. Although simulation has been widely used in the assessment of gas systems as well as power systems, there is a significant gap in simulation models that are able to address the coupling of both systems. In this paper, a simulation framework that models and simulates the gas and power network in an integrated manner is proposed. The framework consist of a transient model for the gas system and a steady state model for the power system based on AC-Optimal Power Flow. The gas and power system model are coupled through an interface which uses the coupling equations to establish the data exchange and coordination between the individual models. The bidirectional interlink between both systems considered in this studies are the fuel gas offtake of gas fired power plants for power generation and the power supply to LNG terminals and electric drivers installed in gas compressor stations and underground gas storage facilities. The simulation framework is implemented into an innovative simulation tool named SAInt (Scenario Analysis Interface for Energy Systems) and the capabilities of the tool are demonstrated by performing a contingency analysis for a real world example. Results indicate how a disruption triggered in one system propagates to the other system and affects the operation of critical facilities. In addition, the studies show the importance of using transient gas models for security of supply studies instead of successions of steady state models, where the time evolution of the line pack is not captured correctly.

In this paper, the boiling flow inside a nanochannel with 700000 argon particle has been simulated by molecular dynamics (MD) simulation. This approach has been employed to analysis the superheated flow and its heat transfer pattern as well. For all simulations an external thrust force varying from 1 PN to 12 PN is exerted on inlet nanoparticles along the channel to have the forced annular boiling flow. Computations reveal that saturation condition and superheat degree have significant impacts on the liquid-vapor interface. Furthermore, because of the major influence of surface tension throughout a nanochannel, the x-velocity of liquid film and vapor core has not considerable fluctuations and stay smooth. All provided results show the behaviors completely similar to the available outcomes in the literature.

The effects of the climate crisis are affecting ecosystems at different scales and magnitudes. This paper focuses on a massive Mediterranean mussel die-off observed along the middle Italian Adriatic coast in the summer of 2022. We considered the possible environmental causes of this phenomenon and carried out a climatic analysis of the last decade. We performed field surveys in different locations along a 16 Km coastal stretch (from Martinsicuro (TE) on the South, to Grottammare (AP) on the North). The study area includes two marine Sites of Community Importance under the European Natura 2000 network. The mussels die-off has interested practically all the natural mussel-beds colonizing the study area. Mussels are sessile filter-feeding organisms inhabiting the intertidal zone, therefore, are highly exposed to variations in environmental conditions such as temperature and nutrient load. We discuss the possible causes of this die-off, proposing that high temperature and the scarce availability of food acted simultaneously as stress factors, generating local unsustainable living conditions for this species.

Surgical simulators are crucial in early craniofacial and plastic surgical training, necessitating synthetic materials that accurately replicate tissue properties. Recent critiques of our lab’s currently deployed silicone surrogate have highlighted numerous areas for improvement. To further refine our models, our group's objective is to find a composition of materials that is closest in fidelity to native oral mucosa during surgical rehearsal by expert craniofacial surgeons. Fifteen platinum silicone-based surrogate samples were constructed with variable hardness and slacker percentages. These samples underwent evaluation of tactile sensation, hardness, needle puncture, cut resistance, suture retention, defect repair, and tensile elasticity. Expert craniofacial surgeon evaluators provided focused qualitative feedback on selected top-performing samples for further assessment and statistical comparisons. Evaluation revealed surrogate characteristics with satisfactory to good performance. Sample 977 exhibited the highest performance, and comparison with the original surrogate (sample 810) demonstrated significant improvements in critical areas, emphasizing the efficacy of the refined composition. The study identified a silicone composition that directly addresses the feedback received by our team’s original silicone surrogate. The study underscores the delicate balance between biofidelity and practicality in surgical simulation. The need for ongoing refinement in surrogate materials is evident to optimize training experiences for early surgical learners.

Surgical simulators are crucial in early craniofacial and plastic surgical training, necessitating synthetic materials that accurately replicate tissue properties. Recent critiques of our lab’s currently deployed silicone surrogate have highlighted numerous areas for improvement. To further refine our models, our group's objective is to find a composition of materials that is closest in fidelity to native oral mucosa during surgical rehearsal by expert craniofacial surgeons. Fifteen platinum silicone-based surrogate samples were constructed with variable hardness and slacker percentages. These samples underwent evaluation of tactile sensation, hardness, needle puncture, cut resistance, suture retention, defect repair, and tensile elasticity. Expert craniofacial surgeon evaluators provided focused qualitative feedback on selected top-performing samples for further assessment and statistical comparisons. Evaluation revealed surrogate characteristics with satisfactory to good performance. Sample 977 exhibited the highest performance, and comparison with the original surrogate (sample 810) demonstrated significant improvements in critical areas, emphasizing the efficacy of the refined composition. The study identified a silicone composition that directly addresses the feedback received by our team’s original silicone surrogate. The study underscores the delicate balance between biofidelity and practicality in surgical simulation. The need for ongoing refinement in surrogate materials is evident to optimize training experiences for early surgical learners.

The field dynamics of neutral and charged matter (treated as gauge fields) beyond the event horizon of black holes are investigated. As an extension of the work done in Ganesan (2023) [1], the analysis was done using the combined dynamics of the two-dimensional Yang-Mills and Liouville gravity fields beyond the event horizon. The quantum interactions and symmetry breaking mechanisms of the combined two-dimensional fields are discussed. The second part of this paper explores the quantum dynamics occurring at the event horizon. By considering the structure of the event horizon as a Möbius strip (embedded in three-dimensional space), a formulation for the lower bound of the first energy eigenvalue of a quantum particle (electron) at the event horizon is obtained.

Although Shannon mutual information has been widely used, its effective calculation is often difficult for many practical problems, including those in neural population coding. Asymptotic formulas based on Fisher information sometimes provide accurate approximations to the mutual information but this approach is restricted to continuous variables because the calculation of Fisher information requires derivatives with respect to the encoded variables. In this paper, we consider information-theoretic bounds and approximations of the mutual information based on Kullback--Leibler divergence and Rényi divergence. We propose several information metrics to approximate Shannon mutual information in the context of neural population coding. While our asymptotic formulas all work for discrete variables, one of them has consistent performance and high accuracy regardless of whether the encoded variables are discrete or continuous. We performed numerical simulations and confirmed that our approximation formulas were highly accurate for approximating the mutual information between the stimuli and the responses of a large neural population. These approximation formulas may potentially bring convenience to the applications of information theory to many practical and theoretical problems.

Inverse Sumudu transform multiple shifting properties are used to design methodology for solving ordinary differential equations. Then algorithm applied to solve Whittaker and Zettl equations to get their new exact solutions and profiles which shown through Maple complex graphicals. Table of inverse Sumudu transforms for elementary functions given for supporting the differential equations solving using inverse Sumudu transform.

The event camera generates an event stream based on changes in brightness, retaining only the characteristics of moving objects, addresses the high power consumption associated with using high-frame-rate cameras for high-speed eye tracking tasks. However, the asynchronous incremental nature of event camera output has not been fully utilized, and there are also issues related to missing event datasets. Combining the temporal information encoding and state-preserving properties of spiking neural network (SNN) with event camera, a near-range eye tracking algorithm is proposed as well as a novel event-based dataset for validation and evaluation. According to experimental results, the proposed solution outperforms artificial neural network (ANN) algorithms, while computational time remains only 12.5\% of that of traditional SNN algorithms. Furthermore, the proposed algorithm allows for self-adjustment of time resolution, with a maximum achievable resolution of 0.081ms, enhancing tracking stability while maintaining accuracy.

Digital machine intelligence has evolved from its inception in the form of computation of numbers to AI, which is centered around performing cognitive tasks that humans can perform, such as predictive reasoning or complex calculations. The state of the art includes tasks that are easily described by a list of formal, mathematical rules or a sequence of event-driven actions such as modeling, simulation, business workflows, interaction with devices, etc., and also tasks that are easy to do “intuitively”, but are hard to describe formally or as a sequence of event-driven actions such as recognizing spoken words or faces. While these tasks are impressive, they fall short in applying common sense reasoning to new situations, filling in information gaps, or understanding and applying unspoken rules or norms. Human intelligence uses both associative memory and event-driven transaction history to make sense of what they are observing fast enough to do something about it while they are still observing it. In addition to this cognitive ability, all bio-logical systems exhibit autopoiesis and self-regulation. In this paper, we demonstrate how machine intelligence can be enhanced to include both associative memory and event-driven transaction history to create a new class of knowledge-based assistants to augment human intelligence. The digital assistants use global knowledge derived from the Large Language Models to bridge the knowledge gap between various participants interacting with each other. We use the general theory of information and schema-based knowledge representation to create the memory and history of various transactions involved in the interactions.

Structured extraction of emergency event information can effectively enhance the ability to re-spond to emergency events. This article focuses on the extraction of Chinese document-level emergency events, which mainly faces two key issues in this field: first, related datasets; Secondly, existing DEE (document-level event extraction) studies mostly use sequence annotation to extract candidate entities in the subtask of candidate entity extraction without considering the problem of role overlapping between candidate entities. On the one hand, this article constructs a Chinese document-level emergency extraction dataset, CDEEE, which first annotates the issues of argu-ment scattering, multiple events, and role overlapping. On the other hand, this article proposes a model RODEE for the problem of role overlapping in DEE tasks. This model first uses two in-dependent modules to represent the head and tail positions of candidate entities, then uses a multiplication attention mechanism to interact with the two to obtain a scoring matrix. Finally, role-overlapping candidate entities are predicted to assist in completing DEE tasks. Experiments were conducted on our manually annotated dataset, CDEEE, and the results showed that RODEE can effectively solve the problem of role overlapping in candidate entities and improve the performance of the DEE model.

To address the requirements of sonar imaging, such as high receiving sensitivity, wide bandwidth, wide receiving angle, an AlN PMUT with an optimized ratio of 0.6 for the piezoelectric layer diameter to backside cavity diameter is proposed in this paper. A sample AlN PMUT is designed and fabricated with SOI substrate-based bulk MEMS process. The characterization test result of the sample demonstrates a -6 dB bandwidth of approximately 500 kHz and a measured receiving sensitivity per unit area of at 1.37 V/μPa/mm2, which significantly surpasses the performance of previously reported PMUTs. The -6 dB horizontal angles of the AlN PMUT at 300 kHz and 500 kHz are measured 68.30° and 54.24°, respectively. To achieve an accurate prediction of its characteristics when being packaged and assembled in a receive array, numerical simulations with the consideration of film stress are conducted. The numerical result shows a maximum deviation of ±7% in the underwater receiving sensitivity across the frequency range of 200 kHz to 1000 kHz and a deviation of about 0.33% in the peak of underwater receiving sensitivity comparing to the experimental data. By such good agreement, the simulation method reveals its capability of providing theoretical foundation for enhancing the uniformity of the AlN PMUTs in future studies.

: In this paper we present an optimization of the planar manufacturing scheme for stretch-free, shape induced metal interconnects to simplify the fabrication with the aim of maximizing the flexibility in the structure regarding to stress and strain. The Formation of trenches between silicon islands are actively used in the lithographic process to create arc shape structures by spin coating resist into the trenches. The resulting resist form is used as a template for the metal lines, which are structured on top. Because this arc shape is beneficial for the flexibility of these bridges. The trench depth as a key parameter for the stress distribution is investigated by applying numerical simulations. The simulated results show that the increase in penetration depth of the metal bridge into the trench increases the tensile load which is converted into a shear Force Q(x), that usually leads to increased strains the structure can generate. For the fabrication the filling of the trenches with resist is optimized by varying the spin speed. Compared to the theoretical resistance, the current-voltage measurements of the metal bridges show a similar behavior and almost every structural variation is capable of functioning as a flexible electrical interconnect in a complete island-bridge array.

In this research article we solve the problem of synchronization and anti-synchronization of chaotic systems described by discrete and time-delayed variable fractional order differential equations. To guarantee the synchronization and anti-synchronization of these systems, we use the well-known PID control theory and the Lyapunov-Krasovskii stability theory for discrete systems of variable fractional order.We illustrate the results obtained through simulation with examples, in which it can be seen that our results are satisfactory, thus achieving synchronization and anti-synchronization of chaotic systems of variable fractional order with discrete time delay.

This paper proposed three-dimensional numerical simulation method by coupling of electrostatic and fluid fields to evaluating the performance of electrical sensor in the concentration measurement of gas/solid two-phase flow. Compared with the static numerical simulation, this real-time dynamic 3-D simulation method can research on a designed capacitance sensor combining the dynamic characteristics of the two-phase flows for concentration measurement. Several fluid-electrostatic models of transmission pipes with different sensor structures are built. Under different test positions and different particle concentrations, the flow characteristics and the corresponding electric signals can be obtained, and the correlation coefficient between the concentration values and the capacitance values are used for performance evaluation of the sensors. The effects of flow regimes on concentration measurement are also been investigated in this paper. To validate the results of simulation, an experimental platform with horizontal straight pipe for phase volume concentration measurement of solid/air two-phase flow is built, and the experimental results agree well with simulation conclusions. The simulation and test results show that the coupling models can give constructive reference opinions for the sensor design and collection of installation position in different transmission pipelines, which are very important for the practical process of pneumatic conveying system.

Abstract Using the Pauli algebra version of the Dirac equation six symmetric but non-equivalent copies of the QED Lagrangian are constructed. The group of symmetries is the group of outer automorphisms of the finite quaternion group, isomorphic to the group of permutations on three letters. Solutions to corresponding Dirac equations form six sectors of fermion fields. The intersections between different sectors can only contain massless fields, implying that the sectors are distinct. If the symmetry could be extended to the full Standard Model Lagrangian it would predict the ratio of dark matter to ordinary matter of 5 : 1 which is close to the ratio of 5.2 : 1 according to current observations.

Discrete distribution estimation is a fundamental statistical tool, which is widely used to perform data analysis tasks in various applications involving sensitive personal information. Due to privacy concerns, individuals may not always provide their raw information, which leads to unpredictable biases in the final results of estimated distribution. Local Differential Privacy (LDP) is an advanced technique for privacy protection of discrete distribution estimation. Currently, typical LDP mechanisms provide same protection for all items in the domain, which imposes unnecessary perturbation on less sensitive items and thus degrades the utility of final results. Although, several recent works try to alleviate this problem, the utility can be further improved. In this paper, we propose a novel notion called Item-Oriented Personalized LDP (IPLDP), which independently perturbs different items with different privacy budgets to achieve personalized privacy protection. Furthermore, to satisfy IPLDP, we propose the Item-Oriented Personalized Randomized Response (IPRR) based on the observation that the sensitivity of data shows an inverse relationship with the population size of respective individuals. Theoretical analysis and experimental results demonstrate that our method can provide fine-grained privacy protection and improve data utility simultaneously.

A stochastic dominance (SD) relation can be defined by two different perspectives: One from the view of distributions, and the other one from the view of expected utilities. In early days, Fishburn investigated SD from the view of distributions and we refer this perspective as Fishburn’s SD. One of his many results was the development of fractional-order SD for continuous distributions. However, discrete fractional-order SD may not be generalized directly since some properties of fractional calculus do not have a discrete counterpart. In this paper, we develop a discrete analogue of fractional-order SD from the view of distributions. We generalize the order of SD by Lizama’s fractional delta operator, show the preservation of SD hierarchy, and formulate the utility classes that are congruent with our SD relations. This work brings a message that some results of discrete SD cannot be generalized directly from continuous SD. We characterize the difference between discrete and continuous fractional-order SD, as well as the way to handle them.

The author demonstrates a stable Lagrangian solid modeling method, tracking the interactions of solid mass particles rather than using a meshed grid. This numerical method avoids the problem of tensile instability often seen with smooth particle applied mechanics by having the solid particles apply stresses expected with Hooke's law, as opposed to using a smoothing function for neighboring solid particles. This method has been tested successfully with a bar in tension, compression, and shear, as well as a disk compressed into a flat plate, and the numerical model consistently matched the analytical Hooke's law as well as Hertz contact theory for all examples. The solid modeling numerical method was then built into a 2-D model of a pressure vessel, which was tested with liquid water particles under pressure and simulated with smoothed particle hydrodynamics. This simulation was stable, and demonstrated the feasibility of Lagrangian specification modeling for fluid–solid interactions.

3D printing facilitates the straightforward construction of microchannels with complex three-dimensional architectures. Here, we demonstrate 3D-printed modular mixing components that operate on the basis of splitting and recombining fluid streams to decrease interstream diffusion length. These are compared to helical mixers that operate on the principle of chaotic advection.

In this paper we introduce a computational model for the simulation of hypervelocity impact (HVI) phenomena which is based on the Discrete Element Method (DEM). Our paper constitutes the first application of DEM to the modeling and simulating of impact events for velocities beyond 5 kms⁻¹. We present here the results of a systematic numerical study on HVI of solids. For modeling the solids, we use discrete spherical particles that interact with each other via potentials. In our numerical investigations we are particularly interested in the dynamics of material fragmentation upon impact. We model a typical HVI experiment configuration where a sphere strikes a thin plate and investigate the properties of the resulting debris cloud. We provide a quantitative computational analysis of the resulting debris cloud caused by impact and a comprehensive parameter study by varying key parameters of our model. We compare our findings from the simulations with recent HVI experiments performed at our institute. Our findings are that the DEM method leads to very stable, energy–conserving simulations of HVI scenarios that map the experimental setup where a sphere strikes a thin plate at hypervelocity speed. Our chosen interaction model works particularly well in the velocity range where the local stresses caused by impact shock waves markedly exceed the ultimate material strength.

Hydraulic fracturing has enabled production from unconventional reservoirs in the U.S., but production rates often decline sharply, limiting recovery factors to under 10%. This study optimizes the CO2 huff-n-puff process for multistage-fractured horizontal wells in Wolfcamp A formation in the Delaware Basin. The potential for enhanced oil recovery and CO2 sequestration simultaneously was addressed using a coupled geomechanics-reservoir simulation. Geomechanical properties were derived from a 1D mechanical earth model and integrated into reservoir simulation to replicate hydraulic fracture geometries. The fracture model was validated using a robust production history matching. Fluid phase behavior analysis refined the equation of state, and 1D slim tube simulations determined a minimum miscibility pressure of 4300 psi for CO2 injection. After the primary production phase, various CO2 injection rates were tested from 1 to 25 MMSCFD/well, resulting in incremental oil recovery ranging from 6.3% to 69.3%. Different injection, soaking, and production cycles were analyzed to determine the ideal operating condition. The optimal scenario improved cumulative oil recovery by 68.8% while keeping the highest CO2 storage efficiency. The simulation approach proposed by this study provides a systematic workflow for evaluating and optimizing CO2 huff-n-puff in hydraulically fractured wells, enhancing the recovery factor of unconventional reservoirs.