bluuebunny/crossref_metadata_2025 huggingface dataset:
The Mirage of Higher Petroleum Prices
Authors: Lynch Michael C.
Published: March 2023

For the next half century, long-term petroleum shortages though often predicted are unlikely, so prices will remain flat.

Introduction

Most petroleum industry price forecasters do not possess a record of which they can be proud. Long-term petroleum market forecasting has been so inaccurate that it has often been described as virtually impossible. To avoid criticism of their performance, many organizations no longer circulate their forecasts. Why have the forecasts been so wrong? Because of failure to predict supply!

Oil

Before the first oil price shock in 1973-74, most forecasts called for flat prices. Even after that price explosion, nearly every forecast continued to assume that prices would be flat. Some studies at that time argued that the new, approximately $10/bbl prices were too high for OPEC to maintain and that a decline to $7 /bbl should be expected. However, a shift occurred in 1977 when the Central Intelligence Agency published two influential reports that projected world demand for oil would approach productive capacity by the early 1980's and would substantially exceed capacity by 1985. The 1979 tripling of oil prices because of short-term factors associated with the Iranian revolution undermined arguments that long-term oil prices would not rise. Many analysts then chose to interpret the events as reflections of long-term resource shortages.
Factors Determining User Satisfaction with Counter Services of Local Government Offices: On-Site Survey at Higashihiroshima City Hall, Japan
Authors: Moteki Yasutoshi
Published: March 2023

This study empirically investigated the major factors determining user satisfaction with counter services in local government offices in Japan. The findings were compared with results from similar surveys conducted in 2020. Data were collected via on-site surveys on user satisfaction or service quality of the local government office using an original three-category questionnaire scale tailored to Japanese local administration characteristics. A total of 1,000 questionnaires were distributed over four weekdays on August 23–26, 2021 at the City Hall; there were 537 respondents (response rate: 53.7%). A regression analysis indicated that the independent variables of category C (service delivery) were the most influential, followed by the B (human-related factors) and A (office hardware) categories (adjusted R2 value: 0.58). This order of importance for each category is consistent with the results of the author's previous survey of users of ward offices in Osaka City and Tokyo Metropolitan Government. For service delivery (C), promptness and short waiting time, do not necessarily affect user satisfaction positively. Regarding staff responses, (1) the speed with which attending counter clerk speaks and (2) the atmosphere and kindness of the attending counter clerk are two important factors for counter-staff responses and the human-related factors category.
Fruit Type Classification using Deep Learning and Feature Fusion
Authors: Gill Harmandeep Singh , G Murugesan , Mehbodniya Abolfazl , Sajja Guna Sekhar , Gupta Gaurav , Bhatt Abhishek
Published: March 2023

Machine and deep learning applications play a dominant role in the current scenario in the agriculture sector. To date, the classification of fruits using image features has attained the researcher’s attraction very much from the last few years. Fruit recognition and classification is an ill-posed problem due to the heterogeneous nature of fruits. In the proposed work, Convolution neural network (CNN), Recurrent Neural Network (RNN), and Long-short Term Memory (LSTM) deep learning methods are used to extract the optimal image features, and to select features after extraction, and finally, use extracted image features to classify the fruits. To evaluate the performance of the proposed approach, the Support vector machine (SVM) unsupervised learning method, Artificial neuro-fuzzy inference system (ANFIS), and Feed-forward neural network (FFNN) classification results are compared, and observed that the proposed fruit classification approach results are quite efficient and promising.
Extrachromosomal Circular DNA Orchestrates Genome Heterogeneity and Outcome in Urothelial Bladder Carcinoma
Authors: Lv Wei , Pan Xiaoguang , Han Peng , Wu Shuang , Zeng Yuchen , Wang Qingqing , Guo Lidong , Xu Mengyang , Wang Fei , Xu Zhe , Li Yi , Teng Huajing , Li Conghui , Qi Kairu , Gao Shang , Yu Tianxi , Cui Xin , Xiang Chongjun , Li Yue , Tan Haotian , Liang Ning , Tao Huiying , Yu Guohua , Mi Jia , Xu Fuyi , Gong Benjiao , Shi Lei , Wang Tao , Bolund Lars , Yang Huan-Ming , Wang Wenting , Li Hanbo , Huang Jinrong , Lin Chunhua , Luo Yonglun
Published: March 2023

Extrachromosomal circular DNA (circulome) is a newly evolved hallmark in cancer whereas its role in shaping the cancer genome heterogeneity is yet to be sufficiently understood. Here, we comprehensively characterize and exploit the circulome-associated genome heterogeneity in 80 urothelial bladder carcinoma (UBC) patients by whole-genome/exome sequencing, Circle-Seq, long-read sequencing, and RNA-seq. Our results reveal a high load and heterogeneity of UBC circulome. Many single locus and chimeric circular DNAs originating from different chromosomes are identified, including extremely chimeric circular DNAs carrying seven oncogenes and circles from nine chromosomes. Circular DNAs contribute to increased oncogene dosage and could influence genome-wide gene expression. Increased expression of DNA repair genes (LIG3, POLQ, BRCA1, and BRCA2) promote circulome generation in UBCs. The circulome is in cis correlated with hypermutation (kataegis), copy number variation, oncogene amplification, structure variation, and poor clinical outcome. These results strongly support circulome associations with UBC genome heterogeneity, progression, and outcome.
Spectral Barcode for Drug Classification
Enabled with a Smartphone Raman Spectrometer
Authors: Kim Un Jeong , Lee Suyeon , Kim Hyochul , Roh Young-Geun , Han Seungju , Kim Hojung , Park Yeonsang , Kim Seokin , Chung Myung Jin , Son Hyungbin , Choo Hyuck
Published: March 2023

Measuring, recoding and analyzing of spectral information of materials as its unique finger print using a ubiquitous smart phone has been craved for by scientists and consumers. We demonstrated the concept of a spectral barcode in drug classification with a smartphone Raman spectrometer. The spectral barcode is a 2D Raman spectral intensity map captured with a CMOS-image sensor integrated with a periodic array of band-pass filters. We classified the type of each drug by capturing its spectrum and using a convolutional neural network (CNN) that we trained to identify 11 major spectral components. This yielded a prediction accuracy of 99%, including drugs in the same category but from different brands. The accuracy of identifying drugs by individual brand was 79.5%, which improved to 83.2% simultaneously using the spectral barcode and RGB image in the CNN. This inherent property-based labeling system will facilitate fundamental research and business opportunities.
Healthcare-seeking of medical students: the effect of socio-demographic factors, health risk behaviour and health status – a cross-sectional study in Hungary
Authors: Umami Afriza , Zsiros Viktória , Maróti-Nagy Ágnes , Máté Zsuzsanna , Sudalhar Sudalhar , Molnár Regina , Paulik Edit
Published: March 2023

Background
Medical students are more likely to have various physical and psychological issues, but less information is available about the healthcare-seeking behaviour for physical and mental health issues. The aim of this study is to determine the factors affecting medical students' healthcare-seeking when visiting a general practitioner (GP) and/or psychologist.
Methods
688 medical students (326 International and 362 Hungarian,) participated in a cross-sectional study. The information was gathered using a self-administered online questionnaire and covered socio-demographic background, health behaviour, general and mental health status and healthcare-seeking. For analysing adjusted associations, multivariable logistic regression models were used.
Results
Overall, 56.8% of medical students visit the GP; and 17.2%, the psychologist. Hungarian medical students visited the GP with chronic diseases, International medical students were more likely to visit a GP when they encountered sexual activity and had chronic diseases. Moreover, there was a significant correlation between sex, alcohol consumption, and perceived stress in the total sample of psychologist visits. When Hungarian medical students were in their clinical years and had a poor self-rated mental health, they were more likely to visit a psychologist. Whereas female international medical students and those who had poor self-rated mental health were more likely to seek psychological help.
Conclusion
Students visit a GP and/or psychologist for a variety of reasons, including socio-demographic background, risky health behaviours, and health issues. Medical schools should encourage help-seeking behaviours and early disclosure of medical students. Their ability to grasp healthcare attitudes and designing treatments will be important for both their academic success and future profession.
Accuracy of Photonic RF Transversal Signal Processors based on Microcomb
Authors: Moss David
Published: March 2023

Photonic RF transversal signal processors, which are equivalent to reconfigurable electrical digital signal processors but implemented with photonic technologies, have been widely used for modern high-speed information processing. With the capability of generating large numbers of wavelength channels with compact micro-resonators, optical microcombs bring new opportunities for realizing photonic RF transversal signal processors that have greatly reduced size, power consumption, and complexity. Recently, a variety of signal processing functions have been demonstrated using microcomb-based photonic RF transversal signal processors. Here, we provide detailed analysis for quantifying the processing accuracy of microcomb-based photonic RF transversal signal processors. First, we investigate the theoretical limitations of the processing accuracy determined by tap number, signal bandwidth, and pulse waveform. Next, we discuss the practical error sources from different components of the signal processors.Finally, we analyze the contributions of the theoretical limitations and the experimental factors to the overall processing inaccuracy both theoretically and experimentally. These results provide a useful guide for designing microcomb-based photonic RF transversal signal processors to optimize their accuracy.
Self-assembled Ni-Rh bimetallic catalyst for the exceptional performance and stability of direct methane protonic ceramic fuel cells
Authors: Hong Kyungpyo , Choi Mingi , Bae Yonggyun , Min Jihong , Lee Jaeyeob , Kim Donguk , Bang Saehee , Lee Han-Koo , Lee Wonyoung , Hong Jongsup
Published: March 2023

Direct methane PCFCs are promising electrochemical devices that address the technical and economic challenges associated with using pure hydrogen, such as the high cost of green production, transportation, and long-term storage. However, Ni, a conventional monometallic catalyst has sluggish reaction kinetics and a low tolerance for carbon cocking under CH4 operation, limiting its wider applications. Herein, we develop a self-assembled Ni-Rh bimetallic catalyst through Ni exsolution and Rh surface decoration in a fuel electrode. The Ni-Rh bimetallic catalyst shows remarkably high catalytic activity with an exceptional performance of ~ 0.50 W/cm2 at 500 ℃. Moreover, the catalyst significantly improves the stability with a degradation rate of 0.02%/h at 500 ℃; this value is ~ 20-fold lower than that of conventional PCFC (0.4%/h). Synchrotron-based in situ X-ray photoelectron spectroscopy reveals that the Ni-Rh bimetallic catalyst initiates a self-carbon cleaning process due to its high-water dissociation reaction, allowing sustainable operation.
Reproducible and Clinically Translatable Deep Neural Networks for Cancer Screening
Authors: Ahmed Syed Rakin , Befano Brian , Lemay Andreanne , Egemen Didem , Rodriguez Ana Cecilia , Angara Sandeep , Desai Kanan , Jeronimo Jose , Antani Sameer , Campos Nicole , Inturrisi Federica , Perkins Rebecca , Kreimer Aimee , Wentzensen Nicolas , Herrero Rolando , Pino Marta del , Quint Wim , de Sanjose Silvia , Schiffman Mark , Kalpathy-Cramer Jayashree
Published: March 2023

Cervical cancer is a leading cause of cancer mortality, with approximately 90% of the 250,000 deaths per year occurring in low- and middle-income countries (LMIC). Secondary prevention with cervical screening involves detecting and treating precursor lesions; however, scaling screening efforts in LMIC has been hampered by infrastructure and cost constraints. Recent work has supported the development of an artificial intelligence (AI) pipeline on digital images of the cervix to achieve an accurate and reliable diagnosis of treatable precancerous lesions. In particular, WHO guidelines emphasize visual triage of women testing positive for human papillomavirus (HPV) as the primary screen, and AI could assist in this triage task. Published AI reports have exhibited overfitting, lack of portability, and unrealistic, near-perfect performance estimates. To surmount recognized issues, we implemented a comprehensive deep-learning model selection and optimization study on a large, collated, multi-institutional dataset of 9,462 women (17,013 images). We evaluated relative portability, repeatability, and classification performance. The top performing model, when combined with HPV type, achieved an area under the Receiver Operating Characteristics (ROC) curve (AUC) of 0.89 within our study population of interest, and a limited total extreme misclassification rate of 3.4%, on held-aside test sets. Our work is among the first efforts at designing a robust, repeatable, accurate and clinically translatable deep-learning model for cervical screening.
Effects of Blood Flow Restriction Exercise on Muscle Endurance and Aerobic Capacity in Different Populations: A Systematic Review and Meta-Analysis
Authors: Xiong Feng , Wang Lu , Xiang Yulu , Feng Jie , Mu Panyun , Deng Qiulin , Hu Yimei
Published: March 2023

There is a lack of comprehensive understanding of the effect on aerobic capacity and muscle endurance by BFR’s application in different populations. SO the target was to elaborate the influence of BFR training on aerobic capacity and muscle endurance in different populations. A systematic review and meta-analysis were conducted. Literature was retrieved in PubMed, Web of Science, the Cochrane Library databases, Embase, CNKI (China National Knowledge Internet) and CBM (China Biology Medicine). 64 studies met the inclusion criteria, and 19 trials were included in the quantitative analysis. The main results showed that aerobic exercise combined with BFR (AE-BFR) and low-load resistance training with BFR (LBFR-RT) significantly improved athletes' aerobic capacity and muscle endurance, compared with aerobic exercise (AE) and low-load resistance training (LL-RT) (WMD = 2.47, p < 0.01; SMD = 1.15, p < 0.01). However, in the healthy, elderly and patients, no similar significant difference was found. In general, BFR training can significantly improve the muscle endurance and aerobic capacity of athletes. However, it remains to be seen whether the benefits of BFR are greater than non-BFR in the healthy and elderly, the impact of BFR on the patient needs to consider the pathophysiological characteristics of different diseases. The current evidence doesn’t support that anaerobic exercise (ANA-E) or high intensity interval training (HIIT) with BFR brings additional aerobic capacity gains. In addition, some new technological attempts deserve attention.
Patient-tailored Transcranial Direct Current Stimulation to Improve Stroke Rehabilitation: Study Protocol of a Randomized Sham-controlled Trial
Authors: Kolmos Mia , Madsen Mads Just , Liu Marie Louise , Karabanov Anje and , Johansen Katrine Lyders , Thielscher Axel , Gandrup Karen , Lundell Henrik , Fuglsang Søren , Thade Esben , Christensen Hanne , Iversen Helle Klingenberg , Siebner Hartwig Roman , Kruuse Christina
Published: March 2023

Background: Many patients do not fully regain motor function after ischemic stroke. Transcranial Direct Current Stimulation (TDCS) targeting the motor cortex may improve motor outcome as an add-on intervention to physical rehabilitation. However, beneficial effects on motor function varies largely among patients within and across TDCS trials. In addition to a large heterogeneity of study designs, this variability may be caused by the fact that TDCS was given as a one-size-fits-all protocol without accounting for anatomical differences between subjects. The efficacy and consistency of TDCS might be improved by a patient-tailored design that ensures precise targeting of a physiological relevant area with an appropriate current strength.
Methods: In a randomized, double-blinded, sham-controlled trial, patients with subacute ischemic stroke and residual upper-extremity paresis will receive two times 20 minutes of focal TDCS of ipsi-lesional primary motor hand area (M1-HAND) during supervised rehabilitation training three times weekly for four weeks. Anticipated 60 patients will be randomly assigned to active or sham TDCS of ipsi-lesional M1-HAND, using a central anode and four equidistant cathodes. The placement of the electrode grid on the scalp and current strength at each cathode will be personalized based on individual electrical field models to induce an electrical current of 0.2 V/m in the cortical target region resulting in current strengths between 1-4 mA. Primary endpoint will be difference in change of Fugl-Meyer Assessment of Upper Extremity (FMA-UE) score between active TDCS and sham at the end of the intervention. Exploratory endpoints will include UE-FMA at 12 weeks. Effects of TDCS on motor network connectivity and interhemispheric inhibition will be assessed with functional MRI and transcranial magnetic stimulation.
Discussion: The study will show the feasibility and test the efficacy of personalized, multi-electrode anodal TDCS of M1-HAND in patients with subacute stroke patients with upper-extremity paresis. Concurrent multimodal brain mapping will shed light into the mechanisms of action of therapeutic personalized TDCS of M1-HAND. Together, the results from this trial may inform future personalized TDCS studies in patients with focal neurological deficits after stroke.
CDKN2B-AS (rs2891168), SOD2 (rs4880) and PON1 (rs662) polymorphisms and susceptibility to coronary artery disease and type 2 diabetes mellitus in Iranian patients
Authors: Yari Abolfazl , Karam Zahra Miri , Meybodi Seyed Mahdi Emami , Saeidi Kolsoum , Sargazi Marzieh Lotfian , Ali Ali Sharif
Published: March 2023

Background: Several large-scale studies found that 9p21.3, superoxide dismutase 2 (SOD2), and paraoxonase 1 (PON1) polymorphisms increases type 2 diabetes mellitus (T2DM) and/or coronary artery disease (CAD) risk. Our research aimed to investigate whether the SNPs of the 9p21.3 locus (rs28911698), SOD2 (rs4880), and PON1 (rs662) genes were associated with the risk of T2DM and/or CAD in the Iranian population. In this case-control study four group subjects including patients with CAD non-T2DM, with CAD and T2DM, non-CAD with T2DM, and non-CAD non-T2DM were recruited to the study from 2019 to 2020.
Methods: Molecular analysis was carried out by allele specific-polymerase chain reaction (AS-PCR) technique for rs4880, Taqman genotyping assay for rs2891168, and PCR followed by restriction fragment length polymorphism (PCR-RFLP) technique for rs662.
Results: The rs2891168 polymorphism presented an elevated risk of CAD in non-T2DM with CAD and with T2DM CAD groups compared to the non-T2DM non-CAD group with GG genotype and dominant model after adjustment (p< 0.05). G-allele in PON1 rs662 polymorphism associated with increased risk of T2DM in T2DM non-CAD, and T2DM CAD groups compared to non-T2DM non-CAD group with dominant model, GG and AG genotypes (p< 0.05). However, SOD2 rs4880 polymorphism presented no significant association with the development of diabetes or CAD.
Conclusion: These results provide prime witness that rs2891168 and rs662 gene variants might have a possible increased risk of CAD and T2DM occurrence, respectively. To obtain more definitive and accurate results in this area, further research is required.
Ultra-low-cost Mechanical Smartphone Attachment for No-Calibration Blood Pressure Measurement
Authors: Xuan Yinan , Barry Colin , De Souza Jessica , Wen Jessica , Antipa Nick , Moore Alison , Wang Edward
Published: March 2023

We propose BPClip, a less than $ 1 USD blood pressure monitor that leverages a plastic clip with a spring-loaded mechanism to enable any smartphone with a flash LED and a camera to measure blood pressure. Unlike prior approaches, our system measured systolic, mean, and diastolic blood pressure using oscillometric measurements that avoid cumbersome per-user calibrations and does not require specialized smartphone models with custom sensors.
Segmentation of tooth enamel microstructure images using classical image processing and U-Net approaches
Authors: Fogalli Giovani Bressan , Line Sérgio Roberto Peres , Baum Daniel
Published: March 2023

Brackground: Tooth enamel is the hardest tissue in human organism, formed by prism layers in regularly alternating directions. These prisms form the Hunter-Schreger Bands (HSB) pattern when under side illumination, which is composed of light and dark stripes resembling fingerprints. We have shown in previous works that HSB pattern is highly variable, seems to be unique for each tooth and can be used as a biometric method for human identification. Since this pattern cannot be acquired with sensors, the HSB region in the digital photograph must be identified and correctly segmented from the rest of the tooth and background. Although these areas can be manually removed, this process is not reliable as excluded areas can vary according to the individual`s subjective impression. Therefore, the aim of this work was to develop an algorithm that automatically selects the region of interest (ROI), thus, making the entire biometric process straightforward.
Results: We used two different approaches: a classical image processing method which we called anisotropy-based segmentation (ABS) and a machine learning method known as U-Net, a convolutional neural network. Both approaches were applied to a set of extracted tooth images. U-Net with some post processing outperformed ABS in the segmentation task with an Intersection Over Union (IOU) of 0.837 against 0.766.
Conclusions: Even with a small dataset, U-Net proved to be a potential candidate for fully automated in-mouth application. However, the ABS technique has several parameters which allow a more flexible segmentation with interactive adjustments specific to image properties.
Mechanical properties of coarse-fine particle mixtures: a laboratory investigation of the effect of fine content
Authors: Chang Liucheng , Wang Ya , Wang Hongyu
Published: March 2023

The impacts of fine content on the microstructure and mechanical behavior of five types of coarse-fine particle mixtures with different fine contents are examined based on laboratory tests. The samples used in these tests all had a void ratio of 0.6. As the fine content decreases, the pore-size distribution of the soil changes from unimodal to bimodal, and the variation rate of the initial elastic modulus with confining pressure becomes great. If the fine content is greater than 71.43%, the pore volume of the mixtures reduces progressively progressively with decreasing the fine content, resulting in the growth of peak deviator stress, cohesiveness, and internal friction angle. If the concentration of the fine grains is less than 71.43%, the pore volume of the mixtures shows an increasing trend, which causes a decrease in peak deviator stress,cohesiveness, and internal friction angle.
Holo-omics analysis reveals the influence of gut microbiota on obesity indicators in Jinhua pigs
Authors: Liu Shuang , Lai Xueshuang , Xie Qinqin , Wang Zhen , Pan Yuchun , Zhang Zhe , Wang Qishan
Published: March 2023

Background

The mechanisms behind obesity are complex and multi-faceted, involving the interplay of both host genomics and gut microbiome. In recent years, research has largely focused on these factors separately, but rarely from the viewpoint of holo-omics, which considers the host and microbiome as an integrated entity. To address this gap in knowledge, the present study aimed to investigate the holo-omics basis of obesity in Jinhua pigs, a Chinese indigenous breed known for its high degree of fat deposition and superior meat quality.

Methods

Six pigs with extreme obesity phenotype were selected from a larger cohort of 18 Jinhua pigs, and the contents of the jejunum, cecum, and colon regions were collected after slaughter at 240 days of age. The data obtained was processed, denoised, and annotated using QIIME2, with expression differences being analyzed using edgeR software.

Results

The results showed significant differences in jejunal microbial diversity and composition between the two groups, with gut transcriptomics also indicating that differentially expressed genes in the jejunum were enriched in lipid metabolism pathways. These findings provide further evidence of the influence of the gut microbiome and host gene expression on fat deposition in Jinhua pigs.

Conclusions

This study provides valuable insights into the mechanisms of fat deposition in Jinhua pigs from the viewpoint of holo-omics. The integration of host transcriptomics and microbiome data helps shed light on the complex interactions between the host and gut microbiome, and highlights the importance of considering both factors in our understanding of obesity.
Haptic feature selection using Statistical Analysis for Haptic Object Reconstruction
Authors: Dewapura Praveena Wimarshani , Abeykoon A. M. Harsha S.
Published: March 2023

Object reconstruction in the haptic dimension is crucial in achieving realization in the virtual world. Prior studies have employed the conventional spring damper model to interpret the behavior of the object and motion parameters were treated as determinants of force feedback. However, haptic information is nonlinear, and more factors could influence haptic feedback recreation than just motion parameters. In recent years, scientists have paid much attention on employing AI technologies with haptics. AI approach is data-driven, and a thorough understanding of the data is critical to its performance. As a result, a detailed statistical analysisis required to discover patterns and relationships within the features and identify significant features as utilizing all the factors affecting haptic object reconstruction will cost higher computational time and resources. Thus, this study focused on statistically analyzing data extracted through a Disturbance Observer (DOB) and Reaction Force Observer(RFOB) based sensorless technique and selecting the significant features to precisely reconstruct an object in a haptic dimension. Furthermore, a mathematical notation was introduced to define a feature and the feature matrix was derived using the notation. Finally, a comparison of the performance of the same AI algorithm was conducted by varying the feature input to the AI model to understand the effect of the feature matrix.
Divide and conquer： Machine learning accelerated design of lead-free solder alloys with high strength and high ductility
Authors: Zhang Tong-Yi , Wei Qinghua , Cao Bin , Yuan Hao , Chen Youyang , You Kangdong , Yu Shuting , Yang Tixin , Dong Ziqiang
Published: March 2023

The attainment of both high strength and high ductility is always the goal for structure materials, because the two properties generally are mutually competing, called strength-ductility trade-off. Nowadays, the data-driven paradigm combined with expert domain knowledge provides the state-of-the-art methodology to design and discovery for structure materials with high strength and high ductility. To enhance both strength and ductility, a joint feature is proposed here to be the product of strength multiplying ductility. The strategy of “divide and conquer” is developed to solve the contradictory problem, that material experimental data of mechanical behaviors are, in general, small in size and big in noise, while the design space is huge, by a newly developed data preprocessing algorithm, named the Tree-Classifier for Gaussian Process Regression (TCGPR). The TCGPR effectively divides an original dataset in a huge design space into three appropriate sub-domains and then three Machine Learning (ML) models conquer the three sub-domains, achieving significantly improved prediction accuracy and generality. After that the Bayesian sampling is applied to design next experiments by balancing exploitation and exploration. Finally, the experiment results confirm the ML predictions, exhibiting novel lead-free solder alloys with high strength high ductility. Various material characterizations were also conducted to explore the mechanism of high strength and high ductility of the alloys.
Archaeological Bolivian maize genomes suggest diversity is associated with Inca cultural expansion and environmental variation in South America
Authors: Chen Huan , Baetsen-Young Amy , Thompson Addie , Day Brad , Lovis William , Wrobel Gabriel
Published: March 2023

Previous archaeological and anthropological studies have demonstrated the myriad ways that cultural and political systems shape access to and preferences for food. However, few studies have carried out biocultural analysis linking specific genotypic/phenotypic traits as evidence of cultural selection in ancient contexts. Here, we address this issue by comparing genomes of maize samples from Bolivia dating to ~500 BP included as an offering with the mummified remains of a young girl to 13 archaeological maize samples spanning at least 5,000 years of evolution, and 87 modern western hemisphere maize samples. Our phylogenetic analysis showed that the archaeological Bolivian maize (aBM) has the closest genetic distance to archaeological maize from ancient Peru, which in turn was derived from central Mexican domesticated varieties. The comparative genomic analysis identified specific modifications in the aBM genome representing traits that were selected by ancient farmers, including shorter growing season, greater drought28 istant, and sweeter taste. The genome of the aBM appears to reflect gene flow from Chile coinciding with the arrival of the Inca empire in the altiplano region surrounding modern-day La Paz, as well as selection for specific traits related to growth in this marginal environment and taste preference. Our study provides insights into the complex biocultural role that Inca culture had in determining the direction of maize diversity in South America.
Effect of the geometry and severity of left ventricular hypertrophy on cardiovascular mortality in patients on chronic hemodialysis
Authors: Huang Yi-Chen , Wu Chung-Kuan
Published: March 2023

Background
Left ventricular hypertrophy (LVH) is a vital risk factor for mortality in patients on dialysis. The association of the geometry and severity of LVH with cardiovascular mortality and mortality in patients on hemodialysis (HD) remains unknown. This study investigated clinical outcomes among patients on HD with different LVH geometric patterns and severity.
Methods
Patients on chronic HD from a single medical center who underwent echocardiography for the assessment of LVH geometric patterns and severity were enrolled. They were divided into concentric and eccentric groups and then subdivided into four groups based on LVH severity: mild-to-moderate eccentric LVH, mild-to-moderate concentric LVH, severe eccentric LVH, and severe concentric LVH. The risks of all-cause mortality and cardiovascular mortality between groups were evaluated using Cox proportional hazard analysis.
Results
Of the 237 enrolled patients on HD with LVH, 131 had concentric LVH, and 106 had eccentric LVH, with 33, 44, 73, and 87 having mild-to-moderate eccentric LVH, mild-to-moderate concentric LVH, severe eccentric LVH, and severe concentric LVH, respectively. Compared with eccentric LVH, the crude HR of mortality and cardiovascular mortality of concentric LVH was 1.60 (0.96–2.66) and 2.03 (1.13–3.65). Severe concentric LVH was a significant risk factor for all-cause mortality and cardiovascular mortality compared with mild-to-moderate eccentric LVH (crude HR: 2.58, 95% CI: 1.00–6.65 and 3.73, 1.13–12.33, respectively). After adjustment for all variables, concentric LVH and severe concentric LVH remained significant risk factors for cardiovascular mortality (adjusted HR: 2.13, 95% CI: 1.13–4.01 and 3.71, 1.07–12.82, respectively).
Conclusions
Concentric LVH, especially severe concentric LVH, was associated with a high risk of cardiovascular mortality among patients on chronic HD.
Soft computing in computer network security protection system with machine learning based on level protection in the cloud environment
Authors: Thomas Merin , Gupta Mohan Vishal , V Gokul Rajan , R Rajalakshmi , Dixit Rinku Sharma , Choudhary Shailee Lohmor
Published: March 2023

Artificial neural networks (ANN), fuzzy inference systems, approximate reasoning, and derivative-free optimization approaches like evolutionary computation, among others, are all components of Soft Computing (SC), an innovative method for building computationally intelligent methods. Due to its widespread use and benefits, cloud computing is currently a major focus for researchers. The distributed nature of cloud computing and its complete reliance on the internet for service provision present security challenges, the most serious of which is insider Distributed Denial of Service (DDoS), which results in complete deactivation of services. Based on machine learning and the cloud, this study proposes a novel method for improving computing network security. Using a trust-based secure cloud environment and the Kernel principal component encoder architecture, the network monitoring is carried out. The soft computing environment-based detection of a cyberattack on the network is the purpose for the security enhancement. Throughput, QoS, latency, and packet delivery ratio for various monitored cyber security datasets are the focus of the experimental analysis.the proposed technique attainednetwork security analysis of 89%, throughput of 98%, QoS of 66%, latencyof 59%, packet delivery ratio of 83%.
A case of influenza A H3N2 complicated with Kawasaki disease and liver function impairment
Authors: Guo Caili , Song Chunlan , Cheng Yibing , Wang Yuanyuan
Published: March 2023

Background:Studies have found that multiple viral infections are related to the occurrence of Kawasaki disease. It has not been reported in the world which H3N2 influenza A and Kawasaki disease occurred simultaneously. Children with influenza virus infection and symptoms associated with Kawasaki disease should be highly alert.
Case presentation:Here we present a 2 -year -old Chinese girl who was treated with oseltamivir and cefoperazone sulbactam for H3N2influenza infection and sepsis. The girl's body temperature remained high and liver enzymes increased significantly. The child was diagnosed with Kawasaki disease by a pediatric cardiologist and The patient recovered completely after being treated with gamma globulin.
Conclusion: We hope that our case report will guide clinicians to be vigilant about Kawasaki disease when high fever persists in children with H3N2 influenza.
Spatial Attention and Quantization based Contrastive Learning Framework for mmWave Massive MIMO Beam Training
Authors: Jia Haohui , Urakami Taisei , Chen Na , Gao Hui , Okada Minoru
Published: March 2023

Identifying the optimal beam is a major challenge in millimeter wave (mmWave) wireless communication systems, considering the impacts of noise and training overhead for processing massive multiple-input multiple-output (MIMO) channel streams. To address these challenges, the conventional schemes are to extract the feature of received signals and directly predict the optimal beam based on deep learning (DL) during the beam training. However, this process highly relies on the quality and prior knowledge of massive MIMO channels. In this paper, we propose a novel spatial attention and quantization based beam contrastive learning framework to draw the inherent transmitting angular relation between the beam signature and sub array channels from the contrastive environmental prediction. Specifically, we propose two DL-associated beam training solutions to improve transmission reliability and reduce the training overhead. The first solution, named quantified phase-based transformer architecture (QPTNet), applies a high spatial resolution codebook to quantify the received array signals along the subcarriers into finite categorical beamformer codeword and predict the optimal beam based on the attention mechanism. The second solution, named self enhanced QPTNet (SE-QPTNet), enhances the capability of identifying the beam signature by competitively predicting the partial channel arrays with the limited training data. Simulation results show that our proposed schemes obtain significantly higher beamforming gain with lower prior channel requirements compared to the existing DL-based schemes, and achieve highly reliable performance for mmWave massive MIMO systems.
Design and Implementation of Virtual Maintenance Training System for Heavy Duty Gearbox
Authors: Zhu Zi-cheng , Chu Wei-jun , Diao Yan , Li Xiang-hui
Published: March 2023

By analyzing the structural performance characteristics of a heavy-duty gearbox, based on the modular design thinking and the requirements of the actual maintenance training application, the virtual prototype of a heavy-duty gearbox, such as the construction of three-dimensional model, collision detection, 3d observation, disassembly sequence planning and evaluation of training in the virtual maintenance training simulation platform and maintenance simulation implementation process. A desktop virtual maintenance training system based on EON simulation platform is constructed. The training results show that the system can accurately guide the trainees to complete the gearbox maintenance training tasks correctly, with good operability and interaction.
Aggregation algorithm based on consensus verification
Authors: Li Shichao , Qin Jiwei
Published: March 2023

Distributed learning, as the most popular solution for training large-scale data for deep learning, consists of multiple participants collaborating on data training tasks. However, the malicious behavior of some during the training process, like Byzantine participants who would interrupt or control the learning process, will trigger the crisis of data security. Although recent existing defense mechanisms use the variability of Byzantine node gradients to clear Byzantine values, it is still unable to identify and then clear the delicate disturbance/attack. To address this critical issue, we propose an algorithm named consensus aggregation in this paper. This algorithm allows computational nodes to use the information of verification nodes to verify the effectiveness of the gradient in the perturbation attack, reaching a consensus based on the effective verification of the gradient. Then the server node uses the gradient as the valid gradient for gradient aggregation calculation through the consensus reached by other computing nodes. The proposed algorithm outperforms existing common aggregation algorithms (Krum, Trimmed Mean, and Bulyan) in the face of delicate disturbances and attacks on the MNIST and CIFAR10 datasets. In our experiments, we show that the proposed consensus aggregation algorithm successfully defends against a variety of Byzantine problems and effectively solves delicate disturbance/attack in Byzantine problems in distributed learning.
Plant managements but not fertilization mediate soil carbon emission and microbial community composition in subtropical Eucalyptus plantations
Authors: Wu Bin , Liu Wenfei , Wu Ying , Thompson Jill , Wu Jianping
Published: March 2023

Background and Aims
The diversity of plant functional groupsin plantations affects soil carbon, but we have limited understanding of the underlying mechanisms for how to plant management affects soil carbon dynamics.
Methods
We conducted a 3-year manipulation experiment of plant functional groups that included understory removal, tree root trenching, and fertilization treatments in 2-year-old and 6-year-old Eucalyptus plantations in the subtropical region, to explore soil carbon emission and microbial community composition.
Results
Soil respiration was significantly suppressed by understory removal (-38%), tree root trenching (-41%), and their interactions (-54%), but that fertilization alone and in interactions had no significant effect. Soil bacterial and fungal communities were also affected by understory removal and tree root trenching. Soil respiration, physicochemical characteristics, microbial diversity, and community composition were significantly affected by plantation age. Reductions in soil carbon emissions were associated with reductions in plant functional groups and soil microbial groups, while increases in soil respiration were associated with soil physicochemical factors, soil temperature, and plantation age.
Conclusion
Our findings highlight that plant managements are of great significance to the soil carbon emission processes in afforested plantations.
Red narrow-linewidth lasing and frequency comb from gain-switched self-injection-locked Fabry-Pérot laser diode
Authors: Shitikov Artem , Galiev Ramzil , Min'kov Kirill , Kondratiev Nikita , Cordette Steevy , Lobanov Valery , Bilenko Igor
Published: March 2023

Narrow-linewidth lasers are in extensive demand for numerous cutting-edge applications. Such lasers operating at the visible range are of particular interest. Self-injection locking of a laser diode frequency to a high-Q whispering gallery mode is an effective and universal way to achieve superior laser performance. We demonstrate ultrastable lasing with less than 10 Hz instantaneous linewidth for 20 μs averaging time at 638 nm using a Fabry-Pérot laser diode locked to a crystalline MgF2 microresonator. The linewidth measured with a β-separation line technique that characterizes 10 ms stability is as low as 850 Hz. Output power exceeds 80 mW. Demonstrated results are among the best for compact visible-range lasers in terms of stability and output power. We additionally report the first demonstration of a gain-switched regime for such stabilized Fabry-Pérot laser diode showing a high-contrast visible frequency comb generation. Tunable linespacing from 10 MHz to 2 GHz is observed. We demonstrated that the beatnote between the lines has sub-Hz linewidth and experiences spectral purification in the self-injection locking regime. This result might be of special importance for spectroscopy in the visible range.
Sampling Based Fuzzy Speech Clustering Systems for Faster Communication with Virtual Robotics Towards Social Applications
Authors: K RAJENDRA PRASAD , Narasimhulu K , KUMAR CH.N SANTHOSH , Reddy N Ramanjaneya
Published: March 2023

For social applications, virtual robotics technologies require the Internet of Things (IoT) and cloud services. IoT based speaker identification is required for virtual social robotics to operate in the most secure manner possible. Moreover, humans communicate using only natural speech to virtual robotics through IoT devices; Virtual robotics are the most emerging and which are required developing the speech-based identification models in the virtual cloud platforms. State-of-the-art of speech models defines the composite technique with the combination of k-means clustering and Gaussian mixture model (GMM) for speaker identification problems. Every speaker has their own annotation, acoustic, and utterance. The Gaussian mixture model (GMM) describes the modelling of features in the speech clustering system for speaker identification applications. These traditional systems are expensive for speech clustering of large datasets when considering the time and space parameters. Sampling based fuzzy-spherical k-means and sampling based fuzzy-mini-batch k-means(mbkm) are proposed to overcome the problem of scalability issue in the implementation of speech based virtual cloud based robotic systems. For demonstrating the effectiveness of the proposed fuzzy based speaker models, extensive experimental research is conducted using a variety of benchmark speech datasets.
On a problem by J. Jarczyk-W. Jarczyk
Authors: Chen Kai , Chen Yong
Published: March 2023

In the note, We use a different method from M. Balcerowskito find all analytic solutions f : C → C of the equation x + f(y + f(x)) = y + f(x + f(y)), which posed by J. Jarczyk-W. Jarczyk in [2].
Rensets and Renaming-Based Recursion for Syntax with Bindings: Extended Version
Authors: Popescu Andrei
Published: March 2023

We introduce renaming-enriched sets (rensets for short), which are algebraic structures axiomatizing fundamental properties of renaming (also known as variable for-variable substitution) on syntax with bindings. Rensets compare favorably in some respects with the well-known foundation based on nominal sets. In particular, renaming is a more fundamental operator than the nominal swapping operator and enjoys a simpler, equationally expressed relationship with the variable-freshness predicate. Together with some natural axioms matching properties of the syntactic constructors, rensets yield a truly minimalistic characterization of λ-calculus terms as an abstract datatype—one involving an infinite set of unconditional equations, referring only to the most fundamental term operators: the constructors and renaming. This characterization yields a recursion principle, which (similarly to the case of nominal sets) can be improved by incorporating Barendregt’s variable convention. When interpreting syntax in semantic domains, our renaming-based recursor is easier to deploy than the nominal recursor. We also provide a variant of our results centered around an axiomatization of term-for-variable substitution, called substitutive sets, which leads to a substitution based recursion principle. Our results have been validated with the proof assistant Isabelle/HOL.
The Design of a MOOC on Health Behaviors: A Practical Blueprint for the Instructional Design of MOOCs
Authors: Müller Andre Matthias , Tan Claire , Goh Charlene , Lim Raymond Boon Tar
Published: March 2023

Massive Open Online Courses (MOOCs) are a primary mode of scaled-up online learning. They have enjoyed great popularity which grew significantly during the Covid-19 pandemic. Despite the growing demand for MOOCs and the desire of institutions to develop high-quality courses, research indicates that the educational impact of MOOCs is limited. Low instructional design quality has been cited as a culprit for the failure of MOOCs to support learning and skill development, prompting calls for a more thorough design of MOOCs. The aims of our work are to describe the systematic development of guiding principles that informed instructional design decisions for our MOOC on health behaviors, and to showcase how these principles were implemented in practice. Through triangulation of various sources of evidence on anticipated learners and the MOOC learning environment, we identified four key aspects we needed to address with our instructional design to facilitate learning. We formulated guiding principles that both addressed these aspects and served as a reference point during instructional design. Our approach to making instructional design decisions can serve as a practical blueprint for other MOOC developers.