Search Results (125)

The integration of artificial intelligence (AI) and advanced deep learning techniques is reshaping intelligent financial forecasting and decision-support systems. This study presents a comprehensive comparative analysis of advanced deep learning models, including state-of-the-art transformer architectures and established non-transformer approaches, for long-term stock market index prediction. Utilizing historical data from major global indices (S&P 500, NASDAQ, and Hang Seng), we evaluate ten models across multiple forecasting horizons. A dual-metric evaluation framework is employed, combining traditional predictive accuracy metrics with critical financial performance indicators such as returns, volatility, maximum drawdown, and the Sharpe ratio. Statistical validation through the Mann–Whitney U test ensures robust differentiation in model performance. The results highlight that model effectiveness varies significantly with forecasting horizons and market conditions—where transformer-based models like PatchTST excel in short-term forecasts, while simpler architectures demonstrate greater stability over extended periods. This research offers actionable insights for the development of AI-driven intelligent financial forecasting systems, enhancing risk-aware investment strategies and supporting practical applications in FinTech and smart financial analytics. Full article

Forecasting financial time series is challenging due to their intrinsic nonlinearity, high volatility, and complex dependencies across temporal scales. This study introduces MSGformer, a novel hybrid architecture that integrates multi-scale graph neural networks (MSGNet) with Transformer encoders to capture both local temporal fluctuations and long-term global trends in high-frequency financial data. The MSGNet module constructs multi-scale representations using adaptive graph convolutions and intra-sequence attention, while the Transformer component enhances long-range dependency modeling via multi-head self-attention. We evaluate MSGformer on minute-level stock index data from the Chinese A-share market, including CSI 300, SSE 50, CSI 500, and SSE Composite indices. Extensive experiments demonstrate that MSGformer significantly outperforms state-of-the-art baselines (e.g., Transformer, PatchTST, Autoformer) in terms of MAE, RMSE, MAPE, and R². The results confirm that the proposed hybrid model achieves superior prediction accuracy, robustness, and generalization across various forecasting horizons, providing an effective solution for real-world financial decision-making and risk assessment. Full article

Accurate electricity wholesale price (EWP) forecasting is crucial for advancing sustainability in the energy sector, as it supports more efficient utilization and integration of renewable energy by informing when and how it should be consumed, dispatched, curtailed, or stored. However, high fluctuations in EWP, often resulting from demand–supply imbalances typically caused by sudden surges in electricity usage and the intermittency of renewable energy generation, and unforeseen external events, pose a challenge for accurate forecasting. Incorporating local temporal information (LTI) in time series, such as hourly price changes, is essential for accurate EWP forecasting, as it helps detect rapid market shifts. However, existing methods remain limited in capturing LTI, either relying on point-wise input sequences or, for fixed-length, non-overlapping segmentation methods, failing to effectively model dependencies within and across segments. This paper proposes the Local-Temporal Convolutional Transformer (LT-Conformer) model for day-ahead EWP forecasting, which addresses the challenge of capturing fine-grained LTI using Local-Temporal 1D Convolution and incorporates two attention modules to capture global temporal dependencies (e.g., daily price trends) and cross-feature dependencies (e.g., solar output influencing price). An initial evaluation in the Australian market demonstrates that LT-Conformer outperforms existing state-of-the-art methods and exhibits adaptability in forecasting EWP under volatile market conditions. Full article

The rapid evolution of autonomous vehicles (AVs) has ignited widespread interest in their potential to transform mobility and transportation ecosystems. However, despite significant technological advances, the acceptance of AVs by the public remains a complex and multifaceted challenge. This state-of-the-art review explores the key factors influencing AV acceptance, focusing on the intersection of artificial intelligence (AI) services, user experience, social dynamics, and regulatory landscapes across diverse global regions. By analyzing trust, perceived safety (PS), cybersecurity, and user interface design, this paper delves into the psychological and behavioral drivers that shape public perception of AVs. It also highlights the role of demographic segmentation and media influence in accelerating or hindering adoption. A comparative analysis of AV acceptance across North America, Europe, Asia, and emerging markets reveals significant regional variations, influenced by regulatory frameworks, economic conditions, and social trends. Also, this review reveals critical insights into the perceived safety associated with AV technology, including legal uncertainties and cybersecurity concerns, while emphasizing the future potential of AVs in urban environments, public transit, and autonomous logistics fleets. This review concludes by proposing strategic roadmaps and policy implications to accelerate AV adoption, offering a forward-looking perspective on how advances in technology, coupled with targeted industry and government initiatives, can shape the future of autonomous mobility. Through a comprehensive examination of current trends and challenges, this paper provides a foundation for future research and innovation aimed at enhancing public acceptance and trust in AVs. Full article

Green synthesized nanoparticles (NPs) are arousing constantly increasing attention due to inherent advantages such as biocompatibility, nontoxicity, and cost-effectiveness. As the state of the art of this rapidly evolving topic demands a punctual update, the present study was focused on reviewing the novelty, feasibility, and effectiveness related to the specific category of red seaweed-derived NPs. Among algae, red seaweeds have already gained consideration in the global market due to their high content of primary and secondary metabolites, supporting multifunctional applications across various industries. This scoping review reveals how this interest has also driven their investigation as a natural source for the sustainable NP fabrication. The fragmentary body of studies was synthesized, identifying red seaweed NPs as a flourishing nanotechnological subgroup and meriting their own space in the scientific literature. Noteworthy, the great majority of the reviewed papers feature efficient controlled release, enhanced bioavailability, and reduced toxicity, making red seaweed NPs elective candidates for the medical sector as anticancer, antimicrobial, and antioxidant agents. Moreover, their parent natural counterparts seem to endow NPs with unexpected specificity toward biological targets such as prokaryotic and tumor cells. Nanotechnological solutions based on red seaweeds pave the way to a new avenue of opportunities and challenges. Full article

The rapid growth in the global apparel market and the rise of online consumption underscore the necessity for intelligent clothing recommendation systems that balance visual compatibility with personalized preferences, particularly price sensitivity. Existing recommendation systems often neglect nuanced consumer price behaviors, limiting their ability to deliver truly personalized suggestions. To address this gap, we propose DeepFMP, a multimodal deep learning framework that integrates visual, textual, and price features through an enhanced DeepFM architecture. Leveraging the IQON3000 dataset, our model employs ResNet-50 and BERT for image and text feature extraction, alongside a comprehensive price feature module capturing individual, statistical, and category-specific price patterns. An attention mechanism optimizes multimodal fusion, enabling robust modeling of user preferences. Comparative experiments demonstrate that DeepFMP outperforms state-of-the-art baselines (LR, FM, Wide & Deep, GP-BPR, and DeepFM), achieving AUC improvements of 1.6–12.2% and NDCG@10 gains of up to 3.2%. Case analyses further reveal that DeepFMP effectively improves the recommendation accuracy, offering actionable insights for personalized marketing. This work advances multimodal recommendation systems by emphasizing price sensitivity as a pivotal factor, providing a scalable solution for enhancing user satisfaction and commercial efficacy in fashion e-commerce. Full article

Stem removal from harvested fruits remains one of the most labor-intensive tasks in fruit harvesting, directly affecting the fruit quality and marketability. Accurate and rapid fruit and stem segmentation techniques are essential for automating this process. This study proposes an enhanced You Only Look Once (YOLO) model, AppleStem (AS)-YOLO, which uses a ghost bottleneck and global attention mechanism to segment apple stems. The proposed model reduces the number of parameters and enhances the computational efficiency using the ghost bottleneck while improving feature extraction capabilities using the global attention mechanism. The model was evaluated using both a custom-built and an open dataset, which will be later released to ensure reproducibility. Experimental results demonstrated that the AS-YOLO model achieved high accuracy, with a mean average precision (mAP)@50 of 0.956 and mAP@50–95 of 0.782 across all classes, along with a real-time inference speed of 129.8 frames per second (FPS). Compared with state-of-the-art segmentation models, AS-YOLO exhibited superior performance. The proposed AS-YOLO model demonstrates the potential for real-time application in automated fruit-harvesting systems, contributing to the advancement of agricultural automation. Full article

This study explores the growing global focus on sustainability in museums and cultural institutions, examining how digital marketing can support both sustainability and cultural identity. It provides insights into best practices, strategies, and challenges faced by cultural organizations, offering recommendations for improving sustainability and digital marketing in the Greek cultural sector. The study employs a mixed-methods approach, including a literature review to establish the international context, an observational analysis of global leaders mainly focusing on the Museum of Modern Art (MoMA) and the Metropolitan Opera (the Met), and primary research through a 30-question survey answered by 26 Greek cultural institutions. The findings reveal that leading global cultural organizations effectively use digital strategies to promote sustainability, enhancing cultural identity, brand, and economic resilience while advancing environmental stewardship and social justice. Greek cultural organizations, primarily facing financial and technical constraints, struggle with strategic integration and digital marketing, with few exceptions. The study concludes that the benefits of sustainable digital marketing outweigh the challenges, as it can significantly enhance cultural values and drive sustainability across environmental, economic, and social dimensions. By adopting a deeper understanding of sustainability and a more strategic, holistic approach, Greek organizations can amplify their impact, strengthen their presence, and contribute to long-term sustainability goals. Full article

With the deadline for the 17 United Nations Sustainable Development Goals (SDGs) on the horizon, the global community is forging a pathway through the ever-more complex global ecosystem to 2030. Whilst household-scale AD systems have seen significant attention, the community and commercial scales remain significantly under-researched. This paper aims to explore the state-of-the-art in energy access, AD and smart metering, and presents three scales of AD system delivery models which can potentially unlock meaningful pathways to energy access and the completion of SDG7. We achieve this through a two-phase qualitative methodology: first, an in-person participatory market systems development workshop in Malawi, and second, by leveraging experts’ knowledge of the Uganda and Malawian biogas sector to develop the case studies that illustrate the three scales of the AD system delivery model. Our findings analyse these delivery models, exploring the disconnection between digester size and delivery model, overcoming delivery model weaknesses through blended approaches to energy access, the role of digitalisation, and the importance of tailoring the delivery models to specific contexts. Ultimately, by drawing on real-world examples of AD system delivery models, this paper concludes by proposing a novel entire ecosystems or systems approach to biogas implementation through the blending of different scales of implementation. Full article

The Cannabis market is experiencing steady global growth. Cannabis herbal extracts (CHE) are interesting and sought-after products for many clinical conditions. The medical potential of these formulations is mainly attributed to neutral cannabinoids, such as cannabidiol (CBD), tetrahydrocannabinol (THC), and cannabinol (CBN), and their non-standardized content poses a significant fragility in these pharmaceutical inputs. High-resolution mass spectrometry portrays a powerful alternative to their accurate monitoring; however, further analytical steps need to be critically optimized to keep up with instrumental performance. In this study, Full Factorial and Box–Behnken designs were employed to achieve a multivariate optimization of CBD, THC, and CBN extraction from human and veterinary commercial CHE using a minimum methanol/hexane 9:1 volume and short operational times. A predictive model was also constructed using the Response Surface Methodology and its accuracy was validated. Agitation and sonication times were identified as the most significant operational extraction parameters, followed by the extraction mixture volume. The final setup of the optimized method represented a significantly faster and cheaper protocol than those in the literature. The selected neutral cannabinoids quantification was conducted using ultra high-performance liquid chromatography coupled to high-resolution tandem mass spectrometry (UHPLC-HRMS/MS) with a precision of <15%, accuracy of 69–98%, sensitivity of 23–39 ng kg⁻¹, and linearity regarding pharmaceutical requirements. State-of-the-art levels of analytical sensitivity and specificity were achieved in the target quantification due to high-resolution mass spectrometry. The developed method demonstrated reliability and feasibility for routine analytical applications. As a proof-of-concept, it enabled the efficient processing of 16 samples of commercial CHE within a three-hour timeframe, showcasing its practicality and reproducibility, and highlighting its potential for broader adoption in similar scenarios for both human and veterinary medicines. Full article

Background: In today’s complex and rapidly changing global markets, supply chain resilience (SCR) has become critical for businesses aiming to maintain continuity and competitive advantage. Disruptions and challenges in the supply chain will always exist; therefore, preparing in advance and improving resilience for the upcoming consequences should be the utmost important goal. Methods: Digital twins (DTs) provide a comprehensive view of product performance, enabling companies to adopt an end-to-end approach to product management. To maximize product and service value, supply chains must also be managed holistically. Results: Therefore, companies will need smarter strategies to balance inventory costs, availability and lead times. The optimal setup of suppliers, manufacturing, logistics and stock locations will ensure high service levels and meet customer expectations. Additionally, supply chains must be resilient, and capable of maintaining performance during disruptions and adapting to demand changes. Conclusions: This paper aims to provide a comprehensive review of the state of the art in digital twin applications within supply chains, focusing on their role in improving visibility, agility and decision-making. This paper explores how digital twins might integrate with emerging technologies such as IoT, AI and blockchain, fostering a more adaptive and robust supply chain ecosystem. Full article

The adoption of agricultural robots is revolutionizing the agricultural sector, offering innovative solutions to optimize production and reduce environmental impact. This review examines the main functions and applications of agricultural robots, with a focus on the crops handled and the technologies employed. The study analyzes the current state of the art regarding the market trend of agricultural robots used in field and greenhouse operations. Several solutions are emerging, some already implemented and others still in the prototype or project stage. These solutions are beginning to spread, though they may still seem far from widespread field application, particularly given the peculiarities and heterogeneity of the global agricultural landscape. In the face of the many benefits associated with the use of agricultural robots, even today some technical bottlenecks and costs limit their widespread use by farmers. The review provides a fairly comprehensive and up-to-date overview of current trends in agricultural automation, suggesting new areas of research to improve the efficiency and adaptability of robotic systems to different types of crops and environments. Full article

The market for plant-based analogs for meat is growing exponentially. In addition to motivations related to the search for health benefits, the consumption of such products is justified by the sustainability of their production since the use of non-renewable resources and the emission of polluting gases is lower than their animal-origin equivalents. However, little information regarding the global panorama of the sustainability of plant-based meat analogues is available, mainly due to the diffuse distribution of food matrices used across the planet. In this sense, this narrative review aimed to describe the state of the art regarding the use of resources and sustainability of the inputs used as protein sources in the manufacture of plant-based meat analogues. From the review carried out, it was possible to observe that the biggest problem in producing these plant-based alternatives lies in using inputs that are not native to the countries where the products are marketed, especially in the case of South American countries. Ingredients widely used in the production of these analogues find better cultivation conditions in the northern hemisphere, as in the case of lentils, peas and chickpeas; thus, South American markets depend on imports, reducing the sustainability of the products. Full article

The increasing adoption of renewable energy sources and the emergence of distributed generation have significantly transformed the traditional energy landscape, leading to the rise of local energy markets. These markets facilitate decentralized energy trading among different market participants at the community level, fostering greater energy autonomy and sustainability. As local energy markets gain momentum, the application of artificial intelligence techniques, particularly reinforcement learning, has gained substantial interest in optimizing energy trading strategies by interacting with the environment and maximizing the rewards by addressing the decision complexities by learning. This paper comprehensively reviews the different energy trading projects initiated at the global level and machine learning approaches and solution strategies for local energy markets. State-of-the-art reinforcement learning algorithms are classified into model-free and model-based methods. This classification examines various algorithms for energy transactions considering the agent type, learning methods, policy, state space, action space, and action selection for state, action, and reward function outputs. The findings of this work will serve as a valuable resource for researchers, stakeholders, and policymakers to accelerate the adoption of the local energy market for a more efficient, sustainable, and resilient energy future. Full article

This study explores the comparative performance of cutting-edge AI models, i.e., Finaance Bidirectional Encoder representations from Transsformers (FinBERT), Generatice Pre-trained Transformer GPT-4, and Logistic Regression, for sentiment analysis and stock index prediction using financial news and the NGX All-Share Index data label. By leveraging advanced natural language processing models like GPT-4 and FinBERT, alongside a traditional machine learning model, Logistic Regression, we aim to classify market sentiment, generate sentiment scores, and predict market price movements. This research highlights global AI advancements in stock markets, showcasing how state-of-the-art language models can contribute to understanding complex financial data. The models were assessed using metrics such as accuracy, precision, recall, F1 score, and ROC AUC. Results indicate that Logistic Regression outperformed the more computationally intensive FinBERT and predefined approach of versatile GPT-4, with an accuracy of 81.83% and a ROC AUC of 89.76%. The GPT-4 predefined approach exhibited a lower accuracy of 54.19% but demonstrated strong potential in handling complex data. FinBERT, while offering more sophisticated analysis, was resource-demanding and yielded a moderate performance. Hyperparameter optimization using Optuna and cross-validation techniques ensured the robustness of the models. This study highlights the strengths and limitations of the practical applications of AI approaches in stock market prediction and presents Logistic Regression as the most efficient model for this task, with FinBERT and GPT-4 representing emerging tools with potential for future exploration and innovation in AI-driven financial analytics. Full article