bluuebunny/crossref_metadata_2025 huggingface dataset:
Antimicrobial usage in broiler chicken production in the United States, 2013–2021
Authors: Singer Randall S. , Schrag Nora F. D. , Ricke Isabel , Apley Michael D.
Published: April 2023

Although efforts to improve antimicrobial stewardship should include the collection of antimicrobial use data, most antimicrobial datasets collected at the national level consist of antimicrobial sales data which cannot inform stewardship. These data lack context, such as information regarding target species, disease indication, and regimen specifics like dose, route and duration. Therefore, the goal of this study was to develop a system for collecting data on the use of antimicrobials in the U.S. broiler chicken industry. This study utilized a public-private partnership to enable collection and protection of sensitive data from an extremely large industry while releasing deidentified and aggregated information regarding the details of antimicrobial use on U.S. broiler chicken farms over time. Participation was voluntary. Data were collected for the period 2013 through 2021 and are reported on a calendar year basis. Using production statistics from USDA:NASS as a denominator, the data supplied by participating companies represented approximately 82.1% of broiler chicken production in the U.S. in 2013, approximately 88.6% in 2017, and approximately 85.0% in 2021. The data that were submitted for 2021 are based on approximately 7,826,121,178 chickens slaughtered and 50,550,817,859 pounds liveweight produced. Granular flock-level treatment records were available for 75–90% of the birds represented in the 2018–2021 dataset. There was no use of antimicrobials in the hatchery for the years 2020 and 2021. Medically important in-feed antimicrobial use decreased substantially, with all in-feed tetracycline use being eliminated by 2020, and the use of virginiamycin being reduced by more than 97% since 2013. Medically important water-soluble antimicrobials are used for the treatment of disease in broiler production. Use decreased substantially for most water-soluble antimicrobials. The most important diseases necessitating treatment were necrotic enteritis and gangrenous dermatitis as well as E. coli-related disease. A focus on reducing the incidence of these diseases would reduce the need for antimicrobial therapy but will require an investment in research to find efficacious and cost-effective interventions for these diseases.
Ganoderma lingzhi culture enhance growth performance via improvement of antioxidant activity and gut probiotic proliferation in Sanhuang broilers
Authors: Liu Xuzhou , Huang Liling , Shi Yan , Wang Xiaoguo , Luo Yanglan , Wei Shiyan , Qin Yanchun , Lu Yuwen , Zhang Wenlong , Ju Ying , Yan Yong , Liao Yuying
Published: April 2023

Introduction

The experiment was conducted to evaluate the effects of Ganoderma lingzhi culture (GLC) as a fermented feed on growth performance, serum biochemical profile, meat quality, and intestinal morphology and microbiota in Sanhuang broilers. In addition, the association between gut bacteria and metabolites was investigated via untargeted metabolomic analysis.

Methods

A total of 192 Sanhuang broilers (112 days old) with an initial body weight of 1.62 ± 0.19 kg were randomly allocated to four treatments, six replicate pens per treatment with 8 broilers per pen. The four treatments contain a control diet (corn-soybean meal basal diet, CON), a positive control diet (basal diet + 75 mg/kg chlortetracycline, PCON), and the experimental diets supplemented with 1.5 and 3% of GLC, respectively. The trial includes phase 1 (day 1–28) and phase 2 (day 29–56).

Results

The results showed that broilers in PCON and GLC-added treatments showed a lower FCR (P < 0.05) in phase 2 and overall period and a higher ADG (P < 0.05) in phase 2. On day 56, the concentrations of serum SOD (P < 0.05), and HDL (P < 0.05) and cecal SCFA contents (P < 0.05) were increased in broilers fed GLC diets. Broilers fed GLC also showed a higher microbiota diversity and an elevated abundance of SCFA-related bacteria in the caecum. The association between intestinal bacteria and metabolites was investigated via correlation analysis. The differential metabolites in the caecum, such as L-beta-aspartyl-L-aspartic acid and nicotinamide riboside, were identified.

Conclusion

In summary, dietary GCL supplementation could increase growth performance to some extent. Moreover, GLC might benefit broilers' health by improving serum HDL content, antioxidant status, SCFAs contents, bacterial diversity, and probiotic proliferation in the caecum.
Genetic comparison of transmissible gastroenteritis coronaviruses
Authors: Wang Pei-Hua , Nawal Bahoussi Amina , Tariq Shah Pir , Guo Yan-Yan , Wu Changxin , Xing Li
Published: April 2023

Transmissible gastroenteritis virus (TGEV) is a porcine coronavirus that threatens animal health and remains elusive despite years of research efforts. The systematical analysis of all available full-length genomes of TGEVs (a total of 43) and porcine respiratory coronaviruses PRCVs (a total of 7) showed that TGEVs fell into two independent evolutionary phylogenetic clades, GI and GII. Viruses circulating in China (until 2021) clustered with the traditional or attenuated vaccine strains within the same evolutionary clades (GI). In contrast, viruses latterly isolated in the USA fell into GII clade. The viruses circulating in China have a lower similarity with that isolated latterly in the USA all through the viral genome. In addition, at least four potential genomic recombination events were identified, three of which occurred in GI clade and one in GII clade. TGEVs circulating in China are distinct from the viruses latterly isolated in the USA at either genomic nucleotide or antigenic levels. Genomic recombination serves as a factor driving the expansion of TGEV genomic diversity.
Tumor PD-L1 expression and molecular profiling are not associated with immune checkpoint inhibitor-induced thyroid dysfunction in advanced NSCLC patients
Authors: Horesh Adi , Pollack Rena , Nechushtan Hovav , Dresner-Pollak Rivka , Neuman Tzahi
Published: April 2023

Background: Immune-checkpoint inhibitors (ICIs) have revolutionized the treatment of advanced non-small cell lung cancer (NSCLC), however are frequently associated with thyroid immune-related adverse events (IRAEs). We investigated the association between patient characteristics, tumor PD-L1 expression and molecular profile with the development of thyroid IRAEs in NSCLC patients.

Methods: Single center, retrospective study including 107 NSCLC patients treated with PD-1/PD-L1 inhibitors from April 2016 to July 2020. All patients were euthyroid at baseline with at least two TSH measurements post-treatment initiation. The primary outcome was the difference in tumor PD-L1 expression in patients who developed any thyroid IRAEs versus those who remained euthyroid. Additional outcomes included development of overt thyroid dysfunction, the association of specific molecular alterations with thyroid IRAEs, and onset of thyroid IRAEs as a function of tumor PD-L1 expression.

Results: Overall, 37 (34.6%) patients developed any thyroid dysfunction and 18 (16.8%) developed overt thyroid dysfunction. Tumor PD-L1 staining intensity was not associated with thyroid IRAEs. TP53 mutation was less likely to be associated with any thyroid dysfunction (p < 0.05) and no association was found between EGFR, ROS, ALK or KRAS mutations. There was no association between PD-L1 expression and time to develop thyroid IRAEs.

Conclusion: PD-L1 expression is not associated with the development of thyroid dysfunction in advanced NSCLC patients treated with ICIs, suggesting that thyroid IRAEs are unrelated to tumor PD-L1 expression.
Effect of Basil, Thyme and Sage Essential Oils as Phytogenic Feed Additives on Production Performances, Meat Quality and Intestinal Microbiota in Broiler Chickens
Authors: Vlaicu Petru , Untea Arabela , Panaite Tatiana , Saracila Mihaela , Turcu Raluca , Dumitru Mihaela
Published: April 2023

As the use of antibiotics has been banned or reduced in certain countries in animal industries, the search for new alternatives to antibiotics has been and will continue to be a research subject in poultry for several years. This study aimed to evaluate the effect of basil, thyme and sage essential oils (EO) in broiler chickens’ diets. A total of 120 Cobb 500 broiler chickens aged 12 days were distributed into four homogeneous groups of thirty chickens with six replicates of five broilers each, raised until 42 days of age. For the diets, corn, soybean meal, corn gluten and wheat were used as the main ingredients for the control diet (CON), and three experimental diets were formulated as follows: a diet containing 0.05% essential oil from basil (EOB), a diet containing 0.05% essential oil from thyme (EOT), and a diet containing 0.05% essential oil from sage (EOS). The results showed that production performances, European Production Efficiency Factor (EPEF), European Broiler Index (EBI) and carcass weight were improved (p < 0.05) in the experimental groups compared with the control; however, no significant effect in anatomical parts development was observed. Lightness (L *) and hue angle (H *) colorimetric meat parameters were (p < 0.05) altered but without an effect on meat texture. The antioxidant capacity and total polyphenols content in the thigh meat and total n-3 and n-6 polyunsaturated fatty acids were higher in the experimental samples compared with the control. The intestinal microbiota was also significantly altered with a lowering of Escherichia coli, Coliforms and staphylococci in the small intestine and caecum and an increased lactobacilli count in the experimental groups compared with the control. Overall, all EO-supplemented diets showed the potential to improve meat quality; however, EOS was more effective in altering the chicken microbiota in the small intestine and caecum.
Proper Delay of Phosphorus Application Promotes Wheat Growth and Nutrient Uptake under Low Phosphorus Condition
Authors: Yi Huixuan , Hu Shengchao , Zhang Yulin , Wang Xudong , Xia Zhenqing , Lei Yuting , Duan Min
Published: April 2023

It is widely known that root morphology has different response mechanisms at different phosphorus (P) levels. However, the effects of P application times on wheat (Triticum aestivum L.) root morphology and, therefore, on growth and nutrient uptake are still unclear. In this study, we investigated the feedback from the physiological indexes of wheat on different P application times after planting. Compared with the P application at planting, the shoot biomass with delayed P application for 21 days (d) increased by 11.8% (p < 0.05). Compared with the P application at planting, the shoot P uptake with delayed P application for 14–21 days increased by 38.4–71.2%, while the shoot N uptake and K uptake with delayed P application for 21 days increased by 16.0% and 14.1% (p < 0.05). Compared with the P application at planting, P use efficiency, N use efficiency, and K use efficiency with delayed P application for 21 days increased by 16.4%, 12.4%, and 12.4%. Delayed P application for 14–21 days promoted wheat shoot growth, nutrient (P, N, and K) uptake, and their use efficiencies. This is particularly important for optimizing the P fertilizer input and nutrient management for wheat growth.
Alternative Cropping Systems for Climate Change
Authors: Dordas Christos
Published: April 2023

Climate change and increased climate variability are significant contemporary issues and create new challenges for agriculture and the whole food production chain [...]
Quantitative Impacts of Climate Change and Human Activities on Grassland Productivity in Otog Banner, China from 2001 to 2020
Authors: Liu Hui , Song Xiaoyu , Wang Rongrong
Published: April 2023

The responses of grassland net primary productivity (NPP) to climate change (CC) and human activities (HA) have received much attention and are inconsistent on different spatial scales. The accurate and quantitative evaluation of the impacts of CC and HA on grassland NPP at a county scale is very important to reveal the external driving factors on grassland NPP and guide the protection of the grassland ecosystem in the arid sandy area of China. In this study, the improved CASA model was adopted to quantify the grassland NPP in Otog Banner, China from 2001 to 2020. The spatiotemporal dynamics of grassland NPP and the relationships between grassland NPP and climate factors in space were analyzed using the methods of simple linear regression and relative sensitivity coefficient. Furthermore, the relative contributions to grassland NPP dynamics caused by CC and HA were explored using the quantitative method based on partial derivative. The results revealed that the mean value of grassland NPP was 175.17 g C m−2 yr−1, and exhibited a significant decrease trend periodically at a rate of 2.14 g C m−2 yr−1 from 2001 to 2020. The spatial distribution of grassland NPP increased from west to east gradually and ranged in 17.48–498.09 g C m−2 yr−1. Grassland NPP exhibited significant linear patterns along the gradients of climate factors, and was the most sensitive to sunshine duration (SSD). Approximately 86.83% of the grassland showed a degradation trend and 39.71% showed a serious degradation trend. The CC contribution to grassland NPP dynamics was 0.593 g C m−2 yr−1, and precipitation was the key driving climate factor, while the contribution of HA was −2.733 g C m−2 yr−1, which was the primary factor leading to large-scale degradation of grassland in Otog banner. This study indicates that the status of the grassland ecosystem in Otog Banner is not optimistic, and measures for grassland ecosystem restoration and improvement need to be further strengthened.
High Levels of Heavy Metal(loid)s Related to Biliary Hyperplasia in Hedgehogs (Erinaceus europaeus)
Authors: Jota Baptista Catarina , Seixas Fernanda , Gonzalo-Orden José M. , Patinha Carla , Pato Pedro , Ferreira da Silva Eduardo , Casero María , Brazio Erica , Brandão Ricardo , Costa Daniela , Mateus Teresa Letra , Oliveira Paula A.
Published: April 2023

Heavy metal(loid) pollution of ecosystems is a current One Health problem. The liver is one of the most affected organs in cases of acute or chronic exposure to abnormal amounts of these substances, inducing histopathologic lesions. In order to assess the influence of heavy metal(loids), forty-five European hedgehogs (Erinaceus europaeus) were submitted to necropsy, and liver samples were collected for a routine histopathology exam and metal(loid)s determination (As, Cd, Co, Cr, Cu and Pb) by ICP-MS. Age was estimated during the necropsy exam. Biliary hyperplasia was the most frequent lesion observed (16/45; 35.56%). No statistically significant associations were found between biliary hyperplasia and age or sex. Metal(loid)s’ concentrations were higher in animals with biliary hyperplasia (except for As). There was a statistically significant difference for both Cd and Co. For As, Cd and Co, cubs and juveniles animals showed significantly lower concentrations than elder individuals. Only for Pb were significant differences found between females and males. As described in the literature, exposure to metal(loid)s may be a cause of biliary hyperplasia, although further research (including the use of biochemical methods) is needed to support these results. To the authors’ knowledge, this is the first report of this association in hedgehogs.
Effects of Dietary Sodium Nitroprusside and NG-Nitro-L-Arginine Methyl Ester on Growth Performance, Some Organs Development Status and Immune Parameters in Broilers
Authors: Bülbül Tuba , Sefergil Şamil , Bülbül Aziz
Published: April 2023

This study was conducted to determine the effects of dietary supplementation of sodium nitroprusside (SNP), a nitric oxide (NO) donor, and NG-nitro-L-arginine methyl ester (L-NAME), an NO synthase inhibitor, on growth performance, organ development, and immunity in broilers. A total of 560 one-day-old mixed-gender broiler chickens (ROSS 308) were divided into one control and seven experimental groups. The experimental groups were fed a basal diet supplemented with 25, 50, 100, and 200 ppm SNP, and 25, 50, and 100 ppm L-NAME in the starter and grower diets. Body weight gain increased in groups receiving 25–100 ppm L-NAME on day 21 and 100 ppm L-NAME on days 0–42. Feed intake increased in the group receiving 100 ppm L-NAME on all days. The feed conversion ratio improved in the group receiving 25 ppm L-NAME on days 0–21, whereas it worsened in groups with 100 and 200 ppm SNP on days 0–42. Serum antibody titers decreased in the 100 ppm L-NAME group on day 21. In conclusion, the supplementation of the NO synthase inhibitor L-NAME to the broilers’ diet had a positive effect on the performance parameters, whereas the NO donor SNP worsened these parameters, especially on days 0–21.
Assessing Alternatives to Locomotion Scoring for Detecting Lameness in Dairy Cattle in Tanzania: Infrared Thermography
Authors: Werema Chacha W. , Laven Linda J. , Mueller Kristina R. , Laven Richard A.
Published: April 2023

Lameness detection is a significant challenge. Locomotion scoring (LS), the most widely used system for detecting lameness, has several limitations, including its subjective nature and the existence of multiple systems, each with its own advantages and disadvantages. Therefore, this study aimed to evaluate whether the foot skin temperature (FST) of hind limbs, as measured using infrared thermography (IRT), could potentially be used as an alternative on Tanzanian dairy farms. Each of the three study farms were visited twice during the afternoon milking on consecutive days, with a total of 170 cows assessed. DairyNZ LS (4-point scale (0–3)) was undertaken on the first day as the cows exited the milking parlour after being milked, while on the following day, the plantar aspect of the hind limbs of the cows was thermally imaged while they were standing in the milking parlour, using a handheld T650sc forward-looking infrared camera. Mean FST was higher for cows with a locomotion score of 1 than those with a score of 0; higher for cows with a locomotion score of 2 than those with a score of 1; and higher for cows with a locomotion score of 3 than those with a score of 2, with each one-unit locomotion score increase being associated with a 0.57 °C increase in mean temperature across all zones. The optimal cut-off point of 38.0 °C for mean temperature across all zones was identified using a receiver operator characteristic curve. This cut-off point had a sensitivity of 73.2% and a specificity of 86.0% for distinguishing cows with a locomotion score ≥ 2 (clinical lameness). The prevalence of clinical lameness across all three farms was 33%, which meant that only 72% of cows with a mean FST across all zones ≥ 38.0 °C had been identified as clinically lame using LS. This study confirmed that IRT has the potential to be used to detect lameness on Tanzanian dairy farms. However, before it can be widely used, improvements in accuracy, especially specificity, are needed, as are reductions in equipment (IR camera) costs.
Novel Antimicrobial Agents for Gram-Negative Pathogens
Authors: Karvouniaris Marios , Almyroudi Maria Panagiota , Abdul-Aziz Mohd Hafiz , Blot Stijn , Paramythiotou Elisabeth , Tsigou Evdoxia , Koulenti Despoina
Published: April 2023

Gram-negative bacterial resistance to antimicrobials has had an exponential increase at a global level during the last decades and represent an everyday challenge, especially for the hospital practice of our era. Concerted efforts from the researchers and the industry have recently provided several novel promising antimicrobials, resilient to various bacterial resistance mechanisms. There are new antimicrobials that became commercially available during the last five years, namely, cefiderocol, imipenem-cilastatin-relebactam, eravacycline, omadacycline, and plazomicin. Furthermore, other agents are in advanced development, having reached phase 3 clinical trials, namely, aztreonam-avibactam, cefepime-enmetazobactam, cefepime-taniborbactam, cefepime-zidebactam, sulopenem, tebipenem, and benapenem. In this present review, we critically discuss the characteristics of the above-mentioned antimicrobials, their pharmacokinetic/pharmacodynamic properties and the current clinical data.
Bacillus subtilis YtpP and Thioredoxin A Are New Players in the Coenzyme-A-Mediated Defense Mechanism against Cellular Stress
Authors: Tossounian Maria-Armineh , Baczynska Maria , Dalton William , Peak-Chew Sew Yeu , Undzenas Kipras , Korza George , Filonenko Valeriy , Skehel Mark , Setlow Peter , Gout Ivan
Published: April 2023

Coenzyme A (CoA) is an important cellular metabolite that is critical for metabolic processes and the regulation of gene expression. Recent discovery of the antioxidant function of CoA has highlighted its protective role that leads to the formation of a mixed disulfide bond with protein cysteines, which is termed protein CoAlation. To date, more than 2000 CoAlated bacterial and mammalian proteins have been identified in cellular responses to oxidative stress, with the majority being involved in metabolic pathways (60%). Studies have shown that protein CoAlation is a widespread post-translational modification which modulates the activity and conformation of the modified proteins. The induction of protein CoAlation by oxidative stress was found to be rapidly reversed after the removal of oxidizing agents from the medium of cultured cells. In this study, we developed an enzyme-linked immunosorbent assay (ELISA)-based deCoAlation assay to detect deCoAlation activity from Bacillus subtilis and Bacillus megaterium lysates. We then used a combination of ELISA-based assay and purification strategies to show that deCoAlation is an enzyme-driven mechanism. Using mass-spectrometry and deCoAlation assays, we identified B. subtilis YtpP (thioredoxin-like protein) and thioredoxin A (TrxA) as enzymes that can remove CoA from different substrates. With mutagenesis studies, we identified YtpP and TrxA catalytic cysteine residues and proposed a possible deCoAlation mechanism for CoAlated methionine sulfoxide reducatse A (MsrA) and peroxiredoxin 5 (PRDX5) proteins, which results in the release of both CoA and the reduced form of MsrA or PRDX5. Overall, this paper reveals the deCoAlation activity of YtpP and TrxA and opens doors to future studies on the CoA-mediated redox regulation of CoAlated proteins under various cellular stress conditions.
Machine Learning-Based Framework for Predicting Creep Rupture Life of Modified 9Cr-1Mo Steel
Authors: Chai Mengyu , He Yuhang , Li Yongquan , Song Yan , Zhang Zaoxiao , Duan Quan
Published: April 2023

Efficient and accurate predictions of creep rupture life are essential for ensuring the integrity of high-temperature components. In this work, a machine learning-based framework is developed for the quick screening of crucial features and accurate prediction of the creep rupture life of modified 9Cr-1Mo steels. A feature screening protocol based on correlation filtering and sequential feature selection techniques is established for identifying critical features that significantly affect the prediction performance from a set of numerous descriptors. Moreover, several machine learning algorithms are employed for model training to examine their ability to map the complex nonlinear interactions between multivariate features and creep life. The results show that the test stress, test temperature, tempering time, and the contents of S and Cr are identified as the crucial features that greatly influence the life prediction performance of modified 9Cr-1Mo steels. Moreover, the Gaussian process regression (GPR) model with these five selected crucial features exhibits the highest prediction accuracy among various machine learning strategies. Finally, an additional dataset out of model training and testing is used to further validate the efficacy of the constructed GPR model. The validated results demonstrate that most creep data are distributed inside the two-factor band lines. Results from this work show that the developed machine learning framework can offer high accuracy and excellent adaptability in predicting the creep life of modified 9Cr-1Mo steels under various environmental conditions.
A Methodological Framework for Bridge Surveillance
Authors: Petti Luigi , Lupo Carmine , De Gaetano Constanza Maria
Published: April 2023

The Italian “Guidelines for risk classification and management, security assessment and monitoring of existing bridges”, published in 2020 after the collapse of the Polcevera viaduct in Northern Italy, present a multilevel methodology that involves on-site operators and universities/research centers carrying out and validating a management process from on-site survey to the bridges’ condition assessment. The main goals of this process are to acquire appropriate knowledge of the current state and its evolution over time of the overall buildings that compose the infrastructures, with the aim to support the managing companies in a decision-making process and the purpose of guaranteeing service in full safety. In particular, the guidelines propose the use of engineered software platforms for data digitalization of the structures with the aim to create a Building Management System (BMS) in which the main historical and current state information is collected and can then be uploaded continually. In 2020, the CUGRI (Inter-University Research Center for the Prediction and Prevention of Major Hazards) and the SAM (Southern Highways Company) launched an innovative surveillance management model established on a multidisciplinary approach based on Geography Markup Language (GML), BIM tools, on-site interdisciplinary inspections, and multi-hazard analysis. The experimented methodology provides the on-site training of inspectors, the elaboration of suitable BIM models according to the above guidelines, and AINOP (National Archive of Public Infrastructures) requirements, and an expert judgement process for preliminary bridge assessment and data validation to support the maintenance managing process. The study presents an innovative operative model for the surveillance process, which integrates on-site expeditious inspections and multidisciplinary expert judgements by using an appropriate digitalization of the bridges with BIM and GIS technologies. The paper illustrates the experimental methodology performed on the A3 highway, which connects Naples to Salerno in Southern Italy, highlighting issues and opportunities, moreover in a first interdisciplinary contribution of object-oriented landslide mapping modelling.
HFD: Hierarchical Feature Detector for Stem End of Pomelo with Transformers
Authors: Hou Bowen , Li Gongyan
Published: April 2023

Transformers have become increasingly prevalent in computer vision research, especially for object detection. To accurately and efficiently distinguish the stem end of pomelo from its black spots, we propose a hierarchical feature detector, which reconfigures the self-attention model, with high detection accuracy. We designed the combination attention module and the hierarchical feature fusion module that utilize multi-scale features to improve detection performance. We created a dataset in COCO format and annotated two types of detection targets: the stem end and the black spot. Experimental results on our pomelo dataset confirm that HFD’s results are comparable to those of state-of-the-art one-stage detectors such as YOLO v4 and YOLO v5 and transformer-based detectors such as DETR, Deformable DETR, and YOLOS. It achieves 89.65% mAP at 70.92 FPS with 100.34 M parameters.
Hierarchical Model Updating Method for Vector Electric-Propulsion Satellites
Authors: Wu Xueqian , Dong Yunfeng
Published: April 2023

Electric propulsion is of great significance to the development of high-efficiency and long-life satellites, and digital twins have gradually become a powerful tool for satellite engineering. Being affected by uncertainty factors such as the complexity and variability of the space environment and the satellite system, the digital twin model cannot accurately reflect the real physical properties. Therefore, it is crucial to update the satellite model to improve prediction accuracy. However, the complex structure and multi-physics process coupling of vector electric-propulsion satellites bring great challenges to model updating. According to the characteristics of the vector electric-propulsion satellite, this paper establishes mathematical models of the whole satellite. Additionally, a hierarchical model updating method is proposed and applied to the model updating case of a satellite with multiple subsystems. The simulation results show that the method is suitable for the model updating of the vector electric-propulsion satellite. Through multiple iterations of closed-loop cycles, the residual errors between the simulation values and the telemetry values can be decreased, and the errors between the estimated values and the true values of state variables can also be decreased by an order of magnitude.
Analytic Time Reentry Cooperative Guidance for Multi-Hypersonic Glide Vehicles
Authors: Xu Hui , Cai Guangbin , Fan Yonghua , Wei Hao , Li Xin , Wang Yongchao
Published: April 2023

Aiming at the cooperative guidance problem of multi-hypersonic glide vehicles, a cooperative guidance method based on a parametric design and an analytical solution of time-to-go is proposed. First, the hypersonic reentry trajectory optimization problem was transformed into a parameter optimization problem. The parameters were optimized to determine the angle of attack profile and the time to enter the altitude velocity reentry corridor. Then, using the quasi-equilibrium glide condition, the estimation form of the remaining flight time was analytically derived to satisfy accurately the cooperative time constraint. Using the remaining time-to-go and range-to-go, combined with the heading angle deviation corridor, the bank angle command was further calculated. Finally, the swarm intelligence optimization algorithm was used to optimize the design parameters to obtain the cooperative guidance trajectory satisfying the time constraint. Simulations showed that the analytical time reentry cooperative guidance algorithm proposed in this paper can accurately meet the time constraints and cooperative flight accuracy. Monte Carlo simulation experiments verified that the proposed algorithm demonstrates a robust performance.
Forest Fragmentation and Landscape Connectivity Changes in Ecuadorian Mangroves: Some Hope for the Future?
Authors: Jaramillo Julio J. , Rivas Carlos A. , Oteros José , Navarro-Cerrillo Rafael M.
Published: April 2023

This study investigates the impact of fragmentation on Ecuador’s coastal mangrove forests. Fragmentation is identified as a primary cause of aquatic ecosystem degradation. We analyzed the relationship between habitat loss, fragmentation, and mangrove connectivity through a multitemporal approach using Global Mangrove Watch and fragmentation and connectivity metrics. The terrain was divided into 10 km2 hexagons, and six fragmentation metrics were calculated. A Getis–Ord Gi* statistical analysis was used to identified areas with the best and worst conservation status, while connectivity analyses were performed for a generic species with a 5 km dispersion. Findings revealed widespread mangrove fragmentation in Ecuador, with geographical differences between the insular region (Galapagos) and the mainland coast. Minimal loss or even expansion of mangrove forests in areas like the Galapagos Islands contrasted with severe fragmentation along the mainland coast. Transformation of forests into fisheries, mainly prawn factories, was the primary driver of change, while only a weak correlation was observed between mangrove fragmentation and conversion to agriculture, which accounts for less than 15% of all deforestation in Ecuador. Fragmentation may increase or decrease depending on the management of different deforestation drivers and should be considered in large-scale mangrove monitoring. Focusing only on mangrove deforestation rates in defining regional conservation priorities may overlook the loss of ecosystem functions and fragmentation.
Effectiveness of Fiber Optic Distributed Acoustic Sensing (DAS) in Vertical Seismic Profiling (VSP) Field Survey
Authors: Mad Zahir Mohamad Hafizal , Abdul Aziz Khairul Mustaqim , Ghazali Ahmad Riza , Abdul Latiff Abdul Halim
Published: April 2023

The evolution of fiber optic technology in the past few decades has led to significant advancements in various fields, including high-speed and long-distance communication, big data transport, optical imaging, and sensing. However, relatively few studies have examined the use of fiber optic sensors (FOSs) as point and distributed sensors in geophysics. Distributed Acoustic Sensing (DAS) is a widely used method for subsurface imaging and monitoring in wells, specifically in Vertical Seismic Profiling (VSP) surveys. This method allows for detailed analysis of subsurface structures and properties of reservoirs. Four different strategies for deploying FOS cables in DAS VSP are evaluated and compared: cementing behind casing, cable behind inflatable liner, strapping to production tubing, and wireline deployment. Cementing the fiber behind casing is considered the most effective method for coupling with the formation. However, the other methods also have their own advantages and limitations. The fiber cable behind inflatable liner, for example, allows for accessibility to the fiber without affecting the acoustic signal, while strapping the fiber to production tubing can still record DAS signals; tubing noise and signal attenuation from the annular fluid, however, can make it difficult to differentiate from the seismic signal. Nonetheless, this method has the benefit of being simpler to deploy and replace in case of failure. Wireline deployment can pick up some acoustic signals in regions where the cable touches the well wall, but in vertical sections where the cable is not in contact with the wall, the signal is attenuated. Results from pilot tests in a field in Canada are discussed and evaluated, and suggestions for improving the VSP signal are provided.
Efficient False Positive Control Algorithms in Big Data Mining
Authors: Liu Xuze , Zhao Yuhai , Xu Tongze , Wahab Fazal , Sun Yiming , Chen Chen
Published: April 2023

The typical hypothesis testing issue in statistical analysis is determining whether a pattern is significantly associated with a specific class label. This usually leads to highly challenging multiple-hypothesis testing problems in big data mining scenarios, as millions or billions of hypothesis tests in large-scale exploratory data analysis can result in a large number of false positive results. The permutation testing-based FWER control method (PFWER) is theoretically effective in dealing with multiple hypothesis testing issues. In reality, however, this theoretical approach confronts a serious computational efficiency problem. It takes an extremely long time to compute an appropriate FWER false positive control threshold using PFWER, which is almost impossible to achieve in a reasonable amount of time using human effort on medium- or large-scale data. Although some methods for improving the efficiency of the FWER false positive control threshold calculation have been proposed, most of them are stand-alone, and there is still a lot of space for efficiency improvement. To address this problem, this paper proposes a distributed PFWER false-positive threshold calculation method for large-scale data. The computational effectiveness increases significantly when compared to the current approaches. The FP-growth algorithm is used first for pattern mining, and the mining process reduces the computation of invalid patterns by using pruning operations and index optimization for merging patterns with index transactions. The distributed computing technique is introduced on this basis, and the constructed FP tree is decomposed into a set of subtrees, each corresponding to a subtask. All subtrees (subtasks) are distributed to different computing nodes. Each node independently calculates the local significance threshold according to the designated subtasks. Finally, all local results are aggregated to compute the FWER false positive control threshold, which is completely consistent with the theoretical result. A series of experimental findings on 11 real-world datasets demonstrate that the distributed algorithm proposed in this paper can significantly improve the computation efficiency of PFWER while ensuring its theoretical accuracy.
Artificial Intelligence Enabled Project Management: A Systematic Literature Review
Authors: Taboada Ianire , Daneshpajouh Abouzar , Toledo Nerea , de Vass Tharaka
Published: April 2023

In the Industry 5.0 era, companies are leveraging the potential of cutting-edge technologies such as artificial intelligence for more efficient and green human-centric production. In a similar approach, project management would benefit from artificial intelligence in order to achieve project goals by improving project performance, and consequently, reaching higher sustainable success. In this context, this paper examines the role of artificial intelligence in emerging project management through a systematic literature review; the applications of AI techniques in the project management performance domains are presented. The results show that the number of influential publications on artificial intelligence-enabled project management has increased significantly over the last decade. The findings indicate that artificial intelligence, predominantly machine learning, can be considerably useful in the management of construction and IT projects; it is notably encouraging for enhancing the planning, measurement, and uncertainty performance domains by providing promising forecasting and decision-making capabilities.
Cooperative Decision-Making for Mixed Traffic at an Unsignalized Intersection Based on Multi-Agent Reinforcement Learning
Authors: Zhuang Huanbiao , Lei Chaofan , Chen Yuanhang , Tan Xiaojun
Published: April 2023

Despite rapid advances in vehicle intelligence and connectivity, there is still a significant period in mixed traffic where connected, automated vehicles and human-driven vehicles coexist. The behavioral uncertainty of human-driven vehicles makes decision-making a challenging task in an unsignalized intersection scenario. In this paper, a decentralized multi-agent proximal policy optimization (MAPPO) based on an attention representations algorithm (Attn-MAPPO) was developed to make joint decisions at an intersection to avoid collisions and cross the intersection effectively. To implement this framework, by exploiting the shared information, the system was modeled as a model-free, fully cooperative, multi-agent system. The vehicle employed an attention module to extract the most valuable information from its neighbors. Based on the observation and traffic rules, a joint policy was identified to work more cooperatively based on the trajectory prediction of all the vehicles. To facilitate the collaboration between the vehicles, a weighted reward assignment scheme was proposed to focus more on the vehicles approaching intersections. The results presented the advantages of the Attn-MAPPO framework and validated the effectiveness of the designed reward function. Ultimately, the comparative experiments were conducted to demonstrate that the proposed approach was more adaptive and generalized than the heuristic rule-based model, which revealed its great potential for reinforcement learning in the decision-making of autonomous driving.
Assessment of the Quality of Video Sequences Performed by Viewers at Home and in the Laboratory
Authors: Klink Janusz , Brachmański Stefan , Łuczyński Michał
Published: April 2023

The paper presents the results of subjective and objective quality assessments of H.264-, H.265-, and VP9-encoded video. Most of the literature is devoted to subjective quality assessment in well-defined laboratory circumstances. However, the end users usually watch the films in their home environments, which may be different from the conditions recommended for laboratory measurements. This may cause significant differences in the quality assessment scores. Thus, the aim of the research is to show the impact of environmental conditions on the video quality perceived by the user. The subjective assessment was made in two different environments: in the laboratory and in users’ homes, where people often watch movies on their laptops. The video signal was assessed by young viewers who were not experts in the field of quality assessment. The tests were performed taking into account different image resolutions and different bit rates. The research showed strong correlations between the obtained results and the coding bit rates used, and revealed a significant difference between the quality scores obtained in the laboratory and at home. As a conclusion, it must be underlined that the laboratory tests are necessary for comparative purposes, while the assessment of the video quality experienced by end users should be performed under circumstances that are as close as possible to the user’s home environment.
Emission Characteristics of Tyre Wear Particles from Light-Duty Vehicles
Authors: Li Jiachen , Zhang Mengzhu , Ge Yunshan , Wen Yi , Luo Jiaxin , Yin Dailin , Wang Chongyao , Wang Changyu
Published: April 2023

Tyre wear particle emissions have gained significant attention due to their harmful effects on the environment and human health. However, studies on tyre wear particles generated under chassis dynamometer conditions are still scarce. This study measures the instantaneous number concentrations and elemental species of tyre wear particles in different light-duty vehicle test cycles. The results show that the particle number (PN) concentrations of the US06 test cycle are much higher than those of the WLTC test cycle due to the larger and more frequent accelerations and decelerations in the former. High PN concentrations are observed during high driving speeds with rapid accelerations, while PN concentrations are much lower during low driving speed with rapid acceleration. Furthermore, tyre tread temperature is found to be related to the formation of tyre wear particles. The PN concentration in the second and third US06 test cycles are similar, indicating that once the tyre temperature exceeds the critical value, the tyres become heated to a steady state, and the PN concentrations will not be affected by the average temperature of the tyre. A low initial tyre temperature can produce high PN concentrations during the cold start phase of test cycles and prolong the time required for tyres to warm up. In addition, the particles contained a high mass fraction of Zn, which can serve as a tracer of tyre wear particles in non-exhaust particle tests of vehicles.
Reinsurance Policy under Interest Force and Bankruptcy Prohibition
Authors: Zhong Yangmin , Huang Huaping
Published: April 2023

In this paper, we solve an optimal reinsurance problem in the mathematical finance area. We assume that the surplus process of the insurance company follows a controlled diffusion process and the constant interest rate is involved in the financial model. During the whole optimization period, the company has a choice to buy reinsurance contract and decide the reinsurance retention level. Meanwhile, the bankruptcy at the terminal time is not allowed. The aim of the optimization problem is to minimize the distance between the terminal wealth and a given goal by controlling the reinsurance proportion. Using the stochastic control theory, we derive the Hamilton-Jacobi-Bellman equation for the optimization problem. Via adopting the technique of changing variable as well as the dual transformation, an explicit solution of the value function and the optimal policy are shown. Finally, several numerical examples are shown, from which we find several main factors that affect the optimal reinsurance policy.
Infinite Series and Logarithmic Integrals Associated to Differentiation with Respect to Parameters of the Whittaker Mκ,μ(x) Function I
Authors: Apelblat Alexander , González-Santander Juan Luis
Published: April 2023

In this paper, first derivatives of the Whittaker function Mκ,μx are calculated with respect to the parameters. Using the confluent hypergeometric function, these derivarives can be expressed as infinite sums of quotients of the digamma and gamma functions. Moreover, from the integral representation of Mκ,μx it is possible to obtain these parameter derivatives in terms of finite and infinite integrals with integrands containing elementary functions (products of algebraic, exponential, and logarithmic functions). These infinite sums and integrals can be expressed in closed form for particular values of the parameters. For this purpose, we have obtained the parameter derivative of the incomplete gamma function in closed form. As an application, reduction formulas for parameter derivatives of the confluent hypergeometric function are derived, along with finite and infinite integrals containing products of algebraic, exponential, logarithmic, and Bessel functions. Finally, reduction formulas for the Whittaker functions Mκ,μx and integral Whittaker functions Miκ,μx and miκ,μx are calculated.
Infinite Series and Logarithmic Integrals Associated to Differentiation with Respect to Parameters of the Whittaker Wκ,μx Function II
Authors: Apelblat Alexander , González-Santander Juan Luis
Published: April 2023

In the first part of this investigation, we considered the parameter differentiation of the Whittaker function Mκ,μx. In this second part, first derivatives with respect to the parameters of the Whittaker function Wκ,μx are calculated. Using the confluent hypergeometric function, these derivatives can be expressed as infinite sums of quotients of the digamma and gamma functions. Furthermore, it is possible to obtain these parameter derivatives in terms of infinite integrals, with integrands containing elementary functions (products of algebraic, exponential, and logarithmic functions), from the integral representation of Wκ,μx. These infinite sums and integrals can be expressed in closed form for particular values of the parameters. Finally, an integral representation of the integral Whittaker function wiκ,μx and its derivative with respect to κ, as well as some reduction formulas for the integral Whittaker functions Wiκ,μx and wiκ,μx, are calculated.
A New Perspective for Multivariate Time Series Decision Making through a Nested Computational Approach Using Type-2 Fuzzy Integration
Authors: Ramirez Martha , Melin Patricia
Published: April 2023

The integration of key indicators from the results of the analysis of time series represents a constant challenge within organizations; this could be mainly due to the need to establish the belonging of each indicator within a process, geographic region or category. This paper thus illustrates how both primary and secondary indicators are relevant for decision making, and why they need to be integrated by making new final fuzzy indicators. Thus, our proposal consists of a type-2 fuzzy integration of multivariate time series, such as OECD country risk classification, inflation, population and gross national income (GNI) by using multiple type-1 fuzzy inference systems to perform time series classification tasks. Our contribution consists of the proposal to integrate multiple nested type-1 fuzzy inference systems using a type-2 fuzzy integration. Simulation results show the advantages of using the proposed method for the fuzzy classification of multiple time series. This is done in order so the user can have tools that allow them to understand the environment and generate comparative analyses of multiple information sources, and finally use it during the process prior to decision making considering the main advantage of modeling the inherent uncertainty.