bluuebunny/crossref_metadata_2025 huggingface dataset:
Environmental Drivers of Functional Structure and Diversity of Vascular Macrophyte Assemblages in Altered Waterbodies in Serbia
Authors: Vukov Dragana , Ilić Miloš , Ćuk Mirjana , Igić Ružica
Published: February 2023

There is a gap in the knowledge about how environmental factors affect functional diversity and trait structures of macrophyte communities in altered waterbodies. We used macrophyte and environmental data collected from 46 waterbodies; we extracted data on 14 traits with 43 attributes for 59 species and calculated seven functional diversity indices. We used redundancy analysis (RDA) to investigate the response of functional diversity indices to the environmental variables. To relate traits to environment we performed the analysis on three data matrices: site by environmental variables (R), site by species (L), and species by traits (Q)—the RLQ analysis, and the 4th corner analyses. The RDA showed that the environmental variables explained 47.43% of the variability in the functional diversity indices. Elevation, hemeroby (integrative measure of the impact of all human intervention) of the land cover classes on the banks, and water conductivity were correlated with all diversity indices. We found that the traits characteristic of floating and emergent plants represents a strategy to increase efficiency in light interception under high nutrient concentrations in lowland waterbodies, while submerged plants dominate nutrient-poorer waterbodies at higher altitudes. Future investigations should be focused on the role of functional diversity and the structure of macrophyte communities in the indication of tradeoffs and/or facilitation between ecosystem services that altered waterbodies provide, in order to guide their adequate management.
Is Shrub Encroachment Driving the Decline of Small Mammal Diversity in Pyrenean Grasslands? A Preliminary Study
Authors: Torre Ignasi , Palau Oriol
Published: February 2023

The Pyrenean highlands hold the southernmost populations of some endemic and rare arvicoline species associated with grasslands. This area, as well as many other areas in the Mediterranean basin, has suffered from land abandonment due to socio-economic changes during the last decades. Those changes represented a reduction of the traditional livestock grazing by goats and sheep which naturally controlled the process of shrub encroachment. Today, maintenance of open habitats such as pasture grasslands needs to be performed by mechanical actions aimed at removing shrubs and woody vegetation. We document a case study on a plot sampled for five consecutive years (2017–2021) in which clear-cutting increased the cover of herbaceous vegetation by removing shrubs, potentially improving pasture quality. Also, changes in the small mammal community were detected, such as an increase in the species density and diversity (Shannon index) after clear-cutting, with the occurrence of typical grassland species (Microtus arvalis, M. lavernedii). Nonetheless, the effects were short-term (lasting two years), and then the community returned to the pre-treatment situation. Prior to intervention, the community was dominated by two generalist and widespread species (A. sylvaticus and C. russula), and the situation was similar two years after the intervention. Small increases in shrub cover produced relevant community changes by decreasing diversity and increasing dominance of the two most common species. These results suggested that shrub encroachment produced the banalization of small mammal communities dominated by generalist species.
Research Regarding Dental Mobility Phenomena in the Clinical Recognition Diagnosis of Temporomandibular Disorders
Authors: Checherita Laura Elisabeta , Antohe Magda Ecaterina , Costin Lupu Iulian , Văscu Mihai Bogdan , Stamatin Ovidiu , Croitoru Irina , Solomon Sorina Mihaela , Teslaru Silvia , Gradinaru Irina , Toma Vasilica , Bogdan Bulancea Petru , Cioloca Daniel Petru , Aungurencei Ovidiu Dumitru , Balcoș Carina Ana Maria , Fătu Ana Maria
Published: February 2023

The main objective of this study is to quantify the implications of the complications of periodontal pathology and dental mobility on the pathology of dysfunctional algo syndrome, a clinical entity with profound implications for the patient’s quality of life. Methodology: Clinical and laboratory evaluation was conducted in the 2018–2022 period, on a group of 110 women and 130 men, aged between 20–69, selected from our practice venue, Policlinica Stomatologica nr. 1 Iasi, Clinical Base of Dentistry Education “Mihail Kogalniceanu” Iasi, “Grigore T. Popa” University of Medicine and Pharmacy Iasi and “Apollonia” University Iasi. Overall, 125 subjects were diagnosed with periodontal disease with complications and TMJ disorders and followed periodontal therapy in the context of oral complex rehabilitation treatments (study group); the results of their clinical evaluation were compared with the results of the evaluation of the control group, made from the other 115 patients). Results: Dental mobility and gingival recession were identified as more frequent in the study sample compared with the control sample, the differences being statistically significant in both cases. In total, 26.7% of patients had different types of TMJ disorders and 22.9% of patients had occlusal changes; the percentages are slightly increased in the study sample compared with the control one, but the recorded differences are not statistically significant. Conclusions: Dental mobility, most of the time, is a negative consequence of periodontal disease, leading to the alteration of the mandibular-cranial relations, materializing in an important proportion as an etiopathogenic factor of the dysfunctional syndrome of the stomatognathic system.
Impact of COVID-19 on Patients with Decompensated Liver Cirrhosis
Authors: Moga Tudor Voicu , Foncea Camelia , Bende Renata , Popescu Alina , Burdan Adrian , Heredea Darius , Danilă Mirela , Miutescu Bogdan , Ratiu Iulia , Bizerea-Moga Teofana Otilia , Sporea Ioan , Sirli Roxana
Published: February 2023

The aim of this study was to assess the impact of COVID-19 infection on patients with decompensated liver cirrhosis (DLC) in terms of acute-on-chronic liver failure (ACLF), chronic liver failure acute decompensation (CLIF-AD), hospitalization, and mortality. In this retrospective study, we analyzed patients with known DLC who were admitted to the Gastroenterology Department with COVID-19. Clinical and biochemical data were obtained to compare the development of ACLF, CLIF-AD, days of hospitalization, and the presence of independent factors of mortality in comparison with a non-COVID-19 DLC group. All patients enrolled were not vaccinated for SARS-CoV-2. Variables used in statistical analyses were obtained at the time of hospital admission. A total of 145 subjects with previously diagnosed liver cirrhosis were included; 45/145 (31%) of the subjects were confirmed with COVID-19, among which 45% had pulmonary injury. The length of hospital stay (days) was significantly longer in patients with pulmonary injury compared to those without (p = 0.0159). In the group of patients with COVID-19 infection, the proportion of associated infections was significantly higher (p = 0.0041). Additionally, the mortality was 46.7% in comparison with only 15% in the non-COVID-19 group (p = 0.0001). Pulmonary injury was associated with death during admission in multivariate analysis in both the ACLF (p < 0.0001) and the non-ACLF (p = 0.0017) group. COVID-19 significantly influenced disease progression in patients with DLC in terms of associated infections, hospitalization length, and mortality.
Optical Coherence Tomography Angiography (OCT-A) in Uveitis: A Literature Review and a Reassessment of Its Real Role
Authors: Herbort Carl P. , Takeuchi Masaru , Papasavvas Ioannis , Tugal-Tutkun Ilknur , Hedayatfar Alireza , Usui Yoshihiko , Ozdal Pinar C. , Urzua Cristhian A.
Published: February 2023

Background: The global and precise follow-up of uveitis has become possible with the availability of dual fluorescein (FA) and indocyanine green angiography (ICGA) since the mid-1990s. Progressively, additional non-invasive imaging methods have emerged, bringing value-added precision to the imaging appraisal of uveitis, including, among others, optical coherence tomography (OCT), enhanced-depth imaging OCT (EDI-OCT) and blue light fundus autofluorescence (BAF). More recently, another complementary imaging method, OCT-angiography (OCT-A), further allowed retinal and choroidal circulation to be imaged without the need for dye injection. Purpose: The purpose of this review was aimed at examining the evidence in published reports indicating whether OCT-A could possibly replace dye angiographic methods, as well as the real practical impact of OCT-A. Methods: A literature search in the PubMed database was performed using the terms OCT-angiography and uveitis, OCTA and uveitis and OCT-A and uveitis. Case reports were excluded. Articles were classified into technical reports, research reports and reviews. Articles in the two latter categories were analyzed in a more detailed, individual fashion. Special attention was paid to whether there were arguments in favor of an exclusive rather than complementary use of OCT-A. Furthermore, a synthesis of the main practical applications of OCT-A in the management of uveitis was attempted. Results: Between 2016 (the year of the first articles) and 2022, 144 articles containing the search terms were identified. After excluding case report articles, 114 articles were retained: 4 in 2016, 17 in 2017, 14 in 2018, 21 in 2019, 14 in 2020, 18 in 2021 and 26 in 2022. Seven articles contained technical information or consensus-based terminology. Ninety-two articles could be considered as clinical research articles. Of those, only two hinted in their conclusions that OCT-A could hypothetically replace dye methods. The terms mostly used to qualify the contribution of the articles in this group were “complementary to dye methods”, “adjunct”, “supplementing” and other similar terms. Fifteen articles were reviews, none of which hinted that OCT-A could replace dye methods. The situations where OCT-A represented a significant practical contribution to the practical appraisal of uveitis were identified. Conclusion: To date, no evidence was found in the literature that OCT-A can replace the classic dye methods; however, it can complement them. Promoting the possibility that non-invasive OCT-A can substitute the invasive dye methods is deleterious, giving the elusive impression that dye methods are no longer inevitable for evaluating uveitis patients. Nevertheless, OCT-A is a precious tool in uveitis research.
Potential of Unenhanced Ultra-Low-Dose Abdominal Photon-Counting CT with Tin Filtration: A Cadaveric Study
Authors: Huflage Henner , Grunz Jan-Peter , Patzer Theresa Sophie , Pannenbecker Pauline , Feldle Philipp , Sauer Stephanie Tina , Petritsch Bernhard , Ergün Süleyman , Bley Thorsten Alexander , Kunz Andreas Steven
Published: February 2023

Objectives: This study investigated the feasibility and image quality of ultra-low-dose unenhanced abdominal CT using photon-counting detector technology and tin prefiltration. Materials and Methods: Employing a first-generation photon-counting CT scanner, eight cadaveric specimens were examined both with tin prefiltration (Sn 100 kVp) and polychromatic (120 kVp) scan protocols matched for radiation dose at three different levels: standard-dose (3 mGy), low-dose (1 mGy) and ultra-low-dose (0.5 mGy). Image quality was evaluated quantitatively by means of contrast-to-noise-ratios (CNR) with regions of interest placed in the renal cortex and subcutaneous fat. Additionally, three independent radiologists performed subjective evaluation of image quality. The intraclass correlation coefficient was calculated as a measure of interrater reliability. Results: Irrespective of scan mode, CNR in the renal cortex decreased with lower radiation dose. Despite similar mean energy of the applied x-ray spectrum, CNR was superior for Sn 100 kVp over 120 kVp at standard-dose (17.75 ± 3.51 vs. 14.13 ± 4.02), low-dose (13.99 ± 2.6 vs. 10.68 ± 2.17) and ultra-low-dose levels (8.88 ± 2.01 vs. 11.06 ± 1.74) (all p ≤ 0.05). Subjective image quality was highest for both standard-dose protocols (score 5; interquartile range 5–5). While no difference was ascertained between Sn 100 kVp and 120 kVp examinations at standard and low-dose levels, the subjective image quality of tin-filtered scans was superior to 120 kVp with ultra-low radiation dose (p < 0.05). An intraclass correlation coefficient of 0.844 (95% confidence interval 0.763–0.906; p < 0.001) indicated good interrater reliability. Conclusions: Photon-counting detector CT permits excellent image quality in unenhanced abdominal CT with very low radiation dose. Employment of tin prefiltration at 100 kVp instead of polychromatic imaging at 120 kVp increases the image quality even further in the ultra-low-dose range of 0.5 mGy.
Pregnancy-Associated Breast Cancer: A Diagnostic and Therapeutic Challenge
Authors: Galati Francesca , Magri Valentina , Arias-Cadena Paula Andrea , Moffa Giuliana , Rizzo Veronica , Pasculli Marcella , Botticelli Andrea , Pediconi Federica
Published: February 2023

Pregnancy-associated breast cancer (PABC) is commonly defined as a breast cancer occurring during pregnancy, throughout 1 year postpartum, or during lactation. Despite being a rare circumstance, PABC is one of the most common types of malignancies occurring during pregnancy and lactation, with growing incidence in developed countries, due both to decreasing age at onset of breast cancer and to increasing maternal age. Diagnosis and management of malignancy in the prenatal and postnatal settings are challenging for practitioners, as the structural and functional changes that the breast undergoes may be misleading for both the radiologist and the clinician. Furthermore, safety concerns for the mother and child, as well as psychological aspects in this unique and delicate condition, need to be constantly considered. In this comprehensive review, clinical, diagnostic, and therapeutic aspects of PABC (including surgery, chemotherapy and other systemic treatments, and radiotherapy) are presented and fully discussed, based on medical literature, current international clinical guidelines, and systematic practice.
Distributed Hypothesis Testing over a Noisy Channel: Error-Exponents Trade-Off
Authors: Sreekumar Sreejith , Gündüz Deniz
Published: February 2023

A two-terminal distributed binary hypothesis testing problem over a noisy channel is studied. The two terminals, called the observer and the decision maker, each has access to n independent and identically distributed samples, denoted by U and V, respectively. The observer communicates to the decision maker over a discrete memoryless channel, and the decision maker performs a binary hypothesis test on the joint probability distribution of (U,V) based on V and the noisy information received from the observer. The trade-off between the exponents of the type I and type II error probabilities is investigated. Two inner bounds are obtained, one using a separation-based scheme that involves type-based compression and unequal error-protection channel coding, and the other using a joint scheme that incorporates type-based hybrid coding. The separation-based scheme is shown to recover the inner bound obtained by Han and Kobayashi for the special case of a rate-limited noiseless channel, and also the one obtained by the authors previously for a corner point of the trade-off. Finally, we show via an example that the joint scheme achieves a strictly tighter bound than the separation-based scheme for some points of the error-exponents trade-off.
Tourism, Remittances, and Foreign Investment as Determinants of Economic Growth: Empirical Evidence from Selected Asian Economies
Authors: Tabash Mosab I. , Anagreh Suhaib , Subhani Bilal Haider , Al-Faryan Mamdouh Abdulaziz Saleh , Drachal Krzysztof
Published: February 2023

This research discovers how international tourism affects the economic growth of selected Asian states, e.g., Bangladesh, China, India, Pakistan, and Sri Lanka, throughout 2001–2019. To attain this objective, we have employed various regression estimation approaches, e.g., Fixed Effect Model (FEM) and Fully Modified Ordinary Least Square (FMOLS) technique. The statistical results of the applied techniques reveal that international tourism activities have a positive and significant effect on the GDP growth rate because such kinds of activities considerably contribute to creating opportunities that lead to hoist economic activities and economic growth. Moreover, an influx of tourism increases tourism activities and operations, which opens further doors to opportunities and generates revenue for the government. Similarly, the GDP per capita has been positively and significantly influenced by international tourism activities. The government and host country should emphasize the activities and operations regarding tourism and should also concentrate on the dynamic role, importance, and sensitivity of tourism operations in under-analyzed economies. This research brings a new arrangement of the variable, which has never been considered in prior literature.
Dynamic Beacon Distribution Mechanism for Internet of Vehicles: An Analytical Study
Authors: Ouladdjedid Lakhdar Kamel , Brik Bouziane
Published: February 2023

In the last decade, with the arrival of the 5G communication technology and the increasing numbers of vehicles being connected to the internet, conventional vehicle ad-hoc networks (VANETs) are evolving towards the internet of vehicles (IoV), which makes the co-existence of IEEE 802.11p and 5G-based technologies very important for the design of a heterogeneous IoV system that takes advantage of both. The IEEE 802.11p standard is still the best candidate to support direct communications for safety critical services. In fact, both the ETSI ITS-G5 and the IEEE 1609 standard families adopt the IEEE 802.11p standard as a medium access control (MAC) mechanism, and they require vehicles to exchange periodic awareness messages to avoid dangerous situations. When the density of vehicles increases, the MAC layer will suffer from radio channel congestion problems, and this may affect the various VANET applications, especially safety applications. Therefore, the decentralized congestion control (DCC) mechanism has been specified by ETSI to mitigate the channel congestion; this was achieved by adapting the transmission parameters, such as the transmit power and data-rate. However, many research studies have demonstrated limitations and a low performance of DCC, especially when the channel load is extremely high. To deal with this, in this paper, we investigate a new promising technique, called the transmission timing control (TTC), to control the channel load for periodic cooperative awareness. It consists of spreading the transmissions over time in order to avoid contention on the transmission channel. The objective of the paper is to propose an analytical study to calculate the probability of successful transmission using TTC. The demonstrated results show the efficiency of our timing control-enabled scheme to deal with the channel load on top of different conditions.
Image-Based Pothole Detection Using Multi-Scale Feature Network and Risk Assessment
Authors: Heo Dong-Hoe , Choi Ji-Yoon , Kim Sang-Baeg , Tak Tae-Oh , Zhang Sheng-Peng
Published: February 2023

Potholes on road surfaces pose a serious hazard to vehicles and passengers due to the difficulty detecting them and the short response time. Therefore, many government agencies are applying various pothole-detection algorithms for road maintenance. However, current methods based on object detection are unclear in terms of real-time detection when using low-spec hardware systems. In this study, the SPFPN-YOLOv4 tiny was developed by combining spatial pyramid pooling and feature pyramid network with CSPDarknet53-tiny. A total of 2665 datasets were obtained via data augmentation, such as gamma regulation, horizontal flip, and scaling to compensate for the lack of data, and were divided into training, validation, and test of 70%, 20%, and 10% ratios, respectively. As a result of the comparison of YOLOv2, YOLOv3, YOLOv4 tiny, and SPFPN-YOLOv4 tiny, the SPFPN-YOLOv4 tiny showed approximately 2–5% performance improvement in the mean average precision (intersection over union = 0.5). In addition, the risk assessment based on the proposed SPFPN-YOLOv4 tiny was calculated by comparing the tire contact patch size with pothole size by applying the pinhole camera and distance estimation equation. In conclusion, we developed an end-to-end algorithm that can detect potholes and classify the risks in real-time using 2D pothole images.
Design of Efficient Methods for the Detection of Tomato Leaf Disease Utilizing Proposed Ensemble CNN Model
Authors: Ulutaş Hasan , Aslantaş Veysel
Published: February 2023

Early diagnosis of plant diseases is of vital importance since they cause social, ecological, and economic losses. Therefore, it is highly complex and causes excessive workload and time loss. Within the scope of this article, nine tomato plant leaf diseases as well as healthy ones were classified using deep learning with new ensemble architectures. A total of 18.160 images were used for this process. In this study, in addition to the proposed two new convolutional neural networks (CNN) models, four other well-known CNN models (MobileNetV3Small, EfficientNetV2L, InceptionV3 and MobileNetV2) are used. A fine-tuning method is applied to the newly proposed CNNs models and then hyperparameter optimization is performed with the particle swarm optimization algorithm (PSO). Then, the weights of these architectures are optimized by the grid search method and triple and quintuple ensemble models are created and the datasets are classified with the help of the five-fold cross-validation. The experimental results demonstrate that the proposed ensemble models stand out with their fast training and testing time and superior classification performances with an accuracy of 99.60%. This research will help experts enable the early detection of plant diseases in a simple and quick manner and prevent the formation of new infections.
In-Line Gas Sensor Based on the Optical Fiber Taper Technology with a Graphene Oxide Layer
Authors: Stasiewicz Karol Antoni , Jakubowska Iwona , Moś Joanna , Kosturek Rafał , Kowiorski Krystian
Published: February 2023

This article investigates the possibilities of gas detection using a tapered optical fiber coated with a graphene oxide layer. Measurement is based on changes in light beam propagation depending on the process of gas absorption to the graphene oxide layer. In this paper, we investigated the light change in a double-clad tapered optical fiber in a wide optical range. We present a special platform constructed for the deposition of additional functional materials that enable the preparation of the sensor module. Our results present differences in light transmission for three different kinds of gasses pure nitrogen, pure hydrogen, and a mixture of propane–butane. Measurements were provided in a wide range of 500 nm–1800 nm to find the most sensitive ages for which we are able to detect mentioned absorption and their interaction with light. Obtained results for pure gasses for which the refractive indices are similar to the air show the greatest changes for the visible range 750 nm–850 nm, and for propane–butane, changes are much visible in the whole investigated range.
GDPR Personal Privacy Security Mechanism for Smart Home System
Authors: Jhuang Yun-Yun , Yan Yu-Hui , Horng Gwo-Jiun
Published: February 2023

In the era of vigorous development of the Internet of Things (IoT), the IoT has been widely used in people’s daily life. Before the user starts using an IoT product, the developer provides a privacy consent form for the user to fill in. However, the content of the consent form is usually too long for the user to read, and the user neglects the provisions related to privacy use, which often results in personal information being recorded in the database of the product without the user’s knowledge. To protect users’ informed use, we propose a privacy protection standard of the general data protection regulation (GDPR) law applicable to smart-family-related applications and data security with a consensus mechanism. We also propose a unified device data format agreement. Each product can communicate with each other through a smart housekeeper and can collect personal information between its own products and users based on the personal data protection law. Through practice, we demonstrate the feasibility of this open system. In addition, we also collected 70 questionnaires. If the GDPR specification is placed on smart appliances, about 90% of people can accept smart appliances. If smart appliances can be compatible with different brands’ unified standards, about 97% of people can accept smart appliances. Therefore, we recommend the introduction of GDPR specifications for smart home appliances.
An Implementation Methodology for the Online Tracking Simulation Architecture of the 660 MW USCFB
Authors: Yang Chen , Wang Xiaosheng , Zhang Zonglong
Published: February 2023

This paper studies the implementation strategy of an online tracking simulation system for the 660 MW ultra supercritical circulating fluidized bed boiler (USCFB). The establishment of the online system can be divided into offline and online phases. In the offline phase, the high-precision model and initial condition database of the boiler are first established. After the initial conditions are loaded, the initial conditions are adjusted through mass and energy compensation to make the initial state of the model consistent with the current operating state of the unit. In the online phase, this paper analyzes the global sensitivity of the 660 MW USCFB, establishes an online tracking mechanism based on the PI controller, and conducts simulation tests. The experiment shows that the tracking simulation system established in this paper can track the analog signal quickly and accurately.
Anomaly Detection Algorithm for Photovoltaic Cells Based on Lightweight Multi-Channel Spatial Attention Mechanism
Authors: Chen Aidong , Li Xiang , Jing Hongyuan , Hong Chen , Li Minghai
Published: February 2023

With the proposed goal of “Carbon Neutrality”, photovoltaic energy is gradually gaining the leading role in energy transformation. At present, crystalline silicon cells are still the mainstream technology in the photovoltaic industry, but due to the similarity of defect characteristics and the small scale of the defects, automatic defect detection of photovoltaic cells (PV) by electroluminescence (EL) imaging is a challenging task. In order to better meet the growing demand for high-quality photovoltaic cell products in intelligent manufacturing and use, and ensure the safe and efficient operation of photovoltaic power stations, this paper proposes an improved abnormal detection method based on Faster R-CNN for the surface defect EL imaging of photovoltaic cells, which integrates a lightweight channel and spatial convolution attention module. It can analyze the crack defects in complex scenes more efficiently. The clustering algorithm was used to obtain a more targeted anchor frame for photovoltaic cells, which made the model converge faster and enhanced the detection ability. The normalized distance between the prediction box and the target box is minimized by considering the DIoU loss function for the overlapping area of the boundary box and the distance between the center points. The experiment shows that the average accuracy of surface defect detection for EL images of photovoltaic cells is improved by 14.87% compared with the original algorithm, which significantly improves the accuracy of defect detection. The model can better detect small target defects, meet the requirements of surface defect detection of photovoltaic cells, and proves that it has good application prospects in the field of photovoltaic cell defect detection.
System Economy Improvement and Risk Shortening by Fuel Cell-UPFC Placement in a Wind-Combined System
Authors: Chakraborty Mitul Ranjan , Dawn Subhojit , Saha Pradip Kumar , Basu Jayanta Bhusan , Ustun Taha Selim
Published: February 2023

It is important to understand the features of an integrated renewable energy power system, especially for deregulated systems. The greatest obstacle to assimilating renewable energy generators with the existing electrical system is their unpredictability. Because wind energy is inconsistent, incorporating it into an established power system necessitates more planning. The effects of wind farm (WF) incorporation with fuel cells and a unified power flow controller (UPFC) on electric losses, voltage profile, generating price, and the economics of the system in a deregulated power market are examined in this paper. An impact analysis of integrating wind farms into controlled and uncontrolled situations is conducted. At two randomly selected locations in India, the real-time statistics of the actual wind speed (AWS) and forecasted wind speed (FWS) were merged for this study. The surplus charge rate and deficit charge rate are intended to evaluate the imbalance cost which is arising from the difference between anticipated and true wind speeds to determine the economics of the system. Customers are always trying to find electricity that is reliable, inexpensive, and efficient due to the reconfiguration of the power system. As a consequence, the security limitations of the system may be surpassed or might function beyond the safety limit, which is undesirable. In the last section, heuristic algorithms, such as sequential quadratic programming (SQP), artificial bee colony algorithms (ABC), and moth-flame optimization algorithms (MFO), are employed to analyze economic risk. In the real-time energy market, it also covers how the fuel cells and UPFC are utilized to rectify the WF integration’s deviation. Economic risk evaluation approaches include value-at-risk (VaR) and conditional value-at-risk (CVaR). A modified IEEE 30-bus test system is used throughout the whole project.
Numerical Optimization of Triple-Phase Components in Order-Structured Cathode Catalyst Layer of a Proton Exchange Membrane Fuel Cell
Authors: Ye Miao , Rong Long , Ma Xu , Yang Weiwei
Published: February 2023

Proton exchange membrane fuel cell (PEMFC) is generally regarded as a promising energy conversion device due to its low noise, high efficiency, low pollution, and quick startup. The design of the catalyst layer structure is crucial in boosting cell performance. The traditional catalyst layer has high oxygen transmission resistance, low utilization rate of Pt particles and high production cost. In this study, we offer a sub-model for an order-structured cathode catalyst layer coupled to a three-dimensional (3D) two-phase macroscopic PEMFC model. In the sub-model of the cathode catalyst layer, it is assumed that carbon nanowires are vertically arranged into the catalyst layer structure, platinum particles and ionomers adhere to the surface, and water films cover the cylindrical electrode. The impacts of triple-phase contents in the catalyst layer on cell performance are investigated and discussed in detail after the model has been validated using data from existing studies. The results show that when the triple-phase contents ratio of the order-structured cathode catalyst layer is the best, the overall cell power density of the cell can be maximized, that is, the Pt loading of 0.15 mg cm−2, carbon loading of 1.0 mg cm−2, and ionomer volume fraction of 0.2. The above study may provide guidance for constructing the PEMFC catalyst layer with high catalyst utilization and high performance.
Simultaneous Solution of Helical Coiled Once-Through Steam Generator with High-Speed Water Property Library
Authors: Wu Yingjie , Jiang Zhuo , Zhang Han , Liu Lixun , Tang Huanran , Guo Jiong , Li Fu
Published: February 2023

Efficient simulation of the helical coiled once-through steam generator (H-OTSG) is crucial in the design and safety analysis of the high-temperature gas-cooled reactor (HTGR). The physical property and phase transformation of water in the steam generator brings great challenges during simulation. The water properties calculation routine occupies a large part of the computational time in the steam generator solution process. Thus, a thermohydraulic property library is developed based on the IAPWS-IF97 formulation in this work to reduce the computational cost. Here the formulation adopts the backward equation method to avoid iterations in thermodynamic property calculation. Moreover, two Newton-method-based simultaneous solutions are implemented as implicitly nonlinear solvers, including Jacobian-Free Newton–Krylov (JFNK) and Newton–Krylov (NK) methods, due to its excellent computational performance. These simultaneous solution algorithms are combined with the developed water property library to simulate the H-OTSG efficiently. The numerical analysis is performed based on the transient and steady-state cases of the HTR-10 steam generator. Successful simulations of HTR-10 steam generator cases demonstrate the capability of the newly developed method.
Multi-Objective Optimization Strategy for Permanent Magnet Synchronous Motor Based on Combined Surrogate Model and Optimization Algorithm
Authors: Yu Yinquan , Pan Yue , Chen Qiping , Hu Yiming , Gao Jian , Zhao Zhao , Niu Shuangxia , Zhou Shaowei
Published: February 2023

When a permanent magnet synchronous motor (PMSM) is designed according to the traditional motor design theory, the performance of the motor is often challenging to achieve the desired goal, and further optimization of the motor design parameters is usually required. However, the motor is a strongly coupled, non-linear, multivariate complex system, and it is a challenge to optimize the motor by traditional optimization methods. It needs to rely on reliable surrogate models and optimization algorithms to improve the performance of the PMSM, which is one of the problematic aspects of motor optimization. Therefore, this paper proposes a strategy based on a combination of a high-precision combined surrogate model and the optimization method to optimize the stator and rotor structures of interior PMSM (IPMSM). First, the variables were classified into two layers with high and low sensitivity based on the comprehensive parameter sensitivity analysis. Then, Latin hypercube sampling (LHS) is used to obtain sample points for highly sensitive variables, and various methods are employed to construct surrogate models for variables. Each optimization target is based on the acquired sample points, from which the most accurate combined surrogate model is selected and combined with non-dominated ranking genetic algorithm-II (NSGA-II) to find the best. After optimizing the high-sensitivity variables, a new finite element model (FEM) is built, and the Taguchi method is used to optimize the low-sensitivity variables. Finally, finite element analysis (FEA) was adopted to compare the performance of the initial model and the optimized ones of the IPMSM. The results showed that the performance of the optimized motor is improved to prove the effectiveness and reliability of the proposed method.
Artificial Intelligence for Wind Turbine Condition Monitoring
Authors: Vidal Yolanda
Published: February 2023

The global energy system is undergoing an undeniable change [...]
FDD in Building Systems Based on Generalized Machine Learning Approaches
Authors: Nelson William , Culp Charles
Published: February 2023

Automated fault detection and diagnostics in building systems using machine learning (ML) can be applied to commercial buildings and can result in increased efficiency and savings. Using ML for FDD brings the benefit of advancing the analytics of a building. An automated process was developed to provide ML-based building analytics to building engineers and operators with minimal training. The process can be applied to buildings with a variety of configurations, which saves time and manual effort in a fault analysis. Classification analysis is used for fault detection and diagnostics. An ML analysis is defined which introduces advanced diagnostics with metrics to quantify a fault’s impact in the system and rank detected faults in order of impact severity. Explanations of the methodology used for the ML analysis include a description of the algorithms used. The analysis was applied to a building on the Texas A&M University campus where the results are shown to illustrate the performance of the process using measured data from a building.
An Integrated Approach to Reservoir Characterization for Evaluating Shale Productivity of Duvernary Shale: Insights from Multiple Linear Regression
Authors: Hui Gang , Gu Fei , Gan Junqi , Saber Erfan , Liu Li
Published: February 2023

In the development of unconventional shale resources, production forecasts are fraught with uncertainty, especially in the absence of a full, multi-data study of reservoir characterization. To forecast Duvernay shale gas production in the vicinity of Fox Creek, Alberta, the multi-scale experimental findings are thoroughly evaluated. The relationship between shale gas production and reservoir parameters is assessed using multiple linear regression (MLR). Three hundred and five core samples from fifteen wells were later examined using the MLR technique to discover the fundamental controlling characteristics of shale potential. Quartz, clay, and calcite were found to comprise the bulk of the Duvernay shale. The average values for the effective porosity and permeability were 3.96% and 137.2 nD, respectively, whereas the average amount of total organic carbon (TOC) was 3.86%. The examined Duvernay shale was predominantly deposited in a gas-generating timeframe. As input parameters, the MLR method calculated the components governing shale productivity, including the production index (PI), gas saturation (Sg), clay content (Vcl), effective porosity (F), total organic carbon (TOC), brittleness index (BI), and brittle mineral content (BMC) (BMC). Shale gas output was accurately predicted using the MLR-based prediction model. This research may be extended to other shale reservoirs to aid in the selection of optimal well sites, resulting in the effective development of shale resources.
Productivity and Cost of Retention Harvesting Operation in Conifer Plantations
Authors: Tsushima Toshiyuki , Watanabe Ichiro , Akashi Nobuhiro , Ozaki Kenichi
Published: February 2023

Retention forestry is a harvesting system that intentionally retains important forest structures at harvest time. We examined the effect of dispersed retention on the productivity and cost of harvesting operations in a large-scale field experiment in conifer plantations in Hokkaido, Japan. For dispersed retention, we retained broad-leaved trees in three levels (10, 50, or 100 trees/ha). We used daily operator reports and investigated time consumption (h/m3) of felling and pre-hauling operations and the total cost to roadside. Compared with clearcutting, mean felling time was 7% and 17% longer, and mean pre-hauling time was 20% and 19% longer in 50 and 100 retained trees/ha, respectively. The other operations were not affected by tree retention because they were conducted at pre-existed strip roads or landings where no trees were retained. Increased time consumption caused an increased cost of felling and pre-hauling. Compared with clearcutting, the combined cost of felling and pre-hauling was 14% and 18% higher in 50 and 100 retained trees/ha, respectively. However, the total cost to roadside increased only by 3% compared with clearcutting because these two operations in total occupied 19% of the overall cost, and the cost of the other operations was not affected by tree retention. This suggests that the impact of tree retention on the total cost was small and that the harvesting system used in the present study can reduce cost increment due to tree retention unless trees are not retained on strip roads.
Bioeconomic Analysis of In-Pond Raceway System Production of Foodsize and Stocker Hybrid Catfish (Channel Catfish Ictalurus punctatus ♀ × Blue Catfish, I. furcatus ♂)
Authors: Fantini-Hoag Leticia , Hanson Terry , Chappell Jesse
Published: February 2023

The U.S. catfish industry is seeking production systems that are efficient, intensive, and profitable. Growing foodsize and stocker-sized fish in the same pond is attractive as it is often difficult to obtain larger-sized stockers early each year. This case study evaluated the performance and economics of producing foodsize hybrid catfish and stocker-sized fingerlings in in-pond raceway systems (IPRS) placed into four 0.4 ha ponds. Growout raceways (RW1/RW2) in ponds 1 and 2 were 63 m3, and 45 m3 in ponds 3 and 4 (RW3/RW4). Each pond had one (14 m3) stocker unit raceway and a 5 HP of aeration that maintained adequate dissolved oxygen levels. Average growout production was 12,050 kg/ha in 63 m3 raceways and 12,078 kg/ha in 45 m3 raceways (228 days of production) and achieved harvest weights ranging from 564 to 661 g. The raceway stocker unit had production yields ranging from 3537 to 4388 kg/ha (143 days of production) and achieved harvest weights ranging from 123 to 234 g. Stocker units in ponds 1 and 2 generated 8540 stocker–fingerlings (21,102 fingerlings/ha) and units in ponds 3 and 4 generated 7954 fingerlings (19,654 fingerlings/ha). An investment of USD 39,996 was needed for ponds 1 and 2 and USD 21,196 for ponds 3 and 4. When scenarios were analyzed financially, positive financial net returns occurred when farm level investment decreased, leading to reduced payback periods, increased net present values, and higher internal rates of return. IPRS used stocker units to culture fingerlings for future stocking of foodsize fish. IPRS provided good inventory control, and high production yields compared to traditional pond culture of catfish.
Aquatic Pollutants: Risks, Consequences, Possible Solutions and Novel Testing Approaches
Authors: Estêvão Maria Dulce
Published: February 2023

It is undeniable that there is a wide variety of pollutants in the various water bodies around the planet, including organic and inorganic compounds, pathogenic microorganisms, and microplastics [...]
Mycotoxin Monitoring, Regulation and Analysis in India: A Success Story
Authors: Chatterjee Sujata , Dhole Archana , Krishnan Anoop A. , Banerjee Kaushik
Published: February 2023

Mycotoxins are deleterious fungal secondary metabolites that contaminate food and feed, thereby creating concerns regarding food safety. Common fungal genera can easily proliferate in Indian tropical and sub-tropical conditions, and scientific attention is warranted to curb their growth. To address this, two nodal governmental agencies, namely the Agricultural and Processed Food Products Export Development Authority (APEDA) and the Food Safety and Standards Authority of India (FSSAI), have developed and implemented analytical methods and quality control procedures to monitor mycotoxin levels in a range of food matrices and assess risks to human health over the last two decades. However, comprehensive information on such advancements in mycotoxin testing and issues in implementing these regulations has been inadequately covered in the recent literature. The aim of this review is thus to uphold a systematic picture of the role played by the FSSAI and APEDA for mycotoxin control at the domestic level and for the promotion of international trade, along with certain challenges in dealing with mycotoxin monitoring. Additionally, it unfolds various regulatory concerns regarding mycotoxin mitigation in India. Overall, it provides valuable insights for the Indian farming community, food supply chain stakeholders and researchers about India’s success story in arresting mycotoxins throughout the food supply chain.
CEBPA-Regulated Expression of SOCS1 Suppresses Milk Protein Synthesis through mTOR and JAK2-STAT5 Signaling Pathways in Buffalo Mammary Epithelial Cells
Authors: Fan Xinyang , Qiu Lihua , Zhu Wei , Huang Lige , Tu Xingtiao , Miao Yongwang
Published: February 2023

Milk protein content is a key quality indicator of milk, and therefore elucidating its synthesis mechanism has been the focus of research in recent years. Suppressor of cytokine signaling 1 (SOCS1) is an important inhibitor of cytokine signaling pathways that can inhibit milk protein synthesis in mice. However, it remains elusive whether SOCS1 plays roles in the milk protein synthesis in the buffalo mammary gland. In this study, we found that the mRNA and protein expression levels of SOCS1 in buffalo mammary tissue during the dry-off period was significantly lower than those during lactation. Overexpression and knockdown experiments of SOCS1 showed that it influenced the expression and phosphorylation of multiple key factors in the mTOR and JAK2–STAT5 signaling pathways in buffalo mammary epithelial cells (BuMECs). Consistently, intracellular milk protein content was significantly decreased in cells with SOCS1 overexpression, while it increased significantly in the cells with SOCS1 knockdown. The CCAAT/enhancer binding protein α (CEBPA) could enhance the mRNA and protein expression of SOCS1 and its promoter activity in BuMECs, but this effect was eliminated when CEBPA and NF-κB binding sites were deleted. Therefore, CEBPA was determined to promote SOCS1 transcription via the CEBPA and NF-κB binding sites located in the SOCS1 promoter. Our data indicate that buffalo SOCS1 plays a significant role in affecting milk protein synthesis through the mTOR and JAK2-STAT5 signaling pathways, and its expression is directly regulated by CEBPA. These results improve our understanding of the regulation mechanism of buffalo milk protein synthesis.
Bioactive Nutrient Retention during Thermal-Assisted Hydration of Lupins
Authors: Perera Dilini , Kumar Gaurav , Devkota Lavaraj , Dhital Sushil
Published: February 2023

Lupin, an arid pulse, is gaining popularity as a super food due to its superior nutritional properties. However, it has not been considered for large scale thermal processing, e.g., canning. The present work evaluated the best time/temperature combination to hydrate lupins for canning with minimum losses of bioactive nutrients, pre-biotic fibre, and total solids during hydration. The two lupin species showed a sigmoidal hydration behaviour, which was adequately modelled by the Weibull distribution. The effective diffusivity, Deff, increased from 7.41 × 10−11 to 2.08 × 10−10 m2/s for L. albus and 1.75 × 10−10 to 1.02 × 10−9 m2/s for L. angustifolius with increasing temperature, namely, from 25 °C to 85 °C. The lag phase decreased from 145 min to 56 min in L. albus and 61 min to 28 min in L. angustifolius. However, based on the effective hydration rate, reaching the equilibrium moisture, minimum loss of the solids, and prebiotic fibre and phytochemicals, 200 min hydration at 65 °C can be regarded as the optimum temperature of hydration. The findings are thus relevant for designing the hydration protocol to achieve the maximum equilibrium moisture content and yield with the minimum loss of solids (phytochemicals and prebiotic fibres) for L. albus and L. angustifolius.