bluuebunny/crossref_metadata_2025 huggingface dataset:
Interplay between Myokine Profile and Glycemic Control in Type 2 Diabetes Mellitus Patients with Heart Failure
Authors: Berezin Alexander A. , Obradovic Zeljko , Novikov Evgen V. , Boxhammer Elke , Lichtenauer Michael , Berezin Alexander E.
Published: November 2022

Type 2 diabetes mellitus (T2DM) remains a powerful predictor of progressive heart failure (HF), but it is not clear whether altered glycemic control interferes with HF progression via an impaired profile of circulating myokines. The aim was to investigate plausible effects of glucose control on a myokine signature in T2DM patients affected by chronic HF. We selected 372 T2DM patients from the local database and finally included 314 individuals suffering from chronic HF and subdivided them into two groups according to glycosylated hemoglobin (HbA1c) levels (<6.9% and ≥7.0%). Echocardiography and Doppler examinations along with biomarker measurements were performed at the baseline of the study. The results showed that irisin levels were significantly lower in patients with HbA1c ≥ 7.0% than in those with HbAc1 < 6.9%, whereas concentrations of apelin, myostatin and adropin did not significantly differ between these two groups. We also identified numerous predictors of poor glycemic control, but only N-terminal brain natriuretic propeptide (odds ratio [OR] = 1.07; 95% confidence interval [CI] = 1.02–1.10, p = 0.04) and irisin (OR = 1.09; 95% CI = 1.04–1.17, p = 0.001) remained independent predictors of the dependent variable. In conclusion, we found that decreased levels of irisin were associated with poor glycemic control in T2DM patients with HF regardless of clinical conditions and other biomarkers.
Bronchoalveolar Lavage Fluid-Isolated Biomarkers for the Diagnostic and Prognostic Assessment of Lung Cancer
Authors: Kalkanis Alexandros , Papadopoulos Dimitrios , Testelmans Dries , Kopitopoulou Alexandra , Boeykens Eva , Wauters Els
Published: November 2022

Lung cancer is considered one of the most fatal malignant neoplasms because of its late detection. Detecting molecular markers in samples from routine bronchoscopy, including many liquid-based cytology procedures, such as bronchoalveolar lavage fluid (BALF), could serve as a favorable technique to enhance the efficiency of a lung cancer diagnosis. BALF analysis is a promising approach to evaluating the tumor progression microenvironment. BALF’s cellular and non-cellular components dictate the inflammatory response in a cancer-proliferating microenvironment. Furthermore, it is an essential material for detecting clinically significant predictive and prognostic biomarkers that may aid in guiding treatment choices and evaluating therapy-induced toxicities in lung cancer. In the present article, we have reviewed recent literature about the utility of BALF analysis for detecting markers in different stages of tumor cell metabolism, employing either specific biomarker assays or broader omics approaches.
Computational Pipeline for Glomerular Segmentation and Association of the Quantified Regions with Prognosis of Kidney Function in IgA Nephropathy
Authors: Kawazoe Yoshimasa , Shimamoto Kiminori , Yamaguchi Ryohei , Nakamura Issei , Yoneda Kota , Shinohara Emiko , Shintani-Domoto Yukako , Ushiku Tetsuo , Tsukamoto Tatsuo , Ohe Kazuhiko
Published: November 2022

The histopathological findings of the glomeruli from whole slide images (WSIs) of a renal biopsy play an important role in diagnosing and grading kidney disease. This study aimed to develop an automated computational pipeline to detect glomeruli and to segment the histopathological regions inside of the glomerulus in a WSI. In order to assess the significance of this pipeline, we conducted a multivariate regression analysis to determine whether the quantified regions were associated with the prognosis of kidney function in 46 cases of immunoglobulin A nephropathy (IgAN). The developed pipelines showed a mean intersection over union (IoU) of 0.670 and 0.693 for five classes (i.e., background, Bowman’s space, glomerular tuft, crescentic, and sclerotic regions) against the WSI of its facility, and 0.678 and 0.609 against the WSI of the external facility. The multivariate analysis revealed that the predicted sclerotic regions, even those that were predicted by the external model, had a significant negative impact on the slope of the estimated glomerular filtration rate after biopsy. This is the first study to demonstrate that the quantified sclerotic regions that are predicted by an automated computational pipeline for the segmentation of the histopathological glomerular components on WSIs impact the prognosis of kidney function in patients with IgAN.
Tissue Characteristics of Endometrial Carcinoma Analyzed by Quantitative Synthetic MRI and Diffusion-Weighted Imaging
Authors: Wang Yiang , He Mengge , Cao Peng , Ip Philip P. C. , Lin Chien-Yuan , Liu Weiyin , Lee Chia-Wei , Lee Elaine Y. P.
Published: November 2022

Background: This study investigates the association of T1, T2, proton density (PD) and the apparent diffusion coefficient (ADC) with histopathologic features of endometrial carcinoma (EC). Methods: One hundred and nine EC patients were prospectively enrolled from August 2019 to December 2020. Synthetic magnetic resonance imaging (MRI) was acquired through one acquisition, in addition to diffusion-weighted imaging (DWI) and other conventional sequences using 1.5T MRI. T1, T2, PD derived from synthetic MRI and ADC derived from DWI were compared among different histopathologic features, namely the depth of myometrial invasion (MI), tumor grade, cervical stromal invasion (CSI) and lymphovascular invasion (LVSI) of EC by the Mann–Whitney U test. Classification models based on the significant MRI metrics were constructed with their respective receiver operating characteristic (ROC) curves, and their micro-averaged ROC was used to evaluate the overall performance of these significant MRI metrics in determining aggressive histopathologic features of EC. Results: EC with MI had significantly lower T2, PD and ADC than those without MI (p = 0.007, 0.006 and 0.043, respectively). Grade 2–3 EC and EC with LVSI had significantly lower ADC than grade 1 EC and EC without LVSI, respectively (p = 0.005, p = 0.020). There were no differences in the MRI metrics in EC with or without CSI. Micro-averaged ROC of the three models had an area under the curve of 0.83. Conclusions: Synthetic MRI provided quantitative metrics to characterize EC with one single acquisition. Low T2, PD and ADC were associated with aggressive histopathologic features of EC, offering excellent performance in determining aggressive histopathologic features of EC.
Hereditary Hemorrhagic Telangiectasia—A Case Series Experience from a Liver Transplant Center in Romania
Authors: Pavel Christopher , Cabel Teodor , Dinuță Dragoș , Zaharia Alexandru , Dima Simona Olimpia , Sandru Vasile , Grasu Mugur Cristian , Mihaila Mariana
Published: November 2022

Hereditary hemorrhagic telangiectasia (HHT) has significant morbidity due to multiorgan involvement and an unpredictable disease course. We analyzed the data of 14 patients diagnosed with HHT. The case series comprised 14 patients with a median age at presentation of 48 years old (41–74 years). In twelve patients (85.7%), the diagnosis was confirmed by using the Curacao Criteria. The most common reason for admission was epistaxis, with 9 patients (57%) presenting with nosebleed refractory to prolonged self-tamponade. The biochemical abnormalities identified were elevations in AP and gamma-GT; liver synthetic function was generally normal, even though 21% of patients had clinical or imaging findings for cirrhosis. Nosebleeds were the main reason for admission and significantly impacted quality of life through anemia and frequent hospital admissions. However, the visceral manifestations seemed to be more serious. The hepatic arteriovenous malformations (AVMs) appeared to remain asymptomatic or led to minimal changes for the majority of patients; some cases were associated with liver and biliary tract ischemia, necrosis leading to acute liver failure and even death. Hepatic AVMs can also lead to high-output heart failure due to arterio-venous shunting. The most frequent AVM was hepatic artery to hepatic vein, with secondary hepatic vein dilation and hemodynamic consequences.
Embedded AMIS-Deep Learning with Dialog-Based Object Query System for Multi-Class Tuberculosis Drug Response Classification
Authors: Prasitpuriprecha Chutinun , Pitakaso Rapeepan , Gonwirat Sarayut , Enkvetchakul Prem , Preeprem Thanawadee , Jantama Sirima Suvarnakuta , Kaewta Chutchai , Weerayuth Nantawatana , Srichok Thanatkij , Khonjun Surajet , Nanthasamroeng Natthapong
Published: November 2022

A person infected with drug-resistant tuberculosis (DR-TB) is the one who does not respond to typical TB treatment. DR-TB necessitates a longer treatment period and a more difficult treatment protocol. In addition, it can spread and infect individuals in the same manner as regular TB, despite the fact that early detection of DR-TB could reduce the cost and length of TB treatment. This study provided a fast and effective classification scheme for the four subtypes of TB: Drug-sensitive tuberculosis (DS-TB), drug-resistant tuberculosis (DR-TB), multidrug-resistant tuberculosis (MDR-TB), and extensively drug-resistant tuberculosis (XDR-TB). The drug response classification system (DRCS) has been developed as a classification tool for DR-TB subtypes. As a classification method, ensemble deep learning (EDL) with two types of image preprocessing methods, four convolutional neural network (CNN) architectures, and three decision fusion methods have been created. Later, the model developed by EDL will be included in the dialog-based object query system (DBOQS), in order to enable the use of DRCS as the classification tool for DR-TB in assisting medical professionals with diagnosing DR-TB. EDL yields an improvement of 1.17–43.43% over the existing methods for classifying DR-TB, while compared with classic deep learning, it generates 31.25% more accuracy. DRCS was able to increase accuracy to 95.8% and user trust to 95.1%, and after the trial period, 99.70% of users were interested in continuing the utilization of the system as a supportive diagnostic tool.
Fight against Future Pandemics: UAV-Based Data-Centric Social Distancing, Sanitizing, and Monitoring Scheme
Authors: Gupta Rajesh , Bhattacharya Pronaya , Tanwar Sudeep , Sharma Ravi , Alqahtani Fayez , Tolba Amr , Țurcanu Florin-Emilian , Raboaca Maria
Published: November 2022

The novel coronavirus disease-2019 (COVID-19) has transformed into a global health concern, which resulted in human containment and isolation to flatten the curve of mortality rates of infected patients. To leverage the massive containment strategy, fifth-generation (5G)-envisioned unmanned aerial vehicles (UAVs) are used to minimize human intervention with the key benefits of ultra-low latency, high bandwidth, and reliability. This allows phased treatment of infected patients via threefold functionalities (3FFs) such as social distancing, proper sanitization, and inspection and monitoring. However, UAVs have to send massive recorded data back to ground stations (GS), which requires a real-time device connection density of 107/km2, which forms huge bottlenecks on 5G ecosystems. A sixth-generation (6G) ecosystem can provide terahertz (THz) frequency bands with massive short beamforming cells, intelligent deep connectivity, and physical- and link-level protocol virtualization. The UAVs form a swarm network to assure 3FFs which requires high-end computations and are data-intensive; thus, these computational tasks can be offloaded to nearby edge servers, which employ local federated learning to train the global models. It synchronizes the UAV task formations and optimizes the network functions. Task optimization of UAV swarms in 6G-assisted channels allows better management and ubiquitous and energy-efficient seamless communication over ground, space, and underwater channels. Thus, a data-centric 3FF approach is essential to fight against future pandemics, with a 6G backdrop channel. The proposed scheme is compared with traditional fourth-generation (4G) and 5G-networks-based schemes to indicate its efficiency in traffic density, processing latency, spectral efficiency, UAV mobility, radio loss, and device connection density.
An Adaptive Beetle Swarm Optimization Algorithm with Novel Opposition-Based Learning
Authors: Wang Qifa , Cheng Guanhua , Shao Peng
Published: November 2022

The Beetle Swarm Optimization (BSO) algorithm is a high-performance swarm intelligent algorithm based on beetle behaviors. However, it suffers from poor search speeds and is prone to local optimization due to the size of the step length. To address this further, a novel improved opposition-based learning mechanism is utilized, and an adaptive beetle swarm optimization algorithm with novel opposition-based learning (NOBBSO) is proposed. In the proposed NOBBSO algorithm, the novel opposition-based learning is designed as follows. Firstly, according to the characteristics of the swarm intelligence algorithms, a new opposite solution is obtained to generate the current optimal solution by iterations in the current population. The novel opposition-based learning strategy is easy to converge quickly. Secondly, an adaptive strategy is used to make NOBBSO parameters self-adaptive, which makes the results tend to converge more easily. Finally, 27 CEC2017 benchmark functions are tested to verify its effectiveness. Comprehensive numerical experiment outcomes demonstrate that the NOBBSO algorithm has obtained faster convergent speed and higher convergent accuracy in comparison with other outstanding competitors.
Blockchain Application Analysis Based on IoT Data Flow
Authors: Li Juxia , Zhang Xing , Shi Wei
Published: November 2022

In the Internet of Things (IoT) system, data leakage can easily occur due to the differing security of edge devices and the different processing methods of data in the transmission process. Blockchain technology has the advantages of good non-tamperability, decentralization, de-trust, openness, and transparency, and it can protect data security on the Internet of Things. This research integrates the means by which data flow can be combined with blockchain technology to prevent privacy leakage throughout the entire transportation process from sender to receiver. Through a keyword search of the last five years, 94 related papers in Web of Science and IEEE Xplore were extracted and the complex papers and frameworks explained using a reconstruction graph. The data processing process is divided into five modules: data encryption, data access control, data expansion, data storage, and data visualization. A total of 11 methods combining blockchain technology to process IoT data were summarized. The blockchain application technology in the IoT field was summarized objectively and comprehensively, and a new perspective for studying IoT data flow was given.
Application of FCM Clustering Algorithm in Digital Library Management System
Authors: Shi Yuqing
Published: November 2022

Traditional library systems are gradually being replaced by digital libraries. Digital libraries are developing from simple database-based storage and retrieval to knowledge-based implementation. The fuzzy C-means (FCM) clustering algorithm is an example of data collection and data processing technology. It evaluates and draws conclusions based on mathematics, large data, and other technologies. In order to better improve the digital library management system, this paper applied FCM clustering algorithm to the digital library management system. Based on the in-depth study of the FCM clustering algorithm, this paper built a digital library management system. The clustering algorithm was used to cluster library borrowing records and reader information. It provided technical support and suggestions on library collection construction and book purchase and promoted book management to form a good spitting cycle. The experimental results extracted during the evaluation phase demonstrated that the overall error rate of the suggested FCM clustering algorithm for information clustering is 3.66%, which is better than the existing comparative models. This shows that applying the FCM clustering algorithm to a digital library management system has some practical significance.
A Quasi-Wireless Intraoperatory Neurophysiological Monitoring System
Authors: Alonso Rivas Eduardo , Giannetti Romano , Rodríguez-Morcillo García Carlos , Matanza Domingo Javier , Muñoz Frías José Daniel , Scandurra Graziella , Ciofi Carmine , Vega-Zelaya Lorena , Pastor Jesús
Published: November 2022

Intraoperative Neurophysiological Monitoring is a set of monitoring techniques that reads electrical activity generated by the nervous system structures during surgeries. In non-trivial surgeries, neurophysiologists require a significant number of electrical signals to be picked up to check the effects of the surgeon’s actions in real time or to confirm that the correct nerves are selected. As a result, cabling the patient in the operating room can become cumbersome. The proposed WIONM module solves part of the problem by converting a good part of those cables into a wireless connection that is substantially transparent to the human operator and the existing medical instrumentation.
Investigations on the Modulation Strategies for Performance Improvement of a Controlled Wind Energy System
Authors: Durgam Rajababu , Karampuri Ramsha , Rangarajan Shriram S. , Subramaniam Umashankar , Collins E. Randolph , Senjyu Tomonobu
Published: November 2022

The challenges faced in an isolated wind energy conversion system (WECS) are larger transient times, high steady-state error, and larger harmonic content. To overcome these issues, an adaptive voltage controller (AVC) along with the load current observer (LCO) could be the better proposition. However, the AVC and LCO, in conjunction with the conventional space vector pulse width modulation (SVPWM) technique to operate the three-phase inverter of WECS, would not be able to further improve these parameters. This paper proposes the use of the unified voltage SVPWM (UVSVPWM) technique along with the AVC and LCO, which could improve the transient behavior by about 30% as well as reduce the harmonic content of the load voltage and current by about 70% and 2%, respectively. This paper considers an isolated WECS connected to the linear load, which is operated under balanced as well as unbalanced load conditions. The proposed control technique is verified for both the balanced and unbalanced cases using MATLAB/Simulink.
DNN Beamforming for LEO Satellite Communication at Sub-THz Bands
Authors: Kumar Rajnish , Arnon Shlomi
Published: November 2022

The 6G communication system will be designed at sub-THz frequencies due to increasing demand in data rates, emerging new applications and advanced communication technologies. These high-performing systems will heavily rely on artificial intelligence (AI) for efficient and robust design of transceivers. In this work, we propose a deep neural network (DNN) beamformer that will replace the use of phase shifters for a massive array of antenna elements employed at the ground station for wideband LEO satellite communication at sub-THz bands. We show that the signal processing algorithm employed using DNN is capable to match the performance of a true-time delay beamformer as the angle of arrival of the received wideband signal at the ground station is changing due to rapid movement of the LEO satellite. The implementation of DNN beamformer will be able to reduce the cost of receiver and provide a way for the efficient and compact design of the massive array beamforming for wideband LEO satellite applications.
Semantic Segmentation Algorithm-Based Calculation of Cloud Shadow Trajectory and Cloud Speed
Authors: Wang Shitao , Sun Mingjian , Shen Yi
Published: November 2022

Cloud covering is an important factor affecting solar radiation and causes fluctuations in solar energy production. Therefore, real-time recognition and the prediction of cloud covering and the adjustment of the angle of photovoltaic panels to improve power generation are important research areas in the field of photovoltaic power generation. In this study, several methods, namely, the principle of depth camera measurement distance, semantic segmentation algorithm, and long- and short-term memory (LSTM) network were combined for cloud observation. The semantic segmentation algorithm was applied to identify and extract the cloud contour lines, determine the feature points, and calculate the cloud heights and geographic locations of the cloud shadows. The LSTM algorithm was used to predict the trajectory and speed of the cloud movement, achieve accurate and real-time detection, and track the clouds and the sun. Based on the results of these methods, the shadow area of the cloud on the ground was calculated. The recursive neural LSTM network was also used to predict the track and moving speed of the clouds according to the cloud centroid data of the cloud images at different times. The findings of this study can provide insights to establish a low-cost intelligent monitoring predicting system for cloud covering and power generation.
Recent Advancements in Evacuated Tube Solar Water Heaters: A Critical Review of the Integration of Phase Change Materials and Nanofluids with ETCs
Authors: Uniyal Arun , Prajapati Yogesh K. , Ranakoti Lalit , Bhandari Prabhakar , Singh Tej , Gangil Brijesh , Sharma Shubham , Upadhyay Viyat Varun , Eldin Sayed M.
Published: November 2022

Evacuated tube solar water heaters are gaining more attention in the present market scenario as compared to conventional collectors. Such collectors are versatile because no solar tracking is required and the operating temperature range is also broad. Comparatively, it is cost-effective and may attain higher thermal efficiency. However, like other collectors, continuous energy supply is sometimes hampered by the intermittent nature of solar radiation. This problem can be partially resolved by using phase change materials (PCM) in the evacuated tube solar collector (ETC). PCMs can store the energy during the sunshine hours, which can be released when solar energy is not available. In the literature, several studies are available pertaining to the use of PCMs in ETC-based solar water heaters. The literature indicates that the integration of PCMs with ETCs has several merits. Nevertheless, systematic, and comprehensive review papers dedicated to such integrated energy storage systems with ETC solar water heaters are not available. Hence, the objective of this work is to compile the relevant experimental, numerical, and theoretical works reported in the literature. The present paper broadly reviews the recent design modifications, PCM integration with different kinds of ETC water heaters, and their life cycle assessment. Furthermore, studies in the literature pertaining to the application of nanoparticles in ETC systems are also discussed, and finally, a roadmap for this energy storage system is provided.
Contrasting Soil Microbial Functional Potential for Phosphorus Cycling in Subtropical and Temperate Forests
Authors: Zhou Sha , Li Yi , Wang Jieying , He Liyuan , Wang Jun , Guo Yaoxin , Zhao Fazhu
Published: November 2022

Microorganisms play important roles in phosphorus (P) cycling via their regulation of P uptake and transport, P mineralization and solubilization, and the mediation of P deficiency in forest biomes. However, the dynamics of microbial P functional genes and the underlying regulatory mechanisms in different forest biomes (e.g., temperate vs. subtropical) have yet to be sufficiently clarified. In this study, we applied a metagenomics approach to investigate changes in the abundance of three microbial P functional gene groups (P starvation response regulation genes, P uptake and transport genes, and P solubilization and mineralization genes) along a subtropical–temperate gradient of forest biomes (23° N–45° N) in China. Our results revealed that the abundances of P starvation response regulation genes in temperate forest biomes were significantly higher than those in the subtropics (p < 0.05), although not in the cases of the other two P functional gene types (p > 0.05). Moreover, in both temperate and subtropical forests, Acidobacteria, Actinobacteria, Proteobacteria, and Verrucomicrobia were identified as key phyla associated with P cycling; moreover, we found dominate species of Acidobacteria and Proteobacteria at genus level were higher in subtropical zones than that of temperate zones, in most cases. Furthermore, our results showed that significant correlation was found between P functional genes and microbial α-diversity along latitude gradient. Furthermore, in both forest biomes, microbial community α-diversity was significantly positively correlated with P starvation response regulation genes (p < 0.05), whereas α-diversity was significantly positively related to P uptake and transport genes in temperate forest biomes (p < 0.001), although not in subtropical forests (p > 0.05). In addition, we found that whereas soil substrates showed significant negative relationships with P solubilization and mineralization genes in temperate forest biomes (p < 0.05), this was not the case in subtropical forests. Collectively, these findings indicate that the responses of microbial P functional genes to the environmental variation in temperate forests are more sensitive than those in subtropical forests, thereby providing a theoretical foundation for further elucidation of the differential regulatory roles of these genes in different forest biomes.
The Adaptive Capacity of Alien and Rare Species in China
Authors: Zhou Quanlai , Xin Zhiming , Wang Yongcui , Miao Renhui , Liu Zhimin , Zong Lu , Li Xuehua , Ma Qun , Liang Wei , Yu Haibin , Wang Lixin
Published: November 2022

Alien and rare plants tend to adapt to contrasting biotic and abiotic conditions. However, adaptability assessments of alien and rare plants using biological and habitat characteristics in stressful and disturbed environments are limited. We collected and demonstrated the biological and habitat characteristics and assessed the adaptive capacity of alien and rare plants in China using the analytic hierarchy process. Biological characteristics, such as dispersal strategy, sexual and asexual reproduction modes, life form, and habitat characteristics (e.g., habitat type and distribution spatial extent), are important indicators of the adaptability of alien and rare plants to stressful and disturbed environments. Alien plants have a higher adaptive capacity to disturbed environments than rare plants, while rare plants have a higher adaptive capacity to stressful environments than alien plants. Stressful and disturbed environments constrain the adaptive capacity of alien and rare plants, respectively. However, the constraint on alien plants from stressful environments is weaker than that on rare plants from disturbed environments. Understanding the adaptive capacity of alien and rare plants will help researchers and policymakers develop strategies for preventing the invasion of alien plants as well as protecting rare and endangered plants.
Analysis and Prediction of the IPv6 Traffic over Campus Networks in Shanghai
Authors: Sun Zhiyang , Ruan Hui , Cao Yixin , Chen Yang , Wang Xin
Published: November 2022

With the exhaustion of IPv4 addresses, research on the adoption, deployment, and prediction of IPv6 networks becomes more and more significant. This paper analyzes the IPv6 traffic of two campus networks in Shanghai, China. We first conduct a series of analyses for the traffic patterns and uncover weekday/weekend patterns, the self-similarity phenomenon, and the correlation between IPv6 and IPv4 traffic. On weekends, traffic usage is smaller than on weekdays, but the distribution does not change much. We find that the self-similarity of IPv4 traffic is close to that of IPv6 traffic, and there is a strong positive correlation between IPv6 traffic and IPv4 traffic. Based on our findings on traffic patterns, we propose a new IPv6 traffic prediction model by combining the advantages of the statistical and deep learning models. In addition, our model would extract useful information from the corresponding IPv4 traffic to enhance the prediction. Based on two real-world datasets, it is shown that the proposed model outperforms eight baselines with a lower prediction error. In conclusion, our approach is helpful for network resource allocation and network management.
Lamiaceae as Feed Additives in Fish Aquaculture
Authors: Orso Graziella , Imperatore Roberta , Coccia Elena , Ashouri Ghasem , Paolucci Marina
Published: November 2022

The growing demand for high-quality food has induced a rapid expansion of the aquaculture sector. On the other hand, this sector has to overcome numerous challenges and problems triggered by the adoption of intensive farming systems, such as stress and high susceptibility to diseases. The improper use of chemicals and antibiotics has led to the development of antibiotic resistance in fish, with consequent health risks for consumers. Natural additives are increasingly used in aquaculture and, among these, medicinal plants are constantly under investigation as safe and environmentally friendly alternatives to chemicals. Great attention has been paid to Lamiaceae plants as feed additives capable of enhancing the growth performance, immune system, and antioxidant status of farmed fish. The aim of this review is to provide an updated picture of the employment of the Lamiaceae species (oregano, rosemary, sage, thyme, and mint) to enhance farmed fish health. The benefits of oregano, rosemary, sage, thyme, and mint feed supplementation on growth performance, immune system, antioxidant status, hemato-biochemical parameters, and resistance to stress, parasites, and bacteria have been described, highlighting weaknesses and drawbacks and proposing possible implementations.
Measurement of Mass Transfer Intensity in Gas–Liquid Medium of Bioreactor Circuit Using the Thermometry Method
Authors: Starodumov Ilya , Nizovtseva Irina , Lezhnin Sergey , Vikharev Sergey , Svitich Vladislav , Mikushin Pavel , Alexandrov Dmitri , Kuznetsov Nikolay , Chernushkin Dmitri
Published: November 2022

The development of energy-efficient solutions for large-scale fermenters demands a deep and comprehensive understanding of hydrodynamic and heat and mass transfer processes. Despite a wide variety of research dedicated to measurements of mass transfer intensity in bubble flows, this research subject faces new challenges due to the topical development of new innovative bioreactor designs. In order to understand the fluid dynamics of the gas–liquid medium, researchers need to develop verified CFD models describing flows in the bioreactor loop using a progressive physical and mathematical apparatus. In the current paper, we represent the results of evaluating the key performance indicator of the bioreactor, namely the volumetric mass transfer coefficient (kLa) known as a parameter of dominant importance for the design, operation, scale-up, and optimization of bioreactors, using the developed thermometry method. The thermometry method under consideration was examined within a series of experiments, and a comparative analysis was provided for a number of various regimes also being matched with the classical approaches. The methodology, experiment results, and data verification are given, which allow the evaluation of the effectiveness and prediction of the fluid flows dynamics in bioreactors circuits and ultimately the operational capabilities of the fermenter line.
Neural Networks for Improving Wind Power Efficiency: A Review
Authors: Shin Heesoo , Rüttgers Mario , Lee Sangseung
Published: November 2022

The demand for wind energy harvesting has grown significantly to mitigate the global challenges of climate change, energy security, and zero carbon emissions. Various methods to maximize wind power efficiency have been proposed. Notably, neural networks have shown large potential in improving wind power efficiency. In this paper, we provide a review of attempts to maximize wind power efficiency using neural networks. A total of three neural-network-based strategies are covered: (i) neural-network-based turbine control, (ii) neural-network-based wind farm control, and (iii) neural-network-based wind turbine blade design. In the first topic, we introduce neural networks that control the yaw of wind turbines based on wind prediction. Second, we discuss neural networks for improving the energy efficiency of wind farms. Last, we review neural networks to design turbine blades with superior aerodynamic performances.
Gelatin-Based Film as a Color Indicator in Food-Spoilage Observation: A Review
Authors: Shaik Mannur Ismail , Azhari Muhammad Farid , Sarbon Norizah Mhd
Published: November 2022

The color indicator can monitor the quality and safety of food products due to its sensitive nature toward various pH levels. A color indicator helps consumers monitor the freshness of food products since it is difficult for them to depend solely on their appearance. Thus, this review could provide alternative suggestions to solve the food-spoilage determination, especially for perishable food. Usually, food spoilage happens due to protein and lipid oxidation, enzymatic reaction, and microbial activity that will cause an alteration of the pH level. Due to their broad-spectrum properties, natural sources such as anthocyanin, curcumin, and betacyanin are commonly used in developing color indicators. They can also improve the gelatin-based film’s morphology and significant drawbacks. Incorporating natural colorants into the gelatin-based film can improve the film’s strength, gas-barrier properties, and water-vapor permeability and provide antioxidant and antimicrobial properties. Hence, the color indicator can be utilized as an effective tool to monitor and control the shelf life of packaged foods. Nevertheless, future studies should consider the determination of food-spoilage observation using natural colorants from betacyanin, chlorophyll, and carotenoids, as well as the determination of gas levels in food spoilage, especially carbon dioxide gas.
The Lipid-Soluble Bioactive Substances of Fagopyrum esculentum Varieties under Different Tillage and Nitrogen Fertilisation
Authors: Dziedzic Krzysztof , Kurek Szymon , Podolska Grażyna , Drzymała-Czyż Sławomira , Mildner-Szkudlarz Sylwia , Sun Wei , Walkowiak Jarosław
Published: November 2022

Yield and grain composition play an important role in food production. The aim of this study was to determine the content of phytosterols, squalene, and tocopherols in four varieties of common buckwheat grains: Kora, Panda, Smuga, and Korona depending on the tillage and nitrogen doses employed. The grains were cultivated at the Experimental Station of the State Research Institute of Soil Science and Plant Cultivation in Osiny, Poland, with plow tillage, without plow tillage cultivation, and with nitrogen fertilizers (0, 50, and 100 kg N2 ha−1). Gas chromatography with a mass spectrometer was used to assess all the parameters studied. The cultivation methods did not affect the levels of phytosterols, tocopherols, and squalene, but the highest levels of phytosterols were seen in the Kora variety with 50 kg N2 ha−1 (ranging from 1198 μg to 1800 μg·g−1 of sample weight); therefore, the variety was the best source of phytosterols investigated.
Ergodic Stationary Distribution and Threshold Dynamics of a Stochastic Nonautonomous SIAM Epidemic Model with Media Coverage and Markov Chain
Authors: Liu Chao , Chen Peng , Cheung Lora
Published: November 2022

A stochastic nonautonomous SIAM (Susceptible individual–Infected individual–Aware individual–Media coverage) epidemic model with Markov chain and nonlinear noise perturbations has been constructed, which is used to research the hybrid dynamic impacts of media coverage and Lévy jumps on infectious disease transmission. The uniform upper bound and lower bound of the positive solution are studied. Based on defining suitable random Lyapunov functions, we researched the existence of a nontrival positive T-periodic solution. Sufficient conditions are derived to discuss the exponential ergodicity based on verifying a Foster–Lyapunov condition. Furthermore, the persistence in the average sense and extinction of infectious disease are investigated using stochastic analysis techniques. Finally, numerical simulations are utilized to provide evidence for the dynamical properties of the stochastic nonautonomous SIAM.
Approximation by Quantum Meyer-König-Zeller Fractal Functions
Authors: Kumar Deependra , Chand Arya K. B. , Massopust Peter R.
Published: November 2022

In this paper, a novel class of quantum fractal functions is introduced based on the Meyer-König-Zeller operator Mq,n. These quantum Meyer-König-Zeller (MKZ) fractal functions employ Mq,nf as the base function in the iterated function system for α-fractal functions. For f∈C(I), I closed interval in R, it is shown that a sequence of quantum MKZ fractal functions {fn(qn,α)}n=0∞ exists which converges uniformly to f without altering the scaling function α. The shape of fn(qn,α) depends on q as well as the other iterated function system parameters. For f,g∈C(I), f≥g>0, we show that a sequence {fn(qn,α)}n=0∞ exists with fn(qn,α)≥g>0 converging to f. Quantum MKZ fractal versions of some classical Müntz theorems are also presented. For q=1, the box dimension and some approximation-theoretic results of MKZ α-fractal functions are investigated in C(I). Finally, MKZ α-fractal functions are studied in Lp spaces with p≥1.
GenomeBits Characterization of MPXV
Authors: Canessa Enrique
Published: November 2022

Genome sequences of the monkeypox virus (MPXV) causing the current outbreak are being reported from an increasing number of countries. We present a letter-to-numerical sequence study via GenomeBits signal mapping in order to characterize the evolution of the MPXV via simple statistical analysis. Histograms, empirical and theoretical cumulative distribution curves and the resulting scatter plots for the base nucleotides A and C versus their complementary base nucleotides T and G are discussed. GenomeBits may help the surveillance of emergent infectious diseases.
Genome-Wide Identification and Analysis of DOF Gene Family in Eugenia uniflora L. (Myrtaceae)
Authors: Waschburger Edgar Luis , Guzman Frank , Turchetto-Zolet Andreia Carina
Published: November 2022

Eugenia uniflora is a Brazilian native plant species with great ecological and economic importance. It is distributed throughout the Atlantic forest, where two distinct populations show local adaptation to the contrasting conditions of restinga and riparian forest. Among various TFs described in plants, the DOF TF family has been reported to affect flowering and vascular development, making them promising candidates for characterization in E. uniflora. In this study, 28 DOF genes were identified by a genome-wide analysis, of which 20 were grouped into 11 MCOGs by Bayesian phylogeny, suggesting a shared functionallity between members. Based on RNA-seq experiments, we have detected eight drought responsive genes, and SNPs identification revealed population unique polymorphisms, implying a role in local adapatation mechanisms. Finally, analysis of conserved motifs through MEME revealed 15 different protein motifs, and a promoter region analysis returned 40 enriched TF binding motifs, both reporting novel biological functions circa the DOF gene family. In general, the DOF family is found to be conserved both in sequence and expression. Furthermore, this study contributes to both DOF literature and the genetic exploration of native species, elucidating their genetic potential and bringing to light new research topics, paving the way to future studies.
Micromorphological Analysis of Archaeological Abenaki Pit Features from the Fort Odanak Site (CaFe-7), Québec, Canada
Authors: Robert Sarah , Bhiry Najat , Bain Allison
Published: November 2022

Built in the early 18th century on the banks of the Saint-François River (Quebec, QC, Canada), the fortified Jesuit mission of Saint-François-de-Sales was an important Abenaki centre during the colonial period. Between 2010 and 2021, archaeological excavations conducted by the Waban-Aki Nation led to the discovery of the mission’s remains at the Fort Odanak site (CaFe-7) in the historical centre of Odanak (Quebec, QC, Canada), and revealed numerous pit features likely used for storage or refuse disposal. A sedimentological and micromorphological investigation was undertaken in two areas of the site to identify the function and use of four pit features and to clarify site formation and evolution over time. Our study indicates that all pit features were used as refuse facilities prior to abandonment, but two of them were possibly used for storage. Chronological (14C) results indicate that Indigenous people frequented the site during the 16th century, before the establishment of the Jesuit mission, and that an initial domestic Abenaki occupation occurred during the second half of the 18th century in one of the sampled sectors. The use of traditional pit features by the Abenaki of Odanak seems to have persisted into the late 19th century.
Inter-Rater and Intra-Rater Reliability of Return-to-Work Screening Tests for UK Firefighters Following Injury
Authors: Noll Liam , Moran Jason , Mallows Adrian
Published: November 2022

The aim of this study was to assess the inter-rater and intra-rater reliability of a return-to-work (RTW) screening test to be used on UK firefighters following injury. The inter rater and intra-rater reliability of eight tasks involved in a screening test was used to assess readiness to RTW for UK firefighters following injury. These tasks included the following: (1) putting on and removing a breathing apparatus set (BA), (2) a ladder lift simulation, (3) a ladder carry simulation, (4) a light portable pump (LPP) lift and carry simulation, (5) a hose run, (6) a ladder climb with leg lock, (7) a casualty evacuation and (8) a confined space crawl simulation. The inter-rater reliability between each individual screening task was interpreted as Excellent (ICC = 0.94–1.00) for eleven (68.75%) of the screening task videos and as Good (ICC = 0.75–0.88) for five (31.25%) of the screening task videos. Intra-rater reliability was interpreted as Excellent (ICC = 1) for twenty-six participants (74.3%), Good (ICC = 0.76–0.88) for eight participants (22.9%) and Moderate for one participant (2.8%). Due to the reliability of this screening test, it allows conclusions to be made from the results which can inform a RTW decision for a firefighter.
Assessing Physicochemical Parameters, Bioactive Profile and Antioxidant Status of Different Fruit Parts of Greek Eggplant Germplasm
Authors: Kadoglidou Kalliopi I. , Krommydas Konstantinos , Ralli Parthenopi , Mellidou Ifigeneia , Kalyvas Apostolos , Irakli Maria
Published: November 2022

Eggplant is an economically important vegetable with a potential for functional food production, mainly due to its high fruit antioxidant capacity. The goal of the present study was to investigate the main physicochemical and antioxidant parameters, and assess the bioactive profiles, of 19 eggplant genotypes of diverse origin, including Greek commercial varieties and hybrids, landraces and the related species S. macrocarpon. For that reason, the physicochemical traits (dry matter, pH, total soluble solids and total acidity) were assessed in the eggplant fruit and some important bioactive compounds (total phenols (TPC), total flavonoids (TFC), total monomeric anthocyanin (TAC), chlorogenic acid (CA) and its isomers neo- and crypto-CA) were assessed both in fruit pulp and peel. In addition, the antioxidant capacity was assessed according to ABTS•+, DPPH• and FRAP assays. The results revealed significant differences between the studied genotypes for all the evaluated traits, for both fruit parts. Solanum macrocarpon showed a distinct bioactive profile and was superior for most of the pulp traits (TFC, neo-CA, crypto-CA, ABTS•+, DPPH• and FRAP). Among the eggplant materials, the landrace ‘KD054/07’ had very high values for pH and some pulp traits (TPC, CA, ABTS•+ and FRAP), while the commercial F1 hybrid ‘Nilo’ was superior for dry matter and most of the peel traits (TPC, TFC, ABTS•+ and FRAP). The Greek commercial variety ‘Langada’ performed well for TAC and peel CA, ABTS•+ and FRAP, while ‘Tsakoniki’ had very high anthocyanin and pulp TPC content. These results constitute a source of information for a subset of the Greek eggplant germplasm and could contribute both to the promotion of Greek varieties of high bioactive and antioxidant value, as well as to the targeted selection of parents in breeding programs.
Water Footprint Assessment for Irrigated Paddy Cultivation in Walawe Irrigation Scheme, Sri Lanka
Authors: Janani Higgoda K. , Abeysiriwardana Himasha Dilshani , Rathnayake Upaka , Sarukkalige Ranjan
Published: November 2022

Water footprint (WF) is a comprehensive summation of the volume of freshwater consumed directly and indirectly in all the steps of the production chain of a product. The water footprint concept has been widely used in agricultural water resources management. Water for irrigation is supplied in Sri Lanka to farmers at no cost, and thus the question is arising, whether the current management strategies the authorities and the farmers follow are appropriate to achieve productive water utilization. Therefore, this study aims at evaluating the water footprint of rice production in an irrigation scheme in the dry zone of Sri Lanka, the Walawe irrigation scheme. Due to the unreliability of the rainfall in the study area paddy cultivation depends entirely on irrigation, thus, the WFblue, in other terms the volume of water evaporated from the irrigation water supply is considered as the total WF (WFtot) in this study. Actual crop evapotranspiration (equivalent to ETblue) was estimated based on the Penman-Monteith (P-M) model integrating effective rainfall, and crop coefficient published in Sri Lankan Irrigation Design Guidelines. The study spanned for three irrigation years from 2018–2021. Actual irrigation water issued to the field was estimated based on the data recorded by the government body responsible for irrigation water management of the area—Mahaweli Authority of Sri Lanka. The total volume of percolated water was computed employing the water balance method while assuming runoff is negligible. Results show that the average annual WFblue found to be 2.27 m3/kg, which is higher than global and national WFtot. As the crop yield in the study area (6.5 ton/ha) is also higher than the global (4.49 ton/ha) and national (3.5 ton/ha) yields, a conclusion was drawn that the irrigation water usage (CWUTblue) in the area may be significantly higher. It was then noted the higher CWUTblue was due to relatively higher evapotranspiration in the area. Thus, it is vital to reduce excess water usage by shifting irrigation practices from flooded irrigation to the System of Rice Intensification (SRI).