Unleashing the Power of Multi-Agent Reinforcement Learning for Algorithmic Trading in the Digital Financial Frontier and Enterprise Information Systems

In the rapidly evolving landscape of today’s digital economy,Financial Technology(Fintech)emerges as a trans-formative force,propelled by the dynamic synergy between Artificial Intelligence(AI)and Algorithmic Trading.Our in-depth investigation delves into the intricacies of merging Multi-Agent Reinforcement Learning(MARL)and Explainable AI(XAI)within Fintech,aiming to refine Algorithmic Trading strategies.Through meticulous examination,we uncover the nuanced interactions of AI-driven agents as they collaborate and compete within the financial realm,employing sophisticated deep learning techniques to enhance the clarity and adaptability of trading decisions.These AI-infused Fintech platforms harness collective intelligence to unearth trends,mitigate risks,and provide tailored financial guidance,fostering benefits for individuals and enterprises navigating the digital landscape.Our research holds the potential to revolutionize finance,opening doors to fresh avenues for investment and asset management in the digital age.Additionally,our statistical evaluation yields encouraging results,with metrics such as Accuracy=0.85,Precision=0.88,and F1 Score=0.86,reaffirming the efficacy of our approach within Fintech and emphasizing its reliability and innovative prowess.

Neuroprotective effects of resveratrol on retinal ganglion cells in glaucoma in rodents:A narrative review

Glaucoma,an irreversible optic neuropathy,primarily affects retinal ganglion cells(RGC)and causes vision loss and blindness.The damage to RGCs in glaucoma occurs by various mechanisms,including elevated intraocular pressure,oxidative stress,inflammation,and other neurodegenerative processes.As the disease progresses,the loss of RGCs leads to vision loss.Therefore,protecting RGCs from damage and promoting their survival are important goals in managing glaucoma.In this regard,resveratrol(RES),a polyphenolic phytoalexin,exerts antioxidant effects and slows down the evolution and progression of glaucoma.The present review shows that RES plays a protective role in RGCs in cases of ischemic injury and hypoxia as well as in ErbB2 protein expression in the retina.Additionally,RES plays protective roles in RGCs by promoting cell growth,reducing apoptosis,and decreasing oxidative stress in H_(2)O_(2)-exposed RGCs.RES was also found to inhibit oxidative stress damage in RGCs and suppress the activation of mitogen-activated protein kinase signaling pathways.RES could alleviate retinal function impairment by suppressing the hypoxia-i nducible factor-1 alpha/vascular endothelial growth factor and p38/p53 axes while stimulating the PI3K/Akt pathway.Therefore,RES might exert potential therapeutic effects for managing glaucoma by protecting RGCs from damage and promoting their survival.

Improving Supply Chain Strategies towards Mitigating the Effect of Crude Oil Theft and Pipeline Vandalism in the Nigerian Oil and Gas Industry: A Case Study of the Nigerian National Petroleum Corporation (NNPC)

Generically, SCM may be said to include all activities carried out to ensure proper functioning of the supply chain. The activities included in proper management of a supply chain broadly include logistics activities, planning and control of the flow of information and materials in a firm, management of relationships with other organizations and government intervention, However, crude oil theft and pipeline vandalism are established products supply chain disruptors in Nigeria which are rendering the task of running an efficient petroleum supply chain onerous. This paper aims to establish the importance of effective supply chain strategies for companies in the oil and gas industry with special focus on the Nigerian oil and gas sector and the strategies by which the state oil and gas corporation in this sector may mitigate disruptions to its supply chain. This study investigates the enhancement of supply chain strategies towards meeting the challenge of crude oil theft and pipeline vandalism, using the Nigerian National Corporation (NNPC) as a case study. Based on this study, data were collected from two sources: A summary of incident reports obtained from NNPC and an interview with personnel in the PPMC Department. Incident report refers to a report produced when accidents such as equipment failure, injury, loss of life, or fire occur at the work site. Content analysis is utilized to evaluate data obtained from interview responses, CBN financial stability reports, NDIC annual reports, circulars, banking supervision reports and implementation guidelines. The study found out that NNPC should endeavor to sustain its value chain and ward of pipeline vandals and crude oil thieves by engaging in community partnership, detailing security outfits to ensure its pipelines’ right of way and bridging. Concluded that the oil supply chain of the Nigerian National Petroleum Corporation has been plagued by disruptions in the form of crude oil theft and pipeline vandalism which has had debilitating effects on its value.

Comparison of Type I Error Rates of Siegel-Tukey and Savage Tests among Non-Parametric Tests

This study aimed to examine the performance of the Siegel-Tukey and Savage tests on data sets with heterogeneous variances. The analysis, considering Normal, Platykurtic, and Skewed distributions and a standard deviation ratio of 1, was conducted for both small and large sample sizes. For small sample sizes, two main categories were established: equal and different sample sizes. Analyses were performed using Monte Carlo simulations with 20,000 repetitions for each scenario, and the simulations were evaluated using SAS software. For small sample sizes, the I. type error rate of the Siegel-Tukey test generally ranged from 0.045 to 0.055, while the I. type error rate of the Savage test was observed to range from 0.016 to 0.041. Similar trends were observed for Platykurtic and Skewed distributions. In scenarios with different sample sizes, the Savage test generally exhibited lower I. type error rates. For large sample sizes, two main categories were established: equal and different sample sizes. For large sample sizes, the I. type error rate of the Siegel-Tukey test ranged from 0.047 to 0.052, while the I. type error rate of the Savage test ranged from 0.043 to 0.051. In cases of equal sample sizes, both tests generally had lower error rates, with the Savage test providing more consistent results for large sample sizes. In conclusion, it was determined that the Savage test provides lower I. type error rates for small sample sizes and that both tests have similar error rates for large sample sizes. These findings suggest that the Savage test could be a more reliable option when analyzing variance differences.

Ensuring Security and Privacy in the Internet of Things: Challenges and Solutions

The Internet of Things (IoT) represents a revolutionary paradigm, enabling a vast array of devices to be ubiquitously interconnected via the Internet, thereby facilitating remote control and management of these devices. This pervasive integration into daily life brings significant convenience but also raises substantial concerns regarding the security of personal data collected and stored online. As the number of connected devices grows, the urgency to address privacy and security issues becomes paramount. IoT systems are particularly susceptible to threats that could compromise consumer privacy and security, affecting their practical deployment. Recent research efforts have focused on enhancing the security of IoT devices, including the exploration of blockchain technologies to mitigate these concerns. This paper aims to elucidate the security and privacy challenges inherent in IoT systems by examining vulnerabilities at each layer of the IoT protocol stack. It identifies key security requirements and reviews existing solutions designed to protect IoT systems from a layered perspective, thereby providing a comprehensive overview of the current landscape of IoT security and highlighting the critical need for robust security measures as the adoption of IoT continues to expand.

Applying the Technology Acceptance Model (TAM) in Information Technology System to Evaluate the Adoption of Decision Support System

With the beginning of the information systems’ spreading, people started thinking about using them for making business decisions. Computer technology solutions, such as the Decision Support System, make the decision-making process less complex and simpler for problem-solving. In order to make a high-quality business decision, managers need to have a great deal of appropriate information. Nonetheless, this complicates the process of making appropriate decisions. In a situation like that, the possibility of using DSS is quite logical. The aim of this paper is to find out the intended use of DSS for medium and large business organizations in USA by applying the Technology Acceptance Model (TAM). Different models were developed in order to understand and predict the use of information systems, but the information systems community mostly used TAM to ensure this issue. The purpose of the research model is to determine the elements of analysis that contribute to these results. The sample for the research consisted of the target group that was supposed to have completed an online questionnaire about the manager’s use of DSS in medium and large American companies. The information obtained from the questionnaires was analyzed through the SPSS statistical software. The research has indicated that, this is primarily used due to a significant level of Perceived usefulness and For the Perceived ease of use.

Research on the Influence of Anchor Attributes on Consumers’Online Behaviors in Social E-Commerce Platforms:The Moderating Effect of Platform Contextual Factors

As e-commerce continues to mature,the advantages of live streaming within the industry have become increasingly apparent,offering significant growth opportunities.Social e-commerce platforms,which are user-centered,integrate social networks with e-commerce by leveraging social interactions to drive product sales and enhance the overall consumer shopping experience.This type of e-commerce fosters engagement and promotes products by merging online communities with shopping behavior,creating a more interactive and dynamic marketplace.It not only retains the traditional e-commerce trading and marketing functions but also adds a social dimension,making live stream anchors crucial figures connecting consumers with products.These anchors can attract consumers with their appearance and charm,and use their expertise on live streaming platforms to guide consumers by recommending live content.They can also interact with their audiences and potentially influence them to purchase the recommended goods.It is evident that the attributes of anchors in live streaming rooms significantly impact consumers’online behavior.Therefore,researching how platform contextual factors regulate consumers’online behavior is of great practical significance.This study employs multilevel regression analysis to support its hypotheses using data.The findings indicate that contextual factors of the platform significantly influence online behavior,enhancing the positive relationship between user attachment and online activities.

Application of Pomegranate Rind Extract for Improvement of Functional Properties of Various Materials—A Critical Review

Pomegranate rind is abundantly available as a waste material. Pomegranate Rind Extract (PRE) can be applied to cotton fabrics for its natural colours, as a mordanting agent and also for imparting certain functional properties such as fire retardancy and antimicrobial properties. This paper reviews the feasibility of Pomegranate Rind Extract to improve the functional properties of cellulosic fabrics. Studies show that varying concentrations and higher temperatures that were used to apply the extract on the fabric, resulted in enhanced functional properties. At a particular concentration, the treated fabric showed a 15 times lower burning rate in comparison with the control fabric. Also, antimicrobial efficacy has been observed against Gram-positive and Gram-negative bacteria. Due to the natural colouring material, it can be used as a natural dye on cotton material. The fire retardancy of pomegranate rind extract was tested on jute material under varying alkalinity. Research has indicated that pomegranate rind extract could be used to dye polyamide as well. The rubbing and wash fastness of the finished fabrics is good. The light fastness was fair, and its antibacterial efficiency against tested bacteria was good.

Research on the Training Model of Electronic Business Professionals in Higher Vocational Colleges Based on Modern Apprenticeship

For vocational education, modern apprenticeship is of great importance to its teaching quality, and the application of modern apprenticeship in training e-commerce professionals in higher vocational colleges can promote the training of e-commerce professionals. This paper briefly introduces the modern apprenticeship system, and has analyzed the existing problems in the teaching of e-commerce specialty in higher vocational colleges and the training model of modern apprenticeship for e-commerce professionals in higher vocational colleges, and finally, it puts forward several problems which we should pay attention to in the application of the modern apprenticeship to improve the teaching level of the major of e-commerce and promote the development of contemporary vocational education.

Impact of taxes on the 2030 Agenda for Sustainable Development:Evidence from Organization for Economic Cooperation and Development(OECD)countries

Multiple ecological and socioeconomic problems have occurred worldwide,raising the awareness of sustainability.This study aims to examine the impact of taxes on Sustainable Development Goals(SDGs)in the context of Organization for Economic Co-operation and Development(OECD)countries.This research used effective average tax(EAT),tax on personal income(TPI),tax on corporate profits(TCP),and tax on goods and services(TGS)as the variables of taxes,and employed secondary data from 38 OECD countries covering 2000–2021.The study also used Breusch-Pagan Lagrange Multiplier(LM),Pesaran Scaled LM,Bias-Corrected Scaled LM,and Pesaran Cross-sectional dependence(CSD)tests to analyze the existence of crosssectional dependency.Then,we established the stationarity of variables through second-generation panel unit root tests(Cross-sectional Augmented Dickey-Fuller(CADF)and Cross-sectional Im,Pesaran,and Shin(CIPS)),and confirmed the long-run cointegration of the variables by using secondgeneration panel cointegration test(Westerlund cointegration test).The results showed that EAT,TPI,TCP,and TGS are positively associated with SDGs.However,the change in TPI has a smaller effect on SDGs than the change in EAT or TCP or TGS.The result of panel causality indicated that EAT,TPI,and TGS have a unidirectional causal relationship with SDGs.The study also found that TCP has a bi-directional causal relationship with SDGs.Moreover,the finding indicated that the OECD countries need to focus on tax policies to achieve the 2030 Agenda for Sustainable Development.This study is based on the theory of optimal taxation(TOT),which suggests that tax systems should be designed to maximize social welfare.Finally,we suggests the importance of taking a comprehensive approach for the managers and policy-makers when analyzing the impact of taxes on SDGs.

The Effect of Latinization on Reading Time and Understanding: Greeklish in Communication and Social Media

The increasing prevalence of technology in society has an impact on young people’s language use and development. Greeklish is the writing of Greek texts using the Latin instead of the Greek alphabet, a practice known as Latinization, also employed for many non-latin alphabet languages. The primary aim of this research is to evaluate the effect of Greeklish on reading time. A sample of 732 young Greeks were asked about their habits when communicating through e-mail and social media with their friends and they then participated in an experiment in which they were asked to read and understand two short texts, one written in Greek and the other in Greeklish. The findings of the research show that nearly one third of the participants use Greeklish. The results of the experiment conducted reveal that understanding is not affected by the alphabet used but reading Greeklish is significantly more time consuming than reading Greek independently of the sex and the familiarity of the participants with Greeklish. The findings suggest that amending social and communication media with software utilities related to Latinization such as language identifiers and converters may reduce reading time and thus facilitate written communication among the users.

Differential Calculus the Study of the Growth and Decay of an Entity’s Population

Population Growth and Decay study of the growth or the decrease of a population of a given entity, is carried out according to the environment. In an infinite environment, i.e. when the resources are unlimited, a population P believes according to the following differential equation P’ = KP, with the application of the differential calculus we obtasin an exponential function of the variable time (t). The function of which we can predict approximately a population according to the signs of k and time (t). If k > 0, we speak of the Malthusian croissant. On the other hand, in a finite environment i.e. when resources are limited, the population cannot exceed a certain value. and it satisfies the logistic equation proposed by the economist Francois Verhulst: P’ = P(1-P).

A Class of Algorithms for Solving LP Problems by Prioritizing the Constraints

Linear programming is a method for solving linear optimization problems with constraints, widely met in real-world applications. In the vast majority of these applications, the number of constraints is significantly larger than the number of variables. Since the crucial subject of these problems is to detect the constraints that will be verified as equality in an optimal solution, there are methods for investigating such constraints to accelerate the whole process. In this paper, a technique named proximity technique is addressed, which under a proposed theoretical framework gives an ascending order to the constraints in such a way that those with low ranking are characterized of high priority to be binding. Under this framework, two new Linear programming optimization algorithms are introduced, based on a proposed Utility matrix and a utility vector accordingly. For testing the addressed algorithms firstly a generator of 10,000 random linear programming problems of dimension n with m constraints, where , is introduced in order to simulate as many as possible real-world problems, and secondly, real-life linear programming examples from the NETLIB repository are tested. A discussion of the numerical results is given. Furthermore, already known methods for solving linear programming problems are suggested to be fitted under the proposed framework.

The Benefits of Promoting SDG 17 to Combat Climate Change

193 members of the United Nations approved the 17 Sustainable Development Goals in September 2015.According to the 2030 Agenda,the SDGs contemplated the ending of poverty,the protection of the Earth and the promotion of prosperity for all.Sustainable Development Goal 17(SDG 17)deals specifically with the creation of global alliances for development.The underlying assumption respecting this point is that these stakeholder partnerships encourage the interchange of knowledge,experience,technology,and other resources to administer efficiently the other sixteen SDGs.Although SDG 17 is very well established in theory,in practice there are still appreciable downfalls as to how to successfully make this theory become a reality.This short review will analyse the potential viability of SDG 17“partnerships for the goals”with respect to SDG 7(affordable and clean energy),and thereupon SDG 13(associated with climate action)utilising two south-western France two wind farm initiatives.

Optimized Phishing Detection with Recurrent Neural Network and Whale Optimizer Algorithm

Phishing attacks present a persistent and evolving threat in the cybersecurity land-scape,necessitating the development of more sophisticated detection methods.Traditional machine learning approaches to phishing detection have relied heavily on feature engineering and have often fallen short in adapting to the dynamically changing patterns of phishingUniformResource Locator(URLs).Addressing these challenge,we introduce a framework that integrates the sequential data processing strengths of a Recurrent Neural Network(RNN)with the hyperparameter optimization prowess of theWhale Optimization Algorithm(WOA).Ourmodel capitalizes on an extensive Kaggle dataset,featuring over 11,000 URLs,each delineated by 30 attributes.The WOA’s hyperparameter optimization enhances the RNN’s performance,evidenced by a meticulous validation process.The results,encapsulated in precision,recall,and F1-score metrics,surpass baseline models,achieving an overall accuracy of 92%.This study not only demonstrates the RNN’s proficiency in learning complex patterns but also underscores the WOA’s effectiveness in refining machine learning models for the critical task of phishing detection.

Selective and Adaptive Incremental Transfer Learning with Multiple Datasets for Machine Fault Diagnosis

The visions of Industry 4.0 and 5.0 have reinforced the industrial environment.They have also made artificial intelligence incorporated as a major facilitator.Diagnosing machine faults has become a solid foundation for automatically recognizing machine failure,and thus timely maintenance can ensure safe operations.Transfer learning is a promising solution that can enhance the machine fault diagnosis model by borrowing pre-trained knowledge from the source model and applying it to the target model,which typically involves two datasets.In response to the availability of multiple datasets,this paper proposes using selective and adaptive incremental transfer learning(SA-ITL),which fuses three algorithms,namely,the hybrid selective algorithm,the transferability enhancement algorithm,and the incremental transfer learning algorithm.It is a selective algorithm that enables selecting and ordering appropriate datasets for transfer learning and selecting useful knowledge to avoid negative transfer.The algorithm also adaptively adjusts the portion of training data to balance the learning rate and training time.The proposed algorithm is evaluated and analyzed using ten benchmark datasets.Compared with other algorithms from existing works,SA-ITL improves the accuracy of all datasets.Ablation studies present the accuracy enhancements of the SA-ITL,including the hybrid selective algorithm(1.22%-3.82%),transferability enhancement algorithm(1.91%-4.15%),and incremental transfer learning algorithm(0.605%-2.68%).These also show the benefits of enhancing the target model with heterogeneous image datasets that widen the range of domain selection between source and target domains.

LSTM Based Neural Network Model for Anomaly Event Detection in Care-Independent Smart Homes

This study introduces a long-short-term memory(LSTM)-based neural network model developed for detecting anomaly events in care-independent smart homes,focusing on the critical application of elderly fall detection.It balances the dataset using the Synthetic Minority Over-sampling Technique(SMOTE),effectively neutralizing bias to address the challenge of unbalanced datasets prevalent in time-series classification tasks.The proposed LSTM model is trained on the enriched dataset,capturing the temporal dependencies essential for anomaly recognition.The model demonstrated a significant improvement in anomaly detection,with an accuracy of 84%.The results,detailed in the comprehensive classification and confusion matrices,showed the model’s proficiency in distinguishing between normal activities and falls.This study contributes to the advancement of smart home safety,presenting a robust framework for real-time anomaly monitoring.

Probability Laws Derived from the Gamma Function

Several densities or probability laws of continuous random variables derive from the Euler Gamma function. These laws form the basis of sampling theory, namely hypothesis testing and estimation. Namely the gamma, beta, and Student law, through the chi-square law and the normal law are all distributions resulting from applications of Euleur functions.

Optimizing Resource Management for IoT Devices in Constrained Environments

Embedded computing device implementation on the Internet of Things (IoT) requires careful assessment of their intrinsic resource limitations. These constraints are not limited to memory and processing capabilities but extend to the network interfaces, particularly due to the low-power radio standards that these devices typically employ. The IPv6 protocol is shown to be a strong option for guaranteeing interoperability in the IoT, mostly because of its large address space, the range of current IP-based protocols, and its intrinsic versatility. Considering these benefits, we investigate if current IP-based network management protocols can be implemented on devices with limited resources. We investigate the resource needs in particular for implementing Network Configuration Protocol (NETCONF) and Simple Network Management Protocol (SNMP) on an 8-bit AVR-based device. Our investigation reveals the specific memory and processing demands of these protocols, providing valuable insights into their practicality and efficiency in constrained IoT environments. This study underscores the potential and challenges of leveraging IPv6-based network management protocols to enhance the functionality and interoperability of IoT devices while operating within stringent resource limitations.

A Dimensional Reduction Approach Based on Essential Constraints in Linear Programming

This paper presents a new dimension reduction strategy for medium and large-scale linear programming problems. The proposed method uses a subset of the original constraints and combines two algorithms: the weighted average and the cosine simplex algorithm. The first approach identifies binding constraints by using the weighted average of each constraint, whereas the second algorithm is based on the cosine similarity between the vector of the objective function and the constraints. These two approaches are complementary, and when used together, they locate the essential subset of initial constraints required for solving medium and large-scale linear programming problems. After reducing the dimension of the linear programming problem using the subset of the essential constraints, the solution method can be chosen from any suitable method for linear programming. The proposed approach was applied to a set of well-known benchmarks as well as more than 2000 random medium and large-scale linear programming problems. The results are promising, indicating that the new approach contributes to the reduction of both the size of the problems and the total number of iterations required. A tree-based classification model also confirmed the need for combining the two approaches. A detailed numerical example, the general numerical results, and the statistical analysis for the decision tree procedure are presented.