Miracle-dz/UniFAR-FTUs huggingface dataset:
Smoking burden, MPOWER, future tobacco control and real-world challenges in China: reflections on the WHO report on the global tobacco epidemic 2021

Transl Lung Cancer Res 2022;11(1):117-121 | https://dx.doi. org/10.21037/tlcr-22-27 On July 27, 2021, the World Health Organization (WHO) released its latest (the eighth) report on the global tobacco epidemic (1). The report tracks the progress made by countries in tobacco control since 2008, including China. Of note, the report highlights the global progress especially implementations according to the six MPOWER policies [“monitor tobacco use and prevention policies (M); protect people from tobacco smoke (P); offer help to quit tobacco use (O); warn about the dangers of tobacco (W); enforce bans on tobacco advertising, promotion & sponsorship (E); and raise taxes on tobacco (R)”], but raises new challenges including electronic nicotine delivery systems (1). Given China’s critical role in global tobacco control, we write this article to highlight where China is now in tobacco control, including the smoking burden as well as the current progress, future direction and real-world challenges based on the MPOWER policies.
Evaluation of the Sequencing and Merging Procedures at Three European Airports Using Opensky Data

With the development of aircraft equipment, conventional navigation shifted towards performance-based navigation (PBN) procedures, which significantly improved the efficiency of airport arrivals. Availability of the open-access Automatic Dependent Surveillance-Broadcast (ADS-B) data enables research targeting careful and detailed analysis of the arrival performance. In this work, we demonstrate how we use historical data provided via Opensky Network to investigate various aspects of arrival performance at three European airports implementing different sequencing and merging techniques (Dublin, Stockholm-Arlanda, and Vienna). We create a number of datasets of different size and features, and apply a set of performance metrics characterizing horizontal and vertical efficiency, sequencing and metering effort, as well as environmental efficiency at the chosen airports.
Partial Disassembly of the Nuclear Pore Complex Proteins during Semi-Closed Mitosis in Dictyostelium discoideum

Dictyostelium cells undergo a semi-closed mitosis, during which the nuclear envelope (NE) persists; however, free diffusion between the cytoplasm and the nucleus takes place. To permit the formation of the mitotic spindle, the nuclear envelope must be permeabilized in order to allow diffusion of tubulin dimers and spindle assembly factors into the nucleus. In Aspergillus, free diffusion of proteins between the cytoplasm and the nucleus is achieved by a partial disassembly of the nuclear pore complexes (NPCs) prior to spindle assembly. In order to determine whether this is also the case in Dictyostelium, we analysed components of the NPC by immunofluorescence microscopy and live cell imaging and studied their behaviour during interphase and mitosis. We observed that the NPCs are absent from the contact area of the nucleoli and that some nucleoporins also localize to the centrosome and the spindle poles. In addition, we could show that, during mitosis, the central FG protein NUP62, two inner ring components and Gle1 depart from the NPCs, while all other tested NUPs remained at the NE. This leads to the conclusion that indeed a partial disassembly of the NPCs takes place, which contributes to permeabilisation of the NE during semi-closed mitosis.
Adaptive Fixed-Time Control for Attitude Consensus of Disturbed Multi-Spacecraft Systems With Directed Topologies

In this paper, the problem of leaderless fixed-time attitude consensus is studied for rigid spacecraft systems, where each spacecraft is subject to disturbance and the communication graph is directed. Based on the backstepping technique, a distributed control protocol is proposed to solve fixed-time attitude consensus problem. Then, to remove the dependence of control gains on the global information, an adaptive fixed-time control for attitude consensus is presented. Unlike the most existing consensus protocols, the parameters in the controllers for the spacecraft are chosen independently and can be different from each other. Finally, the performances of the proposed control protocol is illustrated through a numerical simulation.
Potential field‐based path planning for emergency collision avoidance with a clothoid curve in waypoint tracking

A novel potential field‐based model curve fitting method (PF‐MCF) is presented in this paper to handle emergency collision avoidance in waypoint tracking (following waypoints from a leading vehicle). It is reported that the PF has high performance on real‐time obstacle avoidance in robotic path planning. However, some inherent limitations exist in the PF, such as no passage between closely spaced obstacles and oscillations in the presence of obstacles, which is rarely discussed in the research field of PF‐based autonomous vehicles (AVs), especially in waypoint tracking. To solve these problems, we propose the PF‐MCF method to transform the clothoid curve into a quadratic programming form and solve the optimization under reasonable constraints to consider the different waypoints from the PF and the leading vehicle. The proposed PF‐MCF method is validated via MATLAB/Simulink and CarSim to avoid multiple obstacle vehicles in waypoint tracking compared with the two latest methods (PF‐based model predictive controller and PF‐based curve fitting method). The simulation results confirm the efficiency of finding a passage in closely spaced obstacle vehicles and eliminating oscillations (under 0.5 [deg] in tire steering angle) in the presence of obstacles compared to other latest methods.
Analysis of expressive illocutionary act in the movie entitled the conjuring 2

Language is one the foremost necessary ability folks use to interact with others. The fascinating phenomena in illocutionary act is the act are often use not just concerning what the speaker vocalization. The study aims to investigate the kinds of expressive illocutionary act found in the movie The conjuring 2 and also the context situation supports in the movie the conjuring 2. This study the writer used some methods there are descriptive qualitative and quantitative method. And also used some theories, first from Searle (1979) theory of types illocutionary act, Halliday and Hassan (1985) theory context situation. Based on the data, there are 32 utterances found in the movie with 6 types there are (22%) of thanking, (16%) of apologizing, (22%) of attitude, (28%) of greeting, (9%) of wishing, (3%) of congratulating and field, tenor, and mode as the context of situation found in the movie the conjuring 2.
Online Teaching and learning: Classroom Practices, Implementation, and its Impact on Engineering Science and Fundamental Thermodynamics Courses

Covid-19 has disrupted the education system worldwide. The sudden outbreak has forced the education system especially the tertiary education to shift to online teaching and learning which serve as the best solution for the continuation of learning during the Covid-19 pandemic. This study aims at evaluating the online classroom practices and its implementation as well as the impact of online teaching and learning from the student perspectives at Universiti Kuala Lumpur, Malaysian Institute of Marine Engineering Technology that enrolled in Engineering Science and Fundamental Thermodynamics courses. The analysis was carried out using the data collected through the questionnaire responses. Approximately 80% of students from Engineering Science course and just about 46% of students from Fundamental Thermodynamics course like the approached used during the online teaching and learning. This indicated that around 54% of students from Fundamental Thermodynamics course preferred the conventional methods more. However, students from both courses revealed that they were satisfied with the student-teacher interaction during the online teaching and learning with more than 90% agreed on this. The study also disclosed positive feedback on the acceptance and usage of the online teaching and learning with around 78% of students from Engineering Science course and 58% of students from Fundamental Thermodynamics course rated good and above despite the challenge faced by both academicians and students throughout this new way of teaching and learning. The results from this survey may lead to the improvement of the online classroom execution for these two courses.
Cetacean spatial trends from 2005 to 2019 in Svalbard, Norway

This study uses cetacean sighting data, acquired via a citizen science programme, to update distributions and spatial trends of whales and dolphins in waters around the Svalbard Archipelago during the period 2005–2019. Distributions, based on kernel density estimates, from an early period (2005–2019) and a recent period (2015–19) were compared to identify potential shifts in distribution in this area, which is experiencing rapid warming and concomitant sea-ice losses. Among the three Arctic endemic cetaceans, white whales (Delphinapterus leucas, also known as beluga) had a stable, coastal distribution throughout the study, whereas narwhals (Monodon monoceros) and bowhead whales (Balaena mysticetus) were observed only north of the archipelago, but with increasing frequency during the recent period. White-beaked dolphins (Lagenorhynchus albirostris) had a stable distribution along the continental shelf break, west and south of Svalbard. Sperm whale observations shifted from west of Bjørnøya during the early period to being concentrated around the north end of Prins Karls Forland, west of Spitsbergen during the recent period. The four summer-resident baleen whales—blue whales (Balaenoptera musculus), fin whales (Balaenoptera physalus), humpback whales (Megaptera novaeangliae) and minke whales (Balaenoptera acutorostrata)—have shifted their distributions from the continental shelf break west of Spitsbergen during the early period into fjords and coastal areas during the recent period. These changes coincide with increased inflows of Atlantic Water into the fjords along the west coast of Spitsbergen and across the north of the archipelago.
The weight distribution searches of quaternary self-orthogonal codes based on a mended LMBP neural network

Aim To find better weight distributions quantum code from the relationship between weight distribution and dual distance parameters.Methods Firstly the input vector was proposed from the code.Secondly a mended LMBP neural network is trained with some samples that have been constructed.Thirdly the weight distribution is extracted by using the trained neural network.Results An efficient neural network is developed which could get 89%precise rate.Conclusion The neural network methods could be used in quantum code field study and cut down the complexity of the algorithm.
PRE-SERVICE TEACHER EDUCATORS ASSESSMENT OF ADEQUACY OF GOOGLE CLASSROOM APPLICATION AS TEACHING TOOLS IN TEACHER EDUCATION PROGRAMME, NIGERIA

Despite the increasing number of technologies and levels of educational technology provided by teacher education programmes, valid and high-level integration of technology into learning and teaching processes is not still as expected, like theÂ Google classroom for learning. The study purposively investigated the adequacy of Google classroom application as a teaching tool in the training of pre-service teachers by their educators. Three specific purposes and questions were raised. The study adopted a descriptive survey design. Pre-service teacher educators of the Faculty of Education, University of Ilorin constituted the target population. A simple random sampling procedure was adopted in the selection of 107 out of 197 Pre-service educators. A -14 item five-point-Likert scale researchers designed a questionnaire with psychometric properties of content validity of 0.90 and a reliability index of 0.81 was used to elicit the needed data from the respondents. The statistics collected were analysed using percentage, mean ranking and standard deviation. Moreover, the findings among others revealed that Google classroom application is not adequate for training pre-service teachers. Thus, it was recommended that Google classroom application should not be adopted in the teaching of pre-service teachersâ€™ education programme since it was found not adequate
Artificial Intelligence-Based Assistance System for Visual Inspection of X-ray Scatter Grids

Convolutional neural network (CNN)-based approaches have recently led to major performance steps in visual recognition tasks. However, only a few industrial applications are described in the literature. In this paper, an object detection application for visual quality evaluation of X-ray scatter grids is described and evaluated. To detect the small defects on the 4K input images, a sliding window approach is chosen. A special characteristic of the selected approach is the aggregation of overlapping prediction results by applying a 2D scalar field. The final system is able to detect 90% of the relevant defects, taking a precision score of 25% into account. A practical examination of the effectiveness elaborates the potential of the approach, improving the detection results of the inspection process by over 13%.
Investigation of Size Effect in Absorption Spectra of Silver and Gold Nanoparticles

The absorption cross-section of gold and silver nanoparticles has been demonstrated in confined wavelength spectra based on Mie's theory. For this purpose, the numerical study performed with COMSOL for defined particle size to clarify absorption spectra and final results have been compared with experimental data to express the absorption peak occurs in higher wavelength for large particle size which is in around 530 nanometers for gold and 400 nanometer for silver particles. These results show that particle size affects directly on absorption spectra of metallic
Method of restoring the geofield values based on data from a highly mobile geosensors network using an automatic adaptive technique for determining the parameters of the local regression kernel

The article deals with the matter of restoring the geofield based high-intensity spatio-temporal data streams received from a highly mobile geosensors network in real time. It includes dozen thousands of active measuring devices that measure at a frequency of up to 5 Hz and are installed on mobile platforms moving at speeds of up to 80–100 km/h. For solving this task, we propose to apply the method of local smoothing. To adapt a kernel of local regression to the conditions of a specific task, such as the features of the terrain or the simulated geofield, as well as the characteristics of the geosensor network, the authors suggested using the kernel of local regression obtained through using stochastic optimization methods. The application of genetic algorithms and the method of simulating annealing for this purpose are considered. The structure of a generalized method based on this idea is presented in the article. The results of model experiments aimed to confirm the operability of this method and those of evaluating the performance of this method in single-threaded mode are given.
Fire disturbance effects on plant taxonomic and functional β‐diversity mediated by topographic exposure

Abstract Although the diversity–disturbance relationship has been extensively studied, the differences in responses of taxonomic vs. functional diversity to natural disturbances (i.e., fire) call for an improved understanding of this relationship. Here, we investigated how fire disturbance influenced plant taxonomic and functional diversity in Golestan National Park, in northeastern Iran. We evaluated the response of α‐ and β‐plant diversity considering both taxonomic and functional diversity and different β‐diversity components (i.e., turnover and nestedness) as a function of fire regime, topographic exposure, and their interactive effect. We considered different indices of functional diversity including functional richness, functional evenness, functional divergence, functional dispersion, Rao's quadratic entropy, and community‐weighted mean (CWM). Functional diversity indices were computed using four leaf traits related to species growth strategy and fire response including leaf thickness and leaf length, specific leaf area (SLA) and leaf dry matter content (LDMC). Taxonomic and functional diversity had contrasting response to fire disturbance. Fire significantly decreased taxonomic α‐diversity similarly in both north and south exposures. β‐diversity increased in south exposures but decreased in north exposures. Fire decreased functional richness, increased CWM of SLA, and decreased CWM of LDMC. In contrast, abundance‐weighted metrics of functional diversity (functional evenness, functional divergence, functional dispersion, Rao's quadratic entropy) were not impacted by fire disturbance. Finally, the main contributors to heterogeneity were driven by a fire × exposure interaction, suggesting that fire disturbance interacts with topographic exposure. Our results suggest that taxonomic and functional α‐ and β‐diversity have contrasting responses to fire illustrating the need to consider both dimensions to understand how disturbance impacts plant communities. At large spatial scale, species turnover and nestedness appear as essential parameters to maintain species‐rich communities in response to fire disturbance.
Applications of Computational and Statistical Models for Optimizing the Electrochemical Removal of Cephalexin Antibiotic from Water

One of the most serious effects of micropollutants in the environment is biological magnification, which causes adverse effects on humans and the ecosystem. Among all of the micro-pollutants, antibiotics are commonly present in the aquatic environment due to their wide use in treating or preventing various diseases and infections for humans, plants, and animals. Therefore, an aluminum-based electrocoagulation unit has been used in this study to remove cephalexin antibiotics, as a model of the antibiotics, from water. Computational and statistical models were used to optimize the effects of key parameters on the electrochemical removal of cephalexin, including the initial cephalexin concentration (15–55 mg/L), initial pH (3–11), electrolysis time (20–40 min), and electrode type (insulated and non-insulated). The response surface methodology-central composite design (RSM-CCD) was used to investigate the dependency of the studied variables, while the artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) methods were applied for predicting the experimental training data. The results showed that the best experimental and predicted removals of cephalexin (CEX) were 88.21% and 93.87%, respectively, which were obtained at a pH of 6.14 and electrolysis time of 34.26 min. The results also showed that the ANFIS model predicts and interprets the experimental results better than the ANN and RSM-CCD models. Sensitivity analysis using the Garson method showed the comparative significance of the variables as follows: pH (30%) > electrode type (27%) > initial CEX concentration (24%) > electrolysis time (19%).
Assessment of predictive uncertainty within the framework of water demand forecasting using the Model Conditional Processor (MCP)

Abstract This paper presents an application of the Model Conditional Processor (MCP), originally proposed by Todini (2008) within the hydrological framework, to assess the predictive uncertainty in water demand forecasting related to water distribution systems. The MCP enables us to assess the probability distribution of the future water demand conditional on the forecasts provided by two or more deterministic forecasting models. In the numerical application described here, where two years of hourly water demand data for a town in northern Italy are considered, two forecasting models are applied in order to forecast hourly water demands from 1 to 24 hours ahead: the first model has a modular structure comprising a periodic component which reflects the long-term effects and a persistence component which represents the short-term memory of the process; the latter is based on neural networks. The results highlight the effectiveness of the approach, provided that the data set used for the MCP parameterization is properly selected so as to be actually representative of the accuracy of the real-time water demand forecasting models.
WEIGHT STATUS AND NUTRITION IN SIBERIAN ADOLESCENTS

The purpose of the study was to evaluate the nutrition in adolescents with different weight status. Material and methods.In April-May 2019,a cross-sectional population survey was conducted on a random representative sample of schoolchildren aged 14-18 years of both sexes, 612 people were examined: 249 boys (40.9%), 360 girls (59.1%). Diet recall was taken in 537 persons.The survey program included a standard questionnaire,anthropometry,and assessment of diet using 24-hour dietary recall (537 persons). Results. Based on IOTF criteria, 5 body mass index categories were identified:underweight,normal weight,overweight,obesity,extreme obesity.Because of low prevalence of obesity and extreme obesity in the study these groups were combined with the overweight group and further 3 groups were analyzed:underweight,normal weight and overweight.The prevalence of overweight and obesity among adolescents in Novosibirsk was 22.1% in boys and 14.4% in girls. Disturbances in the structure of nutrition were revealed both in the studied sample of adolescents in general, and in the group of boys and girls with overweight and obesity. Quotas of fat energy in general, saturated fatty acids, easily digestible sugars exceed recommended levels. The fiber content was below the recommended level by more than 2 times. Above 80% of overweight adolescents underestimate energy intake. Percent of under-reporting of energy intake in boys was 45,8% in underweight group; 48,6% in normal weight group;80,6% in overweight group.In girls – 26,8%,54,1% and 85,1%, respectively.Conclusions. The results show that both adolescents with overweight and obesity, as well as with normal indicators of physical development, need to optimize nutrition
Blockchain and Fog Computing in IoT-Driven Healthcare Services for Smart Cities

Nowadays, technology has been evolving rapidly. Due to the consequent impact of smart technologies, it becomes a ubiquitous part of life. These technologies have led to the emergence of smart cities that are geographic areas driven by advanced information and communication technologies. In the context of smart cities, IoT, blockchain, and fog computing have been found as the significant drivers of smart initiates. In this recognition, the present study is focused on delineating the impact and potential of blockchain, IoT, and fog computing on healthcare services in the context of smart cities. In pursuit of this objective, the study has conducted a systematic review of literature that is most relevant to the topic of the paper. In order to select the most relevant and credible articles, the researcher has used PRISMA and AMSTAR that have culminated in the 10 most relevant articles for the present study. The findings revealed that IoT, blockchain, and fog computing had become drivers of efficiency in the healthcare services in smart cities. Among the three technologies, IoT has been found to be widely incorporated. However, it is found to be lacking in terms of cost efficiency, data privacy, and interoperability of data. In this recognition, blockchain technology and fog computing have been found to be more relevant to the healthcare sector in smart cities. Blockchain has been presented as a promising technology for ensuring the protection of private data, creating a decentralized database, and improving the interoperability of data while fog computing has been presented as the promising technology for low-cost remote monitoring, reducing latency and increasing efficiency.