Search Results (1,525)

Digital technologies are increasingly used to understand and influence consumer food waste behavior, yet evidence on their applications and effectiveness remains fragmented. This systematic literature review synthesizes research at the intersection of consumer behavior, technological innovation, and food waste. Peer-reviewed articles published in English were identified through searches of Scopus and Web of Science, resulting in a final sample of 22 studies. Thematic analyses were conducted to examine publication patterns, research contexts, methodological approaches, and emerging research gaps. The literature shows a shift from predominantly theoretical and exploratory work towards more applied, technology-centered research. Scientific production is concentrated in Australia, China, Italy, the Netherlands, and the United Kingdom, while the selected studies are mainly published in journals focused on environmental sustainability, food-chain management, and consumer psychology. Two broad research approaches were identified: quantitative studies using cross-sectional surveys and statistical modeling, in which technology is treated as an explanatory factor, and experimental or design-oriented studies that employ technology as a direct intervention. However, the evidence base is constrained by extensive reliance on self-reported data, recall and social-desirability bias, and cross-sectional designs that limit causal and long-term conclusions. Future research should therefore prioritize longitudinal, mixed-methods, and objective measurement approaches, while examining emotional and psychological responses to food-waste-prevention technologies. The implementation of such technologies also requires attention to usability, user fatigue, privacy and ethical concerns, and psychological and cultural barriers to adoption. Overall, the findings indicate that digital technologies offer promising but insufficiently validated opportunities to support food waste reduction and that stronger empirical evidence is needed to guide their effective and responsible development. Full article

Anomaly detection in water-related time-series data often reveals important environmental problems and serves as a starting point for scientific discoveries. Machine learning has become the mainstream method and a research hotspot for anomaly detection in recent years. This review examines 106 research articles from the Web of Science database published over the past 10 years. Unlike other surveys, this review focuses on anomalies arising from the water-related variables themselves rather than from equipment malfunctions. The work assesses the overall trends in the application and development of machine learning models for water-related anomaly detection. It classifies machine learning-based anomaly-detection models from two dimensions: development stage and anomaly-detection paradigm. Our analysis covers the mechanisms, strengths, limitations, and applications of various machine learning-based anomaly-detection models across different paradigms, highlighting current challenges and prospective research directions in water-related anomaly detection. Full article

Retrieval-augmented generation (RAG) systems for scientific literature require evidence-based choices of document segmentation, representation, retrieval, and generation components, particularly when the source collection varies in topical specificity and document structure. This study addresses the lack of an end-to-end, component-level comparison of these choices for soil science question answering. A three-stage evaluation was conducted across general, domain-specific, and geospatial soil science corpora. The corpus combines foundational soil science books, peer-reviewed research articles, European soil monitoring material, and geospatial mapping publications, thereby covering both broad disciplinary concepts and specialized scientific evidence. The study compares four chunking strategies, three embedding models, five retrieval methods, and five large language models. In Experiment 1, semantic chunking with text-embedding-3-large achieved the highest aggregate retrieval scores (recall@1 = 0.824; MRR = 0.819), whereas text-embedding-3-small delivered practically comparable performance at lower cost. In Experiment 2, hybrid reciprocal rank fusion achieved recall@5 values of 0.957, 0.960, and 0.647 for the general, domain-specific, and geospatial corpora, respectively; the cross-encoder reranker showed weaker rank quality on scientific content. In Experiment 3, model responses attained BERTScore values of 0.909–0.927 and faithfulness of at least 0.993; these automated measures indicate low contradiction with retrieved context but do not establish answer completeness or human-perceived correctness. The study provides a reproducible component-level evaluation design, characterizes the effect of corpus specificity on RAG retrieval, and identifies a practical configuration for soil science literature retrieval. Among the models retained for direct aggregate comparison, Llama 3.1 8B offered the most favorable observed balance of answer quality, latency, cost, and model openness. Full article

Remote and digital laboratories have become critical resources for sustaining experimental activities in natural sciences and engineering when direct access to physical facilities is constrained. This scoping review of 470 articles published between 2021 and 2026 examines the current state of research on remote laboratory development and educational implementation, identifying disciplinary patterns, pedagogical approaches, and reported learning outcomes. The analysis, based on systematic searches of Web of Science, Scopus, and Springer Nature, reveals a marked asymmetry: research concentrates overwhelmingly on university engineering, whilst chemistry, physics, and especially biology remain underrepresented, the corpus concentrates geographically in Europe, North America, and Asia, and production accelerated sharply during the pandemic before stabilizing or declining. A critical finding is that much of the literature (particularly in engineering) treats student learning as secondary validation of technical infrastructure rather than as a primary research question. The review identifies a consistent and significant gap in rigorous investigations of scientific skill development in remote laboratories for natural sciences, and limited evidence, within the analyzed search strategy, of explicit pedagogical or didactic frameworks for integrating these technologies into science teaching sequences. These findings point to a clear research priority: the field requires concrete didactic and pedagogical models for integrating remote laboratories into natural science education, with sustained attention to authentic scientific reasoning and experimental competencies, particularly at pre-university levels where the gap is most pronounced. Full article

This review examines the physical foundations, predictive models, emerging technologies and recent research trends in bioimpedance for clinical nutrition. A bibliometric analysis of cumulative scientific production from 1980 to 2025 shows that weighted logistic models provide a good descriptive fit to the evolution of the literature (R² ≥ 0.999). Bioimpedance research remains in an expanding phase, with its projected inflection point lying beyond 2026. These fits are descriptive and model-dependent: high coefficients of determination on cumulative counts are not in themselves evidence of predictive validity, and inflection points beyond the observation window are extrapolations. We introduce the Measurement-Independent Factor (MIF), which isolates the anthropometric contribution from the bioimpedance measurement in predictive equations. Phase Angle (PhA) has emerged as the most extensively studied bioimpedance descriptor, since it avoids the population-specific prediction equations required for estimates such as FFM or FM, although its absolute value remains dependent on measurement conditions, instrumentation and biological characteristics. Body-composition estimates obtained with devices from different manufacturers should not be interpreted interchangeably. Electrode technologies, bioimpedance wearables, and bioimpedance imaging are reviewed as major emerging research directions. A second bibliometric analysis covering the most-cited literature from 2020 to 2026 and 1655 original articles published during 2025–2026 show that recent research focuses primarily on obesity and adiposity, together with sarcopenia and frailty, whereas critical and acute care remain underrepresented. Full article

Background/Objectives: Artificial intelligence (AI) has transformed computational antibody engineering by enabling accurate prediction of antibody structures, rational optimization of therapeutic properties, and de novo antibody design. Recent advances in deep learning, protein language models, and generative AI have fundamentally changed the way antibodies are discovered and engineered. This review aims to present the historical evolution of computational antibody engineering, from early structure-based design strategies to modern AI-driven approaches, while highlighting the major computational tools, publicly available databases, current limitations, and future directions of the field. Methods: A comprehensive narrative review of the literature was conducted using PubMed, Scopus, Web of Science, and Google Scholar. Original research articles, methodological studies, and review papers published between 1985 and 2026 were evaluated. Publications were selected according to their scientific relevance, methodological quality, and contribution to the historical development of computational antibody engineering. Results: The review describes the progression of antibody engineering from phage display and structure-based computational methods to machine learning, deep learning, protein language models, and generative artificial intelligence. It summarizes key public databases supporting antibody research, discusses advances in antibody structure prediction and developability assessment, and reviews recent generative models capable of designing antibody sequences and structures. Current challenges, including limited experimental validation, dataset bias, prediction of highly flexible regions, model interpretability, and clinical translation, are also discussed. Conclusions: Artificial intelligence has fundamentally reshaped computational antibody engineering by integrating sequence, structural, and functional information into increasingly accurate predictive and generative frameworks. Although important challenges remain, recent developments indicate that AI-driven approaches will play an increasingly central role in the discovery and optimization of next-generation therapeutic antibodies. Full article

Rare earth elements (REE) are critical to 21st-century technology, from electronics and defense applications to renewables and beyond. The processing of REE is primarily based on minerals (bastnasite, monazite, and xenotime), but alternative resources (coal ash, E-waste, and permanent magnets) are also gaining increasing interest. Adsorption remains one of the most efficient, environmentally friendly extraction methods despite its lengthy mixing time. In the present research article, a hemp biochar prepared by vacuum pyrolysis at 700 °C was examined for its applicability in the adsorption of selected REE (La³⁺, Nd³⁺, Dy³⁺) from synthetic solutions. An innovative technique, Resonant Vibratory Mixing (RVM), was applied to improve adsorption kinetics, with factors including time (5–30 min) and intensity (30–70%) at room temperature. Using the Thermo Scientific 4000 M shaker for mixing, the maximum adsorption capacities were 77.56 mg/g for Dy³⁺, 75.85 mg/g for La³⁺, and 72.65 mg/g for Nd³⁺ using 100 mg of hemp biochar and 10 mL solutions (1000 mg/L). The adsorption capacity of 100 mg hemp biochar was 79.79 mg/g for Dy³⁺, followed by 77.61 mg/g for La³⁺ and 75.75 mg/g for Nd³⁺, using RVM for only 40 min at 70% intensity. RVM increased the adsorption capacities of all REE in only 40 min. Surface and structural analyses were carried out using Scanning Electron Microscope (SEM), Fourier Transform Infrared Spectroscopy (FTIR), Brunauer-Emmett-Teller analysis (BET), Zeta Potential, and Carbon/Hydrogen/Nitrogen (CHN) methods. The adsorption recoveries of all REE in the single-element system were higher than 98.5%. However, in a multi-element system, the adsorption recoveries of La³⁺, Nd³⁺, and Dy³⁺ were 83.7%, 96.2%, and 99.2%, respectively, demonstrating that hemp biochar has low selectivity for Dy³⁺ and Nd³⁺. The adsorption process could be well described by the Langmuir isotherm and the pseudo-second-order kinetic model, indicating monolayer adsorption and chemical process involvement. Based on the characterization analysis of hemp biochar, electrostatic interaction was the dominant mechanism in this study. REE desorption using 0.5 M nitric acid was the most efficient, with >80% of REE recovered. The combination of hemp biochar as an adsorbent and RVM as a mixing technique demonstrated excellent performance in synthetic solutions; the reusability and application of hemp biochar to natural solutions require further study. Full article

Recent advances in digital technologies are transforming the production and visualization of 3D models for Augmented Reality (AR), Virtual Reality (VR), and cultural heritage reconstruction. Within this evolving context, ensuring scientifically grounded, transparent, and interpretable reconstruction processes remains essential, particularly for the hypothetical reconstruction of lost or unbuilt architecture. This article discusses the theoretical framework defined by the Critical Digital Model (CDM) and the Scientific Reference Model (SRM), both of which aim to define the 3D model as a scientific product generated through a transparent and falsifiable research process. The proposed approach integrates a structured reconstruction methodology based on source analysis, semantic segmentation, and iterative validation, distinguishing between Raw and Informative Models and applying these principles to selected case studies. Attention is devoted to visualization as an integral component of the reconstruction process. Rather than serving solely as a presentation tool, visualization is examined as a means of analysis, interpretation, and communication of uncertainty. Different visualization strategies—including photorealistic, non-photorealistic, uncertainty-driven, and diplomatic representations—are assessed together with XR visualization modalities, from static images and spherical panoramas to fully interactive VR environments. The results demonstrate how immersive and methodologically grounded visualization approaches can enhance both scholarly investigation and public dissemination, supporting informed choices according to specific research and communication objectives. Full article

Islamic intellectual history offers little support for a general conflict between the religious sciences (tafsīr, ḥadīth, fiqh, and kalām) and the rational and natural sciences (mathematics, medicine, astronomy, physics, geography, and philosophy): Muslim scholars typically cultivated both, reading the created world as one of God’s signs (āyāt) alongside the revealed text, and a number of the tradition’s most celebrated figures, Ibn Sīnā, al-Fārābī, al-Ghazālī, and Ibn Rushd among them, achieved distinction in both domains at once. This article examines, through the biography and corpus of a single fifteenth-century Ottoman scholar, how religious and scientific learning were institutionally and intellectually intertwined in the early Ottoman period. Mollā ʿAbd al-Wājid al-Kutāhī (d. 838/1435) worked at a high level in both fields at once, leaving a commentary that carried Marāgha-school astronomy into Anatolia, the work for which Western scholarship has chiefly known him, and a major Ḥanafī legal commentary; his career is accordingly a direct case study of the relationship between religion and science. The study traces his two works dedicated to Sultan Murad II, the legal al-Ikhtiyārāt fī Sharḥ al-Nuqāya and the astronomical Sharḥ al-Mulakhkhaṣ fī al-Hayʾa. It proposes a two-stage draft/fair-copy (musawwada/mubayyaḍa) composition model based on a codicological analysis of twenty-four manuscripts of the legal work and a preliminary census of the four known copies of the astronomical commentary. The astronomical work is a commentary on al-Jaghmīnī’s al-Mulakhkhaṣ, the most widely used Ptolemaic textbook in Islamic education; the same base text was also glossed by Qāḋīzāde al-Rūmī from the same Fanārī milieu, linking ʿAbd al-Wājid to the Samarqand school. The article’s central contribution to debates on religion and science comes from an episode preserved in the Mawlawī hagiography Safīna-yi Nafīsa-yi Mawlawīyān: the Mawlawī shaykh Jalāl al-Dīn Ergun, in conversation with ʿAbd al-Wājid, deliberately invokes astronomy and mathematics (hayʾa, riyāḍiyyāt) to teach that these sciences ultimately belong to the category of “māsiwā” (all that is other than God) and that natural philosophy is a stepping stone toward self-knowledge and, through it, knowledge of God. This primary-source testimony documents a hierarchical, integrative model of natural philosophy within fifteenth-century Ottoman intellectual culture rather than an adversarial one, offering historical evidence against the applicability of the conflict thesis in this Ottoman-Islamic context and a regional, biographically documented instance of the broader integrative pattern that characterizes the classical and post-classical Islamic tradition of learning. Full article

This article re-examines the French military physician and physiologist Jules Héricourt’s (1850–1938) treatment of what he termed intoxications d’origine externe, intoxications of external origin, in his popularising 1904 book Les Frontières de la Maladie: Maladies Latentes et Maladies Atténuées. Héricourt grouped alcohol, tea, coffee, tobacco, and carbon monoxide under a single clinical logic: each, in his account, produced disease gradually, cumulatively, and at exposure levels too low to be recognised as pathological. Through close, critical reading of the primary text, supplemented by biographical and historiographical contextualisation, this study asks how Héricourt characterised each form of intoxication, on what evidentiary basis, and what his synthesis contributes to the historiography of chronic toxic exposure. The analysis shows that most of Héricourt’s specific clinical claims lack a stated evidentiary basis and that several derive directly from earlier authors; his scientific authority rested chiefly on his experimental physiological work with Charles Richet rather than on documented clinical toxicology. The article argues that the book’s historiographical significance lies not in medical originality but in its role as a widely read popularisation of the idea that ordinary domestic and social habits could produce disease through prolonged, low-intensity, unrecognised exposure, an idea that resonates, at the level of general logic, with later models of chronic and cumulative toxic exposure. This resonance is treated as suggestive rather than as evidence of direct influence. Full article

In this article, general operating principles for modern photocatalytic solar water-splitting systems are reviewed from a physics perspective, and their fundamental limitations are identified. Several potential approaches to overcome the identified limitations are proposed, and a new solar water-splitting system unifying those approaches based on plasmonic metal nanoparticles linked by a metal-insulator junction is described. Based on already existing scientific knowledge, some probable features of the proposed system are briefly discussed, and an initial theoretical analysis of electromagnetic wave-propagation modeling was performed with COMSOL Multiphysics software. In addition, some rectification capabilities for the metal insulator–metal junction embedded in the system are calculated using a simplified Simmons model for tunneling currents. A probable approach for initial system synthesis with existing nanotechnology techniques is proposed, and its limitations and probable bottlenecks are marked. Full article

Global biodiversity policy increasingly depends on community-led conservation, yet the comparative evidence base contains little from South Asia’s frontier regions. This article asks how a long-running grassroots organisation in a politically and ecologically marginal region combined community mobilisation, vernacular knowledge, scientific evidence, and engagement with public institutions in pursuing conservation outcomes, and which features of that process may be relevant beyond Northeast India. Four campaigns of Nature’s Beckon, founded in Dhubri, Assam, in 1982, are compared as distinct types of intervention: species-led protected-area mobilisation at Chakrashila; landscape-scale conservation against extractive pressure at Dihing Patkai; species research with public ecological education; and community-managed institution-building. The available evidence indicates a documented and substantial, though not exclusive, role in campaigns associated with the notification of two protected areas whose current notified areas total approximately 279.83 km². Advocacy alone does not adequately explain these outcomes: where a formal government decision was required, sustained organisational capacity became consequential only when it coincided with a favourable political and administrative opening. Measured against four design features associated with successful community-based conservation, the model corresponds strongly to capacity-building investment and external linkage, in qualified form to equitable benefit-sharing, and only partly to tenure security. The article develops an ecology of the margins framework and specifies which elements appear transferable and which do not. Full article

Background/Objectives: Microplastics (MPs) and nanoplastics (NPs) have emerged as ubiquitous environmental contaminants resulting from the extensive production, use, and degradation of plastic materials. Human exposure occurs primarily through contaminated food and drinking water, with inhalation representing an additional important route. Growing concern has focused on the ability of these particles, particularly NPs, to cross biological barriers, enter the systemic circulation, and reach human tissues. The aim of this review was to summarize current evidence on dietary exposure to MPs and NPs, their gastrointestinal bioavailability and toxicokinetics, and their potential systemic health effects, with particular emphasis on organ-specific responses, underlying biological mechanisms, and the strength and limitations of the available evidence. Methods: A comprehensive narrative review of the scientific literature published between 2000 and 2026 was conducted using PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar. Original research articles and review papers addressing dietary exposure, occurrence in food and drinking water, migration from food-contact materials, gastrointestinal absorption, translocation, biodistribution, bioaccumulation, elimination, molecular mechanisms, and potential organ-specific or systemic health effects were included. Publications without full-text availability, conference proceedings, editorials, commentaries, duplicate publications, and studies without relevance to human exposure or health were excluded. Results: Food, drinking water, beverages, and food-contact materials represent important sources of human exposure to MPs and NPs. Following ingestion, most larger particles are eliminated through the gastrointestinal tract, whereas smaller MPs and particularly NPs may cross biological barriers and potentially reach the systemic circulation and distant tissues. Experimental studies consistently identify interconnected biological responses involving oxidative stress, inflammation, mitochondrial dysfunction, barrier impairment, immune dysregulation, genotoxicity, apoptosis, and endocrine disruption. These mechanisms have been associated with alterations in the gastrointestinal, respiratory, cardiovascular, nervous, urinary, reproductive, endocrine, and skeletal systems and with biological processes relevant to carcinogenesis. However, most mechanistic evidence derives from in vitro and animal models, whereas human evidence remains limited and predominantly observational. Consequently, the extent to which these experimental findings translate into clinically significant effects in humans remains uncertain. Conclusions: Current evidence supports the biological plausibility of systemic effects associated with MNP exposure but is insufficient to establish causal relationships between chronic dietary exposure and specific human diseases. The detection of MNPs in human tissues and reported associations with pathological conditions should therefore be interpreted cautiously. Standardized analytical methods, improved characterization of realistic human exposure, and well-designed longitudinal epidemiological studies integrating quantitative exposure assessment with validated clinical outcomes are required to clarify dose–response relationships, long-term health effects, and the clinical significance of MNP exposure. Full article

The article explores the evolution of energy transition research through a bibliometric co-occurrence analysis of author keywords extracted from 146 scientific publications. The analysis reveals a shift from the technical aspects of energy systems and traditional decision-support approaches (2014–2018) towards climate change, decarbonization, renewable energy, investments, and energy policy (2018–2021). Recent studies (2022–2024) increasingly emphasize renewable energy, sustainable development, and intelligent decision-support systems, reflecting the growing digitalization of energy systems and the transition towards intelligent energy management. Based on these findings, the study develops a Digital-Twin-Oriented Techno-Economic Decision-Support Framework (DTOTEDSF) for optimizing and managing carbon-reduction strategies under dynamic energy transition conditions. Rather than representing a fully implemented digital twin (DT), the proposed framework constitutes the analytical foundation for its future development. It integrates techno-economic modeling, optimization, scenario analysis, and sensitivity assessment into a unified decision-support methodology. To demonstrate its practical applicability, the framework was applied to four industrial CCS case studies in the cement sector using publicly available technical and economic data. Its analytical core combines technical, economic, and optimization models to evaluate CCS performance under alternative operating conditions. Consequently, the proposed framework provides a methodological basis for the future implementation of fully operational DTs and contributes to the development of intelligent decision-support tools for industrial decarbonization and the sustainable energy transition. Full article

Generative Artificial Intelligence (AI) has become a rapidly expanding area of scientific research, generating a growing body of literature across technical and applied domains. This study provides a comprehensive bibliometric analysis of global generative AI research to characterize its publication growth, disciplinary and geographical distribution, institutional participation, funding patterns, citation performance, and thematic development. The analysis covers 22,758 Scopus-indexed journal articles and conference papers published between 2020 and 2025, identified using the phrase “generative artificial intelligence” enclosed in double quotation marks in TITLE-ABS-KEY fields. A reproducible computational workflow was used to examine publication output, document types, subject areas, countries, institutions, funding sponsors, citation patterns, and keyword co-occurrence. Citation analysis incorporated annualized citation rates and cohort-normalized annual citation rates to improve comparisons across publication years. Results show a pronounced acceleration in publication output after 2022, with an approximate 105% compound annual growth rate over 2020–2025. Computer Science remained the largest subject area, while substantial representation extended across Engineering, Social Sciences, Medicine, Mathematics, and other domains. Publication activity was concentrated among leading countries and institutions, with the United States and China recording the highest output. Funding analysis identified major national and international sponsors through publication–sponsor associations. Citation performance varied substantially across cohorts, with the 2023 cohort exhibiting the highest cohort-normalized annual citation rate (1.58). Keyword analysis revealed a thematic shift from early AI and GAN-related research toward generative AI and large language model themes, alongside education, innovation, human–AI interaction, and responsible AI. The findings provide an evidence-based, multidimensional characterization of the rapidly evolving generative AI research landscape. Full article