bluuebunny/crossref_metadata_2025 huggingface dataset:
Optimization of the Structural and Motion Parameters of Blade Cutters in Paddy Field Pulping Machines
Authors: Ren Jinbo , Chen Chongcheng , Bao Difa , Weng Wuxiong , Zheng Shuhe
Published: January 2024

Blade cutters are a component in paddy field pulping machines that perform mud splashing, and the design of their structural and motion parameters will directly affect the splashed-mud volume and pulping-machine efficiency. Therefore, the optimization of the blade cutter’s structural and motion parameters is an important approach for improving the operating performance of paddy field pulping machines. In this study, based on the central-composite-design (CCD) method and a response-surface-method-based variance analysis, a regression-forecast model for the relationship between the splashing performance of the blade cutter and the blade’s structural and motion parameters was constructed to determine the influence of these parameters on the multi-dimensional splashing performance of blade cutters. Additionally, with the construction of a multi-objective performance-optimization model for pulping-machine blade cutters, the predicted optimal structural and motion parameters could be obtained based on the genetic algorithm. The ideal operating performance could be achieved when the blade turning radius was 180 mm, with a bending angle of 125°, a sub-cutter dip angle of 63°, a forward velocity of 0.15 m/s, and a rotating speed of 158 r/min. Verification of the optimization results in a bench test showed that the mean relative errors between the theoretical and experimental values of the mud volume and power consumption were 9.13% and 8.86%, respectively, revealing the high accuracy of the mud-volume and power-consumption models. Furthermore, there was a significant reduction in blade-cutter unit power consumption of 19.13%. These research results can provide a theoretical reference and technical support for blade-cutter optimization and improving pulping-machine performance.
Comparative Evaluation of Color Correction as Image Preprocessing for Olive Identification under Natural Light Using Cell Phones
Authors: Mojaravscki David , Graziano Magalhães Paulo S.
Published: January 2024

Integrating deep learning for crop monitoring presents opportunities and challenges, particularly in object detection under varying environmental conditions. This study investigates the efficacy of image preprocessing methods for olive identification using mobile cameras under natural light. The research is grounded in the broader context of enhancing object detection accuracy in variable lighting, which is crucial for practical applications in precision agriculture. The study primarily employs the YOLOv7 object detection model and compares various color correction techniques, including histogram equalization (HE), adaptive histogram equalization (AHE), and color correction using the ColorChecker. Additionally, the research examines the role of data augmentation methods, such as image and bounding box rotation, in conjunction with these preprocessing techniques. The findings reveal that while all preprocessing methods improve detection performance compared to non-processed images, AHE is particularly effective in dealing with natural lighting variability. The study also demonstrates that image rotation augmentation consistently enhances model accuracy across different preprocessing methods. These results contribute significantly to agricultural technology, highlighting the importance of tailored image preprocessing in object detection models. The conclusions drawn from this research offer valuable insights for optimizing deep learning applications in agriculture, particularly in scenarios with inconsistent environmental conditions.
The Archaeology of Field Systems in Al-Andalus
Authors: Kirchner Helena
Published: January 2024

The Berber and Arab conquest of the Iberian Peninsula in 711 C.E. led to a profound transformation of the agricultural landscape. The layout of the irrigated areas, both rural and urban, is recognisable because it is the result of social and technological choices. But irrigated agriculture was not the only option in Al-Andalus. Rainfed agriculture is supposed to have been the main form of agriculture in large areas of the centre and west of the peninsula, although the field systems have been scarcely identified. In regions where irrigation was the preferred option, rainfed crops were complementary. In regions where dry farming was the only possible agriculture, there were settlement networks linked to livestock breeding and to droveways and pasture areas. The original selections made by the Berber and Arab farmers can still be recognized despite the expansion that has mainly taken place since modern times. However, the more recent and destructive capitalist agriculture is erasing the last vestiges of the Andalusi agricultural landscape.
A Treatise on Reconnoitering the Suitability of Fuzzy MARCOS for Assessment of Conceptual Designs
Authors: Olabanji Olayinka Mohammed
Published: January 2024

The development of an equipment starts from an effective design activity. The concept selection process is an activity that is entailed in the design stage, and its relevance in the design process cannot be overemphasized because it informs the choice of optimal conceptual design from a set of alternative designs. Hence, there is a need to accrue efforts to the concept selection process because of its importance. This article presents the identification of optimal conceptual design as a multicriteria decision-making model by assessing the suitability of fuzzy Measurement Alternatives and Ranking according to COmpromise Solution (MARCOS). The fuzzy MARCOS model was developed to access four alternative conceptual designs of briquetting machines considering eight design features with several sub-features. The results obtained from the decision analysis showed that the fuzzy MARCOS model was able to rank the designs based on their performance and the final values of the overall utility function. The overall utility function is based on the utility degree of the conceptual design alternatives in terms of the best and worst designs identified by the model. The utility degree created a platform for comparison on how the design alternatives varied from the best and worst designs. The results obtained from the MARCOS method were validated using the TOPSIS method and modified TOPSIS method, and the results obtained showed that the MARCOS method is in conformity with the validation results.
Assessing the Biomechanical, Kinematic, and Force Distribution Properties of the Foot Following Tarsometatarsal Joint Arthrodesis: A Systematic Review
Authors: Balu Abhinav Reddy , Baumann Anthony N. , Burkhead Daniel , Talaski Grayson M. , Anastasio Albert T. , Walley Kempland C. , Adams Samuel B.
Published: January 2024

The Lisfranc joint connects the forefoot to the midfoot. Tarsometatarsal (TMT) arthrodesis is commonly employed for Lisfranc joint injuries; however, there is active discussion regarding the optimal method of fixation for TMT arthrodesis. The purpose of this systematic review is twofold: to assess the stability of various constructs used in TMT arthrodesis and to evaluate joint motion and force distribution in the foot following arthrodesis. The PubMed, CINAHL, MEDLINE, and Web of Science databases were searched for articles evaluating biomechanical and kinetic properties of TMT arthrodesis constructs in accordance with PRISMA guidelines. The preliminary search yielded 367 articles and the final review included 14 articles with 195 cadaveric and 70 synthetic bone constructs. Plantar plates and intramedullary screw fixation at the first TMT joint were consistently found to bear significantly greater loads and resist diastasis more effectively than crossed screws. Furthermore, whole foot and lateral column arthrodesis significantly elevate calcaneocuboid and lateral column pressures. This increase was not observed with isolated fourth or fifth TMT arthrodesis. TMT arthrodesis should aim to avoid the lateral column and fuse as few joints as possible. Overall, plantar plates are an effective construct for first TMT arthrodesis due to their ability to withstand both compressive and tensile forces while maintaining stable alignment of the foot via reinforcement of the transverse arch. Intramedullary fixation devices are an alternative option that provide a high degree of active compression across the joint space while avoiding irritation of surrounding soft tissue structures.
Unsupervised Wavelet-Feature Correlation Ratio Markov Clustering Algorithm for Remotely Sensed Images
Authors: Wang Zhaohui
Published: January 2024

The spectrums of one type of object under different conditions have the same features (up, down, protruding, concave) at the same spectral positions, which can be used as primary parameters to evaluate the difference among remotely sensed pixels. The wavelet-feature correlation ratio Markov clustering algorithm (WFCRMCA) for remotely sensed data is proposed based on an accurate description of abrupt spectral features and an optimized Markov clustering in the wavelet feather space. The peak points can be captured and identified by applying a wavelet transform to spectral data. The correlation ratio between two samples is a statistical calculation of the matched peak point positions on the wavelet feature within an adjustable spectrum domain or a range of wavelet scales. The evenly sampled data can be used to create class centers, depending on the correlation ratio threshold at each Markov step, accelerating the clustering speed by avoiding the computation of Euclidean distance for traditional clustering algorithms, such as K-means and ISODATA. Markov clustering applies several strategies, such as a simulated annealing method and gradually shrinking the clustering size, to control the clustering convergence. It can quickly obtain the best class centers at each clustering temperature. The experimental results of the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Thermal Mapping (TM) data have verified its acceptable clustering accuracy and high convergence velocity.
MBPPE: A Modular Batch Processing Platform for Electroencephalography
Authors: Qiu Jinggong , Chen Ming , Feng Guofu
Published: January 2024

To ensure the accuracy and reliability of subsequent analysis, research on electroencephalogram (EEG) signals typically requires preliminary processing of large datasets to eliminate noise and artifacts. Traditional batch processing methods require substantial hardware resources while lacking flexible automated workflows and user-friendly interactions. To address these challenges, we have implemented a modular batch processing platform for EEG (MBPPE) that offers both local execution and private deployment options to meet the demands of efficient signal processing from individuals to laboratories. We modularize the processing methods and organize them into pluggable multi-task batch processes, providing asynchronous processing solutions. In addition, we extend user functions by introducing plugins and promoting collaborative interaction through data sharing, access control, and comment communication. Simultaneously, interactive features are integrated into the visualization design, enabling users to process and analyze data more intuitively and naturally. Currently, the platform integrates several commonly used data preprocessing and analysis techniques, providing a novel solution for batch processing of EEG signals.
NodeVector: A Novel Network Node Vectorization with Graph Analysis and Deep Learning
Authors: Altuntas Volkan
Published: January 2024

Network node embedding captures structural and relational information of nodes in the network and allows for us to use machine learning algorithms for various prediction tasks on network data that have an inherently complex and disordered structure. Network node embedding should preserve as much information as possible about important network properties where information is stored, such as network structure and node properties, while representing nodes as numerical vectors in a lower-dimensional space than the original higher dimensional space. Superior node embedding algorithms are a powerful tool for machine learning with effective and efficient node representation. Recent research in representation learning has led to significant advances in automating features through unsupervised learning, inspired by advances in natural language processing. Here, we seek to improve the representation quality of node embeddings with a new node vectorization technique that uses network analysis to overcome network-based information loss. In this study, we introduce the NodeVector algorithm, which combines network analysis and neural networks to transfer information from the target network to node embedding. As a proof of concept, our experiments performed on different categories of network datasets showed that our method achieves better results than its competitors for target networks. This is the first study to produce node representation by unsupervised learning using the combination of network analysis and neural networks to consider network data structure. Based on experimental results, the use of network analysis, complex initial node representation, balanced negative sampling, and neural networks has a positive effect on the representation quality of network node embedding.
Alveolar Organoids in Lung Disease Modeling
Authors: Purev Enkhee , Bahmed Karim , Kosmider Beata
Published: January 2024

Lung organoids display a tissue-specific functional phenomenon and mimic the features of the original organ. They can reflect the properties of the cells, such as morphology, polarity, proliferation rate, gene expression, and genomic profile. Alveolar type 2 (AT2) cells have a stem cell potential in the adult lung. They produce and secrete pulmonary surfactant and proliferate to restore the epithelium after damage. Therefore, AT2 cells are used to generate alveolar organoids and can recapitulate distal lung structures. Also, AT2 cells in human-induced pluripotent stem cell (iPSC)-derived alveolospheres express surfactant proteins and other factors, indicating their application as suitable models for studying cell–cell interactions. Recently, they have been utilized to define mechanisms of disease development, such as COVID-19, lung cancer, idiopathic pulmonary fibrosis, and chronic obstructive pulmonary disease. In this review, we show lung organoid applications in various pulmonary diseases, drug screening, and personalized medicine. In addition, stem cell-based therapeutics and approaches relevant to lung repair were highlighted. We also described the signaling pathways and epigenetic regulation of lung regeneration. It is critical to identify novel regulators of alveolar organoid generations to promote lung repair in pulmonary diseases.
Current Technologies Unraveling the Significance of Post-Translational Modifications (PTMs) as Crucial Players in Neurodegeneration
Authors: Zafar Saima , Fatima Shehzadi Irum , Schmitz Matthias , Zerr Inga
Published: January 2024

Neurodegenerative disorders, such as Parkinson’s disease, Alzheimer’s disease, and Huntington’s disease, are identified and characterized by the progressive loss of neurons and neuronal dysfunction, resulting in cognitive and motor impairment. Recent research has shown the importance of PTMs, such as phosphorylation, acetylation, methylation, ubiquitination, sumoylation, nitration, truncation, O-GlcNAcylation, and hydroxylation, in the progression of neurodegenerative disorders. PTMs can alter protein structure and function, affecting protein stability, localization, interactions, and enzymatic activity. Aberrant PTMs can lead to protein misfolding and aggregation, impaired degradation, and clearance, and ultimately, to neuronal dysfunction and death. The main objective of this review is to provide an overview of the PTMs involved in neurodegeneration, their underlying mechanisms, methods to isolate PTMs, and the potential therapeutic targets for these disorders. The PTMs discussed in this article include tau phosphorylation, α-synuclein and Huntingtin ubiquitination, histone acetylation and methylation, and RNA modifications. Understanding the role of PTMs in neurodegenerative diseases may provide new therapeutic strategies for these devastating disorders.
A Quantitative Method for the Evaluation of Deep Vein Thrombosis in a Murine Model Using Three-Dimensional Ultrasound Imaging
Authors: Xie Yanjun , Huang Yi , Stevenson Hugo C. S. , Yin Li , Zhang Kaijie , Islam Zain Husain , Marcum William Aaron , Johnston Campbell , Hoyt Nicholas , Kent Eric William , Wang Bowen , Hossack John A.
Published: January 2024

Deep vein thrombosis (DVT) is a life-threatening condition that can lead to its sequelae pulmonary embolism (PE) or post-thrombotic syndrome (PTS). Murine models of DVT are frequently used in early-stage disease research and to assess potential therapies. This creates the need for the reliable and easy quantification of blood clots. In this paper, we present a novel high-frequency 3D ultrasound approach for the quantitative evaluation of the volume of DVT in an in vitro model and an in vivo murine model. The proposed method involves the use of a high-resolution ultrasound acquisition system and semiautomatic segmentation of the clot. The measured 3D volume of blood clots was validated to be correlated with in vitro blood clot weights with an R2 of 0.89. Additionally, the method was confirmed with an R2 of 0.91 in the in vivo mouse model with a cylindrical volume from macroscopic measurement. We anticipate that the proposed method will be useful in pharmacological or therapeutic studies in murine models of DVT.
Anticancer Drug Discovery Based on Natural Products: From Computational Approaches to Clinical Studies
Authors: Chunarkar-Patil Pritee , Kaleem Mohammed , Mishra Richa , Ray Subhasree , Ahmad Aftab , Verma Devvret , Bhayye Sagar , Dubey Rajni , Singh Himanshu , Kumar Sanjay
Published: January 2024

Globally, malignancies cause one out of six mortalities, which is a serious health problem. Cancer therapy has always been challenging, apart from major advances in immunotherapies, stem cell transplantation, targeted therapies, hormonal therapies, precision medicine, and palliative care, and traditional therapies such as surgery, radiation therapy, and chemotherapy. Natural products are integral to the development of innovative anticancer drugs in cancer research, offering the scientific community the possibility of exploring novel natural compounds against cancers. The role of natural products like Vincristine and Vinblastine has been thoroughly implicated in the management of leukemia and Hodgkin’s disease. The computational method is the initial key approach in drug discovery, among various approaches. This review investigates the synergy between natural products and computational techniques, and highlights their significance in the drug discovery process. The transition from computational to experimental validation has been highlighted through in vitro and in vivo studies, with examples such as betulinic acid and withaferin A. The path toward therapeutic applications have been demonstrated through clinical studies of compounds such as silvestrol and artemisinin, from preclinical investigations to clinical trials. This article also addresses the challenges and limitations in the development of natural products as potential anti-cancer drugs. Moreover, the integration of deep learning and artificial intelligence with traditional computational drug discovery methods may be useful for enhancing the anticancer potential of natural products.
Role of Functionalized Peptides in Nanomedicine for Effective Cancer Therapy
Authors: Kim Kibeom , Park Myoung-Hwan
Published: January 2024

Peptide-functionalized nanomedicine, which addresses the challenges of specificity and efficacy in drug delivery, is emerging as a pivotal approach for cancer therapy. Globally, cancer remains a leading cause of mortality, and conventional treatments, such as chemotherapy, often lack precision and cause adverse effects. The integration of peptides into nanomedicine offers a promising solution for enhancing the targeting and delivery of therapeutic agents. This review focuses on the three primary applications of peptides: cancer cell-targeting ligands, building blocks for self-assembling nanostructures, and elements of stimuli-responsive systems. Nanoparticles modified with peptides improved targeting of cancer cells, minimized damage to healthy tissues, and optimized drug delivery. The versatility of self-assembled peptide structures makes them an innovative vehicle for drug delivery by leveraging their biocompatibility and diverse nanoarchitectures. In particular, the mechanism of cell death induced by self-assembled structures offers a novel approach to cancer therapy. In addition, peptides in stimuli-responsive systems enable precise drug release in response to specific conditions in the tumor microenvironment. The use of peptides in nanomedicine not only augments the efficacy and safety of cancer treatments but also suggests new research directions. In this review, we introduce systems and functionalization methods using peptides or peptide-modified nanoparticles to overcome challenges in the treatment of specific cancers, including breast cancer, lung cancer, colon cancer, prostate cancer, pancreatic cancer, liver cancer, skin cancer, glioma, osteosarcoma, and cervical cancer.
A Literature Review: The Mechanisms and Treatment of Neuropathic Pain—A Brief Discussion
Authors: Rugnath Renira , Orzechowicz Casey , Newell Clayton , Carullo Veronica , Rugnath Anesh
Published: January 2024

Classically, neuropathic pain is described as a pain caused by a lesion or disease of the somatosensory system. However, one must note that the presence of somatosensory pathology alone does not guarantee a progression to neuropathic pain. This is due, in part, to the fact that neuropathic pain is a notoriously complex disease process, involving sensitization of both the central and peripheral nervous systems. Its causes are also numerous and varied, including trauma, the compression of a nerve, autoimmune disorders, diabetes, and infections. Due to the various manifestations, causes, and symptoms of neuropathic pain, the treatment of this disease process has proved challenging for generations of physicians. This section aims to elaborate on newly proposed mechanisms for pharmacological and targeted therapies, such as neurostimulation, which aim to reduce the negative somatosensory effects of neuropathic pain.
Compassion Catalysts: Unveiling Proactive Pathways to Job Performance
Authors: Choi Yongjun , Ko Sung-Hoon
Published: January 2024

This study aims to provide a better understanding of the mechanisms underlying the relationship between the experiences of compassion and job performance. Specifically, we test if positive emotion and job crafting could help explain the experience of the compassion–job performance link. Using a sample of 312 employees in large-sized domestic companies located in South Korea, we found that the experience of compassion was positively related to job performance. In addition, we adopted the motivational approach to demonstrate that the positive relationship between the experience of compassion and job performance was sequentially mediated by positive emotion and job crafting. Our study advances the literature on workplace compassion by introducing job crafting as a novel driver in explaining the positive effects of compassion and contributes novel insights into the mechanisms underlying the relationship between compassion and job performance. Our findings also suggest that to enhance employees’ job performance and facilitate employees’ proactive behaviors (i.e., job crafting), organizations must foster a compassionate work environment by placing high importance on compassion.
Study on Arching Mechanism of Bridge Pile Foundation: Taking the Shiyangtai No.1 Bridge as an Example
Authors: Wang Lian-Hua , Sun Guo-Zheng , Xu Jiang-Bo , Wu Xiong , Hou Xin-Min , Han Ze-Min
Published: January 2024

The structure of a bridge has certain peculiarities, and its pile foundations are susceptible to uplift or settlement deformation due to various factors. This can result in bridge deck cracking, structural instability, tilting, and even irreversible damage, which significantly impacts the bridge’s stability and driving safety. This study focuses on the Shiyangtai No.1 Bridge and aims to investigate the factors that cause abnormal rise and fall deformations of bridge pile foundations. The study combines macro and micro analysis, physical characteristic testing of the overlying soil under the bridge pile foundation, and numerical simulation of the bridge pile foundation in the goaf. The study discusses in-depth the formation mechanism of the abnormal uplift of some pile foundations of the Shiyangtai No.1 Bridge based on the analysis of the factors influencing the abnormal rise and fall deformation of the bridge pile foundations at home and abroad. The expansive soil beneath the pile foundation is weak, and the force generated by the water expansion is insufficient to cause the pile foundation to rise to 309 mm. The results indicate that the pile foundation of the bridge is not affected by the expansion characteristics of the overlying soil. The collapse of the goaf roof generates double lateral thrust from the accumulation body at the bottom of the goaf and the upper collapse arch. This causes staggered bending uplift of the sandstone soil layer, resulting in upward squeezing pressure that causes the bridge pile foundation to rise. Therefore, the coal mining area is the main factor influencing the abnormal uplift of the pile foundation of the Shiyangtai No.1 Bridge.
Analysis of the Mechanical Performance of High-Strength Nano-Modified Cement Mortars for Overlays
Authors: Szymanowski Jacek , Sadowski Łukasz
Published: January 2024

This article provides the results of the analysis of the mechanical performance of high-strength nano-modified cement mortars for overlays. In order to find the optimal addition amount of those nanoparticles for which an improvement in the mechanical performance of high-strength nano-modified cement mortars for overlays in floors will be noticeable in terms of their mechanical, functional, and adhesive properties, the mechanical performance ratio (MPR) was used. Mechanical performance analyzes were carried out for the six most common variants of overlays. It has been shown that from the point of view of the mechanical performance of the high-strength overlay, it is optimal to use the addition of SiO2 nanospheres in the amount of 0.5% and TiO2 tetragonal crystalline nanoparticles in the amount of 1% of the cement mass.
Advances in Cross-Coupling Reactions Catalyzed by Aromatic Pincer Complexes Based on Earth-Abundant 3d Metals (Mn, Fe, Co, Ni, Cu)
Authors: Cruz-Navarro Jesús Antonio , Sánchez-Mora Arturo , Serrano-García Juan S. , Amaya-Flórez Andrés , Colorado-Peralta Raúl , Reyes-Márquez Viviana , Morales-Morales David
Published: January 2024

The increase of noble-metal-free catalysis in organic chemistry is a trending topic in constant growth due to the price increase of noble metals and their scarce abundance. As a result, many earth-abundant transition-metal complexes containing nickel, iron, or cobalt have been successfully applied in the homogeneous catalysis of a wide number of cross-coupling reactions, and the use of pincer complexes based on these earth-abundant metals was recently explored, affording interesting results. Thus, this review provides a general overview of earth-abundant 3D pincer complexes and their application during the last decade as catalysts in cross-coupling reactions such as Kumada–Corriu, Suzuki–Miyaura, Sonogashira, C–S cross-coupling, and C–N bond-forming reactions.
Modulation of Extracellular Matrix Composition and Chronic Inflammation with Pirfenidone Promotes Scar Reduction in Retinal Wound Repair
Authors: Jahnke Laura , Perrenoud Virginie , Zandi Souska , Li Yuebing , Conedera Federica Maria , Enzmann Volker
Published: January 2024

Wound repair in the retina is a complex mechanism, and a deeper understanding of it is necessary for the development of effective treatments to slow down or even prevent degenerative processes leading to photoreceptor loss. In this study, we harnessed a laser-induced retinal degeneration model (532-nm laser photocoagulation with 300 μm spot size, 60 ms duration and 60 mV pulse), enabling a profound molecular elucidation and a comprehensive, prolonged observation of the wound healing sequence in a murine laser-induced degeneration model (C57BL/6J mice, 6–12 weeks) until day 49 post-laser. Our observations included the expression of specific extracellular matrix proteins and myofibroblast activity, along with an analysis of gene expression related to extracellular matrix and adhesion molecules through RNA measurements. Furthermore, the administration of pirfenidone (10 mg/kg via drinking water), an anti-inflammatory and anti-fibrotic compound, was used to modulate scar formation after laser treatment. Our data revealed upregulated collagen expression in late regenerative phases and sustained inflammation in the damaged tissue. Notably, treatment with pirfenidone was found to mitigate scar tissue formation, effectively downregulating collagen production and diminishing the presence of inflammatory markers. However, it did not lead to the regeneration of the photoreceptor layer.
Cloud-Based Infrastructure and DevOps for Energy Fault Detection in Smart Buildings
Authors: Horvath Kaleb , Abid Mohamed Riduan , Merino Thomas , Zimmerman Ryan , Peker Yesem , Khan Shamim
Published: January 2024

We have designed a real-world smart building energy fault detection (SBFD) system on a cloud-based Databricks workspace, a high-performance computing (HPC) environment for big-data-intensive applications powered by Apache Spark. By avoiding a Smart Building Diagnostics as a Service approach and keeping a tightly centralized design, the rapid development and deployment of the cloud-based SBFD system was achieved within one calendar year. Thanks to Databricks’ built-in scheduling interface, a continuous pipeline of real-time ingestion, integration, cleaning, and analytics workflows capable of energy consumption prediction and anomaly detection was implemented and deployed in the cloud. The system currently provides fault detection in the form of predictions and anomaly detection for 96 buildings on an active military installation. The system’s various jobs all converge within 14 min on average. It facilitates the seamless interaction between our workspace and a cloud data lake storage provided for secure and automated initial ingestion of raw data provided by a third party via the Secure File Transfer Protocol (SFTP) and BLOB (Binary Large Objects) file system secure protocol drivers. With a powerful Python binding to the Apache Spark distributed computing framework, PySpark, these actions were coded into collaborative notebooks and chained into the aforementioned pipeline. The pipeline was successfully managed and configured throughout the lifetime of the project and is continuing to meet our needs in deployment. In this paper, we outline the general architecture and how it differs from previous smart building diagnostics initiatives, present details surrounding the underlying technology stack of our data pipeline, and enumerate some of the necessary configuration steps required to maintain and develop this big data analytics application in the cloud.
Augmented Reality Escape Classroom Game for Deep and Meaningful English Language Learning
Authors: Voreopoulou Angeliki , Mystakidis Stylianos , Tsinakos Avgoustos
Published: January 2024

A significant volume of literature has extensively reported on and presented the benefits of employing escape classroom games (ECGs), on one hand, and on augmented reality (AR) in English language learning, on the other. However, there is little evidence on how AR-powered ECGs can enhance deep and meaningful foreign language learning. Hence, this study presents the design, development and user evaluation of an innovative augmented reality escape classroom game created for teaching English as a foreign language (EFL). The game comprises an imaginative guided group tour around the Globe Theatre in London that is being disrupted by Shakespeare’s ghost. The game was evaluated by following a qualitative research method that depicts the in-depth perspectives of ten in-service English language teachers. The data collection instruments included a 33-item questionnaire and semi-structured interviews. The findings suggest that this escape game is a suitable pedagogical tool for deep and meaningful language learning and that it can raise cultural awareness, while enhancing vocabulary retention and the development of receptive and productive skills in English. Students’ motivation and satisfaction levels toward language learning are estimated to remain high due to the game’s playful nature, its interactive elements, as well as the joyful atmosphere created through active communication, collaboration, creativity, critical thinking and peer work. This study provides guidelines and support for the design and development of similar augmented reality escape classroom games (ARECGs) to improve teaching practices and foreign language education.
Search for Novel Phases in Y-Ba-Cu-O Family
Authors: Djurek Danijel
Published: January 2024

In order to search for possible residual minor phases in the Y-Ba-Cu-O family, powdered mixtures of Y2O3 + BaCO3 + CuO and, independently, superconducting compound YBa2Cu3O7−x have been treated in evacuated cells and elevated temperatures. YBa2Cu3O7−x was reduced to YBa2Cu3O5 by use of the special home-designed Taconis–Knudsen vacuum device. Subsequent doping by oxygen converts produced insulator YBa2Cu3O5 to semiconductor or metal YBa2Cu3O5+x (0 < x < 0.3). In addition to YBa2Cu3O5, 0.05 volume percent of the minor delafossite phase Y2Cu2O4 was spotted in the powder mixture 1/2 Y2O3 + 2BaCO3 + 6Cu2O, heated up to 818 °C in an inert gas atmosphere. An attempt to prepare the insulating bulk delafossite samples was successful, and subsequent doping by oxygen produced novel metallic phases.
Correction: Neumeier et al. The Loneliness of Migraine Scale: A Development and Validation Study. Clin. Transl. Neurosci. 2022, 6, 12
Authors: Clinical and Translational Neuroscience Editorial Office
Published: January 2024

The Clinical and Translational Neuroscience Editorial Office would like to make the following correction to the Academic Editor listed on this published paper [...]
Correction: Agache et al. Nail Ultrasound in Psoriasis and Psoriatic Arthritis—A Narrative Review. Diagnostics 2023, 13, 2236
Authors: Agache Mihaela , Popescu Claudiu C. , Enache Luminita , Dumitrescu Bianca M. , Codreanu Catalin
Published: January 2024

In the original publication [...]
Severity of Atelectasis during Bronchoscopy: Descriptions of a New Grading System (Atelectasis Severity Scoring System—“ASSESS”) and At-Risk-Lung Zones
Authors: Khan Asad , Bashour Sami , Sabath Bruce , Lin Julie , Sarkiss Mona , Song Juhee , Sagar Ala-Eddin S. , Shah Archan , Casal Roberto F.
Published: January 2024

Atelectasis during bronchoscopy under general anesthesia is very common and can have a detrimental effect on navigational and diagnostic outcomes. While the intraprocedural incidence and anatomic location have been previously described, the severity of atelectasis has not. We reviewed chest CT images of patients who developed atelectasis in the VESPA trial (Ventilatory Strategy to Prevent Atelectasis). By drawing boundaries at the posterior chest wall (A), the anterior aspect of the vertebral body (C), and mid-way between these two lines (B), we delineated at-risk lung zones 1, 2, and 3 (from posterior to anterior). An Atelectasis Severity Score System (“ASSESS”) was created, classifying atelectasis as “mild” (zone 1), “moderate” (zones 1–2), and “severe” (zones 1–2–3). A total of 43 patients who developed atelectasis were included in this study. A total of 32 patients were in the control arm, and 11 were in the VESPA arm; 20 patients (47%) had mild atelectasis, 20 (47%) had moderate atelectasis, and 3 (6%) had severe atelectasis. A higher BMI was associated with increased odds (1.5 per 1 unit change; 95% CI, 1.10–2.04) (p = 0.0098), and VESPA was associated with decreased odds (0.05; 95% CI, 0.01–0.47) (p = 0.0080) of developing moderate to severe atelectasis. ASSESS is a simple method used to categorize intra-bronchoscopy atelectasis, which allows for a qualitative description of this phenomenon to be developed. In the VESPA trial, a higher BMI was not only associated with increased incidence but also increased severity of atelectasis, while VESPA had the opposite effect. Preventive strategies should be strongly considered in patients with risk factors for atelectasis who have lesions located in zones 1 and 2, but not in zone 3.
(Nano)Granules-Involving Aggregation at a Passage to the Nanoscale as Viewed in Terms of a Diffusive Heisenberg Relation
Authors: Gadomski Adam
Published: January 2024

We are looking at an aggregation of matter into granules. Diffusion plays a pivotal role here. When going down to the nanometer scale (the so-called nanoscale quantum-size effect limit), quantum mechanics, and the Heisenberg uncertainty relation, may take over the role of classical diffusion, as viewed typically in the mesoscopic/stochastic limit. A d-dimensional entropy-production aggregation of the granules-involving matter in the granule-size space is considered in terms of a (sub)diffusive realization. It turns out that when taking a full d-dimensional pathway of the aggregation toward the nanoscale, one is capable of disclosing a Heisenberg-type (diffusional) relation, setting up an upper uncertainty bound for the (sub)diffusive, very slow granules-including environment that, within the granule-size analogy invoked, matches the quantum limit of h/2πμ (μ—average mass of a granule; h—the Planck’s constant) for the diffusion coefficient of the aggregation, first proposed by Fürth in 1933 and qualitatively foreseen by Schrödinger some years before, with both in the context of a diffusing particle. The classical quantum passage uncovered here, also termed insightfully as the quantum-size effect (as borrowed from the quantum dots’ parlance), works properly for the three-dimensional (d = 3) case, making use of a substantial physical fact that the (nano)granules interact readily via their surfaces with the also-granular surroundings in which they are immersed. This natural observation is embodied in the basic averaging construction of the diffusion coefficient of the entropy-productive (nano)aggregation of interest.
Entropy Estimators for Markovian Sequences: A Comparative Analysis
Authors: De Gregorio Juan , Sánchez David , Toral Raúl
Published: January 2024

Entropy estimation is a fundamental problem in information theory that has applications in various fields, including physics, biology, and computer science. Estimating the entropy of discrete sequences can be challenging due to limited data and the lack of unbiased estimators. Most existing entropy estimators are designed for sequences of independent events and their performances vary depending on the system being studied and the available data size. In this work, we compare different entropy estimators and their performance when applied to Markovian sequences. Specifically, we analyze both binary Markovian sequences and Markovian systems in the undersampled regime. We calculate the bias, standard deviation, and mean squared error for some of the most widely employed estimators. We discuss the limitations of entropy estimation as a function of the transition probabilities of the Markov processes and the sample size. Overall, this paper provides a comprehensive comparison of entropy estimators and their performance in estimating entropy for systems with memory, which can be useful for researchers and practitioners in various fields.
Enhancing Employability Skills of Biology Graduates through an Interdisciplinary Project-Based Service Learning Experience with Engineering and Translation Undergraduate Students
Authors: González-Cespón José Luis , Alonso-Rodríguez José Antonio , Rodríguez-Barcia Susana , Gallego Pedro Pablo , Pino-Juste Margarita R.
Published: January 2024

We describe an interdisciplinary experience based on the application of the Project-Based Service Learning (PBSL) methodology for six learning outcomes directly related to employability skills. The objective was to assess students’ learning outcomes, the level of satisfaction of students and lecturers with the PBSL, and the advantages and disadvantages of the implementation of this learning methodology. Interdisciplinary teamwork of undergraduate science, engineering, and humanities students was required to design real-world projects to develop their transferable skills, through the process of learning by doing. Students perceived that PBSL favored employability skills such as knowledge acquisition, self-directed learning, problem-solving, critical thinking, communication, and teamwork skills. Students and lecturers showed high satisfaction with PBSL and outlined its advantages as an active and experiential learning methodology. In conclusion, the implementation of the collaborative and interdisciplinary PBSL methodology is decidedly recommended to enhance Biology, Engineering, and Translation graduates’ learning satisfaction and their future employability.
Is It Possible to Apply Inquiry in the First Level of Primary School without Hindering the Acquisition of Scientific Competencies? Perspectives of Pupils and Their Pre-Service Teacher
Authors: Delgado-Iglesias Jaime , Bobo-Pinilla Javier , Reinoso-Tapia Roberto , Vega-Agapito María Victoria
Published: January 2024

The aim of this study is to assess the application of inquiry as a teaching strategy during the practicum of a pre-service teacher and to verify the acquisition of scientific skills by her students. The importance of this study is the application of inquiry as a teaching strategy at lower levels of basic education to contribute to scientific skills and scientific literacy. The sample consisted of 27 pupils in the first level of primary education and one pre-service teacher in a Spanish school. The methodology used was a case study with a participatory experimental design, incorporating guided discovery and teacher questioning focused on plant growth. The results indicate that the pupils improved their understanding and assimilation of content related to plant functions, scientific procedures, and critical thinking. The pre-service teacher demonstrated mastery of the inquiry-based methodology, confirming an adequate level of both pedagogical and scientific competence. Some suggestions are provided to enhance her training. The study concludes by demonstrating that it is possible to implement inquiry at any educational level, despite the potential limitations of the students. It is recommended that pre-service teachers receive comprehensive training to enable them to facilitate the acquisition of scientific competence among their students.
Innovative Transformer Life Assessment Considering Moisture and Oil Circulation
Authors: Liang Zhengping , Fang Yan , Cheng Hao , Sun Yongbin , Li Bo , Li Kai , Zhao Wenxuan , Sun Zhongxu , Zhang Yiyi
Published: January 2024

Power transformers are one of the most expensive and important equipment in the power system. Significant differences exist in the insulation lifespan of transformers that have been in operation for more than 20 years, and using identical maintenance or scrapping methods may result in significant economic losses. However, most existing transformer life assessment methods only consider the impact of moisture content on the life decay rate without considering the impact of oil circulation cooling modes, which leads to some evaluation errors. In this study, we established a new transformer life assessment method that considers the influence of moisture content and oil circulation cooling modes, which is more accurate than most life assessment methods. Then, the proposed life evaluation method was validated on the on-site transformers, demonstrating its accuracy and effectiveness. The novelty of this study is that it establishes a new on-site transformer life assessment method that considers the comprehensive effect of moisture content and oil circulation cooling mode, which helps to evaluate the remaining lifespan of power transformers more accurately and thus extends the transformer lifespan systematically.
A Generic Model for Accurate Energy Estimation of Electric Vehicles
Authors: Alhanouti Muhammed , Gauterin Frank
Published: January 2024

A systematic simulation model is proposed in this research paper to estimate the energy consumption of electric vehicles. The main advantage of this model is that it is made in a generic and simplified way in order to be adaptable to different electric vehicles. The overall electrical power corresponding to the performed maneuver is estimated considering: a tabular form of electric motor efficiency, mechanical power losses, a generalized efficiency map of the power electronics, the auxiliary power losses, and an electro-thermal Lithium-Ion battery pack model. The battery model was developed in a previous work, which simulates the open circuit voltage curves at different temperatures and the alteration in the internal resistance of the battery cells. The proposed model is validated with experimental data from the maneuver tests. The battery model proved high accuracy in estimating the voltage values relevant to the WLTP2 driving cycle on the chassis roller test bench. Furthermore, the mechanical and electrical power were estimated with excellent matching compared to actual test field driving test measurements, giving only the measured vehicle speed and auxiliary power losses. Finally, the state of charge change is predicted accurately along the performed test field dynamic maneuver.