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Objectives: Guidewire occasionally creates a loop-like appearance between the right subclavian artery and brachiocephalic artery when performing coronary angiography (CAG) with right radial artery (RtRA) access. We called this occurrence a looped guidewire at the brachiocephalic artery (looped GW at BA). It is associated with difficulties in catheter manipulation. This study aimed to assess the predictors of forming a looped GW at the BA., Methods: We examined 175 (mean age, 71.3 ± 9.5 years; 74.9% men) consecutive patients who underwent elective CAG with the RtRA access. Looped GW at the BA was defined as a loop-like appearance of the 0.035-inch GW between the right subclavian and brachiocephalic artery on a frontal view or left anterior oblique. To identify the predictors of looped GW at BA, patient characteristics and examination data obtained before CAG were compared between patients with and without looped GW at the BA., Results: The prevalence of looped GW at BA was 10.9%. The cardio-ankle vascular index (CAVI), which reflects arterial stiffness, was significantly different in patients with or without looped GW at BA (9.8 ± 1.0 vs. 8.9 ± 1.5, p   = 0.0092). The area under the receiver-operating characteristic curve of CAVI to predict looped GW at BA was 0.745, with 0.94 sensitivity and 0.57 specificity in a cutoff point of CAVI ≥9.0., Conclusions: Looped GW at BA can be ruled out by CAVI and is associated with high arterial stiffness., Competing Interests: Declaration of conflicting interestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

In recent years, the problem caused by waste disposable paper cups (WDPCs) has became a topic of great concern to scientists, and commercial companies have also begun to take an interest in developing processes for tackling the issue. In this review, the inherent problems and social barriers during the recycling of WDPCs are described. This review presents the major conclusions of previously published works focused on the utilisation of WDPCs for material and energy purposes. The commercial utilisation of WDPCs is also described. Some suggestions for better recycling of WDPCs are given in the final part of this work. [ABSTRACT FROM AUTHOR]

This research explores the flow penetration in porous media by virtue of capillary action and geometric control of the liquid imbibition rate in microfluidic paper-based analytical devices (μPADs) having applications in food quality management, medical diagnostics, and environmental monitoring. We examine changes in flow resistance and membrane geometry, aiming to understand factors influencing capillary penetration rates for various practical applications. We conducted experiments and simulations using lateral porous membranes and altered the flow resistance by changing the liquids or the paper channel geometry by adding cavities. From experiments, it was revealed that by creating a circular cavity in the paper channel, the penetration rate was sufficiently altered. Moreover, increasing the cavity size and type of liquid (w.r.t. viscosity) also caused a decrease in the flow rate. Imbibition rates were also influenced by the position of the cavities in the paper channel. The maximum delay for water was almost 2 times with a 16 mm circular cavity located at 3 cm from strip bottom edge. Overall, we attained a maximum delay in the case of castor oil which was almost 85 times slower than water and 3.7 times slower than olive oil. A good agreement was observed with CFD analysis. We believe that this research would help in developing advance techniques to enhance the flow control strategies in μPADs and indicators. [ABSTRACT FROM AUTHOR]

The present paper is an attempt to consider some of the modem conceptions of biology in relation to the human species. With regard to species in general the Darwinian theory [...]

In recent years, the problem caused by waste disposable paper cups (WDPCs) has became a topic of great concern to scientists, and commercial companies have also begun to take an interest in developing processes for tackling the issue. In this review, the inherent problems and social barriers during the recycling of WDPCs are described. This review presents the major conclusions of previously published works focused on the utilisation of WDPCs for material and energy purposes. The commercial utilisation of WDPCs is also described. Some suggestions for better recycling of WDPCs are given in the final part of this work.

flexible, hydrophobic, and multilayered poly(vinyl alcohol) (PVA) film evolved to replace a commercially available nonbiodegradable easy seal-paper (ES-PAPER) sealing film. First, environmentally friendly fillers, such as cellulose nanocrystals (CNCs) or cellulose nanofibers (CNFs), were added to produce PVA+CNC/CNF composites via blade coating and solution casting to strengthen the mechanical properties of PVA. Subsequently, biodegradable and hydrophobic materials, such as poly(ethylene glycol)–poly(lactic acid) (PEG–PLA) and neat PLA, were added to prepare multilayered PEG–PLA and PLA hydrophobic composites using double-sided solution casting. The hydrophobicity of PVA was enhanced through heat treatment. Finally, the mechanical properties of the as-prepared PVA film were compared with those of a commercially available ES-PAPER sealing film. PVA+CNC/CNF composites exhibit excellent transparency and mechanical properties, whereas PVA +CNCs 3.0 wt% have the highest Young’s modulus and tensile strength, which are, respectively, 3% and 96% higher than the Young’s modulus and tensile strength of an ES-PAPER sealing film. With regard to strain at break, the prepared PVA film also exhibited a value many times larger than that of the ES-PAPER sealing film because of good filler dispersibility, which significantly enhanced the durability of the sealing film [ABSTRACT FROM AUTHOR]

Smart building equipment monitoring is a well-established field focused on enhancing contemporary building comfort. The proliferation of Internet connectivity, facilitated by the internet of things (IoT), has transformed buildings from static structures into interactive environments. IoT has witnessed substantial growth across various aspects of daily life, from monitoring environmental conditions to managing building systems and storing data in the cloud. One critical application is the intelligent monitoring and control of building equipment, such as air conditioners, to optimize energy efficiency-a matter of increasing concern for building owners, design experts, and system integrators. Achieving comprehensive energy savings demands a meticulous approach to energy-efficient design and control. This paper's primary objective is to explore and analyze IoT-based energy-saving optimization techniques for intelligent building equipment, integrating building information modeling (BIM) technology. It particularly delves into the energy conservation control algorithm for air-conditioning systems. The research presents a challenge rooted in energy-saving optimization, established upon specific objective functions, followed by a detailed explanation of the energy-saving control algorithm. To validate their approach, the paper outlines a comprehensive experimental design. Over three sessions in August, they conducted control experiments in two distinct areas. Area 1 implemented the energy-saving control methodology discussed in the paper, utilizing virtual parameter enhancement mechanisms, while Area 2 adhered to conventional control methods. The results were enlightening. Area 1 demonstrated superior energy efficiency, consuming 735 kWh compared to Area 2's 819 kWh, signifying an impressive 11.43% reduction in energy consumption thanks to the optimized control strategy. This research underscores the practicality and significance of implementing IoT-based energy-saving strategies, with a focus on smart thermostats, HVAC controllers, and daylight sensors, in intelligent building equipment management to achieve substantial energy conservation gains. [ABSTRACT FROM AUTHOR]

Contaminated fresh produce remains a prominent catalyst for food-borne illnesses, prompting the need for swift and precise pathogen detection to mitigate health risks. This paper introduces an innovative strategy for identifying food-borne pathogens in fresh produce samples from local markets and grocery stores, utilizing optical sensing and machine learning. The core of our approach is a photonics-based sensor system, which instantaneously generates optical signals to detect pathogen presence. Machine learning algorithms process the copious sensor data to predict contamination probabilities in real time. Our study reveals compelling results, affirming the efficacy of our method in identifying prevalent food-borne pathogens, including Escherichia coli (E. coli) and Salmonella enteric, across diverse fresh produce samples. The outcomes underline our approach’s precision, achieving detection accuracies of up to 95%, surpassing traditional, time-consuming, and less accurate methods. Our method’s key advantages encompass real-time capabilities, heightened accuracy, and cost-effectiveness, facilitating its adoption by both food industry stakeholders and regulatory bodies for quality assurance and safety oversight. Implementation holds the potential to elevate food safety and reduce wastage. Our research signifies a substantial stride toward the development of a dependable, real-time food safety monitoring system for fresh produce. Future research endeavors will be dedicated to optimizing system performance, crafting portable field sensors, and broadening pathogen detection capabilities. This novel approach promises substantial enhancements in food safety and public health. [ABSTRACT FROM AUTHOR]

In the production of air conditioners, there are various issues such as complex requirements, redundant stations, excessive man-hours, and low production line balance rate. This paper aims to address these problems by analyzing the historical data of H Company’s commercial air conditioner production line. The data is categorized into five aspects: station, working hours, standard working hours, labor capacity, and presence of bottleneck processes. To optimize and improve the second production line, this paper applies the production line balance management method based on data mining. It utilizes the decision tree model in data mining and incorporates lean production knowledge from industrial engineering. The goal is to identify crucial factors that affect the balance of the production line and address the issues caused by these factors. The aim is to reduce and eliminate redundant working hours and enhance the balance rate of the production line. By implementing the approach outlined in this paper, the bottleneck time of the second production line was reduced from 96.67 s to 74.6 s, and the production line balance rate increased from 68% to 85%. [ABSTRACT FROM AUTHOR]

Coastal states are currently transitioning their traditional socio-economic activities into a single platform to address the emerging issues of the coast and marine environment. For countries like Pakistan, managing multiple coastal and marine activities is a significant challenge. The lack of proper management policies and an inefficient decision-making process put various types of pressure on the ecological functions of Pakistan's coastal and marine areas. The Government of Pakistan has not yet prioritized coastal and marine affairs in its policy agenda, resulting in a halt to the process of sustainable development. Moreover, a lack of financial allocation for large-scale coastal and marine projects, a shortage of trained human resources, insufficient coordination among organizations, and unstable political decisions and governance impede the sustainable delivery of the projects. This paper discusses Pakistan's coastal and marine policy targets and highlights various issues through PESTLE analysis, which encompasses political (P), economic (E), social (S), technological (T), legal (L), and environmental (E) factors that limit the advancement of sustainable coastal and marine development. This paper aims to determine the advantages of adopting marine spatial planning (MSP) in Pakistan and how its implementation can bring social, ecological, and economic prosperity. The study provides a baseline for the country's coastal and marine policymakers regarding the advancement of a blue economy through MSP. [ABSTRACT FROM AUTHOR]

A crucial stage in eukaryote gene expression involves mRNA splicing by a protein assembly known as the spliceosome. This step significantly contributes to generating and properly operating the ultimate gene product. Since non-coding introns disrupt eukaryotic genes, splicing entails the elimination of introns and joining exons to create a functional mRNA molecule. Nevertheless, accurately finding splice sequence sites using various molecular biology techniques and other biological approaches is complex and time-consuming. This paper presents a precise and reliable computer-aided diagnosis (CAD) technique for the rapid and correct identification of splice site sequences. The proposed deep learning-based framework uses long short-term memory (LSTM) to extract distinct patterns from RNA sequences, enabling rapid and accurate point mutation sequence mapping. The proposed network employs one-hot encodings to find sequential patterns that effectively identify splicing sites. A thorough ablation study of traditional machine learning, one-dimensional convolutional neural networks (1D-CNNs), and recurrent neural networks (RNNs) models was conducted. The proposed LSTM network outperformed existing state-of-the-art approaches, improving accuracy by 3% and 2% for the acceptor and donor sites datasets. [ABSTRACT FROM AUTHOR]

When the vehicle is in motion, the elastic deformation of the flexible subframe significantly influences ride comfort. Therefore, it is crucial to investigate the impact of flexible subframes on vehicle ride comfort. In order to enhance the reliability and optimization efficiency of our research, this paper incorporates the concept of elastic deformation in the flexible subframe into the investigation of vehicle ride comfort, and proposes a multi-objective optimization approach to enhance the overall vehicle ride comfort. The vibration mathematical model elucidates how flexible subframes affect vehicle ride comfort and establishes a rigid-flexible coupling model for a specific vehicle with a flexible subframe to analyze the impact of its elastic deformation on vehicle ride comfort through simulation experiments. Subsequently, a radial basis function approximation model is established, and the multi-objective particle swarm optimization and non-dominated sorting genetic algorithm II algorithms are employed to conduct multi-objective optimization of the stiffness of the subframe bushing with the aim of enhancing vehicle ride comfort. The findings indicate that the flexible subframe has a significant impact on vehicle ride comfort. Specifically, on bump roads, peak values of vertical and longitudinal seat accelerations decrease while lateral seat acceleration increases. On random roads, peak values of longitudinal and lateral seat accelerations increase while vertical acceleration decreases. Furthermore, the stiffness of the subframe bushing optimized by the non-dominated sorting genetic algorithm II algorithm further enhances vehicle ride comfort and aligns more closely with the optimization requirements in this study. [ABSTRACT FROM AUTHOR]

The current COVID-19 pandemic situation has stimulated an unplanned clinical research paradigm which is evident from the surge of clinical trial registrations and the increasing number of COVID-related publications. We aimed to explore the standards for research conduction, publications and retraction of articles related to COVID-19 pharmacotherapy research during the pandemic. We analysed data from the contemporary literatures on studies reporting pharmacological agents for COVID-19 using MEDLINE, PubMed, WHO database and Google Scholar between January 01, 2020 and March 20, 2021. The initial search revealed a total of 61,801 articles. Based on the inclusion criteria, a total of 124 studies related to various pharmacological agents were included in the final analysis. Most of the studies were reported from the United States (n = 30, 24%). Of the 124 studies, 50 (40%) were randomized controlled trials (RCTs). Immunomodulatory drugs-related (n = 17, 34%) and COVID-19 vaccine-related studies (n = 14, 28%) were the main topics in the relevant RCTs. The median days for dissemination of findings in journals were 114 days (IQR 61–189). A comparative analysis revealed that RCTs were disseminated earlier (median 79 days; IQR 52–131) when compared to observational studies (median = 144 days; IQR 69–206) (p = 0.003). Six papers were retracted from high impact journals; in which the average period till publication was 33 days. Retraction of papers occurred within 10–48 days. Expedited reviews, research approval and early publications of COVID-19 related pharmaceutical studies could have an impact on the quality of publications. However, the huge number of publications in short time creates confusion for readers during the early phases of the pandemic. Retraction of papers is alarming but ensures research integrity and correctness of scientific information. These abbreviated processes could affect patient care and public awareness. It is imperative to follow rapid but rigours ethical standards for research approval and peer-review process for publications during health pandemics. [ABSTRACT FROM AUTHOR]

As the Internet and Internet of Things (IoT) continue to develop, Heterogeneous Information Networks (HIN) have formed complex interaction relationships among data objects. These relationships are represented by various types of edges (meta-paths) that contain rich semantic information. In the context of IoT data applications, the widespread adoption of Trigger-Action Patterns makes the management and analysis of heterogeneous data particularly important. This study proposes a meta-path-based clustering method for heterogeneous IoT data called I-RankClus, which aims to improve the modeling and analysis efficiency of IoT data. By combining ranking with clustering algorithms, the PageRank algorithm was used to calculate the intraclass influence of objects in the network. The HITS algorithm then transfers the influence to the core objects, thereby optimizing the classification of objects during the clustering process. The I-RankClus algorithm does not process each meta-path individually, but instead integrates multiple meta-paths to enhance the interpretability and clustering performance of the model. The experimental results show that the I-RankClus algorithm can process complex IoT datasets more effectively than traditional clustering methods and provide more accurate clustering outcomes. Furthermore, through a detailed analysis of meta-paths, this study explored the influence and importance of different meta-paths, thereby validating the effectiveness of the algorithm. Overall, the research presented in this paper not only improves the application effects of HINs in IoT data analysis but also provides valuable methods and insights for future network data processing [ABSTRACT FROM AUTHOR]

Ground experimentation is an important method to verify the performance and quality of spacecraft structures. How to simulate the force and heat fields that a spacecraft may be subjected to during flight is a challenge for experiment design. In this paper, the static strength experimental method of spacecraft in the combined force and heat fields is studied. Furthermore, the coupling cases of the spacecraft during the flight process are simulated using an airbag and a resistive heater. Finally, it can be concluded that the accuracy of the proposed experimental method as well as the structural strength of the spacecraft can meet technical requirements during flight. [ABSTRACT FROM AUTHOR]

The avoidance of causality in the design, analysis and interpretation of non-experimental studies has often been criticised as an untenable scientific stance, because theories are based on causal relations (and not associations) and a rich set of methodological tools for causal analysis has been developed in recent decades. Psychology researchers (n = 106 with complete data) participated in an online study presenting a causal statement about the results of a fictitious paper on the potential effect of drinking clear water for years on the risk of dementia. Two randomised groups of participants were then asked to reflect on the conflict between the goal of approaching a causal answer and the prevailing norm of avoiding doing so. One of the two groups was also instructed to think about possible benefits of addressing causality. Both groups then responded to a list of 19 items about attitudes to causal questions in science. A control group did this without reflecting on conflict or benefits. Free-text assessments were also collected during reflection, giving some indication of how and why causality is avoided. We condense the exploratory findings of this study into five new hypotheses about the how and why, filtered through what can be explained by cognitive dissonance reduction theory. These concern the cost of addressing causality, the variety of ways in which dissonance can be reduced, the need for profound intervention through teaching and social aspects. Predictions are derived from the hypotheses for confirmation trials in future studies and recommendations for teaching causality. Open data are provided for researchers' own analyses. [ABSTRACT FROM AUTHOR]

Microplastics have been considered a new type of pollutant in the marine environment and have attracted widespread attention worldwide in recent years. Plastic particles with particle size less than 5 mm are usually defined as microplastics. Because of their similar size to plankton, marine organisms easily ingest microplastics and can threaten higher organisms and even human health through the food chain. Most of the current studies have focused on the investigation of the abundance of microplastics in the environment. However, due to the limitations of analytical methods and instruments, the number of microplastics in the environment can easily lead to overestimation or underestimation. Microplastics in each environment have different detection techniques. To investigate the current status, hot spots, and research trends of microplastics detection techniques, this review analyzed the papers related to microplastics detection using bibliometric software CiteSpace and COOC. A total of 696 articles were analyzed, spanning 2012 to 2021. The contributions and cooperation of different countries and institutions in this field have been analyzed in detail. This topic has formed two main important networks of cooperation. International cooperation has been a common pattern in this topic. The various analytical methods of this topic were discussed through keyword and clustering analysis. Among them, fluorescent, FTIR and micro-Raman spectroscopy are commonly used optical techniques for the detection of microplastics. The identification of microplastics can also be achieved by the combination of other techniques such as mass spectrometry/thermal cracking gas chromatography. However, these techniques still have limitations and cannot be applied to all environmental samples. We provide a detailed analysis of the detection of microplastics in different environmental samples and list the challenges that need to be addressed in the future. [ABSTRACT FROM AUTHOR]

Professor Bajram Preza was a career neurologist with a strong background in research both in neurology and psychiatry. After a period of study in Sarajevo in the immediate post-WWII period, he completed his studies in medicine and a fellowship in neuropsychiatry in Nizhny Novgorod (formerly Gorky). A highly prolific author, he holds the laurels of the first medical dissertation sustained in the University of Tirana (1958) as well as for publishing the first student’s textbook on medicine (Semiotics of nervous diseases, 1964) in Albania. He led the Clinic of Neurology in Tirana for more than three decades, while relentlessly lecturing, publishing and editing a diversity of medical papers, translations and origin Professor Bajram Preza was a career neurologist with a strong background in research both in neurology and psychiatry. After a period of study in Sarajevo in the immediate post-WWII period, he completed his studies in medicine and a fellowship in neuropsychiatry in Nizhny Novgorod (formerly Gorky). A highly prolific author, he holds the laurels of the first medical dissertation sustained in the University of Tirana (1958) as well as for publishing the first student’s textbook on medicine (Semiotics of nervous diseases, 1964) in Albania. He led the Clinic of Neurology in Tirana for more than three decades, while relentlessly lecturing, publishing and editing a diversity of medical papers, translations and original works that have shaped the professional education of entire generations of future Albanian physicians. [ABSTRACT FROM AUTHOR]

To explore the impact of land-use change on carbon storage, this study coupled the InVEST model and the FLUS model to analyse the spatial and temporal characteristics of carbon storage in the Qiantang River source region from 2000 to 2030. The carbon storage in the study area is evaluated which declined rapidly from 166.22 × 106 t in 2000 to 164.41 × 106 t in 2020, and the spatial distribution of carbon storage could be characterized by “the northwest and the southwest of region with higher, the east and the centre of the region with lower”. The carbon storage was simulated based on the historical trend development scenario, the food security scenario, and the ecological protection scenario. The carbon storage with the food security scenario could achieve 162.74 × 106 t in 2030. The carbon storage with the ecological protection scenario had an increase of 62.60 t/km2 compared to the historical natural tendency development. Interestingly, the food security scenario had the smallest carbon loss value which is about $1.39 × 109, and its net carbon storage value was the largest which is about $3.71 × 109. The results of this study could provide a scientific reference for the conservation of carbon storage and land use management for climate change and sustainable development. This paper also can lay the foundation for subsequent further studies such as artificial intelligence. [ABSTRACT FROM AUTHOR]

At present, airspace congestion and flight delays have become widespread concerns. This study aims to optimize the sequencing of arrival flights in the terminal area of multirunway airports. Considering the constraints of multiple runways, slant intervals and moving flight positions, this article establishes an optimization model for arrival flight sequencing in a multirunway airport terminal area. Accordingly, an improved sparrow search algorithm (ISSA) is proposed based on Chebyshev chaotic mapping, the golden sine strategy, and the variable neighborhood strategy. Through six basic test functions, the ISSA is compared with particle swarm optimization, the whale optimization algorithm, the genetic algorithm, and other algorithms to verify its superiority. Finally, two sets of instance data from Kunming Changshui Airport were used for experiments. The results show that the total delay times (TDTs) of small-scale flights (number of aircraft: 29) and large-scale flights (number of aircraft: 147) are 55.3% and 20.5% lower, respectively, than those of the first-come-first-served algorithm. The superiority of the ISSA designed in this article is verified, and it can significantly reduce the TDTs of arrival flights. It is suitable for optimizing arrival flights during peak hours at most airports. This approach provides theoretical support for optimizing the sorting of flights in terminal areas. [ABSTRACT FROM AUTHOR]

Carbon neutrality is an important policy in the current global response to climate change and has been widely recognized by various industries. In the process of promoting carbon neutrality, new energy plays a pivotal role. In this study, the definition and connotation of new energy and its role and specific operation in the energy transition of carbon neutrality are firstly explained. Promoting new energy development requires significant green and low-carbon investments. Taking China as an example, this paper analyzes the opportunities brought by the carbon neutral process to the field of green finance and analyzes the main features and development trends of green finance in China at present. Then this paper proposes policy recommendations to strengthen the development of green finance in China in terms of improving the green financial policy system, enhancing the supply capacity of green financial services, and optimizing the supporting environment for green financial development. Finally, this paper analyzes the measures and experiences of the United States in promoting low-carbon development and proposes countermeasures for China's low-carbon development on the basis of the five major relationships that need attention in China's carbon-neutral process. That is, strengthen the top-level design and improve the regulatory policy system; optimize the energy structure and increase the proportion of clean energy; optimize the industrial structure and reduce energy consumption in key industries; build a complete low-carbon technology system and promote low-carbon technology research and development and demonstration applications, and encourage local conditions to explore low-carbon development paths. The development of green finance can contribute to the advancement of new energy technologies, thus contributing to the achievement of carbon neutrality goals. [ABSTRACT FROM AUTHOR]

The digital image correlation method is a high precision and non-contact ground deformation monitoring method, which is widely used in surface deformation monitoring. However, according to its principle, different surface characteristics have great influence on the monitoring results. In this paper, a regional digital image slope monitoring method is established to meet the monitoring requirements of different areas of the same slope. Firstly, the features of different regions in the image are identified according to the optical region detection, and then the deformation calculation method is matched according to the features of different regions. Finally, according to the binocular vision principle, the three-dimensional displacement of the slope is obtained. Taking a highway slope in Qinhuangdao as an example, we realised all-weather and full-field deformation monitoring of slope, and verified the applicability and feasibility of regional digital image slope monitoring method. The applicability of digital image correlation method in slope monitoring is improved. [ABSTRACT FROM AUTHOR]

The state of health (SOH) of power battery reflects the difference between the current performance of the battery and the time it left the factory. Accurate prediction of it is the key to improving battery cycle efficiency. This paper studies the application of data-driven algorithms in power battery health estimation. Firstly, Using the data of actual operating vehicles which are monitoring in the data platform as the research objects. The charging event segmentation algorithm is designed for the full amount of data, and the K-means clustering model is used to extract slow charging events. Secondly, feature engineering is performed on the data, including the use of Pearson and Spearman coefficients analysis for numerical features, the use of one-hot encoding for category features to determine the final input features of SOH model. Eventually, using the Ridge linear regression model to predict the health status of the power battery. The research shows that the MAE is less than 5%, which meets the needs of practical use. In addition, this paper comparing Ridge with three other models named Linear Regression, Lasso, and Elastic Net. The result showed that the linear regression model with L2 regularization is more applicable in low-dimensional feature application scenarios without cell data in prediction of SOH. [ABSTRACT FROM AUTHOR]

In this paper, the process of crack propagation is investigated using the extended finite element method at the mesoscale to study the drill pipe fracture mechanism. Firstly, the property of the S135 drill pipe was analyzed through physical and chemical experiments and the scanning electron microscope method. After that, a grain distribution model of the drill pipe material at the mesoscale was established by the Python scripting language on ABAQUS platform. Furthermore, the extended finite element method was applied to study crack dynamic propagation. And the distribution of stress and strain during the crack propagation were obtained at the mesoscale grain model. Finally, by the mesomechanics "homogenization" method, the stress and strain of the crack propagation model at different times were analyzed, and the influence of crack propagation on drill pipe material was obtained. Simulation results show that, although drill pipe material at the macroscopic scale is in the elastic stage, plastic zone and micro-crack propagation may also exist at the mesoscale. The proposed method in this paper studied the stress distribution in the crack tip during the propagation, which is a benefit for exploring the fracture mechanism of drill pipe. [ABSTRACT FROM AUTHOR]

Innovative product design is essentially an activity involving creative cognitive thinking. Therefore, research on the innovative design process and of methods, computer-aided innovation tools should be conducted based on systematic exploration based on the principles of innovative cognitive thinking. We aim to uncover some general principles that can serve as a systematic thinking framework for product designers, provide a feasible framework and method of innovative thinking for designers, and provide theoretical and methodological support for further development of computer-aided innovation platforms. This paper summarizes the sources and content of studies on the idea generation and design processes behind product innovation. Specifically, we break down the general innovative design process of products, outlining the cognitive mechanisms and propose an integrated application strategy that incorporates multiple methods. And taking "the solution program to the coking of oily sludge pyrolysis equipment" as an example, this paper demonstrated and verified the effectiveness and practicability of the method and strategy proposed. Based on the results on improvements in the equipment, the designers put forward some creative and implementable design schemes. The entire thinking process was relatively smooth and efficient. The example presented in this paper shows that the proposed method can effectively guide/assist/motivate designers to think creatively. [ABSTRACT FROM AUTHOR]

Commonly used fastener positioning methods include pixel statistics (PS) method and template matching (TM) method. For the PS method, it is difficult to judge the image segmentation threshold due to the complex background of the track. For the TM method, the search in both directions of the global is easily affected by complex background, as a result, the locating accuracy of fasteners is low. To solve the above problems, this paper combines the PS method with the TM method and proposes a new fastener positioning method called local unidirectional template matching (LUTM). First, the rail positioning is achieved by the PS method based on the gray-scale vertical projection. Then, based on the prior knowledge, the image of the rail and the surrounding area of the rail is obtained which is referred to as the 1-shaped rail image; then, the 1-shaped rail image and the produced offline symmetrical fastener template is pre-processed. Finally, the symmetrical fastener template image is searched from top to bottom along the rail and the correlation is calculated to realize the fastener positioning. Experiments have proved that the method in this paper can effectively realize the accurate locating of the fastener for ballastless track and ballasted track at the same time. [ABSTRACT FROM AUTHOR]

In this paper, a new apodized fiber Bragg grating (FBG) structure, the Chebyshev apodization, is proposed. The Chebyshev polynomial distribution has been widely used for the optimal design of antennas and filters, but it has not been used for designing FBGs. Unlike the function of traditional Gaussian-apodized FBGs, the Chebyshev polynomial is a discrete function. We demonstrate a new methodology for designing Chebyshev-apodized FBGs: the grating region is divided by discrete n sections with uniform gratings, while the index change is used to express the Chebyshev polynomial. We analyze the Chebyshev-apodized FBGs by using coupled mode theory and the piecewise-uniform approach. The reflection spectrum and the dispersion of Chebyshev-apodized FBGs are calculated and compared with those of Gaussian FBGs. Moreover, a sidelobe suppression level (SSL), a parameter of the Chebyshev polynomial, along with the maximum ac-index change of FBGs are analyzed. Assume that the grating length is 20mm, SSL is 100 dB, the section number is 40, and the maximum ac-index change is 2 × 10−4. The reflection spectrum of Chebyshev apodized FBGs shows flattened sidelobes with an absolute SSL of −95.9 dB (corresponding to SSL=100 dB). The simulation results reveal that at the same full width at half maximum, the Chebyshev FBGs have lower sidelobe suppression than the Gaussian FBGs, but their dispersion is similar. We demonstrate the potential of using Chebyshev-apodized FBGs in optical filters, dispersion compensators, and sensors; Chebyshev apodization can be applied in the design of periodic dielectric waveguides. [ABSTRACT FROM AUTHOR]

Introduction: Curriculum learning through the wisdom tree massive open online course platform not only gets rid of the limitations of specialty, school and region, eliminates the limitations of time and space in traditional teaching, but also effectively solves the problem of educational equity. Objectives: This paper proposes an intelligent algorithm combining decision tree, support vector machine, and simulated annealing to obtain the best classification accuracy and decision rules for university students' satisfaction with the wisdom tree massive open online course platform. Methods: This study takes the university students in Fuzhou city information management department as the survey object, and adopts the electronic questionnaire survey method. A total of 1136 formal questionnaires were responded, and 1028 valid questionnaires were obtained after data cleaning and deleting invalid questionnaires (the effective rate was 90.49%). In this paper, the reliability and validity of the questionnaire were tested by IBM SPSS-20.0 software, and six explanatory variables including function, achievement, exercise, quality, richness, and interaction were obtained by principal component analysis. Then, the questionnaire data is converted to CSV (comma separated values) format for analysis. This paper proposes an intelligent algorithm combining decision tree, support vector machine, and simulated annealing to obtain the best classification accuracy and decision rules for university students' satisfaction with the wisdom tree massive open online course platform. In this paper, the proposed algorithm is compared with decision tree, random forest, k-nearest neighbor, and support vector machine to verify its performance. Results: The experimental results show that training set classification accuracy of decision tree, random forest, k-nearest neighbor, only support vector machine and the proposed algorithm (simulated annealing + support vector machine) are 92.21%, 96.10%, 95.67%, 97.29%, and 99.58%, respectively. Conclusion: The proposed algorithm simulated annealing + support vector machine does increase the classification accuracy. At the same time, the 11 decision rules generated by simulated annealing + decision tree can provide useful information for decision makers. [ABSTRACT FROM AUTHOR]

The generator is the most popular mobile power device and backup power device in the world. It is very important for human life. Therefore, it is important to develop more efficient combustion technology in order to save energy and reduce air pollution. In this paper, a novel technology of hydrogen and oxygen compound gasoline fuel is developed. Hydrogen and oxygen gases are produced from an electrolytic cell and then mixed with the intake gasoline and air. The compound fuel is sucked into the engine combustion chamber. The hydrogen and oxygen gases can be produced immediately without any storage device of hydrogen. The experimental results show that this technology can increase the power generation and decrease emission pollution due to promoting combustion efficiency. In addition, the spark plug seat temperature increases due to higher heat value of hydrogen. This technique can reduce carbon monoxide and HC, but increase carbon dioxide. The research and development of this technique can achieve the goals of energy saving, emission reduction, relative safety, easy refitting and low refitting expense. Moreover, this research possesses academic innovation and industrial application. [ABSTRACT FROM AUTHOR]

This paper presents a non-isolated single switch converter with high voltage gain. Its circuit topology is combined with coupled-inductor, clamp circuit, and voltage lift capacitor techniques. The proposed converter has several advantages: First, the circuit is controlled by only single pulse width modulation (PWM) for the power switch, which keeps the circuit simple. Secondly, the proposed converter is used as a clamping circuit,which let the energy of the leakage inductance can be circulated to the capacitor, so that the voltage spike on the active switch can be suppressed, and improves efficiency. This paper will introduce the principle of action, theoretical analysis, and experimental waveform in order. Finally, in the case of input voltage of 48 V, output voltage of 400 V, and output power of 1 kW, the performance of the proposed converter is verified. As a result, the maximum efficiency is up to 96.5% and full load efficiency is 92.3%. [ABSTRACT FROM AUTHOR]

The gap leakage between the impeller ring leads to the change of the pump cavity flow characteristics, resulting in the uneven pressure distribution of shroud, which causes the axial force of the cavity to change. In this paper, the flow in the front and the rear cavity of the multistage centrifugal pump was taken as the research object. Through the numerical method, the radial flow velocity, the leakage flow size and its direction, the core zone rotation factor of the front and the rear cavity of the multistage pump impeller at all stages and the axial force of pump cavity were studied. The results show that the leakage in the front cavity of multistage pump impeller at all stages flows inward along radial direction (i.e. it flows from the inlet of pump cavity to the front ring clearance). The rotation factor in core zone is higher than 0.5, and with the increase of rotation factor, the axial force of the front pump cavity increased. The leakage in the rear pump cavity flows outward along radial direction (i.e. it flows from the rear ring to the inlet of pump cavity). The rotation factor in core zone is less than 0.5, and with the increase of rotation factor, the axial force decreased gradually. Besides, the radial velocity and rotation factor in the front and the rear multistage pump of impellers were obviously along the axial direction at three regions, the regions are pump case boundary layer, core zone and impeller boundary layer. The flow in the core zone is dominated by circumferential circular motion, and the radial velocity in the core zone is 0. It is shown that the direction of the leakage in the pump cavity and the rotation effect of the flow micelle in the mainstream core zone are the main factors affecting the axial force of the pump cavity, and the research results can provide theoretical guidance for the calculation and suppression of axial force of multistage pumps. [ABSTRACT FROM AUTHOR]

In this paper, the wing body fusion method is used to complete the design of underwater glider. On this basis, the traditional optimization algorithm of underwater gliding wing shape is improved. Based on the improved Hicks Henne algorithm and genetic algorithm, the shape optimization of underwater glider is completed. Through the further optimization of the overall performance, the overall shape of the glider is improved and the maximum lift drag ratio is increased. Finally, the physical experiment of the optimized shape is carried out according to the experimental water area of the circulating water tank. Through the comparative analysis of the data, the accuracy of the numerical calculation is verified. [ABSTRACT FROM AUTHOR]

High Velocity Compaction (HVC) is a key technology to realize the performance of powder metallurgy parts "three high and one low" (high density, high precision, high performance, and low cost). The main purpose of this paper is to summarize the research results at home and abroad on the forming mechanism, forming influencing factors and theoretical basis of high velocity compaction. This paper analyzes and summarizes the literature and monographs at home and abroad through literature research methods to obtain the research results of high velocity powder metallurgy compaction, so as to fully and correctly understand the research progress, development trends and application prospects of high velocity compaction. This paper expounds the technical characteristics, advantages and limitations of high velocity compaction, and analyzes and looks forward to its development trend, application prospect and expansion field, So as to provide reference and reference for the future research and application direction of high velocity compaction technology. [ABSTRACT FROM AUTHOR]

The half-car suspension has the coupling of pitch angle and front and rear suspension. Especially when the suspension model has a series of uncertainties, the traditional linear control method is difficult to be applied to the half-car suspension model. At present, there is no systematic method to solve the suspension power. According to the energy storage characteristics of the elastic components of the suspension, the power calculation formula is proposed in this paper. This paper proposes a composite adaptive backstepping control scheme for the half-car active suspension systems. In this method, the correlation information between the output error and the parameter estimation error is used to construct the adaptive law. According to the energy storage characteristics of the elastic components of the suspension, the power calculation formula is introduced. The compound adaptive law and the ordinary adaptive law have good disturbance suppression, both of which can solve the pitching angle problem of the semi-car suspension, but the algorithm of the compound adaptive law is superior in effect. In terms of vehicle comfort, the algorithm of the general adaptive law can achieve stability quickly, but compared with the composite adaptive law, its peak value and jitter are higher, while the algorithm of the composite adaptive law is relatively gentle and has better adaptability to human body. In terms of vehicle handling, both control algorithms can maintain driving safety under road excitation, and the compound adaptive algorithm appears to have more advantages. Compared with the traditional adaptive algorithm, the power consumption of the composite adaptive algorithm is relatively lower than that of the former in the whole process. The simulation results show that the ride comfort, operating stability and safety of the vehicle can be effectively improved by the composite adaptive backstepping controller, and the composite adaptive algorithm is more energy-saving than the conventional adaptive algorithm based on projection operator. [ABSTRACT FROM AUTHOR]

The seepage properties of natural gravel are one of the problems to be considered in seepage project designs. In this paper, the seepage properties of the natural gravel with particle sizes of 5, 20 and 60 mm were investigated under different laying conditions. The effect of the particle size, laying depth, bulk density and pressurized head on the seepage properties of the natural gravel was analyzed by using the combined methods of theoretical analysis with physical model test. The results showed that the seepage flow in the natural gravel was non-laminar flow in the test conditions described in this paper. Meanwhile, the relationship between particle size, laying depth, bulk density, pressurized heads and seepage property was established. The seepage discharge increased with the increase of the pressurized head and particle size, and decreased with the increasing of laying depth and bulk density. The critical laying depth and bulk density can be obtained when the seepage discharge becomes zero. The empirical formula of the seepage discharge of natural gravel with different particle sizes, laying depths, bulk densities and pressurized heads was obtained with the method of nonlinear regression, which can be expressed as: Q = 5. 9546 d 0. 3713 γ − 0. 2974 L − 0. 1156 H 0. 1307 − 5. 6614. The empirical formula was experimentally validated. The maximum relative error did not exceed 6.73%, proving that the empirical formula of the seepage discharge of natural gravel was rational. The results can provide an important reference to further studying the seepage properties of macropore media, and form a theoretical basis for applying the natural gravel in the seepage projects. [ABSTRACT FROM AUTHOR]

This paper addresses the adaptive control of a class of time-varying nonlinear systems. Under the framework of Immersion and Invariance (I&I) adaptive control, a set of sufficient conditions is obtained to stabilize the concerned time-varying nonlinear systems. It is shown that the presented controller can also be utilized to complete tracking control for a class of nonholonomic constraint systems if the desired trajectories satisfy certain conditions. The effectiveness of the new adaptive controller is demonstrated by some numerical simulations on a nonholonomic mobile robot. [ABSTRACT FROM AUTHOR]

In peer-to-peer (P2P) social lending, it is important to predict the repayment of borrowers. P2P lending data are generated in real-time, but most of them are pending to decide the repayment because the deadline is not yet expired. Adding the unexpired data with appropriate labels into the training set could improve the performance of a prediction model, but the pseudo-labels cannot be certainly precise. In this paper, we propose an ensemble classifier composed of diverse convolutional neural networks (CNNs) of GoogLeNet, ResNet and DenseNet for the repayment prediction in social lending with the pseudo-labels approximated by an uncertainty handling scheme. The additional data labeled by Dempster-Shafer fusion of two semi-supervised learning methods boost up training of various models of CNNs, which are combined by weighted voting. A diversity measure is applied to constructing a pool of different models of CNNs that extract the effective features in the social lending data with labeling noise and predict the borrower’s loan status. The experiment with the real dataset of 855,502 cases from Lending Club confirms that the diverse ensemble combined with weighted voting achieves the highest performance and outperforms conventional methods. [ABSTRACT FROM AUTHOR]

A kinase anchoring protein (AKAP) 12 is a scaffolding protein that improves the specificity and efficiency of spatiotemporal signal through assembling intracellular signal proteins into a specific complex. AKAP12 is a negative mitogenic regulator that plays an important role in controlling cytoskeletal architecture, maintaining endothelial integrity, regulating glial function and forming blood-brain barrier (BBB) and blood retinal barrier (BRB). Moreover, elevated or reduced AKAP12 contributes to a variety of diseases. Complex connections between AKAP12 and various diseases including chronic liver diseases (CLDs), inflammatory diseases and a series of cancers will be tried to delineate in this paper. We first describe the expression, distribution and physiological function of AKAP12. Then we summarize the current knowledge of different connections between AKAP12 expression and various diseases. Some research groups have found paradoxical roles of AKAP12 in different diseases and further confirmation is needed. This paper aims to assess the role of AKAP12 in physiology and diseases to help lay the foundation for the design of small molecules for specific AKAP12 to correct the pathological signal defects. [ABSTRACT FROM AUTHOR]

In order to solve the problem of freezing damage treatment and anti-freezing design for railway tunnels in operation in the seasonal frozen area, this paper proposes a polyurethane-corrugated steel plate insulation lining, and studies its thermal insulation effect, relying on a freezing damage treatment project of an existing passenger-dedicated tunnel for on-site application. In this paper, the insulation lining is applied to the actual project and the insulation effect is verified by the field monitoring. The temperature test section was set in the anti-freezing design section. The polyurethane-corrugated steel plate insulation lining proposed in this paper is mainly composed of corrugated steel plate, polyurethane insulation layer and waterproof layer. The polyurethane-corrugated steel insulation lining is applied by using self-made rail flat battery car, door-type scaffolding, arch slide and other construction equipments. The on-site temperature test shows that the insulation effect is obvious. The polyurethane-corrugated steel plate insulation lining has the advantages of fast construction speed, good thermal insulation effect, preventing concrete from peeling off blocks, easy removal and replacement. According to the field temperature test, under the natural temperature outside the tunnel from −18°C to −22°C in winter of 2018–2020, the temperature of the interface between surrounding rock and lining of the tunnel monitoring sections are all above 0°C so that the freezing damage is eliminated from the root. Research results can provide a theoretical basis for the anti-freezing projects of tunnels in seasonal frozen area. [ABSTRACT FROM AUTHOR]

This paper presents work towards a formal framework to support model-based integrated design and optimization of mechatronic products for early-phase conceptual design. This paper describes an integrated design framework through the introduction of its software implementation and a specific use case. The contribution is to introduce mathematical formalism to define the concepts, semantics, computation rules and system architectures of the formal framework. The advantage of the formal definitions is to clearly expose functionality and the limitations of the design framework and facilitate the software implementation. The modelling capability of the framework is enhanced to include non-linear mechatronic components, such as a two degrees-of-freedom arm. Further, an optimal proportional–integral–derivative control component is added to the software library supporting the framework. [ABSTRACT FROM AUTHOR]

Past swarm intelligence algorithms for solving UAV path planning problems have suffered from slow convergence, lack of complex constraints and guidance for local optimisation. It no longer meets the requirements of the Multi-UAV Cooperative Reconnaissance Mission Planning (MUCRMP) problem in the context of multi-radar detection. In this paper, a global optimisation model with the objective of a shorter distance within radar detection range of the UAV is proposed at first, including the planning of reconnaissance sequence between and within target groups, relative position to targets. More importantly, the imaging characteristics of the UAV and its minimum turning radius have been considered in depth in this study. Then an improved synthetic heuristic algorithm is proposed to solve the model, which obtains valuable reconnaissance mission plan. Finally, an example solution for a problem with 68 target point sizes is carried out, and the validity and feasibility of the model and algorithm are illustrated through the analysis given. Compared with the existing algorithms, the improved synthetic heuristic algorithm can give better anti-radar attributes to the UAV and efficiently improved the convergence speed in the specific reconnaissance mission. [ABSTRACT FROM AUTHOR]

Blowout is a major safety concern for well control in oil and gas well engineering and well killing is an important technical means to avoid blowout. Mastering the initial state of the wellbore is the basis to ensure well killing. The traditional gas column model cannot effectively explain the development law of wellbore pressure. Therefore, this paper is based on the actual killing conditions and the principle of gas-liquid two-phase flow. This paper establishes the physical model and mathematical model of the initial state of the wellbore annulus under overflow conditions. By means of the finite difference method and cyclic iteration calculation, the initial state of the wellbore annulus can be accurately inverted, and the calculation results are verified by an example. The results show that, when the wellhead pressure is determined, the wellbore pressure distribution and annular air phase distribution are more reasonable and the initial length of the overflow section is larger, the calculation results of the inversion method are closer to the actual engineering situation. The findings of this study can help to better understand wellbore state before killing and provide a theoretical basis for optimizing killing parameters, which is of great significance for avoiding well control safety risks during killing. [ABSTRACT FROM AUTHOR]

The structural adequacy challenges of railroad track structures have received considerable attention globally. Track defects and failures due to weak strength and buckling effect account for onethird of all railroad accidents. The current paper provides a comprehensive study of the recent work on the structural adequacy/bearing capacity of rail systems from mechanical engineering perspectives; existing techniques for track stiffness/modulus evaluations, including standstill and continuous methods. Further, this paper demonstrates the current techniques for track stiffness/ modulus evaluation. Prevailing track modulus techniques, while accurate but time-taking, effortful, requires a track closure and provides only single-point information. Also, this review provides a suggestion on the non-destructive and non-invasive technologies for example Ground-penetrating radar (GPR) and evaluation of the substructures of tracks as they have great potential for image subsurface features. [ABSTRACT FROM AUTHOR]

Considering that the distinctions among static hand gestures are the difference between fingers sticking out, a method of grouping and classifying hand gestures step by step by using the information of the quantity, direction, position and shape of the outstretched fingers was proposed this paper. Firstly, the gesture region was segmented by using the skin color information of the hand, and the gesture direction was normalized by using the direction information of the gesture contour lines. Secondly, the finger was segmented one by one by using convex decomposition in the hand gesture image based on the convex characteristic of the gesture shape. Thirdly, the features of quantity, direction, position and shape of the segmented fingers were extracted. Lastly, a hierarchical decision classifier embedded with deep sparse autoencoders was constructed. The quantity of fingers was used to divide the gesture images into groups first, then the direction, position and shape features of the fingers were used to subdivide and recognize gestures within each group. The experimental results show that the proposed method is robust as lighting, direction and scale changes and significantly superior to the traditional method both in the recognition rate and the recognition stability. [ABSTRACT FROM AUTHOR]

Recent advances in three-dimensional printing technologies provide one way not only to speed up freeform fabrication but also to exert programmable control over mechanical properties. Besides, origami-inspired structures, origami-inspired metamaterials, and even origami-inspired robotics primarily demonstrate the promising potential for innovative inspirations of engineering solutions. The motivation of this work is to explore a fully programmable robotic perspective with a fusion of programmable metamaterials, programmable mechanics, and programmable fabrication. First, we proposed an illustrative roadmap for transforming an origami model into a fully programmable robotic system. Then, we introduced an origami spring model and revealed its shape-shifting geometry and intrinsic metamaterial mechanisms, especially the rarely switchable behavior from transverse compression to longitudinal stretchability, and the curvilinear deployment. Furthermore, we addressed the fabrication challenges of three-dimensional printable origami sheets considering three-dimensional printability, foldability with high elasticity, and good damage tolerance. Finally, we developed a fully soft manipulator in terms of the highly reversible compressibility of origami spring metamaterials. And we also devised a peristaltic crawling robot with undulatory movements induced by inclination deployment effect of origami spring metamaterials. Conceivably, the proposed fully programmable robotic system was demonstrated starting from programmable metamaterials, programmable mechanics, and programmable fabrication to programmable robotic behaviors. The contribution of this work also suggested that robotic morphing could be tunable by hierarchical programming from modeling and fabrication to actions. [ABSTRACT FROM AUTHOR]

At present, new energy time-sharing rental vehicles have become a new generation of travel modes, playing a positive role in environmental protection and being regarded as one of the most promising travel modes in the future. However, due to the rapid pace of market expansion, the problems in their development have come one after another, such as the leakage of personal information, difficulty refunding the deposit and other phenomena frequently occurring, causing problems for consumer user experience. The reasons are as follows: government policies and regulations need to be perfected; the level of enterprise operation needs to be observed, financing is difficult, and the level of consumer participation in supervision is low. Based on the traits of bounded rationality in the internet plus the periodic rental market, this paper introduces the government into the market subject and constructs a three-party dynamic evolution game model between the government, enterprises, and consumers to reveal the control strategy. This paper conducts a concrete analysis to verify the model using a case study by revealing the game process between the regulation strategy and behavior decisions of enterprises and consumers and provides a theoretical basis and reference for policy-making and decision-making. The results showed that when the key parameters are in different numerical ranges, the system has four evolutionary stability results. By appropriately increasing the number of subsidies and penalties, increasing the proportion of the compensation coefficient to consumers, and urging enterprises to reduce operating costs, government participation is conducive to the healthy development of the new energy timesharing automobile industry. [ABSTRACT FROM AUTHOR]