Search Results (3,014)

Ecologically sustainable economic development is increasingly recognized as essential to global efforts to improve and protect environmental and socio-economic conditions. The transportation sector is also important regarding the movement of human beings and goods. Fossil fuels are primarily used in transport vehicles and emit carbon dioxide into the atmosphere. Hence, innovative investments in the transportation system and the use of renewable energy play a key role in overcoming this lingering problem. This study utilizes nonlinear autoregressive distributed lag (NARDL) methods to uncover key drivers influencing innovative investments in the transportation sector and the impact of renewable energy use on environmental sustainability in France between 1995 and 2020. The results indicate that renewable energy use and transport infrastructure innovations positively and negatively impact environmental sustainability. Both variables have different influences on the dependent variable depending on the economic shock period. Based on the outcomes, this study offers the following significant policy insights: (i) France could invest in innovations in renewable energy sourcing and incentivize switching from combustion engine-based transport systems. (ii) France should commit to the Europe 2020 strategy for green growth to ensure resource efficiency and promote environmental sustainability, which requires a coordinated effort to invest in smart transport systems that leverage technologies like the Internet of Things, artificial intelligence, and big data analytics. (iii) Given that two-thirds of France’s electricity is produced from nuclear sources, the government needs to implement policies in the renewable energy sector to reduce over-reliance on nuclear energy sourcing. Full article

Occupational therapy (OT) is a client-centered health profession focused on enhancing individuals’ ability to perform meaningful activities and daily tasks, particularly for those recovering from injury, illness, or disability. As a core component of rehabilitation, it promotes independence, well-being, and quality of life through personalized, goal-oriented interventions. Identifying and measuring the role of artificial intelligence (AI) in the human–computer interaction (HCI) within OT is critical for improving therapeutic outcomes and patient engagement. Despite AI’s growing significance, the integration of AI-driven HCI in OT remains relatively underexplored in the existing literature. This scoping review identifies and maps current research on the topic, highlighting applications and proposing directions for future work. A structured literature search was conducted using the Scopus and PubMed databases. Articles were included if their primary focus was on the intersection of AI, HCI, and OT. Out of 55 retrieved articles, 26 met the inclusion criteria. This work highlights three key findings: (i) machine learning, robotics, and virtual reality are emerging as prominent AI-driven HCI techniques in OT; (ii) the integration of AI-enhanced HCI offers significant opportunities for developing personalized therapeutic interventions; (iii) further research is essential to evaluate the long-term efficacy, ethical implications, and patient outcomes associated with AI-driven HCI in OT. These insights aim to guide future research efforts and clinical applications within this evolving interdisciplinary field. In conclusion, AI-driven HCI holds considerable promise for advancing OT practice, yet further research is needed to fully realize its clinical potential. Full article

Background: The integration of artificial intelligence (AI) into mental health care is reshaping psychological support systems, particularly for digitally literate populations such as nursing students. Given the high prevalence of anxiety and depression in this group, understanding their perceptions of AI-driven mental health support is critical for effective implementation. Objectives: to evaluate nursing students’ perceptions toward AI-driven mental health support and examine its relationship with anxiety, depression, and their attitudes to seeking professional psychological help. Methods: A cross-sectional survey was conducted among 176 undergraduate nursing students in northern Jordan. Results: Students reported moderately positive perceptions toward AI-driven mental health support (mean score: 36.70 ± 4.80). Multiple linear regression revealed that prior use of AI tools (β = 0.44, p < 0.0001), positive help-seeking attitudes (β = 0.41, p < 0.0001), and higher levels of psychological distress encompassing both anxiety (β = 0.29, p = 0.005) and depression (β = 0.24, p = 0.007) significantly predicted more positive perceptions. Daily AI usage was not a significant predictor (β = 0.15, p = 0.174). Logistic regression analysis further indicated that psychological distress, reflected by elevated anxiety (OR = 1.42, p = 0.002) and depression scores (OR = 1.32, p = 0.003), along with stronger help-seeking attitudes (OR = 1.35, p = 0.011), significantly increased the likelihood of using AI-based mental health support. Conclusions: AI-driven mental health tools hold promises as adjuncts to traditional counseling, particularly for nursing students experiencing psychological distress. Despite growing acceptance, concerns regarding data privacy, bias, and lack of human empathy remain. Ethical integration and blended care models are essential for effective mental health support. Full article

The proliferation of toxigenic fungi in food and the subsequent production of mycotoxins constitute a significant concern in the fields of public health and consumer protection. This review highlights recent strategies and emerging methods aimed at preventing fungal growth and mycotoxin contamination in food matrices as opposed to traditional approaches such as chemical fungicides, which may leave toxic residues and pose risks to human and animal health as well as the environment. The novel methodologies discussed include the use of plant-derived compounds such as essential oils, classified as Generally Recognized as Safe (GRAS), polyphenols, lactic acid bacteria, cold plasma technologies, nanoparticles (particularly metal nanoparticles such as silver or zinc nanoparticles), magnetic materials, and ionizing radiation. Among these, essential oils, polyphenols, and lactic acid bacteria offer eco-friendly and non-toxic alternatives to conventional fungicides while demonstrating strong antimicrobial and antifungal properties; essential oils and polyphenols also possess antioxidant activity. Cold plasma and ionizing radiation enable rapid, non-thermal, and chemical-free decontamination processes. Nanoparticles and magnetic materials contribute advantages such as enhanced stability, controlled release, and ease of separation. Furthermore, this review explores recent advancements in the application of artificial intelligence, particularly machine learning methods, for the identification and classification of fungal species as well as for predicting the growth of toxigenic fungi and subsequent mycotoxin production in food products and culture media. Full article

The human proteome—the entire collection of proteins expressed by the human genome—represents a dynamic and intricate landscape of biological function. Proteins are the workhorses of the body, driving processes from cellular communication to immune defense, and their alterations underpin many diseases. Understanding the proteome has become a cornerstone of modern biomedical research, offering insights into disease mechanisms, diagnostic tools, and personalized treatments through precision medicine. This commentary explores the current state of human proteome research; its applications in disease understanding, diagnostics, and therapeutic advancements; and the exciting prospects that lie ahead. Full article

Gliomas are the most prevalent and aggressive form of primary brain tumors. The clinical challenge in managing patients with this disease revolves around the difficulty of diagnosis, both at onset and during treatment, and the scarcity of prognostic outcome indicators. Radiomics involves the extraction of quantitative features from medical images with the help of artificial intelligence, positioning it as a promising tool to be integrated into the care of glioma patients. Using data from 52 studies and 12,482 patients over two years, this review explores how radiomics can enhance the initial diagnosis of gliomas, especially helping to differentiate treatment stages that may be difficult for the human eye to do otherwise. Radiomics has also been able to identify patient-specific tumor molecular signatures for targeted treatments without the need for invasive surgical biopsy. Such an approach could lead to earlier interventions and more precise individualized therapies that are tailored to each patient. Additionally, it could be integrated into clinical practice to improve longitudinal diagnosis during treatment and predict tumor recurrence. Finally, radiomics has the potential to predict clinical outcomes, helping both patients and providers set realistic expectations. While this field is continuously evolving, future research should conduct such studies in larger, multi-institutional cohorts to enhance generalizability and applicability in clinical practice and focus on combining radiomics with other modalities to improve its predictive accuracy and clinical utility. Full article

Large amounts of high-quality data are required to train artificial intelligence (AI) models; however, curating such data through human intervention remains cumbersome, time-consuming, and error-prone. In particular, erroneous annotations and statistical imbalances in object detection datasets can significantly degrade model performance in real-world autonomous driving scenarios. This study proposes an automated pruning framework and quality assurance strategy for 2D object detection datasets to address these issues. The framework is composed of two stages: (1) noisy label identification and deletion based on labeling scores derived from the inference results of multiple object detection models, and (2) statistical distribution whitening based on class and bounding box size diversity metrics. The proposed method was designed in accordance with the ISO/IEC 25012 data quality standards to ensure data consistency, accuracy, and completeness. Experiments were conducted on widely used autonomous driving datasets, including KITTI, Waymo, nuScenes, and large-scale publicly available datasets from South Korea. An automated data pruning process was employed to eliminate anomalous and redundant samples, resulting in a more reliable and compact dataset for model training. The results demonstrate that the proposed method substantially reduces the amount of training data required, while enhancing the detection performance and minimizing manual inspection efforts. Full article

This study investigated how generative Artificial Intelligence (AI) systems—now increasingly integrated into public services—respond to different technical configurations, and how these configurations affect the perceived quality of the outputs. Drawing on an experimental evaluation of Govern-AI, a chatbot designed for professionals in the social, educational, and labor sectors, we analyzed the impact of the temperature parameter—which controls the degree of creativity and variability in the responses—on two key dimensions: accuracy and comprehensibility. This analysis was based on 8880 individual evaluations collected from five professional profiles. The findings revealed the following: (1) the high-temperature responses were generally more comprehensible and appreciated, yet less accurate in strategically sensitive contexts; (2) professional groups differed significantly in their assessments, where trade union representatives and regional policy staff expressed more critical views than the others; (3) the type of question—whether operational or informational—significantly influenced the perceived output quality. This study demonstrated that the AI performance was far from neutral: it depended on technical settings, usage contexts, and the profiles of the end users. Investigating these “behind-the-scenes” dynamics is essential for fostering the informed governance of AI in public services, and for avoiding the risk of technology functioning as an opaque black box within decision-making processes. Full article

Evapotranspiration (ET) has a significant role in various natural and human systems, such as water cycle balance, climate regulation, ecosystem health, agriculture, hydrological cycle, water resource management, and climate studies. Among various approaches that are employed for estimating ET, the Penman–Monteith equation is known as the widely accepted reference approach. However, the extensive data requirement of this method is a crucial challenge that limits its usage, particularly in data-scarce regions. Therefore, as an alternative approach, artificial intelligence (AI) models have gained prominence for estimating evapotranspiration because of their capacity to handle complicated relationships between meteorological variables and water loss processes. These models leverage large datasets and advanced algorithms to provide accurate and timely ET predictions. The current research aims to review previous studies addressing the application of the AI model in ET modeling under four main categories: neuron-based, tree-based, kernel-based, and hybrid models. The results of this study indicated that traditional models like the Penman–Monteith (PM) require extensive input data, while AI-based approaches offer promising alternatives due to their ability to model complex nonlinear relationships. Despite their potential, AI models face challenges such as overfitting, interpretability, inconsistent input variable selection, and lack of integration with physical ET processes, highlighting the need for standardized input configurations, better pre-processing techniques, and incorporation of hydrological and remote sensing data. Full article

Quantum technologies are increasingly pervasive, underpinning the operation of numerous electronic, optical and medical devices. Today, we are also witnessing rapid advancements in quantum computing and communication. However, access to quantum technologies in computation remains largely limited to professionals in research organisations and high-tech industries. This paper demonstrates how traditional neural networks can be transformed into neuromorphic quantum models, enabling anyone with a basic understanding of undergraduate-level machine learning to create quantum-inspired models that mimic the functioning of the human brain—all using a standard laptop. We present several examples of these quantum machine learning transformations and explore their potential applications, aiming to make quantum technology more accessible and practical for broader use. The examples discussed in this paper include quantum-inspired analogues of feedforward neural networks, recurrent neural networks, Echo State Network reservoir computing, and Bayesian neural networks, demonstrating that a quantum approach can both optimise the training process and equip the models with certain human-like cognitive characteristics. Full article

Viruses that infect bacteria (bacteriophages or phages) have a history of use in both biomedicine and basic molecular biology. Here, I briefly outline the pre-1940 use of phages in biomedicine and then more comprehensively outline the subsequent use of phages in determining the basics of molecular biology. Finally, I outline work that appears to form the foundation for a future, phage-enhanced biomedicine that generally extends medicine in the areas of anti-bacterial therapy (including vaccinology), anti-tumor therapy, and understanding the basic process of amyloid-associated neurodegenerative diseases. The following are general conclusions. (1) In the future, the discipline of phage-based biomedicine will be enhanced by more extensive merging with the discipline of basic phage biology (including molecular biology) and evolution. These two disciplines have been separated post-1940. (2) Biomedicine, in general, will be assisted if the focus is on key problems and key observations, thereby leaving details to later work. (3) Simplicity of strategy is a virtue that can be implemented and should be pursued with phages. (4) Capacity for directed evolution provides phages with generative (artificial intelligence-like) means for increasing biomedical effectiveness without using human design. Two related quotes set the stage (references at the end of the text). “But see that the imagination of nature is far, far greater than the imagination of man” (physicist Richard Feynman). “Nature, in all its variations and seeming paradoxes, speaks to those who pay attention and gives hints and clues to basic facts” (a thought attributed to Felix d’Herelle, a self-trained biologist who developed biological phage isolation and characterization). The integration of natural phenomenon-focused basic science and medical practice is an underlying theme. Full article

Industry 5.0 transforms industrial ecosystems via artificial intelligence (AI), human–machine collaboration, and sustainability-focused innovations. This systematic literature review examines Industry 5.0′s role in energy transition through digital transformation, sustainable supply chains, and energy efficiency strategies. Key findings highlight AI-driven smart grids, blockchain-enabled energy transactions, and digital twin simulations as enablers of low-carbon, adaptive industrial operations. This review uniquely integrates technological, managerial, and policy perspectives, providing actionable insights for policymakers and industry leaders. Industry 5.0 enhances innovative energy management, renewable energy integration, and flexible energy distribution, strengthening resilience and sustainability. It fosters environmental responsibility, social impact, and circular economy principles, laying the foundation for a low-carbon economy and accelerating the global energy transition. Full article

Slaughter facilities use a variety of tools to evaluate animal handling, including but not limited to live audits, the use of remote video auditing, and some AI technologies. The objective of this study was to determine the similarity between AI and human evaluator assessments of critical cattle handling outcomes in a slaughter plant. One hundred twelve video clips of cattle handling and stunning from a slaughter plant in the United Kingdom were collected. The AI identified the presence or absence of: Stunning, Electric Prod Usage, Falling, Pen Crowding, and Questionable Handling Events. Three human evaluators scored the videos for these outcomes. Four different datasets were generated, and Jaccard similarity indices were generated. There was high similarity (JI > 0.90) for Stunning, Electric Prod Usage, and Falls between the evaluators and the AI. There was high consistency (JI > 0.80) for Pen Crowding. There were differences (JI ≥ 0.50) between the humans and the AI when identifying Questionable Animal Handling Events but the AI was adept at identifying events for further review. The implementation of AI to assist with cattle handling in a slaughter facility environment could be an added tool to enhance animal welfare programs. Full article

This systematic literature review investigates the integration of artificial intelligence (AI) and the Internet of Things (IoT) in marketing, with a focus on their application in enhancing consumer engagement, personalization, and strategic decision-making. Using the Scopus database and a refined keyword search strategy, the study identified 223,671 initial records, which were narrowed down to 121 peer-reviewed academic articles after applying strict inclusion and exclusion criteria. Thematic analysis revealed that foundational technologies—such as machine learning, big data, and deep learning—dominate the field, while marketing strategy, decision systems, and customer experience emerge as central application areas. Co-citation and keyword network analyses indicate a technocentric and interdisciplinary knowledge structure, but also expose significant gaps in research related to ethics, regulation, consumer trust, and small business contexts. The review highlights opportunities for future research in underexplored areas such as sentiment analysis, sustainability, and human–AI interaction. For practitioners, the findings underscore the strategic importance of AI and IoT in driving personalized, data-driven marketing, while emphasizing the need for ethical transparency and regulatory alignment. Limitations include reliance on a single database, potential exclusion of relevant studies due to keyword constraints, and a focus on peer-reviewed journal articles only. This review addresses key gaps in the literature by offering a focused synthesis of current research and proposing directions for more balanced and responsible innovation in AI-enabled marketing. Full article

Systematic reviews require labor-intensive screening processes—an approach prone to bottlenecks, delays, and scalability constraints in large-scale reviews. Large Language Models (LLMs) have recently emerged as a powerful alternative, capable of operating in zero-shot or few-shot modes to classify abstracts according to predefined criteria without requiring continuous human intervention like semi-automated platforms. This review focuses on the central challenges that users in the biomedical field encounter when integrating LLMs—such as GPT-4—into evidence-based research. It examines critical requirements for software and data preprocessing, discusses various prompt strategies, and underscores the continued need for human oversight to maintain rigorous quality control. By drawing on current practices for cost management, reproducibility, and prompt refinement, this article highlights how review teams can substantially reduce screening workloads without compromising the comprehensiveness of evidence-based inquiry. The findings presented aim to balance the strengths of LLM-driven automation with structured human checks, ensuring that systematic reviews retain their methodological integrity while leveraging the efficiency gains made possible by recent advances in artificial intelligence. Full article