Great Convergence Research Articles

New Late Cretaceous Paleomagnetic Results From the Eastern Qiangtang Terrane: Implications for the Postcollisional Extrusion and Convergence of the Southeastern Tibetan Plateau

AbstractTo refine the postcollisional extrusion and convergence history of the southeastern Tibetan Plateau, we report a quality paleomagnetic data set from Upper Cretaceous redbeds of the eastern Qiangtang Terrane (QT), where is situated at the front edge of eastern Himalayan Syntaxis. The 32 site‐mean directions provide a mean pole at 75.4°N, 176.8°E (A95 = 2.4°) and a paleolatitude of 31.8 ± 2.4°N for the Mangkang area. Comparison with reliable Late Cretaceous paleomagnetic data from the eastern QT and East Asia indicates that the eastern QT experienced a southward extrusion of 940 ± 310 km after the Late Cretaceous. Comparing Late Cretaceous paleolatitudes estimated from the eastern QT and Lhasa terrane (LT) reveals a paleolatitude difference of 2,390 ± 620 km. Such a great latitudinal convergence can be attributed to the fact that some continental terranes originally located between the eastern LT and QT were laterally extruded after the Late Cretaceous.

Dynamic location model for designated COVID-19 hospitals in China.

In order to effectively cope with the situation caused by the COVID-19 pandemic, cases should be concentrated in designated medical institutions with full capability to deal with patients infected by this virus. We studied the location of such hospitals dividing the patients into two categories: ordinary and severe. Genetic algorithms were constructed to achieve a three-phase dynamic approach for the location of hospitals designated to receive and treat COVID-19 cases based on the goal of minimizing the cost of construction and operation isolation wards as well as the transportation costs involved. A dynamic location model was established with the decision variables of the corresponding 'chromosome' of the genetic algorithms designed so that this goal could be reached. In the static location model, 15 hospitals were required throughout the treatment cycle, whereas the dynamic location model found a requirement of only 11 hospitals. It further showed that hospital construction costs can be reduced by approximately 13.7% and operational costs by approximately 26.7%. A comparison of the genetic algorithm and the Gurobi optimizer gave the genetic algorithm several advantages, such as great convergence and high operational efficiency.

Inverse aerodynamic design for distributed propulsion wing with expected circulation distribution

Distributed Propulsion Wing (DPW) technology offers significant advantages in terms of flight energy savings, but the strong aerodynamic coupling between the propulsive internal flow and aerodynamic external flow brings significant design challenges. As the primary DPW profile design is of great significance, this paper proposes a hybrid method to solve the inverse problem mainly based on the formula relationship between the required aerodynamic loads and the profile shape, which is more direct and instructive compared with traditional parametric iterative methods. The aerodynamic characteristics are described by the circulation distribution in the Fourier series form, then the mean camber line of the profile is solved through the re-derived airfoil theory considering disk’s influence. Further CFD correction methods are also proposed. To validate the effectiveness and feasibility of the proposed hybrid inverse method, several DPW profile design tests are then conducted. Finally, the relationship between 2D and realistic 3D unit shape is also researched. The results show that the proposed inverse design method has great accuracy and convergence speed in the design tests, and shows good robustness against changes of the design parameters. The 2D profile shape and the actual 3D shape of DPW unit can establish an aerodynamic-propulsion equivalent relationship based on the same internal mass fluxes.

Assessment of the seismic capacity of existing RC buildings short columns or soft storey, in accordance with the Second-Degree Pre-earthquake Inspection

The seismic capacity assessment of two three-storey reinforced concrete buildings constructed prior to 1984 is being conducted in this paper. Specifically, the case of short columns or soft storey on the ground floor is investigated and a comparison is made regarding the seismic response obtained for each case. The approximate method of the Second-degree pre-earthquake inspection is applied for the case of known and unknown of reinforcement amounts. The obtained results are then compared to the corresponding of a Non-linear static analysis. The primary criterion for the comparison is the failure index of the buildings, as derived from each method. The buildings are categorized into seismic categories according to the Second-degree pre-earthquake inspection, which are compared with the seismic classifications determined by KANEPE. The results of the failure indices and seismic categories according to the Second-degree pre-earthquake inspection were in great convergence with the corresponding results of failure indices in terms of acceleration and the seismic classifications according to KANEPE obtained when the Non-linear static analysis is applied. This convergence is particularly pronounced when known amounts of reinforcement are considered for the vertical elements. Furthermore, the seismic vulnerability of buildings with soft storey or short columns was confirmed in both methods in a similar manner.

Parametric design and modeling method of carbon fiber reinforcement plastic-laminated components applicable for multi-material vehicle body development

Abstract Combined application of steel, aluminum, and carbon fiber reinforcement plastic (CFRP) is the main direction of future lightweight body development. However, the anisotropy and additional lamination design variables of CFRP parts pose significant challenges for the development of multi-material bodies. This study establishes a parametric design method for the variable-thickness lamination scheme based on non-uniform rational B-splines, it can be coupled with existing parametric design methods for structural shapes to formulate a complete parametric design and modeling of CFRP components. On this basis, a homogenized intermediate material property is derived from classic laminate theory by introducing lamination assumptions, it enables a stepwise multi-material body optimization method to solve the challenge that components’ material design variables switching between CFRP and alloy will introduce/eliminate lamination design variables iteratively, posing a great optimization convergence difficulty. The proposed parametric modeling method for CFRP components was validated by experimental tests of a fabricated roof beam, and the proposed optimization method was applied to a vehicle body, achieving 15.9%, 23.9%, 18.6%, and 12.2% increase in bending and torsional stiffness and modal frequencies; 20.2%, 9.3%, and 12.7% reduction of weight and peak acceleration in frontal and side collisions. This study enables the forward design of multi-material bodies compatible with CFRP parts.

Demographic Globalization: Contribution to Global Economic Growth in Long-Term Perspective

Demography is one of the important factors of the Great Convergence. We estimate the contribution of the demographic factor to the prospective GDP dynamics for all countries of the world up to 2100. We use a long-term economic growth model that is based on estimates of labor productivity growth rates and incorporates the demographic forecast for both population and life expectancy. Globally, our model estimates long-term economic growth of world GDP at an average annual rate of 1.7 per cent, of which the demographic factor accounts for 0.54 percentage points, or about one-third (31.8 per cent) of the projected world economic growth in the twenty-first century. In our forecasts, we have relied on the logic of the previous research on convergence, which considered a sufficiently high level of the human capital development and a sufficient degree of economic openness as necessary conditions for convergence, which have now largely been achieved throughout the world precisely through the globalization processes (especially in the core and the semi-periphery of the World System). However, the inclusion of the demographic projections in our forecasts allows us to emphasize an important feature of the perspective convergence – it is not the semi-periphery, but rather the periphery of the World System, or the ‘bottom billion’ according to Collier, that is likely to converge particularly rapidly with both the core and the semi-periphery (such as the BRICS countries) thanks to favorable demographic structure. The paper has been prepared within the framework of RANEPA state assignment in 2023.

Modeling of a power consumption by bus in the real operating conditions

In spite of numerous researches concerning a power consumption by the vehicles, during everyday operation there are the significant deviations from the consumptions indicated by the factories-manufacturers. It is caused by a great number of different factors, the main part of which take the operating conditions. In order to determine the power consumption of a vehicle in real operating conditions, it is necessary to solve a difficult problem with many unknowns. While making the investigations, the authors have used a multi-criteria approach that takes into consideration the guided, unguided and conditionally guided factors that influence the vehicles power consumption. The in research work approach provides a modeling of a power consumption by the vehicles with a simultaneous changeability of factors in the determined limits. The researches were made on a base of Т70110 trolleybus. There was made an investigation of influence of every factor on a power consumption, in addition to that there were made 7000 simulations, on the base of which, using a law of large numbers, there were found the corresponding changeability ranges. During the researches there were made 122 simulations of a power consumption, as a result of which there was determined an area of values of a power consumption.The obtained theoretical results are very well correlated with the experimental data, and a deviation is equal to 3…6 % for normal operating conditions. A comparison of the received data of a power consumption with the researches results, obtained by another researchers, also shows a great convergence.

A Deep-Reinforcement-Learning-Based Computation Offloading With Mobile Vehicles in Vehicular Edge Computing

Vehicular edge networks involves edge servers that are close to mobile devices to provide extra computation resource to complete the computation tasks of mobile devices with low latency and high reliability. Considerable efforts on computation offloading in vehicular edge networks have been developed to reduce the energy consumption and computation latency, in which roadside units (RSUs) are usually considered as the fixed edge servers. Nonetheless, the computation offloading with considering mobile vehicles as mobile edge servers in vehicular edge networks still needs to be further investigated. To this end, in this paper, we propose a deep reinforcement learning based computation offloading with mobile vehicles in vehicular edge computing, namely DRL-COMV, in which some vehicles (such as autonomous vehicle) are deployed and considered as the mobile edge servers that move in vehicular edge networks and cooperate with fixed edge servers to provide extra computation resource for mobile devices, in order to assist in completing the computation tasks of these mobile devices with great QoE (i.e.,low latency) for mobile devices. Particularly, the computation offloading model with considering both mobile and fixed edge servers is conducted to achieve the computation tasks offloading through vehicle-to-vehicle (V2V) communications, and a collaborative route planning is considered for these mobile edge servers to move in vehicular edge networks with objective of improving efficiency of computation offloading. Then, a deep reinforcement learning approach with designing rational reward function is proposed to determine the effective computation offloading strategies for multiple mobile devices and multiple edge servers with objective of maximizing both QoE (i.e., low latency) for mobile devices. Through performance evaluations, our results show that our proposed DRL-COMV scheme can achieve a great convergence and stability. Additionally, our results also demonstrate that our DRL-COMV scheme also can achieve better both QoE and task offloading requests hit ratio for mobile devices in comparison with existing approaches (i.e., DDPG, IMOPSOQ and GABDOS).

Game-theoretic robotic offloading via multi-agent learning for agricultural applications in heterogeneous networks

Intelligent robotics, as a frontier field of widespread attention, is increasingly applied to realize cyber-physical-social system (CPSS). Taking Agriculture 4.0 as one use case, using various agricultural robot applications can reduce labor costs and improve operation efficiency, but limited by the onboard resources, these mobile robots need to make full use of network and computation resources provided by infrastructures in the smart farm to enhance their capabilities.On the one hand, the heterogeneity of networks brings more options for access selection; however, on the other hand, the mobility of robots and the Quality of Service (QoS) requirements of different agricultural applications pose great challenges to the current offloading work. Therefore in this paper, we introduce a stochastic game framework to address these challenges faced by smart agriculture, and propose a multi-agent task offloading and network selection (MATONS) scheme, which utilizes reinforcement learning to offload tasks from robots to central cloud or edge servers. The robots cooperatively optimize the QoS perceived and the energy usage by adopting a joint Nash equilibrium strategy, which requires the decision of offloading along with the optimal transmission technology and corresponding access nodes, i.e., WiFi or cellular network. Simulation results verify that MATONS has great convergence and stability under different scales of robots in the farm, and can achieve superior performance than mainstream algorithms in various scenarios.

Crosstalk between autophagy and bladder transitional cell carcinoma by autophagy-related lncRNAs.

The aim of this study was to investigate the crosstalk between autophagy and bladder transitional cell carcinoma (TCC) by autophagy-related long noncoding RNAs (lncRNAs). A total of 400 TCC patients from The Cancer Genome Atlas were enrolled in this study. We identified the autophagy-related lncRNA expression profile of the TCC patients and then constructed a prognostic signature using the least absolute shrinkage and selection operation and Cox regression. Risk, survival, and independent prognostic analyses were carried out. Receiver operating characteristic curve, nomogram, and calibration curves were explored. Gene Set Enrichment Analysis was employed to verify the enhanced autophagy-related functions. Finally, we compared the signature with several other lncRNA-based signatures. A 9-autophagy-related lncRNA signature was established by least absolute shrinkage and selection operation-Cox regression that was significantly associated with overall survival in TCC. Among them, 8 of the 9 lncRNAs were protective factors while the remaining was a risk factor. The risk scores calculated by the signature showed significant prognostic value in survival analysis between the high- or low-risk groups. The 5-year survival rate for the high-risk group was 26.0% while the rate for the low-risk group was 56.0% (P < .05). Risk score was the only significant risk factor in the multivariate Cox regression survival analysis (P < .001). A nomogram connecting this signature with clinicopathologic characteristics was assembled. To assess the performance of the nomogram, a C-index (0.71) was calculated, which showed great convergence with an ideal model. The Gene Set Enrichment Analysis results demonstrated 2 major autophagy-related pathways were significantly enhanced in TCC. And this signature performed a similar predictive effect as other publications. The crosstalk between autophagy and TCC is significant, and this 9 autophagy-related lncRNA signature is a great predictor of TCC.

Minimizing tardiness and makespan for distributed heterogeneous unrelated parallel machine scheduling by knowledge and Pareto-based memetic algorithm

This work aims to deal with the distributed heterogeneous unrelated parallel machine scheduling problem (DHUPMSP) with minimizing total tardiness (TDD) and makespan. To solve this complex combinatorial optimization problem, this work proposed a knowledge and Pareto-based memetic algorithm (KPMA) which contains the following features: 1) four heuristic rules are designed including the shortest processing time rule, the minimum factory workload rule, the minimum machine finish time rule, and the earliest due date rule. Meanwhile, a hybrid heuristic initialization is developed to construct a population with great convergence and diversity; 2) four problem feature-based heuristic neighborhood structures are designed to increase the success rate of local search; and 3) a simple elite strategy is developed to enhance the usage of historical elite solutions. Finally, to evaluate the performance of KMPA, it is compared to five state-of-art and run on 20 instances with different scales. The results of numerical experiments show that the proposed hybrid heuristic initialization can efficiently save computation resources to improve the initialized convergence. In addition, the knowledge-based neighborhood structures can vastly accelerate exploration. Moreover, the elite strategy can efficiently improve the diversity of the final non-dominated solutions set. The proposed KPMA has better performance than the state-of-art and has a strong ability to solve DHUPMSP.

The use of a pumice stone in removal of petroleum hydrocarbons from industrial wastewater through coagulation and flocculation

Background: Petroleum hydrocarbons that are released from industrial processes cause damage to aquatic life and degrade ecosystems by accumulating in the food chain, allowing living organisms to produce toxic secretions. Methods: In this study, a pumice stone was used to remove total petroleum hydrocarbons (TPHs) from industrial wastewater in the south refinery company in Iraq/Basrah by coagulation and flocculation processes using the design of experiments (DOE) approach using the method of analyzing screening designs and measurement of the samples using GC instrument. Results: The maximum removal was 99% at pH 3, coagulation dose of 0.5 g, and time of 60 min, and a comparison was made between the DOE and the multiple linear regression (MLR) to determine the effectiveness of the system used, which proved its incredible effectiveness. Conclusion: According to the results, a great convergence between the actual and prediction results of removal was found, while MLR was very far from the actual and predicted removal results. The high efficiency of pumice stone was found in removing petroleum hydrocarbons from the industrial wastewater of the South Refineries Company. Pumice stone is widely available in the market and cheap economically.

Development of salinity intrusion model (Munaf Q. 2019) In the Shatt Al-Arab River

Mathematical model (Munaf Q. 2019) was developed to predict the intrusion of salt into the Shatt Al-Arab River, which addresses the problem of the difficulty of measurements and prediction of the level of salinity along the Shatt Al-Arab River during the measurement period and in a very short time, where it is possible to identify the level of salinity along the Shatt Al-Arab River when it changes at the estuary. In this article, a mathematical model was used with an automatic computing system to predict the penetration of the salt intrusion into the Shatt Al-Arab, based on the development of a previous mathematical model. The results obtained through the application of the model showed that there is a great convergence between the results at a high level and at most of the stations under study (Al-Fadaqhiyah, Seyhan, Basrah, Al-Hartha) for the field-measured values with the theoretical values respectively, as follows: 27-26 ppt., 2.6-2.9 ppt., 1.85-1.96 ppt., 0.86-0.92 ppt., when the flow is 50m3/sec. When the flow is 5m3/sec, the results are as follows: 30.4-30.2 ppt., 25.3-25.1 ppt., 11.4-12.6 ppt., 1.50-1.97 ppt., Therefore the adoption of this mathematical model can be enhanced to predict the penetration of the salt as a result of any situation that may occur at any time along the Shatt Al-Arab River. The reason for using mathematical models is because it is an easy, cheap and fast way of obtaining the desired results to address some of the problems that occur for many reasons, the most important of which are direct events that may occur as a result of a sudden change in the variables dependent on them.

INFLUENCE OF AL2O3 NANO ADDITIVES ON THE VISCOSITY AND THERMAL CONDUCTIVITY OF DOUBLE DISTILLED WATER

The nano fluid consist of Al2O3 nanoparticles was dissolved by double distilled water (DDW) in three concentrations, 0.5% wt, 1%wt, and 1.5%wt of nanomaterials. The effect of adding nano materials on the viscosity and thermal conductivity was calculated experimentally at room temperature. Some mathematical models were used to calculate the viscosity and the thermal conductivity and compare it with the experimental work. The results showed that viscosity and thermal conductivity increased with increasing concentration of Al2O3 nanoparticles, and there is a great convergence between the theoretical and measured viscosity and thermal conductivity. The SEM images showed that there is homogeneity in the distribution of the nano fluid at 1%wt., and some agglomerations were detected at a concentration 1.5%. The EDS spectrum showed that the percentage of aluminum increased gradually with increasing the Al2O3 concentration. The X-Ray diffraction indicated slight increase in the intensity of diffraction with increasing the Al2O3 concentration.

Optimal power flow-based approach for grid dispatch problems through Rao algorithms

In order to fulfill the load demand and operational restrictions, the grid dispatch problem's optimization goal is to reduce fuel cost through variation of active and reactive power of the generators within fixed limits and to uplift the security of the power plants during voltage instability through proper allocation of static voltage compensator to ensure minimum power loss. This paper proposes a collaborative optimization of economic load dispatch and reactive power dispatch using three metaphor-less algorithms called Rao algorithms, where the current solution will interact with the finest and inferior solutions in the population, as well as a randomly selected solution. These strategies are free from any specific control parameters. These algorithms are formulated on the IEEE 30 bus system. The outcomes of the suggested techniques are compared with those of existing optimization approaches such as Bat algorithm (BOA), Modified Harmonic search algorithm (MSG-HS), Root tree optimization (RTO), Adaptive Gravitational search optimization (AGSO), Whale optimization algorithm (WOA), Fruit fly optimization (MOFOA), Crow search algorithm (CSA), Quasi-Oppositional Teaching learning based optimization (QOTLBO), and Jaya algorithm (JA) which demonstrates that the proposed strategies have great convergence characteristics and are the most systematic way to addressing these complicated power system issues. They are unable to converge on a local optimal solution prematurely while maintaining a high level of precision.

Численное моделирование участка реки в трехмерной постановке задачи для корректировки параметров проектируемых русловых карьеров

Extraction of alluvial sand and gravel materials is a large industry. It has been widely developed in many regions of Russia, which has an increasing impact on the riverbed processes in the form of changes in the morphological structure of riverbeds and floodplains under the influence of changing flow hydraulics. Changes in the hydrological regime and morphology of the riverbed can bring negative effects and progress over time, as well as at a considerable distance from the mining sites, as can be seen from the examples, which indicates the need for their comprehensive study. Leading higher educational institutions and research organizations in the field of hydrology, water transport and fisheries study the problems of the impact of anthropogenic changes in river beds. Summarizing the experience, it is recommended to predict the impact of a potential quarry on the hydrological conditions and morphology of the whole river section by numerical modeling of the channels in 3-D problem formulation. Different technological schemes of open pit mining are considered and modeled, the most acceptable (sparing) option minimizing the negative impact on the environment is selected and justified. In order to control the results obtained by numerical simulation, studies were also carried out using the methods recommended by the regulatory literature. Analysis of the results revealed their great convergence. The final results of the research were compared with the allowable parameters in the regulatory documents, recommendations were made on improving the quarry mining technology for minimizing its negative impact on the channel processes and the resulting transport logistics.

Phase-Only Pattern Synthesis for Spaceborne Array Antenna Based on Improved Mayfly Optimization Algorithm

A new optimization algorithm—Improved Mayfly Optimization Algorithm (IMOA)—is proposed in this paper to fulfill the low sidelobe level (SLL) design requirements of the spaceborne array antenna. MOA is a new heuristic algorithm inspired by the flying behavior and mating process of mayflies. It has a unique speed updating system with great convergence, strong stability, fast solution speed, and high precision. Based on the MOA, IMOA not only introduces the adaptive inertial weight factor to enhance the search ability, but also uses the Levy flight strategy and the golden sine operator to improve the disadvantage of easily falling into the local optimal solution. Firstly, according to the antenna pattern requirements of high gain and low sidelobe, an optimization problem model is carried out. Then, the IMOA is applied to solve the problem by only controlling the phase under a given secondary amplitude distribution. Simulation results show that IMOA has great advantages in the maximum sidelobe level (MSLL) suppression and convergence speed. Finally, the EM simulations are conducted on the 528-element planar array antenna. The maximum sidelobe level suppression performances in the test are very consistent with the theoretical simulation, which verifies the feasibility and effectiveness of the proposed IMOA.

Algorithms for audio inpainting based on probabilistic nonnegative matrix factorization

Audio inpainting, i.e., the task of restoring missing or occluded audio signal samples, usually relies on sparse representations or autoregressive modeling. In this paper, we propose to structure the spectrogram with nonnegative matrix factorization (NMF) in a probabilistic framework. First, we treat the missing samples as latent variables, and derive two expectation–maximization algorithms for estimating the parameters of the model, depending on whether we formulate the problem in the time- or time-frequency domain. Then, we treat the missing samples as parameters, and we address this novel problem by deriving an alternating minimization scheme. We assess the potential of these algorithms for the task of restoring short- to middle-length gaps in music signals. Experiments reveal great convergence properties of the proposed methods, as well as competitive performance when compared to state-of-the-art audio inpainting techniques.

Detection of pulmonary embolism severity using clinical characteristics, hematological indices, and machine learning techniques.

Pulmonary embolism (PE) is a cardiopulmonary condition that can be fatal. PE can lead to sudden cardiovascular collapse and is potentially life-threatening, necessitating risk classification to modify therapy following the diagnosis of PE. We collected clinical characteristics, routine blood data, and arterial blood gas analysis data from all 139 patients. Combining these data, this paper proposes a PE risk stratified prediction framework based on machine learning technology. An improved algorithm is proposed by adding sobol sequence and black hole mechanism to the cuckoo search algorithm (CS), called SBCS. Based on the coupling of the enhanced algorithm and the kernel extreme learning machine (KELM), a prediction framework is also proposed. To confirm the overall performance of SBCS, we run benchmark function experiments in this work. The results demonstrate that SBCS has great convergence accuracy and speed. Then, tests based on seven open data sets are carried out in this study to verify the performance of SBCS on the feature selection problem. To further demonstrate the usefulness and applicability of the SBCS-KELM framework, this paper conducts aided diagnosis experiments on PE data collected from the hospital. The experiment findings show that the indicators chosen, such as syncope, systolic blood pressure (SBP), oxygen saturation (SaO2%), white blood cell (WBC), neutrophil percentage (NEUT%), and others, are crucial for the feature selection approach presented in this study to assess the severity of PE. The classification results reveal that the prediction model's accuracy is 99.26% and its sensitivity is 98.57%. It is expected to become a new and accurate method to distinguish the severity of PE.

Law as Peace (Or Why I Am Still a Hard-Core Positivist)

Abstract The fact that the most prominent brands of positivism and natural law theory, namely their Razian and Finnisian variants, are in agreement about some hitherto hotly contested issues has over the decades become more of an entertaining curio than an actual concern that ruffles any theoretical feathers. Whatever its benefits, this great convergence requires positivism to forfeit much of what has originally made it a theoretically appealing position. One of the many reasons for the curious alignment of positivism with natural law theory has been a focus on the explanatory power and theoretical import of the phenomenon of friendship. If we allow the countervailing forces of enmity to be given their due weight, a different model of law emerges, namely law as peace, which is more resistant to the contortions and ultimate self-abandonment that ensues from the alignment of positivism with natural law theory.