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Along with the unceasing growth of worldwide economic and the associated issues on resources, energy and environment, clean energy generating technologies that are based on recyclable materials, if possible, may become the future trend of development. Here, we report the design of a cheap, lightweight, and recyclable single-electrode triboelectric nanogenerator (TENG) that utilizes waste paper as the triboelectric material. Under the current strategy, we successfully developed green energy machines without vastly increasing the mining of various critical minerals around the world. The as-designed TENG could not only collect and convert mechanical energy into electricity with sound efficiency, but also has the merit for continuous reuse and quick construction. The maximum output power density is as high as 171 mW·m−2 at a resistance of 130 MΩ and could be integrated into a book for monitoring reading actions, thus providing a new approach to the low-cost, green and sustainable self-powered electronic systems.

COVID-19 has sparked a worldwide pandemic, with the number of infected cases and deaths rising on a regular basis. Along with recent advances in soft computing technology, researchers are now actively developing and enhancing different mathematical and machine-learning algorithms to forecast the future trend of this pandemic. Thus, if we can accurately forecast the trend of cases globally, the spread of the pandemic can be controlled. In this study, a hybrid CNN-LSTM model was developed on a time-series dataset to forecast the number of confirmed cases of COVID-19. The proposed model was evaluated and compared with 17 baseline models on test and forecast data. The primary finding of this research is that the proposed CNN-LSTM model outperformed them all, with the lowest average MAPE, RMSE, and RRMSE values on both test and forecast data. Conclusively, our experimental results show that, while standalone CNN and LSTM models provide acceptable and efficient forecasting performance for the confirmed COVID-19 cases time series, combining both models in the proposed CNN-LSTM encoder-decoder structure provides a significant boost in forecasting performance. Furthermore, we demonstrated that the suggested model produced satisfactory predicting results even with a small amount of data.

Deep learning is an idea technique for image classification. Imaging flow cytometer enables high throughput cell image acquisition and some have integrated with real-time cell sorting. The combination of deep learning and imaging flow cytometer has changed the landscape of high throughput cell analysis research. In this review, we focus on deep learning technologies applied in imaging flow cytometer for cell classification and real-time cell sorting. This article describes some recent research, challenges and future trend in this area.

This work provides an overview of avionic systems, which can be used as an entry point to learn about their architecture and components, and as a guide for studying the recent development of unmanned aerial systems avionics. The development trend of avionics for unmanned aerial systems tends toward the one used for a large aircraft both in the structure of the architecture and in design and implementation processes. However, due to the differences in the operational environment, challenges remain in specifying the proper requirements, and proving their safety and security. The paper reviews the functionalities and importance of avionic systems, especially in unmanned aerial systems. It also provides the historical background and the future trend of system development in terms of safety, security, and certification for the purpose of integrating these unmanned aerial systems into an urban airspace.

The stereotaxic brain atlas is a fundamental reference tool commonly used in the field of neuroscience. Here we provide a brief history of brain atlas development and clarify three key conceptual elements of stereotaxic brain atlasing: brain image, atlas, and stereotaxis. We also refine four technical indices for evaluating the construction of atlases: the quality of staining and labeling, the granularity of delineation, spatial resolution, and the precision of spatial location and orientation. Additionally, we discuss state-of-the-art technologies and their trends in the fields of image acquisition, stereotaxic coordinate construction, image processing, anatomical structure recognition, and publishing: the procedures of brain atlas illustration. We believe that the use of single-cell resolution and micron-level location precision will become a future trend in the study of the stereotaxic brain atlas, which will greatly benefit the development of neuroscience.

Nanofiber (NF) polymeric composites have received more interest nowadays. The majority of the study focuses on characterizing NF and comparing them to traditional composites in terms of mechanical behavior and usage efficiency. There are different varieties of NFs, each with unique possessions that influence whether or not they are used in particular industrial usage. Because of the natural source of these materials, they have an extensive variety of characteristics that are largely dependent on the gathering position and conditions, assembly it tough to choose the right fiber for precise usage. This study aims to map where every form of fiber was located in numerous assets by providing a detailed analysis of the characteristics of NF employed as composite-based materials reinforcement. Recent research on emerging forms of fibers was also discussed. A bibliometric analysis of NF composite applications is discussed. A future trend analysis of NF applications, as well as the essential innovations to extend their uses were also addressed.

Quality education is the basis, driver, and inspirer of skill education. These two education models can complement and interact with each other. However, few scholars have discussed the current state and future trend of the integration between the two models, not to mention quantifying the integration effect. This paper applied the artificial neural network (ANN) to evaluate the integration effect of quality education and skill education, owing to the advantages of the ANN in processing nonlinear information adaptively. First, the subjects and motivation mechanismss of the integration between the two models were analyzed. Then, an evaluation index system was established for the integration effect. After that, an ANN model was created for the compatibility of evaluation indexes and used to predict the integration effect. Experimental results verified the reasonability of the proposed evaluation index system, and the effectiveness of the proposed model. Finally, the current state of the integration was analyzed based on the prediction results.

Crime is a bone of contention that can create a societal disturbance. Crime forecasting using time series is an efficient statistical tool for predicting rates of crime in many countries around the world. Crime data can be useful to determine the efficacy of crime prevention steps and the safety of cities and societies. However, it is a difficult task to predict the crime accurately because the number of crimes is increasing day by day. The objective of this study is to apply time series to predict the crime rate to facilitate practical crime prevention solutions. Machine learning can play an important role to better understand and analyze the future trend of violations. Different time-series forecasting models have been used to predict the crime. These forecasting models are trained to predict future violent crimes. The proposed approach outperforms other forecasting techniques for daily and monthly forecast.

The role of point-of-care (POC) diagnostics is important in public health. With the support of smartphones, POC diagnostic technologies can be greatly improved. This opportunity has arisen from not only the large number and fast spread of cell-phones across the world but also their improved imaging/diagnostic functions. As a tool, the smartphone is regarded as part of a compact, portable, and low-cost system for real-time POC, even in areas with few resources. By combining near-infrared (NIR) imaging, measurement, and spectroscopy techniques, pathogens can be detected with high sensitivity. The whole process is rapid, accurate, and low-cost, and will set the future trend for POC diagnostics. In this review, the development of smartphone-based NIR fluorescent imaging technology was described, and the quality and potential of POC applications were discussed.

Plant diseases and pests are important factors determining the yield and quality of plants. Plant diseases and pests identification can be carried out by means of digital image processing. In recent years, deep learning has made breakthroughs in the field of digital image processing, far superior to traditional methods. How to use deep learning technology to study plant diseases and pests identification has become a research issue of great concern to researchers. This review provides a definition of plant diseases and pests detection problem, puts forward a comparison with traditional plant diseases and pests detection methods. According to the difference of network structure, this study outlines the research on plant diseases and pests detection based on deep learning in recent years from three aspects of classification network, detection network and segmentation network, and the advantages and disadvantages of each method are summarized. Common datasets are introduced, and the performance of existing studies is compared. On this basis, this study discusses possible challenges in practical applications of plant diseases and pests detection based on deep learning. In addition, possible solutions and research ideas are proposed for the challenges, and several suggestions are given. Finally, this study gives the analysis and prospect of the future trend of plant diseases and pests detection based on deep learning.

Countries across the world are in different stages of COVID-19 trajectory, among which many have implemented the lockdown measures to prevent its spread. Although the lockdown is effective in such prevention, it may put the economy into a depression. Predicting the epidemic progression with government switching the lockdown on or off is critical. We propose a transfer learning approach called ALeRT-COVID using attention-based recurrent neural network (RNN) architecture to predict the epidemic trends for different countries. A source model was trained on the pre-defined source countries and then transferred to each target country. The lockdown measure was introduced to our model as a predictor and the attention mechanism was utilized to learn the different contributions of the confirmed cases in the past days to the future trend. Results demonstrated that the transfer learning strategy is helpful especially for early-stage countries. By introducing the lockdown predictor and the attention mechanism, ALeRT-COVID showed a significant improvement on the prediction performance. We predicted the confirmed cases in one week when extending and easing lockdown separately. Results showed the lockdown measures is still necessary for a number of countries. We expect our research can help different countries to make better decisions on the lockdown measures.

Whole body vibration (WBV) is a common phenomenon for heavy machinery operators such as tractor driver, earth mover operator, cranes, helicopter pilots etc. Whole body vibration is the main cause of musculoskeletal diseases and pain in joints which is gradually decreases the motor abilities of the person. However random human body vibration is a very popular cure for posture correction in Parkinson patients. Whole body vibration can be eradicated by practices such as Ergonomics and many such inventions which are being devised for the same purpose. A condition also arises, when some body parts get damage due to vibration, and need to replace/repair. In contrast to this, biomaterials come into consideration. This review paper gives an overview of whole-body vibration, its health effects, relevant remedies for WBV, role and future trend of biomaterials has been discussed.

The complex nature of the damage evolution in stress corrosion cracking (SCC) leads to explore for new investigation technologies in order to better identify the mechanisms that supervise the initiation and evolution of the damage as well to provide an improvement of knowledge on this critical localized corrosion form during time. Research activities concerning the use of acoustic emission (AE) technique to assess SCC has acquiring considerably relevance in recent decades. The non-invasiveness and the possibility to provide a continuous in situ monitoring of structures and components make this non-destructive technique clearly promising in the field of structural health monitoring. In this concern, this paper aims to be a focused overview on the evaluation of SCC phenomena by AE technique. The main topic of this review is centered on the approaches that can be used in elaborating AE data to better discriminate the mechanisms that contribute to damage propagation in SCC conditions. Based on available literature, investigation approaches assessing AE waveform parameters were classified, evidencing, furthermore, the identified mechanisms that synergistically take place during the material degradation. Eventually, a brief summary and a future trend evaluation was also reported.

In this paper, we propose a game theoretical adversarial intervention detection mechanism for reliable smart road signs. A future trend in intelligent transportation systems is ``smart road signs" that incorporate smart codes (e.g., visible at infrared) on their surface to provide more detailed information to smart vehicles. Such smart codes make road sign classification problem aligned with communication settings more than conventional classification. This enables us to integrate well-established results in communication theory, e.g., error-correction methods, into road sign classification problem. Recently, vision-based road sign classification algorithms have been shown to be vulnerable against (even) small scale adversarial interventions that are imperceptible for humans. On the other hand, smart codes constructed via error-correction methods can lead to robustness against small scale intelligent or random perturbations on them. In the recognition of smart road signs, however, humans are out of the loop since they cannot see or interpret them. Therefore, there is no equivalent concept of imperceptible perturbations in order to achieve a comparable performance with humans. Robustness against small scale perturbations would not be sufficient since the attacker can attack more aggressively without such a constraint. Under a game theoretical solution concept, we seek to ensure certain measure of guarantees against even the worst case (intelligent) attackers that can perturb the signal even at large scale. We provide a randomized detection strategy based on the distance between the decoder output and the received input, i.e., error rate. Finally, we examine the performance of the proposed scheme over various scenarios.

Outbreak of COVID-19, created a disastrous situation in more than 200 countries around the world. Thus the prediction of the future trend of the disease in different countries can be useful for managing the outbreak. Several data driven works have been done for the prediction of COVID-19 cases and these data uses features of past data for future prediction. In this study the machine learning (ML)-guided linear regression model has been used to address the different types of COVID-19 related issues. The linear regression model has been fitted into the dataset to deal with the total number of positive cases, and the number of recoveries for different states in India such as Maharashtra, West Bengal, Kerala, Delhi and Assam. From the current analysis of COVID-19 data it has been observed that trend of per day number of infection follows linearly and then increases exponentially. This property has been incorporated into our prediction and the piecewise linear regression is the best suited model to adopt this property. The experimental results shows the superiority of the proposed scheme and to the best of our knowledge this is a new approach towards the prediction of COVID-19.

Multilegged robots have the potential to serve as assistants for humans, replacing them in performing dangerous, dull, or unclean tasks. However, they are still far from being sufficiently versatile and robust for many applications. This paper addresses key points that might yield breakthroughs for highly dynamic multilegged robots with the abilities of running (or jumping and hopping) and self-balancing. First, 21 typical multilegged robots from the last five years are surveyed, and the most impressive performances of these robots are presented. Second, current developments regarding key technologies of highly dynamic multilegged robots are reviewed in detail. The latest leg mechanisms with serial-parallel hybrid topologies and rigid–flexible coupling configurations are analyzed. Then, the development trends of three typical actuators, namely hydraulic, quasi-direct drive, and serial elastic actuators, are discussed. After that, the sensors and modeling methods used for perception are surveyed. Furthermore, this paper pays special attention to the review of control approaches since control is a great challenge for highly dynamic multilegged robots. Four dynamics-based control methods and two model-free control methods are described in detail. Third, key open topics of future research concerning the mechanism, actuation, perception, and control of highly dynamic multilegged robots are proposed. This paper reviews the state of the art development for multilegged robots, and discusses the future trend of multilegged robots.

The lower extremity exoskeletons (LEE) are used as an assistive device for disabled people, rehabilitation for paraplegic, and power augmentation for military or industrial workers. In all the applications of LEE, the dynamic and static balance, prevention of falling, ensuring controller stability and smooth human-exoskeleton interaction are of critical importance for the safety of LEE users. Although numerous studies have been conducted on the balance and stability issues in LEEs, there is yet to be a systematic review that provides a holistic viewpoint and highlights the current research challenges. This paper reviews the advances in the inclusion of falling recognition, balance recovery and stability assurance strategies in the design and application of LEEs. The current status of research on LEEs is presented. It has been found that Zero Moment Point (ZMP), Centre of Mass (CoM) and Extrapolated Center of mass (XCoM) ideas are mostly used for balancing and prevention of falling. In addition, Lyapunov stability criteria are the dominant methods for controller stability confirmation and smooth human-exoskeleton interaction. The challenges and future trend of this domain of research are discussed. Researchers can use this review as a basis to further develop methods for ensuring the safety of LEE’s users.

As a bone scaffold, meeting all basic requirements besides dealing with other bone-related issues—bone cancer and accelerated regeneration—is not expected from traditional scaffolds, but a newer class of scaffolds called multifunctional. From a clinical point of view, being a multifunctional scaffold means reducing in healing time, direct costs—medicine, surgery, and hospitalization—and indirect costs—loss of mobility, losing job, and pain. The main aim of the present review is following the multifunctional bone scaffolds trend to deal with both bone regeneration and cancer therapy. Special consideration is given to different fabrication techniques which have been applied to yield these materials spanning from traditional to modern ones. Moreover, the hierarchical structure of bone plus bone cancers and available medicines to them are introduced to familiarize the potential reader of review with the pluri-disciplinary essence of the field. Eventually, a brief discussion relating to the future trend of these materials is provided.

Meat adulteration, mainly for the purpose of economic pursuit, is widespread and leads to serious public health risks, religious violations, and moral loss. Rapid, effective, accurate, and reliable detection technologies are keys to effectively supervising meat adulteration. Considering the importance and rapid advances in meat adulteration detection technologies, a comprehensive review to summarize the recent progress in this area and to suggest directions for future progress is beneficial. In this review, destructive meat adulteration technologies based on DNA, protein, and metabolite analyses and nondestructive technologies based on spectroscopy were comparatively analyzed. The advantages and disadvantages, application situations of these technologies were discussed. In the future, determining suitable indicators or markers is particularly important for destructive methods. To improve sensitivity and save time, new interdisciplinary technologies, such as biochips and biosensors, are promising for application in the future. For nondestructive techniques, convenient and effective chemometric models are crucial, and the development of portable devices based on these technologies for onsite monitoring is a future trend. Moreover, omics technologies, especially proteomics, are important methods in laboratory detection because they enable multispecies detection and unknown target screening by using mass spectrometry databases.

Entropy-based dispatching has attracted more and more attention from professional scholars and engineers. The quantity and quality of publications related to this topic are increasing rapidly. To accurately measure this growth, we use bibliometric tools to evaluate publications in the field from 1985 to 2019, based on the Science Citation Index Expanded database. They compare and analyse the relationship between dispatching problems and entropy theory in different research fields, and provide detailed overviews and discussions on hot topics and potential research value based on keywords, citations, H-index, journals, institutions, countries and authors. The analysis shows that the application of information entropy to the quantification of dynamic information in flexible manufacturing systems, noise processing in power systems and channel rate prediction has potential research value. The result reveals that universities in mainland China have made the greatest contribution, which renders the number of publications from China in the lead, followed by the US and the UK. ‘International Journal of Production Research’ and ‘European Journal of Operational Research’ are two of the most noteworthy archives for researchers in this field. For interested readers, integrating entropy theory with economic scheduling, power scheduling, and job-shop scheduling will be the future trend of this field.

Prosthetic hands have made a significant influence on the quality of life of people with upper arm amputation. Research on prosthetic hands today is focused on replicating the functionalities of the biological hands. The present article provides a bibliometric survey on bionic hands, done through a compilation of a scientific publications database on the field of prosthetic hands spanning the last two decades. Through network-based information analysis, meaningful patterns are inferred, and several key questions significant to bionic prosthetic hands are answered. The article gives an insight into the growth, progress, and future trend in bionic hand prostheses, including identifying subject areas and discovering distinct research communities within the subject. The analysis clearly reveals the rapid expansion of bionic hand research over the last two decades, which is expected to rise considerably in near future.

Mycotoxins are secondary metabolites produced by fungus. Many mycotoxin species are highly toxic and are frequently found in cereals and feedstuffs. So, powerful detection methods are vital and effective ways to prevent feed contamination. Traditional detection methods can no longer meet the needs of massive, real-time, simple, and fast mycotoxin monitoring. Rapid detection methods based on advanced material and sensor technology are the future trend. In this review, we highlight recent progress of mycotoxin rapid detection strategies in feedstuffs and foods, especially for simultaneous multiplex mycotoxin determination. Immunoassays, biosensors, and the prominent roles of nanomaterials are introduced. The principles of different types of recognition and signal transduction are explained, and the merits and pitfalls of these methods are compared. Furthermore, limitations and challenges of existing rapid sensing strategies and perspectives of future research are discussed.

In recent years, multiphase motor drives are receiving more attention in industrial and academic fields. In this paper, the characteristics and industrial application background regarding the multiphase motor drives are introduced. And then, the field-oriented control, direct torque control, fault diagnosis, and tolerance control of the multiphase motor drives are presented, compared, and discussed. Finally, the future trend of the multiphase motor drive systems is predicted.

Organic semiconductors for electronic devices have attracted much attention in scientific research and industrial applications. In the past few decades, functional organic field-effect transistors (OFETs) have developed rapidly, especially OFETs with memory function. Here, through a detailed introduction of the background, memory mechanism and structure construction, we make a comprehensive summary of memory applications based on OFETs. Further, we describe four types of OFET memories, i.e. floating-gate OFET memory, electret OFET memory, ferroelectric OFET memory, and optoelectronic OFET memory. Finally, we put forward challenges in the development of OFET memory and look forward to the future trend of the development of OFET memory.

A blockchain is a revolutionary system that connects computer networks in a decentralized and distributed manner. Blockchain allows the process of current transactions to be peer-to-peer (P2P) without relying on a single server. Once entered, information can never be erased. The blockchain contains a certain and verifiable record of every single transaction ever made. This study evaluated the development of research publications in the implementation process and future trend of Blockchain Technology. The aim of this study is to assist researchers in creating a theoretical framework and to provide a preliminary source of references in research mapping in this field. All research publications related to the implementation and future trend of Blockchain Technology in the Scopus database were analyzed using several bibliometric indicators. The authorship, number of citations, journal sources, publishers, institutions, and countries, year of publication, categories, and author keywords were examined.

Background UHPLC-MS/MS is connected in various research facilities for the qualitative and quantitative investigation of a pharmaceutical substance, pharmaceutical items, and biological specimen. Main body The commence review article is an endeavor to offer pervasive awareness around assorted aspects and details about the UHPLC-MS/MS and related techniques with the aim on practice to an estimation of medicinal active agents in the last 10 years. The article also focused on isolation, separation, and characterization of present impurity in drug and biological samples. Conclusion Review article compiles a general overview of medicinally important drugs and their analysis with UHPLC-MS/MS. It gives fundamental thought regarding applications of UHPLC-MS/MS for the study on safety limit. The summary of developed UHPLC-MS/MS methods gives a contribution to the future trend and limitations in this area of research.

What is the future trend of gender (dis)parity in the U.S. higher education and workforce populations? I take a systems approach to develop a dynamic simulation model of gender diversity in U.S. hi...

Lately, the popularity and adoption of serverless computing or Function-as-a-Service have been grown substantially, and it emerges as a better way to manage cost, reliability, availability, and scalability. This paper presents a detail of serverless offerings from leading cloud providers such as AWS, Azure, Google Cloud Platform and some open source. It compares them side by side in the relevant category like compute, storage, database, messaging, API management, and tooling. Also provides comparative analysis on available serverless architectures for the most common use cases within cloud provider's environment. It will also emphasize on benefits, open problems, possible solutions, and the future of the technology.

Networks (i.e., graphs) are often collected from multiple sources and platforms, such as social networks extracted from multiple online platforms, team-specific collaboration networks within an organization, and inter-dependent infrastructure networks, etc. Such networks from different sources form the multi-networks, which can exhibit the unique patterns that are invisible if we mine the individual network separately. However, compared with single-network mining, multi-network mining is still under-explored due to its unique challenges. First ( multi-network models ), networks under different circumstances can be modeled into a variety of models. How to properly build multi-network models from the complex data? Second ( multi-network mining algorithms ), it is often nontrivial to either extend single-network mining algorithms to multi-networks or design new algorithms. How to develop effective and efficient mining algorithms on multi-networks? The objectives of this tutorial are to: (1) comprehensively review the existing multi-network models, (2) elaborate the techniques in multi-network mining with a special focus on recent advances, and (3) elucidate open challenges and future research directions. We believe this tutorial could be beneficial to various application domains, and attract researchers and practitioners from data mining as well as other interdisciplinary fields.

Decentralized autonomous organizations(DAOs) enabled by blockchain and smart contracts is regarded as an effective tool to solve corporate governance problems. It can minimize the contract risks, principal-agent dilemmas, and other issues in corporate governance. However, DAOs also face a variety of governance issues, such as internal governance issues and external governance issues caused by the technical attributes of blockchain and smart contracts. DAO is a typical complex system with the characteristics of both social and engineering complexity. It is difficult for existing governance theories to solve governance problems in DAOs. The parallel intelligence theory based on the artificial systems + computational experiments + parallel execution (ACP) method is a suitable research and practical approach to solve management and control problems for complex systems. Therefore, we propose a parallel governance framework for DAO based on the parallel intelligence theory and discuss its key research issues. Finally, we construct a parallel governance system for GnosisDAO, and conduct computational experiments on its voting mechanism. The results show the defects of GnosisDAO governance mechanism and we thus propose further research directions. We consider that parallel governance represents the future trend of DAO research and practice. This paper is aimed at stimulating further effort and providing helpful guidance and reference for future research works.

The computer technology on new media has changed people's life significantly along with the continuous development of internet, the whole society has entered into the new media era, which has changed the way people work and live. Meanwhile, the traditional brand image requires redesign to adapt to people's new aesthetic demand in new media era. This new brand image combing with the concept of new media has broken through the previous visual rules of brand design, changed the traditional thinking and ways of brand design, which are crucial to investigate the future trend of brand design and to explore the influence of new media on brand design. This research provides reference and suggestions for modern enterprises for their fashion brand design based on new media technology.

Blockchain, as a decentralized platform and distributed ledger database, will promote the centralized system to form a comprehensive ecosystem. A systematic review of blockchain research on informa...

When there is a fire in the airport, railway station, gymnasium and other large indoor spaces, the fire location can be located by video images, and the high-pressure water cannon can be driven to implement automatic fire-fighting through the pan tilt control system of fire monitor, which is the future trend of large space indoor fire-fighting. This paper takes the of fire monitor direction control as the object, analyzes the overall structure, hardware structure, communication, motor control and sensor of PTZ control, and designs the hardware of PTZ direction control system, which has great significance in the application of large space automatic fire monitor control.

The security of a cognitive radio network (CRN) is one of the most important features that should receive great attention, especially with the continuing developments in wireless communication systems. Because the security standards for cognitive radio networks (CRN) are the same as those for public wireless networks. Many attacks affect the security and reliability of the network and the emergence of many challenges, as these attacks are classified on the basis of cognitive radio network layers so that there are different types of attacks for each layer. Security is the most important issue that researchers must address. This paper addresses the review of the literature in this section the different types of approaches the researchers used to provide security for cognitive radio networks are explained. Bearing in mind that security is one of the most important factors that must be focused on and given utmost importance in dealing with cognitive radio networks. Also, this paper deals with a table showing the previous works as it illustrates this table Status, Challenges, and future trends.

Multi-material additive manufacturing of polymers has experienced a remarkable increase in interest over the last 20 years. This technology can rapidly design and directly fabricate three-dimensional (3D) parts with multiple materials without complicating manufacturing processes. This research aims to obtain a comprehensive and in-depth understanding of the current state of research and reveal challenges and opportunities for future research in the area. To achieve the goal, this study conducts a scientometric analysis and a systematic review of the global research published from 2000 to 2021 on multi-material additive manufacturing of polymers. In the scientometric analysis, a total of 2512 journal papers from the Scopus database were analyzed by evaluating the number of publications, literature coupling, keyword co-occurrence, authorship, and countries/regions activities. By doing so, the main research frame, articles, and topics of this research field were quantitatively determined. Subsequently, an in-depth systematic review is proposed to provide insight into recent advances in multi-material additive manufacturing of polymers in the aspect of technologies and applications, respectively. From the scientometric analysis, a heavy bias was found towards studying materials in this field but also a lack of focus on developing technologies. The future trend is proposed by the systematic review and is discussed in the directions of interfacial bonding strength, printing efficiency, and microscale/nanoscale multi-material 3D printing. This study contributes by providing knowledge for practitioners and researchers to understand the state of the art of multi-material additive manufacturing of polymers and expose its research needs, which can serve both academia and industry.

The dangerously contagious virus named "COVID-19" has struck the world strong and has locked down billions of people in their homes to stop the further spread. All the researchers and scientists in various fields are continually developing a vaccine and prevention methods to aid the world from this challenging situation. However, a reliable prediction of the epidemic may help control this contiguous disease until the cure is available. The machine learning techniques are one of the frontiers in predicting this outbreak's future trend and behavior. Our research is focused on finding a suitable machine learning algorithm that can predict the COVID-19 daily new cases with higher accuracy. This research has used the adaptive neuro-fuzzy inference system (ANFIS) and the long short-term memory (LSTM) to foresee the newly infected cases in Bangladesh. We have compared both the experiments' results, and it can be forenamed that LSTM has shown more satisfactory results. Upon study and testing on several models, we have shown that LSTM works better on a scenario-based model for Bangladesh with mean absolute percentage error (MAPE)-4.51, root-mean-square error (RMSE)-6.55, and correlation coefficient-0.75. This study is expected to shed light on COVID-19 prediction models for researchers working with machine learning techniques and avoid proven failures, especially for small imprecise datasets.

Crime is one of the most critical problems of the today’s world which affects the normal life of the society and it breaks the social, and economical flow of a country. The scenario is the same for Bangladesh. This paper focused on prediction of the areas based on crimes instead of the opposite. This will also be beneficial to predict crimes occurred in different cities in certain timeframe. And knowing the future trend the law enforcement and detectives will be able to take precautionary steps which will in turn reduce the rate of such events. Here, Random Forest algorithm is used to predict the regions (AKA district) based on different crime rates.

In this paper, we are predicting and forecasting the COVID-19 outbreak in India based on the machine learning approach, where we aim to determine the optimal regression model for an in-depth analysis of the novel coronavirus in India. We are implementing the two regression models namely linear and polynomial and evaluating the two using the R squared score and error values. The COVID-19 dataset for India is being used to serve the research of this paper. The model is predicting the number of confirmed, recovered, and death cases based on the data available from March 12 to October 31,2020. For forecasting the future trend of these cases, we are utilizing the time series forecasting approach of tableau. Furthermore, the time series forecasting method is being employed to forecast the total count of confirmed cases in the future.

Coronavirus2019 has been seen as an overall pandemic and spreads all over India. Tamilnadu is one of the most affected states in India. The study of the spread of this disease is essential to foresee the requirement in improving the health care facilities. A couple of assessments are being driven using distinctive models to foresee the probable progression of this disease. To report the present scenario, the research uses exploratory data processing and uses time series forecasting approaches to estimate the future trend. Here, the spread of the disease is analyzed in Tamil Nadu and employed Prophet Model to forecast the transmission of COVID-19 in the forthcoming months. Results show that the spread of the disease will be gradually reduced and the state will be back to normal in the forthcoming months.

Road infrastructure is subject to increasingly demanding operating conditions. The loads stressing the bridges is increasingly high, which overloads the existing structures, many of which were designed with lower design loads. The high frequency of heavy vehicle traffic accelerates damage to the structure. Furthermore, other environmental causes (ice, de-icing salt, settling, temperature, etc.) make existing structures unsuitable. The security risk of bridges is most frequently raised by crack. The paper explored a digital and smart bridge crack system for improving the efficiency and risk factor for bridge security diagnosis. Artificial intelligence and deep learning will enhance the inspection of concrete bridge structures, and it will become the future trend of development.

The future trend of integrated joint operations makes the task of battlefield airspace management more important. However, in the face of users’ demands for using airspace, airspace resources are more precious and tense. Especially the conflict resolution between different types of tactical airspace use needs is an important issue in wartime airspace management. The resolution of airspace conflict should be combined with the user attribute decision of airspace use demand—the airspace use demand with low priority attribute such as weapon platform and task type should avoid the airspace use demand with high priority. However, for a certain airspace use requirement, when the priority ranking of different attributes conflicts with the impact of avoidance decision, the decision dilemma will be caused. In this paper, priority information is fused by evidence theory to solve the problem of spatial conflict resolution.

21st European Symposium on Improved Oil Recovery, IOR 2021 -- 19 April 2021 through 22 April 2021 -- -- 169492, Production from Unconventional structures is being expected to be the future trend of oil and gas industry. Although the primary recovery values range between 5 to 8%, liquid-rich shale structures has great amount of hydrocarbon in place and it can be produced by EOR applications such as cyclic gas injection that can boost the production up to 1.7 times. This reference study gives a deep review of EOR mechanisms and the stimulation techniques for unconventionals. A comprehensive investigation about the historical development of the concept, the technologies utilized as well as transferred from other industries, adaptness and impetus of the methods in both conventional and unconventional structures is described with the escalating efficacies that these methods ensured for different reservoir structures. The concepts are exemplified with existing worldwide field case studies and applications by implying the advantages, challenges and drawbacks belong to each story. All parameters are discussed and summarized that lead to conclusions on the criteria of application of enhanced technologies. The concept of EOR and Stimulation is a proven area of application which demonstrated itself with its great number of worldwide applications in conventional structures. Key factor of success for an EOR application is to evaluate each case individually to take the suitable actions of technical and economic concerns. This study underlines the theory, key parameters, methodology, challenges and advantages of a successful EOR application for the wells that include intelligent or standard completions in unconventional structures. It aims to serve as a unique reference study which combines of all the aspects of the employed techniques and their usability in specific cases. There are dozens of publications which includes certain examples of Enhanced Oil and Gas technologies, but a detailed study that includes all the aspects of EOR methods suitable to apply in unconventional reservoirs is missing. This study aims to be a reference study by giving all the details from design to result of a successful EOR application in unconventional reservoirs. © IOR 2021 - 21st European Symposium on Improved Oil Recovery.

Agriculture, as it grows increasingly information-based, will enter the development stage of smart agriculture. At the same time, research on agriculture-related information also demonstrates that it is the future trend for agriculture to become intelligent, precise, standardized, and digitized. This chapter offers a detailed elucidation on the concept of agricultural information technology, key technologies in agricultural information processing technology, and multi-source agricultural information fusion processing technology.

The primary target of this paper is to give an away from of powerful business choices. This investigation makes a focus on the connection of the present business framework and artificial knowledge. As we enter in the period of PCs in mid 1950s, we named that this will change our method of working. Yet, as it develops, it has changed the perspective too. The customary business chiefs for settling on any dynamic utilize their data sources from the market, now and then they give the proper data and some of the time not due to the HR blunders. Now, many decision-making tools (like MIS, EIS, DSS, etc.) prove as a boon to businesses for making sensible and right decisions. By using the current and past data about any situation, these systems predict the future trend of the market. Now we enter in the age of man-made intelligence since last decade. The artificial intelligence in the processing of business data proves itself as a major base for decision making. It will give better outcomes as the suitable data ship off it. The most proper approach to convey a privilege and fast data in the association, the knowledge-based information systems, can be utilized, thus named as business insight frameworks.

The purpose of this paper is to calculate the external noise cost of China’s high-speed railway and forecast its future trend. Firstly, the unit value transfer method after correction is selected as a method of calculation. Secondly, the noise cost calculation model of China’s high-speed railway is established by introducing the correction factors such as GDP-PPP, population density ratio and CPI ratio. At last, the external noise cost since the opening of high-speed railway is calculated. The model validation results show that although the average external noise cost of high-speed railway increases year by year, it does not increase significantly. This is not only related to economic growth and improvement of noise reduction technology, but also related to the increase of investment in noise reduction facilities in the early stage. Compared with it, the total external cost of high-speed railway noise increases obviously, which is positively related to the rapid development of high-speed railway. With the increase of high-speed railway mileage and passenger turnover, the total noise cost increases significantly. It can be predicted that with the implementation of high-speed railway planning in the future, the negative external cost of noise will continue to increase.

Artificial Intelligence (AI) is the latest achievement in the development of human technology. Intelligent design driven by artificial intelligence technology is the inevitable trend of future design development. Intelligent design has the characteristics of user experience as the center, universality to the industry and inevitable risk. It plays an important role in industrial practice and may bring out of control risk [1,2].

The world is struggling and dealing with a new pandemic named Coronavirus. Technologies such as the internet of things, IR4.0, artificial intelligence, and social media applications are becoming crucial tools during these tumultuous times to aid humanity in different perspectives. Many industries and people around the world are utilizing these applications to perform daily life operations. Therefore, technological innovations are continuously improving in every sector. For instance, in many hospitals, COVID-19 patients are monitored using the IoT-based networks on a regular basis with the ability to make decisions with the aid of the artificial intelligence. Industrial Revolution 4.0 is another technology that is helping business in current pandemic situation. In current pandemic, internet and 4G networks are playing important roles. These networks demand heavy bandwidth and show new future industries trends. In this chapter, the authors have discussed IoT, IR 4.0, AI, and social media applications' roles and impact on society, usability, and future trend demands.