bluuebunny/crossref_metadata_2025 huggingface dataset:
Monitoring biosecurity in poultry production: an overview of databases reporting biosecurity compliance from seven European countries
Authors: Delpont Mattias , Salazar Luis G. , Dewulf Jeroen , Zbikowski Artur , Szeleszczuk Piotr , Dufay-Lefort Anne-Christine , Rousset Nathalie , Spaans Annick , Amalraj Arthi , Tilli Giuditta , Piccirillo Alessandra , Devesa Aitor , Sevilla-Navarro Sandra , van Meirhaege Hilde , Kovács László , Jóźwiak Ákos Bernard , Guérin Jean-Luc , Paul Mathilde C.
Published: August 2023

Compliance with required on-farm biosecurity practices reduces the risk of contamination and spread of zoonotic and economically important diseases. With repeating avian influenza epidemics in the poultry industry, the need to monitor and improve the overall level of biosecurity is increasing. In practice, biosecurity compliance is assessed by various actors (e.g., academic, private and public institutions), and the results of such assessments may be recorded and gathered in databases which are seldom shared or thoroughly analyzed. This study aimed to provide an inventory of databases related to the assessment of biosecurity in poultry farms in seven major poultry-producing European countries to highlight challenges and opportunities associated with biosecurity data collection, sharing, and use. The institutions in charge of these databases were contacted and interviewed using a structured questionnaire to gather information on the main characteristics of the databases and the context of their implementation. A total of 20 databases were identified, covering the gamut of poultry species and production types. Most databases were linked to veterinary health authorities or academia, and to a lesser extent interbranch organizations. Depending on the institutions in charge, the databases serve various purposes, from providing advice to enforcing regulations. The quality of the biosecurity data collected is believed to be quite reliable, as biosecurity is mostly assessed by trained farm advisors or official veterinarians and during a farm visit. Some of the databases are difficult to analyze and/or do not offer information concerning which biosecurity measures are most or least respected. Moreover, some key biosecurity practices are sometimes absent from certain databases. Although the databases serve a variety of purposes and cover different production types, each with specific biosecurity features, their analysis should help to improve the surveillance of biosecurity in the poultry sector and provide evidence on the benefits of biosecurity.
Design and Optimization of a Novel Electronic Mechanical Brake Actuator Based on Cam
Authors: Yan Zhoudong , Chen Xinbo , Yan Min , Hang Peng
Published: August 2023

The electronic mechanical brake (EMB) is considered an ideal actuator for brake-by-wire systems. We applied the negative radius roller cam mechanism as the clamping mechanism of the EMB, solving the problem of large size, poor load-bearing capacity, and the inefficiency of the existing EMBs. When designing a cam as a clamping transmission mechanism, it is necessary to take the pressure angle, contact stress, motion law, etc., as goals and constraints. Existing design methods cannot easily solve this problem. Therefore, we propose a new analysis method from the cam profile and combine it with an improved particle swarm optimization (PSO) algorithm to design the cam profiles. This method can handle various complex goals and constraints of the EMB and obtain the required negative radius roller cam profile. Finally, the logical consistency of the profile-based analysis method was verified, and the EMB design objectives and accuracy were compared using ADAMS. Under the same conditions, the result showed that the optimized cam mechanism requires only 40.52% motor power and only 65.65% clearance elimination time compared to the EMB with the lead screw mechanism.
Design and Research of Multimodal Fusion Feedback Device Based on Virtual Interactive System
Authors: Zhang Zhen , Shi Kenan , Ge Pan , Zhang Taisheng , Xu Manman , Chen Yu
Published: August 2023

This paper proposes a kinesthetic–tactile fusion feedback system based on virtual interaction. Combining the results of human fingertip deformation characteristics analysis and an upper limb motion mechanism, a fingertip tactile feedback device and an arm kinesthetic feedback device are designed and analyzed for blind instructors. In order to verify the effectiveness of the method, virtual touch experiments are established through the mapping relationship between the master–slave and virtual end. The results showed that the average recognition rate of virtual objects is 79.58%, and the recognition speed is improved by 41.9% compared with the one without force feedback, indicating that the kinesthetic–tactile feedback device can provide more haptic perception information in virtual feedback and improve the recognition rate of haptic perception.
The Contribution of Tourism to the Development of Central and Eastern European Countries in the New Post-Endemic and Geostrategic Context
Authors: Pripoaie Rodica , Turtureanu Anca-Gabriela , Schin George-Cristian , Matic Andreea-Elena , Crețu Carmen-Mihaela , Pătrașcu Cristina-Gabriela , Sîrbu Carmen-Gabriela , Marinescu Emanuel Ștefan
Published: August 2023

This study explores an important issue in tourism, namely, the competitiveness of tourism. In the present paper, we aim at analyzing the relationship between tourism and national prosperity. The term competitiveness is a research topic intensively addressed in various fields, and it can answer current research questions related to a constantly changing tourism industry. Thus, the possibility of knowing how competitive this particularly important sector is for the five emerging countries considered in the study, namely, the Czech Republic, Hungary, Poland, Romania, and Slovakia, allows public authorities and entrepreneurs to estimate the revenues that can generate the field of hospitality. Also, based on these results, a series of national strategies specific to the tourism industry can be founded to lead to the increase of its competitiveness. We used cross-country multiple regression analysis to determine variables that are particularly important for the competitiveness of the hospitality industry, a fact for which the study has a theoretical and practical applicability. The study offers the possibility to anticipate the values of this index (TTCI), thus enabling government and industry bodies to take the necessary steps to increase specific competitiveness in the international market.
Genome-Wide Identification, Characterization, and Expression Analysis of the Geranylgeranyl Pyrophosphate Synthase (GGPPS) Gene Family Reveals Its Importance in Chloroplasts of Brassica oleracea L.
Authors: Yan Longxiang , Fang Zhiyuan , Zhang Na , Yang Limei , Zhang Yangyong , Zhuang Mu , Lv Honghao , Ji Jialei , Wang Yong
Published: August 2023

GGPPS (geranylgeranyl pyrophosphate synthase) is a crucial enzyme in the terpene biosynthesis pathway. Terpenoids play essential roles in chlorophyll biosynthesis and the development of cabbage (Brassica oleracea L. var. capitata L.), a major cruciferous vegetable worldwide. However, limited information is available regarding B. oleracea GGPPS genes. In this study, we examined 10 BoGGPPS genes from the B. oleracea genome. The subcellular localization prediction suggests that BoGGPPS proteins are mainly expressed in chloroplasts and plastids. Similar BoGGPPS genes exhibited a similar structure and motif. Distribution, collinearity, and Ka/Ks analysis revealed multiple duplication events within the BoGGPPS gene family. Cabbage BoGGPPS may participate in light and hormone responses via analysis of cis-acting elements. Three-dimensional structure analysis demonstrated the abundance of α-helices and random coils among BoGGPPS members, suggesting their important functions. Based on qRT-PCR, we observed notable differences in the transcript levels of BoGGPPS genes between leaves and siliques. Bol028967 exhibited significantly higher transcript levels in WT (Wild-type) siliques compared to in Boas1 (Brassica oleracea albino silique 1), and subcellular localization analysis confirmed its expression in chloroplasts, implying its essential role in chloroplast synthesis. These findings lay the groundwork for further exploration and in-depth functional analysis of BoGGPPS genes and their relationship with terpenoids in the context of chlorophyll synthesis in B. oleracea.
Deep-Learning-Based Rice Disease and Insect Pest Detection on a Mobile Phone
Authors: Deng Jizhong , Yang Chang , Huang Kanghua , Lei Luocheng , Ye Jiahang , Zeng Wen , Zhang Jianling , Lan Yubin , Zhang Yali
Published: August 2023

The realization that mobile phones can detect rice diseases and insect pests not only solves the problems of low efficiency and poor accuracy from manually detection and reporting, but it also helps farmers detect and control them in the field in a timely fashion, thereby ensuring the quality of rice grains. This study examined two Improved detection models for the detection of six high-frequency diseases and insect pests. These models were the Improved You Only Look Once (YOLO)v5s and YOLOv7-tiny based on their lightweight object detection networks. The Improved YOLOv5s was introduced with the Ghost module to reduce computation and optimize the model structure, and the Improved YOLOv7-tiny was introduced with the Convolutional Block Attention Module (CBAM) and SIoU to improve model learning ability and accuracy. First, we evaluated and analyzed the detection accuracy and operational efficiency of the models. Then we deployed two proposed methods to a mobile phone. We also designed an application to further verify their practicality for detecting rice diseases and insect pests. The results showed that Improved YOLOv5s achieved the highest F1-Score of 0.931, 0.961 in mean average precision (mAP) (0.5), and 0.648 in mAP (0.5:0.9). It also reduced network parameters, model size, and the floating point operations per second (FLOPs) by 47.5, 45.7, and 48.7%, respectively. Furthermore, it increased the model inference speed by 38.6% compared with the original YOLOv5s model. Improved YOLOv7-tiny outperformed the original YOLOv7-tiny in detection accuracy, which was second only to Improved YOLOv5s. The probability heat maps of the detection results showed that Improved YOLOv5s performed better in detecting large target areas of rice diseases and insect pests, while Improved YOLOv7-tiny was more accurate in small target areas. On the mobile phone platform, the precision and recall of Improved YOLOv5s under FP16 accuracy were 0.925 and 0.939, and the inference speed was 374 ms/frame, which was superior to Improved YOLOv7-tiny. Both of the proposed improved models realized accurate identification of rice diseases and insect pests. Moreover, the constructed mobile phone application based on the improved detection models provided a reference for realizing fast and efficient field diagnoses.
Use of Food Attractant to Monitor and Forecast Population Dynamics of Cnaphalocrocis medinalis (Lepidoptera: Pyralidae), a Long-Distance Migratory Pest
Authors: Gao Lingyun , Yan Ran , He Wei , Wu Kongming
Published: August 2023

The rice leaf folder, Cnaphalocrocis medinalis, is a significant migratory pest that restricts rice production in Asia and Africa, and monitoring and early warning measures are the basis for its management. Based on its regional migratory path in China, the pest population dynamics were monitored from 2020 to 2021 using food attractants. In this study, we also used internal reproductive system dissection to determine the adult age and reproductive developmental state. The monitoring results indicated that the trapping dynamics of C. medinalis males using food attractants and sex pheromones were approximately similar. Both trapping methods yielded C. medinalis males of different ages, with an identical age structure for both trap types. Dissection analysis of the reproductive system of females trapped using the food attractants indicated that in 2020, the population of C. medinalis in Liling, Hunan Province, was indigenous, while populations in other monitoring sites mainly comprised migrants from other areas. The ovarian development level of females trapped using food attractants showed a positive correlation with the age of males, and there was also a significant positive correlation between the index of ovarian development of females and the mating time. Furthermore, a dynamic prediction method for fecundity in field populations was established based on the quantity of eggs held by the females. This study provides a new method for the monitoring and early warning of the existence of C. medinalis populations.
Plant Spacing Effects on Stem Development and Secondary Growth in Nicotiana tabacum
Authors: Xu Na , Meng Lin , Tang Fang , Du Shasha , Xu Yanli , Kuang Shuai , Lv Yuanda , Song Wenjing , Li Yang , Qi Weicong , Zhang Yu
Published: August 2023

Plant spacing usually refers to distances between plants within and between rows in the field. Different spacing in crop planting would generally influence the size, plant architecture, economic productivity, etc. The present research provided a time course monitoring of stem development in tobacco with different plant spacing. The result showed that cambium activity, vascular bundle thickness, lignin, cellulose, and hemicellulose content, as well as the macronutrient deposition in the stem varied because of the different plant spacing. Furthermore, the genes (NtHB8s and NtNST3s) coding the homologs of HB8 and NST3 transcription factors, which are involved in plant secondary growth, were cloned in tobacco. In the time course, they also indicated diverse expression patterns among altered plant-spacing treatments. Their transcriptomic activities were validated, and the motifs that might bind transcription factors in their promoter regions were predicted. Promoters of NtHB8s and NtNST3s genes were rich in light-response elements; as a result, light might be the main environmental factor in plant spacing to regulate stem secondary growth.
Assessment of Heat Risk of Winter Wheat Cropping Based on Long-Term Meteorological Data
Authors: Li Min , Wu Xuejing , Zhu Yulei , Ullah Najeeb , Song Youhong
Published: August 2023

The frequency of heat events is likely to increase due to global climate change, posing an increasing risk to wheat production. To optimize crop management strategies for coping with future climates, it is crucial to quantify the high-temperature occurrence during cropping seasons. Here, sixty-six years (1955~2020) of meteorological data during wheat reproductive growth were collected from six meteorological stations in the Huaibei Plain of Anhui Province. These data were analyzed to quantify the pattern and characteristics of post-anthesis heat stress for wheat crops. Five levels of annual mean daily maximum temperature (Tmax) were defined, from normal to extreme temperatures. Six crop developmental phases of winter wheat, i.e., phase i to phase vi, were divided from flowering to maturity. The data suggest an annual mean temperature of 17~24 °C from flowering to maturity, with an annual effective cumulative temperature ranging from 725 °C d to 956 °C d. The mean temperature and effective cumulative temperature increased as crop growth progressed, along with more frequent heat events during phase ii (8~14 days after anthesis) and phase iii (15~21 days after anthesis). We also found that the frequency of extremely high temperatures (≥33 °C) from 1990 to 2020 was significantly greater than that from 1957 to 1990. Interestingly, it was found that the intensity of post-anthesis night temperatures also increased with crop growth, i.e., from phase i to phase vi. Wheat grain yield increased with increasing effective accumulative temperature and Tmax, but it started to decline when thresholds of effective accumulative temperature and Tmax were reached. Overall, these findings could provide guidelines for winter wheat cropping in the Huaibei Plain, China, or similar climate and cropping regions.
The Correlation of Machine-Picked Cotton Defoliant in Different Gossypium hirsutum Varieties
Authors: Wang Junduo , Zhang Zeliang , Zhang Nala , Liang Yajun , Gong Zhaolong , Wang Junhao , Ditta Allah , Sang Zhiwei , Li Xueyuan , Zheng Juyun
Published: August 2023

Cotton mechanized harvesting is the development direction of cotton production. The rapid development of mechanized cotton harvesting in Xinjiang has significantly increased the efficiency of cotton harvesting and reduced the harvesting cost. However, in the rapid development of mechanized cotton harvesting, there are also the problems of net yield, recovery rate and poor harvesting quality, which lead to the poor quality competitiveness of mechanized cotton harvesting. In order to solve the problem of mechanized cotton loss, the key is to reduce the problem of cotton miscellaneous, and breed cotton varieties suitable for mechanized harvesting. The purpose of this study was to clarify the key trait correlation of defoliation through the establishment and association analysis of foliation and deciduous phenotype indicators in cotton. In this study, the phenotypic indexes of defoliation and deciduous traits were established through the comprehensive analysis of the defoliation rate of 273 cotton germplasm resources and other 11 related phenotypic traits in the field, in order to provide guidance for the breeding and production of cotton varieties collected by breeders. In addition to peeling rate, an analysis of the association between 11 agronomic parameters and peeling rate and hanging rate revealed that the number of effective branches, chlorophyll SPAD value, fruit branch angle, and hanging rate have substantial correlation in 3 years. Hence The hanging rate, fruit branch angle, effective branch number, and chlorophyll SPAD value can be used as the evaluation indicators of varieties for peeling ability trait index.
Effect of Lavage Solution Type on Bronchoalveolar Lavage Fluid Cytology in Clinically Healthy Horses
Authors: Westermann Cornélie M. , de Bie Annelieke G. , Olave Carla , de Grauw Janny C. , Teske Erik , Couetil Laurent L.
Published: August 2023

Equine bronchoalveolar lavage (BAL) is usually performed with 250–500 mL of isotonic saline at pH 5.5. The acidic pH of saline may cause an increase in airway neutrophil count 48 h after BAL. Other isotonic solutions such as Ringer’s solution, phosphate-buffered saline (PBS) or Plasma-Lyte 148® have a neutral pH of 7.4 and might be a better choice for BAL by not provoking inflammation and the influx of neutrophils into airways. BAL was performed in four healthy horses in four different lung lobes using four different solutions in a randomized crossover design. In each lobe, BAL was performed twice with a 48 h interval using 250 mL of solution. Automated total nucleated cell counts (TNCs) were recorded, and differential cell counts in lavage fluid were determined by two investigators blinded to treatments. The mean volume of BAL fluid retrieved was 51 ± 14%. The mean neutrophil percentage (%N) increased from 1.5 ± 0.9% to 14.7 ± 9.6% at 48 h (p < 0.001) but was not significantly affected by the solution used or the lung lobe sampled. In conclusion, in this study, the influx of neutrophils into airways after BAL was independent of the type of isotonic solution used and the lung lobe sampled. Saline remains an appropriate solution for BAL in horses.
The Triple-E Model: Advancing Equestrian Research with Perspectives from One Health
Authors: Keener Michaela M. , Tumlin Kimberly I.
Published: August 2023

Equestrian sport has various welfare issues and educational needs. To address these complex interactions, we propose an integrated approach called the Triple-E Model, which focuses on the equine, equestrian, and environmental triad. A literature review of existing models suggests that complexities of these interactions are overlooked, despite the significant impact of equine industries on economics, healthcare, and animal welfare. This paper discusses current models and theories used to evaluate equine–equestrian–environmental interactions and introduces the Triple-E Model to foster multidisciplinary collaboration. Unlike the One Health triad, which focuses on disease emergence, transmission, and zoonosis, the Triple-E Model extends to non-infectious research, such as musculoskeletal injury. It promotes collaborative care and rehabilitation within the equestrian community by engaging multidisciplinary, multi-setting, and multi-sectoral teams. Given the nature of human–animal interaction and welfare considerations, this model fills the gap in understanding human–horse interactions. The paper highlights the limitations of existing models and explains how the Triple-E Model guides and encourages holistic team collaboration in the equestrian community.
In the Alphaproteobacterium Hyphomicrobium denitrificans SoxR Serves a Sulfane Sulfur-Responsive Repressor of Sulfur Oxidation
Authors: Li Jingjing , Törkel Kaya , Koch Julian , Tanabe Tomohisa Sebastian , Hsu Hsun Yun , Dahl Christiane
Published: August 2023

In organisms that use reduced sulfur compounds as alternative or additional electron donors to organic compounds, transcriptional regulation of genes for enzymes involved in sulfur oxidation is needed to adjust metabolic flux to environmental conditions. However, little is known about the sensing and response to inorganic sulfur compounds such as thiosulfate in sulfur-oxidizing bacteria. In the Alphaproteobacterium Hyphomicrobium denitrificans, one strategy is the use of the ArsR–SmtB-type transcriptional regulator SoxR. We show that this homodimeric repressor senses sulfane sulfur and that it is crucial for the expression not only of sox genes encoding the components of a truncated periplasmic thiosulfate-oxidizing enzyme system but also of several other sets of genes for enzymes of sulfur oxidation. DNA binding and transcriptional regulatory activity of SoxR are controlled by polysulfide-dependent cysteine modification. The repressor uses the formation of a sulfur bridge between two conserved cysteines as a trigger to bind and release DNA and can also form a vicinal disulfide bond to orchestrate a response to oxidizing conditions. The importance of the sulfur bridge forming cysteines was confirmed by site-directed mutagenesis, mass spectrometry, and gel shift assays. In vivo, SoxR interacts directly or indirectly with a second closely related repressor, sHdrR.
Radial Gradient Seismic Metamaterials with Ultra-Low Frequency and Ultra-Wide Band Gap
Authors: Yang Qian , Su Kun , Li Lixia , Li Yan , Bai Jin
Published: August 2023

In this paper, a radial gradient seismic metamaterial (RGSM) is proposed. The structural unit cell is composed of an external square soil embedded with a triangular-cross-sectioned steel ring, which is filled at different angles of multiple steel rings to form a supercell. The dispersion curve and attenuation spectrum of the unit cell are calculated by the finite element method, and the opening mechanism of the band gap is explained by analyzing the modes at the band gap boundary. The influence of geometric parameters and material parameters on the band gap is further studied, and the optimized supercell radial gradient seismic metamaterial (OS-RGSM) structure is designed through structure and parameter optimization. The ultra-low broadband excellent band gap in the range of 2.35–20 Hz for seismic Lamb waves is realized, and its three-dimensional frequency response and displacement field diagram are calculated. In addition, the attenuation characteristics of the optimized supercell seismic metamaterial on the seismic surface wave are calculated and analyzed. It is found that the attenuation can reach more than 50% in the ultra-low frequency range of 3.5–9 Hz. The seismic wave barrier is verified by the vibration transmission characteristics of RGSM under finite period and dynamic time history analysis. The results show that RGSM can effectively shield from seismic Lamb waves in the ultra-wideband with the starting frequency of 2.35 Hz and can also effectively attenuate the seismic surface wave in semi-infinite space.
CEMLB-YOLO: Efficient Detection Model of Maize Leaf Blight in Complex Field Environments
Authors: Leng Shengjie , Musha Yasenjiang , Yang Yulin , Feng Guowei
Published: August 2023

Northern corn leaf blight is a severe fungal disease that adversely affects the health of maize crops. In order to prevent maize yield decline caused by leaf blight, we propose the YOLOv5-based object detection lightweight models to rapidly detect maize leaf blight disease in complex scenarios. Firstly, the Crucial Information Position Attention Mechanism (CIPAM) enables the model to focus on retaining critical information during downsampling to reduce information loss. We introduce the Feature Restructuring and Fusion Module (FRAFM) to extract deep semantic information and make the feature map fusion across maps at different scales more effective. Thirdly, we add the Mobile Bi-Level Transformer (MobileBit) to the feature extraction network to help the model understand complex scenes more effectively and cost-effectively. The experimental results demonstrate that the proposed model achieves 87.5% mAP@0.5 accuracy on the NLB dataset, which is 5.4% higher than the original model.
Investigation of Surface Defects in Optical Components Based on Reflection Mueller Matrix Spectroscopy
Authors: Li Ruyi , Wu Feibin , Huang Huiling , Cai Weibin , Zhang Yantong , Han Jun
Published: August 2023

Nanoscale defects on the surface of ultra-precision optical elements seriously affect the beam quality in optical systems. In response to the challenge of detecting nanoscale defects on optical component surfaces, we propose a method for the detection and classification of various types of defects on optical component surfaces via reflection Mueller matrix spectroscopy (RMMS). Firstly, an electromagnetic scattering theoretical model for various types of defects on the surface of optical elements and the incident and scattered fields were established by combining the bidirectional reflection distribution function (BRDF) and the Rayleigh–Rice vector scattering theory. Then, the optimal conditions for RMMS measurements were determined by numerically simulating the BRDF. On this basis, the surface roughness and pockmarks of the optical test plate were simulated and analyzed via RMMS, and the results were verified experimentally; then, dirty particles and pockmarks above the surface of the optical element and subsurface bubble defects (SSBD) were simulated and analyzed via RMMS. The results showed that some elements of the Mueller matrix could significantly distinguish defects on the surface of the optical element with dimensions smaller than the visible wavelength, and the dimensions of various types of defects of the element could be inverted using the values of the Mueller matrix elements. This method provides a theoretical basis and reference for the detection and classification of various types of defects in precision optical components.
DCIDS—Distributed Container IDS
Authors: Levy Rocha Savio , Lopes de Mendonca Fabio Lucio , Staciarini Puttini Ricardo , Rabelo Nunes Rafael , Amvame Nze Georges Daniel
Published: August 2023

Intrusion Detection Systems (IDS) still prevail as an important line of defense in modern computing environments. Cloud environment characteristics such as resource sharing, extensive connectivity, and agility in deploying new applications pose security risks that are increasingly exploited. New technologies like container platforms require IDS to evolve to effectively detect intrusive activities in these environments, and advancements in this regard are still necessary. In this context, this work proposes a framework for implementing an IDS focused on container platforms using machine learning techniques for anomaly detection in system calls. We contribute with the ability to build a dataset of system calls and share it with the community; the generation of anomaly detection alerts in open-source applications to support the SOC through the analysis of these system calls; the possibility of implementing different machine learning algorithms and approaches to detect anomalies in system calls (such as frequency, sequence, and arguments among other type of data) aiming greater detection efficiency; and the ability to integrate the framework with other tools, improving collaborative security. A five-layer architecture was built using free tools and tested in a corporate environment emulated in the GNS3 software version 2.2.29. In an experiment conducted with a public system call dataset, it was possible to validate the operation and integration of the framework layers, achieving detection results superior to the work that originated the dataset.
Information Gap Decision Theory-Based Stochastic Optimization for Smart Microgrids with Multiple Transformers
Authors: Rong Shuang , Zhao Yanlei , Wang Yanxin , Chen Jiajia , Guan Wanlin , Cui Jiapeng , Liu Yanlong
Published: August 2023

Multi-microgrid collaborative scheduling can promote the local consumption of renewable energy in the smart grid and reduce the operating costs of the power grid park. At the same time, the access of the distributed energy storage (ES) system provides an opportunity to further enhance the park’s peak shaving and valley filling capacity, thereby reducing costs. However, the uncertainty of photovoltaic (PV) power generation and load demand seriously affects the profit maximization of the microgrid in the park. To address this challenge, this paper proposes a stochastic optimal scheduling strategy for industrial park smart microgrids with multiple transformers based on the information gap decision theory (IGDT). We first introduce a revenue maximization model for industrial parks, incorporating a two-part tariff system and distributed ES. Subsequently, we employ an envelope constraint model to accurately represent the uncertainty associated with PV generation and load demand. By integrating these components, we establish the IGDT stochastic optimization scheduling model for industrial parks with multiple transformers. Finally, we simulate and analyze the performance of the proposed IGDT model under various cost deviation factors during typical spring and summer days. The simulation results demonstrate the effectiveness of the proposed control strategy in mitigating the impact of PV generation and load uncertainty on industrial parks. The IGDT-based scheduling approach provides an efficient solution for maximizing revenue and enhancing the operational stability of industrial park microgrids.
Evolution of Atmospheric Carbon Dioxide and Methane Mole Fractions in the Yangtze River Delta, China
Authors: Jiang Kai , Ma Qianli , Zang Kunpeng , Lin Yi , Chen Yuanyuan , Liu Shuo , Qing Xuemei , Qiu Shanshan , Xiong Haoyu , Hong Haixiang , Li Jiaxin , Fang Shuangxi
Published: August 2023

As the most economically developed region in China, the Yangtze River Delta (YRD) region contributed to ~17% of the total anthropogenic CO2 emissions from China. However, the studies of atmospheric CO2 and CH4 in this area are relatively sparse and unsystematic. Here, we analyze the changing characters of those gases in different development periods of China, based on the 11-year atmospheric CO2 and CH4 records (from 2010 to 2020) at one of the four Chinese sites participating in the World Meteorological Organization/Global Atmospheric Watch (WMO/GAW) program (Lin’an regional background station), located in the center of YRD region, China. The annual average atmospheric CO2 and CH4 mole fractions at LAN have been increasing continuously, with growth rates of 2.57 ± 0.14 ppm yr−1 and 10.3 ± 1.3 ppb yr−1, respectively. Due to the complex influence of regional sources and sinks, the characteristics of atmospheric CO2 and CH4 varied in different periods: (i) The diurnal and seasonal variations of both CO2 and CH4 in different periods were overall similar, but the amplitudes were different. (ii) The elevated mole fractions in all wind sectors tended to be uniform. (iii) The potential source regions of both gases expanded over time. (iv) The growth rate in recent years (2016–2020) changed significantly less than that in the earlier period (2010–2015). Our results indicated that the CO2 and CH4 mole fractions were mainly correlated to the regional economic development, despite the influence of special events such as the G20 Summit and COVID-19 lockdown.
Climate Change Will Lead to a Significant Reduction in the Global Cultivation of Panicum milliaceum
Authors: Jiang Pan , Jiang Junyi , Yang Cong , Gu Xinchen , Huang Yi , Liu Liang
Published: August 2023

Panicum milliaceum is a specialty crop that maintains the economic stability of agriculture in arid and barren regions of the world. Predicting the potential geographic distribution of Panicum milliaceum globally and clarifying the ecological needs of Panicum milliaceum will help to advance the development of agriculture, which is important for the maintenance of human life and health. In this study, based on 5637 global distribution records of Panicum milliaceum, we used the MaxEnt model and ArcGIS software, the Beijing Climate Center Climate System Model (BCC-CSM2-MR) was selected to predict the potential global geographic distribution of Panicum milliaceum in the present and future in combination with the environmental factor variables; we evaluated the significant factors constraining the potential geographic distribution of Panicum milliaceum by combining the contributions of environmental factor variables; and we assessed the accuracy of the MaxEnt model by using AUC values and Kappa statistics. The results showed that the MaxEnt model was highly accurate, the simulation results were credible, and the total suitable area of Panicum milliaceum in the world is 4563.82 × 104 km2. The high habitat area of Panicum milliaceum is 484.95 × 104 km2, accounting for 10.63% of the total habitat area, and is mainly distributed in the United States, the Russian Federation, France, Ukraine, Australia, Germany, etc. The soil factor (hswd) was the most important environmental factor limiting the potential geographic distribution of Panicum milliaceum, followed by the precipitation factor (Precipitation of the Driest Month, bio14) and temperature factor (Mean Temperature of the Wettest Quarter, bio8). Under four future climate change scenarios, the area of the potential geographic distribution of Panicum milliaceum decreased to different extents at different levels compared to the contemporary period. Therefore, climate change may significantly affect the global distribution pattern of Panicum milliaceum cultivation in the future and thus reshape global Panicum milliaceum production and trade patterns.
Long Memory Cointegration in the Analysis of Maximum, Minimum and Range Temperatures in Africa: Implications for Climate Change
Authors: Yaya OlaOluwa S. , Adesina Oluwaseun A. , Olayinka Hammed A. , Ogunsola Oluseyi E. , Gil-Alana Luis A.
Published: August 2023

This paper deals with the analysis of the temperatures in a group of 36 African countries. By looking at the maximum, minimum and the range (the difference between the maximum and the minimum) and using a long memory model based on fractional integration and cointegration, we first show that all series display a long memory pattern, with a significant positive time trend in 29 countries for the maximum temperatures and in 33 for the minimum ones. Looking at the range, the estimated value for the order of integration is smaller than the one based on maximum or minimum temperatures in 17 countries. Performing fractional cointegration tests between the maximum and minimum temperatures, our results indicate that the two series cointegrate in the classical sense (i.e., with a short memory equilibrium relationship) in a group of 11 countries, and there is another group of eight countries displaying cointegration in the fractional sense. The remaining 17 countries with no evidence of cointegration are therefore at a very high risk of climate change due to the absence of long-term co-movement in their maximum and minimum temperatures. Findings in this paper are of tremendous interpretations and relevance for the analysis and climate projections in Africa.
Almost Ćirić Type Contractions and Their Applications in Complex Valued b-Metric Spaces
Authors: Sarwar Muhammad , Shah Syed Khayyam , Mitrović Zoran D. , Mukheimer Aiman , Mlaiki Nabil
Published: August 2023

In this article, we present the use of a unique and common fixed point for a pair of mappings that satisfy certain rational-type inequalities in complex-valued b-metric spaces. We also provide applications related to authenticity concerns in integral equations. Our results combine well-known contractions, such as the Ćirić contraction and almost contractions.
Ransomware Detection Using Machine Learning: A Survey
Authors: Alraizza Amjad , Algarni Abdulmohsen
Published: August 2023

Ransomware attacks pose significant security threats to personal and corporate data and information. The owners of computer-based resources suffer from verification and privacy violations, monetary losses, and reputational damage due to successful ransomware assaults. As a result, it is critical to accurately and swiftly identify ransomware. Numerous methods have been proposed for identifying ransomware, each with its own advantages and disadvantages. The main objective of this research is to discuss current trends in and potential future debates on automated ransomware detection. This document includes an overview of ransomware, a timeline of assaults, and details on their background. It also provides comprehensive research on existing methods for identifying, avoiding, minimizing, and recovering from ransomware attacks. An analysis of studies between 2017 and 2022 is another advantage of this research. This provides readers with up-to-date knowledge of the most recent developments in ransomware detection and highlights advancements in methods for combating ransomware attacks. In conclusion, this research highlights unanswered concerns and potential research challenges in ransomware detection.
Automatic Medical Report Generation Based on Cross-View Attention and Visual-Semantic Long Short Term Memorys
Authors: Gu Yunchao , Li Renyu , Wang Xinliang , Zhou Zhong
Published: August 2023

Automatic medical report generation based on deep learning can improve the efficiency of diagnosis and reduce costs. Although several automatic report generation algorithms have been proposed, there are still two main challenges in generating more detailed and accurate diagnostic reports: using multi-view images reasonably and integrating visual and semantic features of key lesions effectively. To overcome these challenges, we propose a novel automatic report generation approach. We first propose the Cross-View Attention Module to process and strengthen the multi-perspective features of medical images, using mean square error loss to unify the learning effect of fusing single-view and multi-view images. Then, we design the module Medical Visual-Semantic Long Short Term Memorys to integrate and record the visual and semantic temporal information of each diagnostic sentence, which enhances the multi-modal features to generate more accurate diagnostic sentences. Applied to the open-source Indiana University X-ray dataset, our model achieved an average improvement of 0.8% over the state-of-the-art (SOTA) model on six evaluation metrics. This demonstrates that our model is capable of generating more detailed and accurate diagnostic reports.
Divergent Pharmacology and Biased Signaling of the Four Melanocortin-4 Receptor Isoforms in Rainbow Trout (Oncorhynchus mykiss)
Authors: Ji Ren-Lei , Liu Ting , Hou Zhi-Shuai , Wen Hai-Shen , Tao Ya-Xiong
Published: August 2023

The melanocortin-4 receptor (MC4R) is essential for the modulation of energy balance and reproduction in both fish and mammals. Rainbow trout (Oncorhynchus mykiss) has been extensively studied in various fields and provides a unique opportunity to investigate divergent physiological roles of paralogues. Herein we identified four trout mc4r (mc4ra1, mc4ra2, mc4rb1, and mc4rb2) genes. Four trout Mc4rs (omMc4rs) were homologous to those of teleost and mammalian MC4Rs. Multiple sequence alignments, a phylogenetic tree, chromosomal synteny analyses, and pharmacological studies showed that trout mc4r genes may have undergone different evolutionary processes. All four trout Mc4rs bound to two peptide agonists and elevated intracellular cAMP levels dose-dependently. High basal cAMP levels were observed at two omMc4rs, which were decreased by Agouti-related peptide. Only omMc4rb2 was constitutively active in the ERK1/2 signaling pathway. Ipsen 5i, ML00253764, and MCL0020 were biased allosteric modulators of omMc4rb1 with selective activation upon ERK1/2 signaling. ML00253764 behaved as an allosteric agonist in Gs-cAMP signaling of omMc4rb2. This study will lay the foundation for future physiological studies of various mc4r paralogs and reveal the evolution of MC4R in vertebrates.
Being Eaten Alive: How Energy-Deprived Cells Are Disposed of, Mediated by C-Reactive Protein—Including a Treatment Option
Authors: Sheriff Ahmed , Kunze Rudolf , Brunner Patrizia , Vogt Birgit
Published: August 2023

In medicine, C-reactive protein (CRP) has become established primarily as a biomarker, predicting patient prognosis in many indications. Recently, however, there has been mounting evidence that it causes inflammatory injury. As early as 1999, CRP was shown to induce cell death after acute myocardial infarction (AMI) in rats and this was found to be dependent on complement. The pathological effect of CRP was subsequently confirmed in further animal species such as rabbit, mouse and pig. A conceptual gap was recently closed when it was demonstrated that ischemia in AMI or ischemia/hypoxia in the severe course of COVID-19 causes a drastic lack of energy in involved cells, resulting in an apoptotic presentation because these cells cannot repair/flip-flop altered lipids. The deprivation of energy leads to extensive expression on the cell membranes of the CRP ligand lysophosphatidylcholine. Upon attachment of CRP to this ligand, the classical complement pathway is triggered leading to the swift elimination of viable cells with the appearance of an apoptotic cell by phagocytes. They are being eaten alive. This, consequently, results in substantial fibrotic remodeling within the involved tissue. Inhibiting this pathomechanism via CRP-targeting therapy has been shown to be beneficial in different indications.
A Novel Heteromorphous Convolutional Neural Network for Automated Assessment of Tumors in Colon and Lung Histopathology Images
Authors: Iqbal Saeed , Qureshi Adnan N. , Alhussein Musaed , Aurangzeb Khursheed , Kadry Seifedine
Published: August 2023

The automated assessment of tumors in medical image analysis encounters challenges due to the resemblance of colon and lung tumors to non-mitotic nuclei and their heteromorphic characteristics. An accurate assessment of tumor nuclei presence is crucial for determining tumor aggressiveness and grading. This paper proposes a new method called ColonNet, a heteromorphous convolutional neural network (CNN) with a feature grafting methodology categorically configured for analyzing mitotic nuclei in colon and lung histopathology images. The ColonNet model consists of two stages: first, identifying potential mitotic patches within the histopathological imaging areas, and second, categorizing these patches into squamous cell carcinomas, adenocarcinomas (lung), benign (lung), benign (colon), and adenocarcinomas (colon) based on the model’s guidelines. We develop and employ our deep CNNs, each capturing distinct structural, textural, and morphological properties of tumor nuclei, to construct the heteromorphous deep CNN. The execution of the proposed ColonNet model is analyzed by its comparison with state-of-the-art CNNs. The results demonstrate that our model surpasses others on the test set, achieving an impressive F1 score of 0.96, sensitivity and specificity of 0.95, and an area under the accuracy curve of 0.95. These outcomes underscore our hybrid model’s superior performance, excellent generalization, and accuracy, highlighting its potential as a valuable tool to support pathologists in diagnostic activities.
Chinese Personality Traits and Mental Health: A Meta-Analysis
Authors: Wang Liangsheng , Zhang Yong , Fu Zhiyuan
Published: August 2023

This meta-analysis aimed to synthesize the evidence on the relationship between Chinese personality traits and mental health. Through literature search and screening, a total of 70 original articles and 72 independent samples with a total of 65,133 participants were included. The results showed that: (1) Chinese mental health was significantly correlated with three dimensions of personality: psychoticism, neuroticism, and extraversion (correlation coefficients were 0.234, 0.438, and −0.101, respectively); (2) each factor of mental health was significantly positively correlated with psychoticism and neuroticism; only the factor of interpersonal sensitivity was significantly negatively correlated with extraversion; (3) subject type has a significant moderating effect on the relationship between mental health and neuroticism and extraversion, but has no significant moderating effect on the relationship between mental health and psychoticism; (4) publication type does not play a moderating role in the relationship between mental health and the three personality dimensions. This meta-analysis confirmed that personality traits have a significant predictive effect on mental health in Chinese people. However, the relationship between personality and mental health varied considerably across dimensions and groups.
Study on Impact Resistance of All-Lightweight Concrete Columns Based on Steel Fiber Reinforced and Various Axial Compression Ratio
Authors: Wang Xiuli , Wu Qinyuan , Gao Zhenguo , Sha Lirong
Published: August 2023

Concrete columns in service are exposed to threats such as accidental impacts and explosions, which pose potential risks to the safety of buildings. Although fully lightweight concrete elements prepared from non-sintered fly ash ceramic pellets and pottery sand are widely used in engineering practice, the dynamic response of such elements under impact loading is not supported by adequate research data. Therefore, in this study, the dynamic response of all-lightweight concrete columns under impact loading with different axial compression ratios (0.1, 0.2, and 0.3) was investigated by means of drop hammer impact tests, and the potential of shear wave steel fibers in mitigating structural damage and preventing structural failure was investigated. The results of the study reveal that the specimens primarily exhibit shear and bending damage under impact loading. With an axial compression ratio of 0.1, the specimen is dominated by bending damage. As the axial compression ratio increases from 0.1 to 0.3, the specimen’s damage mode transitions to shear damage dominance. This change results in a larger impact force and displacement response while experiencing lower displacement acceleration. Additionally, the introduction of steel fibers improves the strength and stiffness of the specimens, shifting their behavior from shear to bending damage. Consequently, this reduces impact damage, mid-span displacement, and displacement acceleration while enhancing the specimen’s response to the impact force and its capacity for deformation energy dissipation.