Search Results (2,601)

Background/Objectives: Downy mildew, caused by Peronosclerospora and Sclerophthora species, is a major constraint to maize production in tropical and subtropical regions, with yield losses of 30–100%. This systematic review synthesised evidence on methods used to screen maize for downy mildew resistance and assessed their effectiveness, reliability, and associated markers. Methods: PubMed, Google Scholar, ScienceDirect, and CAB Abstracts were searched (last searched 22 October 2025) for English-language studies (1990–2025) evaluating phenotypic or molecular screening methods. Risk of bias was assessed using the RoB 2 framework. Narrative synthesis was conducted following a protocol registered on the Open Science Framework. Results: Twelve studies met the inclusion criteria, predominantly from India and Cambodia. Spreader row systems (seven studies) and conidial spray inoculation (six studies) were the most common field methods, while the glasshouse sandwich technique generated the highest disease pressure. Cross-method correlations were strong (r = 0.92–0.99), and heritability estimates ranged from 0.50 to 0.97. QTL mapping identified resistance loci on chromosomes 2, 3, and 6, with chromosome 6 stable across multiple pathogen species. Evidence certainty was moderate for method effectiveness and low for molecular markers. Conclusions: Established phenotypic screening methods reliably discriminate resistant germplasm; however, standardised protocols, broader geographic validation, and independent molecular marker confirmation are needed. Full article

In the academic year 2022/2023, an orientation course addressed to high school students was proposed at Politecnico di Milano. The course was conducted using active methodologies referring to the Problem-Based Learning pedagogical framework. Most of the time was dedicated to an open-ended style physics laboratory in which students worked in small groups exploring different areas of physics. The main aim of the course was to foster science self-efficacy and motivation, in order to enable students to prepare themselves for passing the test required to enrol in any engineering programme at Politecnico di Milano University. To investigate the effectiveness of the course, a statistical analysis of students’ attendance and students’ results on the test was performed. The results of the analysis show that students who attended the course had a slightly better improvement in their test scores compared to those who did not attend. The impact of the course seems to be more effective for female students. The results confirm the validity of active and open-ended activities to increase scholastic performance and to enable students in autonomous preparation. Using these strategies in an orientation course can help students make more informed choices about the university pathway best suited to them, thereby reducing issues related to student dropout. Full article

Ion-selective electrodes (ISEs) are widely used analytical tools for the determination of specific ions in a variety of analytical applications due to their simplicity, selectivity, and low cost. Recent developments in materials science and digital fabrication have opened new opportunities for redesigning ISEs using modern manufacturing techniques. Here, we present a new application of 3D printing for fabricating potassium-selective electrodes using a simplified membrane composition. The 3D printing cocktail was prepared by mixing potassium tetraphenylborate, silver sulfide or graphite, and industrial ABS (acrylonitrile Butadiene Styrene) polymer. Membranes were tested both without and with the addition of ZnO nanoparticles. Incorporation of ZnO NPs significantly enhanced the electrode slope, while graphite-based membranes exhibited faster response, with potential stabilizing within 3–7 s across a concentration range of 4.88 × 10⁻⁵ mol L⁻¹ to 1.00 × 10⁻² mol L⁻¹. The optimized 3D printed membrane containing 0.6% ZnO NPs showed near-Nernstian behaviour (slope: 59.178 mV per decade and R² = 0.9989), a limit of detection of 2.06 × 10⁻⁵ mol L⁻¹ and high selectivity against common interfering ions. These results demonstrate that 3D printing combined with a suitable membrane composition and nanoparticle incorporation provides a versatile platform for rapid, reproducible, and high-performance potassium ISEs. Full article

Medieval literature often seems to be a remote, irrelevant, incomprehensible world of narrative texts lost in heroic, religious, or courtly themes, limited to stories about King Arthur, courtly lovers, military heroes, and religious martyrs, saints, and prophets. In reality, as any expert can easily confirm, when we turn our full attention to pre-modern literature from across Europe (and also other parts of the world), we can often recognize the true extent to which poets utilized their narratives for spiritual, philosophical, religious, scientific, and medical explorations that have much to tell us today and prove to be deeply meaningful in a timeless manner. One key aspect, which was shared among virtually all medieval artists, poets, and theologians, consisted of the unique experience by an individual who is entitled through a physical opening to see into the depth or the height of all existence and can thus discover a wholly different world. Through this motif of the gaze, an entire epiphanic realization can set in, which thus quickly transforms the purely entertaining narrative medium into a narrative catalyst of profound spiritual experiences, helping the individual to gain inspiration from the Godhead (e.g., mysticism). Indeed, numerous times, medieval poets employed the motif of the visionary gaze, developed in very concrete terms, to trace and explain the process of perspicuity and accompanying acuity which ultimately leads to new intellectual, emotional, and religious understandings and experiences. While many intellectuals already embraced this notion of a visionary concept of spiritual comprehension, it might come as a surprise that secular and religious poets also operated quite intentionally with the concept of a hole in the wall or some other opening as a springboard for intellectual and spiritual experiences, directly drawing from the concepts of the optical sciences as understood at that time. Oddly but highly significantly, Christian and pagan notions tend to intersect in those narrative moments, particularly in late medieval literature, merging the visionary experience with the monstrous within human society, associating the gaze with the erotic and religious dimension. Full article

Hyperspectral remote sensing (HRS) is gaining widespread adoption within the geoscience and Earth observation communities. It fosters diverse applications, including precision agriculture, soil science, mineral exploration, and carbon detection, to name a few. Recent technological advancements facilitated a growing number of satellite missions as well as an increase in the availability of commercial sensors and platforms, such as drones. A significant challenge in deploying the varied platforms and sensors is the design and optimization of the hyperspectral surveys. Forward modelling simulators are valuable for optimizing mission parameters and estimating imaging performance. Limited accessibility of open-source simulators presents an obstacle for users who seek to benefit from such tools. To bridge this gap, HySIMU (Hyperspectral SIMUlator) was developed and described herein. It is an open-source, forward modelling toolkit that combines and integrates a primary processing pipeline with various open-source packages into a transparent and modular workflow. It offers a cost-effective approach to evaluating the performance of hyperspectral surveys. HySIMU is designed to simulate hyperspectral imagery based on user-defined targets, platforms, and sensor parameters. Features include (i) a ground truth data cube builder for customizable input parameters, (ii) a terrain-based solar and view geometry calculator for illumination modelling, (iii) integrated open-source radiative transfer models for incorporating atmospheric effects, and (iv) spatial resampling filters. In this manuscript, the initial framework for HySIMU is presented with some example applications, including two validation studies with real hyperspectral images. As remote sensing technologies advance, forward modelling toolkits such as HySIMU play a crucial role in refining mission designs and assessing survey feasibility. The scalability for arbitrary hyperspectral sensors, platforms, and spectral libraries ensures broad applicability. Of particular importance is support for parameter optimization for both scientific and commercial HRS campaigns. Full article

Research using latent profile analysis (LPA) has yielded inconsistent results regarding the number of personality profiles among athletes, the specific configuration of the Big Five traits, and their interpretation. This study seeks to explore personality types by excluding additional variables from the LPA model, aiming to assess how well personality profiles are universal (independent of gender and cultural context) and can predict academic achievement in student athletes. A cross-sectional study was conducted using a paper-and-pencil questionnaire among 424 student athletes from two universities in Poland and Ukraine. The average age of participants was 20 years old (M = 20.01; SD = 2.48), 62% were male, 53% lived in Poland, and 58% studied Sports Sciences vs. 42% Physical Education. The Mini-International Personality Item Pool (Mini-IPIP) was used to assess the Big Five personality traits, and grade point average (GPA) was used to measure students’ academic achievements in the last semester. The LPA identified four personality profiles: (1) Restrained Neurotic (Profile 1, 32%), Open Extravert (Profile 2, 42%), Competitive Neurotic (Profile 3, 17%), and Cooperative Perfectionist (Profile 4, 8%). Profiles 1, 3, and 4 showed similarly low levels of emotional stability, extraversion, and intellect but differed significantly in agreeableness and conscientiousness. Gender and country differences across athletes representing specific profiles were also noted. Profile 2 showed the strongest link with academic achievement. Hierarchical multiple linear regression showed that LPA profiles explained only 2% of GPA variance, compared to Big Five personality traits (9%) and demographic variables, such as sex, country, and study major (8%), which were also included in the following steps in the regression model, explaining only 9% and 8%, respectively. Most student athletes (52%) with personality profiles 1 (Restrained Neurotic), 3 (Competitive Neurotic), and 4 (Cooperative Perfectionist) may require psychological training to better cope with negative emotions and stress arising in competitive and academic settings. Profile 2 (Open Extravert) seems to be the most adaptive and potentially successful personality type. Personality types are, at least to some extent, related to gender and country of residence. More cross-cultural research is required to further verify the types of athletic personalities. Full article

The transition from cash-based transactions to digital wallet usage represents a structural change in the business practices of micro and small enterprises (MSEs) in emerging economies. This study aims to synthesize scientific evidence on digital wallet adoption among microentrepreneurs, analyze the geographical distribution of research, and consolidate key empirical findings, with a specific focus on Asia and Latin America. These regions are of particular interest because they share high levels of economic informality, strong reliance on cash-based transactions, and rapid expansion of digital financial technologies, while also facing institutional, regulatory, and infrastructural constraints that shape technology adoption among microentrepreneurs. A systematic review was conducted following the PRISMA 2020 guidelines. Searches were performed in the Scopus and Web of Science databases, including open access empirical studies published between 2021 and 2025 in English or Spanish. After applying predefined eligibility criteria and removing duplicates, 39 studies were included in the final analysis. The results indicate that most publications originate from Asian countries, particularly India, Indonesia, Malaysia, and Vietnam, whereas Latin America is mainly represented by Colombia and Peru. Across both regions, digital wallet adoption is consistently influenced by trust, perceived security, perceived usefulness, and ease of use, while perceived risk and institutional weaknesses emerge as contextual barriers. Although several primary studies adopt a consumer-level analytical perspective, their findings are extrapolated to microentrepreneur contexts by emphasizing transaction-related behaviors directly linked to business operations. This review acknowledges that the predominance of consumer-focused evidence represents a limitation when interpreting firm-level outcomes. Overall, the findings suggest that digital wallet adoption among microentrepreneurs is a socio-technical process shaped by behavioral, institutional, and regulatory factors rather than technology alone. Full article

This study offers a comprehensive assessment of financial market modeling through a PRISMA-based systematic review, bibliometric analysis, and content synthesis. We examined 67 review articles (1990–2024) from Web of Science to build a conceptual framework, and 4982 articles (1990–2024) were analyzed with Biblioshiny. Five main clusters emerge: AI and deep learning for prediction; hybrid models that combine traditional and computational approaches; theoretical foundations, including the Efficient Market Hypothesis and critiques; high-frequency prediction and volatility analysis; and modeling of cryptocurrencies and digital assets. Temporal patterns show a shift from traditional econometrics to hybrid and deep learning methods, heightened attention to uncertainty and volatility during crises, rapid growth in crypto-focused modeling, and increased use of sentiment/news data after 2017. The content analysis highlights key gaps and future directions: standardized open benchmarks and reproducible frameworks; regime-sensitive validation; interpretable hybrid models that merge econometric structure with machine-learning flexibility; and wider applicability across assets, markets, and data types. The study provides a structured guide to intellectual and applied modeling, supporting future advances in forecasting, risk management, and policy design. Full article

This review paper reviews research literature on Diamond Open Access (DOA) journals—sometimes also called Platinum Open Access—that was produced after this journal segment started to become a priority in European research policy around 2020. It contextualizes the current science policy debate, critically examines different understandings of DOA, and reviews studies on the role of such journals in scholarly communication. Most existing research consists of quantitative studies focusing on aspects such as the number of DOA journals, their publication output, the diversity of the landscape in terms of subject areas, languages, publishing entities, indexing in major databases, awareness and perception among scholars, cost analyses, as well as insights into the internal operations of DOA journals. The review shows that research on DOA journals is partly influenced by the science policy discourse in at least two ways: first, through the normativity inherent in that discourse, and second, through the temporality of policy-driven research of practical relevance, which leaves important aspects of the phenomenon understudied. Moreover, research on the DOA journal landscape has implications beyond understanding this particular journal segment, as it also challenges established views of the global system of scholarly communication. Full article

Background/Objectives: Cognitive impairment affects multiple brain functions, and its early detection is essential to prevent progression to dementia; artificial intelligence has shown considerable potential in this field. This scoping review aims to map the impact of artificial intelligence–based tools for the early detection of cognitive impairment by identifying the main technologies used, examining their effectiveness, and exploring their ethical implications. Methods: A scoping review was conducted between April and May 2025 following the PRISMA-ScR methodological framework; the review protocol was previously registered on the Open Science Framework. PubMed, Scopus, and Cochrane databases were searched using natural language and controlled vocabulary terms via Medical Subject Headings. The search was limited to articles published between 2020 and 2025, in English or Spanish, with free full-text access. Methodological quality was assessed using CASPe, JBI, and MMAT. Results: A total of 14 studies were included after the selection and critical appraisal process. The findings show that artificial intelligence–based tools such as deep-learning models applied to neuroimaging, speech and gait analysis, electronic health record analysis, and mobile health applications demonstrate promising accuracy in detecting early cognitive changes. These technologies enable the identification of subtle patterns that may be difficult to detect using conventional clinical assessments. Conclusions: AI-based tools can provide substantial support for clinical decision-making by effectively identifying subtle changes that are imperceptible to human intelligence. However, their use also raises ethical issues related to patient privacy and data security. Full article

Background/Objectives: This systematic review and meta-analysis evaluated the clinical effectiveness of regenerative surgical treatments compared with open flap debridement (OFD) in the management of peri-implantitis and, secondarily, assessed whether more advanced regenerative approaches, including guided bone regeneration (GBR), platelet-rich fibrin (PRF), and hyaluronic acid (HA), provide additional clinical benefit compared with bone grafting alone. Methods: A comprehensive search of PubMed, Scopus, and Web of Science was conducted in accordance with PRISMA guidelines and the PICO model, covering the period from 1993 to 2024. From 2119 identified articles, 63 full-text papers were reviewed, and 12 studies met all inclusion criteria. These studies compared regenerative treatments with OFD and bone grafting using clinical outcomes of probing pocket depth (PPD), radiographic bone level (RBL), bleeding on probing (BOP), suppuration (SUP), mucosal recession (REC), and clinical attachment level (CAL). Meta-analysis was performed using a random-effects model. Results: Regenerative treatments demonstrated superior outcomes in radiographic bone level gain compared with OFD (p < 0.001), while no statistically significant differences were observed for PPD (p = 0.77), BOP (p = 0.13), SUP (p = 0.42), REC (p = 0.14), or CAL (p = 0.96). Comparisons between bone grafting and other regenerative materials also showed no statistically significant differences. Conclusions: Regenerative procedures improved radiographic bone outcomes but did not consistently outperform OFD in soft tissue parameters, and no advanced regenerative modality demonstrated clear clinical superiority over bone grafting alone. Further high-quality randomized controlled trials with standardized methodologies are needed to establish clinical guidelines for peri-implantitis surgery. Full article

Background/Objectives: Lumbar interbody fusion (LIF) is widely utilized to treat multilevel degenerative lumbar spine pathologies. This systematic review aimed to comprehensively review lateral and posterior multilevel LIF procedures and their clinical and radiographic outcomes. Methods: Following the PRISMA guidelines, a search of PubMed, Embase, Web of Science, and Cochrane identified eligible studies. Patient demographics, as well as clinical and radiographic outcomes were collected. Risk of bias was assessed using the MINORS criteria, while randomized trials were evaluated using the RoB-2 tool. An extensive subgroup analysis was completed when that was possible. Results: A total of 45 studies were included consisting of 5623 patients. The pooled outcomes indicated that TLIF demonstrated the lowest operative duration (198.7 ± 77.83 min) and LOS (5.09 ± 2.5 days), alongside favorable ODI (33.68 ± 6.43), VAS leg pain (5.39 ± 0.66), and VAS back pain (4.67 ± 0.79) score gains. Comparative evidence found that LLIF and OLIF provided advantageous radiographic improvement to the posterior approaches. Comparative evidence on techniques challenged the use of autogenous bone within PLIF, PEEK over HA/PA66 cages, and found no advantages in unilateral decompression within TLIF. There was minimal clinical difference in evidence assessing MIS (minimally invasive) vs. open-TLIF or unilateral vs. bilateral pedicle screw fixation (PSF). Conclusions: This is the first systematic review of the multilevel LIF literature, revealing that while pooled data favored TLIF, a publication bias was detected, and comparative evidence reported advantages for lateral and oblique approaches. Given the lack of conclusive evidence, robust study designs are needed to guide clinical decision-making for multilevel lumbar pathology. Full article

The increasing adoption of Large Language Models (LLMs) in higher education has created a need for high-quality, publicly available benchmarks for automated assessment. Existing datasets often rely on synthetic responses or lack detailed human feedback. This paper presents a multilingual dataset of 3041 authentic student answers to 50 open-ended Computer Science questions, collected from real university assessments during the 2025–2026 academic year. The dataset includes the original student responses (Spanish) and their parallel translations (English), instructor (or teacher) defined ideal answers, blind human grading with qualitative feedback, and structured evaluations from three state-of-the-art LLMs (DeepSeek-chat-V3.2, Qwen-flash-2025-07-28, Gemini-2.5-flash-lite-001) using a unified JSON schema. This resource enables reproducible research in automated grading, feedback generation, and cross-lingual educational NLP. Full article

Background: Artificial intelligence (AI) shows promising results in lymphoma detection, prediction, and classification. However, translating these findings into practice requires a rigorous assessment of potential biases, clinical utility, and further validation of research models. Objective: The goal of this study was to summarize existing studies on artificial intelligence models for the histopathological detection of lymphoma. Design: This study adhered to the PRISMA Extension for Scoping Reviews (PRISMA-ScR) guidelines. A systematic search was conducted across three major databases (Scopus, PubMed, Web of Science) for English-language articles and reviews published between 2016 and 2025. Seven precise search queries were applied to identify relevant publications, accounting for variations in study modality, algorithmic architectures, and disease-specific terminology. Results: The search identified 612 records, of which 36 articles met the inclusion criteria. These studies presented 36 AI models, comprising 30 diagnostic and six prognostic applications, with Convolutional Neural Networks (CNNs) being the predominant architecture. Regarding data sources, 83% (30/36) of datasets utilized Hematoxylin and Eosin (H&E)-stained images, while the remainder relied on diverse modalities, including IHC-stained slides, bone marrow smears, and other tissue preparations. Studies predominantly utilized retrospective, private cohorts with sample sizes typically ranging from 50 to 400 patients; only a minority leveraged open-access repositories (e.g., Kaggle, TCGA). The primary application was slide-level multi-class classification, distinguishing between specific lymphoma subtypes and non-neoplastic controls. Beyond diagnosis, a subset of studies explored advanced prognostic tasks, such as predicting chemotherapy response and disease progression (e.g., in CLL), as well as automated biomarker quantification (c-MYC, BCL2, PD-L1). Reported diagnostic performance was generally high, with accuracy ranging from 60% to 100% (clustering around 90%) and AUC values spanning 0.70 to 0.99 (predominantly >0.90). Conclusions: While AI models demonstrate high diagnostic accuracy, their translation into practice is limited by unstandardized protocols, morphological complexity, and the “black box” nature of algorithms. Critical issues regarding data provenance, image noise, and lack of representativeness raise risks of systematic bias, hence the need for rigorous validation in diverse clinical environments. Full article

Video-based photogrammetric reconstruction is increasingly used when high-resolution still images are unavailable. However, limited spatial resolution, compression artifacts, and motion blur often reduce geometric accuracy. Recent advances in learning-based image super-resolution (SR) offer a promising preprocessing method, but their geometric reliability within photogrammetric workflows remains not well understood. This study provides a controlled quantitative evaluation of learning-based super-resolution for video-based 3D reconstruction. Low-resolution video frames are enhanced using two representative methods: an open-source real-world SR model (Real-ESRGAN ×4) and a commercial solution (Topaz Video AI ×4). All datasets are processed with the same Structure-from-Motion and Multi-View Stereo pipelines and tested against terrestrial laser scanning (TLS) reference data. Results show that super-resolution significantly increases reconstruction density and improves the recovery of fine-scale surface details, while also leading to greater local surface variability compared with reconstructions from the original video; photogrammetric stability remains consistent despite these changes. The findings highlight a fundamental trade-off between reconstruction completeness and local geometric accuracy and clarify when enhanced video imagery via super-resolution can be a reliable source for 3D reconstruction. These results are especially important for spatial data science workflows and AI-powered 3D modeling and digital twin applications. Full article