bluuebunny/crossref_metadata_2025_split huggingface dataset:
Development and trending of deep learning methods for wind power predictions
Authors: Liu Hong , Zhang Zijun 
Published: April 2024

AbstractWith the increasing data availability in wind power production processes due to advanced sensing technologies, data-driven models have become prevalent in studying wind power prediction (WPP) methods. Deep learning models have gained popularity in recent years due to their ability of handling high-dimensional input, automating data feature engineering, and providing high flexibility in modeling. However, with a large volume of deep learning based WPP studies developed in recent literature, it is important to survey the existing developments and their contributions in solving the issue of wind power uncertainty. This paper revisits deep learning-based wind power prediction studies from two perspectives, deep learning-enabled WPP formulations and developed deep learning methods. The advancement of WPP formulations is summarized from the following perspectives, the considered input and output designs as well as the performance evaluation metrics. The technical aspect review of deep learning leveraged in WPPs focuses on its advancement in feature processing and prediction model development. To derive a more insightful conclusion on the so-far development, over 140 recent deep learning-based WPP studies have been covered. Meanwhile, we have also conducted a comparative study on a set of deep models widely used in WPP studies and recently developed in the machine learning community. Results show that DLinear obtains more than 2% improvements by benchmarking a set of strong deep learning models. Potential research directions for WPPs, which can bring profound impacts, are also highlighted.
Cold tolerance of the mealybug parasitoid Anagyrus vladimiri
Authors: Gilliéron Florence , Romeis Jörg , Collatz Jana 
Published: April 2024

AbstractWe investigated the lower thermal limits of Anagyrus vladimiri Triapitsyn (Hymenoptera: Encyrtidae), a natural enemy of mealybugs. Parasitoids were cooled to measure supercooling points and the lower lethal temperature LLT50. To investigate survival after long-term cold exposure, parasitoid adults and eggs, larvae, and pupae within their host mummy were gradually acclimated. Adults were then exposed for three days to 7 °C, 5 °C, 3 °C, and 1 °C, and immatures for varying durations to 5, 1, and − 4 °C. Parasitoids were investigated for survival and reproduction. To assess the impact of fluctuating temperature, parasitoid pupae were subjected to daily warming to 10 °C from baseline temperatures of 5 °C, 1 °C, and − 4 °C during four-day-cold exposure. Finally, eggs, pupae and adults were exposed to winter conditions in Switzerland in a semi-field setup. The LLT50 was − 17.24 °C for adults and 0.94 °C for pupae. Both values were above the supercooling points. No adult survived three days at 3 °C and lower. Likewise, no emergence occurred from eggs or larvae exposed for four days and longer to 1 °C or seven days to 5 °C. Pupae were cold-hardier surviving seven days at 5 °C and three days at 1 °C. Parasitoids surviving cold exposure were still able to reproduce. Daily warming decreased emergence of cold exposed pupae at a baseline temperature of 1 °C but not at 5 °C and − 4 °C. No eggs, pupae and adults survived winter conditions in the semi-field experiment. We thus consider A. vladimiri a chill-susceptible species with very limited cold-tolerance of the investigated population and low chances of survival during winter in Northern Switzerland.
Coordination chemistry suggests that independently observed benefits of metformin and Zn2+ against COVID-19 are not independent
Authors: Lockwood Thomas D. 
Published: April 2024

AbstractIndependent trials indicate that either oral Zn2+or metformin can separately improve COVID-19 outcomes by approximately 40%. Coordination chemistry predicts a mechanistic relationship and therapeutic synergy. Zn2+deficit is a known risk factor for both COVID-19 and non-infectious inflammation. Most dietary Zn2+is not absorbed. Metformin is a naked ligand that presumably increases intestinal Zn2+bioavailability and active absorption by cation transporters known to transport metformin. Intracellular Zn2+provides a natural buffer of many protease reactions; the variable “set point” is determined by Zn2+regulation or availability. A Zn2+-interactive protease network is suggested here. The two viral cysteine proteases are therapeutic targets against COVID-19. Viral and many host proteases are submaximally inhibited by exchangeable cell Zn2+. Inhibition of cysteine proteases can improve COVID-19 outcomes and non-infectious inflammation. Metformin reportedly enhances the natural moderating effect of Zn2+on bioassayed proteome degradation. Firstly, the dissociable metformin–Zn2+complex could be actively transported by intestinal cation transporters; thereby creating artificial pathways of absorption and increased body Zn2+content. Secondly, metformin Zn2+coordination can create a non-natural protease inhibitor independent of cell Zn2+content. Moderation of peptidolytic reactions by either or both mechanisms could slow (a) viral multiplication (b) viral invasion and (c) the pathogenic host inflammatory response. These combined actions could allow development of acquired immunity to clear the infection before life-threatening inflammation. Nirmatrelvir (Paxlovid®) opposes COVID-19 by selective inhibition the viral main protease by a Zn2+-independent mechanism. Pending safety evaluation, predictable synergistic benefits of metformin and Zn2+, and perhaps metformin/Zn2+/Paxlovid® co-administration should be investigated.
Virtual, augmented reality and learning analytics impact on learners, and educators: A systematic review
Authors: Sakr Asmaa , Abdullah Tariq 
Published: April 2024

AbstractVirtual and Augmented Reality technologies have emerged as promising tools in the education sector, offering new possibilities for immersive learning experiences. Many researchers have focused their research on examining the potential of these technologies in education from different perspectives. However, it was discovered that there are research gaps in current systematic reviews regarding the examination of the impact of Virtual, Augmented Reality and Learning Analytics utilization on various types of learners and educators across different educational systems, including K-12 Education, Higher Education, Vocational, and Industrial Training, in addition to the educational systems’ research tendencies and their adoption of these technologies. Therefore, our study aims to address these gaps by searching various studies in Google Scholar, Scopus, and the IEEE Xplore databases. By following the PRISMA protocol, 150 research papers were selected for analysis, and our findings show that improving motivation and attention, improving learners’ understanding & performance, and increasing knowledge retention are the most significant impacts on all types of learners. For educators, we found that these technologies have a prominent effect on assisting educators in teaching and training and reducing the burden. Furthermore, we discovered that Higher Education and Augmented Reality were the dominant educational system and the technology type in the selected studies. We also found that most Virtual and Augmented reality researchers preferred to use questionnaires and online surveys for data collection. We further identified that analyzing learners’ traces when interacting with Virtual and Augmented Reality applications can improve learners’ performance and learning experience. Our review offers valuable insights into how integrating these technologies with Learning Analytics can benefit learners and educators and how educational institutions and industrial organizations can take advantage of adopting these technologies.
Mitochondrial dysfunction in Fragile X syndrome and Fragile X-associated tremor/ataxia syndrome: prospect use of antioxidants and mitochondrial nutrients
Authors: Pagano Giovanni , Lyakhovich Alex , Pallardó Federico V. , Tiano Luca , Zatterale Adriana , Trifuoggi Marco 
Published: April 2024

Abstract
Fragile X syndrome (FXS) is a genetic disorder characterized by mutation in the FMR1 gene, leading to the absence or reduced levels of fragile X Messenger Ribonucleoprotein 1 (FMRP). This results in neurodevelopmental deficits, including autistic spectrum conditions. On the other hand, Fragile X-associated tremor/ataxia syndrome (FXTAS) is a distinct disorder caused by the premutation in the FMR1 gene. FXTAS is associated with elevated levels of FMR1 mRNA, leading to neurodegenerative manifestations such as tremors and ataxia.
Mounting evidence suggests a link between both syndromes and mitochondrial dysfunction (MDF). In this minireview, we critically examine the intricate relationship between FXS, FXTAS, and MDF, focusing on potential therapeutic avenues to counteract or mitigate their adverse effects. Specifically, we explore the role of mitochondrial cofactors and antioxidants, with a particular emphasis on alpha-lipoic acid (ALA), carnitine (CARN) and Coenzyme Q10 (CoQ10). Findings from this review will contribute to a deeper understanding of these disorders and foster novel therapeutic strategies to enhance patient outcomes.
Series solution of time-fractional mhd viscoelastic model through non-local kernel approach
Authors: Saeed Syed Tauseef , Inc Mustafa , Alqarni Mohammed Z. , Radwan Neyara 
Published: April 2024

AbstractThe study of ramped condition in the context of unsteady incompressible magnetohydrodynamic Casson fluid flow over a moving vertical plate is a complex and important topic in fluid dynamics and heat transfer. This scenario combines several physical phenomena and has practical applications in various engineering and scientific fields. In this study, Casson fluid is considered unsteady under the influence of magnetic field. The fractional mathematical model is proposed by considering the effect of chemical reaction parameter of the flowing fluid. The governing equations are transformed into the dimensionless form and developed fractional models like Caputo-Fabrizio and Atangana-Baleanu Derivative. We used the Laplace transform technique to find the solution of the dimensionless governing equation analytically. The transformed solutions for velocity, energy and momentum balances developed in terms of series. MATHCAD software is being used for numerical computations and the physical attributes of material and fractional parameters are discussed. To analyze their behavior clearly, two-dimensional graphical results are plotted for velocity profile and temperature as well. It has been concluded that the fluid’s velocity are reduced for larger values of the fractional parameter and Prandtl number and is maximum for small values of both parameters. Further, the velocity behavior becomes larger for isothermal condition as compared to ramped conditions.
Fungal phthalimidines-chemodiversity, bioactivity and biosynthesis of a unique class of natural products
Authors: El Maddah Fayrouz , Nazir Mamona , Ahmad Rabbia , Khurshid Sobia , Saleem Muhammad , Libor Benjamin 
Published: April 2024

AbstractA wide range of natural products important for the engineering and drug design of pharmaceuticals comprise largely of nitrogen-based heterocycles. Fungal natural products have proven to be a rich source of the industrially-important molecules, many of which are promising drug leads. Although, natural products containing a phthalimidine core tends not to be given distant classification, but compounds containing these structures exhibit antimicrobial, anthelmintic, antimalarial and insecticidal activities, and are among the potential target for discovering new drug candidates. Intriguingly, these are primarily isolated from fungal sources and to a very lesser extent from plants or bacteria. This review surveys fungal-derived phthalimidine metabolites published until the end of 2022, isolated from both terrestrial and aquatic or marine sources with emphasis on their unique chemistry, bioactivities, biogenesis and taxonomic classification. Their unique chemistry and diverse bioactivities (including antiviral, antiproliferative, antioxidant and antimicrobial) provide a chemical library with high medicinal potential, representing a treasure trove for synthetic chemists.
Graphical Abstract
Photospheric Stereoscopy: Direct Estimation of Solar Surface-Height Variations
Authors: Romero Avila Amanda , Inhester Bernd , Hirzberger Johann , Solanki Sami K. 
Published: April 2024

AbstractThe orbit of the Solar Orbiter mission carries it and the Polarimetric and Helioseismic Imager (PHI), which is onboard, away from the Sun–Earth line, opening up the first ever possibility of doing stereoscopy of solar photospheric structures. We present a method for a stereoscopic analysis of the height variations in the solar photosphere. This method enables the estimation of relevant quantities, such as the Wilson depression of sunspots and pores. We demonstrate the feasibility of the method using simulated Stokes-$I$
I
continuum observations of an MHD simulation of the solar-surface layers. Our method estimates the large-scale variations in the solar surface by shifting and correlating two virtual images, mapped from the same surface feature observed from two different vantage points. The resulting vector is then introduced as an initial height estimate in the least-squares Broyden–Fletcher–Goldfarb–Shanno (BFGS) optimization algorithm to reproduce smaller scale structures. The height estimates from the simulated images reproduce well the overall height variations of the MHD simulation. We studied which viewing angles give the best results and found the optimal separation of the view points to be between $10^{\circ }$

10
∘

and $40^{\circ }$

40
∘

; but neither viewing direction should be inclined by more than $30^{\circ }$

30
∘

from the vertical to the solar surface. The method yields reliable results if the data have a signal-to-noise ratio of 50 or higher. The influence of the spatial resolution of the observed images is considered and discussed.
Dialogue, Horizon and Chronotope: Using Bakhtin’s and Gadamer’s Ideas to Frame Online Teaching and Learning
Authors: Rule Peter 
Published: April 2024

AbstractThe information explosion and digital modes of learning often combine to inform the quest for the best ways of transforming information in digital form for pedagogical purposes. This quest has become more urgent and pervasive with the ‘turn’ to online learning in the context of COVID-19. This can result in linear, asynchronous, transmission-based modes of teaching and learning which commodify, package and deliver knowledge for individual ‘customers’. The primary concerns in such models are often technical and economic – technology as a cost-effective ‘solution’ to educational challenges. In this paper I argue for the importance of dialogic learning space in teaching and learning by means of Information and Communication Technologies, whether in the form of fully online learning, blended learning or face-to-face encounters using ICT affordances. Although the 20th Century theorists Mikhail Bakhtin (1895–1975) and Hans-Georg Gadamer (1900–2002) produced their seminal works before the advent of ICTs, they were both concerned with the quality and authenticity of human engagement with texts and with other persons and contexts. Besides a shared interest in dialogue as an ontological feature of human life and being, they both used spatiotemporal concepts for understanding and interpreting texts. The article draws on Gadamer’s notions of dialogue and horizon, and Bakhtin’s notions of dialogue and chronotope, to conceptualize dialogic possibilities for online education. Its purpose is to provide a framework, grounded in Bakhtin’s and Gadamer’s ideas, for a dialogic approach to online teaching and learning in higher education.
A matter of connection: the importance of habitat networks for endangered butterflies in anthropogenic landscapes
Authors: Ancillotto Leonardo , Mosconi Fabio , Labadessa Rocco 
Published: April 2024

AbstractCities are expanding at fast rates across the world, representing one of the main drivers of biodiversity loss due to habitat replacement. Nonetheless, urban and peri-urban areas often feature green spaces that may offer opportunities to wildlife and even represent safe havens for endangered species. Nonetheless, the key drivers that shape wildlife responses to urban landscapes, and in turn their ability to persist within cities, are far from being fully understood. Here we focus on an ecologically specialized butterfly, the endemic Italian festoon Zerynthia cassandra, as a model to assess how endangered species may survive in highly modified urban landscapes. The relatively low mobility and high host plant specialization make Z. cassandra an excellent target for studies in urban ecology, as they make the species able to exploit small suitable patches while at the same time potentially sensitive to habitat fragmentation and loss due to urbanization and land reclamation. We thus first document the relatively widespread occurrence of potentially suitable sites within two highly modified landscapes of central and southern Italy, with 25 and 35% of sites actually occupied by Z. cassandra. By modeling the probability of butterfly occurrence as a function of environmental characteristics, we found that Z. cassandra is strongly influenced by functional connectivity among suitable sites in urban landscapes, as well as by the abundance of Aristolochia host plants, and by the availability of profitable land cover classes in the immediate surroundings of potential oviposition sites. Our results indicate not only that networks of urban and peri-urban green spaces may host populations of protected and endangered species, but that management should also focus on the urban matrix in order to provide connecting corridors, as key assets to guarantee species persistence in cities.
Diffuse large B-cell lymphoma with DNA copy number changes in a Japanese black calf
Authors: Maezawa Masaki , Watanabe Ken-ichi , Kobayashi Yoshiyasu , Yoshida Kio , Chambers James K. , Uchida Kazuyuki , Maruyama Reo , Inokuma Hisashi 
Published: April 2024

AbstractA 2-month-old Japanese Black calf exhibited mandibular and superficial cervical lymph node swelling. Fine needle aspiration cytology of the superficial cervical lymph node revealed large lymphoblast-like cells with mitoses. Hematological examination revealed remarkable lymphocytosis with atypical lymphocytes. Increased activities of serum total lactate dehydrogenase and thymidine kinase were detected. At necropsy, generalized swelling of lymph nodes was observed. Histopathological analysis revealed diffuse proliferation of medium-sized round centroblastic neoplastic cells that were positive for CD20, CD79α, PAX5, and BLA-36, and negative for CD3, CD5, CD10, and CD34. The calf was diagnosed with centroblastic diffuse large B-cell lymphoma (DLBCL) based on these findings. Analysis of DNA copy number variation revealed an increased copy number for the GIMAP family relative to that in healthy cattle. Moreover, decreases in copy numbers of GBP-1, MIR3141, OR5P1E, and PTPRG relative to those in healthy cattle were also observed. Because DNA copy number variation represent a major contribution to the somatic mutation landscapes in human tumors, these findings suggest that DNA copy number changes might have contributed to the onset of DLBCL in the present case.
Identification of natural frequencies of machine tools during milling: comparison of the experimental modal analysis and the operational modal analysis
Authors: Berthold Jan , Kolouch Martin , Regel Joachim , Dix Martin 
Published: April 2024

AbstractModal parameters (natural frequencies, mode shapes and modal damping) help to understand the dynamic behaviour of complex systems like machine tools. There are several approaches for finding the modal parameters. The Experimental Modal Analysis (EMA) has proven to be effective at standstill of a machine tool. The excitation, realized with impulse hammer or shaker, and excited responses at several locations are measured. Alternatively, the Operational Modal Analysis (OMA) can be deployed for finding the modal parameters during operation. Here, responses to excitation resulting from operation are only measured. The modal parameters are mathematically identified from the measured signals in both cases but with different methods. This paper discusses, to what extent both approaches (EMA and OMA) can lead to plausible identification of natural frequencies of a machine tool during milling. Concerning the EMA, attention is paid to capturing the excitation. Process forces can be assumed to be the most significant excitation. However, there are other excitation sources beside the process forces (e.g. drives, hydraulic and pneumatic aggregates), which are considered by this assumption to be a part of disturbances with consequence for the identification of the modal parameters. Regarding the OMA, attention is paid to the fact that the excitation is assumed to be broadband like the white noise. Unfortunately, this assumption does not match the characteristics of a real excitation. This paper contains the identification of natural frequencies of a machine tool during milling within both approaches. The achieved results are compared and discussed.
Transcriptomics of Human Brain Tissue in Parkinson’s Disease: a Comparison of Bulk and Single-cell RNA Sequencing
Authors: Fiorini Michael R. , Dilliott Allison A. , Thomas Rhalena A. , Farhan Sali M. K. 
Published: April 2024

AbstractParkinson’s disease (PD) is a chronic and progressive neurodegenerative disease leading to motor dysfunction and, in some cases, dementia. Transcriptome analysis is one promising approach for characterizing PD and other neurodegenerative disorders by informing how specific disease events influence gene expression and contribute to pathogenesis. With the emergence of single-cell and single-nucleus RNA sequencing (scnRNA-seq) technologies, the transcriptional landscape of neurodegenerative diseases can now be described at the cellular level. As the application of scnRNA-seq is becoming routine, it calls to question how results at a single-cell resolution compare to those obtained from RNA sequencing of whole tissues (bulk RNA-seq), whether the findings are compatible, and how the assays are complimentary for unraveling the elusive transcriptional changes that drive neurodegenerative disease. Herein, we review the studies that have leveraged RNA-seq technologies to investigate PD. Through the integration of bulk and scnRNA-seq findings from human, post-mortem brain tissue, we use the PD literature as a case study to evaluate the compatibility of the results generated from each assay and demonstrate the complementarity of the sequencing technologies. Finally, through the lens of the PD transcriptomic literature, we evaluate the current feasibility of bulk and scnRNA-seq technologies to illustrate the necessity of both technologies for achieving a comprehensive insight into the mechanism by which gene expression promotes neurodegenerative disease. We conclude that the continued application of both assays will provide the greatest insight into neurodegenerative disease pathology, providing both cell-specific and whole-tissue level information.