bluuebunny/crossref_metadata_2025 huggingface dataset:
Job satisfaction and perceived workload in the context of personality typology among subway train drivers
Authors: Gottwald Dalibor , Lejsková Pavla
Published: November 2023

Job satisfaction is a highly relevant topic across all sectors of the national economy worldwide. A contented employee significantly enhances a company’s performance compared to a dissatisfied one. Simultaneously, a contented employee increases the human capital value of the company, which has a substantial impact on its overall value. However, employee satisfaction is not a static state; it can be influenced by various factors, one of which is perceived workload resulting from the nature of the job. The aim of this article is to investigate whether there is a demonstrable impact of perceived workload on employee satisfaction. This relationship is examined within the context of subway train drivers in the Czech Republic. In exploring this relationship, we also delve into the psychological factor of whether there is a connection between perceived workload and the satisfaction of subway train drivers based on their personality types according to the MBTI test. These dependencies are assessed through correlation analysis using a comprehensive dataset obtained. In the course of the research, data were collected from the entire basic sample of respondents, namely from 654 subway train drivers. The results confirmed the existing influence of perceived workload on employee satisfaction and, concurrently, the influence of perceived workload on employee satisfaction based on the personality typology determined by the MBTI test. These findings are undoubtedly beneficial for the management of transportation companies, particularly those involved in passenger transportation, specifically in the provision of subway services.
Teachers as the agent of change for student mental health: the role of teacher care and teacher support in Chinese students’ well-being
Authors: Wang Tingting
Published: November 2023

Introduction

Students in any academic setting typically encounter a wide range of academic problems and challenges, which may endanger their mental health. Since the mental health or well-being of students directly affects their classroom performance, factors helping students overcome their academic challenges need to be widely researched. Considering this, extensive research has been carried out to uncover the personal and situational factors that help students solve their academic problems and improve their well-being. Moreover, due to the invaluable role of teachers in students’ mental conditions, several investigations to date have assessed the impact of teacher communicative behaviors on student well-being.

Methods

To pursue this line of inquiry, the current research assessed the role of teacher support and teacher care in fostering Chinese students’ well-being. To do this, three self-report questionnaires were distributed to 338 undergraduate students. Then, Pearson product–moment and linear regression were performed through IBM SPSS Amos (Version 26) to analyze students’ answers to the self-report questionnaires.

Results

The analysis outcomes divulged a series of strong and positive connections between teacher support, teacher care, and student well-being. The results also showed that teacher support and teacher care can significantly contribute to Chinese students’ well-being.

Implications

Teachers and teacher educators may find these outcomes useful and informative.
Language of mothers and fathers in interaction with their autistic children
Authors: Perzolli Silvia , Bentenuto Arianna , de Falco Simona , Venuti Paola
Published: November 2023

Introduction

Verbal language is one of the most immediate and significant means parents have to express affect and information to their children. Parental speech directed to children has been thoroughly examined in typical development. However, the characteristics of parental speech directed to children with neurodevelopmental disorders are far less well documented, and no recent studies have been carried out that involve autistic1 children and their fathers. Therefore, the present study aims to analyze and compare maternal and paternal speech directed to young autistic children, focusing on fathers’ elements of speech in comparison with maternal language.

Methods

N = 88 dyads participated in this study. 44 autistic children (41 males and 3 females) (chronological age: M = 40.01 months; SD = 11.96) in interaction with their fathers (paternal age M = 41.84 years; SD = 7.02) and the same 44 children in interaction with their mothers (maternal age M = 37.37 years; SD = 5.45). The language was verbatim transcribed using ELAN software (ELAN Version 6.4, 2022) and coded with an observational tool (Penman) for analyzing functions and referents of speech after reaching a satisfactory level of agreement between two independent transcribers.

Results

No differences emerged considering the affective aspects of speech. However, mothers seem to direct more informative salient statements (W = 1,259; p = 0.02) and call the child’s attention more often than fathers (W = 1,253.5; p = 0.02). Regarding referents of informative speech, fathers focused more on the child’s internal states rather than mothers (W = 727; p = 0.04).

Discussion

These results reveal that fathers seem to display a relationship-based approach focused on a non-intrusive style with few demands while talking with their children, providing a complementary role to mothers that allows complete and harmonious stimulation of all areas of child development.
Forensic, legal, and clinical aspects of deaths associated with implanted cardiac devices
Authors: Federspiel Jan M. , Potente Stefan , Abeln Karen B. , Hennemann Kai , Heinbuch Sara , Burkhard Katrin , Richl Madita , Kettner Mattias , Lux Constantin , Schmidt Peter , Verhoff Marcel A. , Ramsthaler Frank
Published: November 2023

As the population ages, the prevalence of heart failure and individuals wearing an implanted cardiac device is increasing. The combination of different underlying pathophysiologies and (the combination of) implanted cardiac devices can become a challenge with regard to the determination of cause and manner of death in such individuals. Additionally, heart disease is frequently associated with mental disease, ranging from anxiety and depression to suicidality and suicide (attempts). At the same time, the correct diagnosis of cause and manner of death is the basis for quality assurance, further therapeutic advances, legal safety, and suicide prevention. By that, an interdisciplinary field between legal medicine, clinicians, and law enforcement opens up. In this field, the different participants can simultaneously benefit from and need each other. For example, legal medicine experts need investigatory results and clinical expertise for the interpretation of readout data of implanted cardiac devices in order to correctly determine the cause of death. A correctly determined cause of death can assist law enforcement and help clinicians to further improve various therapeutic approaches based on correct mortality data collection. In addition, it is the basis for identification of suicides of device carriers, allowing psychological and psychiatric experts to better understand the burden of mental disease in this particular cohort. Against this interdisciplinary background, this manuscript summarizes information about psychiatric comorbidities and suicidality while being on a device. Thereby, basic information on complications and malfunctions of implanted cardiac devices, device-associated deaths with particular emphasis on device manipulation is displayed as basic information needed for correct determination of the cause of death. Also, legal and ethical issues in this field are outlined. The final result is a proposal of an interdisciplinary assessment workflow for a conjoint approach to improve the diagnosis of deaths associated with implanted cardiac devices. It will allow for a differentiation between an individual who died with or due to the device.
The Hyperledger fabric as a Blockchain framework preserves the security of electronic health records
Authors: Hasnain Muhammad , Albogamy Fahad R. , Alamri Saeed S. , Ghani Imran , Mehboob Bilal
Published: November 2023

The Hyperledger Fabric (HF) framework is widely studied for securing electronic health records (EHRs) in the healthcare sector. Despite the various cross-domain blockchain technology (BCT) applications, little is known about the role of the HF framework in healthcare. The purpose of the systematic literature review (SLR) is to review the existing literature on the HF framework and its applications in healthcare. This SLR includes literature published between January 2015 and March 2023 in the ACM digital library, IEEE Xplore, SCOPUS, Springer, PubMed, and Google Scholar databases. Following the inclusion and exclusion criteria, a total of 57 articles emerged as eligible for this SLR. The HF framework was found to be useful in securing health records coming from the Internet of Medical Things (IoMT) and many other devices. The main causes behind using the HF framework were identified as privacy and security, integrity, traceability, and availability of health records. Additionally, storage issues with transactional data over the blockchain are reduced by the use of the HF framework. This SLR also highlights potential future research trends to ensure the high-level security of health records.
Eco-industrial transformation in Uganda: performance evaluation of industrial parks using the international EIP framework
Authors: Negesa Doryn , Geme Tom , Nijman-Ross Elke , Ntawuhiganayo Elisée Bahati
Published: November 2023

This study investigates the transformation of traditional industrial parks into eco-industrial parks within Uganda, with a focus on the Kampala Industrial and Business Park and the Mbale Industrial Park. Utilizing the International Framework for Eco-Industrial Parks as a guiding tool, the research evaluates the parks’ alignment with global eco-industrial park standards. Eco-industrial parks, serving as catalysts for industrial ecology development, promote sustainable practices, resource optimization, and symbiotic inter-industrial relationships. The analysis underscores the pivotal roles of various stakeholders, including the government, enterprises, international cooperators, financial institutions, local communities, and educational and research entities. While Uganda has shown commitment in policy formulation toward industrial transformation, the practical realization of eco-industrial parks remains in its early stages. Challenges such as waste management, energy supply, and community engagement are evident. However, the potential for eco-industrial park development is promising, especially with enhanced international cooperation, financial backing, and community involvement. The study concludes by emphasizing the need for region-specific indicators, tailored educational initiatives, and the selection of Mbale as a pilot city for eco-industrial park development, setting a precedent for the country’s sustainable industrial trajectory.
How Do Rivers Divide Amazonian Animals?
Authors: Oliveira Hernani F. M. , Santos Marcella G. , Bosque Renan J. , Goebel Larissa G. A. , Longo Gabriela R. , Silva Daiana C. , Zangrandi Priscilla L. , Colli Guarino R. , Domingos Fabricius M. C. B.
Published: November 2023

Rivers can be difficult barriers for animals to cross, especially animals that cannot swim or fly. The Amazon region has many of the largest rivers in the world, which limits the movements of many animals that cannot cross them. Thus, some animal species occur on one side of a river but not on the other side. Isolation of animal species caused by rivers or other physical barriers can generate what are called centers of endemism, which are regions that have unique species not seen anywhere else. In this article, we will explain how rivers create barriers to animal movement and how centers of endemism can contribute to the fascinating biodiversity of the Amazon region.
The ecological and etiological investigation of ticks and rodents in China: results from an ongoing surveillance study in Zhejiang Province
Authors: Wang Jinna , Luo Mingyu , Li Tianqi , Liu Ying , Jiang Guoqin , Wu Yuyan , Liu Qinmei , Gong Zhenyu , Sun Jimin
Published: November 2023

Objectives

This study aimed to analyze the population density of vector ticks and reservoir hosts rodents, and to investigate the relevant pathogen infection in Zhejiang Province, China.

Methods

In this surveillance study, the data of ticks density were collected with the tick picking method on animal body surface and the drag-flag method, while the rodent density with the night trapping method. The samples of ticks were examined for the severe fever with thrombocytopenia syndrome virus (SFTSV), and blood serum and organs from rodents were subjected for SFTSV, hantavirus, Leptospira, Orientia tsutsugamushi (O. tsutsugamushi) and Yersinia pestis (Y. pestis) screening in the laboratory.

Results

From 2017 to 2022 in Zhejiang Province, 16,230 parasitic ticks were found in 1848 positive animals, with the density of parasitic ticks of 1.29 ticks per host animal, and a total of 5,201 questing ticks were captured from 1,140,910 meters of vegetation distance with the questing tick density of 0.46 ticks/flag·100 m. Haemaphysalis longicornis (H. longicornis) was the major species. A total of 2,187,739 mousetraps were distributed and 12,705 rodents were trapped, with the density of 0.58 per 100 trap-nights. Rattus norvegicus was the major species. For SFTSV screening, two groups nymphal ticks of H. longicornis were tested to be positive. For the rodents samples, the Leptospira had a positive rate of 12.28% (197/1604), the hantavirus was 1.00% (16/1604), and the O. tsutsugamushi was 0.15% (2/1332). No positive results were found with SFTSV and Y. pestis in the rodents samples.

Conclusion

Findings from this study indicated that the ticks and rodents were widely distributed in Zhejiang Province. Particularly, the positive detection of SFTSV, Leptospira, hantavirus and O. tsutsugamushi in ticks or rodents from this area suggested that more attention should be paid to the possibilities of relevant vector-borne diseases occurrence.
Molecular evidence of Borrelia theileri and closely related Borrelia spp. in hard ticks infesting domestic animals
Authors: Khan Mehran , Almutairi Mashal M. , Alouffi Abdulaziz , Tanaka Tetsuya , Chang Shun-Chung , Chen Chien-Chin , Ali Abid
Published: November 2023

Ticks pose significant threats to hosts by transmitting Borrelia spp., which are grouped into Lyme borreliae, relapsing fever borreliae (RF), and reptiles- and monotremes-associated borreliae. The RF borreliae encompass a group of Borrelia species predominantly transmitted by soft ticks, but some of its members can also be transmitted by hard ticks. Information on the detection and genetic characterization of tick-borne RF borreliae, including Borrelia theileri, is notably rare in Asia, particularly in Pakistan. Herein, we employed molecular techniques to detect borreliae in hard ticks collected from domestic animals in Khyber Pakhtunkhwa, Pakistan. Ticks were subjected to morphological analysis, followed by DNA extraction and PCR amplification of partial fragments of borrelial 16S rRNA and flaB genes. A total of 729 ticks were collected from 264 hosts, with Haemaphysalis cornupunctata (12.9%; 94/729) being the most prevalent, followed by Hyalomma anatolicum (11.7%; 85/729), Rhipicephalus microplus (10.0%; 73/729), Haemaphysalis kashmirensis (9.1%; 66/729), Haemaphysalis bispinosa (8.5%; 62/729), Rhipicephalus sanguineus (8%; 58/729), Haemaphysalis montgomeryi (6.2%; 45/729), Rhipicephalus turanicus (5.5%; 40/729), Hyalomma dromedarii and Ixodes kashmirensis (4.4%; 32/729 each), Rhipicephalus haemaphysaloides (4.1%; 30/729), Haemaphysalis sulcata and Hyalomma scupense (3.8%; 28/729 each), Haemaphysalis danieli (2.9%; 21/729), Hyalomma kumari (2.6%; 19/729), and Hyalomma isaaci (2.2%; 16/729). Based on 16S rRNA detection of Borrelia spp., only R. turanicus yielded positive results, resulting in an overall infection rate of 0.3% (2/160), while using flaB-based detection, four tick species including R. microplus, R. turanicus, Ha. sulcata, and Ha. cornupunctata showed positive results, yielding an overall infection rate of 6.9% (11/160). The amplified DNA fragments of borrelial 16S rRNA and flaB in R. turanicus from goats shared maximum identities of 100 and 99.40% with Borrelia theileri, respectively. Amplified borrelial flaB fragments in R. microplus from cows and sheep displayed 100% identity with B. theileri, while flaB fragments in Ha. cornupunctata and Ha. sulcata from goats revealed identities of 99.32 and 99.75% with undetermined RF Borrelia spp., respectively. Phylogenetic analysis revealed clustering of B. theileri from R. microplus and R. turanicus with the same species, while Borrelia spp. from Ha. cornupunctata and Ha. sulcata with undetermined RF Borrelia spp. Notably, this research marks the first documentation of B. theileri in R. turanicus and the identification of RF Borrelia spp. in Ha. cornupunctata and Ha. sulcata.
Optimizing Physics-Informed Neural Network in Dynamic System Simulation and Learning of Parameters
Authors: Oluwasakin Ebenezer O. , Khaliq Abdul Q. M.
Published: November 2023

Artificial neural networks have changed many fields by giving scientists a strong way to model complex phenomena. They are also becoming increasingly useful for solving various difficult scientific problems. Still, people keep trying to find faster and more accurate ways to simulate dynamic systems. This research explores the transformative capabilities of physics-informed neural networks, a specialized subset of artificial neural networks, in modeling complex dynamical systems with enhanced speed and accuracy. These networks incorporate known physical laws into the learning process, ensuring predictions remain consistent with fundamental principles, which is crucial when dealing with scientific phenomena. This study focuses on optimizing the application of this specialized network for simultaneous system dynamics simulations and learning time-varying parameters, particularly when the number of unknowns in the system matches the number of undetermined parameters. Additionally, we explore scenarios with a mismatch between parameters and equations, optimizing network architecture to enhance convergence speed, computational efficiency, and accuracy in learning the time-varying parameter. Our approach enhances the algorithm’s performance and accuracy, ensuring optimal use of computational resources and yielding more precise results. Extensive experiments are conducted on four different dynamical systems: first-order irreversible chain reactions, biomass transfer, the Brusselsator model, and the Lotka-Volterra model, using synthetically generated data to validate our approach. Additionally, we apply our method to the susceptible-infected-recovered model, utilizing real-world COVID-19 data to learn the time-varying parameters of the pandemic’s spread. A comprehensive comparison between the performance of our approach and fully connected deep neural networks is presented, evaluating both accuracy and computational efficiency in parameter identification and system dynamics capture. The results demonstrate that the physics-informed neural networks outperform fully connected deep neural networks in performance, especially with increased network depth, making them ideal for real-time complex system modeling. This underscores the physics-informed neural network’s effectiveness in scientific modeling in scenarios with balanced unknowns and parameters. Furthermore, it provides a fast, accurate, and efficient alternative for analyzing dynamic systems.
Numerical Analysis of SO2 Absorption inside a Single Water Drop
Authors: Amoresano Amedeo , Langella Giuseppe , Iodice Paolo , Roscioli Silvia
Published: November 2023

This paper introduces a numerical model dedicated to simulating SO2 adsorption during the dynamic interplay between combustion gases and water droplets. The research delves into essential chemical–physical parameters governing mass transfer in these interphase interactions. The proposed simplified model provides preliminary results regarding the granulometric curve of sprays, particularly focusing on the minimal droplet size crucial for effective wet scrubber operation. Our findings underscore a critical diameter below which the spray loses its efficacy under varied boundary conditions. Notably, a single droplet with a maximum diameter of 2 mm absorbs more SO2 than smaller counterparts, peaking at 4.36 × 10−5 g of SO2 within the simulation timeframe. Furthermore, the study explores a specific water mass, revealing that smaller droplets, such as 1 mm, significantly optimize the absorption process. These droplets achieve a SO2 absorption quantity over 5.77 times greater than that of a 2 mm droplet. This research serves as an initial tool for optimizing droplet distribution in sprays, thereby enhancing capture efficiency. The insights presented here offer valuable guidance for designing efficient wet scrubber systems, crucial for pollution control in industrial and environmental applications.
Spatial Distribution and Inter-City Transport of PM2.5 Concentrations from Vehicles in the Guanzhong Plain in Winter
Authors: Lu Pan , Tuheti Abula , Deng Shunxi , Li Guanghua , Liu Jiayao
Published: November 2023

Regional atmospheric environmental problems have become increasingly prominent due to continuous urbanization in China. In this study, the Weather Research and Forecasting (WRF) model coupled with the California Puff (CALPUFF) air quality model was applied to analyze the spatial distribution and inter-city transport of primary and secondary PM2.5 concentrations from vehicles in the Guanzhong Plain (GZP) in January 2019. The results show that the secondary PM2.5 concentration emitted by vehicles was more easily dispersed than primary PM2.5. The maximum hourly average concentrations of primary PM2.5, secondary inorganic aerosol (SIA), and secondary organic aerosol (SOA) were about 18, 9, and 2 µg/m3, respectively. Exhaust emission and secondary NO3− were the main contributors to the total PM2.5 concentration from vehicles, accounting for about 52% and 32%, respectively. The maximum contribution of vehicle emissions to the ambient PM2.5 concentration was about 19%. Inter-city transport contributed about 33% of the total PM2.5 concentration from vehicles in cities in the GZP on average. Among the PM2.5 components transported in each city, SIA was the most abundant, followed by primary PM2.5, and SOA was the least. These findings will provide valuable insights for mitigating the regional PM2.5 pollution caused by near-surface sources in urban agglomerations.
Constrained Binary Optimization Approach for Pinned Node Selection in Pinning Control of Complex Dynamical Networks
Authors: Alanis Alma Y. , Hernandez-Barragan Jesus , Ríos-Rivera Daniel , Sanchez Oscar D. , Martinez-Soltero Gabriel
Published: November 2023

In complex dynamical networks, pinning control techniques are often applied to control a small fraction of the nodes in order to stabilize the network with reduced control effort and energy, facilitating adequate development of the complex network. Selecting the controlled nodes is a key challenge to achieving optimal performance. Theoretical analysis of the network provides the minimum quantity of nodes to control but does not specify which ones should be controlled. Analytically, controllability analysis of the entire network would be ideal, but this becomes difficult for complex networks with a large number of nodes and non-linear dynamics. Another option is to evaluate all possible combinations with the minimum number of necessary nodes or the nodes that can be controlled, but this presents a computational challenge due to the large number of possible combinations. Therefore, the remaining option is the use of metaheuristic algorithms for the rapid and practical evaluation of these combinations. In this work, we propose to optimize the selection of nodes for pinning control based on binary optimization algorithms, subject to control and development constraints. The proposed approach involves finding a binary combination with a fixed number of controlled nodes that best stabilizes the network state to zero. This paper includes a comparative study among state-of-the-art binary optimization algorithms and modified classic optimization algorithms. The applicability of the proposed approach is validated through simulations considering a dynamical discrete-time complex network.
Automatic Facial Palsy Detection—From Mathematical Modeling to Deep Learning
Authors: Vrochidou Eleni , Papić Vladan , Kalampokas Theofanis , Papakostas George A.
Published: November 2023

Automated solutions for medical diagnosis based on computer vision form an emerging field of science aiming to enhance diagnosis and early disease detection. The detection and quantification of facial asymmetries enable facial palsy evaluation. In this work, a detailed review of the quantification of facial palsy takes place, covering all methods ranging from traditional manual mathematical modeling to automated computer vision-based methods. Moreover, facial palsy quantification is defined in terms of facial asymmetry indices calculation for different image modalities. The aim is to introduce readers to the concept of mathematical modeling approaches for facial palsy detection and evaluation and present the process of the development of this separate application field over time. Facial landmark extraction, facial datasets, and palsy grading systems are included in this research. As a general conclusion, machine learning methods for the evaluation of facial palsy lead to limited performance due to the use of handcrafted features, combined with the scarcity of the available datasets. Deep learning methods allow the automatic learning of discriminative deep facial features, leading to comparatively higher performance accuracies. Datasets limitations, proposed solutions, and future research directions in the field are also presented.
Recent Advances in Optically Controlled PROTAC
Authors: Ouyang Muzi , Feng Ying , Chen Hui , Liu Yanping , Tan Chunyan , Tan Ying
Published: November 2023

Proteolysis-targeting chimera (PROTAC) technology is a groundbreaking therapeutic approach with significant clinical potential for degrading disease-inducing proteins within targeted cells. However, challenges related to insufficient target selectivity raise concerns about PROTAC toxicity toward normal cells. To address this issue, researchers are modifying PROTACs using various approaches to enhance their target specificity. This review highlights innovative optically controlled PROTACs as anti-cancer therapies currently used in clinical practice and explores the challenges associated with their efficacy and safety. The development of optically controlled PROTACs holds the potential to significantly expand the clinical applicability of PROTAC-based technology within the realm of drug discovery.
Albumin Thiolation and Oxidative Stress Status in Patients with Aortic Valve Stenosis
Authors: Savini Carlo , Tenti Elena , Mikus Elisa , Eligini Sonia , Munno Marco , Gaspardo Anna , Gianazza Erica , Greco Arianna , Ghilardi Stefania , Aldini Giancarlo , Tremoli Elena , Banfi Cristina
Published: November 2023

Recent evidence indicates that reactive oxygen species play an important causative role in the onset and progression of valvular diseases. Here, we analyzed the oxidative modifications of albumin (HSA) occurring on Cysteine 34 and the antioxidant capacity of the serum in 44 patients with severe aortic stenosis (36 patients underwent aortic valve replacement and 8 underwent a second aortic valve substitution due to a degenerated bioprosthetic valve), and in 10 healthy donors (controls). Before surgical intervention, patients showed an increase in the oxidized form of albumin (HSA-Cys), a decrease in the native reduced form (HSA-SH), and a significant reduction in serum free sulfhydryl groups and in the total serum antioxidant activity. Patients undergoing a second valve replacement showed levels of HSA-Cys, free sulfhydryl groups, and total antioxidant activity similar to those of controls. In vitro incubation of whole blood with aspirin (ASA) significantly increased the free sulfhydryl groups, suggesting that the in vivo treatment with ASA may contribute to reducing oxidative stress. We also found that N-acetylcysteine and its amide derivative were able to regenerate HSA-SH. In conclusion, the systemic oxidative stress reflected by high levels of HSA-Cys is increased in patients with aortic valve stenosis. Thiol–disulfide breaking agents regenerate HSA-SH, thus paving the way to the use these compounds to mitigate the oxidative stress occurring in the disease.
Is the β3-Adrenoceptor a Valid Target for the Treatment of Obesity and/or Type 2 Diabetes?
Authors: Dwaib Haneen S. , Michel Martin C.
Published: November 2023

β3-Adrenoceptors mediate several functions in rodents that could be beneficial for the treatment of obesity and type 2 diabetes. This includes promotion of insulin release from the pancreas, cellular glucose uptake, lipolysis, and thermogenesis in brown adipose tissue. In combination, they lead to a reduction of body weight in several rodent models including ob/ob mice and Zucker diabetic fatty rats. These findings stimulated drug development programs in various pharmaceutical companies, and at least nine β3-adrenoceptor agonists have been tested in clinical trials. However, all of these projects were discontinued due to the lack of clinically relevant changes in body weight. Following a concise historical account of discoveries leading to such drug development programs we discuss species differences that explain why β3-adrenoceptors are not a meaningful drug target for the treatment of obesity and type 2 diabetes in humans.
Satellite Telemetry Insights into the Winter Habitat Use and Movement Ecology of Common and Demoiselle Cranes
Authors: Ram Mohan , Gadhavi Devesh , Sahu Aradhana , Srivastava Nityanand , Rather Tahir Ali , Jhala Lahar , Kapadi Priyank , Vala Karshan , Zala Yashpal , Modi Vidhi , Jhala Dushyantsinh , Patel Akshita , Baraiya Sneha , Devaliya Dhaval
Published: November 2023

Using satellite telemetry, it is possible to track long-distance migrant birds with high accuracy and greater spatial coverage. However, prior to 2014, less than 1% of bird species in India had been monitored using this technology. Between January and February 2022, we deployed leg-mounted solar GPS/GSM satellite transmitters to Common Cranes and Demoiselle Cranes (two each) to study home ranges, movement, migration patterns, and habitat use. We used 95% kernels to define the total home range size and 50% kernels to delimit the core areas. The winter habitat use was assessed using Generalized Linear Models (GLM). The average home range of Common Cranes and Demoiselle Cranes was estimated as 161.22 ± 172.08 km2 and 971.40 ± 1023.57 km2, respectively. During migration, Common Crane-1 and Common Crane-2 traveled an average of 471.19 ± 442.42 and 176.97 ± 24.82 km per day, and Demoiselle Crane-1 and Demoiselle Crane-2 covered an average daily distance of 168.10 ± 203.77 and 192.97 ± 250.72 km, respectively. Water bodies and croplands were the most important habitat variables influencing crane presence positively. In recent years, the share of food grain crops within the study area has declined from 43% in 1994–1995 to 36% in 2014–2015, while the share of cotton crops has doubled from 11% to 20%, indicating a probable cause of concern in the near future.
Mutation Status and Glucose Availability Affect the Response to Mitochondria-Targeted Quercetin Derivative in Breast Cancer Cells
Authors: Przybylski Paweł , Lewińska Anna , Rzeszutek Iwona , Błoniarz Dominika , Moskal Aleksandra , Betlej Gabriela , Deręgowska Anna , Cybularczyk-Cecotka Martyna , Szmatoła Tomasz , Litwinienko Grzegorz , Wnuk Maciej
Published: November 2023

Mitochondria, the main cellular power stations, are important modulators of redox-sensitive signaling pathways that may determine cell survival and cell death decisions. As mitochondrial function is essential for tumorigenesis and cancer progression, mitochondrial targeting has been proposed as an attractive anticancer strategy. In the present study, three mitochondria-targeted quercetin derivatives (mitQ3, 5, and 7) were synthesized and tested against six breast cancer cell lines with different mutation and receptor status, namely ER-positive MCF-7, HER2-positive SK-BR-3, and four triple-negative (TNBC) cells, i.e., MDA-MB-231, MDA-MB-468, BT-20, and Hs 578T cells. In general, the mito-quercetin response was modulated by the mutation status. In contrast to unmodified quercetin, 1 µM mitQ7 induced apoptosis in breast cancer cells. In MCF-7 cells, mitQ7-mediated apoptosis was potentiated under glucose-depleted conditions and was accompanied by elevated mitochondrial superoxide production, while AMPK activation-based energetic stress was associated with the alkalization of intracellular milieu and increased levels of NSUN4. Mito-quercetin also eliminated doxorubicin-induced senescent breast cancer cells, which was accompanied by the depolarization of mitochondrial transmembrane potential. Limited glucose availability also sensitized doxorubicin-induced senescent breast cancer cells to apoptosis. In conclusion, we show an increased cytotoxicity of mitochondria-targeted quercetin derivatives compared to unmodified quercetin against breast cancer cells with different mutation status that can be potentiated by modulating glucose availability.
RFS+: A Clinically Adaptable and Computationally Efficient Strategy for Enhanced Brain Tumor Segmentation
Authors: Duman Abdulkerim , Karakuş Oktay , Sun Xianfang , Thomas Solly , Powell James , Spezi Emiliano
Published: November 2023

Automated brain tumor segmentation has significant importance, especially for disease diagnosis and treatment planning. The study utilizes a range of MRI modalities, namely T1-weighted (T1), T1-contrast-enhanced (T1ce), T2-weighted (T2), and fluid-attenuated inversion recovery (FLAIR), with each providing unique and vital information for accurate tumor localization. While state-of-the-art models perform well on standardized datasets like the BraTS dataset, their suitability in diverse clinical settings (matrix size, slice thickness, manufacturer-related differences such as repetition time, and echo time) remains a subject of debate. This research aims to address this gap by introducing a novel ‘Region-Focused Selection Plus (RFS+)’ strategy designed to efficiently improve the generalization and quantification capabilities of deep learning (DL) models for automatic brain tumor segmentation. RFS+ advocates a targeted approach, focusing on one region at a time. It presents a holistic strategy that maximizes the benefits of various segmentation methods by customizing input masks, activation functions, loss functions, and normalization techniques. Upon identifying the top three models for each specific region in the training dataset, RFS+ employs a weighted ensemble learning technique to mitigate the limitations inherent in each segmentation approach. In this study, we explore three distinct approaches, namely, multi-class, multi-label, and binary class for brain tumor segmentation, coupled with various normalization techniques applied to individual sub-regions. The combination of different approaches with diverse normalization techniques is also investigated. A comparative analysis is conducted among three U-net model variants, including the state-of-the-art models that emerged victorious in the BraTS 2020 and 2021 challenges. These models are evaluated using the dice similarity coefficient (DSC) score on the 2021 BraTS validation dataset. The 2D U-net model yielded DSC scores of 77.45%, 82.14%, and 90.82% for enhancing tumor (ET), tumor core (TC), and the whole tumor (WT), respectively. Furthermore, on our local dataset, the 2D U-net model augmented with the RFS+ strategy demonstrates superior performance compared to the state-of-the-art model, achieving the highest DSC score of 79.22% for gross tumor volume (GTV). The model utilizing RFS+ requires 10% less training dataset, 67% less memory and completes training in 92% less time compared to the state-of-the-art model. These results confirm the effectiveness of the RFS+ strategy for enhancing the generalizability of DL models in brain tumor segmentation.
The Effects of Vitamin D on Preventing Hyperglycemia and a Novel Approach to Its Treatment
Authors: Monapati Suchitra , Kaki Pavani , Gurajapu Mary Stella , Subhas Prathibha Guttal , Kudipudi Harinadha Baba
Published: November 2023

The dietary reference levels for vitamin D were established with an emphasis on its role in bone health; however, with the identification of vitamin D receptors in all body tissues novel associations with other metabolic disorders, such as diabetes, are being researched. Aside from its standard function as the main regulator of calcium absorption, vitamin D also controls the calcium pool, mediates the activity of beta cell calcium-dependent endopeptidases, encourages the conversion of proinsulin to insulin, increases insulin output, and raises insulin activity in peripheral insulin target tissues. Both immune cells and pancreatic beta cells include vitamin D receptors. A deficiency of vitamin D causes glucose intolerance and affects insulin secretion. Different pathogenic characteristics of the disease are linked to a number of vitamin D-related genes. It has been proven that vitamin D supplementation lowers the risk of type 1 and type 2 diabetes and its associated problems. In this article, we discussed a few prospective clinical trials on vitamin D that are necessary to clearly demonstrate the role of vitamin D in the prevention and management of diabetes.
Development of a Hardware-in-the-Loop Platform for the Validation of a Small-Scale Wind System Control Strategy
Authors: Martínez-Nolasco Juan , Sámano-Ortega Víctor , Botello-Álvarez José , Padilla-Medina José , Martínez-Nolasco Coral , Bravo-Sánchez Micael
Published: November 2023

The use of renewable energies contributes to the goal of mitigating climate change by 2030. One of the fastest-growing renewable energy sources in recent years is wind power. Large wind generation systems have drawbacks that can be minimized using small wind systems and DC microgrids (DC-µGs). A wind system requires a control system to function correctly in different regions of its operating range. However, real-time analysis of a physical wind system may not be feasible. An alternative to counteract this disadvantage is using real-time hardware in the loop (HIL) simulation. This article describes the implementation of an HIL platform in an NI myRIO 1900 to evaluate the performance of control algorithms in a small wind system (SWS) that serves as a distributed generator for a DC-µG. In the case of an SWS, its implementation implies nonlinear behaviors and, therefore, nonlinear equations, and this paper shows a way to do it by distributing the computational work, using a high-level description language, and achieving good accuracy and latency with a student-oriented development kit. The platform reproduces, with an integration time of 10 µs, the response of the SWS composed of a 3.5 kW turbine with a fixed blade pitch angle and no gear transmission, a permanent magnet synchronous generator (PMSG), and a three-phase full-bridge AC/DC electronic power converter. The platform accuracy was validated by comparing its results against a software simulation. The compared variables were the PMSG currents in dq directions, the turbine’s angular speed, and the DC bus’s voltage. These comparisons showed mean absolute errors of 0.04 A, 1.9 A, 0.7 rad/s, and 9.5 V, respectively. The platform proved useful for validating the control algorithm, exhibiting the expected results in comparison with a lab-scale prototype using the same well-known control strategy. Using a well-known control strategy provides a solid reference to validate the platform.
Levelized Cost Analysis for Blast Furnace CO2 Capture, Utilization, and Storage Retrofit in China’s Blast Furnace–Basic Oxygen Furnace Steel Plants
Authors: Gu Changwan , Xie Jingjing , Li Xiaoyu , Gao Xu
Published: November 2023

As the largest carbon emitter in China’s manufacturing sector, the low-carbon transition of the steel industry is urgent. CO2 capture, utilization, and storage (CCUS) technology is one of the effective measures to reduce carbon emissions in steel industry. In this paper, a comprehensive assessment model of source–sink matching-levelized cost in China’s steel industry is constructed to evaluate the potential, economy, and spatial distribution of CCUS retrofits of blast furnaces in the BF-BOF steel industry. The results show that, if no extra incentive policy is included, the levelized cost of carbon dioxide (LCOCD) of 111 steel plants with a 420.07 Mt/a CO2 abatement potential ranges from −134.87 to 142.95 USD/t. The levelized cost of crude steel (LCOS) range of steel plants after the CCUS retrofits of blast furnaces is 341.81 to 541.41 USD/t. The incentives such as carbon market and government subsidies will all contribute to the early deployment of CCUS projects. The CCUS technology could be prioritized for deployment in North China, Northwest China, and East China’s Shandong Province, but more powerful incentives are still needed for current large-scale deployment. The research results can provide references for the early deployment and policy formulation of CCUS in China’s steel industry.
A Neural Network-Based Method for Real-Time Inversion of Nonlinear Heat Transfer Problems
Authors: Chen Changxu , Pan Zhenhai
Published: November 2023

Inverse heat transfer problems are important in numerous scientific research and engineering applications. This paper proposes a network-based method utilizing the nonlinear autoregressive with exogenous inputs (NARX) neural network, which can achieve real-time identification of thermal boundary conditions for nonlinear transient heat transfer processes. With the introduction of the NARX neural network, the proposed method offers two key advantages: (1) The proposed method can obtain inversion results using only surface temperature time series. (2) The heat flux can be estimated even when the state equation of the system is unknown. The NARX neural network is trained using the Bayesian regularization algorithm with a dataset comprising exact surface temperature and heat flux data. The neural network takes current and historical surface temperature measurements as inputs to calculate the heat flux at the current time. The capability of the NARX method has been verified through numerical simulation experiments. Experimental results demonstrate that the NARX method has high precision, strong noise resistance, and broad applicability. The composition of the input data, the surface temperature measurement noise, and the boundary heat flux shape have been studied in detail for their impact on the inversion results. The NARX method is a highly competitive solution to inverse heat transfer problems.
Steady-State and Transient Operation of Solid Oxide Fuel Cell Systems with Anode Off-Gas Recirculation within a Highly Constrained Operating Range
Authors: Hollmann Jan , Kabelac Stephan
Published: November 2023

Based on a prototype presented in a prior publication, this research investigates the operational characteristics of a methane-fueled solid oxide fuel cell (SOFC) system with anode off-gas recirculation (AOGR) for electrical energy supply on sea-going vessels. The proposed first-principle system model utilizes a spatially segmented SOFC stack and lumped balance of plant components validated on the component level to accurately depict the steady-state and transient operating behavior. Five operational limitations are chosen to highlight permissible operating conditions with regard to stack and pre-reformer degradation. Steady-state operating maps are presented, emphasizing efficient operating conditions at maximum stack fuel utilization and minimal permissible oxygen-to-carbon ratio. Exemplary transient load changes illustrate increasing system control complexity caused by gas flow delays due to the spatially distributed plant layout. Actuation strategies are presented and underline the need for a top-level model predictive system controller to assure a dynamic and efficient operation within the defined constraints.
White Spaces Unveiled: Investigating the Restorative Potential of Environmentally Perceived Characteristics in Urban Parks during Winter
Authors: Bao Yu , Gao Ming , Zhao Chunli , Zhou Xudan
Published: November 2023

Creating attractive urban green spaces in severely cold and harsh climates is significant for promoting peoples’ health and perceived restoration. However, there is little evidence regarding the urban green spaces in wintery and cold climates and its restorative benefits. This study utilized a pixel grid approach to quantify winter landscape characteristics and a self-reporting method to assess the restorative benefits of audiovisual interactions. The results show the following: (1) Different types of roads in urban parks have significant differences in their level of restorativeness, and the restorativeness benefits of the primary path in winter parks are the strongest. (2) The presence of snowy elements in winter landscapes can enhance park users’ potential to experience restorative characteristics in relation to “being away”. Moreover, there exists a noteworthy positive correlation between deciduous trees and their restoration benefits. (3) People’s perceptions of the tranquility of the soundscape and the duration of environmental exposure are critical mediators in the impact of the restorative path effect. (4) Compared with women, men have a higher restorative level in both the landscape and soundscape. This elucidates the restorative role of white space landscapes and soundscapes in public psychological perception when proposing appropriate forest-based healthcare strategies. It also provides theoretical guidance and optimization schemes for the overall planning, health planning, and design of white spaces shaped by cold urban green spaces.