Reasoning System Research Articles

Analysis of the Influence and Propagation Law of Urban Rail Transit Disruptions: A Case Study of Beijing Rail Transit

In the context of the network operation of urban rail transit systems, disruptions caused by signal interruptions influence not only the operation of the service at a single station but also the level of service of the whole network. Moreover, it is even possible to induce the cascading failure of the urban rail transit network. Therefore, it is essential to maintain the real-time dynamic monitoring of abnormal stations in urban rail transit systems for security reasons. Based on the large amounts of automated fare collection (AFC) data, a real-time calculation method to estimate the influence intensity of the passenger flow is presented, the spatiotemporal distribution of the influence characteristics is analyzed, and the propagation law of disruptions in the urban rail transit network is explored. First, the fluctuation threshold of passenger flow in a normal situation for all stations was calculated. Accordingly, abnormal stations influenced by the disruption were identified. Then, an evaluation method for calculating the influence intensity of the passenger flow was proposed. Finally, a real-world case study based on the Beijing rail transit system was conducted. All abnormal stations were identified dynamically and displayed in real time, and the distribution and propagation law of abnormal stations were constructed by spatiotemporal diagrams. The influence intensity of passenger flow was analyzed in detail from the perspective of the whole network and representative stations. The results revealed that transfer stations were more vulnerable to the effects of disruption, and the duration for which these stations were affected was longer than that of ordinary stations. Moreover, short-distance travelers were less affected by the disruption than long-distance travelers. The method proposed in this paper can provide a theoretical basis for rail management departments to grasp the characteristics of passenger flow in real time, formulate disposal measures dynamically, and provide more accurate information services for passengers.

Stability and bifurcation analysis for a fractional-order cancer model with two delays

Considering that healthy tissue cells and tumor cells restrain each other’s the average growth rate with a corresponding delay, a fractional-order cancer model with time delays is established. Firstly, by selecting different delays as bifurcation parameters, the delay-independent stability and Hopf bifurcation conditions of such system are derived. Then, some interesting dynamical phenomena of the system under different initial values can be observed, such as a pair of coexisted stable periodic solution, equilibrium point transition, and even chaos. Finally, some numerical simulations are exhibited to substantiate the obtained results. The work reveals that the fractional order and delays play an important role in the stability behavior and bifurcation characteristics of the system, and that a suitable control strategy of the system can be designed by selecting reasonable system parameters (fractional orders and time delays).

Research on New Employee Training Management of Dalian New Oriental Training School Co., Ltd

As an important link of human resource management, training is the main means of preserving and increasing the value of human resources. In particular, new employee training is the basis and premise for new employees to carry out their work, which can effectively improve the competence of new employees in actual work, thereby promoting the improvement of enterprise economic efficiency, and has been valued by more and more enterprises.Dalian New Oriental Training School Co., Ltd is a branch of New Oriental Education Technology Group and is currently the largest education and training enterprise in Dalian market. Since its establishment, the company has been in a state of rapid development, and a large number of new employees will be recruited and trained every year. However, due to the immature training system of new employees and imperfect training guidance, the turnover rate of new employees is high, and the training costs paid by enterprises cannot be effectively converted into economic benefits. According to the main links of the training management model, this paper collects and organizes Dalian new oriental training school co., Ltd using the document method, questionnaire survey method and in-depth interview method Relevant information in the management of new employee training, and analyzed the problems in the management of existing training. According to the existing problems, combined with the main theoretical models such as training transfer theory, training demand analysis model, and Kirkoff training evaluation model, targeted optimization measures are proposed to improve the main links in training management and improve the training management performance of new employees. Dalian new oriental training school co., Ltd needs to do a good job in the process of “training needs analysis, training design, training implementation, training effect evaluation” and other processes to optimize the existing training management, and also needs to ensure the existing optimization measures from the institutional level, personnel level and resource level. Through Dalian new oriental training school co., Ltd The research on new employee training mainly has the following conclusions of training management: First, new employee training should serve the organizational strategy, and take the organizational strategy and customer needs as the direction of new employee training; In addition, a reasonable training system and process should be built in training management. Second, the training needs analysis should fully consider the needs of new employees and enhance the sense of belonging of new employees. Third, in the early stage of training, it is necessary to enhance the organizational loyalty and commitment of new employees, help new employees establish values that are highly compatible with the organizational culture, and reduce the turnover rate of new employees. Fourth, before training, it is necessary to create the necessary environment to make new employees feel valued; In addition, the focus of new employee training supervision is on training attitudes. Fifth, Dalian new oriental training school co., Ltd new employee training effect evaluation, should be based on constructive evaluation, supplemented by summary evaluation, continuous tracking of behavior layer and result evaluation, use evaluation to improve existing training.

Discovery of quality markers in the rhizome of Atractylodes chinensis using GC–MS fingerprint and network pharmacology

BackgroundThe rhizome of Atractylodes chinensis (RAC), also known as “Cangzhu”, is a well-known Chinese herbal medicine (CHMs). Due to the scarcity of wild resources, cultivated RAC and counterfeit products derived from the rhizome of Atractylodes japonica (RAJ) are also prevalent on the market. Diverse origins lead to varying quality. At present, the Chinese Pharmacopoeia (2020 edition) uses atractylodin as the only quantitative index component for RAC, and there is a lack of research on quality markers (Q-markers) that are closely related to efficacy. PurposeThis work aims to discover the Q-markers of RAC based on chemical and pharmacological strategies. MethodsThe chemical composition of RAC was analyzed and identified through gas chromatography-mass spectrometry (GC–MS). The ″Chromatographic Fingerprint Similarity Evaluation System for Traditional Chinese Medicine″ was utilized to assess the similarity of the samples and identify the characteristic peaks. Network pharmacology was employed to screen targets and pathways for characteristic components of the RAC, construct “active ingredients–targets–pathways” network diagrams, and predict the Q-markers. The Q-markers for RAC were identified through an integrated analysis of the chemical components, network pharmacology, pharmacological activities and clinical applications. In addition, a preliminary evaluation of 28 batches of “Cangzhu” from different origins were conducted by GC–MS and chemical pattern recognition. ResultsA total of 16 specific components of RAC were identified by GC–MS fingerprint. The network pharmacology study found that these 16 components primarily function through 29 core targets and 23 pathways. Three compounds, hinesol, atractylon, and β-eudesmol were identified as the potential Q-markers for RAC. The evaluation of 28 batches of “Cangzhu” showed that these three Q-markers could effectively distinguish RAC from RAJ, and RAJ could not be used as a substitute for RAC. There was no significant difference between cultivated RAC and wild RAC, and the quality of cultivated RAC was more uniform than wild RAC. ConclusionThis study identified and verified three potential Q-markers for RAC by using a comprehensive strategy combing of “GC–MS fingerprinting – network pharmacology – chemical pattern recognition”. It provides a basis for the establishment of a more reasonable RAC quality evaluation system. Furthermore, it provides a reference for the discovery of Q-markers in other CHMs.

Carbon Emission Influencing Factors and Scenario Prediction for Construction Industry in Beijing–Tianjin–Hebei

In this paper, the factors causing the change in carbon emissions from direct energy consumption in the construction industry in Beijing–Tianjin–Hebei are decomposed using the logarithmic mean divisia index (LMDI) method to analyze the effect values and contribution rates of each macrofactor. Based on the decomposition results and given relevant national policies, five scenarios are set up for each influencing factor, and a regression stochastic impact on population, affluence, and technology (STIRPAT) with ridge regression analysis is applied to each scenario combination for scenario prediction, forming a scientific and reasonable theoretical system to predict the future time of carbon peaking and carbon neutrality in the construction industry of Beijing–Tianjin–Hebei. The results show that (1) energy intensity and energy structure have a suppressive effect on direct energy consumption carbon emissions in the construction industry in Beijing–Tianjin–Hebei, and the industrial structure, economy, and population will promote an increase in carbon emissions. Energy intensity and the economy have a more significant effect on carbon emissions in the construction industry. (2) The peak year of carbon emissions varies with different scenarios, and the energy efficiency scenario achieves peak carbon in 2028, the earliest peak time, and the lowest peak, as it is the optimal emission reduction projection scenario.

Evaluation of Urban Ecological Livability from a Synergistic Perspective: A Case Study of Beijing City, China

The ecological livability of a city reflects the overall civilization degree and sustainable development ability of this region. The establishment of a scientific and reasonable evaluation index system for eco-livable cities helps analyze the level of sustainable development of cities and the quality of life of residents, thus promoting high-quality development of cities. However, few scholars construct a universal evaluation index system for eco-livable cities from synergistic perspective. This paper establishes a multidimensional evaluation index system based on the Theory of Synergism of Five Laws (considering five aspects of economy, society, technology, environment and nature) and analyzes the trends of the evaluation value and coupling coordination degree of five systems in Beijing from 2010 to 2021 by using the systematic methods of a five-law analysis and a five-law synergy integration included in the Theory of Synergism of Five Laws, respectively. The results show that the ecological livability of Beijing generally maintains an upward trend and reaches a well-coordinated level in 2021, but the development level of each system is constrained by different factors. Based on the above results, this paper proposes policy recommendations to improve the ecological livability of cities in combination with the comprehensive model of policy-oriented coupling of multiple subjects to promote the coupling of five systems, with a view to providing references for decision-making in building an eco-livable city.

Knowledge Authoring for Rules and Actions

AbstractKnowledge representation and reasoning (KRR) systems describe and reason with complex concepts and relations in the form of facts and rules. Unfortunately, wide deployment of KRR systems runs into the problem that domain experts have great difficulty constructing correct logical representations of their domain knowledge. Knowledge engineers can help with this construction process, but there is a deficit of such specialists. The earlier Knowledge Authoring Logic Machine (KALM) based on Controlled Natural Language (CNL) was shown to have very high accuracy for authoring facts and questions. More recently, KALMFL, a successor of KALM, replaced CNL with factual English, which is much less restrictive and requires very little training from users. However, KALMFL has limitations in representing certain types of knowledge, such as authoring rules for multi-step reasoning or understanding actions with timestamps. To address these limitations, we propose KALMRA to enable authoring of rules and actions. Our evaluation using the UTI guidelines benchmark shows that KALMRA achieves a high level of correctness (100%) on rule authoring. When used for authoring and reasoning with actions, KALMRA achieves more than 99.3% correctness on the bAbI benchmark, demonstrating its effectiveness in more sophisticated KRR jobs. Finally, we illustrate the logical reasoning capabilities of KALMRA by drawing attention to the problems faced by the recently made famous AI, ChatGPT.

RF plugging of multi-mirror machines

One of the main challenges of fusion reactors based on magnetic mirrors is the axial particle loss through the loss cones. In multi-mirror (MM) systems, the particle loss is addressed by adding mirror cells on each end of the central fusion cell. Coulomb collisions in the MM sections serve as the retrapping mechanism for the escaping particles. Unfortunately, the confinement time in this system only scales linearly with the number of cells in the MM sections and requires an unreasonably large number of cells to satisfy the Lawson criterion. Here, it is suggested to reduce the outflow by applying a traveling radio frequency (RF) electric field that mainly targets the particles in the outgoing loss cone. The Doppler shift compensates for the detuning of the RF frequency from the ion cyclotron resonance mainly for the escaping particles resulting in a selectivity effect. The transition rates between the different phase space populations are quantified via single-particle calculations and then incorporated into a semi-kinetic rate equations model for the MM system, including the RF effect. It is found that for optimized parameters, the confinement time can scale exponentially with the number of MM cells, orders of magnitude better than a similar MM system of the same length but without the RF plugging, and can satisfy the Lawson criterion for a reasonable system size.

The Impact of "Reverse Feedback" on Elderly Labor Participation

This article is based on the 2018 China Health and Elderly Care Tracking Survey (CHARLS) data and uses the Logit model and propensity score matching method (PSM) to estimate the impact of "reverse feeding" behavior on elderly labor participation. Research has shown that providing financial support to children by elderly parents can encourage them to enter the labor market; The behavior of intergenerational care will inhibit their entry into the labor market; Providing economic support for children is more significant for elderly individuals living alone and men, while the negative impact of intergenerational care is more significant among women and elderly people living with their children. Therefore, it is necessary to further optimize the supply and service of early childhood care resources, protect the rights and interests of women to participate in labor, and improve the elderly care security system to build a scientific and reasonable elderly employment security system.

NL2LTL – a Python Package for Converting Natural Language (NL) Instructions to Linear Temporal Logic (LTL) Formulas

This is a demonstration of our newly released Python package NL2LTL which leverages the latest in natural language understanding (NLU) and large language models (LLMs) to translate natural language instructions to linear temporal logic (LTL) formulas. This allows direct translation to formal languages that a reasoning system can use, while at the same time, allowing the end-user to provide inputs in natural language without having to understand any details of an underlying formal language. The package comes with support for a set of default LTL patterns, corresponding to popular DECLARE templates, but is also fully extensible to new formulas and user inputs. The package is open-source and is free to use for the AI community under the MIT license. Open Source: https://github.com/IBM/nl2ltl. Video Link: https://bit.ly/3dHW5b1

Reflection on Standardized Training for Resident Physicians in Cardiology

The standardized training of residents is an important part of Graduate medical education for medical students after graduation. The Affiliated Hospital of Weifang Medical University, as a Teaching hospital approved as a training base, has accumulated rich clinical teaching experience. In the practice of residential training in the Department of Cardiology, we have found that by creating a positive learning atmosphere, improving teacher management mechanisms, establishing efficient training models, drawing on reasonable evaluation systems, taking into account the cultivation of scientific research and teaching abilities, and emphasizing humanistic quality education, it is possible to cultivate higher quality medical talents.

Multi-orbit lunar GNSS constellation design with distant retrograde orbit and Halo orbit combination

The Moon is the closest natural satellite to mankind, with valuable resources on it, and is an important base station for mankind to enter deep space. How to establish a reasonable lunar Global Navigation Satellite System (GNSS) to provide real-time positioning, navigation, and timing (PNT) services for Moon exploration and development has become a hot topic for many international scholars. Based on the special spatial configuration characteristics of Libration point orbits (LPOs), the coverage capability of Halo orbits and Distant Retrograde Orbit (DRO) in LPOs is discussed and analyzed in detail. It is concluded that the Halo orbit with a period of 8 days has a better coverage effect on the lunar polar regions and the DRO has a more stable coverage effect on the lunar equatorial regions, and the multi-orbital lunar GNSS constellation with the optimized combination of DRO and Halo orbits is proposed by combining the advantages of both. This multi-orbital constellation can make up for the fact that a single type of orbit requires a larger number of satellites to fully cover the Moon, using a smaller number of satellites for the purpose of providing PNT services to the entire lunar surface. We designed simulation experiments to test whether the multi-orbital constellations meet the full lunar surface positioning requirements, and compare the coverage, positioning, and occultation effects of the four constellation designs that pass the test, and finally obtain a set of well-performing lunar GNSS constellations. The results indicate that the multi-orbital lunar GNSS constellation combining DRO and Halo orbits can cover 100% of the Moon surface, provides there are more than 4 visible satellites at any time on the Moon surface, which meets the navigation and positioning requirements, and the Position Dilution of Precision (PDOP) value is stable within 2.0, which can meet the demand for higher precision Moon surface navigation and positioning.

Research on two popular recommendation algorithms for anime

Anime is a popular eastern art form whose audience is mainly young people. In recent years, many people choose to watch anime on the websites. The recommendation system plays a very important part in improving the user experience and saving time. This paper focuses on two basic recommendation algorithms based on machine learning and deep learning methods, including the content-based and collaborative filtering method. The data used in this paper was downloaded from a public dataset on Kaggle. This paper shows how the two methods perform in the anime dataset and compares the results of the two methods. However, two methods have their own advantages in different conditions. The content-based method works when the user wants some related contents. The collaborative filtering method works better while considering more factors other than contents. These two methods can be combined under a new algorithm to form an even more reliable and reasonable recommendation system in the future studies.

The application of probabilistic programming in statistics

This article focuses on three aspects of Probabilistic reasoning systems for applications, including Predict future events, Infer the cause of events, and learn from past events to better predict future events. In addition, the article explains that the data will become more accurate as the number of samples increases, and finally, the article describes the application of Turing-Completed Systems in statistics, a kind of probabilistic programming system. This article starts with a soccer competition as an example, then will explain the concepts in depth behind the example.

Modernization of Higher Education Governance in China: Contexts and Ideas

Abstract The governance of higher education in China is a systematic project. Only by coordinating various systems can we build a scientific and reasonable national higher education governance system and comprehensively improve the governance capacity and performance of the country. To achieve this general goal, we need to promote overall progress and key breakthroughs, strengthen the combination of top-level design and correctly handle the relationship between the state and society, government, and market, central and local, government administration and public participation, economic efficiency, and social equity on the basis of the rule of law.

The Minimum Cost and the Highest Energy Efficiency of Major PtX Products: A Potential Analysis under Ideal Conditions

The transformation toward sustainable energy systems requires substantial amounts of H2 and PtX derivatives to mitigate severe consequences of climate change. Renewable energy will be the primary energy source in the future which will dictate the integration and energy efficiency of processes in various aspects of the industry. Herein, major PtX vectors are discussed, namely, methanol, ammonia, and sustainable aviation fuels. These products currently represent around 4% of the global anthropogenic CO2 emissions. The theoretical minimum feedstock of H2, CO2, and N2 required for these products is evaluated under ideal process conditions. Based on this simplified strategy, the lowest production costs and the highest theoretical energy efficiency achievable are evaluated. As a potential analysis, setting the boundaries can be technologically realized. A comparison with fossil‐based counterparts PtX is introduced, reflecting the need for a reasonable CO2 certification system and effective mechanisms to allow the successful market penetration of PtX vectors. The scale of production to cover the market demand of the addressed PtX vectors, in terms of electrolyzer capacity, is discussed. The verdict reflects the need to ramp up renewable power generation and electrolyzer capacities for a sustainable transition of the considered sectors based on PtX derivatives.

A novel breast cancer detection architecture based on a CNN-CBR system for mammogram classification

This paper presents a novel framework for breast cancer detection using mammogram images. The proposed solution aims to output an explainable classification from a mammogram image. The classification approach uses a Case-Based Reasoning system (CBR). CBR accuracy strongly depends on the quality of the extracted features. To achieve relevant classification, we propose a pipeline that includes image enhancement and data augmentation to improve the quality of extracted features and provide a final diagnosis. An efficient segmentation method based on a U-Net architecture is used to extract Regions of interest (RoI) from mammograms. The purpose is to combine deep learning (DL) with CBR to improve classification accuracy. DL provides accurate mammogram segmentation, while CBR gives an explainable and accurate classification. The proposed approach was tested on the CBIS-DDSM dataset and achieved high performance with an accuracy (Acc) of 86.71 % and a recall of 91.34 %, outperforming some well-known machine learning (ML) and DL approaches.

Better Together: Unifying Datalog and Equality Saturation

We present egglog, a fixpoint reasoning system that unifies Datalog and equality saturation (EqSat). Like Datalog, egglog supports efficient incremental execution, cooperating analyses, and lattice-based reasoning. Like EqSat, egglog supports term rewriting, efficient congruence closure, and extraction of optimized terms. We identify two recent applications -- a unification-based pointer analysis in Datalog and an EqSat-based floating-point term rewriter -- that have been hampered by features missing from Datalog but found in EqSat or vice-versa. We evaluate our system by reimplementing those projects in egglog. The resulting systems in egglog are faster, simpler, and fix bugs found in the original systems.

Modified Taxonomy method for double-valued neutrosophic number MADM and applications to physical education teaching quality evaluation in colleges and universities

The evaluation of college physical education (PE) teaching quality is an indispensable part of the teaching process. Building a scientific, comprehensive, reasonable and effective evaluation system is crucial to improving the quality of college PE classroom teaching. This process is not easy, and requires long-term efforts and persistence. The PE teaching quality evaluation in Colleges and Universities is frequently viewed as the multiple attribute decision making (MADM) issue. In such paper, Taxonomy method is designed for MADM under double-valued neutrosophic sets (DVNSs). First, the score function of DVNSs and Criteria Importance Through Intercriteria Correlation (CRITIC) method is used to derive the attribute weights. Second, then, the optimal choice is obtained through calculating the smallest double-valued neutrosophic number (DVNN) development attribute values from the DVNN positive ideal solution (DVNNPIS). Finally, a numerical example for PE teaching quality evaluation is given to illustrate the built method.

Application of well testing technology in YT1 condensate gas well

YT1 is an offshore condensate gas well. During gas testing, well testing is carried out to determine the productivity of the well. The productivity equation is established by modified isochronal well testing. The flow pressure change trend with production under different working systems is compared with the dew point pressure of the gas reservoir under the condition of retrograde condensation to determine the reasonable production differential pressure to avoid the abnormality of productivity equation and calculate the open flow and the reasonable working system. Formation parameters are calculated such as formation pressure, skin factor, effective permeability, flow coefficient, and test radius through pressure build-up test while reservoir type, boundary, and dynamically controlled reserves are determined. According to the analysis results of productivity and reserves, considering the investment cost of offshore gas reservoir development, storage, and transportation, we can comprehensively evaluate the economic rationality of YT1 well construction and production, providing reference experience for improving the benefits construction and production level of the offshore condensate gas reservoir.