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.

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.

Optimization and Innovation of the Operation Model of Mobile Social E-commerce under AI Empowerment:Taking the Female Community Platform“Little Red Book”as an Example

This article takes the female community platform“Little Red Book”as an example to explore the optimization and innovation of mobile community e-commerce operation mode under Artificial Intelligence(AI)empowerment.Firstly,the relevant concepts were defined,and then the unique attributes of mobile community e-commerce were analyzed.As a typical representative of mobile community e-commerce,Little Red Book introduces the background and characteristics of its platform,analyzes its mobile community operation mode,and focuses on exploring how to establish a mobile community e-commerce platform and effective operation mode under the empowerment of AI technology,to provide some reference and inspiration for the development and operation of Little Red Book and other e-commerce platform enterprises.

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.

Correspondence of bubble size and frother partitioning in flotation

The size of bubbles created in the flotation process is of great importance to the efficiency of the mineral separation achieved.Meanwhile,it is believed that frother transport between phases is perhaps the most important reason for the interactive nature of the phenomena occurring in the bulk and froth phases in flotation,as frother adsorbed in the surface of rising bubbles is removed from the bulk phase and then released into the froth as a fraction of the bubbles burst.This causes the increased concentration in the froth compared to the bulk concentration,named as frother partitioning.Partitioning reflects the adsorption of frother on bubbles and how to influence bubble size is not known.There currently exists no such a topic aiming to link these two key parameters.To fill this vacancy,the correspondence between bubble size and frother partitioning was examined.Bubble size was measured by sampling-for-imaging(SFI)technique.Using total organic carbon(TOC)analysis to measure the frother partitioning between froth and bulk phases was determined.Measurements have shown,with no exceptions including four different frothers,higher frother concentration is in the bulk than in the froth.The results also show strong partitioning giving an increase in bubble size which implies there is a compelling relationship between these two,represented by CFroth/CBulk and D32.The CFroth/CBulkand D32 curves show similar exponential decay relationships as a function of added frother in the system,strongly suggesting that the frother concentration gradient between the bulk solution and the bubble interface is the driving force contributing to bubble size reduction.

Generalization of the linguistic aggregation operator and its application in decision making

A generalization of the linguistic aggregation functions （or operators） is presented by using generalized and quasiarithmetic means. Firstly, the linguistic weighted generalized mean （LWGM） and the linguistic generalized ordered weighted averaging （LGOWA） operator are introduced. These aggregation functions use linguistic information and generalized means in the weighted average （WA） and in the ordered weighted averaging （OWA） function. They are very useful for uncertain situations where the available information cannot be assessed with numerical values but it is possible to use linguistic assessments. These aggregation operators generalize a wide range of aggregation operators that use linguistic information such as the linguistic generalized mean （LGM）, the linguistic OWA （LOWA） operator and the linguistic or- dered weighted quadratic averaging （LOWQA） operator. We also introduce a further generalization by using quasi-arithmetic means instead of generalized means obtaining the quasi-LWA and the quasi-LOWA operator. Finally, we develop an application of the new approach where we analyze a decision making problem regarding the selection of strategies.

Dynamic spillovers between the term structure of interest rates,bitcoin,and safe‑haven currencies

This study examines the connectedness between the US yield curve components(i.e.,level,slope,and curvature),exchange rates,and the historical volatility of the exchange rates of the main safe-haven fiat currencies(Canada,Switzerland,EURO,Japan,and the UK)and the leading cryptocurrency,the Bitcoin.Results of the static analysis show that the level and slope of the yield curve are net transmitters of shocks to both the exchange rate and its volatility.The exchange rate of the Euro and the volatility of the Euro and the Canadian dollar exchange rate are net transmitters of shocks.Meanwhile,the curvature of the yield curve and the Japanese Yen,Swiss Franc,and British Pound act mainly as net receivers.Our static connectedness analysis shows that Bitcoin is mainly independent of shocks from the yield curve’s level,slope,and curvature,and from any main currency investigated.These findings hint that Bitcoin might provide hedging benefits.However,similar to the static analysis,our dynamic analysis shows that during different periods and particularly in stressful times,Bitcoin is far from being isolated from other currencies or the yield curve components.The dynamic analysis allows us to observe Bitcoin’s connectedness in times of stress.Evidence supporting this contention is the substantially increased connectedness due to policy shocks,political uncertainty,and systemic crisis,implying no empirical support for Bitcoin’s safe-haven property during stress times.The increased connectedness in the dynamic analysis compared with the static approach implies that in normal times and especially in stressful times,Bitcoin has the property of a diversifier.The results may have important implications for investors and policymakers regarding their risk monitoring and their assets allocation and investment strategies.

Performance Analysis of Machine Learning-Based Intrusion Detection with Hybrid Feature Selection

More businesses are deploying powerful Intrusion Detection Systems(IDS)to secure their data and physical assets.Improved cyber-attack detection and prevention in these systems requires machine learning(ML)approaches.This paper examines a cyber-attack prediction system combining feature selection(FS)and ML.Our technique’s foundation was based on Correlation Analysis(CA),Mutual Information(MI),and recursive feature reduction with cross-validation.To optimize the IDS performance,the security features must be carefully selected from multiple-dimensional datasets,and our hybrid FS technique must be extended to validate our methodology using the improved UNSW-NB 15 and TON_IoT datasets.Our technique identified 22 key characteristics in UNSW-NB-15 and 8 in TON_IoT.We evaluated prediction using seven ML methods:Decision Tree(DT),Random Forest(RF),Logistic Regression(LR),Naive Bayes(NB),K-Nearest Neighbors(KNN),Support Vector Machines(SVM),and Multilayer Perceptron(MLP)classifiers.The DT,RF,NB,and MLP classifiers helped our model surpass the competition on both datasets.Therefore,the investigational outcomes of our hybrid model may help IDSs defend business assets from various cyberattack vectors.

Influence of bubble diameter and solids concentration on bubble stability: Development of a novel analytical approach

The properties and thickness of the bubbles in the froth control the flotation process. There is no work showing how to measure bubble film composition and thickness by a straightforward manner. In this work, a novel approach, a custom-designed bubble cell associated with layer interferometry(in the UV-vis region) and FT-IR spectroscopy was used to investigate the effect of solid particle type(hydrophilic vs hydrophobic), concentration and bubble diameter on stability of a bubble blown in air. Stability was quantified by measuring bubble lifetime and hydrated film thickness. Kerosene with silicone oil as a foaming agent was used to evaluate the impact of bubble diameter(test series I). Frother solutions(MIBC, Dowfroth 250, Hexanol and F-150) were used for the solid type concentration experiments(test series II). In the first series of experiments, it was determined that as the diameter of a bubble increased from 10 to 25 mm, so did the hydrated film thickness from 350 to 1000 nm. In the second series, as the silica concentration increased(0 to 10%), an increase in bubble lifetime and hydrated film thickness was resulted(130%-250%). An impact of solid hydrophobicity was found but to a lesser degree than expected. It is possible that the small particle size(<0.1 m) of silica was responsible for this behavior. The findings are used to interpret the effect of solids in flotation froth.

Predicting Fintech Innovation Adoption:the Mediator Role of Social Norms and Attitudes

Digital innovation is challenging the traditional way of offering financial services to companies;the so-called Fintech phenomenon refers to startups that use the latest technologies to offer innovative financial services.Within the framework of the Theory of Planned Behavior(TPB)and the Theory of Reasoned Action(TRA),the primary purpose of this paper is to develop a causal-predictive analysis of the relationship between Subjective Norms,Attitudes,and Perceived Behavioral Control with the Intention to Use and Behavioral Use of the Fintech services by companies.Partial Least Squares Structural Equation Modeling methodology was used with data collected from a survey of 300 companies.Our findings support the TRA and TPB models and confirm their robustness in predicting companies’intention and use of Fintech services.Financial technology innovators must understand the processes involved in users’adoption to design sound strategies that increase the viability of their services.Studying the antecedents of behavioral intention to adopt Fintech services can greatly help understand the pace of adoption,allowing these players to attract and retain customers better.This study contributes to the literature by formulating and validating TPB to predict Fintech adoption,and its findings provide useful information for banks and Fintech companies and lead to an improvement in organizational performance management in formulating marketing strategies.

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.

Experiences in using gas dispersion measurements to evaluate metallurgical performance of scavenger cleaner and recleaner circuit at Vale's Thompson Mill

The performance of a flotation circuit is largely the result of the operator's response to visual clues. This includes manipulation of the gas input and how it is distributed to cells in a bank. A new gas dispersion technology was presented which was conducted to perform characterization tests in Outokumpu 30 m3 and 50 m3 flotation cells installed at Thompson Vale's concentrator, and subsequent data analysis. The experimental program was designed to establish "as-found" baseline conditions for each cell of the two-parallel banks in the scavenger-cleaner and recleaner circuit, to select and characterize one typical cell in the two banks with either different frother concentrations or different air flow rates, and establish what variables can be manipulated in future characterization work. A three-parameter model was developed in order to link the bubble size and frother concentration. This relationship can be used to correlate gas dispersion change to improved metallurgical performance.

Habitable Land Will Soon Become the World’s Scarcest Resource: Why Appalachia Should Choose Climate Change Havens over Millionaire Estates and Golf Courses

This research advocates for the construction of Climate Change Haven Communities across the Appalachian Region. The proposed development plan can be extended to the northern tier states across the US and also to the northern and mountainous regions of Europe and Asia. We present an analogy to the earlier climate change period of the Last Glacial Maximum/“Ice Age” in which these same northern regions of the planet were covered in ice sheets making them uninhabitable for most humans and many plant and animal species. In some significant ways, the Ice Age scenario can be a reverse-model for our current climate crisis. We also advocate strongly for the prevention of upscale real estate development projects in these same regions of the globe, as these will foreclose the possibility of safely sheltering the millions of persons who will be displaced by climate change over the next 5 to 10 years.

A Markov-switching dynamic regression analysis of the asymmetries related to the determinants of US crude oil production between 1982 and 2019

The structural changes brought about by shale oil revolution have inspired this paper of which the aim is to analyze the potential asymmetries related to the determinants of crude oil production in the USA.Thus,using a Markov-switching dynamic regression model in which parameters change when oil production moves from one regime to the other,it is found that for both oil production and oil relative importance,the regime that was dominant during the 1980s and the early 1990s when oil production in the USA was substantially high is the same regime that has once again become dominant in the decade corresponding to the shale oil revolution.Furthermore,the study reveals the existence of asymmetries in the relationship between US crude oil production and both manufacturing production and the consumer price index.Asymmetries are also found in the relationship between the relative importance US crude oil and manufacturing production.Finally,it is found that the intercept and the variance parameter also vary from one regime to the other,thus justifying the use of regime-dependent models.

Social and Economic Considerations for Creating Sustainable Climate Change Haven Communities

As Climate Change Haven Communities are constructed across the Northern Hemisphere, it will be necessary to attract two types of migrants to populate them. The first group consists of professionals and companies in eco-sustainable businesses, such as law firms, insurance companies, investment firms, banking, technological innovation, mass media, medical research and pharmaceutical research. The second group will consist of persons engaged in organic/eco-sustainable agriculture whose crops and animal husbandry practices can be transferred successfully to Climate Change Haven regions. The present research focuses on the social and economic variables that must be taken into account to insure that each new Climate Change Haven Community becomes successfully integrated with the local population and forms a cohesive, harmonious social structure. Examples are given from the United States, France, Spain, Portugal and Italy.

Impact of Agricultural Modernization,Economic Growth and Industrialization on the International Competitiveness of Agricultural Products:Based on the Empirical Analysis of Cointegration and VEC Model

The expanded agricultural trade deficit in recent years has caused widespread concern about the international competitiveness of Chinese agricultural products.In this paper,guided by Michael Porter's diamond model theory,based on agricultural production and trade data in China and the world from 1986 to 2011,we use principal component analysis,cointegration and vector error correction model,to perform an empirical analysis of the impact of agricultural modernization,economic growth and industrialization on the international competitiveness of Chinese agricultural products.The results show that China's agricultural modernization is slow,the demand for agricultural products caused by economic growth is increased,and excessive rural labor transfer due to industrialization leads to decline in the international competitiveness of Chinese agricultural products.China should increase its efforts to promote the modernization of agriculture and level of industry nurturing agriculture;use WTO rules to create a good international and domestic environment for the development of China's agriculture and improvement of the international competitiveness of agricultural products;give full play to the role of demand in boosting the industrial upgrading of domestic agriculture.

Determination of Ascorbic Acid, Rutin and Antioxidant Capacities of Wild <i/>Allium roseum</i>from Italy

This study is designed to investigate the antioxidant status and the presence of biologically-active compounds in Allium roseum L. and to compare the results with those of Allium sativum L. The content of thiosulfinates (Thio) and the levels of flavonols (quercetin and rutin), ascorbic acid (AA), glutathione reduced (GSH), glutathione oxidized (GSSG) and the antioxidant enzymes activities of glutathione peroxidase (GPx), glutathione reductase (GR) and catalase (CAT), were evaluated in bulbs, bulblets, flowers bulblets, leaves and flowers. Our study shows that bulbs of Allium roseum contain levels significantly higher of GSH, GSSG, AA, Thio, rutin and the activity of GPx and GR significantly higher than bulbs of Allium sativum. Moreover, the bulbs of Allium roseum show a significantly higher content of GPx, GR, CAT, GSH and GSSG than bulblets, flowers bulblets, leaves and flowers of Allium roseum. In Allium roseum, the greatest content of Thio is present in the flowers bulblets, while the levels of AA, quercetin and rutin are greater in the flowers. In conclusion, our result shows how Allium roseum exhibits antioxidant capabilities in all its fresh organs. The bulbs, bulblets, flowers bulblets, leaves and flowers are a good source of important bioactive compounds. Allium roseum possesses properties comparable to garlic indicating its possible nutritional and medicinal value.

（Dis）Economies of Scale in Business Software Systems Development and Enhancement Projects

In the software engineering literature, it is commonly believed that economies of scale do not occur in case of software Development and Enhancement Projects （D＆EP）. Their per-unit cost does not decrease but increase with the growth of such projects product size. Thus this is diseconomies of scale that occur in them. The significance of this phenomenon results from the fact that it is commonly considered to be one of the fundamental objective causes of their low effectiveness. This is of particular significance with regard to Business Software Systems （BSS） D＆EP characterized by exceptionally low effectiveness comparing to other software D＆EP. Thus the paper aims at answering the following two questions： （1） Do economies of scale really not occur in BSS D＆EP？ （2） If economies of scale may occur in BSS D＆EP, what factors are then promoting them？ These issues classify into economics problems of software engineering research and practice.

Competition and Profitability of Banks: Empirical evidence from the Middle East & North African (MENA) Countries

This paper uses generalized method of moments(GMM),Least Squares(LS)and Generalized Linear Model(GLM)to examine the impact of competition on profitability of banks and Stochastic Frontier approach(SFA)is used to estimate of cost efficiency.We have used an unbalanced panel dataset from a sample of emerging economic MENA countries over the period between 2011 and 2017.We find out that have a significant and negative impact of competition on profitability of banks.The empirical findings of this study suggest that(1)MENA banks should more improve the process of managing and monitoring the loan segment business;the result which reducing in the level of credit risk which leads to higher profitability(2)MENA banks should shrink higher level of banking sector development.(3)MENA banks should make full conduct of available funds to engage in various natures of businesses;if there is an issue of insolvency,robust government support would give protection to MENA banks.Finally,it also provides some compulsory policy implications which will be very much beneficial for a wide range of stakeholders.