Substantial Profits Research Articles

Recovery of filler material from mining waste: Techno-economic, and kinetic study

This paper emphasizes the recovery of filler materials from bauxite mining waste by leaching iron oxides (Fe2O3). A combination of oxalic acid and hydrochloric acid is employed synergistically to facilitate the removal of iron oxides (Fe2O3) via the leaching process. Through optimization of the leaching process for iron oxide, it was determined that employing 2 M HCl along with 0.5 M oxalic acid at a temperature of 70°C for 240 minutes yielded a maximum iron removal efficiency of 64.32%. The resulting leached product met the standards outlined by the Bureau of Indian Standards (BIS) for utilization as a filler material. Kinetic investigation utilizing the shrinking core model revealed that the iron leaching process was primarily governed by a reaction step characterized by an activation energy (E) of 17.02 kJ/mol. Economic analysis indicated a return on investment of 523.65%, underscoring the profitability of the process for producing filler material from mining waste, which has the potential to generate substantial profits in the long run.

The Sinking of HBOS: An Invaluable Lesson to the Contemporary Banking and Finance Sector

HBOS plc, a UK-based financial institution, initially appeared as a remarkable success story in the banking industry. 2001 marked a pivotal moment when Halifax and Bank of Scotland joined forces to create HBOS. For the subsequent six years, it achieved substantial double-digit profits, solidifying its position. In 2007, the bank’s market capitalization soared to an impressive £40 billion. While HBOS garnered accolades from analysts and brokers for its apparent success, beneath the surface, its business model proved susceptible to economic fluctuations due to what was identified as a flawed strategy and inadequate risk management practices. These critical shortcomings left the bank ill-prepared to navigate the global financial crisis. In October 2008, HBOS faced a severe crisis and ultimately merged with Lloyds Banking Group. This case delves into the intricacies of HBOS’s strategy, internal and external governance issues and the changing business environment. It serves as an invaluable tool for students seeking to comprehend the factors that led to the company’s downfall. Several pressing questions are raised, including the role of accounting improprieties, the effectiveness of the risk management system, internal governance and the inability of the board and auditors to foresee the impending challenges within the company.

Robust portfolio selection with subjective risk aversion under dependence uncertainty

This paper solves the robust portfolio decision problem with subjective risk aversion under dependence uncertainty. We use spectral risk measures to capture investors’ subjective risk aversion while constructing mixture R-vine copula uncertainty with R-vine copula change-point detection to characterize dependence uncertainty. We introduce worst-case spectral risk measures (WSRM) and formulate portfolio decision problems with WSRM as risk measures. We conduct an empirical study using eight global stock indices. The results demonstrate that our robust portfolio outperforms two other portfolios without incorporating dependence uncertainty or considering dependence uncertainty constructed only through common economic crisis information in static and dynamic investments encompassing the full out-of-sample period and crisis periods across different risk aversion levels. The performance of our dynamic robust portfolio increases with investors’ risk aversion levels. Our robust portfolio strategy can help investors with diverse risk preferences achieve substantial profits and enable those with high-risk aversion to reap the rewards of their risk aversion.

Bioclimatic approaches of modern residential architecture in Cyprus, 1952-1974

. In terms of energy efficiency, bioclimatic architecture aims at minimizing the energy consumption of the building and at achieving control of the microclimate of the indoor spaces. The bioclimatic dimension of residential architecture in Cyprus is examined on the basis of residential buildings, designed in the period 1952-1974 by the Cypriot architect Neoptolemos Michaelides. The selected projects are of a high national architectural significance, as they represent in a most distinct way a critical introduction of modern architecture in Cyprus. In architectural terms, the designs follow the functional disposition, without relying on symbolic or decorative parameters. Following an overview of modern architecture in Cyprus, in the specific period, the main climatic conditions of the region and the thermal comfort criteria are briefly presented. The analysis of five residential buildings, situated in the broader Nicosia district refers to the architectural design strategies, and the construction materials and systems, applied for achieving human comfort, as regards cooling and natural lighting. The study proves that a series of strategic design actions ensure thermal improvements of the buildings and lead to optimum levels of thermal comfort, enabling thus substantial energy profits.

Energy Flows Optimization in a Renewable Energy Community with Storage Systems Integration

Currently, there is increasing implementation of renewable energy communities, where consumers and producers come together to form energy cooperatives. This growing trend has been accompanied by several studies aiming to optimize energy exchanges and sharing inside the community, always taking into account the most favorable tariff regimes for community members. This paper presents an analysis that, based on applying a linear programming model, optimizes energy transactions in a renewable energy community with the integration of storage systems. The results show the developed model's effectiveness, presenting substantial profits for the community.

Robust Financial Market Share Prediction using Intuitionistic Possibility Fermatean Neutrosophic Soft Set

An addition of soft set theory, Neutrosophic soft set theory offers a versatile framework for handling indeterminacy and uncertainty in data. Using this theory for the prediction of market share includes representing market data in a neutrosophic soft-set format, where elements pose truth, indeterminacy, and false degrees. The predictive model is constructed to estimate future market shares with consideration for ambiguity and uncertainty by analyzing previous market factors and trends affecting market dynamics within these frameworks. The stock market prediction pattern is interpreted as a significant action and it is more beneficial. Therefore, stock prices will result in substantial profits from sound taking choices. Thus, stock market forecasting is a main task for investors to spend their money to create maximum profit due to the noisy and stagnant data. Stock market prediction uses learning tools and mathematical strategies. Therefore, this manuscript offers the design of Financial Market Share Prediction using the Intuitionistic Possibility Fermatean Neutrosophic Soft Set (FMSP-IPFNS) technique. In the FMSP-IPFNS technique, a three-stage approach is followed. Firstly, the data normalization process is executed using a min-max scalar approach. Secondly, the prediction process can be carried out using the IPFNS approach. Thirdly, the parameter adjustment of the IPFNS approach takes place using the grasshopper optimization algorithm (GOA). To validate the performance of the FMSP-IPFNS system, a sequence of experimentations were tested. The obtained values demonstrate the promising results of the FMSP-IPFNS system compared to other models

An Accuracy Study of Personalized Recommendation System for E-commerce Based on Big Data Analysis

Abstract E-commerce, as an emerging value chain model for the global economy, has greatly promoted development, while the impact of digitalization on traditional publishing enterprises is increasingly evident. In this paper, we propose a TextRank keyword extraction algorithm based on comprehensive weights, which extracts and assigns keywords that identify user information, behavior, and product characteristics. We then output a keyword weight table for user information, user behavior, and product keywords. Finally, utilizing an optimized collaborative filtering recommendation algorithm, we establish a recommendation model between the user-commodity matrix to build an e-commerce personalized recommendation system that provides users with more accurate customized recommendations. The practical application of the designed personalized recommendation system is examined to evaluate its accuracy. The MAE of this algorithm is smaller than that of user-based (0.8915, 0.9470) or item-based (0.8873, 0.9327) collaborative filtering algorithms, indicating that the improved collaborative filtering algorithm effectively enhances system recommendation accuracy. The direct effect value of recommendation strength is 0.344, with an indirect effect value of 0.018, leading to the highest overall effect value. This study provides users with convenient and attentive services, significantly enhances user experience quality, and generates substantial profits for the e-commerce platform.

Machine learning based method for analyzing vibration and noise in large cruise ships.

Cruise ships are distinguished as special passenger ships, transporting passengers to various ports and giving importance to comfort. High comfort can attract lots of passengers and generate substantial profits. Vibration and noise are the most important indicators for assessing the comfort of cruise ships. Existing methods for analyzing vibration and noise data have shown limitations in uncovering essential information and discerning critical disparities in vibration and noise levels across different ship districts. Conversely, the rapid development in machine learning present an opportunity to leverage sophisticated algorithms for a more insightful examination of vibration and noise aboard cruise ships. This study designed a machine learning-driven approach to analyze the vibration and noise data. Drawing data from China's first large-scale cruise ship, encompassing 127 noise samples, this study sets up a classification task, where decks were assigned as labels and frequencies served as features. Essential information was extracted by investigating this problem. Several machine learning algorithms, including feature ranking, selection, and classification algorithms, were adopted in this method. One or two essential noise frequencies related to each of the decks, except the 10th deck, were obtained, which were partly validated by the traditional statistical methods. Such findings were helpful in reducing and controlling the vibration and noise in cruise ships. Furthermore, the study develops a classifier to distinguish noise samples, which utilizes random forest as the classification algorithm with eight optimal frequency features identified by LightGBM. This classifier yielded a Matthews correlation coefficient of 0.3415. This study gives a new direction for investigating vibration and noise in ships.

Digital Tax Regulation in Facing Society 5.0 Era to Realize Indonesian Tax Sovereignty

Technological advancements have catalyzed the emergence of the digital economy, where digital platforms, leveraging internet access, can penetrate multiple regions and accrue substantial profits unrestricted by national jurisdictions. This scenario presents advantages and convenience but simultaneously challenges a country's tax sovereignty. The traditional tax system, predicated on a company's physical presence for tax liability, finds itself ill-equipped to handle the digital domain's nuances. This research seeks to explore the potential and hurdles in implementing digital taxes in Indonesia, delving into the government's regulatory efforts within the framework of Society 5.0. From an academic standpoint, digital taxes illustrate the disruption in tax collection due to the absence of a tangible company presence. Thus, this study also aims to investigate the determination of tax sovereignty in the context of digital taxation, aspiring to enrich scholarly discourse. Employing a normative juridical approach, this study scrutinizes legal norms and rules while considering empirical facts from the field. The findings indicate that while the introduction of digital taxes in Indonesia holds significant promise and faces considerable challenges, governmental action to update and streamline digital tax regulations is imperative. Furthermore, the principles underlying digital tax proposals, such as Pillar One, require reinforcement to enhance their effective implementation.

Balón parado. El sindicato de futbolistas de México y la huelga de 1971

This article examines the formation of the Mexican football players’ union and their call for a strike in 1971. I assert that the footballers fought to be recognized as workers in a spectacle that generated substantial profits for the organizers but did not acknowledge their labor rights; they suffered from low wages – except for star players – and were denied the right to freely contract with the employer of their choice, among other issues. I argue that three elements explain the articulation of their demands: 1) the consolidation of football as a sporting spectacle during the 1970s; 2) the context of intense union mobilization during the presidency of Luis Echeverría; and 3) the emergence of a certain class consciousness among some footballers. Although they did not achieve their goal of being recognized as subjects of labor rights, I conclude that the union and the Mexican players’ strike were part of a broader international struggle: the contest for the recognition of professional athletes as workers. This research was supported by newspaper sources and oral testimonies from footballers.

A portfolio trading system using a novel pixel graph network for stock selection and a mean-CDaR optimization for portfolio rebalancing

Due to the complexity and dynamic nature of financial markets, portfolio management tasks require continuous adaptation with market intelligence. Furthermore, to make profitable trading decisions, it is essential to predict the future performance of the market. To that end, this study proposes a dynamic portfolio trading system in two stages: stock trend prediction and portfolio optimization. In the first stage, although many researchers proposed powerful classification models to predict BUY/SELL trading signals, some scholars only focused on converting time series' features into multichannel images without considering any spatial relationships between features. Considering the relational capacity of graphs, this study proposes a novel image-based classification model, called Pixel Graph Network (PGN), to analyze the constructed images with propagating information from all neighboring pixels, build up a representation of the images as special graphs, and get advantages of a graph-based learning algorithm. In the second stage, stocks with predicted BUY trading signals in the following days, are fed into the Mean–Conditional Drawdown at Risk (M-CDaR) optimization model to prevent any significant drawdown of the investment portfolio. Moreover, a hierarchical feature selection algorithm has been proposed, combined with a continuous trend labeling method to improve the efficiency of the training process. To demonstrate the superiority of our proposed PGN model in comparison with benchmarks, 18 stocks from the New York Stock Exchange (NYSE) are used for numerical experiments. The obtained results show the improvement and effectiveness of the proposed model in both stock trend prediction and portfolio optimization. In terms of financial performance, the proposed PGN+M-CDaR portfolio-based trading system achieves an average annual return of 112%, an average annual Sortino ratio of 10.78, and an average annual Sharpe ratio of 2.79 which are promising to achieve substantial profits while mitigating downside risk.

Enhancing smallholders' sustainable livelihoods through cassava value chain: a study in Central Highlands, Vietnam

Cassava (Manihot esculenta Crantz) stands out as a remarkably multipurpose crop, playing a pivotal role in sustaining the livelihoods of small-scale farmers and bolstering their income. This study delves into the cassava value chain in Vietnam's Central Highlands, aiming to pinpoint avenues for amplifying production efficiency and refining the overall value chain dynamics. Employing a comprehensive approach, the analysis combines a survey-based questionnaire distributed to 330 smallholder farmers, focused group discussions across three provinces and in-depth interviews with key informants. The primary goal is to unearth strategies that enhance the value chain, elevate stakeholders' incomes, and ensure enduring livelihoods for households. The findings underscore that while farmers contribute significantly to the value chain by generating the highest value added, intermediaries tend to reap the most substantial profits. The intricate web of relationships among stakeholders, spanning from input providers to end-users-primarily starch and ethanol factories-is explored. The study identifies local linkages between farmers and input suppliers, encompassing both spot-market transactions and enduring partnerships. Furthermore, an imbalance in the distribution of gross profit and net profits, favoring middleman and processors disproportionately, is revealed. Nevertheless, the study emphasizes the indispensable role played by intermediaries, asserting that farmers wouldn't fully capitalize on their efforts without this crucial support. Keywords: Cassava value chain, Small-scale farmers, Livelihood sustainability, Stakeholder incomes, Household livelihoods

Cryptic excitement: unveiling market reactions to Facebook's Metaverse and potential manipulation in China's stock market

This study examines the impact of Facebook's rebranding on the Metaverse's renewed interest, revealing potential market manipulation concerns in China. Analyzing Chinese publicly listed firms entering the Metaverse, the research notes a surge in initial disclosures, aligned with Facebook's rebranding and increased investor sentiment. Investors' exaggerated reactions to Metaverse-related announcements are observed, alongside insiders' timed transactions leading to substantial profits during the Metaverse frenzy. The results underscore Chinese regulators' worries, highlighting the susceptibility of investors to companies' initial Metaverse disclosures.

Techno-Economic Analysis of a Highly Renewable and Electrified District Heating Network Operating in the Balancing Markets

In pursuit of Finland’s carbon neutrality objective by 2035, integrating renewable energy sources into the power grid is essential. To address the stochastic nature of these resources, additional sources of flexibility are required to maintain grid stability. Meanwhile, district heating network (DHN) operators in Finland are decommissioning fossil fuel-based combined heat and power plants (CHPs) and electrifying heating systems with heat pumps (HPs) and electric boilers. A techno-economic assessment and the optimized operation of DHN-connected HPs and electric boilers in providing ancillary balancing services were explored in this study. The primary goal was to maximize the potential revenue for DHN operators through participation in the day-ahead electricity market and frequency containment reserve (FCR) balancing markets. Three interconnected DHNs in the Helsinki metropolitan area were optimized based on 2019 data and each operator’s decarbonization strategies for 2025. HPs are expected to achieve the highest profit margins in the FCR-D up-regulation market, while electric boilers could generate substantial profits from the FCR-D down-regulation market. In contrast to other balancing markets studied, the FCR-N market exhibited limited profit potential. Sensitivity analysis indicated that spot electricity prices and CO2 emission allowance prices significantly influence the profitability derived from balancing markets.

The Application of Deep Reinforcement Learning in Stock Trading Models

Deep Reinforcement Learning (DRL), which integrates the perceptual strength of Deep Learning (DL) with the determination strength of Reinforcement Learning (RL), has emerged as an advanced approach in stock trading. This article focuses on summarizing the research on DRL in stock trading over the past five years, with an emphasis on state definition, action design, reward design, and algorithm selection in stock trading models. Many studies have found that the use of DRL in stock trading can effectively improve investment returns and profitability. In the last years, the adoption of DRL in the stock market has increased and researchers have achieved higher returns and substantial profits through continuous model optimization. However, current research on DRL models faces challenges because of the need for large amounts of complex and uncertain stock market data and the impacts of market volatility and the influence of information asymmetry. This review compares the discrepancies in processing logic among various studies and summarizes the progress made in existing research. It also explores the current challenges and limitations, and discusses potential improvement directions for DRL models in stock trading.

Diagnostics for asset pricing models

AbstractThe validity of asset pricing models implies white‐noise pricing errors (PEs). However, we find that the PEs of six well‐known factor models all exhibit a significant reversal pattern and are predictable by their lagged values up to 12 months. Moreover, the predictability of the PEs can produce substantial economic profits. Similar conclusions hold for recently developed machine learning models too. Additional analysis reveals that the significant PE profits cannot be explained by common behavioral biases. Our results imply that much remains to be done and there is a great need to develop new asset pricing models.

Application of Two-sided Matching Theory in Financing of Small and Medium-sized Enterprises

The nation's small and medium-sized businesses (also known as SMEs) constitute an essential component of the economy as a whole; yet, many companies are right now struggling to obtain adequate funding. On the one hand, increasing the amount of capital invested in venture firms by venture capitalists can considerably promote the development of venture enterprises. On the other hand, venture capitalists look for businesses in which they may invest, with the goal of achieving high levels of success and gaining substantial profits. Therefore, in order to make a reasonable and stable match between venture capitalists and SMEs according to their respective preference lists, this paper makes use of the Gale-Shapley algorithm, which is part of the two-sided matching theory. This helps improve cooperation efficiency and maximizes the benefits for both parties. In addition to that, based on the algorithm, this article offers some policy recommendations for the venture capital market in China.

The Transfer Pricing Implications of GILTI for Transfer Pricing in the Pharmaceutical Sector

Pharmaceutical multinationals generate substantial profits from their ownership of valuable intangible assets including patents and marketing intangibles. Their production activities are often vertically integrated with the production of active pharmaceutical ingredients occurring in Western Europe with the fill and finish function occurring in India. The transfer pricing challenges have led to numerous litigations where tax authorities have accused multinationals of shifting profits to tax havens. This article discusses the transfer pricing issues in litigations involving Canada, India and the United States, noting how the applications of market-based approaches versus profits-based approaches lead to very different results.

Distributed dominance graph-based neural multi-objective evolutionary strategy for sponsored search real-time bidding

In industrial online advertising, real-time bidding optimization has brought substantial profits. Existing methods usually set fixed bids for advertisers which causes collusion among advertisers, resulting in unsatisfactory performance. In this work, by considering both advertisers’ and the platform’s objectives, we formulate the problem as a multiobjective optimization problem and propose a neural evolutionary strategy-based framework to learn the optimal bidding strategy. Since the objectives of advertisers and the platform are conflicting to some extent, it is crucial to properly balance them. A straightforward idea is to simply add up the objectives. However, this could limit the diversity of solutions, leading to suboptimal performance. To this end, we innovatively develop a distributed dominance graph-based neural Pareto multiobjective evolutionary strategy, which can learn a better spread of solutions to improve the model performance. In addition, it can distribute the total computational cost among thousands of workers, improving computational efficiency. We conduct a suite of offline experiments as well as the standard online A/B test on the Taobao platform. The experimental results indicate that our method can significantly improve the profits of both advertisers and the platform.

An Overview of Algorithms for Solving Vehicle Routing Problems in the Quantum-Classical Cloud

Introduction. The hope of solving the problem of the avalanche-like growth of requirements for computing power, essential for solving complex routing problems and other problems of combinatorial optimization, relies on the latest quantum computers, in the development of which governments and corporations invest multi-billion investments. The article examines modern routing algorithms and performs their analysis and verification, if the authors of the algorithm provided appropriate test programs. The purpose of the article is to review the current state of development in the field of development of routing algorithms for hybrid quantum-classical clouds, analyze them and propose a classification of algorithms. Results. Modern quantum computers (QCs) make it possible to find approximate solutions to some of mathematical problems faster than classical computers. The inaccuracy of the solutions obtained by the QC is a consequence of physical and technological limitations: calculation errors are caused by thermal noise, a small number of computational elements - qubits and connections between them, which requires the decomposition of the problem and the use of heuristic algorithms. The analysis of approaches to the solution of optimization problems on QC allows us to single out: quantum response and variational search of eigenvalues based on quantum logic gates as the general directions of development of the vast majority of algorithms for solving routing problems. The considered algorithms reduce the vehicle routing problem to a quadratic unconstrained binary optimization problem, which is isomorphic to the Hamilton-Ising model. In this form, the problem is suitable for embedding in QC, which finds an approximate solution that has the best statistical reliability or corresponds to the quantum state with the lowest energy. As a separate class, vehicle routing algorithms for classical computers that use quantum computing to accelerate problem solving can be distinguished. For example, neural networks that calculate weighting factors using QC or an ant algorithm that calculates a pheromone trail in a hybrid cloud. It should be mentioned the quantum-inspired algorithms, which are based on software tools for the simulation of QC and the corresponding libraries and allow creating an effective class of algorithms for solving problems of vehicle routing. Conclusions. Combining hardware quantum annealing with a number of software tools for calculating optimization problems for classical computers in a hybrid quantum-classical cloud service allows to obtain advantages in speed and accuracy of some types of complex optimization problems of a commercial scale, in particular, routing vehicles, which is already bringing substantial profits to a number of corporations. Keywords: vehicle routing problem, quantum computer, annealing, combinatorial optimization, traveling salesman problem, clustering, qubit.