Reasonable Method Research Articles

Digital Capabilities and Social Enterprise Performance: The Mediating Effect of Digital Innovation

Social enterprises are driven by financial objectives to preserve social values and focus on attaining self-financial stability. Digital technologies increase awareness and opportunities for social entrepreneurs. The relationship between digital capabilities and social enterprise performance is associated with digital innovation in a broader social context. This study assesses the connection between digital capability and social enterprise performance. Furthermore, we consider how digital innovation mediates the way in which digital capability influences social enterprise performance. Data were gathered from 326 respondents from various small and medium social enterprises in Bangladesh. The deductive reasoning method was used, and the data were examined through structural equation modelling using IBM SPSS 25 and SmartPLS4. The results indicate that digital capability is positively correlated with social enterprise. In addition, digital innovation was determined to mediate the connection between digital capability and social enterprise performance.

Luminescent Bacteria as Bioindicators in Screening and Selection of Enzymes Detoxifying Various Mycotoxins.

Interest in enzymes capable of neutralizing various mycotoxins is quite high. The methods used for the screening and selection of enzymes that catalyze the detoxification of mycotoxins should be sensitive and fast. However toxic compounds can be generated under the action of such enzymes. Thus, the assessment of the overall reduction in the toxic properties of reaction media towards bioluminescent bacteria seems to be the most reasonable control method allowing a quick search for the effective enzymatic biocatalysts. The influence of a wide range of mycotoxins and glucanases, which hydrolyze toxins with different chemical structures, on the analytical characteristics of luminescent photobacteria as a biosensing element has been studied. Different glucanases (β-glucosidase and endoglucanase) were initially selected for reactions with 10 mycotoxins based on the results of molecular docking which was performed in silico with 20 mycotoxins. Finally, the biorecognizing luminescent cells were used to estimate the residual toxicity of reaction media with mycotoxins after their interaction with enzymes. The notable non-catalytic decrease in toxicity of media containing deoxynivalenol was revealed with luminous cells for both types of tested glucanases, whereas β-glucosidase provided a significant catalytic detoxification of media with aflatoxin B2 and zearalenone at pH 6.0.

Multi-objective cooperative optimization of reservoir scheduling and water resources allocation for inter-basin water transfer project based on multi-stage coupling model

The efficient coupling of a water allocation model with a reservoir operation model is key to maximizing the benefits of inter-basin water transfer (IBWT) projects. Most existing studies of IBWT operations and management do not account for matching water transfer and demand, and instead seek to optimize either the water allocation model or the reservoir scheduling model. In this paper, we propose a multi-stage joint optimization framework for reservoir scheduling and water allocation models that considers the key operation indicators of the project in the water source area and the multi-dimensional objectives in the receiving area. We derived a functional relationship that balances considerations regarding water, the economy, and ecology (WEE) in the receiving area and applied it to the water allocation model. The reservoir operation model was developed by considering the multiple benefits of water transfer and power generation in the water resource area. To reduce the complexity of the model and the tightness of its coupling, the model was divided into multiple stages of optimization and solved using the Multi-objective Evolutionary Algorithm Based on Decomposition (MOEA/D) algorithm. The results showed the allocation schemes of water resources influenced the competition between the economic and ecological benefits. Using the WEE model, a more reasonable water transfer and allocation method can be selected according to different development needs. As such, the water shortage rate of each user in the receiving area is kept below 8%, and there is significant improvement in all indicators compared with the conventional scheme. In addition, this model enables the reservoir group to achieve a power generation capacity of more than 5.64 kWh with the goal of meeting the water transfer demand, maintaining the reservoir empty rate at about 10%, and reducing the risk of water shortages. Our study can serve as a reference for effectively addressing the challenges associated with IBWT projects, and it can inform socioeconomic development in receiving areas while optimizing water transmission and allocation.

A new method of dynamic network security analysis based on dynamic uncertain causality graph

In the context of cloud computing, network attackers usually exhibit complex, dynamic, and diverse behavior characteristics. Existing research methods, such as Bayesian attack graphs, lack evidence correlation and real-time reflection of the network attack events, and high computational complexity for attack analysis. To solve these problems, this study proposes a Dynamic Uncertain Causal Attack Graph (DUCAG) model and a Causal Chain-based Risk Probability Calculation (CCRP) algorithm. The DUCAG model is constructed to represent the uncertain underlying causalities among network attack events, and the CCRP algorithm aims at dynamically updating the causality weights among different network attack events and attacker hypotheses based on alarm information and causal chain reasoning process. By causality simplification and causality reasoning methods, the CCRP efficiently predicts the attacker behaviors and potential attack likelihood under uncertain time-varying attack situations, and is robust to the incompleteness and redundancy in alarm information. Four experiments under different attack scenarios demonstrate that, the DUCAG model can effectively characterize and predict the complex and uncertain attack causalities, in a manner of high time efficiency. The proposed method has application significance on cloud computing platforms by dynamically evaluating network security status, predicting the future behaviors of attackers, and assisting in adjusting network defense strategies.

Research on investment project evaluation: Comparative analysis based on NPV and IRR

Whether a project is viable, determining whether a project is viable requires some metrics. The indicators that are most frequently utilized are the Net Present Value (NPV) and the internal rate of return (IRR). They are very representative and scientific. The same decision can be made by applying both in independent and non-independent projects When there are many contradictions between NPV and IRR, it is necessary to introduce new indicators to continue to judge the project, such as △IRR, incremental IRR method, modified IRR (MIRR) method, selective IRR method, payback period method, Equivalent Annual Annuity (EAA) method and static analysis method. The investment is selected by comparing the internal rate of return △IRR with the benchmark rate of return (ic) or the social discount rate. The MIRR approach determines the IRR of each scheme, compares it to ic, chooses the scheme that have IRR is larger than or the same as ic, and then determines the incremental IRR of the two neighboring schemes to make the choice. In addition to the investment payback period approach and the selective internal rate of return method. EAA method is in the comparison of investment projects, investors should choose the project with higher EAA when comparing. In the process of investment projects, different enterprises can adopt static analysis method to carry out project investment, but there are two forms of static analysis method, such as payback period method and basic income method. Using reasonable indicators and methods to evaluate the investment project, so as to select the best project.

How Investors Choose Assets Among Pfizer, Walgreens, and CVS

Investment is one of the most important economic activities, and a reasonable investment method can help investors continuously obtain stable returns and complete the process of wealth accumulation. The healthcare industry, as a technology and capital-intensive industry, has always been a favored area for investors. This study selected three companies, Pfizer, Walgreens, and CVS, and analyzed financial data from different perspectives, including risk, profitability, and market ratio; Based on these data, this study determined the investment preferences of several types of investors in three companies, and the results showed that value and return investors would invest in Pfizer and Walgreens, while stock repurchase investors would invest in CVS. The analysis in this article is crucial for helping people determine which type of stock to invest in, thereby improving their chances of outperforming the market.

Application of Forward Chaining and Rule-Based Reasoning Methods to Design an Expert System for Pregnant Women Disease Diagnosis in a Private Hospital

This research aims to discover how to create and implement an expert system for prenatal disease diagnosis. To prepare this study, the author employed several data collection techniques, including observation, interviews, and a literature review. The system to be designed was developed using an object-oriented paradigm and a Rapid Application Development (RAD) technique. Multiple development stages are involved in creating RAD, including requirement planning, workshop design, and implementation. Based on the study findings, it was discovered that the PHP programming language, MySQL database management system, Apache web server, UML tool, and RAD system development methodology were used to create an expert system for identifying illnesses in expectant mothers. The forward chaining method is used for this expert system, where the first stages are to create an inference tree and set rules. This system likewise computes the consistency test using the Bayes theorem method. With an 80% accuracy rate, SISPADIBU can be used as a diagnostic tool for disorders related to pregnancy. SISPADIBU can use the user-entered symptoms to present the diagnosis results. According to the survey results, 80% of users said that SISPADIBU was highly user-friendly, that the layout display was simple to use, and that it was constructive in educating users about pregnancy disorders.

Development of PtNi/C and PtSnNi/C Nanocatalysts for Energy Convertion from Ethanol Electrooxidation

Literature describes the importance in studying morphological and structural electrocatalysts features since they influence catalytic activity toward ethanol electrooxidation. Thus, this work consists in studying the relation between structural/composition features and catalytic activity toward ethanol electrooxidation from Pt, PtSn, PtNi and PtSnNi alloys nanoparticles supported on Vulcan Carbon XC72R. These nanoparticles were obtained by the impregnation reduction method at 130 ºC, employing a mixture of water/ethylene glycol (75/25 v/v) with different amounts of Pt, Sn and Ni. The nanostructured catalysts obtained were characterized by Rutherford Backscattering Spectroscopy (RBS), X-Ray Diffraction (XRD), High Resolution Transmission Electronic Microscopy (HRTEM) and Cyclic Voltammetry (CV). Preliminary results showed that the impregnation reduction is a reasonable method to obtain Pt-based nanoparticles with a composition control. XRD analysis showed that tin and nickel addition promoted a crystal lattice dilation and contraction, respectively. Besides, the simultaneous addition of Sn and Ni to Pt did not significantly affect the reticular lattice value. Moreover, crystallite size is diminished with Ni or Ni and Sn addition. PtSn, PtNi and PtSnNi/C nanoparticles have higher catalytic activity than Pt/C toward ethanol electrooxidation, which was verified by a significant current density values increase and an onset potential decrease. The best catalytic activity was observed to PtNi/C electrocatalyst.

Research on Causal Reasoning Method for MOOC Course Effectiveness Based on Multi-path Network Search

Research on Causal Reasoning Method for MOOC Course Effectiveness Based on Multi-path Network Search

Research on knowledge graph-driven equipment fault diagnosis method for intelligent manufacturing

AbstractIn the process of rotating machinery fault diagnosis (RMFD), the lack of feature conditions leads to the problem of low accuracy of traditional rule-based reasoning methods FD. This paper proposed a knowledge graph (KG)-driven device FD method and applied it to RMFD. First, we proposed a multi-level KG construction method to get multi-source data based on each level and analyzed the levels that affected the system state. A single-level KG was constructed through data features, and a multilevel KG with a stereostructure was built using a multi-source data fusion model as data support for FD. Second, we proposed an approach based on multilevel KG and Bayes theory to detect the system state and located the source of faults by combining the KG reasoning based on relational paths, then used the relationships between the structures of rotating mechanical equipment for fault cause reasoning and used the KG as a knowledge base for a reason using machine learning. Finally, the proposed method was validated using a steelworks motor as an example and compared with other ways, such as rule-based FD. The results show that under the condition of missing input features, the accuracy of the proposed method reaches 91.1%, which is significantly higher than other methods and effectively solves the problem of low diagnostic accuracy.

Study on failure mechanism and control of surrounding rock of inclined strata crossing roadway in deep coal mine

China’s coal mines are mainly underground mines, and a large number of roadways have to be excavated underground. It is of great significance for coal mine production to adopt safe and reasonable roadway support methods. In the process of roadway excavation, the rock stratum is inclined and the roadway pass through the layer. Since the surrounding rock conditions of the roadway passing through the layer are more complicated, it is easy to cause deformation of surrounding rock, failure and floor heave, which makes the support work difficult. In order to solve this problem, the mechanical properties of roadway surrounding rock were tested and the failure of roadway surrounding rock was analyzed using the +260 horizontal centralized transportation roadway in Changcheng No.2 mine. The surrounding rock of the roadway was divided into 8 regions, and the stress analysis of the surrounding rock in different regions was carried out. It is found that the left shoulder pit, the right side and the floor of the roadway are prone to damage. The influence of the lateral pressure coefficient, the rock dip angle and the lithology on the failure of the roadway surrounding rock was analyzed by Mohr-Coulomb failure criterion, and the specific failure range of the roadway surrounding rock was obtained. The support optimization design of the roadway was carried out, and the weak area of the surrounding rock was reinforced. The deformation monitoring of roadway surrounding rock after support optimization was carried out. The field monitoring results show that after the optimized support, the displacement of the roof and floor of the roadway section and the two sides are reduced by 43.6% and 40.8% respectively compared with the original scheme, and the deformation of the surrounding rock also shows a trend of gradual stability, and the surrounding rock of the roadway is effectively controlled. The research can provide a new way for the stress and failure analysis of the surrounding rock of the inclined rock roadway.

Effects of different phosphorus fertilizers on cadmium absorption and accumulation in rice under low-phosphorus and rich-cadmium soil.

Rice is the main food crops with the higher capacity for cadmium (Cd) uptake, necessitating the urgent need for remediation measures to address Cd in paddy soil. Reasonable agronomic methods are convenient and favorable for fixing the issue. In this study, a pot experiment was employed to evaluate the effects of two foliar (NaH2PO4, SDP; KH2PO4, PDP) and two solid phosphate fertilizers (double-superphosphate, DSP; calcium-magnesium phosphate, CMP) on uptake and remobilization of Cd in rice plants under the low-P and rich-Cd soil. The results revealed that these four phosphorus fertilizer significantly down-regulated the relative expression of OsNRAMP5 involved in Cd absorption, while up-regulated OsPCS1 expression and increased distribution of Cd into the cell wall in roots. Furthermore, phosphorus fertilizer resulted in a significant decrease in the relative expression of OsLCT1 in stems and OsLCD in leaves, decreased the transfer factor of Cd from shoots to grains, and ulterior reduced the Cd accumulation in three protein components of globulin, albumin, and glutelin, making the average Cd concentration of brown rice decreased by 82.96%. These results comprehensively indicate that in situations with similar soil backgrounds, the recommended application of solid CMP and foliar PDP can alleviate the toxicity of Cd by reducing its absorption and remobilization.

CDs-ICG@BSA nanoparticles for excellent phototherapy and in situ bioimaging

For the diagnosis and treatment of cancer, a great challenge is the fabrication of straightforward, non-toxic, multifunctional green nanomaterials. In this study, carbon quantum dots self-assembled with indocyanine green dye at bovine serum albumin for phototherapy and in situ bioimaging are produced by a flexible hydrothermal method. We find that the synthesized nanoparticles have high tumor photothermal therapeutic activity when exposed to 808 nm light, with a photothermal conversion efficiency up to 61 %. The phototoxicity study revealed the excellent phototherapy of the nanoparticles mainly arises from photothermal therapeutic effect other than photodynamic therapy effect. Simultaneously, it allows biological imaging in the visible and near-infrared ranges because of the significant absorption at 365 nm and 840 nm. The current work offers a simple, environmentally friendly, and reasonable method for developing photothermal drugs with a high photothermal conversion efficiency in the near-infrared region, as well as good biosafety for multifunctional nanomaterials for bioimaging tumor diagnosis and direct phototherapy.

Social Impact Assessment in Urban Security Management Projects: A Case Study from Pakistan

Public safety and security management projects are devised to reduce crime, fear and calamities by prevailing law and order to reduce the harm in society. In a certain context, social impact assessment is a novel way to reveal the extent of effectiveness for these projects. This case study presents an Expert System based methodology regarding social impact assessment for two urban security management projects in Pakistan. An Expert System, DoctuS is employed as a tool to build a rule-based model tool using social impact attributes (as variables) from literature and expert knowledge (Author’s own insight as being involved in projects). Case-based reasoning (CBR) method is employed for the measurement of the impact of urban security management KPIs (Key Performance Indicators) in the two case projects. The empirical findings in this case study approaches the impact assessment by quantitative analysis of crime rates in JMP statistical package and Qualitative Expert knowledge-based model in DoctuS. Thereby, the study evaluates the pre and post ante project situations in order to assess the project’s effectiveness and impact on improved urban safety and security. In the first step, the projects are briefly described, followed by the crime rate analysis under the project jurisdictions and rules between attributes are defined. The second step defines the qualitative rule-based model along with the two cases described, and validated in DoctuS tool. Some attributes and rules have changed, and a new concise model is presented. The Expert System compares the project cases and evaluates the social impact assessment based on the defined KPIs. The Expert system validation presents a novel model for social impact assessment of subject urban security management projects. We propose this methodology and model as useful in investigating social impact assessment of projects specifically dealing with urban safety and security management. However, the generalizability of the findings of this case-study based articles need more sophisticated tests as recommendation for the extension to this study.
 
 Received: 31 May 2023 / Accepted: 19 November 2023 / Published: 5 January 2024

On the ideal number of groups for isometric gradient propagation

Recently, various normalization layers have been proposed to stabilize the training of deep neural networks. Among them, group normalization is a generalization of layer normalization and instance normalization by allowing a degree of freedom in the number of groups it uses. However, to determine the optimal number of groups, trial-and-error-based hyperparameter tuning is required, and such experiments are time-consuming. In this study, we discuss a reasonable method for setting the number of groups. First, we find that the number of groups influences the gradient behavior of the group normalization layer. Based on this observation, we derive the ideal number of groups, which calibrates the gradient scale to facilitate gradient descent optimization. This paper is the first to propose an optimal number of groups that is theoretically grounded, architecture-aware, and can provide a proper value in a layer-wise manner for all layers. The proposed method exhibited improved performance over existing methods in numerous neural network architectures, tasks, and datasets.

An Axiomatic Basis for Computer Programming on the Relaxed Arm-A Architecture: The AxSL Logic

Very relaxed concurrency memory models, like those of the Arm-A, RISC-V, and IBM Power hardware architectures, underpin much of computing but break a fundamental intuition about programs, namely that syntactic program order and the reads-from relation always both induce order in the execution. Instead, out-of-order execution is allowed except where prevented by certain pairwise dependencies, barriers, or other synchronisation. This means that there is no notion of the 'current' state of the program, making it challenging to design (and prove sound) syntax-directed, modular reasoning methods like Hoare logics, as usable resources cannot implicitly flow from one program point to the next. We present AxSL, a separation logic for the relaxed memory model of Arm-A, that captures the fine-grained reasoning underpinning the low-overhead synchronisation mechanisms used by high-performance systems code. In particular, AxSL allows transferring arbitrary resources using relaxed reads and writes when they induce inter-thread ordering. We mechanise AxSL in the Iris separation logic framework, illustrate it on key examples, and prove it sound with respect to the axiomatic memory model of Arm-A. Our approach is largely generic in the axiomatic model and in the instruction-set semantics, offering a potential way forward for compositional reasoning for other similar models, and for the combination of production concurrency models and full-scale ISAs.

A Knowledge-graph based Method for Datum Reference Frame Reasoning

In mechanical engineering, the datum is essential in the tolerance specification, which impacts assembly and functional requirements. The standard mechanical parts, such as shaft and gear, already have a formalized datum reference frame (DRF) design process. However, non-standard parts still mainly rely on manual design. To this end, a knowledge-graph-based DRF reasoning method for non-standard parts is proposed. In this paper, an OWL 2-based knowledge model is constructed according to industrial practice. It combines empirical knowledge, knowledge graphs, and ontology reasoning methods to perform DRF generation automatically and precisely. The finite element method is introduced into this work to evaluate reasoned DRF. The effectiveness and feasibility of the method are demonstrated via a case study.

A Novel Multi-Scale Graph Neural Network for Metabolic Pathway Prediction.

Predicting the metabolic pathway classes of compounds in the human body is an important problem in drug research and development. For this purpose, we propose a Multi-Scale Graph Neural Network framework, named MSGNN. The framework includes a subgraph encoder, a feature encoder and a global feature processor, and a graph augmentation strategy is adopted. The subgraph encoder is responsible for extracting the local structural features of the compound, the feature encoder learns the characteristics of the atoms, and the global feature processor processes the information from the pre-training model and the two molecular fingerprints, while the graph augmentation strategy is to expand the train set through a scientific and reasonable method. The experiment result illustrates that the accuracy, precision, recall and F1 metrics of MSGNN reach 98.17%, 94.18%, 94.43% and 94.30%, respectively, which is superior to the similar models we have known. In addition, the ablation experiment demonstrates the indispensability of MSGNN modules.

Development of Dynamic Balanced Scorecard Using Case-Based Reasoning Method and Adaptive Neuro-Fuzzy Inference System

In recent years, selecting the strategies has been recognized as one of the most challenging issues facing senior and strategy managers of companies. In this regard, this article introduced an integrated framework that helps strategy managers to determine the organization's strategy by analyzing the long-term objectives and visions. The proposed methodology is based on developing and enhancing the performance of the balanced scorecard (BSC) by covering its limitations through being combined with system dynamics (SD) simulation, case-based reasoning (CBR) method, and adaptive neuro-fuzzy inference system (ANFIS) model. The SD model is built based on the company's strategy map to predict the future status of the company based on its selected strategies. The CBR method and ANFIS model are also to develop the sensitivity analysis and policy-making stage utilizing learning and human memory application. An Iranian food and beverage company is considered as a real-world case study to demonstrate how the proposed method could work and validate the research methodology's applicability. As the main finding, the model yields appropriate strategies with minor errors to reach the targets defined by managers. According to the proposed dynamic BSC, managers can select their strategies and observe their financial variables’ outcomes on the planning horizon. Moreover, they can set future targets for their financial variable to receive strategies for their achievement.

Exploring English Translation Strategies Oriented by Big Data Technology

Abstract English translation is not a simple task, and it needs to be translated on the basis of different languages and cultures in order to get the ideal translation effect and promote cross-cultural communication between different languages. In this paper, with the help of web crawler technology to crawl the Chinese-English translation corpus data, and use the inverse document frequency and dynamic planning ideas to align the corpus data, the Chinese-English translation corpus is established. The LDA model is used to mine subject information in the Chinese-English translation corpus, and edge distribution estimation is used to construct the English translation model. For the application of the translation model and the Chinese-English translation corpus in improving the quality and effect of English translation, the word and sentence features, target word blocks, and the comparison of language features of human-computer translation from the Chinese-English corpus are verified in various aspects. The results show that the class/formant ratio of the source language in the corpus is between 56.73% and 75.84%, its average word length fluctuates in the range of [3.91,5.62], and the target word chunks of the English text are mainly based on nominal and verbal structures. The English translation model has a less significant discrepancy in other language features than human translation, except for the difference of 4.526 between sentence lengths. English translation needs to adopt a reasonable annotation method, semantic conversion, and technical tools to improve the quality of English translation.