Search Results (257)

The recent IPCC reports have shown that climate crises are intensifying in the third decade of the 21st century; therefore, policies based on socioeconomic and ecological sustainability must be urgently executed. However, the production organization models and results differ significantly between countries. This paper aims to compare the organic production systems of Denmark, a successful organic player, and Brazil, which has great potential but is poorly structured. Methodologically, due to the lack of quantitative data on the Brazilian organic sector, a qualitative method (SWOT Analysis) was employed to evaluate the Strengths, Weaknesses, Opportunities, and Threats of Danish and Brazilian organic agriculture. Subsequently, eleven criteria were proposed to construct a factorial space in a quadrant system for evaluating scenarios of virtuous and vicious circles. The results have shown that in Denmark, a virtuous circle of interaction between public and private agents has led the country into the global spotlight. In contrast, a vicious circle in Brazil generates obstacles that hinder the growth of organic production. Therefore, identifying these limiting circles also creates opportunities for change, such as recommending actions and public policies to overcome the limitations of Brazil’s organic sector. These findings have implications for Denmark and Brazil and can be replicated in Latin American and African countries, contributing to the global effort to enlarge sustainable food production. Full article

Machine vision (MV) is reshaping numerous industries by giving machines the ability to understand what they “see” and respond without human intervention. This review brings together the latest developments in deep learning (DL), image processing, and computer vision (CV). It focuses on how these technologies are being applied in real operational environments. We examine core methodologies such as feature extraction, object detection, image segmentation, and pattern recognition. These techniques are accelerating innovation in key sectors, including healthcare, manufacturing, autonomous systems, and security. A major emphasis is placed on the deepening integration of artificial intelligence (AI) and machine learning (ML) into MV. We particularly consider the impact of convolutional neural networks (CNNs), generative adversarial networks (GANs), and transformer architectures on the evolution of visual recognition capabilities. Beyond surveying advances, this review also takes a hard look at the field’s persistent roadblocks, above all the scarcity of high-quality labeled data, the heavy computational load of modern models, and the unforgiving time limits imposed by real-time vision applications. In response to these challenges, we examine a range of emerging fixes: leaner algorithms, purpose-built hardware (like vision processing units and neuromorphic chips), and smarter ways to label or synthesize data that sidestep the need for massive manual operations. What distinguishes this paper, however, is its emphasis on where MV is headed next. We spotlight nascent directions, including edge-based processing that moves intelligence closer to the sensor, early explorations of quantum methods for visual tasks, and hybrid AI systems that fuse symbolic reasoning with DL, not as speculative futures but as tangible pathways already taking shape. Ultimately, the goal is to connect cutting-edge research with actual deployment scenarios, offering a grounded, actionable guide for those working at the front lines of MV today. Full article

The continuous growth in global demand for mineral resources and the increasing difficulty of mineral exploration have created bottlenecks for traditional mineral prediction methods in handling complex geological information and large amounts of data. This review aims to explore the latest research progress in machine learning technology in the field of large-scale mineral prediction from 2016 to 2025. By systematically searching the Web of Science core database, we have screened and analyzed 255 high-quality scientific studies. These studies cover key areas such as mineral information extraction, target area selection, mineral regularity modeling, and resource potential evaluation. The applied machine learning technologies include Random Forests, Support Vector Machines, Convolutional Neural Networks, Recurrent Neural Networks, etc., and have been widely used in the exploration and prediction of various mineral deposits such as porphyry copper, sandstone uranium, and tin. The findings indicate a substantial shift within the discipline towards the utilization of deep learning methodologies and the integration of multi-source geological data. There is a notable rise in the deployment of cutting-edge techniques, including automatic feature extraction, transfer learning, and few-shot learning. This review endeavors to synthesize the prevailing state and prospective developmental trajectory of machine learning within the domain of large-scale mineral prediction. It seeks to delineate the field’s progression, spotlight pivotal research dilemmas, and pinpoint innovative breakthroughs. Full article

Honey has a long cultural tradition in Italy, valued for its sensory properties and health benefits. However, in recent years, the beekeeping sector has faced various challenges due to climate change, biodiversity loss, and economic pressures. Therefore, growing consumer awareness of sustainability, traceability, and ethical aspects is influencing food choices and putting niche-market products, such as honey, in the spotlight. This research analysed data from an online survey of Italian consumers to examine their attitudes toward honey. The analysis focused on the primary drivers of consumer behaviour, the state of sustainability efforts, and the importance of certifications and traceability in influencing preferences. The results showed that, beyond taste and health considerations, Italian consumers expressed a strong sensitivity and awareness of the beekeeping sector’s needs and their high engagement in ethical issues, food quality, safety and certification standards, and environmental protection. These findings provide useful insights for producers and policymakers to promote sustainable beekeeping and enhance consumer trust by implementing targeted communication strategies and certification schemes. Full article

In the contemporary international context, the preventive conservation of cultural relics has become a widespread consensus. “Risk management” has emerged as a pivotal research focus at the present stage. However, the preventive protection of cultural relics is confronted with deficiencies in risk assessment and prediction. There is an urgent requirement for research to present a comprehensive and in-depth overview of the frontier technologies applicable to the preventive protection of cultural relics, with a particular emphasis on risk prevention and control. Additionally, it is essential to delineate the prospects for future investigations and developments in this domain. Consequently, this study employs bibliometric methods, applying CiteSpace (6.3.R1) and Biblioshiny (4.3.0) to perform comprehensive visual and analytical examinations of 392 publications sourced from the Web of Science (WoS) database covering the period 2010 to 2024. The results obtained from the research are summarized as follows: First, it is evident that scholars originating from China, Italy, and Spain have exhibited preponderant publication frequencies, contributing the largest quantity of articles. Second, augmented reality, digital technology, and risk-based analysis have been identified as the cardinal research frontiers. These areas have attracted significant scholarly attention and are at the forefront of innovation and exploration within the discipline. Third, the “Journal of Culture Heritage” and “Heritage Science” have been empirically determined to be the most frequently cited periodical within this particular field of study. Moreover, over the past decade, under the impetus and influence of the concept of Intangible Cultural Heritage, virtual reality, digital protection, and 3D models have progressively evolved into the central and crucial topics that have pervaded and shaped the research agenda. Finally, with respect to future research trajectories, there will be a pronounced focus on interdisciplinary design. This will be accompanied by an escalation in the requisites and standards for preventive conservation. Specifically, the spotlight will be cast upon aspects such as the air quality within the preservation environment of cultural relics held in collections, the implementation and efficacy of environmental real-time monitoring systems, the utilization and interpretation of big data analysis and early warning mechanisms, as well as the comprehensive and in-depth risk analysis of cultural relics. These multifaceted investigations will be essential for advancing understanding and safeguarding of cultural heritage. These findings deepen our grasp of how risk management in cultural heritage conservation has progressed and transformed between 2010 and 2024. Furthermore, the study provides novel insights and directions for subsequent investigations into risk assessment methodologies for heritage collections. Full article

Young children’s problematic media use (PMU) is a growing concern, and parents are critical in shaping early digital habits. However, research often overlooks the dyadic interplay between mothers’ and fathers’ attitudes and parenting practices. This study examined how parents’ favorable attitudes toward child screen media (PASU) predict their own (actor) and their partner’s (partner) mediation behaviors, and how these behaviors subsequently mediate the path to children’s PMU. Drawing on survey data from 1802 matched urban Chinese mother–father pairs, we employed an Actor–Partner Interdependence Mediation Model (APIMeM) within a structural equation modeling (SEM) framework. This dyadic model simultaneously tested actor, partner, and indirect mediation paths connecting parental attitudes to PMU via eight specific parenting practices. Results showed that more positive PASUs predicted each parent’s own supportive behaviors (e.g., high-quality dialogue, autonomy support) but not restrictive limits. Partner effects were modest and asymmetric: mothers’ positive attitudes predicted greater knowledge in fathers, whereas fathers’ positive attitudes were linked to lower communication quality from mothers. Of all parenting dimensions, only higher communication quality (both parents) and mothers’ hands-on monitoring directly predicted lower PMU. Mediation analyses confirmed communication quality as the sole reliable pathway: each parent’s favorable attitudes indirectly lowered PMU by enhancing their own dialogue, but fathers’ attitudes simultaneously increased PMU by eroding mothers’ dialogue. These findings spotlight constructive conversation and coordinated dyadic strategies—especially safeguarding maternal dialogue—as critical targets for interventions aimed at curbing early PMU. Full article

The rapid growth of electric vehicles (EVs) and new energy systems has put lithium-ion batteries at the center of the clean energy change. Nevertheless, to achieve the best battery performance, safety, and sustainability in many changing circumstances, major innovations are needed in Battery Management Systems (BMS). This review paper explores how artificial intelligence (AI) and digital twin (DT) technologies can be integrated to enable the intelligent BMS of the future. It investigates how powerful data approaches such as deep learning, ensembles, and models that rely on physics improve the accuracy of predicting state of charge (SOC), state of health (SOH), and remaining useful life (RUL). Additionally, the paper reviews progress in AI features for cooling, fast charging, fault detection, and intelligible AI models. Working together, cloud and edge computing technology with DTs means better diagnostics, predictive support, and improved management for any use of EVs, stored energy, and recycling. The review underlines recent successes in AI-driven material research, renewable battery production, and plans for used systems, along with new problems in cybersecurity, combining data and mass rollout. We spotlight important research themes, existing problems, and future drawbacks following careful analysis of different up-to-date approaches and systems. Uniting physical modeling with AI-based analytics on cloud-edge-DT platforms supports the development of tough, intelligent, and ecologically responsible batteries that line up with future mobility and wider use of renewable energy. Full article

The integration of vision and language processing into a cohesive system has already shown promise with the application of large language models (LLMs) in medical image analysis. Their capabilities encompass the generation of medical reports, disease classification, visual question answering, and segmentation, providing yet another approach to interpreting multimodal data. This survey aims to compile all known applications of LLMs in the medical image analysis field, spotlighting their promises alongside critical challenges and future avenues. We introduce the concept of X-stage tuning which serves as a framework for LLMs fine-tuning across multiple stages: zero stage, one stage, and multi-stage, wherein each stage corresponds to task complexity and available data. The survey describes issues like sparsity of data, hallucination in outputs, privacy issues, and the requirement for dynamic knowledge updating. Alongside these, we cover prospective features including integration of LLMs with decision support systems, multimodal learning, and federated learning for privacy-preserving model training. The goal of this work is to provide structured guidance to the targeted audience, demystifying the prospects of LLMs in medical image analysis. Full article

The transition to clean and renewable energy sources is critically dependent on efficient hydrogen production technologies. This review surveys recent advances in photocatalytic water splitting, focusing on MNb₂O₆ nanomaterials, which have emerged as promising photocatalysts due to their tunable band structures, chemical robustness, and tailored morphologies. The objectives of this work are to (i) encompass the current synthesis strategies for MNb₂O₆ compounds; (ii) assess their structural, electronic, and optical properties in relation to photocatalytic performance; and (iii) elucidate the mechanisms underpinning enhanced hydrogen evolution. Main data collection methods include a literature review of experimental studies reporting bandgap measurements, structural analyses, and hydrogen production metrics for various MNb₂O₆ compositions—especially those incorporating transition metals such as Mn, Cu, Ni, and Co. Novelty stems from systematically detailing the relationships between synthesis routes (hydrothermal, solvothermal, electrospinning, etc.), crystallographic features, conductivity type, and bandgap tuning in these materials, as well as by benchmarking their performance against more conventional photocatalyst systems. Key findings indicate that MnNb₂O₆, CuNb₂O₆, and certain engineered heterostructures (e.g., with g-C₃N₄ or TiO₂) display significant visible-light-driven hydrogen evolution, achieving hydrogen production rates up to 146 mmol h⁻¹ g⁻¹ in composite systems. The review spotlights trends in heterojunction design, defect engineering, co-catalyst integration, and the extension of light absorption into the visible range, all contributing to improved charge separation and catalytic longevity. However, significant challenges remain in realizing the full potential of the broader MNb₂O₆ family, particularly regarding efficiency, scalability, and long-term stability. The insights synthesized here serve as a guide for future experimental investigations and materials design, advancing the deployment of MNb₂O₆-based photocatalysts for large-scale, sustainable hydrogen production. Full article

Background/Objectives: After many years of research and the successful development of therapeutic products by a few industrial actors, the COVID-19 vaccines brought messenger RNAs, as well as other nucleic acid modalities, such as antisense oligonucleotides, small interfering RNA, and aptamers, into the spotlight, eliciting renewed interest from both academia and industry. However, owing to their structure, relative “fragility”, and the (usually) intracellular site of action, the delivery of these therapeutics has frequently proven to be a key limitation, especially when considering endosomal escape, which still needs to be overcome. Methods: By compiling delivery-related data on approved and late clinical-phase ribonucleic acid therapeutics, this review aims to assess the delivery strategies that have proven to be successful or are emerging, as well as areas where more research is needed. Results: In very specific cases, some strategies appeared to be quite effective, such as the N-acetylgalactosamine moiety in the case of liver delivery. Surprisingly, it also appears that for some modalities, efforts in molecular design have led to more “drug-like” properties, enablingthe administration of naked nucleic acids, without any form of encapsulation. This appears to be especially true when local administration, i.e., by injection, is possible, as this provides de facto targeting and a high local concentration, which can compensate for the small proportion of nucleic acids that reach the cytoplasm. Conclusions: Nucleic acid-based therapeutics have come a long way in terms of their physicochemical properties. However, due to their inherent limitations, targeting appears to be crucial for their efficacy, even more so than for traditional pharmaceutical modalities. Full article

The global crude oil market, known for its pronounced volatility and nonlinear dynamics, plays a pivotal role in shaping economic stability and informing investment strategies. Contrary to traditional research focused on price forecasting, this study emphasizes the more investor-centric task of predicting returns for West Texas Intermediate (WTI) crude oil. By spotlighting returns, it directly addresses critical investor concerns such as asset allocation and risk management. This study applies advanced machine learning models, including XGBoost, random forest, and neural networks to predict crude oil return, and for the first time, incorporates sustainability and external risk variables, which are shown to enhance predictive performance in capturing the non-stationarity and complexity of financial time-series data. To enhance predictive accuracy, we integrate 55 variables across five dimensions: macroeconomic indicators, financial and futures markets, energy markets, momentum factors, and sustainability and external risk. Among these, the rate of change stands out as the most influential predictor. Notably, XGBoost demonstrates a superior performance, surpassing competing models with an impressive 76% accuracy in direction forecasting. The analysis highlights how the significance of various predictors shifted during the COVID-19 pandemic. This underscores the dynamic and adaptive character of crude oil markets under substantial external disruptions. In addition, by incorporating sustainability factors, the study provides deeper insights into the drivers of market behavior, supporting more informed portfolio adjustments, risk management strategies, and policy development aimed at fostering resilience and advancing sustainable energy transitions. Full article

Spaceborne high-resolution wide-area SAR image formation processing faces critical challenges induced by orbital curvature, Earth rotation, and spherical ground surfaces. The Spherical Geometry Algorithm (SGA) offers an effective solution to these problems. However, the standard SGA is inherently limited to spotlight mode SAR data processing and cannot be directly extended to other operational modes. To overcome this constraint, this paper proposes an enhanced SGA framework tailored for sliding spotlight mode SAR data processing. Firstly, this paper presents a rigorous analysis of time–frequency relationship variations during the classical SGA processing under sliding spotlight mode, and gives the reasons why the classical SGA can not be directly applied to the data processing in sliding spotlight mode. Then, a modified SGA processing framework is proposed to address the signal sampling ambiguity problem faced by the SGA in processing sliding spotlight mode data. The improved algorithm avoids the sampling ambiguity problem during azimuthal resampling and azimuthal IFFT by introducing an instantaneous Doppler central frequency correction processing before azimuthal resampling and a suitable amount of oversampling during azimuthal resampling. Finally, the effectiveness of the algorithm is verified by measured real data processing. Full article

The paper explores the pressing issue of energy consumption in machine learning (ML) models and their environmental footprint. As ML technologies, especially large-scale models, continue to surge in popularity, their escalating energy demands and corresponding CO₂ emissions are drawing critical attention. The article dives into innovative strategies to curb energy use in ML applications without compromising—and often even enhancing—model performance. Key techniques, such as model compression, pruning, quantization, and cutting-edge hardware design, take center stage in the discussion. Beyond operational energy use, the paper spotlights a pivotal yet often overlooked factor: the substantial emissions tied to the production of ML hardware. In many cases, these emissions eclipse those from operational activities, underscoring the immense potential of optimizing manufacturing processes to drive meaningful environmental impact. The narrative reinforces the urgency of relentless advancements in energy efficiency across the IT sector, with machine learning and data science leading the charge. Furthermore, deploying ML to streamline energy use in other domains like industry and transportation amplifies these benefits, creating a ripple effect of positive environmental outcomes. The paper culminates in a compelling call to action: adopt a dual-pronged strategy that tackles both operational energy efficiency and the carbon intensity of hardware production. By embracing this holistic approach, the artificial intelligence (AI) sector can play a transformative role in global sustainability efforts, slashing its carbon footprint and driving momentum toward a greener future. Full article

Vaccine marketing authorization holders (MAHs) are responsible for the conduction of global vaccine pharmacovigilance on their vaccine products. A safety signal is detected when a new adverse event (AE) or aspect of an AE occurs after exposure to the vaccine and warrants further investigation to determine whether a causal association may exist. Signal detection and evaluation (signal management) begins at the start of vaccine development, before an MAH submits an application for authorization to regulatory authorities, continues through the course of all clinical trials, and carries on beyond development into the post-marketing phase. As long as the vaccine remains authorized anywhere in the world, pharmacovigilance continues. During the time that the COVID-19 vaccine became widely available after authorization and approval, clinical trials were also ongoing, and therefore all clinical development and post-authorization safety information was closely monitored for safety by the MAH. MAH pharmacovigilance activities were adapted to manage the unprecedented volume of safety information that became available within a very short timeframe following worldwide vaccination campaigns. No vaccine had previously been administered to such a large number of individuals in such a short time, nor had there previously been a public health vaccine experience that was the subject of so many medical and non-medical writings. The MAH’s COVID-19 vaccine signal detection methods included the continuous review of accruing clinical trial data and the quantitative and qualitative analyses of spontaneously reported experiences. Review of published and unpublished medical literature and epidemiology-based analyses such as observed vs. expected analysis based on reported adverse events following immunization (AEFIs) played key roles in pharmacovigilance and signal management. All methods of signal detection and evaluation have caveats, but when considered in totality, can advance our understanding of a vaccine’s safety profile and therefore the risk–benefit considerations for vaccinating both individuals and large populations of people. All COVID-19 vaccines authorized for use were subject to an unprecedented level of pharmacovigilance by their individual MAHs, national regulatory authorities, public health organizations, and others during the years immediately following regulatory authorization and full approval. The intense worldwide focus on pharmacovigilance and the need for MAHs and regulatory/health authorities to quickly evaluate incoming safety information, spurred frequent and timely communications between national and regional health authorities and between MAHs and regulatory/health authorities, spotlighting a unique opportunity for individuals committed to patient safety to share important accruing safety information in a collegial and less traditionally formal manner than usual. The global pandemic precipitated by the SARS-CoV-2 virus created a significant impetus for MAHs to develop innovative vaccines to change the course of the COVID-19 pandemic. Pharmacovigilance also had to meet unprecedented needs. In this article, unique aspects of COVID-19 vaccine pharmacovigilance encountered by one MAH will be summarized. Full article

The relentless drive to meet global demand for poultry products has pushed for rapid intensification in chicken farming, dramatically boosting efficiency and yield. Yet, these gains have exposed a host of complex welfare challenges that have prompted scientific scrutiny and ethical reflection. In this review, I critically evaluate recent innovations aimed at mitigating such concerns by drawing on advances in behavioral science and digital monitoring and insights into biological adaptations. Specifically, I focus on four interconnected themes: First, I spotlight the complexity of avian sensory perception—encompassing vision, auditory capabilities, olfaction, and tactile faculties—to underscore how lighting design, housing configurations, and enrichment strategies can better align with birds’ unique sensory worlds. Second, I explore novel tools for gauging emotional states and cognition, ranging from cognitive bias tests to developing protocols for identifying pain or distress based on facial cues. Third, I examine the transformative potential of computer vision, bioacoustics, and sensor-based technologies for the continuous, automated tracking of behavior and physiological indicators in commercial flocks. Fourth, I assess how data-driven management platforms, underpinned by precision livestock farming, can deploy real-time insights to optimize welfare on a broad scale. Recognizing that climate change and evolving production environments intensify these challenges, I also investigate how breeds resilient to extreme conditions might open new avenues for welfare-centered genetic and management approaches. While the adoption of cutting-edge techniques has shown promise, significant hurdles persist regarding validation, standardization, and commercial acceptance. I conclude that truly sustainable progress hinges on an interdisciplinary convergence of ethology, neuroscience, engineering, data analytics, and evolutionary biology—an integrative path that not only refines welfare assessment but also reimagines poultry production in ethically and scientifically robust ways. Full article