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Why I Declare a Conflict of Interest and You Should Not
No full textAcademic publishing is both an indication of scientific contribution and a currency for career advancement. This dual role gives rise to a normative scientific conflict: Does the structural incentive to publish constitute a conflict of interest (COI) that ought to be disclosed? In this paper, we address this conflict through an action research approach, engaging collaboratively and reflexively to answer four related questions: (1) What evidence suggests that researchers face a (financial) COI when publishing? (2) What are the benefits and drawbacks of explicitly acknowledging that publications function as academic currency? (3) How should such conflicts be disclosed? (4) Do mechanisms such as pre-registration and registered reports resolve these concerns? This paper contends that while researchers are clearly incentivised to publish, this interest need not necessarily constitute a conflict or be explicitly disclosed. Treating this issue as a normative scientific conflict does reveal the need for a shift in how researchers understand and navigate the subjective, self-interested dimensions of their work. We propose four key responses: (1) integrating discussions of COIs and biases more extensively into undergraduate science education, (2) promoting greater reflexivity in everyday research practice (e.g., through reflexivity journals, peer-led audit groups, and the reintegration of discussions on the historicity and cultural nature of research into scientific publications), (3) critically investigating institutional incentives and journal policies, and (4) proactively adopting methodological safeguards such as pre-registration. By addressing this conflict through action research, we demonstrate how normative tensions in science can be made productive - supporting both critical reflection and structural improvement.Leverhulme Trust [Early Career Award]; Dutch Ministry of Education, Culture and Science [Starter Grant]; Foundation for Polish Science [START scholarship]We would like to thank all the participants of the "Errors of Omission in Conflict of Interest Statements" hackathons, organized at the 7th Perspectives on Scientific Error Workshop and within the Psychology of Science Collaboration Hub, for their participation and willingness to share their opinions on the matter. We also would like to acknowledge the valuable input of Amelie Gourdon-Kanhukamwe and Tom Heyman
Impact of Slow-Front Impulse Voltage on Positive Streamer-Leader Development in a 10 Meter Rod-Plane Air-Gap
No full textÇeşitli voltaj koşulları altında elektriksel deşarj karakteristiklerinin bilgisi, daha güvenli ve daha verimli yüksek voltajlı yalıtım sistemleri tasarlamanın önemli bir yönüdür. Mevcut çalışma, 250 mikrosaniye, 1000 mikrosaniye ve 2500 mikrosaniyelik bir yükselme süresine sahip yavaş ön pozitif darbe voltajı altında 10 metrelik çubuk-düzlem hava boşluğundaki pozitif akış-lider dinamiklerini araştırmaktadır. Gerçekleştirme, yüksek gerilim mühendisliğinde yalıtım tasarımında temel unsurlardan biri olan uzun aralıklı deşarj davranışına ilişkin bilginin iyileştirilmesini amaçlamaktadır. Deneyler sırasında elde edilen gerilim ve akım dalga formları, polinom regresyonu kullanılarak makine öğrenimi tabanlı bir yaklaşımla analiz edilmiştir. Bu tür analizlerin yanı sıra, farklı bozulma aşamaları için ark uzunluklarını belirlemek amacıyla yüksek hızlı kamera görüntülerine görüntü işleme uygulanmıştır. Ham verilerle başa çıkmak için aşağı örnekleme uygulanmış ve regresyon modelleri için değerlendirme, ortalama karesel hata (MSE) ve R kare değerleri açısından yapılmıştır. Üçüncü dereceden polinom regresyon analizi, R değerleri, RMSE, MSE, MAE, artık grafikler, varyasyon etki faktörü ve daha fazlası dahil olmak üzere standart polinom regresyon analiz testleriyle gösterildiği gibi yüksek doğruluk göstermiştir ve akım, gerilim ve bunlara karşılık gelen zamanı içeren deneyden elde edilen veri setinde de kullanılmıştır. elektrik akımı verileri için. Modelden elde edilen karşılık gelen R kare değerleri mükemmel bir uyumu yansıtmaktadır. Görüntü tabanlı analiz, yaklaşık 10 m'lik bir son sıçrama uzunluğunun düzlem elektroda tam lider gelişimini doğruladığını göstermiştir. Sonuçlar, makine öğrenimi ve görüntü analizinin uzun hava aralıklarında deşarj gelişimini doğru bir şekilde modelleyebileceğini ve ölçebileceğini göstermektedir. Bu bulgular, yüksek voltajlı yalıtım sistemleri tasarımında ilerlemeleri kolaylaştırarak, flama-lider geçişinin daha iyi anlaşılmasını sağlar. Anahtar Sözcükler: Yüksek Gerilim Mühendisliği, Darbe Voltajı, Tipik Yavaş Ön Darbe, Akış Lideri Yayılımı, Uzun Hava Boşluğu, Makine Öğrenmesi, Polinom RegresyonuThe knowledge of the electrical discharge characteristics under various voltage conditions is a crucial aspect of designing safer and more efficient high-voltage insulation systems. The present study investigates positive streamer-leader dynamics in the 10- meter rod-plane air gap under slow front positive impulse voltage having a rise time of 250 microseconds, 1000 microseconds, and 2500 microseconds. The realization is aimed at improving the knowledge of long-gap discharge behavior, which is one of the key aspects in insulation design under high-voltage engineering. The voltage and the current waveforms obtained during experiments were analyzed using a machine-learning-based approach using polynomial regression. Besides such analysis, image processing was applied to high-speed camera footage to determine arc lengths for different breakdown stages. Down-sampling was applied to cope with raw data, and evaluation for the regression models was made in terms of mean squared error (MSE) and R-squared values. The Polynomial regression analysis with third-order degree showed high accuracy as demonstrated by standard polynomial regression analysis tests, including R-values, RMSE, MSE, MAE, residual plots, variation influence factor, and some more were also employed at the dataset obtained from the experiment, which includes current, voltage, and their corresponding time. for the electrical current data. The corresponding R-squared values obtained from the model reflect an excellent fit. The image-based analysis demonstrated that a final jump length of nearly 10 m substantiates full leader development to the plane electrode. The results indicate that machine learning and image analysis can accurately model and quantify discharge development in long air gaps. These findings allow for a greater understanding of the streamer-to-leader transition, facilitating advances in high-voltage insulation systems design. Keywords: High-Voltage Engineering, Impulse Voltage, Typical Slow-Front Impulse, Streamer-Leader Propagation, Long Air Gap, Machine Learning, Polynomial Regressio
A Grey Wolf Optimization Based Approach to Provide Ancillary Services for Battery Owners
No full textAs is known, batteries have started to be used increasingly in both power distribution and transmission networks. This study develops a near-optimal approach for ancillary services in power networks from the perspective of the battery owner. We first model the optimization algorithm for the battery owner, then utilize a grey wolf optimization approach, where near-optimal actions are selected daily from available services. We use real data of frequency, voltage magnitude, combined home and Photovoltaic system, and transformer load to perform the simulations. The simulation results show that battery owners may profit from these services and help the system operators solve the issues such as over-voltage, under-voltage, frequency, and similar. © 2025 Elsevier B.V., All rights reserved.Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, TUBITA
Exploring Children's Causal Language Production: The Role of Mothers and Fathers Across Different Tasks in Dyadic Interactions
No full textCausal language is essential for children's language development, helping them understand and explain the reasons behind events. This study focuses on children's causal language production and the role of parental input, aiming to (1) investigate differences in maternal and paternal language use, (2) analyse children's causal language production across tasks and communication partners, and (3) examine the relationship between parental input and children's causal language skills. Sixty children aged 4-5 and their parents participated in dyadic sessions, which included free play, guided play, and storytelling tasks. Results showed that fathers used more causal language than mothers during free play, and children also produced more causal language with their fathers in this context compared to storytelling. Overall, both maternal and paternal causal language inputs were linked to children's causal language production, highlighting the significant influence of parental input on language development. Nedensel dil, & ccedil;ocuklar & imath;n dil geli & scedil;imi i & ccedil;in temel & ouml;neme sahiptir ve onlar & imath;n olaylar & imath;n ard & imath;ndaki nedenleri anlamalar & imath;na ve a & ccedil;& imath;klamalar & imath;na yard & imath;mc & imath; olur. Bu & ccedil;al & imath;& scedil;ma, & ccedil;ocuklar & imath;n nedensel dil & uuml;retimine ve ebeveyn girdisinin rol & uuml;ne odaklanarak (1) anne ve baba dil kullan & imath;mlar & imath; aras & imath;ndaki farkl & imath;l & imath;klar & imath; incelemeyi, (2) & ccedil;ocuklar & imath;n farkl & imath; g & ouml;revler ve ileti & scedil;im partnerleri ba & gbreve;lam & imath;nda nedensel dil & uuml;retimini analiz etmeyi ve (3) ebeveyn girdisi ile & ccedil;ocuklar & imath;n nedensel dil becerileri aras & imath;ndaki ili & scedil;kiyi ara & scedil;t & imath;rmay & imath; ama & ccedil;lamaktad & imath;r. D & ouml;rt-be & scedil; ya & scedil; aral & imath;& gbreve;& imath;nda 60 & ccedil;ocuk ve ebeveynleri, serbest oyun, y & ouml;nlendirilmi & scedil; oyun ve hikaye anlat & imath;m & imath; g & ouml;revlerini i & ccedil;eren ikili etkile & scedil;im oturumlar & imath;na kat & imath;lm & imath;& scedil;t & imath;r. Bulgular, babalar & imath;n serbest oyun s & imath;ras & imath;nda annelere k & imath;yasla daha fazla nedensel dil kulland & imath;& gbreve;& imath;n & imath; ve & ccedil;ocuklar & imath;n da bu ba & gbreve;lamda babalar & imath;yla etkile & scedil;imde hikaye anlat & imath;m & imath;na k & imath;yasla daha fazla nedensel dil & uuml;retti & gbreve;ini g & ouml;stermi & scedil;tir. Genel olarak, hem anne hem de baba nedensel dil girdileri, & ccedil;ocuklar & imath;n nedensel dil & uuml;retimiyle ili & scedil;kili bulunmu & scedil; ve ebeveyn girdisinin dil geli & scedil;imi & uuml;zerindeki belirgin etkisini ortaya koymu & scedil;tur
Evaluating Cognitive and Emotional Engagement in AI-Assisted Virtual Reality Through EEG
No full textThis study proposes an EEG-based evaluation pipeline for an AI-assisted VR platform designed to deliver immersive cultural heritage experiences for elderly people. EEG data is used to evaluate emotional and cognitive responses while performing real-world versus virtual tasks, offering a reusable evaluation framework for future immersive heritage applications
High- and Low-Frequency Cooperation Based Resource Allocation in Vehicular Edge Computing Via Deep Reinforcement Learning
No full textIn vehicular edge computing (VEC) environment, the increasing task offloading requirements from diverse vehicular applications pose significant challenges to the limited and single communication resources. High- and low-frequency cooperation (HL-FC) has the advantages of large capacity, low latency, large coverage capability, and stable communication link during task offloading. However, how to efficiently allocate communication resources for task offloading in the presence of high- and low-frequency communication resources is a challenge. Furthermore, coupled with the allocation of computing resources and the offloading-decision making, the allocation of high- and low-frequency communication resources is even more complex and challenging. To cope with these challenges, in this paper, we investigate the resource allocation scheme under the high- and low-frequency cooperation in VEC. Specifically, to facilitate the processing of latency-sensitive and computation-intensive tasks, a multi-queue model for task caching is first designed to prioritize latency-sensitive workloads, enabling efficient data buffering and processing. Considering vehicle mobility, we then develop the communication model, task migration model, and the computing model. After that, we formulate a long-term average cost optimization problem that jointly optimizes resource expenditure and latency, which is a NP-hard problem. To obtain the optimal strategy, we leverage the Markov decision process (MDP) to model the optimization problem, which is then solved by our proposed twin delayed deep deterministic policy gradient (TD3)-based two-phase resource allocation scheme (TTRAS). Finally, extensive simulations are conducted to assess and validate the effectiveness of the TTRAS. © 2025 Elsevier B.V., All rights reserved
What Drives the Return and Volatility Spillover Between Defis and Cryptocurrencies
No full textIn this paper, we study the return and volatility connectedness between cryptocurrencies and DeFi Tokens, considering the impact of different uncertainty indices on their connectivity. Initially, we estimate a TVP-VAR model to obtain the total connectedness between the two markets. We find that returns on the cryptocurrencies transmit significantly larger shocks and, thus, are responsible for most variations in the majority of DeFis' returns. Then, to analyse the impact of uncertainty on total return and volatility connectedness, we use four factors, namely, Economic Policy Uncertainty (EPU), The Chicago Board Options Exchange Volatility Index (VIX), Infectious Disease Equity Market Volatility Tracker (ID-EMV) and Geopolitical Risks (GPR). We find that except for geopolitical risks, all three measures have a positive impact on return and volatility connectedness, while GPR exerts a negative impact. Finally, we provide implications for researchers, market participants and policymakers. © 2024 John Wiley & Sons Ltd.Social Science Citation Inde
Segregation of Hourly Electricity Consumption: Quantification of Demand Types Using Fourier Transform
No full textAlthough aggregate electricity consumption provides valuable information for market analysis, it does not provide demand composition, including industrial, residential, illumination, and other uses. The information for subconsumptions is required for the reliable and cost-effective operation of the power system. As a first step towards the segregation of hourly total electricity consumption into its components, we use spectral analysis (Fast Fourier Transform) to determine relative strengths of the harmonics of annual, weekly and daily variations, to quantify the share of electricity consumption for heating, cooling, illumination and industrial activities. The method is applied to the data of France, Sweden, Finland, Norway, Turkiye, Italy, Spain, Greece, Germany, Great Britain, Poland and the Netherlands. Quantitative results obtained via spectral analysis are supplemented by qualitative features observed via time-domain analysis. The consumption ratios for each demand type are calculated using daily, weekly and annual harmonics and the results are presented. © 2025, Econjournals. All rights reserved
Integrating Technology Acceptance Model With Utaut To Increase the Explanatory Power of the Effect of Hci on Students' Intention To Use E-Learning System and Perceive Success
No full textThis study aimed to investigate the potential human-computer interaction factors (HCI) affecting students' behavioural intentions (BI) to use the e-learning system and perceive success. This paper proposes a comprehensive model, integrating the technology acceptance model (TAM) and unified theory of acceptance and use of technology (UTAUT). The data were collected via an online survey conducted on 232 students utilizing the Khas Learn system of Kadir has University in Turkey. The proposed hypotheses were tested by multi-linear regression. The results illustrated that the main predictors of students' success (SS) are behaviour intention, ease of use, usefulness, visual design, and learner interface interactivity which explained 53.6% of perceived success in using the system. While, the main predictors of BI are facilitating condition, effort expectancy, ease of use, and usefulness which explained 71% of the variance in continuance intentions to use e-learning. Therefore, the empirical findings provide strong backing to the technological-social-psychological dimensions extended by HCI main factors, which showed a high explanatory power in accepting e-learning technology and leads to enhance the SS, where five of the model's goodness-of-fit values meet five criteria of structural equation modeling (SEM). © 2013 IEEE.Huntsman Cancer Institute, University of Utah, HCIScience Citation Index Expande
Artificial Intelligence-Enhanced Intrusion Detection Systems for Drone Security: a Real-Time Evaluation of Algorithmic Efficacy in Mitigating Wireless Vulnerabilities
No full textAdvancements in science and technology have provided extensive opportunities and conveniences for mankind. One prime example of these advancements is wireless communication technology. This technology provides users with mobility during communication, initiating a paradigm shift. The convenience of wireless communication technology has initiated the production of versatile devices. Among these technologies developed in recent years for observation and detection purposes in various fields, drones have taken a leading role. Drones, with their versatile applications and access to real-time data, are being used in various operations. With such utilization, humans are increasingly interacting with these systems, leading to natural human-drone interaction. However, in these human-drone interactions, as is the case with many wireless devices, security often becomes an afterthought, leaving many drones vulnerable to cyber attacks. The most effective way to protect against these attackers is to conduct vulnerability analyses of the systems we use against emerging threats and address the detected vulnerabilities. This paper investigates the vulnerabilities of wireless communication regarding remote connectivity usage of a commercial drone, the DJI Ryze Tello, with the aim of examining its weaknesses. In this context, a test environment was created to reveal problems and threats in drone technology through attacks executed on the test environment (DEAUTH ATTACK, Port Scan DOS, DDoS, and MitM). Following the identification of these vulnerabilities, an artificial intelligence-based study was carried out to detect these attacks. In the study, the percentages of attack detection using different algorithms were verified with graphs.Science Citation Index Expande