bluuebunny/crossref_metadata_2025 huggingface dataset:
Effects of combined extreme cold and drought stress on growth, photosynthesis, and physiological characteristics of cool-season grasses
Authors: 李 娟霞 , Bai Xiaoming , Ran Fu , Zhang Caizhong , Yan Yubang , Li Ping , Chen Hui
Published: September 2023

Abiotic stress is an important factor affecting turf establishment and limiting the sustainability of the turf industry. To alleviate the combined effects of cold and drought prevalent in cold- and drought-prone regions, the selection and introduction of turfgrass germplasm suitable for these conditions are essential for successful turf establishment. Thus, we evaluated the effects of combined extreme cold and drought stress on morphological, photosynthetic, and physiological and biochemical traits in 16 wild annual bluegrass (Poa annua) seedlings. We found that there were significant differences (P < 0.05) among the provenance, combined cold and drought stress, and main interaction factors. The combined cold and drought stress altered the morphological characteristics of the 16 germplasm to varying degrees. Furthermore, The combined cold and drought stress significantly also reduced the net photosynthetic rate (Pn), stomatal conductance (Gs), transpiration rate (Tr), instantaneous water use efficiency (WUE), Chlorophyll content, chlorophyll fluorescence parameters, however, ac-cumulated Intercellular CO2 concentration (Ci), relative electrical conductivity (REC), the contents of malondialdehyde (MDA), proline (Pro), soluble protein ( SP), soluble sugar (SS), superoxide anion (O2·−), hydrogen peroxide (H2O2), hydroxyl radical (·OH), and increased the activities of superoxide dismutase activity (SOD), peroxidase activity (POD), catalase activity (CAT), ascorbate peroxidase activity (APX) and glutathione reductase (GR). Comprehensive evaluation using PCA (Principal Component Analysis), affiliation function analysis, and clustered heat maps indicated that ‘HZ’ germplasm had better combined cold and drought tolerance, whereas ‘ZQ’ germplasm was more sensitive to combined cold and drought, which was roughly consistent with the order of morphological damage symptoms. It is recommended that ‘HZ’ seeds be used in planting projects under cold-and drought-prone areas, while ‘ZQ’ seeds are more suitable for use under non-cold and water-deficit conditions.
Optical modulation of hole doping via atomically thin fluorine-functionalized graphene endowed transparent and flexible UV-photodetector
Authors: Chen Chia-Yun , Hsiao Po-Hsuan , Lin Kuan-Han , Wu Tsung-Yen , Nguyen Hien , Lin Ming-Fa
Published: September 2023

Understanding the charge-formation dynamics and current flow of consecutive carbon quantum dot (CQD) layers biased by atomically thin fluorine-functionalized graphene (FF-Gra) as light-driven modulators is manifested. By probing the involved electronic structures and optical transitions, we reveal that the trion population and dynamic phenomena, rather than intuitive photogenerated electron-hole pairs, exhibit as main features of CQD/FF-Gra heterostructures for the modulation of photophysical origin, where the anomalous pathway for coupling out trion state with faster growth configuration than intrinsic exciton-to-trion transition is interpreted by the extrinsic hole doping coupled with exitonic bound states. All these features implicate the outstanding UV-responsive flexible photodetectors with sound optical transparency, attesting external quantum efficiency (EQE) of 1.68x109%, photoresponsivity of 4.66x106 A/W and detectivity of 2.92x1018 Jones based on such nonintuitive quantum-confined hybrid materials.
Plasma electron acceleration driven by a long-wave-infrared laser
Authors: Downer Michael , Zgadzaj Rafal , Welch James , Cao Yuxuan , Amorim Ligia , Cheng Aiqi , Gaikwad APurva , Iapozzutto P. , Kumar Prabhat , Litvinenko Vladimir N , Petrushina Irina , Samulyak Roman , Vafaei-Najafabadi Navid , Joshi Chan , Zhang Chaojie , Babzien Marcus , Fedurin Mikhail , Kupfer Rotem , Kusche Karl , Palmer Mark , Pogorelsky Igor , Polyanskiy Mikhail , Swinson Christina
Published: September 2023

Laser-driven plasma accelerators provide tabletop sources of relativistic electron bunches and femtosecond x-ray pulses, but usually require petawatt-class solid-state-laser pulses of wavelength λsub> ≈ 1 μm. Longer-λsub> lasers can potentially accelerate higher-quality bunches, since they require less power to drive larger wakes in less dense plasma. Here, we report on a self-injecting plasma accelerator driven by a long-wave-infrared laser: a chirped-pulse-amplified CO<2sub> laser (λ 10 μm). Through optical scattering experiments, we observed wakes that 4-ps CO<2sub> pulses with < 1/2 terawatt (TW) peak power drove in hydrogen plasma of density down to 1/100 atmosphere via a self-modulation (SM) instability. Shorter, more powerful CO<2sub> pulses drove wakes in plasma down to 1/1000 atmosphere that captured and accelerated plasma electrons to relativistic energy. Collimated quasi-monoenergetic features in the electron output marked the onset of a transition from SM to ″bubble-regime″ acceleration,
portending future higher-quality accelerators driven by yet shorter, more powerful pulses.
A Dioxygen Compatible Electron Donor-Acceptor (EDA) Catalytic System and its Enabled Aerobic Oxygenation
Authors: Jiao Ning , Wei Jialiang , Meng Junhong , Zhang Caifang , Liu Yameng
Published: September 2023

The photochemical properties of Electron Donor-Acceptor (EDA) complexes present exciting opportunities for synthetic chemistry. However, these strategies often require an inert atmosphere to maintain high efficiency. Herein, we developed a novel EDA complex photocatalytic system through rational design, which overcomes the oxygen-sensitive limitation of traditional EDA photocatalytic systems and enables aerobic oxygenation reactions through dioxygen activation. The mild oxidation system transfers electrons from the donor to the eﬀective catalytic acceptor upon visible light irradiation, which are subsequently captured by molecular oxygen to form the superoxide radical ion, as demonstrated by the specific fluorescent probe, dihydroethidine (DHE). Furthermore, this visible-light mediated oxidative EDA protocol was successfully applied in the aerobic oxygenation of boronic acids. We believe that this photochemical dioxygen activation strategy enabled by EDA complex not only provides a novel and practical approach to aerobic oxygenation but also promotes the design and application of new EDA photocatalysis under ambient conditions.
Shim-free homebuilt 2 T magnet for NMR spectroscopy with sub-ppm resolution and sub-nanomole sensitivity
Authors: Velders Aldrik H , Baas Sander
Published: September 2023

Nuclear Magnetic Resonance (NMR) spectroscopy is quintessential for molecular characterization and quantification in many research areas. However, a major drawback is the complex and expensive instrumentation, which inhibits a greater use and impact. The required magnet is typically several Tesla strong to allow good sensitivity and adapted with complex shimming systems for sufficient field homogeneity in the standard 0.5 mL sample tubes. Here we show that a home-constructed 2.1 T permanent magnet suffices for NMR spectroscopy with chemical shift resolution and sensitivity that can be tailored to the requirements using home-built microcoils. 1D and 2D, 1H and 19F-NMR spectra are obtained without any shimming of the magnet, with resolution down to 0.3 ppm exploiting a 40 nL microcoil and sub-nanomole detection sensitivity is achieved by light-induced hyperpolarization. The presented paradigm of matching transceiver microcoils to the sweet-spot of home-built magnets allows for a more wide-spread use and democratization of NMR spectroscopy.
EEG Signals Classification related to Visual Objects using Long Short-Term Memory Network and Nonlinear Interval Type-2 Fuzzy Regression
Authors: Ahmadieh Hajar , Gassemi Farnaz , Moradi Mohammad Hasan
Published: September 2023

By comprehending how brain activity is encoded and decoded, we can better comprehend how the brain functions. This study presents a method for classifying EEG signals from visual objects that combines an LSTM network with nonlinear interval type-2 fuzzy regression (NIT2FR). Here, ResNet is used to extract features from the images, the LSTM network is used to extract features from the EEG signal, and NIT2FR is used to map the features from the images to the features from the EEG signal. In this paper, type-2 fuzzy logic is utilized to handle this type of uncertainty due to the nonlinearity and noise of the EEG signals, the short sample size of the data, and the varied mental backgrounds of the experiment participants. The Stanford database was used to implement the research technique, and its effectiveness was assessed using the classification accuracy, precision, recall, and F1 score. The LSTM network successfully categorized images using raw EEG data with an accuracy of 55.83%, according to the findings. When compared to classification accuracy obtained with linear type-2, linear and nonlinear type-1 fuzzy, neural network, and polynomial regression, NIT2FR and SVM classifier performed better (68.05%). NIT2FR can therefore perform better in settings with high levels of uncertainty. Additionally, the accuracy outcomes using NIT2FR are 6.03% better than the top outcome of the most recent study that made use of the same dataset. The same process was followed to get the same result for the other performance raters.
Trends of incidence and outcomes of childhood severe malaria in Nigeria: A four-year study from 2019 to 2022.
Authors: Ibrahim Olayinka Rasheed , Alao Michael Abel , Issa Amudalat , Mohammed Bashir , Suleiman Bello Mohammed , Mokuolu Olugbenga Ayodeji
Published: September 2023

Background
Nigeria ranks highest globally in malaria burden, disproportionately affecting children. This study investigated trends in the incidence and outcomes of 948 children with cases of severe malaria in a tertiary hospital in northwestern Nigeria.
Methods
We conducted a retrospective study of children with severe malaria between January 1st, 2019 and December 31st, 2022.We extracted relevant data, including socio-demographics, clinical features, as well as hospitalization outcomes (death or discharge), and the trends analyzed over the period.
Results
Of the 8,295 pediatric admissions during the study period, 948 (11.4%) were cases of severe malaria. The trends of severe malaria (incidence) showed a surge of 17.3% in 2020 from 11.4% in 2019 and subsequently declined to 9.9% in 2022 (p < 0.001). There was a decline in the proportion of under-fives with severe malaria from 57.4% observed in 2019 to 54.8% in 2022, p = 0.019). The crude mortality rate was 7.2% (68/948) and rose from 2.3% in 2019 to 10.3% in 2020 and declined to 8.5% in 2022, p = 0.003. The proportion of malaria deaths (from pediatric deaths) increased from 4.6% in 2019 to 17.3% in 2020, and declined to 9.3% in 2022, p = 0.004]. Among under-fives, there was no significant change in the mortality rate [from 3.2% in 2019 to 10.2% in 2020, 6.4% (2021) and 10.3% in 2022, p = 0.104] and the proportion of deaths in under-fives among malaria deaths (from 66.7% in 2019 to 52.9% in 2022, p = 0.653). Among the clinical features, the presence of cerebral malaria and acute kidney injury had the highest case fatality rate (57.1%).
Conclusions
Despite the initial surge in severe malaria cases during the COVID-19 era, there has been an overall progressive decline in childhood severe malaria. However, among those under-fives, the trends in malaria deaths remained unchanged.
Large eddy simulation on wind-induced interference effects of staggered chamfered square cylinders
Authors: Cao Wenlong , Wang Xueyan , Liu Yubo , Yin Yujie , Yang Jian , An Jianhui
Published: September 2023

Chamfered corners in buildings are the main means to reduce the control effect of wind load on the structure, and the interference effect of chamfered buildings cannot be ignored. At present, only the mutual interference coefficients of square and rectangular section buildings are given in the Chinese code, without the interference effect of chamfered buildings being specified. Therefore, in this paper, aerodynamic force and wind pressure coefficients of chamfered square cylinders of different spacing are obtained by the large eddy simulation method. Wind load characteristics, non-Gaussian characteristics and interference effects of chamfered square cylinders with different arrangements are studied based on aerodynamic coefficients, wind pressure coefficients and interference coefficients. The results show that when the wall y plus value is 1, the large eddy simulation is the most accurate to simulate the wind load and wind field parameters. Besides, the aerodynamic effects, non-Gaussian characteristics and interference effects between the chamfered square cylinders are mainly controlled by the cross-wind interval and the spacing (4.0, 4.0) is the characteristic coordinate. That means, when the spacing is smaller than this co-ordinate, the interference effect of the square cylinder is more obvious. When the spacing coordinate is greater than (4.0, 4.0), the aerodynamic coefficients and non-Gaussian regional distributions of the principal square cylinder and the isolated cylinder are the same, and the interference factor approaches 1.
Resting-state networks and their relationship with MoCA performance in PD patients
Authors: Galvez Victor , Romero-Rebollar César , Estudillo-Guerra María A. , Fernandez-Ruiz Juan
Published: September 2023

Although mild cognitive impairment is a common non-motor symptom experienced by individuals with Parkinson’s Disease, the changes in intrinsic resting-state networks associated with its onset in Parkinson’s remain underexamined. To address the issue, our study sought to examine resting-state network alterations and their association with total performance in the Montreal Cognitive Assessment and its cognitive subdomains in Parkinson’s by means of functional magnetic resonance imaging of 29 Parkinson’s patients with normal cognition, 25 Parkinson’s patients with mild cognitive impairment, and 13 healthy controls. To contrast the Parkinson’s groups with each other and the controls, the images were used to estimate the Z-score coefficient between the regions of interest from the default mode network, the salience network and the central executive network. Our first finding was that default mode and salience network connectivity decreased significantly in Parkinson’s patients regardless of their cognitive status. Additionally, default mode network nodes had a negative and salience network nodes a positive correlation with the global assessment in Parkinson’s with normal cognition; this inverse relationship of both networks to total score was not found in the group with cognitive impairment. Finally, a positive correlation was found between executive scores and anterior and posterior cortical network connectivity and, in the group with cognitive impairment, between language scores and salience network connectivity. Our results suggest that specific resting-state networks of Parkinson’s patients with cognitive impairment differ from those of Parkinson’s patients with normal cognition, supporting the evidence that cognitive impairment in Parkinson’s Disease displays a differentiated neurodegenerative pattern.
An automated fluorescent defect detection system in precision castings by convolutional neural network
Authors: Yu Huipeng , KANG Maodong , Ding Chengyang , Liu Yahui , Wang Jun
Published: September 2023

Fluorescent Penetrant Inspection (FPI) is a widely used Non-Destructive Testing (NDT) method in the aerospace industry to inspect precision casting components. Currently, FPI inspection relies on visual examination, which can lead to challenges in distinguishing between defects and false indications. Moreover, human factors introduce variability in the results, impacting the consistency and reliability of the inspection process. This highlights the desirability of the automation of FPI to increase consistency, reliability and productivity. The deep learning method is gradually replacing the traditional approaches that involve image processing and machine learning classifiers in automated defect detection system. Deep learning method offers automatic feature extraction and high robustness, which contribute to more accurate and efficient defect detection. The use of various convolutional neural networks (CNN) in defect detection for flat superalloy plates processed with FPI and photographed to create digital images was investigated. Among the CNN models, MobileNetV2 exhibited outstanding performance, with a remarkable recall rate of 99.2% and an accuracy of 99.2%. Additionally, the effect of dataset imbalance on model performance was carefully examined. Moreover, the features extracted by the model are visualized using Guided Grad-CAM to reveal the attention of the CNN model to the fluorescent display features. These results underscore the strong capability of deep learning architectures in detect defects in aerospace precision casting components, paving the way for the automation of the entire FPI process.
A Decentralized Smart Healthcare Monitoring System using Deep Federated Learning Technique for IoMT
Authors: V Anbazhagu U. , Tamilarasi K. , Maheswari K. , Ramesh S. , Isaac Samson , Rajaram A.
Published: September 2023

The likelihood of privacy and security problems rises as a result. Additionally, it performs poorly due to a lack of datasets. By utilising a new Federated Learning (FL) model, this research work enhances privacy and decentralises the analysis of medical data. We created a three-tier architecture for our IoMT: In first tier, the medical data is generated by medical sensors. In first, tier we introduced data normalisation technique in order to eliminate the redundant data and uncertain data. This process reduces the complexity of the system. In the second tier, the data is submitted to the decentralised edge servers where the Deep Learning (DL) algorithm is employed. By FL model, each DL is trained with the limited data samples. The DL approach used here is Recurrent Neural Network (RNN) model. The RNN is trained to detect abnormalities in the data received from the first tier. In the final tier, the data is further stored in repositories and enabled to end users. The overall system is analysed with COVID-19 data and proved efficacy in accuracy, precision, recall and f-score.
Nomogram for Predicting Severe Abdominal Pain After Initial Conventional Transarterial Chemoembolisation for Hepatocellular Carcinoma: A Retrospective Study
Authors: Qin Huixia , Xiao Xuhua , Li Jinhai , Ya Houxiang , Liang Fugang , Jin Haiqing , Liao Lianghuan , Li Yaohua , Qin Jiahun , Jue Yu , Jing Gu , Zhou Chunmei , Jin Ming , Miao Ying , Li Shuqun
Published: September 2023

Transarterial chemoembolisation (TACE) is a standard therapy for hepatocellular carcinoma (HCC). However, adverse events, including abdominal pain, are common. This study aimed to investigate and verify the feasibility of a nomogram model to predict severe abdominal pain after first conventional TACE (cTACE) among patients with HCC. Patients with HCC treated with cTACE between October 28, 2019, and August 5, 2022, at a single centre were enrolled (n=216). Patients were divided into training and validation cohorts (ratio, 7:3). A visual analogue scale score between 7 and 10 was considered severe abdominal pain. A total of 127 (58.8%) patients complained of severe abdominal pain after first cTACE treatment. The nomogram considered age and tumour number and size. The nomogram demonstrated good discrimination, with a C-index of 0.749 (95% confidence interval [CI], 0.617, 0.881). Further, the C-index in the validation cohort reached 0.728 (95% CI: 0.592, 0.864). The calibration curves showed ideal agreement between the prediction and real observations, and the nomogram decision curve analysis performed well. The nomogram model can provide an accurate prediction of severe abdominal pain in patients with HCC after first cTACE, aiding in the personalization of pain management and providing novel insights into hospital nursing.
Advancing Rheumatology Practice with AI Assistance: Evaluating ChatGPT's Performance in Real-world Cases
Authors: Parsaei Amirhossein , Torabi Pouya , Naviafar Anahita , Kooshki Ashkan Mohammadi , Masoumi Maryam , Amini Behnam , Azadnajafabad Sina
Published: September 2023

Background
The launch of ChatGPT, an advanced language model created by OpenAI, has sparked attention throughout the world. ChatGPT offers human-like responses and the potential for a wide range of applications, including medical decision-making. However, questions remain regarding its validity, the spread of false information, and its ethical implications for healthcare. While other studies have investigated ChatGPT's diagnostic capabilities, this study fills a research gap by assessing how well it performs in real-world rheumatology case scenarios, offering light on its possible use in managing rheumatologic patients.
Methods
The study encompassed 32 challenging rheumatology cases. Data for each case was divided into four categories: 1) initial presentation, history, and review of systems; 2) physical examinations; 3) workup results; and 4) final diagnosis. Data was transformed into prompts for ChatGPT, simulating real-time interaction. Four stages of questioning were used to progressively build the case. Recommendations were evaluated based on correctness, completeness, and potential harm or cost implications.
Results
The percentage of comprehensive answers (totally correct, totally complete, no extra-costs, no harm) for physical examinations, workups, differential diagnosis, and treatment were 65.6%, 50%, 40,6% and 40,6% respectively. ChatGPT was successful in 65.6% of the cases to suggest the correct diagnosis first in the list of differential diagnoses. None of ChatGPT responses included suggestions that would result in unnecessary costs or harm to the patient. ChatGPT recommendations for physical examinations, workups, differential diagnosis and treatment were totally correct in 75%, 65.63%, 53.13% and 50% of instances; and they were totally complete in 81.25%, 62.50%, 62.50%, 59.38% of instances respectively.
Conclusions
Our study highlights the effectiveness of ChatGPT in supporting rheumatology practice by offering precise and comprehensive suggestions across various stages of patient cases. While the AI model displays potential, its performance is inconsistent when faced with higher levels of scenario complexity.
Evaluation of Serum Zonulin Level and Intestinal Permeability in Patients with Chronic Idiopathic Urticaria and the Relationship between Serum Zonulin Level and Disease Severity
Authors: Ünal Simge , Kartal Selda Pelin , ÖZSOY Ahmet , AYDIN Fevzi Nuri
Published: September 2023

Background
The development of Chronic Spontaneous Urticaria (CSU) and increased intestinal permeability may be related.
Aim
It was aimed to evaluate the role of intestinal permeability in the etiopathogenesis of CSU and the relationship between disease severity and zonulin levels by measuring the zonulin level, which is an indicator of the increase in intestinal permeability, in the serum of CSU and control groups.
Methods
61 CSU patients and 59 healthy controls were included. Demographic characteristics, personal and family history, urticaria activity score, age of onset, duration of last attack, antihistamine dose used, and concomitant diseases of CSU patients were recorded.
Result
Between the patient and control groups, there was a statistically significant difference in zonulin levels (p=0.000). Patients with angioedema had considerably greater zonulin levels than patients without angioedema, and there was a statistically significant difference in zonulin levels depending on whether angioedema was present (p=0.023).
Conslusion
These findings suggest that intestinal permeability may play an important role in the pathogenesis of CSU and angioedema.
Delineation of Intersegmental Plane: Application of Blood Flow Blocking Method in Pulmonary Segmentectomy
Authors: Cai Lei , Wang Changchun , Luo Taobo , Fu Shuang , Liang Jingxiao , Huang Xiancong , Pan Chao , Feng Yiding , Mo Wenkui , Xu Haoting , Chen Qixun , Zeng Jian
Published: September 2023

Background: The Modified Inflation-Deflation Method (MIDM) is widely used in China in pulmonary segmentectomies. We optimized the procedure, which was named as Blood Flow Blocking Method (BFBM), also known as "No-Waiting Segmentectomy”. This method has produced commendable clinical outcomes in segmentectomies. The aim of this research is to confirm whether the intersegmental planes formed by MIDM and BFBM techniques during segmentectomies have high degree of concordance.
Methods: We utilized the Open Sequential Test design in our study. Using both MIDM and BFBM techniques, intersegmental planes were created in the same patient, one after the other. The degree of alignment between the planes formed by the two techniques was assessed by two experienced chief surgeons. Based on the results obtained in each case, a test line was plotted until it intersected the effective or ineffective line.
Results: In every case studied, the intersegmental planes created by the MIDM and BFBM displayed high congruity. The test line crossed the effective line during the 12th case. When comparing the time taken to form the intersegmental plane using either MIDM or BFBM technique, no significant difference was observed. However, the application of the BFBM technique resulted in an average time savings of 13.8 minutes.
Conclusions: In segmentectomies, the intersegmental planes formed by MIDM and BFBM techniques exhibit high concordance. However, given that BFBM affords a time-saving advantage, we propose that BFBM could potentially replace MIDM in performing lung segmentectomies.
Effects of the Mn/Ni Ratio on the Battery Performance of Layered Na-Ni-Mn Oxide Cathode Materials in Sodium-ion Batteries
Authors: Xiao qingmei , Guo Ziting , Huang Jinchao , Zhong Shengwen
Published: September 2023

The development of efficient sodium-ion batteries is essential to overcome the issue of limited lithium sources for preparing lithium-ion batteries. Layered Mn-based cathode materials have significant application potential because of their simple structure and high specific capacities. However, sodium-ion batteries with these cathode materials demonstrate considerable voltage attenuation and phase transition during battery operation. To eliminate these issues, in this study, we investigated the effects of different Mn/Ni ratios in Na-Ni-Mn cathode materials on their structural stability and electrochemical performances. Na0.8MnO2 (NNM-8010), Na0.8Ni0.1Mn0.9O2 (NNM-819), Na0.8Ni0.2Mn0.8O2 (NNM-828), and Na0.8Ni0.3Mn0.7O2 (NNM-837) were synthesized and characterized using X-ray diffraction, X-ray photoelectron spectroscopy, scanning electron microscopy, transmission electron microscopy, and electrochemical analyses. The addition of Ni+ increased the Mn oxidation state from + 3 to + 4, thus reducing the Jahn–Teller effect of Mn3+ and stabilizing the material structure. NNM-819 exhibited the best electrochemical performance. Its initial discharge specific capacity was 198.5mAh g− 1 at a current density of 0.2C, and the capacity retention rate after 100 cycles was 86.9% at 0.5C. Moreover, its capacity retention rate at 1.0C high-rate cycling after 100 cycles remained high 81.9%.
Machine learning-based approach for sentiment analysis from recorded social media movie reviews
Authors: Jassim Mustafa Abdalrassual , Abd Dhafar Hamed , Omri Mohamed Nazih
Published: September 2023

The goal of Sentiment Analysis (SA), especially in social media, is to identify useful information in large amounts of unstructured data from many different sources. Text mining involves extracting revision texts and categorizing them by positive and negative attitudes. However, the value of the extracted features also lies in those that contribute most to the classification process. That's where dimensional reduction comes into play to remove noise and reduce the high area of the feature while maintaining the required accuracy. As part of this work, we propose a new approach for extracting words from a specific text and then classifying them. Thanks to a phase of pre-processing and extraction of words depending on the frequency. For this purpose, we considered a test group database of 50,000 film reviews, of which 25,000 were rated positive and 25,000 negatives. We have selected 4,000 words that have an impact on the feelings of the documents, To predict the rating which is based on a textual customer review. We evaluated the algorithms we proposed, as part of this approach, and compared the effectiveness of the following machine learning techniques as classifiers for SA: extreme gradient boosting (XGBoost), artificial neurons (ANN), and stochastic gradient descent (SGD). with a vectorial construction by applying the techniques of ”Term Frequency” (TF) and that of the ”Term frequency-Inverse Document Frequency” (TF-IDF). We noticed that our approach based on the SGD algorithm recorded the best performance. Indeed, we recorded an accuracy of 88.093\%, a precision of 89\%, a recall of 86\%, and an F1-score of 88\%.
\textbf{Keywords :} Sentiment analysis; Opinion mining; movie review; classification; Machine learning; XGBoost; Artificial neural network; stochastic gradient descent.
Fuzzy Clustering of CT scans to assess image noise
Authors: Ray Monika , Romano Patrick S.
Published: September 2023

Computed Tomography (CT) scans are the largest source of radiation exposure in diagnostic imaging. Studies show that CT scan radiation doses are relatively high in the United States compared to Japan, Israel, and Switzerland even af- ter adjusting for population effects. The increased use of CT scans in the United States has caused great concern in the public health community. Numerous stud- ies have proposed methods for noise estimation in CT scans to help reduce dose. However, there are few efforts that have leveraged fuzzy logic, which is mod- elled after human reasoning and perception to assess the relationship between CT image quality and dose. We show a new application of fuzzy c-means (FCM) clustering to group images. Image characteristics such as noise, contrast, reso- lution, etc. are identified by the algorithm and used to cluster the images. Our dataset included CT scans from the abdominal and pelvic region of adults, re- sulting in a total of 4,951 images, which vary by radiation dose level, image slice thickness, reconstruction filter and machine specifications. One of the disadvan- tages of FCM is its sensitivity to image noise and artifacts. However, we exploit this disadvantage for our purpose to obtain clusters that would correlate well with image noise as calculated by noise algorithms. Our results showed that FCM was successful at clustering images based on image noise if the difference in radiation dose levels was significant. This implies that intelligent image classi- fication algorithms can detect differences in image noise. Results also indicated that the noise from high dose scans were similar to that from low dose scans, thereby indicating that there is potential to lower doses. It was also evident that the choice of the convolution kernel and slice thickness had a greater effect on image noise than radiation dose. We would like to draw attention of the fuzzy logic community to this area of work that will particularly benefit from percep- tion rules in fuzzy logic. While fuzzy logic has been used in image analyses and medical decision-making, it has not been applied to this objective of lowering radiation dose. We aim to show some of the work performed to achieve this goal, aligning with national initiatives to reduce radiation dose that Americans receive, and identified areas that need further exploration.
COVID-19 Under-reporting: Spillovers and Stringent Containment Strategies of Global Cases*
Authors: Wang Yulu , Kumbhakar Subal
Published: September 2023

Owing to the rapid spread of COVID-19 pandemic, accurately determining the true global infection count has become an exceedingly challenging endeavor. In this context, our study delves into the spatial spillover analysis of COVID-19 cases and assesses the impact of containment policy stringency on these spillovers. Furthermore, we explore the extent of under-reporting of COVID-19 cases at a country-specific level.To account for the diverse spatial dependencies, we employ a semiparametric spatial autoregressive model in which the coefficients are smooth unknown functions of countries' stringency indices. Country-specific under-reporting, represented as a one-sided deterministic function of exogenous variables, is estimated using the sieves method.Our analysis relies on COVID-19 infection data spanning from 2020 to 2021 for 57 countries. We have discovered that spillovers exhibit significant variations at different levels of containment stringency. Moreover, the true number of infections is estimated to be 1.38 to 17.12 times higher than the reported cases. These results align with prior research and bear significant policy implications for improving the precision of COVID-19 reporting and managing spillover effects effectively.
JEL Classification No.: C23, C14, I18.
How to Communicate Robot Motion Intent: A Scoping Review
Authors: Pascher Max , Gruenefeld Uwe , Schneegass Stefan , Gerken Jens
Published: September 2023

Robots are becoming increasingly omnipresent in our daily lives, supporting us and carrying out autonomous tasks. In Human-Robot Interaction, human actors benefit from understanding the robot's motion intent to avoid task failures and foster collaboration. Finding effective ways to communicate this intent to users has recently received increased research interest. However, no common language has been established to systematize robot motion intent. This work presents a scoping review aimed at unifying existing knowledge. Based on our analysis, we present an intent communication model that depicts the relationship between robot and human through different intent dimensions (intent type, intent information, intent location). We discuss these different intent dimensions and their interrelationships with different kinds of robots and human roles. Throughout our analysis, we classify the existing research literature along our intent communication model, allowing us to identify key patterns and possible directions for future research.
Clinical activity of TGF-β inhibitor vactosertib in combination with imatinib in desmoid tumors: a multicenter phase 1/2 study
Authors: Ahn Jin-Hee , Lee Jeeyun , Park Changhee , Beom Seung-Hoon , Kim Seung Hyun , Lee Young Han , Yun Kum-Hee , Kim Jeung Eun , Baek Wooyeol , Han Yoon Dae , Kim Sang Kyum , Ryu Hyang Joo , Jung Inkyung , Lee JooHee , Yoon Hong In , Kim Hyo Song
Published: September 2023

BACKGROUND To determine to the activity and safety of TGF-β inhibitor, vactosertib in combination with imatinib in patients with desmoid tumors.PATIENTS AND METHODS In this investigator-initiated, open-label, multicenter, phase I/II trial, patients with desmoid tumors not amendable to locoregional therapies (surgery and/or radiotherapy) or with disease progression following at least one treatment were enrolled. Participants were administered 400 mg imatinib daily in combination with vactosertib (5 days on and 2 days off, twice a day) every 28 days. In phase I, the vactosertib dose was set at 100 mg (level − 1) and 200 mg (level 1) to determine the recommended phase II dose. Phase II assessed the efficacy, with the primary endpoint being progression-free rate (PFR) at 16 weeks. The tolerability of the vactosertib and imatinib combination and pharmacokinetic parameters were also evaluated.RESULTS Twenty-eight patients were enrolled with a median follow-up of 20.7 months. Of the 27 patients evaluated, 7 (25.9%) achieved a confirmed partial response and 19 (70.4%) were stable. The PFR at 16 weeks and 1 year were 96.3% and 81.0%, respectively. Treatment-naïve patients had longer PFS compared to those with 1–2 or 3 prior treatment modalities (1 year PFS 100% vs 90% vs 25%, P = 0.017). The most toxicities were mild to moderate myalgia (n = 10, 37%), anemia (n = 10, 37%), and nausea (n = 9, 33.3%). Common grade 3–4 toxicities included neutropenia (n = 6, 22.2%) and anemia (n = 5, 18.5%).CONCLUSIONS Vactosertib and imatinib combination was well-tolerated, with promising clinical activity in patients with progressive, locally advanced desmoid tumors. This is the first study investigating a novel target agent, a TGF-β inhibitor, in this rare and difficult-to-treat desmoid tumor.
Nb2C and Nb2CO2 MXenes as Anodes in Li-ion Batteries: A Comparative Study by First-Principles Calculations
Authors: Santoy-Flores R. , Escamilla H. N. Fernández , Páez-Ornelas J. I. , Perez-Tijerina E. G. , Guerrero-Sánchez J. , Ponce-Perez R. , Takeuchi N. , Moreno-Armenta Ma. Guadalupe
Published: September 2023

Resume:
The new generation of Li-ion batteries is based on integrating 2D materials into the electrodes to increase energy density while reducing charging time and size. The two-dimensional transition metal carbides or nitrides (MXene) materials offer ideal electronic properties such as metallic behavior, low energy barriers for Li-ion diffusion, and structural stability. This study focuses on Nb2C and Nb2CO2 MXenes, which have shown promising Li-storage capacity, especially the oxidized phase. By using Density Functional Theory (DFT) and thermodynamics criteria, we study the Li intercalation process in both MXenes. Results show that the Li intercalation process in the oxidized phase is more stable. Also, the Li diffusion barriers are 35 meV and 250 meV for the bare and oxidized phase, because of the strong interaction between Li ions and O functional groups. Nb2C and Nb2CO2 MXenes deliver a maximum gravimetric theoretical capacity of 275 and 233 mAh/gr, respectively, with a stable performance.
Characterization of the Sweet Pitaya (Stenocereus thurberi) Fruit Peel Transcriptome: Analysis of Genes Playing a Role in Cuticle Biosynthesis and Identification of Reference Genes
Authors: García-Coronado Heriberto , Hernández-Oñate Miguel-Angel , Tafolla-Arellano Julio-César , Burgara-Estrella Alexel-Jesús , Tiznado-Hernández Martín-Ernesto
Published: September 2023

Background
Cactus (Cactaceae) are plants distributed across arid regions of America with ecological and economic value. One trait that allows the cactus to survive in desert ecosystems is its cuticle, which limits water loss in dry conditions. Nevertheless, the mechanism of cuticle biosynthesis has yet to be described for cactus. Stenocereus thurberi is a cactus endemic from the Sonoran desert, which produces a fruit named sweet pitaya. Transcripts from S. thurberi published in databases are scarce, and no gene expression analysis has been carried out for this species. This study reports for first time the de novo assembly and characterization of the sweet pitaya peel transcriptome.
Results
Two hundred forty-three million of 80–150 base pairs reads with at least 25 of quality in the Phred score were used to get the assembly. The transcriptome includes 174,449 transcripts with an N50 value of 2,110 bp and 85.4% of completeness. Out of the total transcripts, 122,234 (70.07%) were classified as coding RNA, and 43,391 were classified as long non-coding RNA. Functional categorization analysis suggests a response to stress and an active cuticle biosynthesis in fruit pitaya peel. The genes elongation factor 1-alpha (StEF1a), α-tubulin (StTUA), and polyubiquitin 3 (StUBQ3) are reliable reference genes for accurate normalization of gene expression analysis in this specie through qRT-PCR. The cuticle biosynthesis transcripts cytochrome p450 family 77 subfamily A (StCYP77A), Gly-Asp-Ser-Leu motif lipase/esterase 1 (StGDSL1), and ATP binding cassette transporter family G member 11 (StABCG11) showed higher expression at the early stages of fruit development and ripening suggesting an active cuticle compound biosynthesis and transport.
Conclusions
This is the first transcriptome developed in the S. thurberi specie. Further, housekeeping genes suitable for gene expression analysis by qRT-PCR in this specie are reported for first time. The information generated will help to analyze the molecular mechanism of cuticle biosynthesis and another relevant metabolic pathway in S. thurberi and other cactus species. Understanding the role of cuticle in the adaptation to arid environments could help design technologies to ensure fleshy fruit production in the context of the increase in water scarcity for agriculture predicted for the following years.
Nonlinear Normal Modes of Highly Flexible Beam Structures Modelled under the SE(3) Lie Group Framework
Authors: Bagheri Amir Kamyar , Sonneville Valentin , Renson Ludovic
Published: September 2023

This work presents a shooting algorithm to compute the periodic responses of geometrically nonlinear structures modelled under the Special Euclidean (SE) Lie group formulation. The formulation is combined with a pseudo-arclength continuation method, while special adaptations are made to ensure compatibility with the $SE$ framework. Nonlinear normal modes (NNM) of various two-dimensional structures including a doubly-clamped beam, a shallow arch, and a cantilever beam are computed. Results are compared with a reference displacement-based FE model with von Kármán strains. Significant difference is observed in the dynamic response of the two models in test cases involving large degrees of beam deformation and rotation. Differences in the contribution of higher order modes substantially affect the frequency-energy dependence and the nonlinear modal interactions observed between the models. It is shown that the SE model, owing to its exact representation of the beam kinematics, is better suited at adequately capturing complex nonlinear dynamics compared to the von Kármán model.
Inklusive Medienbildung mit digitalen Spielen
Authors: Tillmann Angela , Krupp Bastian , Eggert Susanne , Gurt Michael , Astrath Nils , Fink Johanna Maria , Schäfer Franziska
Published: September 2023

Davon ausgehend, dass auf der einen Seite digitales Spiel und digitale Spielkultur hochbedeutsam für Teilhabe- und Partizipationsprozesse junger Menschen sind, auf der anderen Seite insbesondere junge Menschen mit Behinderung nicht in dem Mass daran teilhaben (können) wie Gleichaltrige ohne Beeinträchtigung, setzt sich das Projekt InGame im Rahmen einer qualitativen Studie mit entsprechenden Potenzialen und Hürden des digitalen Spiels auseinander. Ziel des Projekts ist die Entwicklung von pädagogisch-praktischen Spielsettings sowie eines Leitfadens für den pädagogischen Einsatz des digitalen Spiels in der Kinder- und Jugendhilfe. Im Beitrag werden zunächst die dem Projekt zugrunde liegenden Verständnisse von Inklusion sowie von Behinderung dargelegt. Darauf aufbauend werden mit einem Rückbezug auf die Spiele- und Jugendmedienforschung die sozial-integrativen sowie die peer- und jugendkulturell relevanten Potenziale des digitalen Spiel(en)s herausgearbeitet und erläutert. Diese bieten den Hintergrund für die Entwicklung der theoretisch-konzeptionellen Überlegungen des Projekts zur Erforschung der Potenziale einer handlungsorientierten Medienbildung im Feld des digitalen Spiels. Den Abschluss des Beitrags bildet eine skizzenhafte Beschreibung der Limitationen des Projekts.
Risks of Digital Exclusion
Authors: Böttinger Traugott , Kürzinger Anja , Schulz Lea
Published: September 2023

Teaching in primary schools means taking the heterogeneity of students’ backgrounds, skills, and capacities into account and offering them a commensurately wide variety of learning opportunities. This is particularly the case when it comes to digital-based instruction, which often entails individualized learning and greater responsibility of learners for their own learning processes. To enable participation and reduce risks of exclusion – for students with disabilities and in the context of the digital divide – teachers’ learning support is of great importance. Despite the potential of teacher support for learning with digital media, few studies have investigated this topic. This paper introduces a research study using videography to analyze the learning support given by preservice teachers during active media work. The findings indicate that teachers tend to provide mainly direct instruction, and significantly less diagnostic support aimed at fostering the learning process. The results do not confirm the idea that digital teaching formats provide technical rather than content support. Furthermore, they show that digital learning formats enable participation by all the students in the class. These results are discussed with a view to risks of exclusion and implications for teacher professionalization.
Medienunterstütztes Lernen in der inklusiven beruflichen Bildung für Auszubildende mit Beeinträchtigungen
Authors: Tibbe Tim , Kamin Anna-Maria
Published: September 2023

Das Berufsbildungswerk Bethel (BBW) möchte die Lernplattform Moodle-Workplace einführen, um digital unterstütztes Lernen in der Ausbildung für Menschen mit Beeinträchtigungen zu verankern. Das bedeutet für die Leitung des BBWs, dass technische, personelle und konzeptionelle Voraussetzungen geschaffen werden müssen. Um die Ausbildenden bei der Einführung zu unterstützen, sollen konzeptionelle Hinweise formuliert werden, die unter anderem auf der inhaltsanalytischen Auswertung qualitativer Interviews mit diesen basieren. Der Beitrag geht der Frage nach, wie Moodle-Workplace sinnvoll und zielgruppensensibel in der Ausbildung im BBW-Bethel – und perspektivisch in weiteren Einrichtungen der inklusiven beruflichen Bildung – implementiert werden kann. Die Ergebnisse zeigen, dass sowohl für die Auszubildenden als auch für die Ausbildenden eine strukturierte Einführung notwendig ist, da grosse Unsicherheiten in Bezug auf die Einführung und den Einsatz bestehen. Das ‹10-Stufen-Modell zur Einführung von Lernplattformen in der teilhabeorientierten beruflichen Bildung›, welches von den Autor:innen entwickelt wurde, bietet einen Orientierungsrahmen, um Lernplattformen progressiv und sukzessiv in der inklusiven beruflichen Bildung zu implementieren. Es weist einen exemplarischen Charakter auf und kann zur Veranschaulichung eines schrittweisen Vorgehens dienen, welches zuerst die Schulung der Ausbildenden und im weiteren Verlauf der Auszubildenden in den Blick nimmt.
O Impacto Emocional e as Limitações Geradas por Covid-19 nos Estudantes de Odontologia da Universidade do Pará, Brasil
Authors: Silva Thamirys da Costa , Carvalho Pedro Luiz de , Catelan Anderson , Fagundes Ticiane Cestari
Published: September 2023

O objetivo neste estudo foi analisar os fatores emocionais adquiridos por estudantes do curso de Odontologia no período pandêmico de COVID-19 e as influências relacionadas ao retorno das atividades presenciais. Pesquisa observacional, transversal e analítica, realizada em uma universidade pública do estado do Pará. Foi aplicado na comunidade acadêmica um questionário de pesquisa, próprio, validado pelo Comitê de Ética da Universidade Federal do Pará, por um período de 3 meses (novembro de 2020 a janeiro de 2021). Participaram da pesquisa 188 alunos de Odontologia. Os dados obtidos foram analisados de forma descritiva. Os resultados obtidos na pesquisa mostraram que cerca de 19,2% e 3,7% dos estudantes apresentaram crises emocionais de ansiedade e depressão, respectivamente. Quanto as expectativas para o atendimento, houve aumento de estado opressivo e crises psíquicas (ansiedade, estresse, raiva e medo), considerados influenciadores na cognição comportamental dos atendimentos a serem realizados pós-pandemia. O estudo identificou que as alterações emocionais adquiridas no surto pandêmico desencadearam impacto negativo no rendimento estudantil, todavia, a sintomatologia depressiva esteve expressa principalmente em acadêmicos concluintes.