Miracle-dz/UniFAR-FTUs huggingface dataset:
Highly Fluorescent Dyes Containing Conformationally Restrained Pyrazolylpyrene (Pyrazoolympicene) Chromophore

The triflic-acid-promoted cyclization of 1-phenyl-3-(pyren-1-yl)-1H-pyrazole-4-carbaldehyde afforded a mixture of 9-phenyl-7,9-dihydropyreno (10,1-fg)indazole and 9-phenylpyreno(10,1-fg)indazole-7(9H)-one, readily separable by column chromatography. Both products contained a rigid six-ringed pyrazoolympicene backbone and exhibited bright fluorescence in chloroform solution and a weak fluorescence in the solid state. DFT and TD DFT calculations revealed that the lowest excited state (S1) of these compounds is populated via HOMO →LUMO π-π * transition. Furthermore, the synthesized compounds behaved as weak bases and their emission spectra showed substantial changes upon protonation. Therefore, they may be of interest for sensing of strongly acidic fluorophore environments.
Analysis and Prediction of Students’ Academic Performance Based on Educational Data Mining

The development of intelligent technologies gains popularity in the education field. The rapid growth of educational data indicates traditional processing methods may have limitations and distortion. Therefore, reconstructing the research technology of data mining in the education field has become increasingly prominent. In order to avoid unreasonable evaluation results and monitor the students’ future performance in advance, this paper comprehensively uses the relevant theories of clustering, discrimination and convolution neural network to analyze and predict students’ academic performance. Firstly, this paper proposes that the clustering-number determination is optimized by using a statistic which has never been used in the algorithm of K-means. Then, the clustering effect of K-means algorithm is tested by discriminant analysis. The convolutional neural network is introduced for training and testing data that are labeled with categories. The generated model can be used to predict prospective performance. Finally, in order to validate the prediction results, the effectiveness of the generated model is evaluated by using two metrics in two cross-validation methods. The experimental result demonstrates that the statistic not only solves the difficulty to determine the clustering number in K-means algorithm from an objective and quantitative point of view, but also improves the reliability of prediction results.
Provenance documentation to enable explainable and trustworthy AI: A literature review

ABSTRACT Recently artificial intelligence (AI) and machine learning (ML) models have demonstrated remarkable progress with applications developed in various domains. It is also increasingly discussed that AI and ML models and applications should be transparent, explainable, and trustworthy. Accordingly, the field of Explainable AI (XAI) is expanding rapidly. XAI holds substantial promise for improving trust and transparency in AI-based systems by explaining how complex models such as the deep neural network (DNN) produces their outcomes. Moreover, many researchers and practitioners consider that using provenance to explain these complex models will help improve transparency in AI-based systems. In this paper, we conduct a systematic literature review of provenance, XAI, and trustworthy AI (TAI) to explain the fundamental concepts and illustrate the potential of using provenance as a medium to help accomplish explainability in AI-based systems. Moreover, we also discuss the patterns of recent developments in this area and offer a vision for research in the near future. We hope this literature review will serve as a starting point for scholars and practitioners interested in learning about essential components of provenance, XAI, and TAI.
Performance of Push–Pull Technology in Low-Fertility Soils under Conventional and Conservation Agriculture Farming Systems in Malawi

Push–pull technology (PPT) is one of the most viable low-cost agroecological practices that reduces the effects of insect pest infestations (e.g., stemborer) and parasitic weeds (e.g., Striga) in croplands. PPT was evaluated in low-fertility soils and two farming practices, minimum-tilled conservation agriculture practice (CA), and conventionally tilled practice (CP), in contrasting agroecological zones at the Chitedze, Mbawa, and Chitala stations in Malawi. Stemborer and Striga infestations were also investigated and the suitability levels of two Desmodium species. Farmers’ perceptions of PPT were gathered through a focus group discussion. The performance of PPT varied significantly between treatments, sites, and years on grain yields and the number of cobs that could be assigned to soil attributes. Significant variations were found in the number of exit holes, stemborer damage severity, and the number of Striga-affected plants with severe infestation. In Chitedze, CP recorded significantly shorter maize plants by 14.1, 11.6, and 5.8 cm than CP–PP, CA, and CA–PP, respectively, in 2016–2017. There were no significant differences in plant height between CP–PP, CA, and CA–PP. Similar results were also found in 2017–2018. Focus group discussions among farmers attested to up to 70% reductions in Striga weed and stemborer pests under PPT over the two seasons. Farmers who used push–pull technology reported a 45–50% yield increase. Push–pull was also perceived as a technology that improves soil fertility and controls soil erosion. The study presented the importance of soil physicochemical properties in the performance of the technology, as supported by the high occurrence of Striga asiatica in the country and the low suitability of Greenleaf Desmodium. Results reaffirmed the technology’s agronomic benefits in productivity, pest management, plant vigour, and Striga control. The cost of labour was described as a challenge, and research to identify more suitable Desmodium species is needed. The current study suggests the release of the technology in Malawi, emphasizing the inclusion of Desmodium and Brachiaria as animal fodder for the adoption of the technology.
Molecular dynamics simulations of the Spike trimeric ectodomain of the SARS-CoV-2 Omicron variant: structural relationships with infectivity, evasion to immune system and transmissibility

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant is replacing Delta, the most prevalent variant worldwide from the beginning of 2021 until early 2022. The Omicron variant is highly transmissible and responsible for a new worldwide COVID-19 wave. Herein, we calculated molecular dynamics simulations of the SARS-CoV-2 trimeric spike protein of Wuhan-Hu-1 strain (wild type, WT) and the Omicron variant of concern. Structural analyses reveal that the SpikeOmicron presents more conformational flexibility than SpikeWT, mainly in the N-terminal domain (NTD) and receptor-binding domain (RBD). Such flexibility results in a broader spectrum of different conformations for SpikeOmicron, whereby the RBD can more easily visit an up-conformational state. We reported how the mutations in this variant may influence the intra- and inter-protomer contacts caused by conformational flexibility. Based on our analysis, we suggest that the differences in conformational flexibility between SpikeOmicron and SpikeWT may explain the observed gains in infectivity, immune system evasion and transmissibility in this novel variant. Graphical abstract
Superminiature Eddy Current Probe for Measuring the Electrical Conductivity of Copper Thin Films

The article considers the possibility of applying the eddy current method of non-destructive testing for measuring the electrical conductivity of new material - thin metal films. Copper films of various thickness obtained by physical vapour deposition were used as the measurement object. The deposition method and the hardware and software complex for measuring the electrical conductivity of the film were briefly described. A calibration curve that makes it possible to restore the values of the electrical conductivity of the material by the signal value of the eddy current probe was presented. The test results of films with different characteristics were given, and the distribution of the electrical conductivity of the films depending on the batch was shown. Based on different values of the electrical conductivity in a batch, the difference in deposition quality of various films was found.
Construction of a Comprehensive Mental Health Evaluation System for Clinicians

Objective To detect problems of mental health disorders early and actively by constructing a comprehensive evaluation system of mental health. Methods The evaluation system was constructed by the Minnesota personality questionnaire (MMPI), the personality questionnaire (EPQ), and the depression experience questionnaire (DEQ). Total 341 interns and residents in a general hospital were investigated with the questionnaire about psychological status, and the results were analyzed by SPSS22.0. Results The KMO was 0.879, and the factor load of the seven factors was 0.49 to 0.856. The cumulative variance contribution reached 72.18%, and the overall consistency coefficient was 0.871. The relationship, emotional disorder, paranoia, reflection, and positive response were 0.893, 0.614, 0.867, 0.771 and 0.621, respectively. In this study, the mental health composite scores of all study subjects met a normal distribution, so level 3 scores of clinical interns and residents were established according to the deviation method. Conclusion The constructed index system contains comprehensive indicators, good reliability efficiency, and a level 3 score, which helps individuals and hospitals to detect problems early, and provide guidance and advice for active intervention.
Development of High-Resolution Dedicated PET-Based Radiomics Machine Learning Model to Predict Axillary Lymph Node Status in Early-Stage Breast Cancer

Simple Summary Accurate clinical axillary evaluation plays an important role in the diagnosis of and treatment planning for breast cancer (BC). This study aimed to develop a machine learning model integrating dedicated breast PET and clinical characteristics for prediction of axillary lymph node status in cT1-2N0-1M0 BC non-invasively. The performance of this integrating model in identifying pN0 and pN1 with the AUC was 0.94. We achieved an NPV of 96.88% in the cN0 and PPV of 92.73% in the cN1 subgroup. The higher true positive and true negative rate could delineate clinical subtypes and apply more precise treatment for patients with early-stage BC. Abstract Purpose of the Report: Accurate clinical axillary evaluation plays an important role in the diagnosis and treatment planning for early-stage breast cancer (BC). This study aimed to develop a scalable, non-invasive and robust machine learning model for predicting of the pathological node status using dedicated-PET integrating the clinical characteristics in early-stage BC. Materials and Methods: A total of 420 BC patients confirmed by postoperative pathology were retrospectively analyzed. 18F-fluorodeoxyglucose (18F-FDG) Mammi-PET, ultrasound, physical examination, Lymph-PET, and clinical characteristics were analyzed. The least absolute shrinkage and selection operator (LASSO) regression analysis were used in developing prediction models. The characteristic curve (ROC) of the area under receiver-operator (AUC) and DeLong test were used to evaluate and compare the performance of the models. The clinical utility of the models was determined via decision curve analysis (DCA). Then, a nomogram was developed based on the model with the best predictive efficiency and clinical utility and was validated using the calibration plots. Results: A total of 290 patients were enrolled in this study. The AUC of the integrated model diagnosed performance was 0.94 (95% confidence interval (CI), 0.91–0.97) in the training set (n = 203) and 0.93 (95% CI, 0.88–0.99) in the validation set (n = 87) (both p < 0.05). In clinical N0 subgroup, the negative predictive value reached 96.88%, and in clinical N1 subgroup, the positive predictive value reached 92.73%. Conclusions: The use of a machine learning integrated model can greatly improve the true positive and true negative rate of identifying clinical axillary lymph node status in early-stage BC.
PredictV: A Vehicle Prediction Scheme to Circumvent Occluded Frames

There are many methods to overcome traffic congestion on the roads, but occlusion is still there in most methods, so it is a dire need of the time that researchers have to look into this matter. In the rural and urban areas, heavy congestion on the roads has become the leading cause of occlusion. The PredictV method works on the prediction principle based on existing values and is proved one of the naval approaches for this problem. This scheme uses blob detection for the first frame detection and predicts other vehicles based on percentage increment in the different parameters to identify the vehicles. For improving the quality of the sample, data cleaning has been included in this work. With the help of this approach, congestion has been avoided, and this method prefers to use predicted points to detect vehicles on the frame. The suggested approach is implemented via a MATLAB simulator. It is tested on a large dataset which includes 7152 frames of 6 different videos from the Urban Tracker and KoPer dataset. In total, there are 46876 vehicles present on the frame at first, and the existing methods have detected only 58% and 73% of vehicles, whereas the detection rate is 82% with this suggested approach. The occlusion rate is only 17% on average, which has been reduced via this proposed approach which was 24% and 43% earlier than the existing one. The performance of this suggested method has been tested based on performance and results, which are pretty impressive that is almost 99.74%. In short, the outcome of this prediction technique has been improved now, and the effectiveness has been observed too.
Secure Visible Light Communication System via Cooperative Attack Detecting Techniques

With the recent development of fourth industrial technology, the need for a broadband short-range wireless communication system to realize an ultraconnected, ultra-low latency, and ultra-realistic intelligent information society has emerged. Among the next-generation communication network technologies that can fulfill the technical demands, visible light communication (VLC) is a promising technology that can use illuminated light as a communication light source, which is convenient and environmentally friendly and has high energy and frequency efficiency. However, although VLC has a high level of security owing to the straightness and transparency of visible light, if some of the VLC nodes in a dense mesh network environment are hacked by external attacks, there can be critical performance degradation by jamming attacks. Although several studies have suggested the possibility of VLC jamming attacks, only a few have studied how to effectively detect and respond to these attacks. This study proposes a method to collaboratively detect and respond to jamming attacks in smart LED-based VLC systems. According to the experimental results of this study, the proposed cooperative method showed 91% attack detection accuracy and 1.82 times better than the k-random method. The proposed method showed a minimum detection rate of 84% even in an obstacle-rich environment, proving outstanding attack detection performance 1.68 times better than the k-random method.
A Perspective on the Roles of Adjuvants in Developing Highly Potent COVID-19 Vaccines

Several countries have made unremitting efforts to develop an optimal vaccine in the fight against coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). With the increasing occurrence of SARS-CoV-2 variants, current vaccines show decreased neutralizing activities, especially towards the Omicron variant. In this context, adding appropriate adjuvants to COVID-19 vaccines can substantially reduce the number of required doses and improve efficacy or cross-neutralizing protection. We mainly focus on research progress and achievements associated with adjuvanted COVID-19 subunit and inactivated vaccines. We further compare the advantages and disadvantages of different adjuvant formulations in order to provide a scientific reference for designing an effective strategy for future vaccine development.
Crumpled and Eccentric Nanospheres of Ti3C2T
x
 MXene by Aerosol Jet Printing on Heat Substrate

2D MXene has attracted tremendous attention for developing high‐performance energy storage devices regarding its good metallicity and relatively large accessible surface area. However, the electrochemical performance of MXene‐related devices was hindered due to the pronounced self‐stacking characteristic of MXene. Herein, a convenient aerosol jet printing (AJP) process is developed for the synthesis of MXene nanospheres with crumpled and eccentric structures by modulating the migration and assembly of MXene nanosheets on heat substrate. The existing temperature gradient between the apex and the edge of the deposited droplet causes thermocapillary flows carrying MXene nanosheets toward the edge and the MXene nanosheets are further shaped and assembled along the droplet surface during the solvent evaporation. Interdigital microelectrodes of crumpled MXene nanospheres are tentatively developed for electrochemical performance characterization, a competitive areal capacitance performance is demonstrated in comparison with other MXene‐based devices. Importantly, this work highlights the great potential of the AJP technique for developing functional devices with fascinating hierarchical features as well as further extending applications in miniaturized and intelligent microelectronics.
Music Intervention for Pain Control in the Pediatric Population: A Systematic Review and Meta-Analysis

Music intervention (MI) has been applied as an effective adjunctive treatment for pain control in various clinical settings. However, no meta-analysis has yet been published on the analgesic effects of MI in infants and children. We performed a systematic review of PubMed, EMBASE, Web of Science, and Cochrane Library databases to identify randomized controlled trials (RCTs) with the keywords “pain” AND “music therapy” from inception to January 2022. Primary outcomes were pain intensity and vital signs. Standardized mean difference (SMD) values and the corresponding 95% confidence intervals (CIs) were computed using a random effect model. Subgroup analyses with age groups, types of pain, and music styles were conducted. A total of 38 RCTs involving 5601 participants met the selection criteria. MI significantly decreased the pain levels (SMD = −0.57, p < 0.001), both in the newborn group (p = 0.007) and in the infant/children group (p < 0.001). MI significantly reduced heart rate (SMD = −0.50, p < 0.001) and respiratory rate (SMD = −0.60, p = 0.002) and increased peripheral capillary oxygen saturation (SMD = 0.44, p < 0.001). In subgroup analyses of types of pain, MI had significant effects on prick pain (p = 0.003), chronic and procedural pain (p < 0.001), and postoperative pain (p = 0.018). As for music styles, significant analgesic effects were observed for classical music (p < 0.001), kids’ music (p < 0.001), and pop music (p = 0.001), but not for world music (p = 0.196), special composition (p = 0.092), and multiple music combinations (p = 0.420). In conclusion, our analysis provides supportive evidence about the efficacy of MI, especially classical, kids’, and pop music, in controlling prick, procedural, and postoperative pain in the pediatric population.
Aggression and peer victimization experiences among adolescents who use alcohol with versus without parental permission

ABSTRACT Background Alcohol use among adolescents has been linked to a host of negative social adjustment outcomes, including aggression and peer victimization. However, it is not clear if these associations vary among those who use alcohol with parental permission versus without parental permission, which has implications for prevention and intervention approaches. Methods High school students reported on their past-year alcohol use (i.e., no use, only with parental permission, only without parental permission, and both with and without parental permission), aggressive behavior (i.e., proactive and reactive), and peer victimization (i.e., physical, relational, and cyber) experiences. Results Findings suggest that youth who use alcohol both with and without parental permission are associated with higher levels of both functions of aggression and all forms of peer victimization than those who do not use alcohol and only use alcohol with parental permission. Conclusions These results may indicate that parental permission to use alcohol does not necessarily reduce risk for negative social adjustment outcomes. Additional research investigating contributing factors to youth who use alcohol both with and without parental permission is needed to fully understand risk associated with adolescent alcohol use.
Utilization of biosurfactant produced by Pseudomonas aeruginosa strain LTR1 for the synthesis of the silver nanoparticles and their application as antimicrobial agents

Nitin Deshmukh1, Dilip Kadam2, Pravin Deshmukh3, Gunderao Kathwate4* 1Department of Microbiology, K. N. Bhise Arts, Commerce and Vinayakrao Patil Science College, Bhosare, Solapur, India. 2Department of Microbiology, D. B. F. Dayanand Arts and Science College, Solapur, India. 3Department of Microbiology, Pratapsinh Mohite Patil Mahavidyalaya, Karmala, India. 4Department of Biotechnology, Savitribai Phule Pune University, Pune, India.
Burst bubble recognition based on depth feature

The bursting rate of bubbles is an indicator of bubble stability. To accurately recognize burst bubbles, the Kinect sensor is first introduced to the froth flotation industrial site, then the bubble depth information is collected accurately. Due to the bubble burst is close related with depth change, depth feature is extracted by our proposed method based on depth difference. The proposed method filters potential burst bubbles and registers single bubble region based on color data, and then recognizes bubble bursts in terms of depth data. the experimental results show that our proposed method can effectively identify bubble bursts, the precision and recall reach 0.8547 and 0.8963 respectively.