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With an advancement in artificial intelligence (AI) applications, the use of smart imaging devices has been increased at a rapid rate. Recently, many researchers have utilized deep learning models such as convolutional neural networks (CNN) for image classification models. Compared to the traditional machine learning models, CNN does not require any kind of handcrafted features. It utilizes various filters to extract the potential features of images automatically. Inspired from this, in this paper, we have proposed a novel fruit classification model which utilizes the features of CNN, Long short Term Memory (LSTM) and Recurrent Neural Network (RNN) architectures. Type-II fuzzy enhancement is also used as pre-processing tool to enhance the images. Additionally, to tune the hyper-parameters of the proposed model, TLBO-MCET is also utilized. Extensive experiments are drawn by considering the existing and the proposed fruit classification models. Comparative analysis reveals that the proposed model outperforms the competitive fruit classification models.

Infrared (IR) thermographic assessment of ocular surface temperature (OST) is gaining interest as an adjuvant method to evaluate the ocular surface. It is a quick, non-invasive test that causes minimal, if any, discomfort to patients. The purpose of this article was to summarize research on how OST relates to tear film parameters and dry eye disease (DED). PubMed, Google Scholar, and Scopus searches for specific terms were carried out and eligible articles reviewed. OST of the central cornea is ~34-35°C when measured as a single time-point (typically right after a blink). Dynamically, OST values decrease over time at a rate of ~ −0.01 °C/s in healthy eyes. Single time-point OST values are impacted by temperature, with positive correlations noted with both ambient (1°C↓ results in ~0.16°C↓ in OST) and body (1°C↑ results in ~0.98°C↑ in OST) temperature. Single time-point OST values are also impacted by tear parameters, with negative correlations noted with tear break-up time (TBUT; r=−0.61) and positive correlations with lipid layer thickness (~r=0.50). Dynamically, the rate of OST cooling over the interblink period correlates with various tear parameters including Schirmer’s test scores (r=−0.39), tear meniscus height (r=−0.52) and the rate of tear film break-up (r=−0.74). These data imply that OST decreases more rapidly in individuals with greater tear production, larger tear volumes, and shorter tear break-up times (faster rates of tear film break-up). There are discrepancies in relationships between OST and DED across studies, which is not surprising given that DED encompasses a number of different phenotypic presentations. However, most studies found that OST decreases at a more rapid rate in DED vs. control groups. As such, cooling rate may have utility as a screening tool in DED in combination with established point-of-care tests.

Pekanbaru is the largest cities in Riau province with a very rapid rate of economic growth. This is affecting the business in the hotel sector developing rapidly in the city of Pekanbaru. Augmented Reality (AR) is an innovation that combines virtual and real objects. In this era, people tend to like applications with interactive visual displays. The application of AR technology in the form of LBS on hotels in the city of Pekanbaru based on android is expected to help immigrants or people who need hotel information when visiting the city of Pekanbaru. Users can search for hotels by tracking from the AR camera on the application and knowing the distance and route to get to the hotel. Testing of this application is done by Usability Testing through questionnaires and Blackbox Testing to test functional suitability. Based on the results of the questionnaire found that the application meets the criteria of usability testing where 78,67% of respondents agreed that the application is easy to understand. 71,3% of respondents agreed that the application has an efficient function. 76% of respondents agreed that the appearance of the application is easy to remember. 78,92% of respondents agreed that the application runs well without an error. 72,12% of respondents agreed that the application meets user satisfaction and 82,67% of users would suggest this application as a medium of hotel information in the city of Pekanbaru. Based on the results of Blackbox testing, the application can run smoothly according to existing functional needs.

The automotive industry is going through a transformation. Disruptive technologies and tools are shifting the business model from one of automobiles to one of mobility. To accomplish this shift, automotive companies are embracing acquisitions and partnerships. In a time when the consumer electronics industry is delivering new products to market at a rapid rate, automotive manufacturers must identify ways of getting new products and features to customers faster and with high quality to maintain or increase market share. We provide an analysis of interviews with global automotive company professionals to understand the impact that quality requirements have on innovation and the advanced product design process. The research contributes to the literature on innovation and quality, identifying organizational behaviors and practices that facilitate or obstruct the development of high quality fast-to-market innovations, particularly in the area of mobility.

Because of the increasing use of herbal essential oils (EO) in pharmacology, medicine, and food processing, global EO production grows at a rapid rate, generating large amounts of plant residues wo...

California's Central Valley is responsible for $17 billion of annual agricultural output, producing 1/4 of the nation’s food. However, land in the Central Valley is sinking at a rapid rate (as much...

Medical journal content continues to expand at a rapid rate. This is promising for the future of innovation and patient care but challenging for clinicians and scientists. We feature new journals, new social media platforms, educational advertisements, illuminating Letters to the Editor and enlightening Author Replies, Podcasts, Visual Abstracts, and Infographics. This is a developmental time for medical journal publication.

The children and adolescents of today’s generation are growing up in a media-saturated world. Digital media use has become the most common sedentary leisure time activity among children and adolescents. In the past decade, the development of mobile and digital technologies has taken place at such a rapid rate that researchers have had difficulties reporting evidence within limited timeframes. Growing evidence indicates that screen media usage by teenagers and pre-adolescents has deleterious ramifications on mental wellbeing. In addition to the benefits of screen media for exposure to a wide range of information and quick communication, the use of screen media has been correlated with adverse physical, psychological and social health consequences. This study focuses on the increasing use of screen media and their consequences on the various aspects of mental health of adolescents and preadolescents.

Intelligent gadgets for example smartphones, tablet phones, and personal digital assistants play an increasingly important part in our lives and have become indispensable in our everyday routines. As a result, the market for mobile apps tends to grow at a rapid rate, and mobile app utilization has long eclipsed that of desktop software. The applications based on these smartphones are becoming vulnerable due to the use of open-source operating systems in these smart devices. These applications are vulnerable to smartphones because of memory leaks; they can steal personal data, hack our smartphones, and monitor our private activity, giving anyone significant financial loss. Because of these issues, smartphone security plays a vital role in our daily lives. The Play Store contains unrated applications which any unprofessional developer can develop, and these applications do not pass through the rigorous process of testing and analysis of code leaks. The existing developed system does not include a stringent procedure to examine and investigate source code to detect such vulnerabilities among mobile applications. This paper presented a dynamic analysis-based robust system for Source Code Analysis of Mobile Applications for Privacy Leaks using a machine learning algorithm. Furthermore, our framework is called Source Code Analysis of Mobile Applications (SCA-MA), which combines DynaLog and our machine learning-based classifier for Source Code Analysis of Mobile Applications. Our dataset will contain around 20000 applications to test and analyze vulnerabilities. We will perform dynamic analysis and separate the classification of vulnerable applications and safe applications. Our results show that we can detect vulnerabilities through our proposed system while reviewing code and provide better results than other existing frameworks. We have evaluated our large dataset with the pervasive way so we can detect even small privacy leak which can harm our app. Finally, we have compared results with existing methods, and framework performance is better than other methods.

Vehicle automation is developing at a rapid rate worldwide. However, even lower levels of automation, such as SAE Level-1, are expected to reduce drivers’ workload by controlling either speed or lane position. At the same time, however, drivers’ engagement in secondary tasks may make up for this difference in workload displaced by automation. Previous research has investigated the effects of adaptive cruise control on driving performance and workload, but little attention has been devoted to lane keeping systems (LKS). In addition, the influence of secondary cognitive tasks on Level-1 driving performance is also not well understood. The goal of this study was to examine the effects of secondary cognitive tasks on driving performance and perceived workload while using LKS. Nine participants drove a simulated vehicle in manual and LKS modes, while maintaining a specific headway and performing a secondary cognitive (n-back) task with varying levels of difficulty. Results showed that standard deviation of headway and NASA-TLX workload scores were significantly higher during the most difficulty secondary task. Also, LKS was not found to improve driving performance nor reduce perceived workload. This paper highlights potential performance costs and benefits associated with LKS technology and proposes directions for future research.

Adolescent hookah pipe use is increasing at a rapid rate, thus posing a major public health concern globally. The hookah pipe is a gateway substance to other substances that may be more harmful. Yet, at present, little is known about why adolescents are so drawn to this mechanism. It is this gap that this study attempted to fill. This study, therefore, aimed to review the determinants of adolescent hookah pipe use. An electronic search of 12 databases identified studies investigating determinants of adolescent hookah pipe use. Twenty-five studies sampling a total of 88,988 adolescents who use the hookah pipe were included. This study found that adolescent hookah pipe use is determined by an interplay of family factors, peer/friends factors, individual factors, school factors, the actual hookah pipe mechanism, advertisements, and awareness of hookah pipe lounges or bars. Prevention and early intervention strategies aimed at reducing adolescent hookah pipe use is needed.

During the last 6 months of life, 75% of older adults with preexisting serious illness, such as advanced heart failure, lung disease, and cancer, visit the emergency department (ED). ED visits often mark an inflection point in these patients' illness trajectories, signaling a more rapid rate of decline. Although most patients are there seeking care for acute issues, many of them have priorities other than to simply live as long as possible; yet without discussion of preferences for treatment, they are at risk of receiving care not aligned with their goals. An ED visit may offer a unique "teachable moment" to empower patients to consider their ability to influence future medical care decisions. However, the constraints of the ED setting pose specific challenges, and little research exists to guide clinicians treating patients in this setting. We describe the current state of goals-of-care conversations in the ED, outline the challenges to conducting these conversations, and recommend a research agenda to better equip emergency physicians to guide shared decisionmaking for end-of-life care. Applying best practices for serious illness communication may help emergency physicians empower such patients to align their future medical care with their values and goals.

Since the description of femoroacetabular impingement (FAI) by Ganz in 2003, our understanding of the pathophysiology, management options, and outcomes has evolved and literature continues to be generated on this condition at a rapid rate. FAI has been identified as a primary source of hip pain as well as a generator of secondary osteoarthritis. Improvements in the radiographic detection of cam and pincer morphologies as well as a better understanding of the structural impact of these morphologies have led to improved preoperative planning. Advancements in hip arthroscopy techniques have also led to a higher rate of arthroscopic management of this condition over the initially described open surgical dislocation technique. While arthroscopic management of this condition has become the most common form of surgical management for FAI, inadequate bony resection has been shown to be a frequent source of revision surgery. Therefore, roles for open surgical dislocation and combined mini-open approaches remain, particularly in cases where concern for the inability to fully access the morphology arthroscopically exists.

A key aspect of medical, public health, and diagnostic microbiology laboratories is the accurate identification and rapid reporting and communication to medical staff regarding patients with infectious agents of clinical importance. Microbial taxonomy continues to change at a very rapid rate in the era of molecular diagnostics including whole genome sequencing. This update focuses on taxonomic changes and proposals that may be of medical importance for years 2017 and 2018.

Over the last two decades, robotic surgery has become a mainstay in hospital systems around the world. Leading this charge has been Intuitive Surgical Inc.’s da Vinci robotic system (Sunnyvale, CA, USA). Through its innovative technology and unique revenue model, Intuitive has installed 4,986 robotic surgical systems worldwide in the last two decades. The rapid rate of adoption and diffusion of the surgical robot has been propelled by many important industry-specific factors. In this review, we propose a model that explains the successful adoption of robotic surgery due to its three core groups: the surgeon, the hospital administrator, and the patient.

Australia’s freight task is growing at a rapid rate due to unprecedented population growth, coupled with increased demand from our trading partners in Asia and rapid changes in technology, e-commerce and consumer behavior. A significant proportion of freight is transported using the road network and to remain competitive this network needs to facilitate fast, efficient and effective access. This is a significant challenge with an ageing bridge network, the regularly extreme climatic conditions and the freight industry constantly pushing to increase access and allowable loads.In rising to this challenge, Australia’s infrastructure managers are being innovative, using the latest standards and technology in building new bridges and assessing existing bridges and utilizing the latest technology in managing existing bridge structures. In partnership with academic institutions and with funding from the State and Federal Governments, there has been significant research and development with the monitoring, modelling, assessment and strengthening of bridges.This paper will outline what is currently being done to rise to this significant challenge and also recommend future research and development to continue to meet this challenge.

Objective: To identify the most relevant aspects of global scientific research on biocharin terms of number of articles published, main authors and publishing countries, citation,subjects of scientific journals, funding institutions and general trends.Design/Methodology/Approach: A bibliometric study was carried out in the Scopusdatabase. The word "biochar" was used in the search engine. The search was limited to articles and reviews published from 2009 to March 2020. The VOS viewer software was used to identify the main thematic axes and to glimpse the knowledge gaps that exist to date.Results: A total of 11,444 documents were identified. The trend of work on biochar is onthe rise. China and the United States are the countries with the most publications onbiochar. Jefferson Lehman and Stephen Josephs are the most cited authors on thesubject. Global research on biochar focuses on the mitigating effect of climate changeand the properties that this material has to improve the physicochemical properties ofthe soil. Research on biochar in Mexico is scarce. Study Limitations/Implications: Biochar is a new technology that is not fullyunderstood.Findings/Conclusions: Interest in biochar as a multifaceted solution to agricultural andenvironmental problems is growing at a rapid rate both domestically and internationally.

Background and objective: SAARS-COV-2 is a respiratory illness caused by the novel Coronavirus (COVID-19) disease. The virus goes into the lungs through the respiratory tracks and damages the walls and linings of the air sacs in our lungs, as our body tries to fight it, our lungs become more inflamed and fill with fluid. This makes it harder to breathe. So, at early stages, deep learning applications can be used for screening and prediction at a rapid rate for diagnosing the lungs of patients. This paper uses Transfer learning methods. Four pretrained models were used in this study - VGG-16, VGG-19, Inceptionv3, Xception This paper addresses challenges while using pre-trained models in real-world. Also, high accuracies were achieved on these models.

New forms of protein are being developed at a rapid rate as older forms of protein, particularly meat and poultry, are coming under attack for nutritional, environmental, food safety, and animal welfare issues. To date, the FDA and USDA have split oversight of the new technologies that include genetic engineering and precision fermentation. Because these new products address the problems associated with traditional proteins, consumer demand appears to be overcoming fundamental fears associated with innovative foods. Currently, agencies are struggling with naming issues for the new proteins and, in some cases, possibly being forced to use costly and lengthy premarket approvals. Because of the complexity of new production methods, the speed of development, and the potential benefits, a new system of regulation may be necessary. It would consist of one of the existing agencies becoming a super regulator overseeing private companies that specialize and compete with each other and are regulated quickly and competently.

In the current era of big data, huge amounts of valuable data and information have been generated and collected at a very rapid rate from a wide variety of rich data sources. Social networks are examples of these rich data sources. Embedded in these big data are implicit, previously unknown and useful knowledge that can be mined and discovered by data science techniques such as data mining and social network analysis. Hence, these techniques have drawn attention of researchers. In general, a social network consists of many users (or social entities), who are often connected by “following” relationships. Finding those famous users who are frequently followed by a large number of common followers can be useful. These frequently followed groups of famous users can be of interest to many researchers (or businesses) due to their influential roles in the social networks. However, it can be challenging to find these frequently followed groups because most users are likely to follow only a small number of famous users. In this chapter, we present an efficient and flexible compression model for supporting the analysis and mining of very sparse networks of big data, from which the frequently followed groups of users can be discovered.

Norwegian landscapes are changing at an increasingly rapid rate and therefore systematically structured information about observable landscape variation is required for knowledge-based management of landscape diversity. The purpose of the article is to present the first version of a complete, area covering, evidence-based, landscape type map of Norway, simultaneously addressing geoecological, bioecological and land use related variations. The type system used for the mapping is supported by systematically structured empirical evidence. The results of the mapping procedure, including the geographical distribution and descriptive statistics (abundance and areal coverage), are presented for each of the nine identified major landscape types identified based on coarse-scale landform variation. Within six inland and coastal major types, a large number of minor landscape types are defined based on the composition of geoecological, bioecological, and land use related landscape properties. The results provide new insights into the geography of Norwegian marine, coastal and inland landscapes. The authors discuss potential errors, uncertainties and limitations of the landscape type maps, and address the potential value of the new tool for research, management and planning purposes. They conclude that the results of the study might contribute to knowledge-based spatial planning and management of the unique landscape diversity of Norway. landscape diversity, landscape mapping, landscape type, Norway

Citrus peels are a rich source of essential oils (EOs); these oils have efficient antioxidant and insecticidal properties. However, using these EOs is restricted because of technical obstacles such as their poor solubility in water and rapid rate of vaporization. Nanotechnology enables the formulation of the EOs into promising, efficient nanomaterials. These materials can be used in different fields such as water treatment, production of eco-friendly insecticides, and the food industry. Also, the citrus peels are a great source of nanocellulose, which is considered as a promising material used in water treatment and composite industry. This chapter sheds light on the employment of nanotechnology in the production of different nanomaterials from the agricultural wastes of citrus crops.

We are experiencing the birth pangs of a new era in the lives of the Homos sapiens: how AI, robotics, and automation is changing industry dynamics, socio-economic fundamentals, and what it means to compete. The central lesson we derive in this paper is that a rapid rate of progress or too much connectivity comes at a price one may not always want to pay or even know how to manage.

The fashion industry is evolving over time. The retailers are presenting new business models wherein the industry produces new products at a rapid rate. This has incorporated various new business operations for different types of products and is making supply chains more complex each year.

Since social networking has been popular in the current era of big data, numerous social networking sites (e.g., Instagram, Twitter) have generated huge volumes of social data at a rapid rate. Embedded into these data are valuable information and knowledge. This calls for social network analysis and mining. In this paper, we specifically aim to discover interesting relationships in directed social graphs via a theoretical approach. More specifically, we examine both graph theory and linear algebra approaches to discover interesting entities (e.g., popular followees, second-degree followees) from social networks represented in the form of big directional graphs.

With enormous development of digital technology, data is being generated at rapid rate with various areas of application domains. Data has to be extracted or filtered to find useful information. A basic concept for these analysis tasks and applications are the distance measures to effectively determine how similar two objects are. In this paper, a novel distance measure for microarray dataset is proposed. The bench mark algorithm k-medoids is used for clustering task. The results obtained from the proposed distance measure are compared with other standard distance measures like Manhattan, Euclidean distance measure using Central Nervous System microarray dataset and Prostate Cancer dataset. Dunn Index is used to analyze the cluster validation of the results obtained from the distance measure.

In the current era of big data, high volume of big data can be generated and collected from a wide variety of rich data sources at a rapid rate. Embedded in these big data are useful information and valuable knowledge. Examples include healthcare and epidemiological data such as data related to patients who suffered from viral diseases like the coronavirus disease 2019 (COVID-19). Knowledge discovered from these epidemiological data via data science helps researchers, epidemiologists and policy makers to get a better understanding of the disease, which may inspire them to come up ways to detect, control and combat the disease. In this paper, we present a data science solution for analyzing big COVID-19 epidemiological data. The solution helps users to get a better understanding of information about the confirmed cases of COVID-19. Evaluation results show the benefits of our data science solution in discovering useful knowledge from big COVID-19 data.

Introduction: It is unclear whether atrial fibrillation (AF) is best identified on intracardiac recordings by varying shape, rapid rate or extent of irregularity. Prioritizing these features may improve device diagnosis of AF. Hypothesis: AF can be separated from organized atrial flutter (AFL) by electrogram shape, independent of the contributions of rate or regularity. Methods: In 86 patients (25 female, age 65±11 years) we trained a convolutional neural network (CNN) to classify AF or AFL from 64 unipolar electrograms of persistent AF recorded for 60 seconds. In cases labelled as AF, we modified inputs by progressively regularizing (a) electrogram shape; (b) rate or (c) regularity in timing, to define which switched the classification to AFL. Results: The CNN provided a c-statistic of 0.95 ± 0.05 to identify AF or AFL in independent test cohorts not used for training, using 10-fold cross validation. Fig A shows AF in which progressive regularization of shape and timing from #1 to #4 flipped CNN classification into AFL in 45%. EGMs with 100% consistent shape and timing were classified by their cycle length (CL=1/rate): ~90% AF for CL < 175 ms, ~80% AFL for CL from 200-280 ms. Fig. B shows sequences simulated from patient-specific EGMs of AF that were classified as AF in 91 ± 12% of cases even if regular with CL of 200-280 ms, showing AF classification based on EGM shape alone. Figure B illustrates some ‘AF pathognomic’ electrogram shapes in red. Conclusions: AF may be identified by specific EGM shape patterns independent of regularity or rate. Regularity in shape and timing contribute ~45% to AFL classification, and adding CL explains up to 80%. Studies are required to study the mechanistic basis and clinical implications of specific AF-electrogram shapes.

We aim to provide the cardiovascular clinician with an update on the classification and diagnosis of severe AS and an evidence-based guide to the treatment options available for severe, symptomatic AS. The classification of AS is evolving with increasing recognition of hemodynamically distinct AS subtypes and the importance of transvalvular flow in both the diagnosis and management of severe AS. Transcatheter aortic valve replacement (TAVR) has continued to expand its safety and efficacy to include patients with symptomatic, severe AS at low risk for surgical AVR (SAVR). There is emerging data supporting the use of TAVR among patients with asymptomatic, severe AS, bicuspid aortic valve (BAV) anatomy, and surgical bioprosthetic failure, but additional studies are needed. As treatment options for managing patients with symptomatic, severe AS continue to grow at a rapid rate, it is essential to improve the recognition and diagnosis of the spectrum of clinical and hemodynamic presentations of severe AS. Despite the rapid expansion of TAVR, there remains a role for SAVR among a unique subset of patients. Future research should focus on optimizing the timing of valvular intervention while ensuring the acquisition of longer-term data on the durability of TAVR.

Abstract Aging and average life expectancy have been increasing at a rapid rate, while there is an exponential risk to suffer from brain-related frailties and neurodegenerative diseases as the population ages. Alzheimer’s disease (AD) is the most common neurodegenerative disease worldwide with a projected expectation to blossom into the major challenge in elders and the cases are forecasted to increase about 3-fold in the next 40 years. Considering the etiological factors of AD are too complex to be completely understood, there is almost no effective cure to date, suggesting deeper pathomechanism insights are urgently needed. Metabolites are able to reflect the dynamic processes that are in progress or have happened, and metabolomic may therefore provide a more cost-effective and productive route to disease intervention, especially in the arena for pathomechanism exploration and new biomarker identification. In this review, we primarily focused on how redox signaling was involved in AD-related pathologies and the association between redox signaling and altered metabolic pathways. Moreover, we also expatiated the main redox signaling-associated mechanisms and their cross-talk that may be amenable to mechanism-based therapies. Five natural products with promising efficacy on AD inhibition and the benefit of AD intervention on its complications were highlighted as well. Graphical Abstract

Uterine transplantation is an evolving procedure to allow for childbearing in paitents with absolute uterine factor infertility. The objective of this study was to review the existing literature using a comprehensive PubMed literature search. A systematic medical subheadings search strategy was used with the terms "uterus transplant" and "uterine transplantation". Of the 75 full-text articles assessed for eligibility, 68 were included in the qualitative synthesis. Of these, 9 were included in the meta-analysis on living donor uterine transplant, 5 on deceased donor uterine transplant, and 6 case reports of single uterine transplants. In conclusion, uterus transplant is a nascent field undergoing a rapid rate of evolution as programs mature their data and increase the number of procedures performed. The most recent publications and advances are thus summarized in this article to capture the most up-to-date information.

Healthcare industry is advancing at a lightning speed with extensive usage of IT tools and techniques. The use of machine learning algorithms is not only restricted to the field of computer science. It has sneaked into the healthcare industry too and is assisting the medical practitioners in the prediction of the onset of several diseases based on a particular set of attributes like age, BMI, blood pressure, glucose and insulin level and so on. Diabetes is one such disease that is growing at a very rapid rate and is pretty fatal leading to the requirement of a promising prediction system to diagnose the onset of the disease before it silently attacks the patients and causes the avoidable damage to health. Machine Learning techniques are doable in mining the diabetes dataset to efficiently classify and predict the disease. In this study, four machine learning classification algorithms are compared to find the more viable one in classifying a diabetic and a non-diabetic.

Internet of Things (IoT) allows real-world objects to be connected and managed through the internet. IoT is used to make any device smart and is thus a booming concept worldwide. Being a part of the era of the internet, it has become easier to experiment and develop new ideas. Many such ideas have been implemented, changing people’s lifestyles in general. Smart home technology is known to bring about more comfort and convenience at our homes. We have decided to stretch this concept out further to implement the concept of IoT into the security of our homes. The proposed model focuses on the security and ease of allowing access to the house. The leverage obtained by choosing this over other forms of home security is the ease of allowing access and monitoring our homes at any time and from anywhere. Our overall system is also focused on being made at a budget so that all sections of the society can experience this convenience and safety. In a world where technology is advancing at such a rapid rate, it is to be seen that everyone plays a part and stays up to date.

The change in diet structure is one of the critical features of social transformation, and diet structure is directly related to human health. In China, with rapid economic development, changes in the diet structure of the population have begun and are proceeding at a fairly rapid rate. In order to reveal how the Chinese diet is approaching or deviating from the nutritional goal, a novel index, NDBI (National Dietary Balance Index), is developed in this study to investigate the Chinese diet from 1961 to 2017 at a national level. The results show that the Chinese diet has transitioned from the under-intake stage to the over-intake stage. Before the 1980s, Chinese people ate all foods inadequately except staple foods, after the 1980s, the issue of under-intake began to fade, and the intake of meats even became excessive. The intake of staple foods is always excessive during this period. Currently, the Chinese diet is still unhealthy because of the inadequate intake of dairy products and the excessive intake of staple foods and meats. By evaluating diet structure on a national level, this study can help people to better understand how the Chinese diet deviated from the nutritional goal and provides information for policymakers intervening in China&rsquo, s food consumption.

Today’s world is the internet world, the Internet of Things (IoT) is expanding at a rapid rate increasing technology. A network of connected computers hidden in every corner of our life monitoring and controlling things with minimal IoT supports to connect hardware devices to the internet to process the data for monitoring and security. This system uses some controllers and some sensors help to monitor the real-time parameters like Frequency, Wattage, Voltage, Current etc. are accessed and monitored from remote area by cloud computing called ubidots.com and controls the system operation by computer or smartphone for Automatic under frequency load shedding (AUFLS).This paper presents an application of IoT for Smart Monitoring of Automatic Under frequency Load shedding Scheme at a system coordination centre.

The social demand for dental implantation is growing at a rapid rate, while dentists are faced with the dilemma of implantation failures associated with unfavorable osseointegration. Clinical-friendly osteogenesis, angiogenesis and osteoimmunology around dental implants play a pivotal role in a desirable osseointegration and it’s increasingly appreciated that Hippo-YAP signaling pathway is implicated in those biological processes both in vitro and in vivo in a variety of study. In this article we review the multiple effects of Hippo-YAP signaling in osseointegration of dental implants by regulating osteogenesis, angiogenesis and osteoimmunology in peri-implant tissue, as well as highlight prospective future directions of relevant investigation.

The COVID-19 pandemic has spread across the globe at a rapid rate, affecting large numbers of individuals in different countries with varying health care systems and infrastructure. In the US, New York City has been the epicenter of the COVID-19 outbreak, and the peak impact in this region has come earlier in this location than most other parts of the country. We report our experience preparing for this pandemic in a New York City academic medical center and its regional health care system, the issues confronted during the rise and peak of the number of cases, and the plans for the post-peak recovery and adjustment to the new reality of providing rehabilitation in an environment where COVID-19 remains prevalent.

This pre-print is deposited under a Creative Commons Attribution-NonCommercial 2.5 Canada License. This means that anyone may distribute, adapt, and build upon the work for non-commercial purposes, subject to full attribution. Permission has been granted by Emerald Publishing Ltd. for this version to appear here. If you wish to use this manuscript for commercial purposes, please contact permissions@emerald.com. The version of record is available at https://doi.org/10.1108/JMLC-10-2017-0061., There has been an increase use of mobile money involving retail users and financial institutions (Scobey-Thal, 2015). In 2015, the GSMA (an organization that represent the interests of mobile operators worldwide) states that there are more than 411 million mobile money accounts around the world. Furthermore, mobile money accounts have penetrated 85% of countries, often where a majority of the population do not have access to a formal financial institution (GSMA, 2015). Cisco Systems Inc. (2017) estimates that by 2021, more people will have access to mobile phones than there will be access to running water. Given the enormous growth in the mobile market, it is internationally recognised that this penetration should be capitalised on in the form of financial inclusion. However, with such benefits, come new challenges and the success of mobile money depends on the industry’s ability to adjust to a rapidly changing financial landscape (GSMA, 2015). A combination of exponential mobile growth and emerging financial services creates new opportunities for criminals to exploit vulnerabilities in the system. Although there have not been many cases of mobile money laundering, it is important to consider the potential to commit fraudulent activities. Technology advancements continue to change the very architecture of financial services by becoming complex value chain networks. As these services continue to diversify, there is an urgent need for more adaptive and fluid regulation (Braithwaite, 2013) to identify, address and mitigate threats as they arise (Lokanan, 2017). As a result, anti-money laundering (AML) and counter terrorist financing (CTF) regimes remain operationally fluid, whilst accrediting global financial integrity. This concern lays the foundation for our research question: What can be done to mitigate anti-money laundering and counter-terrorist financing threats posed by mobile money? In this regard, the purpose of the paper is to explore the various characteristics of mobile money that presents a threat to anti-money laundering and counter terrorist financing regimes. The rest of the paper proceeds as follows. First, we conduct a literature review on mobile money. Second, we described the methodology used to identify the main themes in the study. Third, we conducted an analysis of the themes identified in relations to the three stages of money laundering and law enforcement. In this section, we assess the varying threats associated with the mobile money. Finally, we conclude the paper with recommendations on how the threats associated with mobile money can be mitigated.

In this paper, we discuss machine learning methods for classifying gross kicking activity for the term and preterm infants. We examine different combinations of sensors to determine the relative importance of each sensor to gross activity detection. In addition, we discuss methods to correlate infant age to the amount of time an infant performs unilateral vs bilateral kicking and time an infant is at rest. For preterm infants, we examine this same relationship using birth age and adjusted age. From this comparison, we aim to determine which age is a better predictor for movement breakdown. For gross activity recognition, it was determined that a sensor placed on the thigh was less important to overall recognition than a sensor placed on the foot or shin. In addition, a sensor placed on the foot tended to be the most accurate on its own while the thigh sensor tended to be the least accurate. For the relationship between infant age and movement breakdown, it was determined that the amount of time spent at rest increases as age increases. Furthermore, the amount of time spent performing bilateral kicking decreases at a more rapid rate than unilateral kicking as age increases. Finally, we examine how this relationship changes over time for infants observed over multiple months.

Graphical abstract, Highlights • The deep transfer learning model is used to classify COVID-19 infected patients by considering their chest CT images. • The cost-sensitive top-2 smooth loss function is also utilized to enhance the results further. • The deep transfer learning model is trained on a benchmark open dataset of chest CT images., The COVID-19 infection is increasing at a rapid rate, with the availability of limited number of testing kits. Therefore, the development of COVID-19 testing kits is still an open area of research. Recently, many studies have shown that chest Computed Tomography(CT) images can be used for COVID-19 testing, as chest CT images show a bilateral change in COVID-19 infected patients. However, the classification of COVID-19 patients from chest CT images is not an easy task as predicting the bilateral change is defined as an ill-posed problem. Therefore, in this paper, a deep transfer learning technique is used to classify COVID-19 infected patients. Additionally, a top-2 smooth loss function with cost-sensitive attributes is also utilized to handle noisy and imbalanced COVID-19 dataset kind of problems. Experimental results reveal that the proposed deep transfer learning-based COVID-19 classification model provides efficient results as compared to the other supervised learning models.

Robert A. Gross, MD, PhD, FANA, FAAN, the retiring Editor-in-Chief (EIC), has served as an Associate Editor (under former Editor-in-Chief Robert C. Griggs, MD, FAAN), Deputy Editor (under John H. Noseworthy, MD, FAAN), and finally EIC of Neurology® at a turbulent and uncertain time for the scholarly publishing industry. During the 19-year period in which he held these roles, scientific journals proliferated at a rapid rate and the electronic journal came into its own as the canonical version of research articles published. Electronic tracking systems and reviewer databases became the norm. A new business model, open access, appeared, threatening the existence of the subscription model and the publishing world as we knew it. Indeed, publication of scientific material on the world wide web, with the advent of post-peer review journals and preprint servers, threatened the long-held tradition of blinded peer review. The insertion of predatory publishers in the mix complicated article publishing even further.

INTRODUCTION: Three-dimensional (3D) tools have played a significant role in advancing anatomical knowledge, in simulation and clinical practice in Otology. Technology is evolving at a rapid rate with new applications being reported at an overwhelming pace. It is important to continuously review new applications, assess emerging trends, and identify challenges to innovation so clinical translation progresses in an efficient and evidenced-based manner. METHODS: A review of 20 years of literature in 3D technology specific to Otology was undertaken using Medline, Embase, PubMed, and Google Scholar search engines. Trends in the literature were analyzed as applications are evaluated and adopted into clinical practice. A literature review was conducted to identify barriers to translation. RESULTS: There was increasing volume of literature reporting innovations in 3D technology in Otology with a more recent increase in reviews and meta-analysis. The most marked trend was in literature regarding clinical applications of such technology and in 3D printing. Where this may indicate that translation of these technologies is adequate, this is not reflected in routine clinical practice or even in education and training platforms. CONCLUSION: Barriers to translation of 3D tools specific to Otology include ongoing challenges in attaining high-resolution data, rendering parameters and with the advent of 3D printing a multitude of new variables in software, printers, and materials adding complexity to selecting most appropriate options. These need methodical evaluation to selectively customize solutions to clinical challenges so effective translation, scale, and adoption can occur without causing confusion about choices.

The human genome sequencing began in 1999 and was completed in 2000. That scientific accomplishment paved the way for understanding gene functions as they relate to clinical medicine. So, what is PGx testing and how can it help patients? PGx testing is a method of genetically determining how humans metabolize drugs. It is now possible to identify how well genes function that code for the pharmacokinetic cytochrome P-450 enzymes (also named CYP enzymes). PGx testing costs have been reduced from thousands of dollars for testing one or a few genes, to a few hundred dollars for an entire panel of genes. In addition, publication of evidencedbased literature validating the science is occurring at a very rapid rate [1,2]. We now understand how pharmacodynamic proteins work at medication target sites [3].

Technology grows at a rapid rate and trust has involved in a risk judgment between different parties. Authentication and Authorization generally determines the trust while dealing with transactions of data over different channels. Blockchain has become a digital backbone due to its security, time-management, and transparency on data decentralization ledger. This survey paper studies the existing blockchain structure, analyses the research gaps and proposes a new theoretical approach based on neighbor monitoring and updating model. This model may help the patients to decentralize real data and results in the blockchain network. This work discusses in detail about the various security challenges in block chain against 51% attack and double-spend attack by proposing a theoretical framework for the problem.

Massive hemoptysis is generally used to describe the expectoration of a large amount of blood and/or a rapid rate of bleeding, although the precise thresholds that constitute massive hemoptysis are controversial. Unfortunately, few options exist for the treatment of active bleeding from benign airway lesions. This case report highlights a challenging clinical situation where placement of an Amplatzer occlusion device over a proximal bleeding vessel resulted in cessation of hemorrhage until vascular embolization could be performed. The use of the Amplatzer occlusion device within the airways can be an effective intervention to aid in the treatment of massive hemoptysis.

Wearable Technology (WT) devices are growing at a rapid rate for their convenience and capabilities, especially in healthcare and fitness area. As the adoption of WT accelerates and a new level of ...

The implementation of body worn cameras (BWCs) is occurring at a rapid rate and with relatively little information about their potential impacts on both the police and the citizens they serve. The core assumption underlying this widespread adoption is that BWCs will increase self-awareness among police officers as well as citizens, which will in turn reduce negative outcomes and improve police–citizen relations. At the same time, there is a broader movement to emphasize the function of police officers as guardians rather than warriors. This research draws from a mixed method evaluation of a pilot implementation of BWCs in Philadelphia, PA. We draw from focus groups and pre–post survey results to make the case for a wider conceptual frame in understanding the potential for BWCs to transform policing. Specifically, we argue that cameras can provide a tool for police officers to use in emphasizing their role as guardians. We provide evidence from the evaluation to support this view.

To determine: (1) rates and risk factors for progression of lateral and medial full-thickness cartilage defect size in older adults without severe knee osteoarthritis (OA), and (2) whether risk factors for defect progression differ for knees with Kellgren–Lawrence OA grade 3 (moderate) OA versus grades 0–2 (none to mild) OA. Three-hundred and eighty adults enrolled in the Osteoarthritis Initiative were included (43% male, mean age 63.0 SD 9.2 years). Ethical approval was obtained at all study sites prior to enrollment. All participants had full-thickness tibial or weight-bearing femoral condylar cartilage defects on baseline knee MRIs. Baseline OA grade was KL grade 3 in 71.3% and grades 0–2 in 21.7% of participants. Repeat MRIs were obtained at a minimum 2-year follow-up. Independent risk of progression in defect size due to demographic factors, knee alignment, OA grade, knee injury and surgery history, and baseline knee symptoms was determined by multivariate Cox proportional hazards and linear regression modeling. The average increase in defect size over 2 years for lateral defects was 0.18 cm2 (SD 0.60) and for medial defects was 0.49 cm2 (SD 1.09). Independent predictors of medical defect size progression were bipolar defects (beta 0.47 SE 0.08; p

The U.S. health-care costs have increased at a rapid rate over the last several decades. How much responsibility the government should bear with the increase in health-care costs is one of ...

The shallow water Gulf of Mexico is a mature oil and gas province that has produced about 97% of its proved oil and gas reserves circa 2016, so it should come as no surprise that structure inventories in the region have been declining at a rapid rate. Shallow water oil and gas production last peaked in the mid-1990s and the maximum number of standing structures totalled 3972 in 2001, while circa 2016 number 2009. Record levels of decommissioning in recent years are the result of the maturity of field production, sustained low oil and gas prices, and tougher regulatory conditions and oversight. The purpose of this paper is to review structure inventory data, statistics and trends in the Gulf of Mexico in water depth less than 400 ft (122 m) and provide detailed examination of producing, idle and auxiliary structures circa 2016. The paper concludes with decommissioning and installation trends.