bluuebunny/crossref_metadata_2025 huggingface dataset:
Improving Access to Credit for First-Time Home Buyers under Borrower-Based Measures
Authors: Grosz Gabriella , Izsák Gábor , Palicz Alexandr Maxim , Szász Katinka
Published: June 2023

As a result of the dynamic rise in real estate prices in recent years, house prices, and therefore the minimum down payment required to obtain a mortgage, more than tripled by 2022 compared to 2014. This led to a deterioration of access to borrowing opportunities typically for young first-time home buyers aged 35 or younger, especially among clients who are not eligible for family support schemes. In this paper, we provide a detailed overview of the potential side effects of the borrower-based measure framework on first-time home buyers. Relative to their credit risk, these clients may be more severely affected by borrower-based measures, generating negative second-round effects. Therefore, in their case preferential limits may be applied, in line with international practice, notably on the level of down payment. However, when making such a decision, it is important to take into account the development of risks in the housing and credit markets and the macroeconomic environment.
Wild Floral Visitors Are More Important Than Honeybees as Pollinators of Avocado Crops
Authors: Celis-Diez Juan L. , García Camila B. , Armesto Juan J. , Abades Sebastián , Garratt Michael P. D. , Fontúrbel Francisco E.
Published: June 2023

Wild insects provide pollination services in agroecosystems. Avocado is an economically important crop grown in the Chilean Mediterranean climate region and exported worldwide. Avocado pollination is managed using honeybee hives, while the role of wild insects as pollinators remains poorly known. We quantified the relative contributions of wild floral visitors and honeybees to the fruit set of avocados in two contrasting seasons (2015 and 2016) in Central Chile. Observations were made in 60 trees grown nearby (~300 m) to remnant patches of native sclerophyll vegetation. We found that honeybees and wild insects accounted for 48.6% and 51.4% of total floral visitation, respectively, with a 68% taxonomic similarity of floral visitors between seasons. The presence of honeybees significantly modulated the floral visitor composition and modified plant-pollination network parameters, which resulted in the biotic homogenization of the interaction network, through the increase of shared tree–floral visitor interactions, to the detriment of exclusive interactions. Finally, wild insects were more effective than honeybees in pollinating avocado trees and their contribution was greatest during the dry year. Thus, honeybees alone cannot be relied upon to deliver maximum pollination. We highlight ecological intensification practices to encourage wild insect visits and avoid native vegetation replacement with orchards. Therefore, we emphasize that future ecological intensification research should address agricultural practices to promote wild insects and evaluate the role of native vegetation as wild pollinator habitat in agricultural landscapes.
Comparison of the Source–Sink Characteristics between Main Season and Ratooning in Rice (Oryza sativa L.)
Authors: Huang Xinjie , Yang Jing , Zhou Wenxin , Zhang Guilian , Liao Bin , Wahab Abdul , Yi Zhenxie , Tu Naimei
Published: June 2023

Objectives: In order to find out the differences in source–sink characteristics of ratooning rice and main season rice and find approaches to increase the grain yield of ratooning rice, a sowing-by-stage method was adopted to synchronize main season rice with ratooning rice and induce their key growth stages under similar environmental conditions. The source–sink characteristics of four varieties under similar ecological conditions of main and ratooning rice were examined in 2019 and 2020. Results: The main results were: (1) The leaf area index (LAI) of the ratooning rice ranged from 0.54 to 1.44, while that of the LAI of main season rice was 4.67–7.71. The LAI of ratooning rice was much smaller than that of the main season rice; the former was only approximately 1/7–1/5 of the latter. (2) The photosynthesis (Pn) and net assimilation rate (NAR) of the ratooning rice were significantly higher than those of the main season rice before the milking–maturing stages. Still, at the late maturing stage, no definite tendency existed. (3) Ratooning rice transported 14C-assimilate from the flag leaf to the panicle at an estimated 81.43%, while main season rice transported 63.95%. The main stem’s top first and second internodes have been observed to be a major location for the 14C-assimilate in main season rice. (4) The grain yield of main season rice was 6029–7929 kg ha−1 while the grain yield of ratooning rice ranged from 2363–3297 kg ha−1. The sink capacity of the main season rice was approximately 2.4–3.6 times that of ratooning rice. The catalase activity of the rachis branches of the ratooning rice was higher than that of the main season rice. (5) The grain/leaf area (sink/source) ratio in the ratoon season rice was 1.69–2.46 times higher than that of the main season rice. Conclusions: The grain yields of ratooning rice were determined by the interaction of source and sink capacity while those of main season rice were mainly increased by enhancing sink capacity. Choosing varieties with heavier 1000 grain weight, exerting the advantages of higher photosynthetic rate and net assimilation rate of ratooning rice, promoting leaf area, and improving the transportation capacity of carbohydrate are the main approaches to increase the grain yield of ratooning rice.
Between Leisure and Pressure—Veterinarians’ Attitudes towards the Care of Competition Horses in Germany, Austria and Switzerland
Authors: Springer Svenja , Mihatsch Denise Isabell , Grimm Herwig , Jenner Florien
Published: June 2023

Equine veterinarians face a range of challenges when attending competition horses. Athletic goals may significantly impact veterinary decision making, and the veterinarian’s work can be complicated by reputational considerations and rival opinions during an assessment of whether a horse is “fit to compete”. Using an online questionnaire, we found that the majority of German, Austrian and Swiss equine veterinarians (N = 172) surveyed agreed that the owners of competition horses are more likely than owners of leisure horses to approach them with clear treatment ideas, and that the former have higher expectations of the medical services provided. The data also show that the veterinarian’s reputation plays a more important role in the competition sphere. Using a case vignette, we established that, on the grounds of equine welfare, the majority of respondents indicated that they would decide against starting a dressage horse with low-grade lameness in a competition. Those respondents who indicated that they would approve a start of the dressage horse indicated that a horse with a low-grade lameness was fit enough “to compete”. We conclude that clearer definitions of phrases, such as “fit to compete”, may be helpful in guiding veterinarians as they discharge their professional responsibilities during competitions and reduce the reputational stress they experience in this working context.
Cetacean Intracytoplasmic Eosinophilic Globules: A Cytomorphological, Histological, Histochemical, Immunohistochemical, and Proteomic Characterization
Authors: Fernández Antonio , Câmara Nakita , Sierra Eva , Arbelo Manuel , Bernaldo de Quirós Yara , Jepson Paul D. , Deaville Rob , Díaz-Delgado Josué , Suárez-Santana Cristian , Castro Ayoze , Hernández Julia N. , Godinho Ana
Published: June 2023

The nature, etiopathogenesis, and clinicopathologic relevance of the prevalent intracytoplasmic eosinophilic globules (IEGs) within hepatocytes of cetaceans are unknown. This study aims to evaluate the presence and characterize the IEGs in the hepatocytes of cetaceans using histochemical and immunohistochemical electron microscopy, Western blot, lectin histochemistry, and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry techniques. A total of 95/115 (83%) animals (16 species) exhibited histologically evident intracytoplasmic round to oval, single to multiple, hyaline eosinophilic globules within the hepatocytes. These globules were largely PAS-positive, diastase resistant, and were immunopositive for fibrinogen (FB, 97%), albumin (Alb, 85%), and α1-antitrypsine (A1AT, 53%). The IEG positivity for FB and A1AT were correlated with live-stranding, hepatic congestion and a good nutritional status. The cetaceans lacking IEGs were consistently dead stranded and had poor body conditions. The IEGs in 36 bycaught cetaceans were, all except one, FB-positive and A1AT-negative. The IEGs exhibited morphologic and compositional variations at the ultrastructural level, suggesting various stages of development and/or etiopathogenesis(es). The glycocalyx analysis suggested an FB- and A1AT-glycosylation pattern variability between cetaceans and other animals. The proteomic analyses confirmed an association between the IEGs and acute phase proteins, suggesting a relationship between acute stress (i.e., bycatch), disease, and cellular protective mechanisms, allowing pathologists to correlate this morphological change using the acute hepatocytic cell response under certain stress conditions.
Hair Cortisol Concentrations in Feral Horses and the Influence of Physiological and Social Factors
Authors: Medill Sarah A. , Janz David M. , McLoughlin Philip D.
Published: June 2023

Cortisol is a glucocorticoid hormone produced during activation of the hypothalamic–pituitary–adrenal axis (HPA) in response to psychological or physiological demands. High amounts of circulating cortisol can be found in individuals experiencing energetically demanding physiological events, such as pregnancy, lactation, injury, or starvation, but, also, in individuals who may have less obvious HPA activation from social situations. The feral horse population on Sable Island (Nova Scotia, Canada) provides an opportunity to look at hair cortisol concentration (HCC) as a proxy for circulating cortisol concentration to better understand physiological correlates. The horse’s complex social structure also allows us to look at how the population and group structure may influence HPA activation. Hair samples (n = 282) were analyzed from 113 females and 135 males. Females with dependent offspring (foals) had higher HCC than those females without dependent offspring (p = 0.005). Horses in poor body condition were also more likely to have higher HCC (females: p < 0.001, males: p = 0.028); females had greater variation in the body condition index (BCI), which also correlated with foal production. In general, the top-ranked models describing female cortisol levels included age, BCI, presence of a foal, as well as social measures such as harem size and the number of bachelors in the vicinity. The top model describing male cortisol levels included age, BCI, and year of collection only, and the number of bachelors in the home range appeared in subsequent, though still high-ranked, models. Among the variables not of direct interest, we found some significant results relating to hair color and hair texture. Differences in HCC patterns between feral and domestically kept horses (e.g., age and sex) are likely linked to periods of resource limitations, particularly for individuals experiencing energetically demanding processes such as reproduction, illness/parasitism, or related to experiencing the full range of social and reproductive behaviors.
Targeting Triple-Negative Breast Cancer by the Phytopolyphenol Carnosol: ROS-Dependent Mechanisms
Authors: Alsamri Halima , Al Dhaheri Yusra , Iratni Rabah
Published: June 2023

Triple-negative breast cancer (TNBC), which lacks the expression of the three hormone receptors (i.e., estrogen receptor, progesterone receptor, and human epidermal growth factor receptor), is characterized by a high proliferative index, high invasiveness, poor prognosis, early relapse, and a tendency to be present in advanced stages. These characteristics rank TNBC among the most aggressive and lethal forms of breast cancer. The lack of the three receptors renders conventional hormonal therapy ineffective against TNBC. Moreover, there are no clinically approved therapies that specifically target TNBC, and the currently used chemotherapeutic agents, such as cisplatin, taxanes, and other platinum compounds, have a limited clinical effect and develop chemoresistance over time. Phytochemicals have shown efficacy against several types of cancer, including TNBC, by targeting several pathways involved in cancer development and progression. In this review, we focus on one phytochemical carnosol, a natural polyphenolic terpenoid with strong anti-TNBC effects and its ROS-dependent molecular mechanisms of action. We discuss how carnosol targets key pathways and proteins regulating the cell cycle, growth, epigenetic regulators, invasion, and metastasis of TNBC. This review identifies carnosol as a potential novel targeting protein degradation molecule.
Diagnosis and Proposed Treatment for COVID-19 Patients Based on Deep Learning Analysis of Computed Tomography Images
Authors: Knapińska Zofia , Mulawka Jan , Kierzkiewicz Maciej
Published: June 2023

In this contribution, we consider computed tomography (CT) as a diagnostic tool for identifying coronavirus disease 2019 (COVID-19) pneumonia. However, interpreting CT scans can be subjective, leading to interobserver variability and potential misdiagnosis. To address these challenges, a deep learning-based chest approach was developed to create a precise diagnostic tool for COVID-19 pneumonia and a personalized therapeutic strategy for individual patients. The study collected chest CT images from patients with different lung conditions, creating a diverse convolutional neural network (CNN) training material. Three different CNN-based models were tested for diagnostic purposes, with the output stating whether the patient was healthy or infected. The models facilitated selecting regions of interest (ROIs) and extracting the radiomic features from the input data, resulting in satisfactory results with core classification quality measures above the 50% threshold. For therapeutic purposes, a custom U-Net-based model was used to extract lung and infection masks from a provided CT slice. The percentage of the pathologically altered tissue was calculated, and the COVID-19 severity score was computed and then matched with an optimal therapeutic strategy. Overall, the models delivered high-quality results, representing a functioning deep learning-based application that could be advantageous as a doctor-friendly support tool. The use of deep learning techniques in medical imaging shows promising results, improving the accuracy and speed of diagnosis and treatment of not only COVID-19 but also many different diseases.
Anomaly Detection through Grouping of SMD Machine Sounds Using Hierarchical Clustering
Authors: Song Young Jong , Nam Ki Hyun , Yun Il Dong
Published: June 2023

Surface-mounted device (SMD) assembly machines refer to production lines that assemble a variety of products that fit their purposes. As the required products become more diverse, models that oversee product anomaly detection are also becoming increasing linearly. In order to efficiently oversee products, the number of models has to be reduced and products with similar characteristics have to be grouped and overseen. In this paper, we show that it is possible to handle a large number of new products using latent vectors obtained from the autoencoder model. By hierarchically clustering latent vectors, the model finds product groups with similar characteristics and oversees them by group. Furthermore, we validate our multi-product operation strategy for anomaly detection with a newly collected SMD dataset. Experimental results show that the anomaly detection method using hierarchical clustering of latent vectors is a practical management method for SMD anomaly detection.
A Model-Based Intelligent Adjustment Method of Toolface for Bent-Housing Motor
Authors: Deng Tiansheng , Li Qian , Yin Hu , Peng Hao
Published: June 2023

The traditional toolface adjustment of a bent-housing motor is time-consuming and laborious. For the goal of rapid and accurate toolface adjustment, this paper presents an intelligent toolface adjustment method based on the drill string dynamics model and the BP (back propagation) neural network, optimized by a GA (genetic algorithm). Firstly, for the mode of rotating the drill string at the wellhead to change the downhole toolface, the drill string dynamics model is used to calculate the toolface change value. Then, the actual toolface change value is taken as the output data; the calculated toolface change value and the factors that have a significant impact on the change value of the toolface are taken as the input parameters; and the GA-BP neural network is adopted to fit the relationship between the input parameters and the output data. For the mode of changing the toolface by changing the WOB (weight on bit), the WOB change value and other parameters that cause the toolface to change were taken as the input data and the toolface change value was taken as the output data; the relationship between the WOB and the toolface was fitted with the GA-BP neural network. The results show that the prediction of absolute error under the mode of adjusting the toolface by rotating the drill string at the wellhead is within 10.9783°, and the prediction of absolute error under the mode of adjusting the toolface by changing the WOB is within 18.8833°. The intelligent models established under the two modes can meet the requirements of toolface control accuracy. Using the established intelligent prediction model, the toolface can be adjusted to the required value range at one time, which can improve drilling efficiency, and reduce labor intensity and the dependence on the experience of on-site personnel.
Influence of Random Vehicle–Bridge Coupling Vibration on the Anti-Disturbance Performance of Concrete Materials
Authors: Chen Dehua , Zhu Chaoyu , Shi Xuefei , Song Jun
Published: June 2023

Without interrupting the traffic on the old bridge, the connection of the widening bridge will cause disturbance to the concrete in the splicing position. In order to study the anti-disturbance performance of concrete material, the Normal Concrete (NC) material and the Ultra-High-Performance Concrete (UHPC) material were experimentally investigated by the vertical shaking table and X-ray computed tomography scanning. It can be learnt from tests that the compressive strength of NC and UHPC can be increased by about 10% to 20% after disturbance, while the flexural tensile strength and splitting tensile strength of both NC and UHPC can be reduced by about 20% to 25% and 10% to 20%, respectively. The elastic modulus of UHPC is not significantly affected by the vibration disturbance, and that of NC can be increased by about 20%. The setting time difference of the proposed NC material can be controlled within 100 min, and it can improve its anti-disturbance performance. Excessive vibration disturbance affects the internal structure of NC, while it has little effect on the distribution of steel fiber in UHPC. Due to the high cost of UHPC materials, it is recommended to analyze the joint performance requirements before the selection of splicing materials. If the stress requirement is not particularly high, it is still recommended to apply the proposed NC material for the splicing of widening bridge.
Unlocking Efficiency in Fine-Grained Compositional Image Synthesis: A Single-Generator Approach
Authors: Wang Zongtao , Liu Zhiming
Published: June 2023

The use of Generative Adversarial Networks (GANs) has led to significant advancements in the field of compositional image synthesis. In particular, recent progress has focused on achieving synthesis at the semantic part level. However, to enhance performance at this level, existing approaches in the literature tend to prioritize performance over efficiency, utilizing separate local generators for each semantic part. This approach leads to a linear increase in the number of local generators, posing a fundamental challenge for large-scale compositional image synthesis at the semantic part level. In this paper, we introduce a novel model called Single-Generator Semantic-Style GAN (SSSGAN) to improve efficiency in this context. SSSGAN utilizes a single generator to synthesize all semantic parts, thereby reducing the required number of local generators to a constant value. Our experiments demonstrate that SSSGAN achieves superior efficiency while maintaining a minimal impact on performance.
Innovative Materials for Batteries
Authors: Palumbo Oriele
Published: June 2023

In the struggle for the reduction of carbon emission, the use of energy storage systems is becoming more and more widespread, and the wider applications require a continuous upgrading of the batteries’ performances [...]
Modeling Battery Energy Storage Systems Based on Remaining Useful Lifetime through Regression Algorithms and Binary Classifiers
Authors: Gilbert Zequera Rolando , Rjabtšikov Viktor , Rassõlkin Anton , Vaimann Toomas , Kallaste Ants
Published: June 2023

This research work implements an initial methodology for the assessment of Battery Energy Storage Systems (BESSs) based on Remaining Useful Lifetime (RUL), and its main contribution is the modeling and estimation of Health and Charge indicators through regression algorithms and binary classifiers during the battery’s operation. Linear Regression, Ridge Regression, and Lasso Regression are the main algorithms for modeling the State of Health (SOH), while Decision Tree, Naïve Bayes, and Logistic Regression are implemented as binary classifiers to estimate the charge and discharge during battery operation. Additional data science techniques are executed to provide feature selection, validation, and metrics of performance. The results show that binary classifiers achieve a remarkable accuracy, around 95% for charge and discharge predictions, which is supported by experimental battery measurements. Similarly, regression algorithms achieve accuracy results around 97% and provide a basis for determining the Remaining Useful Lifetime (RUL) according to the End-of-Life (EOL) criteria of a BESS.
Configurable Intelligent Design Based on Hierarchical Imitation Models
Authors: Yavich Roman , Malev Sergey , Volinsky Irina , Rotkin Vladimir
Published: June 2023

The deterministic AI system under review is an alternative to neural-network-based machine learning. In its application fields, which are science, technology, engineering, and business, the implementation of rule-based AI systems leads to benefits such as accuracy and correctness of design, and personalization of the process itself and the results. An algorithmic AI suite is based on design and logical imitation models alone, without creating and/or using Big Data and knowledge bases. Excessive complexity of configuration and high design resource capacity, which are inherent in deterministic systems, are balanced by a special methodology. A hierarchical modeling approach gives a quasi-dynamic network effect, symmetric to the analogous effect in neural networks. System performance is improved by deterministic reference training capable of modifying imitation models in online interaction with users. Such training, which serves as an alternative to neural machine learning, can be implemented by means of experimental partially empirical algorithms and system–user dialogues to build reference model libraries (portfolios). Partially empirical algorithms based on experimental design methods and system user dialogues are used to create reference model libraries (portfolios) that form a deterministic training system, which can be an alternative to neural machine learning. Estimated resources can be saved by using modified optimization techniques and by controlling the computational complexity of the algorithms. Since the proposed system in the considered layout has no analogues, and the relevant research and practical knowledge are extremely limited, special methods are required to implement this project. A gradual, phased implementation process involves the step-by-step formation of sets of algorithms with verification tests at each stage. Each test is performed using an iteration method, and each test includes test, tweak, and modification cycles. Final testing should lead to the development of an AI algorithm package, including related methodological and working papers.
Construction of a Dynamic Diagnostic Approach for a Fuzzy-Interval Petri Network
Authors: Lajmi Fatma , Rashdan Mostafa , Neji Bilel , Ghandour Raymond , Dhouibi Hedi
Published: June 2023

Fault diagnosis plays a crucial role in enhancing system dependability and minimizing potential catastrophic consequences for both equipment and human safety. This article presents a research study focused on developing a diagnosis and control approach for discrete event systems using the Petri net Fuzzy Interval (IFPN). The Petri net is utilized as a modeling tool for the target system. The paper describes a case study conducted on an ingredient mixing system, where the objective is to maintain the concentration of ingredients within a valid range. A diagnostic framework is constructed and successfully applied to identify faults in the system. The proposed approach is further validated through simulation tests conducted on a mixing system.
Enhancing Energy Efficiency and Retention of Bioactive Compounds in Apple Drying: Comparative Analysis of Combined Hot Air–Infrared Drying Strategies
Authors: Teymori-Omran Milad , Askari Asli-Ardeh Ezzatollah , Taghinezhad Ebrahim , Motevali Ali , Szumny Antoni , Nowacka Małgorzata
Published: June 2023

The drying process is one of the oldest methods used to obtain food products that could be stored for a long time. However, drying is an energy-intensive process. Additionally, convective drying, due to the high temperature used during the process, results in loss in bioactive substances as well as nutritional value. Thus, in this research, apple slices were dried in a combined hot air–infrared air dryer with four different drying strategies and drying kinetics, internal and external mass transfer (Crank and Dincer models), and then the energy parameters were investigated. The first, second, third, and fourth strategies, respectively, include one-stage drying with a hot air (HA) or infrared energy source (IR), one stage but with two sources of hot air and infrared (HA–IR), and then there are two stages of first hot air and then infrared drying (HA+IR) and vice versa (IR+HA). According to the results, the highest effective moisture diffusion coefficient of the two Crank and Dincer models was equal to 1.49 × 10−9 and 1.55 × 10−8 m2/s, obtained in the HA70–IR750, and the lowest effective moisture diffusion coefficient was equal to 1.8 × 10−10 and 2.54 × 10−9 m2/s, obtained in IR250+HA40. The maximum (10.25%) and minimum (3.61%) energy efficiency were in the IR750 and HA55–IR250 methods, respectively. Moreover, the highest drying efficiency (12.71%) and the lowest drying efficiency (4.19%) were obtained in HA70+IR500 and HA40–IR250, respectively. The value of specific energy consumption was 15.42–51.03 (kWh/kg), the diffusion activation energy was 18.43–35.43 (kJ/mol), and the value of the specific moisture extraction rate (SMER) was in the range of 0.019–0.054 (kWh/kg). Compared to the other strategies, the second strategy (HA–IR) was better in terms of drying time and mass transfer, and the third strategy (HA+IR) was more efficient in terms of energy efficiency and drying efficiency. The infrared drying in the first strategy was better than that in the other methods in the other strategies in terms of retention of bioactive compounds.
Abstractive vs. Extractive Summarization: An Experimental Review
Authors: Giarelis Nikolaos , Mastrokostas Charalampos , Karacapilidis Nikos
Published: June 2023

Text summarization is a subtask of natural language processing referring to the automatic creation of a concise and fluent summary that captures the main ideas and topics from one or multiple documents. Earlier literature surveys focus on extractive approaches, which rank the top-n most important sentences in the input document and then combine them to form a summary. As argued in the literature, the summaries of these approaches do not have the same lexical flow or coherence as summaries that are manually produced by humans. Newer surveys elaborate abstractive approaches, which generate a summary with potentially new phrases and sentences compared to the input document. Generally speaking, contrary to the extractive approaches, the abstractive ones create summaries that are more similar to those produced by humans. However, these approaches still lack the contextual representation needed to form fluent summaries. Recent advancements in deep learning and pretrained language models led to the improvement of many natural language processing tasks, including abstractive summarization. Overall, these surveys do not present a comprehensive evaluation framework that assesses the aforementioned approaches. Taking the above into account, the contribution of this survey is fourfold: (i) we provide a comprehensive survey of the state-of-the-art approaches in text summarization; (ii) we conduct a comparative evaluation of these approaches, using well-known datasets from the related literature, as well as popular evaluation scores such as ROUGE-1, ROUGE-2, ROUGE-L, ROUGE-LSUM, BLEU-1, BLEU-2 and SACREBLEU; (iii) we report on insights gained on various aspects of the text summarization process, including existing approaches, datasets and evaluation methods, and we outline a set of open issues and future research directions; (iv) we upload the datasets and the code used in our experiments in a public repository, aiming to increase the reproducibility of this work and facilitate future research in the field.
The Climate Change Crisis: A Review of Its Causes and Possible Responses
Authors: Gabric Albert J.
Published: June 2023

Anthropogenic climate change (ACC) has evolved into a set of crises due to society’s deep economic dependency on fossil fuels. These multiple crises have been well documented and span diverse ecological, human health and economic settings. Given the scale and breadth of CC impacts, expert labeling of the issues has gradually changed from the somewhat benign sounding “global warming” to the more frightening description of a “climate emergency”. Notwithstanding calls for transformative societal change, serious attempts to confront ACC have been hampered by decades of government policy inaction, various scientific debates, political conservatism and denial and public ignorance or apathy. Meanwhile, atmospheric greenhouse gas concentrations have increased inexorably and show no sign of plateauing. The impacts of ACC are becoming evident sooner than expected, and projections for the future of the planet’s ecosystems and the human population which depends on them are dire. Proposals to geoengineer the climate are currently being hotly debated within the scientific community but may prove to be a last resort if the impacts of unmitigated warming become even more severe.
General Exact Schemes for Second-Order Linear Differential Equations Using the Concept of Local Green Functions
Authors: Vizvari Zoltan , Klincsik Mihaly , Odry Peter , Tadic Vladimir , Sari Zoltan
Published: June 2023

In this paper, we introduce a special system of linear equations with a symmetric, tridiagonal matrix, whose solution vector contains the values of the analytical solution of the original ordinary differential equation (ODE) in grid points. Further, we present the derivation of an exact scheme for an arbitrary mesh grid and prove that its application can completely avoid other errors in discretization and numerical methods. The presented method is constructed on the basis of special local green functions, whose special properties provide the possibility to invert the differential operator of the ODE. Thus, the newly obtained results provide a general, exact solution method for the second-order ODE, which is also effective for obtaining the arbitrary grid, Dirichlet, and/or Neumann boundary conditions. Both the results obtained and the short case study confirm that the use of the exact scheme is efficient and straightforward even for ODEs with discontinuity functions.
Applications of Shell-like Curves Connected with Fibonacci Numbers
Authors: Amourah Ala , Aldawish Ibtisam , Frasin Basem Aref , Al-Hawary Tariq
Published: June 2023

We introduce a new subclass JΣη,δ,μ(p˜) of bi-univalent functions, defined by shell-like curves connected with Fibonacci numbers. Our main results in this paper include estimates of the Taylor–Maclaurin coefficients a2 and a3 for functions in this subclass, as well as solutions to Fekete–Szegö functional problems. We also show novel outcomes resulting from the specialization of the parameters used in our main results.
Boron Nitride-Based Nanomaterials: Synthesis and Application in Rechargeable Batteries
Authors: Mateti Srikanth , Sultana Irin , Chen Ying , Kota Manikantan , Rahman Md Mokhlesur
Published: June 2023

Conventional boron nitride material is a resistant refractory compound of boron and nitrogen with various crystalline forms. The hexagonal form, which corresponds to graphite, is used as a lubricant and an additive to cosmetic products because of its higher stability and softness. Recently, various nanostructured boron nitride materials, including nanosheets, nanotubes, nanoparticles, and nanocomposites with diverse new properties, have been achieved through the development of advanced synthesis techniques as well as a deeper understanding of the properties and related applications. As nanostructured boron nitride materials exhibit high chemical, thermal and mechanical stability, the incorporation of nanostructured boron nitride materials into the key components (electrolytes, separators, and electrodes) of electrochemical systems can alleviate various inherent problems. This review article systematically summarizes the integration of nanostructured boron nitride into electrolytes, separators, and electrodes of lithium-ion, sodium-ion, and lithium-sulfur batteries. Various structures, synthesis methods, properties, and electrochemical performance of nanostructured boron nitride incorporated electrolytes, separators, and electrodes in rechargeable batteries are discussed. The challenges and possibilities for future application of boron nitride-based nanomaterials in electrochemical energy storage systems are also highlighted.
Cognitive Network Science Reveals Bias in GPT-3, GPT-3.5 Turbo, and GPT-4 Mirroring Math Anxiety in High-School Students
Authors: Abramski Katherine , Citraro Salvatore , Lombardi Luigi , Rossetti Giulio , Stella Massimo
Published: June 2023

Large Language Models (LLMs) are becoming increasingly integrated into our lives. Hence, it is important to understand the biases present in their outputs in order to avoid perpetuating harmful stereotypes, which originate in our own flawed ways of thinking. This challenge requires developing new benchmarks and methods for quantifying affective and semantic bias, keeping in mind that LLMs act as psycho-social mirrors that reflect the views and tendencies that are prevalent in society. One such tendency that has harmful negative effects is the global phenomenon of anxiety toward math and STEM subjects. In this study, we introduce a novel application of network science and cognitive psychology to understand biases towards math and STEM fields in LLMs from ChatGPT, such as GPT-3, GPT-3.5, and GPT-4. Specifically, we use behavioral forma mentis networks (BFMNs) to understand how these LLMs frame math and STEM disciplines in relation to other concepts. We use data obtained by probing the three LLMs in a language generation task that has previously been applied to humans. Our findings indicate that LLMs have negative perceptions of math and STEM fields, associating math with negative concepts in 6 cases out of 10. We observe significant differences across OpenAI’s models: newer versions (i.e., GPT-4) produce 5× semantically richer, more emotionally polarized perceptions with fewer negative associations compared to older versions and N=159 high-school students. These findings suggest that advances in the architecture of LLMs may lead to increasingly less biased models that could even perhaps someday aid in reducing harmful stereotypes in society rather than perpetuating them.
Design, Kinematics and Gait Analysis, of Prosthetic Knee Joints: A Systematic Review
Authors: Rasheed Faiza , Martin Suzanne , Tse Kwong Ming
Published: June 2023

The aim of this review article is to appraise the design and functionality of above-knee prosthetic legs. So far, various transfemoral prosthetic legs are found to offer a stable gait to amputees but are limited to laboratories. The commercially available prosthetic legs are not reliable and comfortable enough to satisfy amputees. There is a dire need for creating a powered prosthetic knee joint that could address amputees’ requirements. To pinpoint the gap in transfemoral prosthetic legs, prosthetic knee unit model designs, control frameworks, kinematics, and gait evaluations are concentrated. Ambulation exercises, ground-level walking, running, and slope walking are considered to help identify research gaps and areas where existing prostheses can be ameliorated. The results show that above-knee amputees can more effectively manage their issues with the aid of an active prosthesis, capable of reliable gait. To accomplish the necessary control, closed loop controllers and volitional control are integral parts. Future studies should consider designing a transfemoral electromechanical prosthesis based on electromyographic (EMG) signals to better predict the amputee’s intent and control in accordance with that intent.
Prediction of CD44 Structure by Deep Learning-Based Protein Modeling
Authors: Camponeschi Chiara , Righino Benedetta , Pirolli Davide , Semeraro Alessandro , Ria Francesco , De Rosa Maria Cristina
Published: June 2023

CD44 is a cell surface glycoprotein transmembrane receptor that is involved in cell–cell and cell–matrix interactions. It crucially associates with several molecules composing the extracellular matrix, the main one of which is hyaluronic acid. It is ubiquitously expressed in various types of cells and is involved in the regulation of important signaling pathways, thus playing a key role in several physiological and pathological processes. Structural information about CD44 is, therefore, fundamental for understanding the mechanism of action of this receptor and developing effective treatments against its aberrant expression and dysregulation frequently associated with pathological conditions. To date, only the structure of the hyaluronan-binding domain (HABD) of CD44 has been experimentally determined. To elucidate the nature of CD44s, the most frequently expressed isoform, we employed the recently developed deep-learning-based tools D-I-TASSER, AlphaFold2, and RoseTTAFold for an initial structural prediction of the full-length receptor, accompanied by molecular dynamics simulations on the most promising model. All three approaches correctly predicted the HABD, with AlphaFold2 outperforming D-I-TASSER and RoseTTAFold in the structural comparison with the crystallographic HABD structure and confidence in predicting the transmembrane helix. Low confidence regions were also predicted, which largely corresponded to the disordered regions of CD44s. These regions allow the receptor to perform its unconventional activity.
Aldosterone Contributes to Vasopressin Escape through Changes in Water and Urea Transport
Authors: Wang Yanhua , LaRocque Lauren M. , Ruiz Joseph A. , Rodriguez Eva L. , Sands Jeff M. , Klein Janet D.
Published: June 2023

Hyponatremia (hypo-osmolality) is a disorder of water homeostasis due to abnormal renal diluting capacity. The body limits the degree to which serum sodium concentration falls through a mechanism called “vasopressin escape”. Vasopressin escape is a process that prevents the continuous decrease in serum sodium concentration even under conditions of sustained high plasma vasopressin levels. Previous reports suggest that aldosterone may be involved in the vasopressin escape mechanism. The abilities of aldosterone synthase (Cyp11b2) knockout and wild-type mice to escape from vasopressin were compared. Wild-type mice escaped while the aldosterone synthase knockout mice did not. Both the water channel aquaporin 2 (AQP2) and the urea transporter UT-A1 protein abundances were higher in aldosterone synthase knockout than in wild-type mice at the end of the escape period. Vasopressin escape was also blunted in rats given spironolactone, a mineralocorticoid receptor blocker. Next, the role of the phosphatase, calcineurin (protein phosphatase 2B, PP2B), in vasopressin escape was studied since aldosterone activates calcineurin in rat cortical collecting ducts. Tacrolimus, a calcineurin inhibitor, blunted vasopressin escape in rats compared with the control rats, increased UT-A1, AQP2, and pS256-AQP2, and decreased pS261-AQP2 protein abundances. Our results indicate that aldosterone regulates vasopressin escape through calcineurin-mediated protein changes in UT-A1 and AQP2.
Nutritional Metabolomics in Diet–Breast Cancer Relations: Current Research, Challenges, and Future Directions—A Review
Authors: Vahid Farhad , Hajizadeghan Kimia , Khodabakhshi Adeleh
Published: June 2023

Breast cancer is one of the most common types of cancer in women worldwide, and its incidence is increasing. Diet has been identified as a modifiable risk factor for breast cancer, but the complex interplay between diet, metabolism, and cancer development is not fully understood. Nutritional metabolomics is a rapidly evolving field that can provide insights into the metabolic changes associated with dietary factors and their impact on breast cancer risk. The review’s objective is to provide a comprehensive overview of the current research on the application of nutritional metabolomics in understanding the relationship between diet and breast cancer. The search strategy involved querying several electronic databases, including PubMed, Scopus, Web of Science, and Google Scholar. The search terms included combinations of relevant keywords such as “nutritional metabolomics”, “diet”, “breast cancer”, “metabolites”, and “biomarkers”. In this review, both in vivo and in vitro studies were included, and we summarize the current state of knowledge on the role of nutritional metabolomics in understanding the diet–breast cancer relationship, including identifying specific metabolites and metabolic pathways associated with breast cancer risk. We also discuss the challenges associated with nutritional metabolomics research, including standardization of analytical methods, interpretation of complex data, and integration of multiple-omics approaches. Finally, we highlight future directions for nutritional metabolomics research in studying diet–breast cancer relations, including investigating the role of gut microbiota and integrating multiple-omics approaches. The application of nutritional metabolomics in the study of diet–breast cancer relations, including 2-amino-4-cyano butanoic acid, piperine, caprate, rosten-3β,17β-diol-monosulfate, and γ-carboxyethyl hydrochroman, among others, holds great promise for advancing our understanding of the role of diet in breast cancer development and identifying personalized dietary recommendations for breast cancer prevention, control, and treatment.
Exploring the Regulatory Role of XIST-microRNAs/mRNA Network in Circulating CD4+ T Cells of Hepatocellular Carcinoma Patients
Authors: Huang Lien-Hung , Rau Cheng-Shyuan , Liu Yueh-Wei , Wu Chia-Jung , Chien Peng-Chen , Lin Hui-Ping , Wu Yi-Chan , Huang Chun-Ying , Hsieh Ting-Min , Hsieh Ching-Hua
Published: June 2023

Hepatocellular carcinoma (HCC) is one of the most common cancers and the main cause of cancer-related death globally. Immune dysregulation of CD4+ T cells has been identified to play a role in the development of HCC. Nevertheless, the underlying molecular pathways of CD4+ T cells in HCC are not completely known. Thus, a better understanding of the dysregulation of the lncRNA-miRNA/mRNA network may yield novel insights into the etiology or progression of HCC. In this study, circulating CD4+ T cells were isolated from the whole blood of 10 healthy controls and 10 HCC patients for the next-generation sequencing of the expression of lncRNAs, miRNAs, and mRNAs. Our data showed that there were different expressions of 34 transcripts (2 lncRNAs, XISTs, and MIR222HGs; 29 mRNAs; and 3 other types of RNA) and 13 miRNAs in the circulating CD4+ T cells of HCC patients. The expression of lncRNA-XIST-related miRNAs and their target mRNAs was confirmed using real-time quantitative polymerase chain reaction (qPCR) on samples from 100 healthy controls and 60 HCC patients. The lncRNA–miRNA/mRNA regulation network was created using interaction data generated from ENCORI and revealed there are positive correlations in the infiltration of total CD4+ T cells, particularly resting memory CD4+ T cells, and negative correlations in the infiltration of Th1 CD4+ T cells.
Cushioning Performance of the Biomimetic Cobweb Cushioning Silicone Pad
Authors: Liao Changyu , Tian Ye , Xu Wei , Zhang Jiahang , Sun Zhihui , Liu Zhuang
Published: June 2023

At present, the packing method of “plastic bag–buffer packing–packing paper box” is adopted for bearing packaging. However, the common packing method has a poor packing effect and poor versatility. In this study, a new biomimetic cobweb cushion is proposed to solve the problem of insufficient cushioning capacity of high-precision bearing cushion packaging pads. First, according to the nature of cobweb form, the cobweb cushion structure configuration is determined. Next, based on the structure of the cushion and the relationship between the parameters of radial thread and spiral thread, a mechanical and target optimization model is established. The stress nephogram of bearing and the cobweb cushion are analyzed under three drop heights of 381, 610, and 700 mm, in the finite element simulation software to ensure that the maximum bearings stress is not beyond the material yield strength. Via the 3D printing technology, a cobweb cushion shell cast is made. Drop tests of the bearing were performed, and the results were verified with the finite element simulation analysis. This research can provide technical support for the protection of high-precision bearings from accidental drops during transportation.