Search Results (52)

Background/Objectives: Accurate assessment of periodontal parameters such as bleeding on probing (BoP) and tooth mobility is essential for diagnosis and treatment planning. In contrast to metric measures, these findings represent more subjective clinical parameters, potentially increasing examiner-related variability, particularly among less experienced examiners. This study evaluated agreement patterns between student recordings and educator verification recordings and assessed the influence of educational level and anatomical factors. Methods: BoP and tooth mobility data recorded by undergraduate dental students (ICC1, ICC4, state examination) and verified by licensed dental educators were retrospectively analyzed, comprising 6504 BoP sites and 1084 mobility recordings. Agreement and directional disagreement (under-/over-rating) were analyzed. Statistical analyses evaluated the effects of training stage, anatomical site, and periodontal severity. Associations with probing depth deviations and patient age were also examined. Results: Overall agreement was high for both parameters (BoP: 86%; mobility: 91%). Nevertheless, statistically significant differences were observed across educational levels (global Chi² tests p < 0.001). ICC1 students predominantly underestimated findings, whereas ICC4 students more frequently overestimated them; results stabilized in the state examination. Anatomical location significantly influenced disagreement patterns, with anterior and distal sites showing higher variability. BoP deviations were significantly associated with probing depth inaccuracies (r = 0.6; p < 0.001) and patient age (r = 0.187; p < 0.05). Conclusions: Within the limitations of this retrospective study, student recording accuracy for subjective periodontal parameters appears to be influenced by training stage and anatomical site. These findings may highlight the importance of structured calibration and targeted training strategies to improve diagnostic reliability in undergraduate periodontal education. Full article

This paper investigates how referentiality interacts with the syntax of Hindi–Urdu. It argues that three patterns, namely, object reduplication, association with the focus particle hī, and cross-clausal agreement, are manifestations of a single structural contrast determined by object shift. With our novel observations, we show that only objects that introduce discourse referents undergo displacement to a higher syntactic position, where they can trigger agreement or serve as associates of the focus particle hī. Reduplicated nominals, which lack referential features, must remain inside the VP and are consequently excluded from these dependencies. The analysis formalizes this correlation through a referential licensing condition that restricts movement to SpecvP to arguments bearing a referential feature [+Ref]. This condition derives the observed interactions between object shift and interpretation of the object. The resulting account integrates phenomena of agreement and focus with the semantics of specificity, offering a unified model of how referential interpretation is encoded in the clause structure of Hindi–Urdu. Full article

Generative AI (GenAI) has attracted a surge of attention from higher education constituents after OpenAI released ChatGPT in November 2022. While numerous articles discuss applications and perceptions of GenAI in higher education, no comprehensive review has considered commonalities and differences among various educational stakeholder groups and contexts. In this review, we synthesize the applications, capabilities, and perceptions of GenAI in higher education to provide stakeholders (i.e., students, instructors, researchers, staff, and administrators) with insights into this topic to facilitate GenAI integration in higher education. We reviewed 50 relevant empirical articles published from January 2023 to April 2025 on GenAI in higher education. Our findings demonstrate how GenAI has already been applied and present its potential for implementation across teaching and learning, research, and student affairs in higher education. Among various stakeholders in higher education, students hold a more open and positive attitude toward this rising technology, while instructors and researchers hold mixed attitudes toward GenAI usage, and administrators tend to hold an open but cautious attitude toward GenAI implementation. Addressing common stakeholder concerns and needs, we outline institutional strategies for responsible GenAI integration, including launching GenAI learning hubs, formalizing license agreements, redefining academic originality, and implementing pilot programs. Full article

Background/Objectives: Caries adjacent to restorations remain a leading cause of restoration failure and replacement. Conventional diagnostic methods are limited by subjectivity and restricted visualization. Fluorescence-enhanced three-dimensional (3D) imaging has been proposed to improve detection accuracy, but evidence on its clinical perception and usability remains scarce. The objective of this study was to evaluate the perceived diagnostic value of fluorescence-enhanced 3D imaging in detecting caries adjacent to direct restorations. Methods: A cross-sectional questionnaire-based survey was distributed to undergraduate dental students and licensed dentists (n = 94). Participants assessed images of extracted teeth with direct restorations presented in three formats: conventional photographs, monochromatic 3D models, and 3D models with fluorescence. Responses were analyzed using descriptive statistics, chi-square tests, and Cohen’s kappa to measure inter-rater agreement. Results: Overall, 64.9% of respondents reported that fluorescence-enhanced images improved their diagnostic decision-making, while 29.8% reported partial benefit. Fluorescence was mainly perceived as helpful in defining cavity margins (53.3%) and assessing lesion volume (42.4%). Most participants preferred 3D models with fluorescence over conventional images for diagnostic value. However, inter-rater agreement was generally poor (κ range: –0.05 to 0.25; median κ = 0.02; only 4 images showed weak but statistically significant agreement), with only a few images demonstrating weak but statistically significant agreement. Notably, 39.3% of participants reported prior experience with 3D imaging, which was associated with greater confidence in interpreting fluorescence-enhanced images. Participants with prior 3D imaging experience reported greater confidence in fluorescence interpretation. Conclusions: While fluorescence-enhanced 3D imaging is perceived as a useful adjunct for visualizing lesion margins and depth, it does not currently yield consistent diagnostic agreement across clinicians. Training, calibration, and integration of artificial intelligence support may enhance the clinical reliability of this technology. Full article

This entry explores the implications of generative AI for the underlying foundational premises of copyright law and the potential threat it poses to human creativity. It identifies the gaps and inconsistencies in legal frameworks as regards authorship, training-data use, moral rights, and human originality in the context of AI systems that are capable of imitating human expression at both syntactic and semantic levels. The entry includes: (i) a comparative analysis of the legal frameworks of the United Kingdom, United States, and Germany, using the Berne Convention as a harmonising baseline, (ii) a systematic synthesis of the relevant academic literature, and (iii) insights gained from semi-structured interviews with legal scholars, AI developers, industry stakeholders, and creators. Evidence suggests that existing laws are ill-equipped for semantic and stylistic reproduction; there is no agreement on authorship, no clear licensing model for training data, and inadequate protection for the moral identity of creators—especially posthumously, where explicit protections for likeness, voice, and style are fragmented. The entry puts forward a draft global framework to restore legal certainty and cultural value, incorporating a semantics-aware definition of the term “work”, and encompassing licensing and remuneration of training data, enhanced moral and posthumous rights, as well as enforceable transparency. At the same time, parallel personality-based safeguards, including rights of publicity, image, or likeness, although present in all three jurisdictions studied, are not subject to the same copyright and thus do not offer any coherent or adequate protection against semantic or stylistic imitation, which once again highlights the need for a more unified and robust copyright strategy. Full article

This paper presents a real-time quarry truck monitoring system that combines deep learning and license plate recognition (LPR) for operational monitoring and weighbridge reconciliation. Rather than estimating load volumes directly from imagery, the system ensures auditable matching between detected trucks and official weight records. Deployed at quarry checkpoints, fixed cameras stream to an edge stack that performs truck detection, line-crossing counts, and per-frame plate Optical Character Recognition (OCR); a temporal voting and format-constrained post-processing step consolidates plate strings for registry matching. The system exposes a dashboard with auditable session bundles (model/version hashes, Region of Interest (ROI)/line geometry, thresholds, logs) to ensure replay and traceability between offline evaluation and live operations. We evaluate detection (precision, recall, mAP@0.5, and mAP@0.5:0.95), tracking (ID metrics), and (LPR) usability, and we quantify operational validity by reconciling estimated shift-level tonnage T against weighbridge tonnage T* using Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), R², and Bland–Altman analysis. Results show stable convergence of the detection models, reliable plate usability under varied optics (day, dusk, night, and dust), low-latency processing suitable for commodity hardware, and close agreement with weighbridge references at the shift level. The study demonstrates that vision-based counting coupled with plate linkage can provide regulator-ready KPIs and auditable evidence for production control in quarry operations. Full article

In this paper, we present the application of Entrepreneurial Separation to Transfer Technologies from federally funded research and the difficulties associated with the transfer of intellectual property. With the increased threat (call) by the government to exercise “march-in” rights, which could limit both the licensing terms and what firms charge for goods (e.g., prescription drugs) that come from intellectual property (IP) resulting from federally funded research, researchers may be disinclined to commercialize their IP. While the government wants to exercise its March-In Rights to help consumers, it may be unintentionally harming them. The government is increasingly more vocal about the threat of march-in rights, in part because of the high consumer prices that have resulted from pandemic-related inflationary pressures. This threat has the potential of rolling back 40 years of gains from the Bayh–Dole Act. We present an overview of Entrepreneurial Separation to Transfer Technology and Entrepreneurial Leave Agreements and how they serve as one tool to support the transfer of early-stage technology. In a Volatile, Uncertain, Complex, and Ambiguous environment, university and federal laboratories need all the tools available to facilitate innovation and its commercialization. We present here why the development of these programs can help support their activities. Full article

Accurate assessment of aerodynamic resistance in mine ventilation networks is essential for ensuring operational safety and energy efficiency, yet traditional measurement approaches remain time-consuming and prone to uncertainty. This study presents a novel methodology for constructing digital ventilation models of underground mine workings using markerless LiDAR scanning combined with automated data processing. The proposed procedure includes segmentation of point clouds into sections, calculation of geometric parameters, and direct determination of resistance coefficients, which are subsequently exported to VentSim software. The approach was validated through a case study conducted in a Polish coal mine, where a 369 m ventilation siding was scanned and analyzed. The comparison between numerical simulations and in situ measurements demonstrated strong agreement, with differences not exceeding ±5% for airflow velocity, pressure drop, and total flow rate, while larger deviations were observed for cross-sectional area (+5.1%). The method is limited by potential inaccuracies in determining excavation geometry, which can lead to errors in calculating resistance coefficients, particularly at excavation intersections and at the beginning and end of scanning sections. Point cloud analysis, determination of resistance coefficients for individual sections (segments), spatial transformation, and point cloud reduction, along with integration with VentSim, are based on Python scripts. Calculation results can be easily exported to other computational programs. The proposed approach enables integration with various sensors and allows for assigning this value directly to a given section (segment of the excavation). The method can support the construction of digital twins for mines or underground tunnels. The implementation codes of the developed algorithms have also been made available for educational and scientific purposes under the Modified GNU General Public License v3 (GPLv3). Full article

Background: Pharmacists’ perceptions and practices shape the real-world uptake of generic medicines. From a health-economics perspective, wider generic substitution reduces patient out-of-pocket spending and creates headroom in payer budgets for high-value interventions. We assessed attitudes toward the efficacy, safety, and use of generics and examined sociodemographic correlates among Polish pharmacists. Methods: Analytical cross-sectional survey of licensed pharmacists in Poland was used (June–August 2025). The questionnaire covered reasons for recommending generics in long-term and single-use therapy; doubts about efficacy; views on bioequivalence testing; patient-reported experiences; and Likert-scale opinions on innovation, safety, efficacy, access, and payer savings. Associations were tested with χ² and Mann–Whitney U (α = 0.05). Results: Of 342 respondents (67.5% women; 74.9% community pharmacists), cost was the leading reason to recommend generics in long-term therapy (91.0%), followed by efficacy (53.0%) and safety (51.5%); for single-use prescriptions, cost remained central (76.2%), with lower emphasis on efficacy (47.5%) and safety (45.0%). Pharmacists who never recommend generics were older and more experienced (p = 0.006; p = 0.012). Doubts about generic efficacy were reported by 36.2% overall and more often among women, hospital pharmacists, and those with a specialization; 53.5% of those with doubts would advise switching even to a costlier option. Nearly half supported conducting bioequivalence studies between generics (49.6%). Positive perceptions predominated: 82.9% agreed generics are as effective and 84.6% as safe as originators. Most endorsed system benefits, including payer savings enabling list expansion (73.6%) and improved patient access (92.5%); agreement on access was higher among community pharmacists (p = 0.004). Conclusions: Polish pharmacists largely view generics as clinically equivalent and system-enhancing, with cost the dominant driver of recommendation. Targeted education—especially for hospital settings and specialized pharmacists—and attention to patient-reported experiences may further strengthen confidence and appropriate use of generics. Full article

The rise of digital content has made the need for reliable and practical intellectual property (IP) management systems more critical than ever. Most traditional IP systems are prone to issues such as delays, inefficiency, and data security breaches. This paper introduces SmartRegistry-IP, a system developed to simplify the registration, licensing, and transfer of intellectual property assets in a secure and scalable decentralized environment. By utilizing the InterPlanetary File System (IPFS) for decentralized storage, SmartRegistry-IP achieves a low storage latency of 300 milliseconds, outperforming both cloud storage (500 ms) and local storage (700 ms). The system also supports a high transaction throughput of 120 transactions per second. Through the use of smart contracts, licensing agreements are automatically and securely enforced, reducing the need for intermediaries and lowering operational costs. Additionally, the proof-of-work process verifies all transactions, ensuring higher security and maintaining data consistency. The platform integrates an intuitive graphical user interface that enables seamless asset uploads, license management, and analytics visualization in real time. SmartRegistry-IP demonstrates superior efficiency compared to traditional systems, achieving a blockchain delay of 300 ms, which is half the latency of standard systems, averaging 600 ms. According to this study, adopting SmartRegistry-IP provides IP organizations with enhanced security and transparent management, ensuring they can overcome operational challenges regardless of their size. As a result, the use of blockchain for intellectual property management is expected to increase, helping maintain precise records and reducing time spent on online copyright registration. Full article

Digital Twins (DTs) have become essential tools for the design, diagnostics, and prognostics of mechanical systems. In gearbox applications, DTs are often built using physics-based simulations guided by ISO standards. However, standards-based approaches may suffer from complexity, licensing limitations, and computational costs. The concept of symmetry is inherent in gear mechanisms, both in geometry and in operational conditions, yet practical applications often face asymmetric load distributions, misalignments, and asymmetric and symmetric nonlinear behaviors. In this study, we propose a hybrid method that integrates data-driven modeling with standard-based simulation to develop efficient and accurate digital twins for gear transmission systems. A digital twin of a spur gear transmission is generated using KISSsoft^®, employing ISO standards to compute safety factors across varied geometries and load conditions. An automated MATLAB-KISSsoft^® (COM-interface) enables large-scale data generation by systematically varying key input parameters such as torque, pinion speed, and center distance. This dataset is then used to train a neural network (NN) capable of predicting safety factors, with hyperparameter optimization improving the model’s predictive accuracy. Among the tested NN architectures, the model with a single hidden layer yielded the best performance, achieving maximum prediction errors below 0.01 for root and flank safety factors. More complex failure modes such as scuffing and micropitting exhibited higher maximum errors of 0.0833 and 0.0596, respectively, indicating areas for potential model refinement. Comparative analysis shows strong agreement between the NN outputs and KISSsoft^® results, especially for root and flank safety factors. Performance is further validated through sensitivity analyses across seven cases, confirming the NN’s reliability as a surrogate model. This approach reduces simulation time while preserving accuracy, demonstrating the potential of neural networks to support real-time condition monitoring and predictive maintenance in gearbox systems. Full article

This study aimed to compare the performance of various Large Language Models (LLMs) in answering multiple-choice questions from the last six editions (2017 to 2024) of Revalida exam. The evaluation focused on models capable of processing content in Brazilian Portuguese (PT-BR), including open-source models, namely LLaMA 3.1 (8B parameters), Qwen 2.5 (7B parameters), and their reasoning-oriented distilled variants based on the DeepSeek-R1 architecture, as well as open-access commercial models such as GPT-3.5, GPT-4o, and Gemini. After evaluating the models’ accuracy against the official answer keys, GPT-4o emerged as the top-performing model, achieving an average accuracy of 63.85%. Next, GPT-4o was prompted to justify its answers to the 2024 exam, and its explanations were independently reviewed by three licensed physicians. The evaluators reported full agreement with the clinical reasoning presented, indicating the model’s ability to produce coherent and medically relevant justifications. Lastly, justifications generated by GPT-4o for correctly answered questions from previous exams (2017–2023) were compiled into a knowledge base, which was then used to enhance GPT-4o through retrieval-augmented generation and to fine-tune LLaMA 3.1, leading to measurable performance improvements on the 2024 exam. Despite promising performance, these models still demonstrate variability in responses, hallucinations, and limited reliability in high-stakes contexts. As such, their outputs should always be reviewed by qualified professionals, and human expertise remains essential in clinical decision-making and medical education scenarios, considering the PT-BR language. However, the observed gains from integrating prior exam content indicate that domain-specific adaptation strategies may help mitigate some of these limitations and enhance model alignment. Full article

Background: Objective evaluation of the swimming start is crucial for sprint performance improvement. Traditional visual assessment of its phases—reaction, take-off, flight, and underwater glide—lacks precision. This study addresses the need for more integrated and accessible biomechanical tools by validating IQ LAB software (Version 250319), which is embedded in the SwimPro system and enables immediate video-based motion analysis without external processing. Existing tools like Dartfish (ProSuite 4.0) require separate video handling and licensing, whereas IQ LAB offers a built-in, streamlined solution suitable for applied environments. Methods: We evaluated the concurrent validity of the IQ LAB software, a desktop 2D motion analysis tool, using Dartfish (ProSuite 4.0) as the gold standard. The reliability was assessed using intrarater temporal stability and interrater agreement, considering selected kinematic parameters related to the swimming kick start (to 5 m). A sample of 13 competitive male swimmers (age 17.2 ± 1.1 years) was analyzed across two sessions. Concurrent validity was assessed by comparing IQ LAB outputs to those from Dartfish software in the sagittal plane. Intrarater reliability was measured using a test–retest design across two sessions spaced 7 days apart. Interrater reliability involved two independent raters analyzing the same video data using IQ LAB. Results: IQ LAB and Dartfish kinematic parameters demonstrated strong agreement (Pearson r ≥ 0.95), with no significant systematic differences. The intrarater and interrater reliability were excellent (ICC ≥ 0.94, 95% CI included). The test–retest reliability of the selected parameters across seasons also showed excellent reproducibility (ICC ≥ 0.93). Conclusions: IQ LAB software provides a valid and reliable 2D kinematic assessment of the swimming kick start, offering a practical and accessible tool for coaches and researchers. This study introduces a novel validated software solution for biomechanical analysis in swimming starts. Full article

Shock-to-detonation transition (SDT) is the detonation of explosive charge triggered by the shock pressure from a nearby detonated explosive or an impact at high speed. A good prediction of SDT is a key in the design of explosives’ use, storage, and transportation. Typically, SDT simulation must use designated commercial software; therefore, a high license cost is necessary. This paper presents a simulation of SDT by a cost-effective hydrodynamic code developed on an open-source code framework, OpenFOAM. The code adopted the multi-material Eulerian method, Ignition and Growth reaction rate model, and Riemann solver to solve the shock-induced detonation phenomenon. The code was verified by a Pop plot calculation and a sympathetic detonation simulation. In the Pop plot calculation, the distance-of-run to the detonation of Composition B depending on the initial shock pressure was simulated. The reactant and product phases of Composition B were modeled by the Jone–Wilkins–Lee (JWL) equation of state (EOS). The aluminum plate used to create the initial shock pressure was modeled by shock Mie–Gruneisen (MG) EOS. The predicted distance-of-run against the initial shock pressure was in good agreement with an empirical correlation and experimental data. In the sympathetic detonation simulation, the charge explosive and nearby explosive were Composition B and were modeled by JWL EOS as in the Pop plot calculation and the plexiglass gap was modeled by MG EOS. The simulated critical gap for the sympathetic detonation was well predicted as in the other published data. This implies that the code is valid for SDT simulation. In addition, it is a cost-effective simulation, since the code was developed on open-source code, so massive computation can then be run without license costs. Full article

As the global shift from fossil fuels to the Paris Agreement has accelerated, wind energy has become a key alternative to hydroelectric power. However, existing research often needs to improve in integrating diverse environmental, economic, and climate-related variables when modeling wind energy potential, particularly under future climate change scenarios. Addressing these gaps, this study employs the maximum entropy (MaxEnt) method, a robust and innovative tool for spatial modeling, to identify optimal wind farm sites in Türkiye. This research advances site selection methodologies and enhances predictive accuracy by leveraging a comprehensive dataset and incorporating climate change scenarios. The results indicate that 89% of the current licensed projects will maintain compliance in the future, while 8% will see a decrease in compliance. Furthermore, the wind energy potential in Türkiye is expected to increase because of climate change. These results confirm the suitability of existing project locations and identify new high-potential areas for sustainable wind energy development. This study provides policymakers, investors, and developers actionable insights to optimize wind energy integration into the national energy portfolio, supporting global climate goals by accelerating the adoption of renewable energy sources. Full article