Multifactor Complex Research Articles

DIGITAL ASSETS MARKET HUB

Block chain technology, non-fungible tokens (NFTs), and cryptocurrency have all contributed to the explosive rise of the digital assets sector. The dynamic and cutting-edge digital asset marketplace shown in this abstract is intended to satisfy the various demands of traders, investors, and enthusiasts in the digital asset market. The marketplace offers a user-friendly and safe platform for purchasing, selling, and managing different digital assets, and it is distinguished by its user-centric approach. It offers real-time market data, order book information, and robust analytical capabilities for well-informed decision-making, all with an intuitive interface that streamlines asset transactions. The ability to move assets seamlessly between blockchain networks, multi factor authentication, cold storage, and complex trading algorithms that optimise trading tactics are some of the key characteristics. ----------------------------------------------------------------------------------------------------------------------------------- Keywords Blockchain, Smart Contract, Ethereum, Transparency, Data sharing, Privacy, Byzantine fault tolerance, decentralized systems.

Perancangan Sistem Pandukung Keputusan Pemilihan Perguruan Tinggi Swasta Di Kota Semarang Dengan Metode AHP Untuk Siswa SMK Bina Negara Gubug

The selection of universities has a very important role, at the end of school one of the problems often faced by students is determining where to continue the next level of education, including high school students, they find it difficult to determine which colleges and fields of study they will be capable of. Universities including private universities in the city of Semarang are increasingly making it difficult for students to determine private universities according to their criteria considering the difficulty of selection to enter public universities, in terms of quality or accreditation from these private universities, tuition fees, campus location close to boarding houses, places to eat, markets and a safe and conducive environment that supports them throughout their lives to study, distances that are easily accessible by private vehicles or public transportation, even the facilities obtained on campus are in accordance with the costs incurred or not, there are There are several criteria that are considered when choosing a private university.AHP is a decision support model developed by Thomas L. Saaty. This decision support model will describe a multi-factor problem or complex criteria into a hierarchical form. The AHP method is precise and systematic in the decision-making process according to the criteria set by Kopertis 6 based on data analysis. The research method used is R&D. Research and Development method is research that is used to produce certain products and test the effectiveness of these products. In this study the authors built a decision support system using the AHP method using 5 criteria (Cost, Facilities, Mileage, Quality, Location

Toxicity Studies of DOX~DEX~Fe3O4@ZIF~ Peptides in Vitro and in Vivo

Colorectal cancer is formed and developed through a multi-factor and multi-stage complex pathological process. The review provides an overview of the available colorectal cancer treatments and makes the case for the need to develop specific colon cancer therapeutic products that are safe and stable, with significant therapeutic efficacy and minimal patient sacrifice. The author reviews the progress made in the field of miRNA, nanomedicine system, epidermal growth factor receptor (EGFR), doxorubicin (DOX), dexmedetomidine (DEX) and peptides in the treatment of cancer and point out that more and more researchers are focusing on the treatment of cancer at the genetic level and on the combination of multiple approaches to treat cancer. This review provides an important theoretical reference for the development of multifunctional drugs for the multi-treatment targeting of colorectal cancer.

Correlation between multi-factor phase diagrams and complex electrocaloric behaviors in PNZST antiferroelectric ceramic system

Ferroelectric (FE) phase transition with a large polarization change benefits to generate large electrocaloric (EC) effect for solid-sate and zero-carbon cooling application. However, most EC studies only focus on the single-physical factor associated phase transition. Herein, we initiated a comprehensive discussion on phase transition in Pb_0.99Nb_0.02[(Zr_0.6Sn_0.4)_1−<i>x</i>Ti_<i>x</i>]_0.98O₃ (PNZST100<i>x</i>) antiferroelectric (AFE) ceramic system under the joint action of multi-physical factors, including composition, temperature, and electric field. Due to low energy barrier and enhanced zero-field entropy, the multi-phase coexistence point (<i>x</i> = 0.12) in the composition–temperature phase diagram yields a large positive EC peak of maximum temperature change (Δ<i>T</i>_max) = 2.44 K (at 40 kV/cm). Moreover, the electric field–temperature phase diagrams for four representative ceramics provide a more explicit guidance for EC evolution behavior. Besides the positive EC peaks near various phase transition temperatures, giant positive EC effects are also brought out by the electric field-induced phase transition from tetragonal AFE (AFE_T) to low-temperature rhombohedral FE (FE_R), which is reflected by a positive-slope boundary in the electric field–temperature phase diagram, while significant negative EC responses are generated by the phase transition from AFE_T to high-temperature multi-cell cubic paraelectric (PE_MCC) with a negative-slope phase boundary. This work emphasizes the importance of phase diagram covering multi-physical factors for high-performance EC material design.

Experimental and finite element investigations on hydration heat and early cracks in massive concrete piers

The concrete hydration heat and different construction methods directly affects the early crack resistance of massive concrete structures and may lead to concrete cracks. In this work, the hydration heat test and finite element simulation were conducted to investigate the early cracking resistance and the main influencing factors of the massive concrete bridge pier. The size of the bridge pier was 8 m (length) × 4 m (width) × 22.5 m (height), and the pier was poured in two layers (8.5 m/14 m) with an interval of 37 h. The internal temperature of the pier was tested 16 days after the concrete pouring using pre-buried temperature sensors, and the crack expansion on the surface of the pier was continuously observed during the test period. The test obtained a maximum concrete temperature of 64.25 °C after 100 h after the concrete pouring, and 3 cracks were found on the surface of the pier. The pier simulation model is established according to the actual situation in the test, The maximum temperature inside the bridge pier obtained from the simulation is 61.73 ℃, and the temperature field development pattern and the crack expansion on the pier surface were basically consistent with the test, which confirmed the reliability of the simulation method. Besides, the simulation process found that the maximum main tensile stress on the surface of the bridge pier was 2.77 MPa, which was mainly distributed near the interface of the 2 layers, indicating that the layered pouring scheme adopted in the test was improper. The effect of layered casting schemes on the early cracking resistance of massive concrete has rarely been discussed, and further simulations revealed that: the maximum main tensile stress can be reduced by gradually improving the number of layers, layer height and interval between each layer. By changing the original layered pouring scheme to 3 layers with an interval of 3 days between each layer, the maximum principal tensile stress is reduced to 1.50 MPa, and early cracking of the bridge pier can be avoided. This work provides a general numerical analysis method with high accuracy, parameterization and quantification for early crack control of massive concrete structures in a multi-factor complex environment.

The Differences in the Impact of Economic Structure Adjustment on the Ecological Carrying Capacity of County Education—A Case Study of Chongqing, China

Basic education is a foundation-laying project of national quality education. Improving the ecological carrying level of basic education is of great practical significance to the coordinated and sustainable development of regional society and economy. Based on the panel data of Chongqing from 2011 to 2017, combined with the DPSIR theoretical model, a comprehensive evaluation index system was conducted, and the spatial and temporal evolution characteristics of ecological carrying capacity of county education in Chongqing was explored by using statistical analysis and spatial analysis methods. The multiple linear regression model was used to analyze differences in the impact of economic structure adjustment on the ecological carrying capacity of county education. It was found that the level of ecological carrying capacity of county education in Chongqing showed a steady upward trend in terms of time sequence, and all the five subsystems showed the characteristics of steady development, but the influence of each subsystem on the overall carrying capacity was different. From the formation mechanism, Chongqing’s educational ecological carrying capacity is the result of a multi-factor complex system, and there are obvious differences in the influencing factors at different development stages. Therefore, it is necessary to narrow the regional differences of education ecological carrying capacity, promote the coordinated development of education among regions, and take multiple measures to effectively ensure the high quality and sustainable development of county education in Chongqing.

GDP Economic Forecasting Model Based on Improved RBF Neural Network

Among the existing GDP forecasting methods, time series forecasting and regression model forecasting are the two most commonly used forecasting methods. However, traditional macroeconomic forecasting models are unable to accurately achieve optimal forecasts of highly complex nonlinear dynamic macroeconomic systems due to the influence of multiple confounding factors. In order to solve the above problems, a GDP economic forecasting model based on an improved RBF neural network is proposed. First, the main traditional GDP forecasting methods are analyzed. Then, RBF neural networks are used to solve the problem that traditional forecasting technology methods cannot handle multi-factor complex nonlinearities well. Second, to further improve the convergence speed and accuracy of the RBF neural network learning algorithm, the Shuffled Frog Leaping Algorithm with global search capability and high practicality is fused into the RBF network training. Finally, the improved RBF neural network is used to build a GDP economic forecasting model. The performance of the Shuffled Frog Leaping Algorithm and the improved RBF neural network was tested using the approximation of Hermit polynomials and the Iris classification problem as simulation examples. The experimental results show that the improved RBF neural network-based GDP economic forecasting model achieves more accurate forecasting accuracy than other forecasting methods.

Two-Stage Vehicle Routing Optimization for Logistics Distribution Based on HSA-HGBS Algorithm

Aiming at the problems of complex urban road network, low efficiency of logistics distribution, and the difficulty of large-scale logistics distribution area division and routing planning, this paper proposes a two-stage logistics distribution vehicle routing optimization (VRP) method based on the establishment of a multi-factor complex road network constrained logistics distribution mathematical model. Considering the complex traffic elements and road network topological structure in logistics and distribution, in the first stage, a heuristic simulated annealing (HSA) distribution region partitioning algorithm is proposed with the objective of balancing vehicle task load to divide the urban logistics distribution network under complex road networks, so as to reduce the area scale and path search cost. In the second stage of route decision making, aiming at minimizing the total cost of logistics distribution, combining the VRP problem with complex road network conditions, a heuristic path search method combined with complex road network model constraints is proposed. In this stage, a hybrid genetic beam search(HGBS) algorithm is used to plan the path nodes, reduce the randomness of the model in the initial search for paths by heuristic genetic algorithms, then combine with Beam graph search methods to reduce the space and time used for the search, and use optimization algorithms to improve the accuracy of independent sub-region routing optimization and the rationality of overall physical distribution route selection. Finally, the proposed method is validated in this paper with two practical cases. The experimental results show that the two-stage decision-making algorithm proposed in this paper has certain advantages in partitioning schemes, minimizing total cost and iteration times. Through comparison, the optimization ability of this method for logistics distribution networks is proved.

Моделирование грузового тарифа на контейнерные перевозки железнодорожным транспортом в РФ

Objective: To develop proposals for improving freight rates for container transportation by rail in the Russian Federation in the current context. Methods: Comparative method, analysis, synthesis, statistical method, and system approach have been applied. Results: Multi-factor complex digital models of rates per wagon with containers and per container train have been developed. One of the options for the algorithm for evaluating individual components of these models is proposed, additional comments to the presented models are provided. Practical importance: The findings can be used to improve freight rates for container transportation by rail in the Russian Federation in the current context, as well as for further research in the field of pricing.

Association between 5-HTTLPR and STin2 VNTR polymorphisms in the serotonin transporter gene and clinical response of a selective serotonin reuptake inhibitor (dapoxetine) in lifelong premature ejaculation

Association between 5-HTTLPR and STin2 VNTR polymorphisms in the serotonin transporter gene and clinical response of a selective serotonin reuptake inhibitor (dapoxetine) in lifelong premature ejaculation

New Dynamic Group DEMATEL Decision-Making Method Based on Hesitant Fuzzy Linguistic Term Sets

Decision-making trial and evaluation laboratory (DEMATEL) method is comprehensively applied in the complicated socioeconomic system. Existing literatures on DEMATEL focus on static decision environment at single time node. However, due to the increasingly complex and rapidly changing socioeconomic environment, decision-making has become more complex and dynamic. In this paper, dynamic group DEMATEL method based on hesitant fuzzy linguistic term sets (HFLTSs) is proposed. In the view of the multi-stage, multi-factor and multi-expert complex decision-making situation, the method fully integrates the decision-making information of different periods from the perspective of dynamic evolution mechanism which can effectively link multi rounds DEMATEL decision-making process and reflect the time evolution of dynamic decision-making. Meanwhile, the TOPSIS method is applied to determine the weight of experts, which can effectively solve the influence of the best choice (positive ideal solution) and the worst choice (negative ideal solution) on the decision results in aggregation of group experts' preference information, and fully consider the influence of hesitation and fuzziness of expert preference representation on expert weight. Finally, a numerical comparison is given to verify validity and effectiveness of the proposed approach.

Analysis of modeling, optimization and control methods of multifactor operations and complex technological processes

Analysis of modeling, optimization and control methods of multifactor operations and complex technological processes

The mathematical modeling aspects of multi-factor operations and complex technological processes of machine and equipment parts processing

The mathematical modeling aspects of multi-factor operations and complex technological processes of machine and equipment parts processing

SISTEM PEDUKUNG KEPUTUSAN KELAYAKAN DIKLAT KEPEGAWAIAN DENGAN MENGGUNAKAN ANALYTIC HIERARCHY PROCESS

Education and training is an obligation for a civil servant (PNS) to develop their performance capabilities in the agency they work for. There are several training courses that civil servants can participate in, such as leadership, functional training, and technical training. However, not all employees can participate in the training, because there are special requirements in order to participate in the training. The purpose of this study is to help provide decision recommendations for employees who will conduct training. The method used for the calculation of the DSS is Analytic Hierarchy Process (AHP). Where this decision support model will describe a multi-factor or multi-criteria complex problem into a hierarchy. Then each employee is given the weight of his own assessment of the employee who will conduct the training. Meanwhile, the system development method used is a prototype. The results of this study are in the form of a decision support system, which can facilitate the decision-making process, and can provide recommendations regarding employees who will become training participants.

Should I Stay or Should I Go: eIF3 Remains Ribosome Associated and Is Required for Elongation

Wagner etal. (2020), Bohlen etal. (2020), and Lin etal. (2020) use Sel-TCP-seq or selective ribosome profiling to gain insights into mRNA translation initiation, highlighting distinctions between yeast and higher eukaryotes and a role for eIF3 in elongation.

Role of Nuclear Receptor Condensates in Ligand Induced Co‐operative Gene Regulation

Enhancers are transferable DNA elements that regulate target genes within the confines of Topologically Associated Domains (TADs) through Cohesin‐dependent looping. However, the potential regulatory functions and mechanisms of long‐distance, inter‐TAD enhancer associations remain largely unexplored. We report that 17β‐estradiol (E2) rapidly increases the spatial proximity of a fraction of E2‐responsive enhancers located in widely separated TADs. These enhancers are characterized by the recruitment of mega‐dalton sized multi transcription factor complex and high enhancer RNA (eRNA) induction. Optimal activation of these enhancer targets requires their co‐localization with phase‐separated interchromatin granules (ICGs). Chemical disruption of phase‐separated structures abolishes enhancer complex assembly and, specifically deactivates the most robust signal induced enhancers. Surprisingly, the physical property of the enhancer complex alters with the duration of hormone stimulation. In support of the long distance enhancer cooperativity model, the deletion of the most robust E2 induced enhancers resulted in a quantifiable decrease in the transcriptional activity of homotypic enhancers in the same chromosome. These data reveal previously unappreciated mechanisms for co‐ordinated gene regulation: (i) The biophysical properties of enhancer complex is critical for transcriptional output (ii) Phase separation mediated long‐distance enhancer cooperativity is required for optimal transcriptional activation by acute signaling programs. These findings reveal a new, generalizable model for the acute spatial gene regulation.Support or Funding InformationResearch support for S.J.N include postdoctoral fellowship from American Heart Association (14POST19860025), T32 Fellowship (5T32DK007044), and KO1 career grant (1K01DK121871). D.M. was supported by NIH‐NIDDK Grant 5F32DK112682. MGR is supported by Howard Hughes Medical Institute.Model of E2‐induced changes in the chromatin architecture. E2 stimulation results in ERa dependent recruitment of MegaTrans complex, Condensin complex, and enhancer RNA as phase‐separated ribonucleoprotein condensates. Spatial enhancer association results from “megaloops” formation between the most robust enhancers (E.g. TFF1e1 and NRIP1e3). Maximal cooperative enhancer activation occurs with the co‐localization of the enhancers in the interchromatin granule (ICG).Figure 1

Application of AHP in the Evaluation of Tourism Resources in Ethnic Regions: Case Study of Xichang, Liangshan Yi Autonomous Prefecture, China

Tourism resource evaluation is a comprehensive, multi-factor and multi-criteria complex problem, is the basis of tourism development. It reasonable to regional tourism resources development and utilization plays an important role, and by using AHP method to construct consists of resource conditions, environmental value and facilities management, and other three factor criterion layer and rule layer of 15 children factor of Xichang tourism resources evaluation model.

Risk prediction and factors risk analysis based on IFOA-GRNN and apriori algorithms: Application of artificial intelligence in accident prevention

Risk prediction of disasters is one of the most effective ways to prevent accidents. To solve the problems in multi-factor complex disaster prediction, this paper proposes a new method for risk prediction and factorial risk analysis. Coal and gas outburst accidents were selected as research objects. First, a new coal and gas outburst prediction model was established that consists of 4 levels and 14 factors. Then, the Improved Fruit Fly Optimization Algorithm (IFOA) and the General Regression Neural Network (GRNN) algorithm were combined to establish the IFOA-GRNN prediction model. After that, the sensitivity analysis method was applied to the analysis of the sensitive factors of coal and gas outbursts. Finally, an apriori algorithm was used to mine the disaster information. The method proposed in this paper was applied to the Pingdingshan No. 8 Min. The application results show that the IFOA-GRNN algorithm proposed in this paper has an accuracy rate of 100% for the prediction of accident risk levels. Compared with the Back Propagation (BP), GRNN and FOA-GRNN algorithms, IFOA-GRNN has the characteristics of a smaller prediction error, higher stability and faster convergence. The sensitivity analysis method can judge the sensitive factors of coal and gas outbursts without knowing the mechanisms of the accident. The a priori algorithm can perform good data mining on the combination of high frequency factors leading to accidents and the relationships between the coal and gas outburst levels and factors. The data mining results are very helpful for the prevention and management of coal and gas outbursts.

3084 - Distinct and Overlapping Function for Scl, Lmo2 and Ldb1 in Regulating the Divergence of Hematopoietic and Cardiac Fates

Lmo2 and Ldb1 are non-DNA binding components of a multi-factor complex containing Scl/Tal1, the master transcription factor that regulates hematopoietic fate commitment during embryogenesis. Scl has been shown to be critical for the activation of the hematopoietic transcription program in hemogenic endothelium, and repression of cardiac transcription program in endothelium and endocardium, and hence responsible for the divergence of these mutually exclusive mesodermal fates. However, the specific functions of Lmo2 and Ldb1 in this complex, especially in the repressive function, are poorly understood. Here, we employ a combination of germline and conditionally-deleted Scl, Lmo2 and Ldb1 mouse model to study the dynamic roles of each complex member. Our data suggests that similar to Scl, both Lmo2 and Ldb1 are critical for primitive and definitive erythropoiesis. However, whereas the deletion of Scl completely ablates HSPC specification, the presence of residual CD41+cKit+ HSPCs in Lmo2 and Ldb1 knockout embryos suggests that they may have partially redundant roles during HSPC specification. Lmo2, similar to Scl, is critical for the repression of cardiac fate in hemogenic endothelium and endocardium, but becomes dispensable before Scl for continued cardiac repression in the endocardium. Ldb1-/- embryos lack heart and instead show accumulation of CD31+Pdgfra+ cells in the rostral half. Moreover, hemogenic tissues of Ldb1 mutants do not show accumulation of cardiogenic progenitors but exhibit an expansion of CD31+cKit+ endothelial cells, suggesting that Ldb1 critically regulates the activation of both hematopoietic and cardiac transcriptional program in the endothelium. ChIP-seq revealed that Lmo2 predominantly acts through Scl-bound enhancers. Ldb1 also has an Scl independent function in activating cardiac genes, as well as a distinct, but Scl-dependent, function in regulating genes associated with polycomb-repression such as Ezh2 and Suz12. Our data demonstrates the dynamic and temporal interactions as well as individual functions between the members of Scl complex during the divergence of hematopoietic and cardiac lineages.

A Retrospective Propensity Score-Matched Early Thromboembolic Event Analysis of Prothrombin Complex Concentrate vs Fresh Frozen Plasma for Warfarin Reversal Prior to Emergency Neurosurgical Procedures

Reversal of therapeutic anticoagulation prior to emergency neurosurgical procedures is required in the setting of intracranial hemorrhage. Multifactor prothrombin complex concentrate (PCC) promises rapid efficacy but may increase the probability of thrombotic complications compared to fresh frozen plasma (FFP). To compare the rate of thrombotic complications in patients treated with PCC or FFP to reverse therapeutic anticoagulation prior to emergency neurosurgical procedures in the setting of intracranial hemorrhage at a level I trauma center. Sixty-three consecutive patients on warfarin therapy presenting with intracranial hemorrhage who received anticoagulation reversal prior to emergency neurosurgical procedures were retrospectively identified between 2007 and 2016. They were divided into 2 cohorts based on reversal agent, either PCC (n = 28) or FFP (n = 35). The thrombotic complications rates within 72 h of reversal were compared using the χ2 test. A multivariate propensity score matching analysis was used to limit the threat to interval validity from selection bias arising from differences in demographics, laboratory values, history, and clinical status. Thrombotic complications were uncommon in this neurosurgical population, occurring in 1.59% (1/63) of treated patients. There was no significant difference in the thrombotic complication rate between groups, 3.57% (1/28; PCC group) vs 0% (0/35; FFP group). Propensity score matching analysis validated this finding after controlling for any selection bias. In this limited sample, thrombotic complication rates were similar between use of PCC and FFP for anticoagulation reversal in the management of intracranial hemorrhage prior to emergency neurosurgical procedures.