Miracle-dz/UniFAR-FTUs huggingface dataset:
The Value of CBL-Based Teaching Mode in Training Medical Students' Achievement Rate, Practical Ability, and Psychological Quality

In order to improve the quality of medical student training, the clinical case-based learning (CBL) teaching mode is analyzed in the aspect of the rate of achievement, practical ability, and psychological effect. A total of 86 medical students who came to our hospital from March 2020 to December 2021 are selected and divided into the CBL group and the control group according to the random number table method. The psychological quality of medical students is evaluated by observing their movements and expressions during their internship. In addition, the questionnaire is used to evaluate the scores of a medical student in future planning, occupational health, and teamwork spirit. The compliance evaluation results show that CBL teaching mode can significantly improve the compliance rate of medical students in training and promote the confidence of future role change to a certain extent.
Application of Art Activities in Colleges and Universities Based on BP Neural Network Algorithm

College education has also widely adopted new media technology, which has changed the previous single teaching method and enriched the teaching content. Art education plays an important role in higher education and is an indispensable part of our country’s quality education. The content, methods, and concepts of art education in colleges and universities are also changing with the advent of the new media era. New courses such as multimedia technology, network animation technology, and network advertising design have been added to the teaching curriculum system of colleges and universities for stimulating students’ interest in learning. The online network platform represented by WeChat has had a profound impact in the field of art education. Based on the technical characteristics of art works, this paper briefly classifies the characteristics of art works; the works perform the extraction of local data (picture elements); secondly, the texture feature parameters of each picture element are extracted by using the image histogram processing technology and the gray level co-occurrence matrix statistical analysis method; finally, a relatively mature machine learning model, BP, is introduced. The neural network algorithm learns the technical style characteristics of each part of the ink and wash work, so that on the whole, it can initially achieve the artistic style learning of the entire ink and wash painting.
Location Mechanism and Perception Scheme of Remote Transmission Equipment Layout in Smart Water

Whether the traditional water equipment is equipped with remote transmission unit depends on whether the power consumption of the installed equipment can meet the expected life cycle. In the paper, by using intelligent strategies to save the power consumption of sending data, more sites can be selected to install remote transmitting units. In the process of transmitting data packet sequence, the remote transmission device needs to perceive the environment, interact with the environment and make decisions, and adjust the strategy according to the effect of the decision actions. Therefore, in this paper, the transmission process is modeled as Markov sequence decision model. And the real time signal interference noise ratio of the channel and the transmission delay of the data packet are defined as the state space. The decision action space consists of immediate transmission and delayed one, and the minimum total power consumption is taken as the objective function. The model is solved by Proximal Policy Optimization (PPO) algorithm, and the optimal decision sequence of site selection threshold is obtained.
Squeeze and multi‐context attention for polyp segmentation

Artificial Intelligence‐based Computer Aided Diagnostics (AI‐CADx) have been proposed to help physicians in reducing misdetection of polyps in colonoscopy examination. The heterogeneity of a polyp's appearance makes detection challenging for physicians and AI‐CADx. Towards building better AI‐CADx, we propose an attention module called Squeeze and Multi‐Context Attention (SMCA) that re‐calibrates a feature map by providing channel and spatial attention by taking into consideration highly activated features and context of the features at multiple receptive fields simultaneously. We test the effectiveness of SMCA by incorporating it into the encoder of five popular segmentation models. We use five public datasets and construct intra‐dataset and inter‐dataset test sets to evaluate the generalizing capability of models with SMCA. Our intra‐dataset evaluation shows that U‐Net with SMCA and without SMCA has a precision of 0.86 ± 0.01 and 0.76 ± 0.02 respectively on CVC‐ClinicDB. Our inter‐dataset evaluation reveals that U‐Net with SMCA and without SMCA has a precision of 0.62 ± 0.01 and 0.55 ± 0.09 respectively when trained on Kvasir‐SEG and tested on CVC‐ColonDB. Similar results are observed using other segmentation models and other public datasets. In conclusion, we demonstrate that incorporating SMCA into the segmentation models leads to an increase in generalizing capability of the segmentation models.
Assessment and correction of radiometric calibration for the medium resolution spectral imager on FY-3C

Abstract. As a keystone instrument on the Fengyun-3C (FY-3C) satellite, the medium resolution spectral imager (MERSI) provides global coverage of top-of-atmosphere radiances for a broad range of scientific studies of the earth system. Based on the onboard measurements of the blackbody (BB) and deep space view (SV), the modified radiometric calibration equation was first given for the MERSI thermal emission band. The accuracy of the radiometric calibration was evaluated by comparing the average irradiance corresponding to the spectral response function and the irradiance corresponding to the wavelength, whose relative error was <0.40 % . Moreover, the BB-derived brightness temperature TR calculated by the BB irradiance shows a systematic error relative to the BB equivalent temperature TE obtained by the voltage code values of seven platinum resistors, and the value of TR was lower 0.25 K than that of TE. To further verify the validity of the calibration coefficient, the influences of the BB temperature, wavelength, and emissivity offsets on the derived irradiance and temperature of the earth were analyzed in detail. For the BB temperature changing by 0.10 K, the absolute values of the earth irradiance together with its equivalent temperature were 0.01 Wm − 2 sr − 1 μm − 1 and 0.10 K, respectively, whose relative errors were about 0.16% and 0.40%. When the BB wavelength and emissivity changing by 0.1 μm and 0.005, the variations of the earth irradiance were 0.02 and 0.04 Wm − 2 sr − 1 μm − 1, respectively, whereas the relative errors of the equivalent temperature were 0.07% and 0.11%. These works provided an important theoretical guidance for the satellite data assimilations along with the quantitative remote sensing applications.
Factors Associated With Pain and Function Before Medial Patellofemoral Ligament Reconstruction

Background: Medial patellofemoral ligament (MPFL) reconstruction is performed to treat recurrent patellar instability. Measurement of joint pain and function at the time of surgery has been demonstrated to be a predictor of the final outcomes in many surgical procedures. Purpose/Hypothesis: The purpose of this study was to evaluate the relationship between baseline patient characteristics, mental health, and intraoperative findings and patient-reported knee pain and function at the time of MPFL reconstruction. We hypothesized that patient characteristics and associated pathology would be associated with the degree of pain and dysfunction. Study Design: Cross-sectional study; Level of evidence, 3. Methods: Included were skeletally mature patients who underwent unilateral open MPFL reconstruction between 2015 and 2020 at a single institution. Baseline descriptive information was collected, and the following outcome measures were administered preoperatively: the Veterans RAND 12-Item Health Survey Mental Component Score (VR-12 MCS) and the Knee injury and Osteoarthritis Outcome Score (KOOS) Pain, Physical Function Short Form (PS), and Quality of Life (QoL) subscales. Intraoperative findings were collected in a standardized format. Patient characteristics, preoperative variables, intraoperative findings, and VR-12 MCS were used as risk factors, and multivariate analysis was conducted to assess for relationships with the KOOS subscale scores. Results: In total, 201 patients with patella dislocations were included in this analysis. Intraoperatively, 122 patients (60.7%) had either normal cartilage or grade 1 or 2 cartilage injury, 79 patients (39.3%) had grade 3 or 4 cartilage injury, 35 patients (17.4%) had a loose body, and 3 patients (1.49%) had evidence of synovitis. Younger age (P = .012), male sex (P < .001), never having smoked (P = .029), and lower baseline VR-MCS (P < .001) were significantly associated with higher baseline KOOS Pain scores. Older age (P = .035), female sex (P = .003), higher body mass index (P = .005), and lower baseline VR-12 MCS (P < .001) were significantly associated with higher baseline KOOS PS scores. Younger age (P = .003), male sex (P < .001), lower baseline VR-12 MCS (P < .001), and no dysplasia (P = .023) were significantly associated with higher baseline KOOS QoL scores. Conclusion: Patient age, sex, and baseline VR-12 MCS were associated with all 3 baseline KOOS subscale scores, whereas intraoperative findings outside of trochlear dysplasia were not associated with any of the KOOS subscale scores.
Feature extraction method CNDFA for target contour of coal and gangue based on multifractal

Abstract. Feature extraction is an important factor to improve the recognition rate of coal and gangue. The existing feature extraction methods have some shortcomings in coal and gangue recognition, such as unsatisfactory recognition rate for actual images. Therefore, aiming at the shortcomings of existing coal and gangue feature extraction methods in coal and gangue recognition, the contour feature extraction method is researched based on images analysis to coal and gangue recognition. The center normalization detrended fluctuation analysis (CNDFA) feature extraction algorithm of contour is proposed based on the process of contour feature extraction for coal and gangue. Based on the analysis of the representative features of coal and gangue, the extraction process of target contour features is established based on hardness difference. Combined with the overall features of contour curve after detrending, a center normalized CNDFA feature extraction algorithm is proposed. First, the detrended analysis of contour curve is realized by least square optimal fitting, and then the detrended data are normalized. Finally, the contour features are described quantitatively by multifractal (MF) spectrum to form the geometric features of the target contour curve, which is used to train the support vector machine classifier. The experiment is carried out on the basis of image preprocessing, and the CNDFA method and other feature extraction methods, such as wavelet, gray level co-occurrence matrix, gray level difference statistics, auto-correlation function, and MF, are applied to the contour feature extraction of coal and gangue. Through the comprehensive comparison of the results after different methods recognition in confusion matrix, accuracy, and coal cleanliness, it is concluded that the overall effect of the CNDFA method is better than other methods, and the accuracy is improved by 5% to 25%. The results show that the CNDFA method has better performance. Compared with the other methods, it can better extract the contour features to improve the recognition rate of coal and gangue.
Efficacy of 2-Hydroxyflavanone in Rodent Models of Pain and Inflammation: Involvement of Opioidergic and GABAergic Anti-Nociceptive Mechanisms

The current work examined the pharmacological potential of a selected flavanone derivative 2-hydroxyflavanone as a promising remedy for the treatment and management of pain. The selected flavanone derivative (2-HF) was evaluated for its analgesic and anti-inflammatory potentials following standard pharmacological protocols including hot plate, acetic acid-induced writhing and tail immersion tests. Naloxone and pentylenetetrazol were used to evaluate the potential implication of GABAergic and opioidergic mechanisms. The anti-inflammatory potential of 2-HF was confirmed using carrageenan-, serotonin- and histamine-induced paw edema models as well as a xylene-induced ear edema model. Furthermore, the anti-neuropathic potential of 2-HF was tested using a cisplatin-induced neuropathic pain model. Our sample, at the tested concentrations of 15, 30 and 45 mg kg−1, showed considerable analgesic, anti-inflammatory effects, as well as efficacy against neuropathic pain. Naloxone and pentylenetetrazol at 1 and 15 mg kg−1 antagonized the anti-nociceptive activities of 2-hydroxyflavanone indicating the involvement of opioidergic and GABAergic mechanisms. In the static allodynia model, combination of gabapentin 75 mg kg−1 with 2-HF at 15, 30, 45 mg kg−1 doses exhibited considerable efficacy. In cold allodynia, 2-hydroxyflavanone, at doses of 15, 30 and 45 mg kg−1 and in combination with gabapentin (75 mg kg−1), demonstrated prominent anti-allodynic effects. The paw withdrawal latency was considerably increased in gabapentin + cisplatin treated groups. Moreover, cisplatin + 2-hydroxyflavanone 15, 30, 45 mg kg−1 showed increases in paw withdrawal latency. Likewise, considerable efficacy was observed for 2-hydroxyflavanone in thermal hyperalgesia and dynamic allodynia models. Our findings suggest that 2-hydroxyflavanone is a potential remedy for pain syndrome, possibly mediated through opioidergic and GABAergic mechanisms.
Detection of SARS-CoV-2 in Cancellous Bone of Patients with COVID-19 Disease Undergoing Orthopedic Surgery: Laboratory Findings and Clinical Applications

An emerging issue for orthopedic surgeons is how to manage patients with active or previous COVID-19 disease, avoiding any major risks for the surgeons and the O.R. personnel. This monocentric prospective observational study aims to assess the prevalence of SARS-CoV-2 viral RT-PCR RNA in cancellous bone samples in patients with active or previous COVID-19 disease. We collected data about 30 consecutive patients from our institution from January 2021 to March 2021 with active or previous COVID-19 disease. The presence of SARS-CoV-2 in the samples was determined using two different PCR-based assays. Eighteen of the thirty patients included in the study had a positive nasopharyngeal swab at the time of surgery. Twelve patients had a negative nasopharyngeal swab with a mean days since negativization of 138 ± 104 days, ranging from 23 to 331 days. Mean days of positivity to the nasal swab were 17 ± 17. Twenty-nine out of thirty (96.7%) samples were negative for the presence of SARS-CoV-2 RNA. In one sample, low SARS-CoV-2 load (Cycle threshold (Ct) 36.6. ) was detected but not confirmed using an additional confirmatory assay. The conducted study demonstrates the absence of the viral genome within the analyzed cancellous bone. We think that the use of personal protection equipment (PPE) to only protect from aerosol produced during surgery, both in active and recovered patients, is not strictly necessary. We think that the use of PPE should not be employed by surgeons and the O.R. personnel to protect themselves from aerosols produced from the respiratory tract. Moreover, we think that our results could represent a valid basis for further studies related to the possibility of bone donation in patients that suffered and recovered from COVID-19.
Apache Spark and Deep Learning Models for High-Performance Network Intrusion Detection Using CSE-CIC-IDS2018

Keeping computers secure is becoming challenging as networks grow and new network-based technologies emerge. Cybercriminals' attack surface expands with the release of new internet-enabled products. As many cyberattacks affect businesses' confidentiality, availability, and integrity, network intrusion detection systems (NIDS) show an essential role. Network-based intrusion detection uses datasets like CSE-CIC-IDS2018 to train prediction models. With fourteen types of attacks included, the latest big data set for intrusion detection is available to the public. This work proposes three models, two deep learning convolutional neural networks (CNN), long short-term memory (LSTM), and Apache Spark, to improve the detection of all types of attacks. To reduce the dimensionality, random forests (RF) was employed to select the important features; it gave 19 from 84 features. The dataset is imbalanced; thus, oversampling and undersampling techniques reduce the imbalance ratio. The Apache Spark model produced the best results across all 15 classes, with accuracy as high as 100% for all classes, as seen by the experiments' findings. For the F1-score, Apache Spark showed the highest results with 1.00 for most classes. The findings of the three models showed outstanding results for multiclassification network intrusion detection.
Student Teachers’ Views on Media Education Related to New Literacy Skills

High quality media education promotes media literacy and ensures citizens' smooth participation and engagement in the digital society. Digital media include powerful communication and interaction platforms that require new literacy skills. This study examines what new literacies can be found in the opinion writings of student teachers in relation to media literacy. The research material consists of 37 Finnish student teachers' opinion writings produced during the Media Education 2020 course. The aim of the study is to explore how student teachers identify new literacies as part of media education and how these new literacies are reflected in their opinion writings. As a result of the research, new literacies emerged from the data as a cultural phenomenon of our time. This was reflected in their views on the technological orientation of school and teaching and the importance of media education as a teaching task in schools. An important finding is that the textual subculture of social media is valued as a new form of literacy. The results show that while the narrative value of images, virtual game worlds and easy access to social media via smartphones were identified, the broad understanding of new literacies among student teachers is narrow. Perceptions of media education and new literacies are interlinked and the debate on their relevance as a social phenomenon should continue.
Crop intensification effects on soil quality and organic carbon stocks: a case study of Haramosh Valley in Central Karakorum, Pakistan

ABSTRACT Agricultural operations such as excessive tillage and intense cropping deplete soil organic carbon (SOC), making sustainable agriculture management critical for reducing greenhouse gas (GHG) emissions. This study evaluates the impact of crop intensification on soil quality and soil organic carbon stocks (SOCS) under double cropping (DC) and single cropping pattern (SC) in upper Haramosh of Gilgit, Pakistan. Soil samples were taken from cropping zones (DC and SC) under three depths (0–20, 20–40, and 40–60 cm). Standard methods were used to analyze selected soil quality parameters and SOC. Statistical analysis using ANOVA showed that soil temperature, moisture, pH, SOC, and SOCS highly significantly differed (p < 0.001) for different cropping patterns (DC and SC), whereas bulk density (BD), electrical conductivity (EC), and clay were not significantly different. The SC retained 4.4% more moisture and had lower BD than the DC, while BD increased with increasing depth. The texture of the soil was sandy loam at both cropping zones. The mean SOC and SOCS of SC were greater (by 12%) than in the DC zone. Pearson correlation showed a significant and positive correlation of SOC stock with SOC, moisture (p < 0.01), and EC (p < 0.05), but had a negative correlation with bulk density, pH (p < 0.01), and sand (p < 0.05). DC apparently degraded soil quality and organic carbon reserves, thus reducing the soil health in mountain agriculture.
IMPLEMENTASI METODE SIMPLE ADDITIVE WEIGHTING (SAW) DENGAN PEMBOBOTAN RANK ORDER CENTROID (ROC) DALAM PENGAMBILAN KEPUTUSAN UNTUKSELEKSI PENGGUNA JASA LEASING MOBIL (Studi Kasus : PT.Multindo Auto Finance Cabang Bengkulu)

Leasing atau sewa guna usaha adalah setiap kegiatan pembiayaan perusahaan dalam bentuk penyediaan barang-barang modal untuk digunakan oleh suatu perusahaan untuk jangka waktu tertentu. Penelitian ini bertujuan untuk membangun suatu sistem pendukung keputusan yang dapat digunakan oleh pengguna untuk menyeleksi pengguna jasa Leasing mobil; mengimplementasikan metode Rank Order Centroid (ROC) dan Simple Additive Weighting (SAW) dalam sistem pendukung keputusan seleksi pengguna jasa leasing di PT.Multindo. Sistem ini dibangun dengan menggunakan bahasa pemrograman Java dengan IDE Netbeans 8.2. Metode pengembangan sistem yang digunakan untuk membangun aplikasi ini adalah model waterfall dan Unified Modeling Langauge (UML) sebagai perancangan sistem. Pengujian yang digunakan adalah pengujian white box dengan 100% independent path berhasil dijalankan dan pengujian black box dengan 100% skenario pengujian berhasil. Hasil akhir dari penelitian ini adalah terciptanya sebuah sistem pendukung keputusan yang sesuai dibuktikan dengan akurasi data sebesar 95,7%.
Evaluation of Fatigue Life of Recycled Opaque PET from Household Milk Bottle Wastes

Polyethylene terephthalate (PET) is among the most used thermoplastic polymers in large scale manufacturing. Opaque PET is increasingly used in milk bottles to save weight and to bring a glossy white aspect due to TiO2 nanoparticles. The recyclability of opaque PET is an issue: whereas the recycling channels are well established for transparent PET, the presence of opaque PET in household wastes weakens those channels: opaque bottles cannot be mixed with transparent ones because the resulting blend is not transparent anymore. Many research efforts focus on the possibility to turn opaque PET into resources, as one key to a more circular economy. A recent study has demonstrated the improvement of the mechanical properties of recycled PET through reactive extrusion. In the present work, the lifespan of recycled opaque PET has been evaluated throughout tensile–tensile fatigue loading cycles at various steps of the recycling process: The specimens are obtained from flakes after grinding PET wastes (F-r-OPET), from a subsequent homogenization step (r-OPET-hom) and after reactive extrusion (Rex-r-OPET). Virgin PET is also considered as a comparison. First, tensile tests monitored by digital image correlation have been carried out to obtain the elastic modulus and ultimate tensile stress of each type of PET. The fatigue properties of reactive REx-r-OPET increase, probably associated with the rise of cross-linking and branching rates. The fatigue lifespan increases with the macromolecular weight. The fracture surface analysis of specimens brings new insight regarding the factors governing the fatigue behavior and the damaging mode of recycled PET. TiO2 nanoparticles act as stress concentrators, contributing to void formation at multiple sites and thus promoting the fracture process. Finally, the fatigue life of REx-r-OPET is comparable to those of virgin PET. Upcycling opaque PET by reactive extrusion may be a relevant new route to absorb some of the growing amounts of PET worldwide.