Pertinent Recommendations Research Articles

Deep Learning Based Music Recommendation Systems: A Review of Algorithms and Techniques

Abstract. This paper provides a thorough examination of the utilisation of deep learning in music recommendation systems, which have transformed consumer discovery and engagement with music on streaming platforms. Scalability challenges and the cold-start problem are among the constraints that conventional recommendation methods, such as content-based filtering and collaborative filtering, encounter, which hinder their ability to deliver personalised recommendations that are effective. The processing of multi-modal and sequential data is significantly improved by deep learning methodologies, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and Autoencoders. This results in more precise and contextually pertinent music recommendations. Moreover, hybrid models that amalgamate deep learning with conventional techniques augment recommendation precision by synthesising user interaction data with acoustic characteristics. This paper examines essential performance metrics employed in the assessment of music recommendation systems, including precision, recall, F1-score, and Mean Reciprocal Rank (MRR). It also tackles issues including computational complexity, bias, and ethical dilemmas pertaining to data privacy.

Regulating Artificial Intelligence to Advance Financial Inclusion in South Africa

The emergency of Artificial Intelligence (AI) endowed with capabilities to simulate human intelligence through software-coded operations, has become a topical issue perplexing the minds of regulators, government officials, non-governmental organisations, and the public, across the globe. Linked to this increasing AI debate, is the view that these technologies have the potential to facilitate financial inclusion. Whilst there are concomitant liability and cyber-security related issues associated with AI adoption, the importance of AI in facilitating financial inclusion cannot be overstated. AI can facilitate financial inclusion by enhancing the quality of financial services products and services offered by key players in the South African financial sector, including the capacity to improve the process of opening bank accounts, data analysis, assessment of credit scores, and the management of risk-linked to various financial products. Drawing significant lessons from a select study of the EU and UK model on the regulation of AI, this article argues that there is a need for South Africa to develop an effective regulatory framework governing AI in pursuit of advancing the goals of financial inclusion, among other things. In finality, the article offers pertinent recommendations in search of avenues for developing policies, principles, norms, and rules that govern AI in South Africa to advance financial inclusion and other important related goals.

An explicit solution to harvesting behaviors in a predator–prey system

AbstractThis paper derives closed‐form solutions for a strategic, simultaneous harvesting in a predator–prey system. Using a parametric constraint, it establishes the existence and uniqueness of a linear feedback‐Nash equilibrium involving two specialized fleets and allows for continuous time results for a class of payoffs that have constant elasticity of the marginal utility. These results contribute to the scarce literature on analytically tractable predator–prey models with endogenous harvesting. A discussion based on industry size effects is provided to highlight the role played by biological versus strategic interactions in the multispecies context.Recommendations for Resource Managers This model presents a thorough examination of the economic inefficiencies inherent in the exploitation dynamics of two interdependent species, elucidating the complex interplay between ecological interactions and economic outcomes. The size of the fishing industries constitutes a significant variable that must be integrated into the formulation of pertinent policy recommendations. This constitutes an advancement towards a more time‐consistent approach to Ecosystem‐Based Fishery Management (EBFM).

Analysis of Spatiotemporal Predictions and Drivers of Carbon Storage in the Pearl River Delta Urban Agglomeration via the PLUS-InVEST-GeoDetector Model

Land use and land cover change (LUCC) significantly influences the dynamics of carbon storage in thin terrestrial ecosystems. Investigating the interplay between land use alterations and carbon sequestration is crucial for refining regional land use configurations, sustaining the regional carbon balance, and augmenting regional carbon storage. Using land use data from the Pearl River Delta Urban Agglomeration (PRDUA) from 2010 to 2020, this study employed PLUS-InVEST models to analyze the spatiotemporal dynamics of land use and carbon storage. Projections for the years 2030, 2040, and 2050 were performed under three distinct developmental scenarios, namely, natural development (ND), city priority development (CPD), and ecological protection development (EPD), to forecast changes in land use and carbon storage. The geographic detector model was leveraged to dissect the determinants of the spatial and temporal variability of carbon storage, offering pertinent recommendations. The results showed that (1) during 2010–2020, the carbon storage in the PRDUA showed a decreasing trend, with a total decrease of 9.52 × 106 Mg, and the spatial distribution of carbon density in the urban agglomeration was imbalanced and showed an overall trend in increasing from the center to the periphery. (2) Clear differences in carbon storage were observed among the three development scenarios of the PRDUA between 2030 and 2050. Only the EPD scenario achieved an increase in carbon storage of 1.10 × 106 Mg, and it was the scenario with the greatest potential for carbon sequestration. (3) Among the drivers of the evolution of spatial land use patterns, population, the normalized difference vegetation index (NDVI), and distance to the railway had the greatest influence on LUCC. (4) The annual average temperature, annual average rainfall, and GDP exerted a significant influence on the spatiotemporal dynamics of carbon storage in the PRDUA, and the interactions between the 15 drivers and changes in carbon storage predominantly manifested as nonlinear and double-factor enhancements. The results provide a theoretical basis for future spatial planning and achieving carbon neutrality in the PRDUA.

A survey of 3D Space Path-Planning Methods and Algorithms

Due to their agility, cost-effectiveness, and high maneuverability, Unmanned Aerial Vehicles (UAVs) have attracted considerable attention from researchers and investors alike. Path planning is one of the practical subsets of motion planning for UAVs. It prevents collisions and ensures complete coverage of an area. This study provides a structured review of applicable algorithms and coverage path planning solutions in Three-Dimensional (3D) space, presenting state-of-the-art technologies related to heuristic decomposition approaches for UAVs and the forefront challenges. Additionally, it introduces a comprehensive and novel classification of practical methods and representational techniques for path-planning algorithms. This depends on environmental characteristics and optimal parameters in the real world. The first category presents a classification of semi-accurate decomposition approaches as the most practical decomposition method, along with the data structure of these practices, categorized by phases. The second category illustrates path-planning processes based on symbolic techniques in 3D space. Additionally, it provides a critical analysis of crucial influential approaches based on their importance in path quality and researchers’ attention, highlighting their limitations and research gaps. Furthermore, it will provide the most pertinent recommendations for future work for researchers. The studies demonstrate an apparent inclination among experimenters toward using the semi-accurate cellular decomposition approach to improve 3D path planning.

The Link Effect of ESG Score, Stock Price Volatility, and Tax Payment: Doing Well while Doing Good

This study aims to identify how environmental, social and governance (ESG) performance influences stock price volatility, explicitly focusing on the moderating role of tax engagement. ESG performance is measured by an ESG Score calculated from the weighting of three dimensions: environmental, social and governance. Stock price volatility is measured by the degree of price variations over 12 months based on the last 52 weeks’ prices. A sample of Indonesia-listed firms is used, with 770 observations from 2023. The results show that the ESG Score negatively impacts stock price volatility, which is more significant in the social dimension than in the environmental and governance dimensions. In addition, the tax payment variable moderates the relationship and increases the effect of the ESG Score on stock price volatility. These findings suggest that ESG practices and tax transparency are ethical elements and critical components for financial stability, promoting the high-quality development of listed firms. This study is significant for firms, regulators, policymakers and investors. Overall, it underrates the importance of firms adopting ESG activities and engaging in tax management to mitigate risks and maintain viability in the contemporary business environment. This study provides new empirical evidence regarding the factors driving corporate stock price volatility. In addition, it offers pertinent policy recommendations for businesses and governments regarding the significance of ESG investments.

Knowledge mapping analysis of ground glass nodules: a bibliometric analysis from 2013 to 2023.

In recent years, the widespread use of computed tomography (CT) in early lung cancer screening has led to an increase in the detection rate of lung ground glass nodules (GGNs). The persistence of GGNs, which may indicate early lung adenocarcinoma, has been a focus of attention for scholars in the field of lung cancer prevention and treatment in recent years. Despite the rapid development of research into GGNs, there is a lack of intuitive content and trend analyses in this field, as well as a lack of detailed elaboration on possible research hotspots. The objective of this study was to conduct a comprehensive analysis of the knowledge structure and research hotspots of lung ground glass nodules over the past decade, employing bibliometric methods. The Web of Science Core Collection (WoSCC) database was searched for relevant ground-glass lung nodule literature published from 2013-2023. Bibliometric analyses were performed using VOSviewer, CiteSpace, and the R package "bibliometrix". A total of 2,218 articles from 75 countries and 2,274 institutions were included in this study. The number of publications related to GGNs has been high in recent years. The United States has led in GGNs-related research. Radiology has one of the highest visibilities as a selected journal and co-cited journal. Jin Mo Goo has published the most articles. Travis WD has been cited the most frequently. The main topics of research in this field are Lung Cancer, CT, and Deep Learning, which have been identified as long-term research hotspots. The GGNs-related marker is a major research trend in this field. This study represents the inaugural bibliometric analysis of applied research on ground-glass lung nodules utilizing three established bibliometric software. The bibliometric analysis of this study elucidates the prevailing research themes and trends in the field of GGNs over the past decade. It also furnishes pertinent recommendations for researchers to provide objective descriptions and comprehensive guidance for future related research.

The Impact and Response of China's Capital Market Opening Policy on the Financial System

Due to the liberalization of the Chinese capital market in recent years, many foreign investors have entered the country's capital market. This article outlines the history and present state of China's relative policy, gathers and evaluates pertinent policies, examines the policy's turning points, and finally highlights the difficulties and issues at hand. Based on this, it examines how this act affects financial institutions and presents three arguments for why bringing in foreign money will strengthen the financial market's vibrancy, encourage its modernization, and increase its size. Drawing from the aforementioned analysis, this article puts forth a response strategy aimed at promoting financial institutions' risk control and institutional innovation through capital market liberalization. Additionally, it suggests that the state enhance its oversight of foreign capital inflows to ensure a consistent flow of capital. To sum up, as China's capital market becomes more open to the outside world, it will be important to take advantage of the chances for innovation and growth while also managing the risks and difficulties that come with it. This paper seeks to be able to offer pertinent recommendations for the market's continued development and liberalization.

Comparative Analysis of SAAS Model and NPC Integration for Enhancing VR Shopping Experiences

This article examines the incorporation of the Shopping Assistance Automatic Suggestion (SAAS) model into Virtual Reality (VR) environments in order to improve the online shopping experience. The SAAS model employs sophisticated deep learning methods to offer customized product recommendations, which are conveyed by non-player characters (NPCs) via voice-based interactions. Our goal is to develop an interactive shopping experience that replicates real-life interactions by integrating AI-powered recommendations with immersive VR technology. We gather and standardize data from several open commerce databases, such as Amazon Product and Customer Reviews. The SAAS model, in conjunction with GPT-3, BERT, and T5, undergoes training and testing to evaluate its effectiveness across multiple criteria. The results demonstrate that the SAAS model surpasses other models in delivering contextually aware and pertinent recommendations. The integration process outlines the specific steps involved in capturing, processing, and transforming user interactions in virtual reality (VR) into vocal suggestions provided by non-player characters (NPCs). This strategy improves customization and utilizes the immersive features of virtual reality to effectively engage people. The results of our research establish a higher standard for e-commerce, with the goal of enhancing the user experience of online purchasing by making it more instinctive, engaging, and pleasurable.

Analyzing on Luckin Coffee's Success in 2023 Based on the Marketing Mix Theory of 4Vs

The coffee market in China has experienced notable changes due to rapid technological progress and changing consumer preferences. Luckin Coffee has emerged as a leader in this industry. This study aims to evaluate Luckin Coffee's achievements in 2023 using the Marketing Mix Theory of 4Vs as an analytical framework. It will investigate the operational tactics and marketing initiatives that have contributed to the brand's success in China's competitive coffee industry. This paper examines Luckin Coffee's business strategy, product innovation, co-branding initiatives, and dedication to sustainability to reveal how the company has successfully managed rapid expansion while improving the customer experience and operational efficiency. This article demonstrates that Luckin Coffee's strategy emphasis on market distinctiveness, adding value, and establishing brand resonance with consumers has not only led to its impressive expansion but has also established novel benchmarks for retail innovation in the coffee sector. This paper provides a concise overview of the decisions and actions taken by Luckin Coffee in 2023 regarding their variation strategy, value strategy, and vibration strategy. It also offers pertinent recommendations to Luckin Coffee in these three areas, providing valuable insights for both scholars and professionals on how to effectively utilize technology and strategic marketing to attain sustainable growth and establish dominance in the market.

Disparities in achieving SDG goal of ‘no poverty’ for developing countries: technical efficiency estimates across Asian, African and American nations

ABSTRACT Globally, people acknowledge poverty as the greatest tragedy in human history, a serious threat to sustainable living, and a barrier to the development of human capital. Realising the importance of lowering global poverty, the United Nations incorporated ‘End poverty in all its forms everywhere’ as the first of the 17 SDGs for Sustainable Development. This background motivates us to look into how different lower-middle- or lower-income countries are achieving the first SDG goal. Consequently, the study primarily investigates the efficiency of 43 lower middle- or lower-income countries in Asia, Africa, and America in reaching the SDG goal of ‘No poverty’ by using the Stochastic Production Frontier from 2019 to 2020. Concurrently, the analysis pinpoints the causes of the nation’s inefficiency in accomplishing that objective. According to the study’s findings the Korea Republic is the most efficient nation at reaching the objective of ‘No poverty.’ Guinea is the least efficient. In conjunction with inefficiency issues, the analysis demonstrates that rising CO2 emissions paradoxically promote efficiency in the battle against poverty; conversely, rising corruption increases inefficiency. Conflict significantly impedes any constructive goal, such as poverty reduction. Corruption and intentional homicides serve as proxy indicators of conflict in this context. A rise in conflict variables reduces the nation’s efficacy in lowering poverty rates. The article ends by outlining future research goals and making pertinent policy recommendations.

The Impact of the “Belt and Road” Initiative on Industrial Structure Upgrading Along the Corridor

The “Belt and Road” initiative functions as a novel impetus for China’s external economic development within the framework of the new normal of economic growth. By leveraging panel data from 30 provinces and regions across China for the period of 2010 to 2020, this research assesses the influence of the “Belt and Road” initiative on the enhancement of the industrial structure along its trajectory. The findings indicate that: the most notable influence on the rationalization and progress of the industrial structure is observed in the eastern region, with the central region following suit, whereas the western region has yet to exhibit a significant transformation. The analysis delves into the role of technological innovation, concluding that the initiative primarily catalyzes optimization and upgrading through the effect of technological advancement. The study advances several pertinent policy recommendations to support and enhance these developments.

Calf mortality in Turkish dairy farms: Economic impact, regional disparities, and farm-level drivers

This study investigates the economic burden of calf mortality in Turkish dairy farms and its impact on the national economy. We gathered research data by directly surveying dairy farms in seven provinces, each representing a distinct region of Turkiye. By conducting these surveys, we captured data on various aspects of calf mortality, including losses among non-pregnant cows aged two and older, pregnant cows, and those experiencing complications during birth, as well as losses within the 0–6 month age bracket. These figures were then amalgamated to establish the overall calf loss rate. Using a fractional probit model, we examined the empirical relationship between total calf loss rates and the socio-demographic characteristics of farm operators and their establishments. Our findings revealed that approximately 82 % of farms experienced some degree of calf loss, with the calf loss rate among dairy cattle farming accounting for nearly 20 %. Notably, regional disparities emerged as a key observation, alongside the identification of certain socio-demographic farm characteristics that proved statistically significant. Specifically, factors such as the prevalence of local cattle breeds, the proportion of crossbred bulls, as well as the numbers of heifers and calves, stood out as influential. Further scrutiny, fortified by ANOVA tests and relationships between the number of cows and total calf loss rate, underscored pronounced geographical disparities in post-estimation calf loss rates. Meanwhile, correlation heatmaps illuminated noteworthy relationships between specific cattle traits and the extent of calf losses. These findings not only underscore the severity of the issue but also highlight the urgency of preventive measures. In light of these insights, we offer pertinent policy recommendations to stakeholders and policymakers aimed at mitigating this considerable economic burden effectively.

Understanding stunting and its determinant among children in the most populous state of India using estimation of population mean under ranked set sampling in the presence of missing data

ABSTRACT In real-life scenarios, encountering data with missing values is common, and if not managed carefully from the outset of a study, it can lead to significant biases in survey estimates. Various methods exist for imputing missing values in sampling procedures. Ranked set sampling (RSS) is widely recognized for its superior efficiency compared to simple random sampling. However, limited research has been conducted on ranked set sampling in the presence of missing data. This article introduces novel imputation methods designed to estimate population means in the context of missing data under RSS. These innovative estimators are developed by integrating ratio, exponential, and logarithmic estimators judiciously. Expressions for the bias and mean squared error of the proposed estimators are derived up to the first-order approximation. Through simulation studies and an application to stunting and its determinants among children in Uttar Pradesh, India’s most populous state, the effectiveness of the suggested estimators in handling missing data is demonstrated. Numerical examples involving stunting in Uttar Pradesh, as well as simulated data generated using R software, confirm the superior performance of the proposed estimators over existing methods, as evidenced by comparisons of percentage relative efficiency and mean squared error. The results are promising, indicating improvement over all existing imputation methods. Additionally, pertinent recommendations are provided for survey professionals regarding future research.

Optimal allocation of offshore wind power and energy storage considering source-load power stochasticity

Abstract Large-scale offshore wind generation has been integrated to power grids in China. The annual increase in electric vehicles, air conditioning systems, and other electrical facilities has intensified the randomness and volatility of power supply and demand, presenting significant challenges to the safe and economical operation of power systems. Energy storage systems serve as regulators in the power grid, yet the electrical performance and costs associated with various storage technologies differ considerably. Consequently, the strategic planning and development of energy storage solutions tailored to specific local conditions have emerged as critical areas of research. This paper presents an in-depth analysis of power characteristics across source loads, explores an optimized configuration approach for energy storage, and validates this method through a numerical example. The findings confirm the effectiveness of the proposed configuration strategy and offer pertinent recommendations for the implementation of energy storage solutions.

Organizational Sustainability: A Big Data Framework Using Knowledge Sharing and Innovative Work Behavior

Organizational sustainability is a multifaceted area of exploration for academics and businessmen alike. Sustenance is closely linked to human resource quality, performance, and productivity. The parameters that have a strong impact on the aforementioned factors are information sharing and innovative work behavior, which may transform the sustenance question into a workable and productive human resource strategy. This research focuses on the human resource and respective knowledge-sharing elements to expose the potential of social exchange theory. To analyze the data using Smart PLS, a total of 101 completely filled responses from full-time workers in Lahore city, Pakistan’s readymade garment industry, were utilized. The findings showed that information sharing and creative work practices are positively correlated with organizational sustainability. In addition, sharing knowledge fosters creative work practices. In light of the results, this study offers pertinent research recommendations for sustainable businesses.

Does digital finance drive the green level of transportation companies? Coordination effects of governmental digital preference

This paper examines the influence of digital finance (DF) on the green level of transportation companies (GLTC, n = 112) listed on the Chinese A-share market from 2011 to 2021 through moderation and mediation models. According to the conclusions, first, a significantly positive correlation exists between DF and GLTC. Second, DF indirectly promotes GLTC by enhancing corporate value and alleviating financing constraints. Both enterprise value and financing constraints have a chain mediation effect. Third, the government's digital preference can enhance the role of DF in promoting GLTC and alleviating financing constraints. In addition, governmental digital preference can moderate the impact of financing constraints on GLTC. Fourth, the effect of DF on GLTC exhibits regional, scale, and ownership heterogeneities. Drawing on the conclusions of the empirical analysis, several pertinent recommendations are proposed from micro and macro perspectives.

System dynamics-multiple the objective optimization model for the coordinated development of urban economy-energy-carbon system

The deepening conflict between human societal progress and efforts to combat climate change necessitates immediate coordination among the Economy-Energy-Carbon (EEC) management. The study applies IPCC accounting methods to compute the historical carbon emissions in the Chengdu-Chongqing economic circle from 2005 to 2021. It assesses the developmental efficiency of various subsystems in the EEC system by employing the rank-sum ratio. Taking a typical city in the region (Chongqing) as a case study, this study integrates System Dynamics (SD) with NSGAIII to develop an EEC multi-objective optimization model. It utilizes the VIKOR model to select the optimal solution. Based on this, the analysis explores the coupled and coordinated state of the EEC system from 2022 to 2030 under the optimal solution mode. Research findings indicate that: (1) The overall carbon emissions exhibit a slight increase, where carbon sources surpass the carbon sink created by land use. (2) Compared to results from other preference-based scenarios, in the scenario where subsystem efficiencies are all optimal, the minimum cumulative carbon emissions amount to 1520.52 million tons, with the economy maintaining a favorable status. Moreover, the overall coupling coordination of the EEC is in a relatively favorable state, and it tends to improve annually. (3) To attain the coordinated development of the EEC system by 2030, the energy structure of the representative city must decrease coal consumption by 26.17%, maintain the construction land ratio below 3.7%, and guarantee forest coverage exceeds 57.77%. This study, utilizing complex system simulations and optimization analyses across various scenarios, offers pertinent recommendations for attaining urban low-carbon development while upholding societal advancement.

The impact of new energy industry on environmental and economic benefits: Evidence from China

The aggravation of the global warming crisis, due to excessive consumption of traditional energy, urgently calls for the strategic goal of sustainable development. This study addresses endogeneity concerns by employing panel fixed effect and DID models, employing panel data from China's provincial green finance index spanning from 2016 to 2021. The empirical findings indicate that: (1) the impact of the new energy industry on environmental and economic benefits is more pronounced in inland areas characterized by lower GDP and smaller population size; (2) this effect is driven by green technological innovation, government incentive policies, and job creation. Drawing upon these results, this paper offers a range of pertinent recommendations at governmental, industrial, and societal levels to foster sustainable development, thereby enhancing social welfare.

Impacts of the pilot policy for carbon emissions trading on pollution reduction in China

Global environmental problems are worsening. Among various regulatory measures, market-oriented approaches are most favored by governments worldwide. Using provincial panel data from 2004 to 2020, a multi-period DID model was hereby constructed to empirically study the pollution reduction effects of the pilot policy for carbon emissions trading in China. Findings of this paper could be listed as follows: (1) The pilot policy has achieved notable reductions in waste gas and solid waste, although its effect on wastewater pollutants has been less pronounced; (2) Pollution reduction in the pilot regions can be achieved by optimizing the energy consumption structure; (3) Significant variations in pollution reduction effects are observed between the Northern and Southern pilot regions, with notably stronger policy effects in the north across various pollutants compared to the south. Overall, this paper offers pertinent policy recommendations to aid the Chinese government in enhancing its carbon market.