Search Results (83)

Creating trust in society for new technologies, such as a new types of powertrains, and making them marketable requires transparent, neutral, and independent technical verification. This is crucial for the acceptance and success of electrified vehicles in the used car markets. A key component of electric vehicles is the traction battery, whose current and future condition, particularly regarding aging, determines its residual value and safe operation. This review aims to identify and evaluate methods for predicting the lifetime of onboard traction batteries, focusing on their applicability in technical inspections. A systematic literature and patent review was conducted using targeted keywords, yielding 22 patents and 633 publications. From these, 150 distinct lifetime prediction methods were extracted and categorized into a four-level mind map. These methods are summarized, cited, and structured in detailed tables. The relationships between approaches are explained to clarify the current research landscape. Long Short-Term Memory, Convolutional Neural Networks, and Particle Filters were identified as the most frequently used techniques. However, no methods were found suitable for predicting the lifetime of traction batteries during technical vehicle inspections, which operate under short test durations, limited data access, and diverse real-world operating conditions. Most studies focused on cell-level testing and did not address complete battery systems in operational vehicles. This gap highlights the need for applied research and the development of practical methods to support battery assessment in real-world conditions. Advancing this field is essential to foster confidence in battery systems and enable a sustainable transition to electromobility. Full article

The global production of electronic waste (e-waste) has increased due to the quick turnover of electronic devices, creating urgent problems for resource management and environmental sustainability. As a result, e-waste-derived materials (EWDMs) are being explored in pavement engineering research as sustainable substitutes in line with Sustainable Development Goals (SDGs), specifically SDG 9 (Industry, Innovation, and Infrastructure), 11 (Sustainable Cities and Communities), 12 (Responsible Consumption and Production), and 13 (Climate Action). Therefore, to assess global research production and the effectiveness of EWDMs in asphalt applications, this review combines scientometric mapping and systematic evidence synthesis. A total of 276 relevant publications were identified via a thorough search of Web of Science, Scopus, and ScienceDirect (2010–2025). These were examined via coauthorship structures, keyword networks, and contributions at the national level. The review revealed that China, India, and the United States are prominent research hubs. Additionally, experimental studies have shown that EWDMs, such as printed circuit board powder, fluorescent lamp waste glass, high-impact polystyrene, and acrylonitrile–butadiene–styrene, improve the fatigue life, Marshall stability, rutting resistance (up to 35%), and stiffness (up to 28%). However, issues with long-term field durability, microplastic release, heavy metal leaching, and chemical compatibility still exist. These restrictions highlight the necessity for standardised toxicity testing, harmonised mixed-design frameworks, and performance standards unique to EWDMs. Overall, the review shows that e-waste valorisation can lower carbon emissions, landfill build-up, and virgin material extraction, highlighting its potential in the circular pavement industry and promoting sustainable paving practices in accordance with SDGs 9, 11, 12, and 13. This review suggests that further studies on large-scale field trials, life cycles, and technoeconomic assessments are needed to guarantee the safe, long-lasting integration of EWDMs in pavements. It also advocates for coordinated research, supportive policies, and standardised methods. Full article

Energy efficiency in buildings is a vital subject within sustainable construction and climate change mitigation, yet comprehensive bibliometric analyses mapping the complete evolution of this domain remain limited. This study provides a comprehensive four-decade analysis (1981–2025) of building energy efficiency research using data from the Web of Science database, employing VOSviewer (1.6.20), Bibliometrix (4.3.0), and custom Python (3.12.3) scripts with automated terminology normalization through TF-IDF vectorization (n-grams 2–3) and cosine similarity algorithms (threshold = 0.75). Two critical methodological innovations distinguish this investigation: first, Pruned Exact Linear Time changepoint detection statistically validated 2011 as the field’s statistically validated transition point (Mann–Whitney U test, p < 0.000001, effect size = 2.48), replacing arbitrary decade-based periodization; second, computational keyword harmonization enabled precise thematic evolution mapping across inconsistent terminology. The analysis reveals marked increase in research post-2011, with median annual output increasing from 15 articles (1981–2011) to 840.5 articles (2012–2024), and China emerging as the preeminent research center with 2978 publications. Thematic evolution analysis demonstrates fundamental transformation from seven specialized research themes (i.e., behavior, heat-transfer, simulation, impact, performance, consumption, optimization) in the foundational period to dramatic consolidation into two dominant themes (i.e., performance and simulation) in the contemporary period, reflecting maturation from fragmented, component-focused investigations toward holistic, integrated frameworks. International collaboration network analysis identifies four distinct geographic clusters with China, United States, United Kingdom, and Italy serving as central hubs. These findings provide actionable intelligence for researchers, policymakers, and industry stakeholders, while the computationally enhanced framework offers a replicable methodology for bibliometric analysis in other rapidly evolving interdisciplinary domains. Full article

This study follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, combining bibliometric mapping and mechanistic synthesis to provide a unified evidence-based review of cementitious grouts in ground support systems. The bibliometric layer quantifies global research activity, while the systematic synthesis interprets how material composition, pozzolanic chemistry, and rheology control grout performance and sustainability. This study presents a systematic review complemented by bibliometric analysis to synthesise global research trends and technical advances in grout design. A dataset of 1200 articles was screened, from which 101 journal papers met the inclusion and quality criteria and were analysed in detail. Co-occurrence mapping of author keywords was then used to identify research hotspots and collaborative structures. The bibliometric analysis revealed that Construction and Building Materials is the leading outlet. Co-authorship mapping highlighted strong international collaboration, with leading clusters centred on supplementary cementitious materials, rheology, and microstructural analysis. The technical review consolidates five interrelated themes: reinforcement mechanisms, cementitious grouts, chemical reactions and pozzolanic reactivity, fresh and hardened state properties, and microstructural development with rheological behaviour. Across these themes, supplementary cementitious materials and waste-derived binders have emerged as central to both performance enhancement and carbon reduction, while advanced experimental and modelling techniques have refined understanding of microstructural evolution and grout–rock–bolt interactions. Collectively, the findings underline that cementitious grouts are no longer passive fillers but engineered composites designed for mechanical efficiency, durability, and environmental responsibility. Key research gaps remain in the standardisation of rheological testing, long-term durability under complex field conditions, and integration of life-cycle assessment into grout development. Addressing these challenges will be critical for the design of next-generation grouts capable of meeting the dual imperatives of safety and sustainability in mining and civil engineering. Full article

AI-based educational chatbots can expand access to learning, but many remain limited to text-only interfaces and fixed infrastructures, while purely generative responses raise concerns of reliability and consistency. In this context, we present ROboMC, a portable and multimodal system that combines a validated knowledge base with generative responses (OpenAI) and voice–text interaction, designed to enable both text and voice interaction, ensuring reliability and flexibility in diverse educational scenarios. The system, developed in Django, integrates two response pipelines: local search using normalized keywords and fuzzy matching in the LocalQuestion database, and fallback to the generative model GPT-3.5-Turbo (OpenAI, San Francisco, CA, USA) with a prompt adapted exclusively for Romanian and an explicit disclaimer. All interactions are logged in AutomaticQuestion for later analysis, supported by a semantic encoder (SentenceTransformer—paraphrase-multilingual-MiniLM-L12-v2’, Hugging Face Inc., New York, NY, USA) that ensures search tolerance to variations in phrasing. Voice interaction is managed through gTTS (Google LLC, Mountain View, CA, USA) with integrated audio playback, while portability is achieved through deployment on a Raspberry Pi 4B (Raspberry Pi Foundation, Cambridge, UK) with microphone, speaker, and battery power. Voice input is enabled through a cloud-based speech-to-text component (Google Web Speech API accessed via the Python SpeechRecognition library, (Anthony Zhang, open-source project, USA) using the Google Web Speech API (Google LLC, Mountain View, CA, USA; language = “ro-RO”)), allowing users to interact by speaking. Preliminary tests showed average latencies of 120–180 ms for validated responses on laptop and 250–350 ms on Raspberry Pi, respectively, 2.5–3.5 s on laptop and 4–6 s on Raspberry Pi for generative responses, timings considered acceptable for real educational scenarios. A small-scale usability study (N ≈ 35) indicated good acceptability (SUS ~80/100), with participants valuing the balance between validated and generative responses, the voice integration, and the hardware portability. Although system validation was carried out in the eHealth context, its architecture allows extension to any educational field: depending on the content introduced into the validated database, ROboMC can be adapted to medicine, engineering, social sciences, or other disciplines, relying on ChatGPT only when no clear match is found in the local base, making it a scalable and interdisciplinary solution. Full article

Reforms in the Chinese National Entrance Examination to Universities (CNEEU) and curriculum standards have significantly influenced national education. This study investigates the evolutionary stages, research trends, and emerging topics in the study of geography examinations for the CNEEU, commonly known as the Gaokao in China. Utilizing CiteSpace software, we analyzed academic journal articles indexed in the China National Knowledge Infrastructure (CNKI) database from 1980 to 2023. The analysis focuses on publication volume, regional distribution, core authors, emergent terms, and keyword trends related to geography examinations. The results reveal the following: (1) Research on geography in the CNEEU has experienced three distinct phases since the 1980s—incipient stage, fluctuating growth period, and adjustment period—each characterized by unique emergent keywords reflecting evolving research priorities. (2) The principal research institutions include middle schools, higher normal colleges, and examination research institutes, with scholars from higher normal universities more likely to establish collaborative research groups. (3) Key research topics focus on the content and characteristics of geography examination questions, teaching guidance, test preparation strategies, and the development of simulation questions. (4) Ongoing reforms in the CNEEU and curriculum have significantly influenced the scope, focus, and format of geography examinations, directly impacting geography teaching and learning practice. (5) Emerging trends, including literacy and value-oriented assessment, interdisciplinary integration, and inquiry-based innovation, are expected to shape future research in this field. This study offers valuable insights to support both theoretical research and practical advancement in the design of geography examinations for CNEEU. Full article

Objectives: The incidence of Fuchs endothelial corneal dystrophy (FECD) is growing, and with it, the unmet need for a corneal transplant. Among alternative treatment modalities, only genetic therapy represents a causal therapy. Methods: Following the SNARA protocol, the PubMed and ClinicalTrials databases were searched using the keywords Fuchs endothelial corneal dystrophy, FECD, genetic therapy, and CRISPR-Cas9. Results: FECD is polyfactorial disease and mutations or polymorphisms in at least 15 different genes were connected to the disease. For the early-onset form of the disease, exclusive connection to mutations in COL8A2 was confirmed, while for the late-onset form, the most characteristic mutation is the expansion of the CTG18.1 triplet in the TCF4 gene, making these two possible targets. While the CRISPR-Cas9 approach represents the mainstay of genetic therapy development recently, the application of this method to FECD contains several obstacles, studied in preclinical settings. DT-168 and the Ad-Cas9-Col8a2gRNA molecules were developed for FECD treatment and preclinically tested, and phase I and II clinical studies for DT-168 are also already being performed. Conclusions: The review of the literature proved that genetic therapy for FECD is at the level of preclinical research and that there are several specific challenges connected to the target genetic mutation as well as the delivery of possible treatment and duration of the effect. Further studies in the field might bring solutions in the future for alternative treatments for this common corneal disease. Full article

The global need for tissue and organ transplantation paved the way for plant-based scaffolds as cheap, ethical, and valuable alternatives to synthetic and animal-derived matrices for tissue regeneration. Over the years, the field has outgrown its initial scope, including the development of tissue models, platforms for drug testing and delivery, biosensors, and laboratory-grown meat. In this scoping review, we aimed to shed light on the frequency of the use of different plant matrices, the main techniques for decellularization, the functionalization methods for stimulating mammalian cell attachment, and the main results. To that purpose, we searched the keywords “decellularized” AND “scaffold” AND (“plant” OR “vegetable”) in online-available databases (Science Direct, Scopus, PubMed, and Sage Journals). From the selection and study of 71 articles, we observed a multitude of plant sources and tissues, along with a large and inhomogeneous body of protocols used for decellularization, functionalization and recellularization of plant matrices, which all led to variable results, with different extents of success (mostly in vitro). Since the field of plant-based scaffolds shows high potential for growth in the next few years, driven by emerging biotechnological applications, we conclude that future research should focus on plant sources with low economic and environmental impacts while also pursuing the standardization of the methods involved and a much deeper characterization of the scaffold performance in vivo. Full article

Three-dimensional (3D) printing has become a widely used manufacturing technology, but predicting the mechanical and physical properties of printed parts remains a critical challenge. In particular, tensile strength, hardness, and surface roughness are essential for assessing product quality and reliability. Addressing this gap requires advanced computational approaches. Machine Learning (ML) algorithms have the potential to enhance automation and provide accurate predictions of product quality in 3D printing. The primary objective of this study is to review, classify, and analyze recent research on the application of ML algorithms for predicting mechanical (tensile strength, hardness) and physical (surface roughness) properties in 3D-printed materials. This review aims to identify current research trends, highlight existing gaps in the literature, and propose potential directions for future investigations in this rapidly evolving interdisciplinary field. For this purpose, a literature review was conducted in the Web of Science database using keywords such as “3D printing”, “machine learning”, “tensile strength”, “hardness”, and “roughness. The review systematically examines the material types, ML algorithms, printing parameters, and testing methods reported in the literature, and the frequency of these parameters is visualized through graphs to illustrate research trends. The findings reveal that ML algorithms achieve high accuracy in predicting tensile strength, hardness, and surface roughness, providing valuable insights for process optimization. However, current research still shows limited evidence for direct improvements in efficiency and error reduction, indicating important directions for future work. Full article

In Italy, several changes to academic and professional standards and rules in kinesiology and sport have recently occurred. On the university side, no data collection has started regarding these changes and effects on specific scholars. The aim of this study was to evaluate the opinions of Italian university scholars in Exercise and Sport Sciences regarding recent disciplinary reclassifications, the emergence of the kinesiologist as a formal profession, and related curricular updates. Specifically, this study aimed to measure scholars’ views on the usefulness of unification, hybridization with other fields of knowledge, interdisciplinarity with pedagogy, the distinctiveness of undergraduate education in light of the new kinesiologist profile, and the inclusion of Technical and Laboratory Activities (TLA) credited through the European Credit Transfer System (ECTS). These aspects were explored through an eight-question survey offering three multiple-choice answers. An exploratory survey was distributed to a defined population of 261 Italian scholars (48 full professors, 137 associate professors, and 76 researchers). A total of 83 responses were collected: 14 full professors, 45 associate professors, and 24 researchers (response rate: 31.8%). Descriptive statistics and inferential analyses (Chi-Square tests, Cramér’s V, and Pearson/Spearman correlations) were conducted. Results indicated that 72.3% perceived overlap between pedagogical and medical disciplinary groups, and 85.5% considered practical/laboratory activities essential to the kinesiologist’s role. Significant differences in keyword-sharing perceptions across academic ranks emerged (p = 0.012; V = 0.3), and a near-significant trend was found regarding the importance of discipline-aligned research (p = 0.058; V = 0.3). Full agreement was found on the use of updated scientific evidence in lectures (100%), and 81.9% supported standardized education for the kinesiologist profession (Q6). Positive correlations were observed between support for keyword sharing and belief in its usefulness for promoting interdisciplinarity among full professors (r = 0.58, p = 0.02), associate professors (r = 0.68, p < 0.01), and researchers (r = 0.83, p < 0.01). Conversely, negative correlations emerged between the importance placed on practical activities and support for interdisciplinarity among associate professors and researchers, with values ranging from r = −0.31 to −0.46. The results are significant and tended toward autonomy from pedagogy, training aligned with the bachelor’s and master’s degree kinesiologist, and interdisciplinarity inherent in typical Exercise and Sport Sciences (ESS) keywords. This study should be replicated to increase the sample and to expand the ad hoc questionnaire to other issues. These findings highlight the need for greater alignment between academic training, disciplinary definitions, and professional practice through shared epistemological frameworks and updated descriptors that reflect scientific and labor market developments. Full article

This study examines the semantic dynamics and thematic shifts in artificial intelligence (AI) ethics over time, addressing a notable gap in longitudinal research within the field. In light of the rapid evolution of AI technologies and their associated ethical risks and societal impacts, the research integrates the theory of chance discovery with the KeyGraph algorithm to conduct topic detection through a keyword network built through iterative semantic exploration. ChatGPT is employed for semantic interpretation, enhancing both the accuracy and comprehensiveness of the detected topics. Guided by the double helix model of human–AI interaction, the framework incorporates a dual-layer validation process that combines cross-model semantic similarity analysis with expert-informed quality checks. An analysis of 24 authoritative AI ethics reports published between 2022 and 2024 reveals a consistent trend toward semantic stability, with high cross-model similarity across years (2022: 0.808 ± 0.023; 2023: 0.812 ± 0.013; 2024: 0.828 ± 0.015). Statistical tests confirm significant differences between single-cluster and multi-cluster topic structures (p < 0.05). The thematic findings indicate a shift in AI ethics discourse from a primary emphasis on technical risks to broader concerns involving institutional governance, societal trust, and the regulation of generative AI. Core keywords, such as bias, privacy, and ethics, recur across all years, reflecting the consolidation of an integrated governance framework that encompasses technological robustness, institutional adaptability, and social consensus. This dynamic semantic analysis framework contributes empirically to AI ethics governance and offers actionable insights for researchers and interdisciplinary stakeholders. Full article

Simulation has become a pivotal tool in the design, analysis, and optimization of the built environment, and has been widely adopted by professionals in architecture, engineering, and urban planning. These techniques enable stakeholders to test hypotheses, evaluate design alternatives, and predict performance outcomes prior to construction. Applications span energy consumption, airflow, thermal comfort, lighting, structural behavior, and human interactions within buildings and urban contexts. This study maps the scientific landscape of simulation research in the built environment through a bibliometric analysis of 12,220 publications indexed in Scopus. Using VOSviewer 1.6.20, it conducted citation and keyword co-occurrence analyses to identify key research themes, leading countries and journals, and central publications in the field. The analysis revealed seven primary thematic clusters: (1) human-focused simulation, (2) building-scale energy performance simulation, (3) urban-scale energy performance simulation, (4) sustainable design and simulation, (5) indoor environmental quality simulation, (6) building aerodynamics simulation, and (7) computing in building simulation. By synthesizing these trends and domains, this study provides an overview of the field, facilitating greater accessibility to the simulation literature and informing future interdisciplinary research and practice in the built environment. Full article

The review provides the results of the analysis of research publications in the field of energy economy on a global scale. The review aims to test three hypotheses and build and analyze the main trend lines and clusters to determine the direction of movement of new knowledge in the energy economy research. This review delves into the multifaceted nature of energy transitions, highlighting the pivotal role of policy frameworks, financial instruments, and technological innovation. By examining the socio-economic implications of renewable energy deployment and addressing the challenges associated with energy storage and grid integration, this study contributes to the ongoing discourse on sustainable energy development. The review used scientometric, correlation, and bibliometric methods of analysis. Artificial Intelligence was used to process 411,396 units of information (a special prompt was created). There is a new scientific result: (a) research hypotheses 1 and 3 were accepted, research Hypothesis 2 was rejected; (b) new trend lines showed the directions where the global energy economy is heading; (c) new clusters showed the top five leading countries and top 10 keywords in the field of energy economy; (d) leading journals in the studied area were found. The review indicated an underrepresentation of specialized journals in this field among the top journals. The review also showed that several keywords characterizing the sixth technological paradigm are missing from the top 10 keywords. This result suggests that these research areas were underrepresented in citation-based bibliometric data in energy in 2021–2024. The review is useful to identify promising and problematic areas for future research in the global energy economy. Full article

Wheelchair tennis (WT) is a Paralympic sport designed for athletes with physical impairments. Assessing physical fitness characteristics using appropriate field-based tests and standardized protocols is essential for individualized training, injury prevention, and performance monitoring. However, there is currently limited information on which field-based tests are most suitable and how they should be applied in WT athletes, resulting in inconsistency across studies and practical use. Establishing a standard operating procedure (SOP) enables replicable, cost-effective testing routines that improve data consistency and comparability. We conducted a scoping review to synthesize the existing evidence on field-based physical fitness assessment in WT athletes and to propose a structured SOP for these tests. A comprehensive search was conducted in three electronic databases—NLM PubMed, Web of Science, and Scopus—using predefined keywords and Boolean operators. The inclusion criteria were limited to peer-reviewed, English-language original articles focusing exclusively on field tests in WT athletes. Studies with other populations, reviews, and abstracts were excluded. Eleven studies met the eligibility criteria. This scoping review identified various field tests assessing key fitness components, including cardiorespiratory endurance, muscle strength, agility, and body composition. The most frequently employed tests were the 20 m sprint test, isometric handgrip test, spider test, Illinois Agility Test, and skinfold thickness. These findings highlight the lack of standardized fitness assessments in WT. The proposed SOP offers a practical step toward consistent, replicable, and relevant evaluation in these athletes. Full article

This study presents a comprehensive bibliometric analysis of research trends in evaluating the mechanical properties of timber structures, with a particular emphasis on the modulus of elasticity (MOE) assessed through non-destructive testing (NDT) methods, especially ultrasonic waves. Using VOSviewer software to analyze 129 Scopus-indexed publications, the analysis reveals a marked increase in research activity since the early 2000s and the formation of distinct thematic clusters. The keyword ’non-destructive examination’ consistently emerges as the dominant term, underscoring a sustained and focused scientific interest in this field. Despite this growth, significant gaps remain, notably the lack of standardized methodologies and limited application of ultrasonic NDT techniques for in-service timber structures. This underscores the urgent need for targeted research efforts, including integrating machine learning with ultrasonic analysis, developing standardized testing protocols, exploring hybrid diagnostic approaches, and extending ultrasonic methods to aged and recycled timber. Furthermore, advancing portable, in-situ ultrasonic systems is essential to enable real-time, field-based assessments. This study not only maps the current research landscape but also highlights strategic opportunities to improve the accuracy, reliability, and sustainability of timber mechanical property evaluations, thereby supporting the advancement of timber engineering. Full article