bluuebunny/crossref_metadata_2025_split huggingface dataset:
An in-memory computing architecture based on a duplex 2D material structure for in-situ machine learning
Authors: Wang Xinran , Ning Hongkai , Yu Zhihao , Zhang Qingtian , Wen Hengdi , Gao Bin , Mao Yun , Li Yuankun , Zhou Ying , Zhou Yue , Chen Jiewei , Liu Lei , Wang Wenfeng , Li Taotao , Li Yating , Meng Wanqing , Li Weisheng , Li Yun , Qiu Hao , Shi Yi , Chai Yang , Wu Huaqiang 
Published: March 2023

Abstract
The growing computational demand in artificial intelligence (AI) calls for hardware solutions that are capable of in-situ machine learning, where both training and inference are performed by edge computation. This not only requires extremely energy-efficient architecture (such as in-memory computing, IMC) but also memory hardware with tunable properties to simultaneously meet the demand for training and inference. Here, we report a duplex device structure based on ferroelectric field-effect transistor (FeFET) and atomically thin MoS2 channel and realize a universal IMC architecture for in-situ learning. By exploiting the tunability of ferroelectric energy landscape, the duplex building block demonstrates overall excellent performance in endurance (>1013), retention (>10 years), speed (4.8 ns) and energy consumption (22.7 fJ/(bit·μm2)). We implemented a hardware neural network using arrays of two-transistor-one-duplex-FeFET (2T1D) cells and achieved 99.86% accuracy in non-linear localization task with in-situ trained weights. Simulations show that the proposed device architecture could achieve the same level of performance as graphics processing unit under notably improved energy efficiency. Our device core can be combined with silicon circuitry through three-dimensional heterogeneous integration to give a hardware solution toward general edge intelligence (EI).
Longwave infrared multispectral image sensor system using aluminum-germanium plasmonic filter arrays
Authors: Shaik Noor E Karishma , Widdicombe Bryce , Sun Dechuan , John Sam E , Ryu Dongryeol , Ampalavanapillai Nirmalathas , Unnithan Ranjith R 
Published: March 2023

Abstract
A multispectral camera records image data in various wavelengths across the electromagnetic spectrum to acquire additional information that a conventional camera fails to capture. With the advent of high-resolution image sensors and colour filter technologies, multispectral imagers in the visible wavelengths have become popular with increasing commercial viability in the last decade. However, multispectral imaging in longwave infrared (LWIR; 8 – 14 µm) is still an emerging area due to the limited availability of optical materials, filter technologies, and high-resolution sensors. Images from LWIR multispectral cameras can capture emission spectra of objects to extract additional information that a human eye fails to capture and thus have important applications in precision agriculture, forestry, medicine, and object identification. In this work, we experimentally demonstrate an LWIR multispectral image sensor with three wavelength bands using optical elements made of an aluminum-based plasmonic filter array sandwiched in germanium. To realize the multispectral sensor, the filter arrays are then integrated into a 3D printed wheel stacked on a low-resolution monochrome thermal sensor. Our prototype device is calibrated using a blackbody and its thermal output has been enhanced with computer vision methods. By applying a state-of-the-art deep learning method, we have also reconstructed multispectral images to a better spatial resolution. Scientifically, our work demonstrates a versatile spectral thermography technique for detecting target signatures in the LWIR range and other advanced spectral analyses.
Surgical Site Infections and Antibiotic Prophylaxis in Orthognathic Surgery
Authors: Naros Andreas , Naros Carola Helene , Awad Daniel , Krimmel Michael , Kluba Susanne 
Published: March 2023

Abstract
Background
This study was conducted to determine surgical site infection (SSI) rates and potential risk factors as well as to evaluate antibiotic prophylaxis in orthognathic surgery.
Methods
This retrospective observational study included patients who received orthognathic surgery. SSIs and their management were assessed for up to one year post-operatively. Various predictor variables were assessed.
Results
In total 291 patient met the inclusion criteria (56.7% female). The mean age at surgery was 25.5 ± 8.5 years. Fifty-four patients (18.6%) were diagnosed with a craniofacial malformation. Relevant previous surgeries were documented in about one quarter of included patients (n = 75). Ninety-two percent of patients (n = 267) received intraoperative single-dose antibiotic prophylaxis. Surgical site infections occurred in 12.4% (n = 36) of patients. There was a significant association between postoperative infections and type of surgery (P = .037) as well as type of drainage (P = .002). Statistical analyses also revealed a higher prevalence of smokers (P = .036) and previous surgically assisted rapid palatal expansion (SARPE) (P = .018) in the infection group. Furthermore, no significant relationships were observed between postoperative infections and various co-factors (i.e. antibiotic regime, age at surgery, gender, associated craniofacial malformations, surgery duration, displacement distances, mandibular setback vs. advancement).
Conclusion
Low rates of SSIs occurred following an intraoperative single-dose antibiotic regime. None of the SSIs had a significant effect on the final surgical outcome. Present data do not warrant escalation of the antibiotic regimen. Postoperative smoking and capillary drainage should be avoided.
A novel fractional time-delayed grey model and its applications in predicting enrollments and educational expenditure in China
Authors: Xie Wanli , Liu Caixia 
Published: March 2023

Abstract
Accurate forecasting of the enrollment scale of higher education and education expenditure is the key to scientific decision-making in education. Nevertheless, education data is affected by economy and education system policies, which result in difficulty in modeling. Grey model can build a model from an unclear system with insufficient overall information, and make a fuzzy long-term description of the development law of things. In order to predict the enrollment scale of higher education and education expenditure while improving the prediction accuracy of the existing grey system, we present a novel time-delayed grey model whose time term is a fractional-order accumulation, along with an optimization method that adaptively changes the data structure based on the characteristics of the data, FPGM(1,1,r). The particle swarm optimization algorithm is used to optimize the model's hyperparameters. Results of the experiment indicate that the FPGM(1,1,r) model can effectively fit and forecast the enrollment scale and educational expenditure. In terms of predicting the sustainable development of higher education in China, it is more accurate than other grey models.
“They have to do something:” News Coverage of Climate Change and Generation Z
Authors: Novak Alison 
Published: March 2023

Abstract
In a January 2021 Pew Research Center report, climate change was singled out as a “defining issue” for Generation Z (Tyson et al., 2021). Featuring the findings of a survey of nearly 10,000 members of Generation Z, climate change was repeatedly identified as a top priority impacting the group’s interests, activism actions, and digital media engagement. The Pew study seemingly confirmed what many journalists, political pundits, and even organizational leaders theorized about the newest generation of citizens: climate change permeated the group unlike any other issue. The Pew study launched hundreds of news articles, covering both its findings as well as the growing climate change movement embraced by Generation Z. News articles have the potential to shape intra and inter-group perceptions of (and actions within) generational political and civic engagement. Thus, understanding the nature of this coverage is critical to anticipating and constructing future climate change activism.
Modelling the suitability of agrivoltaics to deliver sustainable energy and food production in East Africa
Authors: Neesham-McTiernan Talitha , Randle-Boggis Richard , Cinderby Steve , Cotton Matthew , Hartley Sue , Lamanna Christine 
Published: March 2023

Abstract
Agrivoltaics address energy, food, and water security concomitantly, yet little research has addressed the opportunities and barriers for this technology in East Africa, a region facing substantial challenges across the water-energy-food nexus. Here, we identify possible use cases for the technology in five East African nations and determine the factors influencing delivery of potential benefits, before assessing the spatial suitability of the technology for two of the emergent use cases: agribusiness and marginal farming. Our findings show there is a moderate to high suitability for AV across both use cases, with a range of biophysical and social factors driving successful outputs; land tenure and solar radiation were the most important for determining suitability. Our analysis indicates where AVs would be best developed to improve access to, and resilience of, energy and food production in East Africa, and our methods could be applied to other locations to support agrivoltaics development internationally.
Effect of Human Chorionic Gonadotropin Injection before Frozen-Thawed Embryo Transfer
Authors: XIN XIN , DONG LI , GUAN LU , WANG YIXUAN , LI JIAXI , WU HAICUI , LIAN FANG 
Published: March 2023

Abstract
Purpose
To investigate the effect of human chorionic gonadotropin (hCG) in hormone replacement regimen for frozen-thawed embryos
Methods
We performed a retrospective cohort study and included patients who underwent frozen embryo transfer (FET) between January 1, 2015, to May 31, 2021. According to the protocols for the FET cycle, the patients were divided into control (n = 238) and hCG groups (n = 216). The clinical pregnancy rate, live birth rate, early abortion rate, late abortion rate, and ectopic pregnancy rate were compared between the two groups.
Results
There was a significant difference in clinical pregnancy rate between the hCG and control groups (54.6 vs. 45.5%, p = 0.048). The ectopic pregnancy rate decreased (0.4% vs. 1.2%), while the live birth rate increased (35.6 vs. 28.6%, p = 0.107) in the hCG group; however, these differences were not statistically significant.
Conclusion
HCG injection in the hormone replacement regimen for FET increased the pregnancy rate.
Predicting therapeutic responses in head and neck squamous cell carcinoma from TP53 mutation and The mutation of TP53 could be detected by cfDNA
Authors: Wei Mei , Liu Yao , Zhi Jingtai , Li Li , Zhang Shengchi , Wang Wei 
Published: March 2023

Abstract
Objectives: Head and neck squamous cell carcinoma (HNSCC) was increasing globally. The mutation of the TP53 was the most common of all somatic genomic changes in HNSCC, and TP53 mutation was associated with the response of immunotherapeutic and chemotherapeutic. Tumor-derived circulating cell-free DNA (cfDNA) was minimally invasive method to determine genetic alterations for cancer. The study aimed to explore the therapeutic responses of TP53 mutation patients with HNSCC and the accuracy of cfDNA to detect TP53 mutation.
Materials and methods: The information of TP53 mutations and patients’ survival time and clinical data in HNSCC was downloaded from The Cancer Genome Atlas (TCGA) Database. The difference of immune infiltration between TP53 mutant group and wild group was compared. The ssGSEA method applied to the transcriptome of HNSCC samples to assess the distribution of immune cell types between two groups. The chemotherapy response was constructed using the R software package, pRRophetic. GSEA enrichment analysis was performed based on TP53 mutation. The next-generation sequencing (NGS) was executed on cfDNA of 9 patients with HNSCC to detect genetic alterations. Tumor biopsy (n = 9) was sequenced using the same technique.
Results: TP53 was the most frequently mutated gene in HNSCC. TP53 mutation was related to the immune cells and expression of immune-associated genes. The TP53 mutation group showed less response to immunotherapeutic but high sensitivity to some chemotherapies compared to wild-type group. TP53 was the most frequently mutated gene (6/9; 66.67%) in cfDNA. 27.27% of the tissue tumor variants were not detected in cfDNA when all TP53 mutations were considered.
Conclusion: TP53 mutation could be used as a specific predictor of treatment response in patients with HNSCC. It was feasible to detect the TP53 mutations from HNSCC patients by cfDNA. The results suggested that the therapeutic response in patients could be predicted by detecting TP53 mutations in cfDNA, and large-scale and prospective studies were needed to validate it.
Epidemic intelligence activities among national public and animal health agencies: a European cross-sectional study
Authors: Dub Timothee , Mäkelä Henna , Kleef Esther van , Leblond Agnes , Mércier Alizé , Hénaux Viviane , Bouyer Fanny , Binot Aurelie , Thiongane Oumy , Lancelot Renaud , Delconte Valentina , Zamuner Léa , Bortel Wim Van , Arsevska Elena 
Published: March 2023

Abstract
Epidemic Intelligence (EI) encompasses all activities related to early identification, verification, analysis, assessment, and investigation of health threats. It integrates an indicator-based (IBS) component using systematically collected surveillance data, and an event-based component (EBS), using non-official, non-verified, non-structured data from multiple sources. We described current EI practices in Europe by conducting a survey of national Public Health (PH) and Animal Health (AH) agencies. We included generic questions on the structure, mandate and scope of the institute, on the existence and coordination of EI activities, followed by a section where respondents provided a description of EI activities for three diseases out of seven disease models. Out of 81 gatekeeper agencies contacted, 34 agencies (42%) from 26 different countries responded, out of which, 32 conducted EI activities. Less than half (15/32; 47%) had teams dedicated to EI activities and 56% (18/34) had Standard Operating Procedures (SOPs) in place. On a national level, a combination of IBS and EBS was the most common data source. Most respondents monitored the epidemiological situation in bordering countries, the rest of Europe and the world. EI systems were heterogeneous across countries and diseases. National IBS activities strongly relied on mandatory laboratory-based surveillance systems. The collection, analysis and interpretation of IBS information was performed manually for most disease models. Depending on the disease, some respondents did not have any EBS activity. Most respondents conducted signal assessment manually through expert review. Cross-sectoral collaboration was heterogeneous. More than half of the responding institutes collaborated on various levels (data sharing, communication, etc.) with neighbouring countries and/or international structures, across most disease models. Our findings emphasise a notable engagement in EI activities across PH and AH institutes of Europe, but opportunities exist for better integration, standardisation, and automatization of these efforts. A strong reliance on traditional IBS and laboratory-based surveillance systems, emphasises the key role of in-country laboratories networks. EI activities may benefit particularly from investments in cross-border collaboration, the development of methods that can automatise signal assessment in both IBS and EBS data, as well as further investments in the collection of EBS data beyond scientific literature and mainstream media.
Modelling the effects of topographic hetero- geneity on distribution of Nitraria tanguto- rum Bobr. species in deserts using LiDAR-data
Authors: Zhou Huoyan , Feng Linyan , Fu Liyong , Sharma Ram P. , Zhou Xiao , Zhao Xiaodi 
Published: March 2023

Abstract
Microclimate ecology is attracting renewed attention because of its fundamental importance in understanding how organisms respond to climate change. A number of hot issues can be investigated in desert ecosystems , including the relationship between species distribution and environmental gradients (e.g., elevation, slope, topographic convergence index, and solar insolation). Species distribution models (SDMs) can be used to understand these relationships. We used the data acquired from the important desert plant Nitraria tangutorum Bobr. communities and desert topographic factors extracted from LiDAR (Light detection and ranging) data of one square kilometers in the Inner Mongolia region of China to construct the SDMs. We evaluated the performance of the SDMs constructed with both the variants of the parametric and nonpara-metric algorithms (bioclimatic Modelling (BIOCLIM), Domain, Mahalanobi, generalized linear model (GLM), generalized additive model (GAM), random forest (RF), and support vector machine (SVM)). The area under the receiver operating characteristic curve was used to evaluate the algorithms. The SDMs constructed with RF appeared to be the best based on the area under the curve (0.7733). We also generated Nitraria tangu-torum Bobr. distribution maps with the constructed SDMs and the suitable habitat area of the Domain model. Based on the suitability map, we conclude that Nitraria tangutorum Bobr. is more suited to the southern part with a slope of 0-20 degree at an elevation of approximately 1010 m. This is the first attempt of modelling the effects of topographic heterogeneity on desert species distribution on a small scale. The presented SDMs will have important applications for predicting species distribution and will be useful for preparing conservation and management plans for desert ecosystems on a small scale.
Application and effect simulation of image recognition technology based on machine vision feature parameters in art teaching
Authors: Surong Guo , Jicheng Xu , Chunming Han 
Published: March 2023

Abstract
Under the new era background, the multi cultures of various countries in the world are spreading and infiltrating each other, and the art education at this stage is in the stage of innovation and development. It is necessary to improve the teaching content and teaching methods of schools in an all-round way, deepen the research of education curriculum reform, especially to combine these new technologies with art classroom teaching to achieve the transformation of humanity. Today, with the great development trend of visual media, it has gradually entered the era of reading and map reading, which is a period of information flow and communication. The basic function of image is to transfer and convey image information. Therefore, in art teaching, it and graphics are complementary. In order to fully carry out art teaching, this paper applies the image recognition technology of machine vision feature parameters to art teaching and effect simulation. Machine vision technology analyzes the required resolution and color scale from digital imaging, and uses pre processed images and features to lock the images in the machine vision system, obtain the texture and proportion of art images according to the area, width and proportion of objects, and complete classification and recognition, The whole image production process is more efficient, and finally can produce high-resolution art images. The results show that, due to the application of image recognition technology of machine vision feature parameters in art teaching and effect simulation, it has achieved good performance results. It has maximized the image recognition methods of students, thus cultivating students' artistic perception through artistic literacy, and promoting the balanced development of students.
Atrial Fibrillation Detection Based on Machine Learning From a Large-Scale Data Set of 12-Lead Electrocardiograms
Authors: Chuang Bo Sheng , Yang Albert C. 
Published: March 2023

Abstract
Background:
Atrial fibrillation (AF) is a dangerous cardiac arrhythmia that increases the risk of stroke and heart failure. Despite its severity, AF diagnosis still heavily depends on the expertise of healthcare professionals. The real-time detection of paroxysmal AF is currently limited due to the absence of automated methods. An efficient machine learning algorithm for AF detection would thus be of immense clinical value.
Objective:
The aim of this study is to develop a clinical valuable machine learning algorithm that can accurately detect AF.
Methods:
This study used the 12-lead ECG recordings obtained from 2020 PhysioNet Challenge data sets. We employed the Welch method to extract the power spectral features of 12-lead electrocardiograms (ECGs) in the range of 0.083 to 24.92 Hz and used multiple machine learning methods to classify sinus rhythm and AF on the basis of these power spectral features. Our machine learning approach is based on the observation that normal ECGs present predictable and stable heartbeats, whereas those of AF are more chaotic and feature fibrillatory waves.
Results:
A LightGBM model achieved the highest performance, obtaining an average F1 score of 0.988 across the 12 leads. The interlead variation of accuracy was low, and aVR lead yielded the highest F1 score of 0.993.
Conclusion:
In conclusion, a machine learning model based on LightGBM may provide a real-time approach for AF detection in intensive care units or in wearable devices used in remote settings.
Amyotrophic Lateral Sclerosis in three Italian regions: prevalence and incidence based on health administrative databases
Authors: Bacigalupo Ilaria , Finocchietti Marco , Paoletti Olga , Bargagli Anna Maria , Brunori Paola , Lombardi Niccolò , Sciancalepore Francesco , Agabiti Nera , Kirchmayer Ursula , Group Caesar Study 
Published: March 2023

Abstract
Background
Studies of the epidemiology of amyotrophic lateral sclerosis (ALS) are numerous, with incidence and prevalence estimates varying widely depending on population characteristics, geographical areas, and available data sources. The aim of this study was to estimate prevalence and incidence of ALS in three Italian regions (Latium, Tuscany, and Umbria) using health administrative databases.
Methods
ALS patients residing in Latium, Umbria and Tuscany were identified through an algorithm based on data from three different health administrative databases: hospital discharges, exemptions from health care co-payment, and emergency departments (study period 2014-2019). We calculated crude, age- and gender specific prevalence estimates on December 31, 2019 and standardised incidence rates of ALS by region, year, and sex between 2014-2019. Moreover, using a clinical dataset available in the Lazio region, we calculated the proportion of individuals correctly identified as ALS cases by the algorithm in this region.
Results
A total of 1,031 persons affected by ALS, aged ≥ 18 years at the prevalence day were identified: 408 cases in Tuscany, 546 in Latium, and 77 in Umbria. Diseases specific co-payment exemptions accounted for the biggest contribution for case detection, ranging between 24.7% in Umbria and 30.6% in Latium. The algorithm produced standardised overall ALS prevalence rates similar between the three regions, varying between 12.31/100,000 in Tuscany, 11.52/100,000 in Latium and 9.90/100,000 in Umbria. The 5-year crude rates were higher in men, and in people aged 65-79 years. Among 310 patients included in the clinical dataset, 263 (84.8%) were identified by the algorithm based on health administrative databases.
Conclusions
ALS prevalence and incidence in three regions of Central Italy are rather similar but slightly higher than those previously reported. This finding is plausible given that previous results relate to at least ten years ago and evidenced of increasing trends. Overall, our results encourage the use of administrative data to produce occurrence estimates, useful to both, epidemiological surveillance and research and healthcare policies.