bluuebunny/crossref_metadata_2025 huggingface dataset:
The Effect of Silica Sol on the Stain Resistance of Exterior Wall Latex Coatings through Natural Exposure to Sunlight
Authors: Dong Lian-Jie , Li Cheng-Di , Peng Jia , Luo Jia-Hong , Hong Yun
Published: November 2023

Stain resistance is one of the important characteristics of exterior wall latex coatings in cities. Adding silica sol to the coating can increase its stain resistance. However, there is currently limited research on the long-term natural exposure test of latex coatings. This paper first investigates the influence of different amounts of silica sol on the elongation, water absorption, and stain resistance of coatings and obtains a better percentage of silica sol addition. Then, heat storage tests were conducted to obtain the viscosity and pH changes of the coating. Afterwards, outdoor natural exposure tests were conducted for up to 12 months to obtain the stain resistance of the coating with the addition of silica sol. The results indicate that the stain resistance value of the coating with added silica sol was significantly better than that without added silica sol after 12 months of natural exposure to sunlight, increasing by 65.8%. The formation of a network structure of Si O-Si bonds in the silica sol enhances the hardness and rigidity of the coating while also allowing it to enter the capillary tubes of the coating caused by prolonged exposure to sunlight, avoiding cracking of the coating and preventing the entry of dust and impurities. Therefore, the stain resistance of the coating is improved. These research results will contribute to the better application of exterior wall latex coatings in architecture.
Electrochemically Obtained Poly(3,4-ethylenedioxythiophene) Layers for Electroanalytical Determination of Lipoic Acid
Authors: Karabozhikova Vasilena , Tsakova Vessela
Published: November 2023

Poly(3,4-ethylenedioxythiophene) (PEDOT) is a conducting polymer with intrinsic redox activity often used to facilitate electrooxidation reactions. PEDOT coatings with different thicknesses are obtained via electrochemical polymerization in the presence of either polysterensulfonate (PSS) or dodecylsulfate (SDS) anions. The electrooxidation of alfa lipoic acid (ALA) is studied depending on the thickness of the polymer coatings and the counterions used for their synthesis. The kinetics of ALA oxidation is found to differ for thin and thick PEDOT coatings with diffusion limitations observed for thin layers. For thick coatings, the rate-determining step varies from adsorption to diffusion depending on the ALA concentration. The type of counterion affects both the ALA oxidation peak currents and the peak potential. SDS-doped PEDOT coatings show a shift in the oxidation peak to positive potentials and higher ALA oxidation currents. The effect is commented in terms of a larger electroactive surface area and possible specific hydrophobic polymer/analyte interactions. For thin PEDOT coatings, the concentration dependence of the voltammetric peaks is linear in a wide concentration range (40 to 1000 µM), whereas the use of differential pulse voltammetry results in a linear response in a lower concentration range (8–200 µM) suitable for practical applications.
Credit Risk Prediction Based on Psychometric Data
Authors: Duman Eren , Aktas Mehmet S. , Yahsi Ezgi
Published: November 2023

In today’s financial landscape, traditional banking institutions rely extensively on customers’ historical financial data to evaluate their eligibility for loan approvals. While these decision support systems offer predictive accuracy for established customers, they overlook a crucial demographic: individuals without a financial history. To address this gap, our study presents a methodology for a decision support system that is intended to assist in determining credit risk. Rather than solely focusing on past financial records, our methodology assesses customer credibility by generating credit risk scores derived from psychometric test results. Utilizing machine learning algorithms, we model customer credibility through multidimensional metrics such as character traits and attitudes toward money management. Preliminary results from our prototype testing indicate that this innovative approach holds promise for accurate risk assessment.
A Tourist-Based Framework for Developing Digital Marketing for Small and Medium-Sized Enterprises in the Tourism Sector in Saudi Arabia
Authors: Alnajim Rishaa Abdulaziz , Fakieh Bahjat
Published: November 2023

Social media has become an essential tool for travel planning, with tourists increasingly using it to research destinations, book accommodation, and make travel arrangements. However, little is known about how tourists use social media for travel planning and what factors influence their intentions to use social media for this purpose. This thesis aims to understand tourists’ intentions to use social media for travel planning. Specifically, it investigates the factors influencing tourists’ intentions to use social media for planning travel to Saudi Arabia. It develops a machine learning (ML) classification model to assist Saudi tourism SMEs in creating effective digital marketing strategies for social media platforms. A survey was conducted with 573 tourists interested in visiting Saudi Arabia, using the Design Science Research (DSR) approach. The findings support the tourist-based theoretical framework, showing that perceived usefulness (PU), perceived ease of use (PEOU), satisfaction (SAT), marketing-generated content (MGC), and user-generated content (UGC) significantly impact tourists’ intentions to use social media for travel planning. Tourists’ characteristics and visit characteristics influenced their intentions to use MGC but not UGC. The tourist-based ML classification model, developed using the LinearSVC algorithm, achieved an accuracy of 99% when evaluated using the K-Fold Cross-Validation (KF-CV) technique. The findings of this study have several implications for Saudi tourism SMEs. First, the results suggest that SMEs should focus on developing social media content that is perceived as useful, easy to use, and satisfying. Second, the findings suggest that SMEs should focus on using MGC in their social media marketing campaigns. Third, the results suggest that SMEs should tailor their social media marketing campaigns to the characteristics of their target tourists. This study contributes to the literature on tourism marketing and social media by providing a better understanding of how tourists use social media for travel planning. Saudi tourism SMEs can use the findings of this study to develop more effective digital marketing strategies for social media platforms.
Public Perception of ChatGPT and Transfer Learning for Tweets Sentiment Analysis Using Wolfram Mathematica
Authors: Su Yankang , Kabala Zbigniew J.
Published: November 2023

Understanding public opinion on ChatGPT is crucial for recognizing its strengths and areas of concern. By utilizing natural language processing (NLP), this study delves into tweets regarding ChatGPT to determine temporal patterns, content features, and topic modeling and perform a sentiment analysis. Analyzing a dataset of 500,000 tweets, our research shifts from conventional data science tools like Python and R to exploit Wolfram Mathematica’s robust capabilities. Additionally, with the aim of solving the problem of ignoring semantic information in the LDA model feature extraction, a synergistic methodology entwining LDA, GloVe embeddings, and K-Nearest Neighbors (KNN) clustering is proposed to categorize topics within ChatGPT-related tweets. This comprehensive strategy ensures semantic, syntactic, and topical congruence within classified groups by utilizing the strengths of probabilistic modeling, semantic embeddings, and similarity-based clustering. While built-in sentiment classifiers often fall short in accuracy, we introduce four transfer learning techniques from the Wolfram Neural Net Repository to address this gap. Two of these techniques involve transferring static word embeddings, “GloVe” and “ConceptNet”, which are further processed using an LSTM layer. The remaining techniques center on fine-tuning pre-trained models using scantily annotated data; one refines embeddings from language models (ELMo), while the other fine-tunes bidirectional encoder representations from transformers (BERT). Our experiments on the dataset underscore the effectiveness of the four methods for the sentiment analysis of tweets. This investigation augments our comprehension of user sentiment towards ChatGPT and emphasizes the continued significance of exploration in this domain. Furthermore, this work serves as a pivotal reference for scholars who are accustomed to using Wolfram Mathematica in other research domains, aiding their efforts in text analytics on social media platforms.
Human Leukocyte Antigen B*27-Negative Spondyloarthritis: Clinical, Serological, and Radiological Features of a Single-Center Cohort
Authors: Mariani Francesco Maria , Alunno Alessia , Di Ruscio Evy , Altieri Piera , Ferri Claudio , Carubbi Francesco
Published: November 2023

The strong genetic association between HLA-B*27 and spondyloarthritis (SpA) accounts for about 90% of the susceptibility to axial SpA (axSpA), and the presence of HLA-B*27 is helpful in classifying patients according to the Assessment of SpondyloArthritis International Society (ASAS) classification criteria. However, over the years, other HLA-B alleles have been associated with an increased risk of developing SpA; on this basis, the aim of our study was to describe the demographic, clinical, and radiological characteristics of a cohort of SpA patients who were negative for HLA-B*27. We identified 85 patients with a clinical diagnosis of SpA displaying HLA-B alleles other than HLA-B*27; HLA-B*51 emerged as the most prevalent allele (N = 33, 39%), regardless of the fulfilment of either the axial or the peripheral ASAS criteria. The second most prevalent allele in the full cohort (N = 16, 19%) and in the patients fulfilling either the axial or the peripheral criteria was HLA-B*35. The third most prevalent allele in the full cohort was HLA-B*18 (N = 12, 15%), which was also the second most prevalent allele in the patients fulfilling neither of the two sets of criteria. Overall, the clinical picture was similar across the subgroups fulfilling the different sets of ASAS criteria; however, the patients not fulfilling any ASAS criteria had a higher likelihood of having arthritis compared to the patients fulfilling the axial criteria, whereas the Bath Ankylosing Spondylitis Functional Index was significantly higher in those patients fulfilling the axial criteria compared to those who did not fulfill any criteria. Our results indicate that other HLA alleles, beyond HLA-B*27, could be useful in facilitating SpA diagnosis, particularly in patients with a clinical picture which is consistent with SpA but does not fulfill the ASAS classification criteria.
The Labour Share, Government Expenditure and Income Inequality of Post-Soviet Countries
Authors: Sergi Bruno S. , Balashova Svetlana , Ratner Svetlana
Published: November 2023

This study analyses the influence of economic growth on inequality, concentrating on the role of governments as mediators. The period studied is from 2000 to 2020, encompassing 11 post-Soviet countries. The primary estimation method used is the two-stage least squares for panel data. Despite the differences in the economic and political systems at the current development stage, the post-Soviet countries share a common pattern in terms of the relationship between economic growth, the labour income share and the level of inequality, which we first show in this article. Government expenditure has the potential to reduce inequality. However, its effectiveness depends largely on government efficiency and the development of democratic institutions. Despite the increase in government spending on education, more is needed to reduce income inequality. Increased economic performance, productivity, and high-quality state institutions are necessary for this change.
A Neighborhood-Similarity-Based Imputation Algorithm for Healthcare Data Sets: A Comparative Study
Authors: Wilcox Colin , Giagos Vasileios , Djahel Soufiene
Published: November 2023

The increasing computerisation of medical services has highlighted inconsistencies in the way in which patients’ historic medical data were recorded. Differences in process and practice between medical services and facilities have led to many incomplete and inaccurate medical histories being recorded. To create a single point of truth going forward, it is necessary to correct these inconsistencies. A common way to do this has been to use imputation techniques to predict missing data values based on the known values in the data set. In this paper, we propose a neighborhood similarity measure-based imputation technique and analyze its achieved prediction accuracy in comparison with a number of traditional imputation methods using both an incomplete anonymized diabetes medical data set and a number of simulated data sets as the sources of our data. The aim is to determine whether any improvement could be made in the accuracy of predicting a diabetes diagnosis using the known outcomes of the diabetes patients’ data set. The obtained results have proven the effectiveness of our proposed approach compared to other state-of-the-art single-pass imputation techniques.
Featured Articles in the Section of Optoelectronics
Authors: Stathatos Elias
Published: November 2023

Optoelectronics, as a rapidly growing field of technology, plays a crucial role in the development of electronic devices that emit or detect light [...]
Tiny Machine Learning Zoo for Long-Term Compensation of Pressure Sensor Drifts
Authors: Pau Danilo , Ben Yahmed Welid , Aymone Fabrizio Maria , Licciardo Gian Domenico , Vitolo Paola
Published: November 2023

Pressure sensors embodied in very tiny packages are deployed in a wide range of advanced applications. Examples of applications range from industrial to altitude location services. They are also becoming increasingly pervasive in many other application fields, ranging from industrial to military to consumer. However, the inexpensive manufacturing technology of these sensors is strongly affected by environmental stresses, which ultimately affect their measurement accuracy in the form of variations in gain, hysteresis, and nonlinear responses. Thermal stresses are the main source of sensor behavior deviation. They are particularly insidious because even a few minutes of high temperature exposure can cause measurement drift for many days in the sensor responses. Therefore, conventional calibration techniques are challenged in their adequacy to achieve high accuracy and over the entire deployment life of the sensor. To manage this, several costly and time-consuming calibration procedures have to be performed. Machine learning (ML) techniques are known, supported by the universal approximation theorem, to provide effective data-driven solutions to the above problems. In this context, this paper addresses two case studies, corresponding to post-soldering thermal stresses and exposure to moderately high temperatures, for which two separate datasets have been built and 53 different tiny ML models (collected into a zoo) have been devised and compared. The ML zoo has been constructed with models such as artificial neural networks (ANN), random forest (RFR), and support vector regressors (SVR), able to predict the error introduced by the thermal drift and to compensate for the drift of the measurements. The models in the zoo also satisfy the memory, computational, and accuracy constraints associated with their deployment on resource-constrained embedded devices to be integrated at the edge. Quantitative results achieved by the zoo are reported and discussed, as well as their deployability on tiny micro-controllers. These results reveal the suitability of a tiny ML zoo for the long-term compensation of MEMS pressure sensors affected by drift in their measurements.
The Formation of a Flame Front in a Hydrogen–Air Mixture during Spark Ignition in a Semi-Open Channel with a Porous Coating
Authors: Golovastov Sergey , Bivol Grigory , Kuleshov Fyodor , Golub Victor
Published: November 2023

An experimental study of ignition and flame front propagation during spark initiation in a hydrogen–air mixture in a semi-open channel with a porous coating is reported. The bottom surface of the channel was covered with a porous layer made of porous polyurethane or steel wool. The measurements were carried out for a stoichiometric mixture (equivalence ratio ER = 1.0) and for a lean mixture (ER = 0.4) of hydrogen with air, where ER is the molar excess of hydrogen. The flame front was recorded with a high-speed camera using the shadow method. Depending on the pore size, the velocity of the flame front and the sizes of disturbances generated on the surface of the flame front were determined. Qualitative features of the deflagration flame front at ER = 0.4, consisting of disturbances resembling small balls of flame, were discovered. The sizes of these disturbances significantly exceed the analytical values for the Darrieus–Landau instability. The effect of coatings made of porous polyurethane or steel wool is compared with the results obtained for an empty smooth channel. Depending on the hydrogen concentration in the hydrogen–air mixture, the velocity of the flame front compared to a smooth channel was three times higher when the channel was covered with steel wool and five times higher when the channel was covered with porous polyurethane.
Fish Pathogens: Infection and Biological Control
Authors: da Costa Arthur Roberto , da Costa Mateus Matiuzzi , de Carvalho Azevedo Vasco Ariston , de Padua Pereira Ulisses
Published: November 2023

Over the last few decades, diseases have emerged as a major bottleneck in fish farming, especially with the emergence of several fish pathogens and increasing resistance to treatments [...]
How Interdisciplinary Interventions Can Improve the Educational Process of Children Regarding the Nutritional Labeling of Foods
Authors: Castagnoli Juliana de Lara , Santos Elisvânia Freitas dos , Novello Daiana
Published: November 2023

This research aimed to evaluate the effect of interdisciplinary educational interventions on children’s attitudes, knowledge, preferences, and perceptions about different nutrition labels. Four hundred and ten elementary school children, aged between seven and ten years, participated in the research. The children completed questionnaires on attitudes, knowledge, and preferences about nutrition labeling and on perceived healthiness of a food product considering different types of nutrition labels (Pre-Intervention). They then participated in educational interventions as a strategy to address nutrition labeling of foods (Intervention). Finally, the Pre-Intervention questionnaires were reapplied (Post-Intervention). The intervention improved children’s attitudes and knowledge about nutrition labeling. It also showed that this public has a greater preference for labels printed on the front of the food package. The label in the form of a warning was considered the most favorable for comparing the healthiness of the food product among children, while the table was the least preferred. It is concluded that interdisciplinary educational interventions are effective in improving children’s attitudes, knowledge, preferences, and perceptions about different nutrition labels on a food product. The front label model is the most suitable for food packaging aimed at children.
Hypolipidemic, Hypoglycemic, and Ameliorative Effects of Boiled Parsley (Petroselinum crispum) and Mallow (Corchorus olitorius) Leaf Extracts in High-Fat Diet-Fed Rats
Authors: Almutairi Albandari A. , Ahmed Waheeba E. , Algonaiman Raya , Alhomaid Raghad M. , Almujaydil Mona S. , Althwab Sami A. , Elhassan Ard ElShifa M. , Mousa Hassan Mirghani
Published: November 2023

Obesity is a major health concern associated with serious conditions such as type 2 diabetes and cardiovascular diseases. This study investigated the potential anti-obesity effects of heat-treated parsley and mallow extracts (PE and ME, respectively) in high-fat diet (HFD)-fed rats. The selected herbs underwent three heat treatments (boiling, blanching, and microwaving), and the most effective treatment was orally administered to the HFD rats for eight weeks. All three treatments effectively increased the total phenolic content (TPC) and antioxidant capacity of the herbs, with boiling treatment exhibiting the most significant increase. Boiled herbs demonstrated approximately 29% higher TPC and an impressive 348% increase in antioxidant activity compared to the other treatments. Oral administration of the boiled herb extracts to the HFD rats resulted in significant reductions in body weight, total cholesterol, triglycerides, and LDL cholesterol levels, while elevating the HDL cholesterol levels compared to the positive control rats. Additionally, the boiled herb extracts exhibited antioxidant, hepatoprotective, and nephroprotective effects. Notably, PE displayed more significant anti-obesity properties compared to ME, potentially due to higher TPC and antioxidant activity observed in PE compared to ME. In conclusion, this study highlights the potential positive effects of boiled parsley against obesity and recommends boiling treatment as the preferred method when heat treatment is required for herbs.
Indirect DNA Transfer and Forensic Implications: A Literature Review
Authors: Sessa Francesco , Pomara Cristoforo , Esposito Massimiliano , Grassi Patrizia , Cocimano Giuseppe , Salerno Monica
Published: November 2023

Progress in DNA profiling techniques has made it possible to detect even the minimum amount of DNA at a crime scene (i.e., a complete DNA profile can be produced using as little as 100 pg of DNA, equivalent to only 15–20 human cells), leading to new defense strategies. While the evidence of a DNA trace is seldom challenged in court by a defendant’s legal team, concerns are often raised about how the DNA was transferred to the location of the crime. This review aims to provide an up-to-date overview of the experimental work carried out focusing on indirect DNA transfer, analyzing each selected paper, the experimental method, the sampling technique, the extraction protocol, and the main results. Scopus and Web of Science databases were used as the search engines, including 49 papers. Based on the results of this review, one of the factors that influence secondary transfer is the amount of DNA shed by different individuals. Another factor is the type and duration of contact between individuals or objects (generally, more intimate or prolonged contact results in more DNA transfer). A third factor is the nature and quality of the DNA source. However, there are exceptions and variations depending on individual characteristics and environmental conditions. Considering that secondary transfer depends on multiple factors that interact with each other in unpredictable ways, it should be considered a complex and dynamic phenomenon that can affect forensic investigation in various ways, for example, placing a subject at a crime scene who has never been there. Correct methods and protocols are required to detect and prevent secondary transfer from compromising forensic evidence, as well as the correct interpretation through Bayesian networks. In this context, the definition of well-designed experimental studies combined with the use of new forensic techniques could improve our knowledge in this challenging field, reinforcing the value of DNA evidence in criminal trials.
Beyond the Tide: A Comprehensive Guide to Sea-Level-Rise Inundation Mapping Using FOSS4G
Authors: Juhász Levente , Xu Jinwen , Parkinson Randall W.
Published: November 2023

Sea-level rise (SLR) is a critical consequence of climate change, posing significant threats to coastal regions worldwide. Accurate and efficient assessment of potential inundation areas is crucial for effective coastal planning and adaptation strategies. This study aimed to explore the utility of free and open-source software for geospatial (FOSS4G) tools for mapping SLR inundation, providing cost-effective solutions that are accessible to researchers and policymakers. We employed a combination of geospatial data, including high-resolution elevation models, tidal data, and projected SLR scenarios. Utilizing widely available FOSS4G tools, like QGIS, GDAL/OGR, and GRASS GIS, we developed an integrated workflow to map inundation extents, using a passive bathtub approach for various SLR scenarios. We demonstrate the approach through a case study in Virginia Key, Florida, however, the methodology can be replicated in any area where the input datasets are available. This paper demonstrates that FOSS4G tools offer a reliable and accessible means to map SLR inundation, empowering stakeholders to assess coastal vulnerabilities and to devise sustainable adaptation measures. The open-source approach facilitates collaboration and reproducibility, fostering a comprehensive understanding of the potential impacts of SLR on coastal ecosystems and communities.
An Analysis of Slope Stability in the Penipe–Baños Road by Applying Empirical Methods, Kinematic Analysis and Remote Photogrammetry Techniques
Authors: Jordá-Bordehore Luis , Albán Lissette G. , Valenzuela Ramiro C. , Bravo Gianella , Menoscal-Menoscal Melanie , Larreta Erwin , Garces Daniel , Mulas Maurizio
Published: November 2023

The purpose of this work is to analyze the stability of four slopes along the Penipe–Baños road, which is situated in the provinces of Chimborazo and Tungurahua and where there are occasionally rockfalls that hinder passage and endanger road users. The methodology used to conduct the analysis was based on data collection with the help of remote techniques such as structure from motion, which allows us to obtain slope data using photogrammetry. Empirical methods such as slope mass rating, Q-slope, the kinematic method and the Rockfall Hazard Rating System method were used. These methods were evaluated with Rocfall3 software for the analysis of the fall trajectory of rock blocks. The results of this work show that the slopes studied do not represent a greater risk to the road than other slopes close to those studied, but these could not be analyzed due to their lack of accessibility and the danger of obtaining data under those conditions. The study of these different methods demonstrates the reliability of low-cost, remote techniques in the facilitation of analysis in places with similar conditions.
Mobile Film Festival Africa and Postcolonial Activism
Authors: Weaver-Hightower Rebecca
Published: November 2023

This paper enters into a debate of how new and potentially more accessible technologies might affect freedom of expression for heretofore disenfranchised peoples and postcolonial social and political development. This essay examines short films produced on camera phones by amateur African filmmakers for one of the many existent mobile phone film festivals: Mobile Film Festival Africa held in 2021. Mobile Film Festival, an annual and international festival of short-length movies, was founded in 2005 based on the principle “1 Mobile, 1 Minute, 1 Film”. Because of the highly destructive mining in Africa required to obtain the minerals necessary for mobile phone production, because of the Western narratives of progress mobile phone sales build upon, and because of the fact that mobile phones are instruments of capitalism that largely feed big Western countries, mobile phones are themselves tools of neocolonialism and digital colonialism. Thus, a film festival that markets itself as a means of social progress but that relies upon mobile phones in Africa provides an interesting and quite complicated case study. Two of the award-winning films from this festival recognize in different ways the complicated relationship between mobile phones and postcolonial activism.
Effects of a Comic Booklet Intervention Aimed at Preventing Second-Hand Smoke Exposure for Pregnant Women in Indonesia: A Randomised Controlled Trial
Authors: Inaoka Kimiko , Octawijaya Ishak Halim , Mamahit Citra Gabriella , Karundeng Jeand’arc Florentia , Wariki Windy Mariane Virenia , Ota Erika
Published: November 2023

Second-hand smoke (SHS) has adverse effects for pregnant women and foetuses. This controlled and randomized clinical trial evaluated the efficacy of a comic booklet intervention in promoting SHS avoidance among pregnant women and appropriate smoking behaviours among their male partners. We allocated 140 couples to the experimental group (EG), who received the comic booklet and a reminder sticker, and 146 couples to the control group (CG), who received usual care. The primary outcomes were women’s self-reported SHS exposure and their male partners’ smoking behaviours. Secondary outcomes included knowledge and awareness of SHS. Independent t-tests revealed that three months post-intervention, more male partners in the EG had appropriate self-reported smoking behaviours with a small effect size (Cohen’s d = 0.35, 95% CI [0.08, 0.62], p-value = 0.01). Significantly more pregnant women in the EG recognised their partners’ appropriate smoking behaviours with a nearly middle effect size (Cohen’s d = 0.43, 95% CI [0.16, 0.70], p-value ≤ 0.01). Cues to action showed a significant difference between groups with a small effect size (Cohen’s d = 0.36, 95% CI [0.09, 0.63], p-value = 0.01), as evaluated by male partners. These findings suggest that the comic booklet intervention might be effective against SHS exposure by providing several cues to action through knowledge and awareness of SHS.
Potentials and Challenges of Chatbot-Supported Thesis Writing: An Autoethnography
Authors: Schwenke Nicolas , Söbke Heinrich , Kraft Eckhard
Published: November 2023

The release of the large language model-based chatbot ChatGPT 3.5 in November 2022 has brought considerable attention to the subject of artificial intelligence, not only to the public. From the perspective of higher education, ChatGPT challenges various learning and assessment formats as it significantly reduces the effectiveness of their learning and assessment functionalities. In particular, ChatGPT might be applied to formats that require learners to generate text, such as bachelor theses or student research papers. Accordingly, the research question arises to what extent writing of bachelor theses is still a valid learning and assessment format. Correspondingly, in this exploratory study, the first author was asked to write his bachelor’s thesis exploiting ChatGPT. For tracing the impact of ChatGPT methodically, an autoethnographic approach was used. First, all considerations on the potential use of ChatGPT were documented in logs, and second, all ChatGPT chats were logged. Both logs and chat histories were analyzed and are presented along with the recommendations for students regarding the use of ChatGPT suggested by a common framework. In conclusion, ChatGPT is beneficial for thesis writing during various activities, such as brainstorming, structuring, and text revision. However, there are limitations that arise, e.g., in referencing. Thus, ChatGPT requires continuous validation of the outcomes generated and thus fosters learning. Currently, ChatGPT is valued as a beneficial tool in thesis writing. However, writing a conclusive thesis still requires the learner’s meaningful engagement. Accordingly, writing a thesis is still a valid learning and assessment format. With further releases of ChatGPT, an increase in capabilities is to be expected, and the research question needs to be reevaluated from time to time.
Physical, Organoleptic, and Phytochemical Valuation of the Promising Greek Kiwifruit Genotype Arta Kiwifruit
Authors: Kosta Anna , Denaxa Nikoleta-Kleio , Tsafouros Athanasios , Ntanos Efstathios , Stathi Efthalia , Tani Eleni , Roussos Peter Anargyrou
Published: November 2023

To evaluate a new kiwifruit genotype named “Arta Kiwifruit”, a comparative analysis of fruit physical characteristics and biochemical composition in comparison to the widely cultivated Hayward cultivar took place, both at harvest and after a storage period for two successive years. The findings revealed that “Arta Kiwifruit” holds a significant advantage, as it features a larger fruit size, is approximately 34% heavier than “Hayward”, and boasts a distinct shape characterized by a bigger length. Both genotypes exhibited similar dry matter content. No significant difference was observed in protein content, or total phenolic compounds between the two genotypes, while “Hayward” exhibited a significantly higher concentration of sugars and higher sweetness index. “Hayward” demonstrated also increased levels of chlorophyll at harvest, along with higher concentrations of carotenoids. “Hayward” during the first year presented higher antioxidant activity both at harvest and after storage. On the other hand, “Arta Kiwifruit” displayed elevated amino acid concentrations compared to “Hayward”, both at harvest and after storage. Phenotypic traits, as well as the genetic analysis using inter-simple sequence repeats (ISSR) markers, further confirmed the distinct genetic profile of “Arta Kiwifruit”, highlighting its potential significance for kiwifruit breeding programs and commercial use.
Mixed-Methods Approach to Land Use Renewal Strategies in and around Abandoned Airports: The Case of Beijing Nanyuan Airport
Authors: Cai Haoxian , Duan Wei
Published: November 2023

Urban airports are typically large infrastructures with significant cultural, economic, and ecological impacts; meanwhile, abandoned airports are common worldwide. However, there is limited knowledge regarding transformation strategies for the renewal of abandoned airports and their surrounding regions in historically and culturally rich areas. We use Beijing’s Nanyuan Airport as a case study, combining the historic urban landscape approach, land use and land cover change, and counterfactual simulations of land use patterns to construct a comprehensive analytical framework. Our framework was used to analyze the long-term land use patterns of the study area, determine its value, and improve perception from a macro- and multi-perspective. We discovered that the traditional knowledge and planning systems in the study area have largely disappeared, but Nanyuan Airport’s impact on the surrounding land use patterns is unique and significant. By considering the characteristics and mechanisms of land use in the study area, we aimed to find a balance point between the historical context and future potential. As such, we propose optimized recommendations with the theme of connection and development engines. Our findings supplement the planning knowledge of relevant areas and provide a springboard for interdisciplinary research in landscape planning.
Effects of Extracorporeal Shock Wave Therapy in Patients with Mild-to-Moderate Carpal Tunnel Syndrome: An Updated Systematic Review with Meta-Analysis
Authors: Zhang Lei , Yang Ting , Pang Long , Li Yinghao , Li Tao , Zhang Chunsen , Yao Lei , Li Ran , Tang Xin
Published: November 2023

Background: Carpal tunnel syndrome (CTS) is the most common entrapment syndrome, primarily affecting women between the ages of 40 and 70, and conservative treatments are the first option for mild-to-moderate CTS. However, the comparisons between extracorporeal shock wave therapy (ESWT) and other non-surgical methods in the treatment of mild-to-moderate CTS remain controversial, and an updated systematic review is needed. Methods: An electronic search was performed, and all available articles until August 2023 were included in the analysis. The overall quality of evidence was assessed by the GRADE approach. Meta-analyses were conducted using Manager V.5.3.3. Pooled effect sizes were expressed as the weighted mean difference (WMD) with 95% confidence intervals (CIs). Results: A total of 19 RCTs were included. Low-level quality evidence showed that ESWT outperformed the control intervention in terms of functional improvements, pain relief, electrodiagnostic parameters, and cross-sectional area of the median nerve at any time point of follow-up. Compared to local corticosteroid injection (LCI), there were statistically better improvements in functional improvements, pain relief, and electrodiagnostic parameters at 3 and 6 months of follow-up. Conclusions: There is low-level quality evidence to show that both fESWT and rESWT are more clinically effective than controls in symptom relief, functional enhancement, and electrophysiologic parameters’ improvement for patients with mild-to-moderate CTS at any time point of follow-up. Compared with LCI, ESWT yielded similar short-term (<1 month) but better medium- (1–6 months) and long-term (>6 months) improvements in pain relief and functional recovery with fewer potential complications.
Adjunctive Therapeutics in the Management of Cardiopulmonary Resuscitation: A Narrative Literature Review
Authors: Hoffer Megan , Pena Robert C. F. , Tran Quincy K. , Pourmand Ali
Published: November 2023

Nearly 565,000 patients will suffer from prehospital and inpatient cardiac arrest in the United States per annum. Cardiopulmonary resuscitation and all associated interventions used to achieve it remain an essential focus of emergency medicine. Current ACLS guidelines give clear instructions regarding mainstay medications such as epinephrine and antiarrhythmics; however, the literature remains somewhat controversial regarding the application of adjunctive therapeutics such as calcium, magnesium, sodium bicarbonate, and corticosteroids. The available data acquired in this field over the past three decades offer mixed pictures for each of these medications on the effects of core metrics of cardiopulmonary resuscitation (e.g., rate of return of spontaneous circulation, survival-to-hospitalization and discharge, 24 h and 30 d mortality, neurological outcome), as well as case-specific applications for each of these interventions (e.g., polymorphic ventricular tachycardia, electrolyte derangements, acidosis, post-arrest shock). This narrative literature review provides a comprehensive summary of current guidelines and published data available for these four agents and their use in clinical practice.
Effectiveness of Antiviral Therapy on Long COVID: A Systematic Review and Meta-Analysis
Authors: Choi Yu Jung , Seo Yu Bin , Seo Jun-Won , Lee Jacob , Nham Eliel , Seong Hye , Yoon Jin Gu , Noh Ji Yun , Cheong Hee Jin , Kim Woo Joo , Kim Eun Jung , Song Joon Young
Published: November 2023

Antiviral treatment reduces the severity and mortality of SARS-CoV-2 infection; however, its effectiveness against long COVID-19 is unclear. This study aimed to evaluate the effectiveness of antiviral drugs in preventing long COVID and related hospitalizations/deaths. Scientific and medical databases were searched from 1 January 2020 to 30 June 2023. We included observational cohort studies comparing individuals receiving early antiviral therapy for COVID-19 and those receiving supportive treatment. A fixed-effects model was used to merge the effects reported in two or more studies. The risk of post-acute sequelae of COVID-19 (PASC) was combined as an odds ratio (OR). Six studies were selected, including a total of 3,352,235 participants. The occurrence of PASC was 27.5% lower in patients who received antiviral drugs during the early stages of SARS-CoV-2 infection (OR = 0.725; 95% confidence interval [CI] = 0.409–0.747) than in the supportive treatment group. Moreover, the risk of PASC-associated hospitalization and mortality was 29.7% lower in patients receiving early antiviral therapy than in the supportive treatment group (OR = 0.721; 95% CI = 0.697–0.794). Early antiviral therapy was associated with a reduced risk of PASC and related hospitalization or death. Thus, early antiviral therapy is recommended for at-risk individuals.
Current Progress and Challenges of Using Artificial Intelligence in Clinical Dentistry—A Narrative Review
Authors: Surlari Zinovia , Budală Dana Gabriela , Lupu Costin Iulian , Stelea Carmen Gabriela , Butnaru Oana Maria , Luchian Ionut
Published: November 2023

The concept of machines learning and acting like humans is what is meant by the phrase “artificial intelligence” (AI). Several branches of dentistry are increasingly relying on artificial intelligence (AI) tools. The literature usually focuses on AI models. These AI models have been used to detect and diagnose a wide range of conditions, including, but not limited to, dental caries, vertical root fractures, apical lesions, diseases of the salivary glands, maxillary sinusitis, maxillofacial cysts, cervical lymph node metastasis, osteoporosis, cancerous lesions, alveolar bone loss, the need for orthodontic extractions or treatments, cephalometric analysis, age and gender determination, and more. The primary contemporary applications of AI in the dental field are in undergraduate teaching and research. Before these methods can be used in everyday dentistry, however, the underlying technology and user interfaces need to be refined.
Pancreatic Ductal Adenocarcinoma: Update of CT-Based Radiomics Applications in the Pre-Surgical Prediction of the Risk of Post-Operative Fistula, Resectability Status and Prognosis
Authors: Pacella Giulia , Brunese Maria Chiara , D’Imperio Eleonora , Rotondo Marco , Scacchi Andrea , Carbone Mattia , Guerra Germano
Published: November 2023

Background: Pancreatic ductal adenocarcinoma (PDAC) is the seventh leading cause of cancer-related deaths worldwide. Surgical resection is the main driver to improving survival in resectable tumors, while neoadjuvant treatment based on chemotherapy (and radiotherapy) is the best option-treatment for a non-primally resectable disease. CT-based imaging has a central role in detecting, staging, and managing PDAC. As several authors have proposed radiomics for risk stratification in patients undergoing surgery for PADC, in this narrative review, we have explored the actual fields of interest of radiomics tools in PDAC built on pre-surgical imaging and clinical variables, to obtain more objective and reliable predictors. Methods: The PubMed database was searched for papers published in the English language no earlier than January 2018. Results: We found 301 studies, and 11 satisfied our research criteria. Of those included, four were on resectability status prediction, three on preoperative pancreatic fistula (POPF) prediction, and four on survival prediction. Most of the studies were retrospective. Conclusions: It is possible to conclude that many performing models have been developed to get predictive information in pre-surgical evaluation. However, all the studies were retrospective, lacking further external validation in prospective and multicentric cohorts. Furthermore, the radiomics models and the expression of results should be standardized and automatized to be applicable in clinical practice.
Experimental and Numerical Investigations into Heat Transfer Using a Jet Cooler in High-Pressure Die Casting
Authors: Bohacek Jan , Mraz Krystof , Krutis Vladimir , Kana Vaclav , Vakhrushev Alexander , Karimi-Sibaki Ebrahim , Kharicha Abdellah
Published: November 2023

During high-pressure die casting, a significant amount of heat is dissipated via the liquid-cooled channels in the die. The jet cooler, also known as the die insert or bubbler, is one of the most commonly used cooling methods. Nowadays, foundries casting engineered products rely on numerical simulations using commercial software to determine cooling efficiency, which requires precise input data. However, the literature lacks sufficient investigations to describe the spatial distribution of the heat transfer coefficient in the jet cooler. In this study, we propose a solver using the open-source CFD package OpenFOAM and free library for nonlinear optimization NLopt for the inverse heat conduction problem that returns the desired distribution of the heat transfer coefficient. The experimental temperature measurements using multiple thermocouples are considered the input data. The robustness, efficiency, and accuracy of the model are rigorously tested and confirmed. Additionally, temperature measurements of the real jet cooler are presented.
Adjoint Data Assimilation of the Flow on the Southern Flank of Georges Bank: March–June 1999
Authors: Chen Changsheng , Xu Qichun
Published: November 2023

An adjoint data assimilation method was incorporated into the ECOM-si coastal ocean circulation model and applied to assimilate the flow field on the southern flank of Georges Bank from March to June 1999. The model was driven by tidal forcing consisting of ten tidal constituents at the open boundary, observed winds, and surface heat fluxes. Numerical experiments were conducted following a strategy to understand the critical issues affecting the efficiency and accuracy of the assimilated flow field. The adjoint data assimilation method significantly improved the computational accuracy of subtidal currents, especially for the along-isobath velocity. The integration window length and iteration number were two parameters affecting the assimilation convergence rate toward the observations. In such a nonlinear dynamical system, using a window length close to the M2 tidal period could make the adjoint model difficult to converge, no matter how many iterations were made. Reducing the time interval for the window length speeded up the convergence rate, but it was paid out with the sacrifice of statistical confidence. This assimilation experiment used a 6-h window length, which led to a faster convergence rate in the first ten iterations. The best-assimilated fields that satisfied the error criteria were obtained as the iteration number increased.
Research on Carbon Intensity Prediction Method for Ships Based on Sensors and Meteorological Data
Authors: Zhang Chunchang , Lu Tianye , Wang Zhihuan , Zeng Xiangming
Published: November 2023

The Carbon Intensity Index (CII) exerts a substantial impact on the operations and valuation of international shipping vessels. Accurately predicting the CII of ships could help ship operators dynamically evaluate the possible CII grate of a ship at the end of the year and choose appropriate methods to improve its CII grade to meet the IMO requirement with minimum cost. This study developed and compared five CII predicting models with multiple data sources. It integrates diverse data sources, including Automatic Identification System (AIS) data, sensor data, meteorological data, and sea state data from 2022, and extracts 21 relevant features for the vessel CII prediction. Five machine learning methods, including Artificial Neural Network (ANN), Support Vector Regression (SVR), Least Absolute Shrinkage and Selection Operator (LASSO), Extreme Gradient Boosting (XGBoost), and Random Forest (RF), are employed to construct the CII prediction model, which is then applied to a 2400 TEU container ship. Features such as the mean period of total swell, mean period of wind waves, and seawater temperature were considered for inclusion as inputs in the model. The results reveal significant correlations between cumulative carbon emissions intensity and features like cumulative distance, seawater temperature, wave period, and swell period. Among these, the strongest correlations are observed with cumulative distance and seawater temperature, having correlation coefficients of 0.45 and 0.34, respectively. Notably, the ANN model demonstrates the highest accuracy in CII prediction, with an average absolute error of 0.0336, whereas the LASSO model exhibits the highest error of 0.2817. Similarly, the ANN model provides more accurate annual CII ratings for the vessel. Consequently, the ANN model proves to be the most suitable choice for cumulative CII prediction.