Extremophilic bacteria are important groups of extremophilic organisms that have been studied during the last years. They are considered as a source of enzymes due to great diversity and can survive under extreme conditions. Many enzymes produced by these microorganisms are of great importance and have found applications in several industries. Due to their activity and stability under extreme conditions, these enzymes offer new alternatives for current biotechnological and industrial applications. They have a wide range of potential uses and have been a nuclear subject of many different investigations. To date, some of the enzymes produced by extremophilic bacteria are currently being assessed thier industrials applications. Despite, benefits that present these enzymes, their potentials remain largely unexplored. These enzymes pose new opportunities for new line of research, and biotechnological applications. This review provides a summary on diversity and biotechnological and industrial applications of some enzymes produced by extremophilic bacteria.

Urban rail transit has become the main mode to ease traffic congestion. Researches on the dynamic variation regularity of passenger flow distribution and passenger flow volume of urban rail transit hub platform have important implication in hub capacity design, operation organization and risk prevention. This paper proposes a passenger flow distribution integrating model (PFDIM) based on the basic theory analysis and basic parameter models of passenger flow distribution in hub platform. Simulation designs of PFDIM are built with Java and Anylogic, which contain simulation system functional framework, implementation path, simulation processes and simulation models. By case study, the performance comparison between two simulation methods indicates that simulation designs are scientific and accordant with the reality scene; calculation results prove that PFDIM has a good performance on describing the dynamic variation regularity of passenger flow distribution. Moreover, the experiments with different departure intervals could provide the reference for train scheduling under the viewpoint of traffic safety and service level.

The global spread of coronavirus disease (COVID -19) worldwide has had a significant effect on social and economic growth. The contamination keeps on advancing quickly and eccentrically, representing a significant test to its recognition and conclusion. Coronaviruses are commonly recognized by seclusion from tests, regardless of whether natural or clinical, utilizing some atomic science procedures, which can take a few days. In this work an analytical review of virus transmission, methods of diagnosing COVID -19 using artificial intelligence techniques to classify images and types of biosensors. At long last, the deformities and points of interest of each kind of sensor are recognized and examined. This exploration gives an explanatory audit of the utilization of crown infection COVID-19 in 2019. Related examinations were led utilizing five dependable databases, for example, Science Direct, IEEE Xplore, Scopus, Web of Science, and PubMed. An acceptable investigation is remembered for this audit, which can be depended upon as a logical database to put resources into another technique for recognizing COIVD-19.

Problem solving is one of the most important components of the human cognition that affects for ages the progress of the human society. Mathematical modelling is a special type of problem solving concerning problems related to science or everyday life situations. The present study is a review of the author’s earlier works on problem solving and mathematical modelling from the scope of Education. Its real goal is that it presents in a systematic way and in a few pages only the results of many years research on the subject. This helps the reader to get a comprehensive idea about a very important topic belonging to the core of Mathematics Education, which is very useful to those wanting to study deeper the subject and get directions for further research in the area.

Indonesia is among the countries developing the concept of smart city. The national government envisions Indonesia 2030 which shall implement the smart city towards sustainable development. Many regions in Indonesia have already implemented the concept of smart city, but still on the level of smart government and smart technology, lacking on the smart citizen component of a smart city. This paper aims to analyze the citizens behavior of using technology on Sleman smart citizen 2021 through the lapor sleman mobile application. This research uses mixed method of quantitative and qualitative approaches in examining the data. The authors distributed 100 questionares and conducted interviews to the government which is department of communication and information, Sleman and the several community that ever use the lapor sleman application. The result of the study reveals the following: firstly, performance expectancy, social influence, and behavioral intention are three independent variables which affect the users’ behavior. Secondly, the behavioral intention has the biggest significant effect on users’ behavior. Thirdly, the citizen behavior of using technology has affect the making a smart citizen. Therefore, using technology in term of lapor sleman mobile application can make the citizens as active participants in public life, in terms of social cooperation, freedom of expression and flexibility. However, there are still many problems in the use of lapor sleman mobile application such as the limited access to only android smartphones, lack of technical know-how, and people’s reluctance to use the lapor sleman mobile application.

Building Information Modelling (BIM) has been more popular in the construction industry as a consequence of the many advantages it offers, such as increased productivity, improved project outcomes, and reduced expenses. However, there are many obstacles that must be overcome before BIM can be widely used. This research uses a poll of construction industry experts to investigate the challenges associated with using BIM in projects. Cost of implementation, inadequate standards and procedures, and a lack of knowledge with BIM were some of the hurdles that were measured in the study. Statistical techniques were used to examine the data, and the findings indicate that the high cost of implementation and the absence of sufficient standards and procedures are the two main obstacles to the widespread adoption of BIM. Problems with supply chain management and a lack of experience with BIM are also mentioned as obstacles. The report also suggests solutions, such as a more focused teaching effort on BIM-related issues and the creation of a specialised BIM curriculum. Professionals in the construction industry may utilise the study’s results to their advantage by expanding their knowledge of the obstacles standing in the way of widespread adoption of BIM.

Summary CoV-GLUE is an online web application for the interpretation and analysis of SARS-CoV-2 virus genome sequences, with a focus on amino acid sequence variation. It is based on the GLUE data-centric bioinformatics environment and provides a browsable database of amino acid replacements and coding region indels that have been observed in sequences from the pandemic. Users may also analyse their own SARS-CoV-2 sequences by submitting them to the web application to receive an interactive report containing visualisations of phylogenetic classification and highlighting genomic variation of potentially high impact, for example linked to primer mismatches.Availability and implementation Available at http://cov-glue.cvr.gla.ac.uk. Implemented using GLUE, an open source framework for the development of virus sequence data resources. Contact josh.singer@glasgow.ac.uk

The present grievous situation of the tuberculosis disease can be improved by efficient case management and timely follow-up evaluations. With the advent of digital technology this can be achieved by quick summarization of the patient-centric data. The aim of our study was to assess the effectiveness of the ODK Scan paper-to-digital system during testing period of three months. A sequential, explanatory mixed-method research approach was employed to elucidate technology use. Training, smartphones, application and 3G enabled SIMs were provided to the four field workers. At the beginning, baseline measures of the data management aspects were recorded and compared with endline measures to see the impact of ODK Scan. Additionally, at the end, users’ feedback was collected regarding app usability, user interface design and workflow changes. 122 patients’ records were retrieved from the server and analysed for quality. It was found that ODK Scan recognized 99.2% of multiple choice bubble responses and 79.4% of numerical digit responses correctly. However, the overall quality of the digital data was decreased in comparison to manually entered data. Using ODK Scan, a significant time reduction is observed in data aggregation and data transfer activities, however, data verification and form filling activities took more time. Interviews revealed that field workers saw value in using ODK Scan, however, they were more concerned about the time consuming aspects of the use of ODK Scan. Therefore, it is concluded that minimal disturbance in the existing workflow, continuous feedback and value additions are the important considerations for the implementing organization to ensure technology adoption and workflow improvements.

Water mist fire suppression technology has attracted an increasing interest from the field of fire protection services such as fire safety for buildings, ships, spacecraft, libraries and museums due to its non-toxic and high efficiency in the suppression of a wide variety of fires. To support the technological development of water mist fire suppression system and its application areas, this review introduces the concept of water mist system and discusses its suppression mechanisms in comparison with other fire protection systems. The recent application areas of water mist system are surveyed for class A fires involving combustible solid materials such as wood, paper and textiles; class B fires involving flammable liquids such as petrol, oils, lubricants, paints and waxes; class C fires involving flammable gasses such as natural gas and liquefied petroleum gas; fires involving electrical (class E) equipment such as computers and information technology facilities; and the class F fires involving flammable cooking oils and fats. Finally, the paper concludes the review by identifying the current research trends, and providing the future direction for water mist technology and applications.

The paper presents a study of two novel visual motion onset stimulus-based brain–computer interfaces (vmoBCI). Two settings are compared with afferent and efferent to a computer screen center motion patterns. Online vmoBCI experiments are conducted in an oddball event–related potential (ERP) paradigm allowing for “aha–responses” decoding in EEG brainwaves. A subsequent stepwise linear discriminant analysis classification (swLDA) classification accuracy comparison is discussed based on two inter–stimulus–interval (ISI) settings of 700 and 150 ms in two online vmoBCI applications with six and eight command settings. A research hypothesis of classification accuracy non–significant differences with various ISIs is confirmed based on the two settings of 700 ms and 150 ms, as well as with various numbers of ERP response averaging scenarios.The efferent in respect to display center visual motion patterns allowed for a faster interfacing and thus they are recommended as more suitable for the no–eye–movements requiring visual BCIs.

Self-assembled structures mostly arises through enzyme-regulated phenomena in nature under persistent conditions. Enzymatic reactions are one of main biological processes in fabrication and construction of supramolecular hydrogel networks required for biomedical applications. The enzymatic processes provide a unique opportunity to integrate hydrogel formation. In most of cases, structure and substrates of hydrogels are adjusted by enzyme catalysis due to the chemo-, regio- and stereo-selectivity of enzymes. Hydrogels processed by using various enzyme schemes showed remarkable characteristics as dynamic frames for cells, bioactive molecules and drugs in biomedical applications. A novel class of enzyme-mediated crosslinking hydrogels mimics the extracellular matrices by displaying unique physicochemical properties and functionalities like water-retention capacity, drug loading ability, biodegradability, biocompatibility, biostability, bioactivity, optoelectronic properties, self-healing ability, shape memory ability. In recent years, many enzymatic systems investigated hydrogel cross-linking. Results of biocompatible hydrogel products show that these mechanisms of crosslinking can fulfill requirements for variety of biomedical applications including tissue engineering, wound healing and drug delivery.

The way people innovate and create new ideas and bring them to the market is undergoing a fundamental change from closed innovation to open innovation. Why and how do firms perform open innovation? Firms’ open innovation is measured through the levels of firms’ joint patent applications. Next, we analyze network structures and characters of firms’ joint patent applications such as betweenness and degree centrality, structure hole, and closure. From this research, we drew conclusions as follows. First, the structure of collaboration networks has both direct and indirect effects on firms’ innovative performance. Second, in the process of joint patent applications, there is a long tail phenomenon in networks of joint patent applications. Third, the number of patents and International Patent Classification (IPC) subclasses together constitute a meaningful measure of the innovation performance of firms.

In the paper, the authors survey integral representations of the Catalan numbers and the Catalan–Qi function, discuss equivalent relations between these integral representations, supply alternative and new proofs of several integral representations, collect applications of some integral representations, and present sums of several power series whose coefficients involve the Catalan numbers.

Cloud computing refers to applications delivered as services over the Internet. Cloud systems employ policies that are inherently dynamic in nature and that depend on temporal conditions defined in terms of external events, such as the measurement of bandwidth, use of hosts, intrusion detection or specific time events. In this paper, we investigate an optimized resource management scheme named v-Mapper. The basic premise of v-Mapper is to exploit application-awareness concepts using software-defined networking (SDN) features. This paper makes three key contributions to the field: (1) We propose a virtual machine (VM) placement scheme that can effectively mitigate the VM placement issues for data-intensive applications; (2) We propose a validation scheme that will ensure that a service is entertained only if there are sufficient resources available for its execution and (3) We present a scheduling policy that aims to eliminate network load constraints. An evaluation was carried out with various benchmarks and demonstrated that v-Mapper shows improved performance over other state-of-the-art approaches in terms of average task completion time, service delay time and bandwidth utilization. Given the growing importance of supporting large-scale data processing and analysis in datacentres, the v-Mapper system has the potential to make a positive impact in improving datacentre performance in the future.

This article presents a self balanced multistage DC-DC step-up converter for photovoltaic applications. Proposed converter topology is designed for unidirectional power transfer and provides a doable solution for photovoltaic applications where voltage is required to be stepped up without magnetic components (Transformer-less and Inductor-less). The output voltage obtained from renewable sources will be low and must be stepped up by using a DC-DC converter for photovoltaic applications. K diodes and K capacitors along with two semiconductor control switch are used in the K-stage proposed converter to obtain an output voltage which is (K+1) times the input voltage. The conspicuous features of proposed topology are i) Magnetic components free (Transformer-less and Inductor-less). ii) Continuous input current iii) Low voltage rating semiconductor devices and capacitors iv) Modularity v) Easy to add a higher number of levels to increase voltage gain vi) Only two control switches with alternating operation and simple control. The proposed converter is compared with recent existing transformer-less and Inductor-less power converter in term of voltage gain, number of devices and cost. The application of proposed circuit is discussed in detail. The proposed converter has been designed with rated power of 60W, input voltage is 24V, output voltage is 100V and switching frequency is 100 kHz. The performance of the converter is verified through experimental and simulation results.

Halloysite nanotubes (HNTs) are natural occurring mineral clay nanotubes that have excellent potential application in different fields. However, HNTs are affected by size, surface electron and hydrogen bond formation on the surface which lead to weak affinity and reunion at certain extent. It is very significant to modify the HNTs’ surface to expand its applications. In this review, the structural characteristics, performance and the related applications of surface modification HNTs are reviewed and summarized. We focus on the surface modify methods of HNTs, the effect of surface modification on materials and related applications in various regions. In addition, future prospects and the meaning of surface modification were also be discussed in HNTs studies. This review provided a reference for the application of HNTs modifications in new nanomaterials fields.

The Android-based IoT platform just like the existing Android provides an environment that makes it easy to utilize Google's infrastructure services including development tools and APIs through which it helps to control the sensors of IoT devices. Applications running on the Android-based IoT platform are often UI free and are used without the user’s consent to registered permissions. It is difficult to respond to the misuse of permissions as well as to check them when they are registered indiscriminately while updating applications. This paper analyzes the versions of before and after an application the update running on the Android-based IoT platform and the collected permission lists. It aims to identify the same permissions before and after the update, and deleted and newly added permissions after the update were identified, and thereby respond to security threats that can arise from the permissions that is not needed for IoT devices to perform certain functions.

Web-Based GIS is a platform that deals with the representation, management and storage of locational information for real-time online retrieval, viewing and manipulations. Web-based GIS incorporate all the functions of information systems: gathering, storing, retrieving, visualizing and dissemination of spatial information on the Internet. In modern-day GIS serve as vital means to not only geo-referenced locational information that are been retrieved but also the development in web-based technologies as well as there involvements in decision making processes. In recent time the increased used of GIS on the Internet have totally transformed and made three distinct changes across our GIS environment. These are: (i) how are GIS data being accessed (ii) what are modes of its transmission and (iii) what available GIS functionalities are within the environment for analysis. This paper examines the applications and technologies associated with web-based GIS architectures with an emphasis on the internet GIS based structure and case studies of GIS Web-based applications in MapInfo, Microsoft Silverlight Technology and the ArcGIS Server using ArcGIS JavaScript API.

In the paper, the authors survey integral representations (including the Lévy--Khintchine representations) and applications of some bivariate means (including the logarithmic mean, the identric mean, Stolarsky's mean, the harmonic mean, the (weighted) geometric means and their reciprocals, and the Toader--Qi mean) and the multivariate (weighted) geometric means and their reciprocals, derive integral representations of bivariate complex geometric mean and its reciprocal, and apply these newly-derived integral representations to establish integral representations of Heronian mean of power 2 and its reciprocal.

Location-Based Services (LBS) have been widely deployed for the connected vehicle (CV) applications such as vehicle navigation,vehicle tracking and location-based augmented reality. The current LBS deployments have limitations in supporting time-critical CV use cases, including vehicle to vehicle (V2V), vehicle to infrastructure (V2I) and vehicle-to-people (V2P) safety applications. The paper presents the new LBS framework based on the publish-subscribe communication paradigm, to enable device-to-device (D2D) connections through use of selected application protocols in the application layer of the TCP/IP layered protocol model. Two publish-subscribe application protocols, Distributed Data Service (DDS) real-time publish and subscribe (DDS-RTPS) and Message Queue Telemetry Transport (MQTT), are introduced to support the LBS D2D applications. A number of test scenarios with Mosquitto MQTT and OpenDDS under 4G-mobile broadband (MBB) services are designed to assess the transmit/receive round-trip time (RTT) and packet-loss rate (PLR) with settings of a publisher to multiple subscribers, to simulate the connections to multiple vehicles. The transmission frequency is set for 10 Hz and the message sizes vary from 100 to 2000 Bytes. The PLRs are defined as the percentages of the delayed messages beyond a delay limit. Static test results with OpenDDS show that for the RTT delay beyond the limit of 100 ms, the total PLRs range between 5.25% and 8.76% for the message size of 50 to 2000 Bytes. Vehicle testing results with Mosquitto show that PLRs for the RTT delays between 200 ms and 1000 ms are 0.63%, 3.58% and 5.77%, for connections with 1, 4 and 10 vehicles, respectively. The results demonstrate the potential of the D2D LBS framework for medium-demanding CV safety applications such as V2P and V2I use cases, taking advantages of the 4G-MBB services and 5G extreme mobile broadband (eMBB) services and mobile devices generally available with all road users.

In the paper, by virtue of a Lévy–Khintchine representation and an alternative integral representation for the weighted geometric mean, the authors establish a Lévy–Khintchine representation and an alternative integral representation for the Toader–Qi mean. Moreover, the authors also collect an probabilistic interpretation and applications in engineering of the Toader–Qi mean.

As the geolocation capabilities of smartphones continue to improve, developers have continued to create more innovative applications that rely on this location information for their primary function. This can be seen with Niantic's release of Pokémon GO, which is a massively multiplayer online role playing and augmented reality game. This game became immensely popular within just a few days of its release. However, it also had the propensity to be a distraction to drivers resulting in numerous accidents, and was used to as a tool by armed robbers to lure unsuspecting users into secluded areas. This facilitates a need for forensic investigators to be able to analyze the data within the application in order to determine if it may have been involved in these incidents. Because this application is new, limited research has been conducted regarding the artifacts that can be recovered from the application. In this paper, we aim to fill the gaps within the current research by assessing what forensically relevant information may be recovered from the application, and understanding the circumstances behind the creation of this information. Our research focuses primarily on the artifacts generated by the Upsight analytics platform, those contained within the bundles directory, and the Pokémon Go Plus accessory. Moreover, we present our new application specific analysis tool that is capable of extracting forensic artifacts from a backup of the Android application, and presenting them to an investigator in an easily readable format. This analysis tool exceeds the capabilities of UFED Physical Analyzer in processing Pokémon GO application data.

Early reports demonstrated the presence of cells with stem-like properties in bone marrow, with these cells having both hematopoietic and mesenchymal lineages. Over the years, various investigations have purified and characterized mesenchymal stromal/stem cells (MSCs) from different human tissues as cells with multi-lineage differentiation potential under the appropriate conditions. Due to their appealing characteristics and potential, MSCs are leveraged in many applications including medicine, oncology, bioprinting and as recent as treatment of COVID-19. To date, reports indicate mesenchymal stromal/stem cells have varied differentiation capabilities into different cell types and demonstrate immunomodulating and anti-inflammatory properties. Reports indicate that different MSCs microenvironments or niche and the resulting heterogeneity may influence their behavior and differentiation capacity. The potential clinical applications of mesenchymal stromal/stem cells have led to an avalanche of research reports on their properties and hundreds of clinical trials being undertaken. The future looks bright and promising for mesenchymal stem cell research with many clinical trials under way to prove their utility in many applications and in the clinic. This report provides an update on the potential broader use of mesenchymal stromal/stem cells, review early observations of the presence of these cells in the bone marrow and their magnificent differentiation capabilities and immunomodulation.

We replace the current industry-standard empirical forecasts of oil production from hydrofractured horizontal wells in shales with a statistically and physically robust, accurate and precise approach, using the Bakken shale as an illustration. The proposed oil production forecasting method extends our previous work on predicting fieldwide gas production in the Barnett shale and merges it with our new scaling of oil production in shales. We first divide the existing 14,678 horizontal oil wells in the Bakken into 12 static samples in which depositional settings and completion technologies are similar. For each sample, we construct a purely data-driven P50 well prototype by merging the GEV distribution fits of oil production from appropriate well cohorts. We fit the parameters of our physics-based scaling curve to the statistical well prototypes, and obtain their smooth extrapolations to 30 years on production. By calculating the number of potential wells of each Bakken region, and scheduling future drilling programs, we stack up the extended well prototypes to achieve the most plausible forecast. We predict that Bakken will ultimately produce 5 billion barrels of oil from the existing wells, with the possible increments of 2 and 6 billion barrels from core and noncore areas.

Autonomous and cooperative vehicle systems represent a key priority in the automotive realm. Networked driving simulation can be utilized as a safe, cost-effective experimental replica of real traffic environments in order to support and accelerate the development of such systems. In networked driving simulation, different independent systems collaborate to achieve a common task: multi-driver traffic scenario simulation. Yet distinct system complexity levels are necessary to fulfill the requirements of various application scenarios, such as development of vehicle systems, analysis of driving behavior, and training of drivers. With myriad alternatives of available systems and components, developers of networked driving simulation are typically confronted with high design complexity. There are no systematic approaches to date for the design of networked driving simulation in accordance with the specific requirements of the concerned application scenarios. This paper presents a novel design method for networked driving simulation. The method consists mainly of a procedure model that is accompanied by a configuration software. The procedure model includes the necessary phases for the systematic design of application-oriented platforms for networked driving simulation. The configuration software embeds supportive decision-making processes that enable developers to apply the design method and easily create different system models. The design method was validated by generating system models and developing platforms of networked driving simulation for three different application scenarios.

On estimation scales ranging from 0.1 nm to 100 nm, the nanoscale is part of the capacitance components of the physical-synthetic and natural environment. Dimensionality, morphology, structure, uniformity, and agglomeration are all used to classify nanoparticles. Its functionality and effect on the environment and species are influenced by its shape and morphology. The priority research is to determine the effects of nanoparticles on any biological entity that is necessary when designing nanotechnology-based biotechnological and biomedical products. Bacteria have a remarkable ability to reduce metal ions, making them one of the most promising candidates for nanoparticle biosynthesis. Nanoparticles have been researched in the biomedical field for antimicrobial, biosensor, diagnostic imaging, and drug delivery applications. These natural technologies appear to be capable of producing stable nanoparticles with well-defined dimensions, morphologies, and compositions by optimizing reaction conditions and selecting the best bacteria. This work includes a list of the most commonly used microorganisms and associated Nanoparticles, as well as a discussion of current biotechnology and biomedical developments.

In the recent concrete industry, high fluidity concrete is being widely used for the pouring of dense reinforced concrete. Normally, in the case of high fluidity concrete, it includes high binder contents, so it is necessary to replace part of the cement through admixtures such as fly ash to procure economic feasibility and durability. This study shows the mechanical properties and field applicability of high fluidity concrete that using mass of fly ash as alternative materials of cement. The high fluidity concrete mixed with 50% fly ash was measured to manufacture concrete that applies low water/binder ratio to measure the mechanical characteristics as compressive strength and elastic modulus. Also, in order to evaluate the field applicability, high fluidity concrete containing high volume fly ash was evaluated that fluidity, compressive strength, heat of hydration and drying shrinkage of concrete.

The increased availability of high resolution remote sensor data for precision agriculture 1 applications permits users to aquire deeper and more relevant knowledge about crops states that lead 2 inevitably to better decisions. The algorithm libraries being developed and evolved around these 3 applications rely on multi-spectral or hyper-spectral data acquired by using manned or unmanned 4 platforms. The current state of the art makes thorough use of vegetational indicies to guide the 5 operational management of agricultural land plots. One of the most challenging sub-problems is 6 to correctly identify and separate crop from soil. Thresholding techniques based on Normalized 7 Difference Vegetation Index (NDVI) or other such similar metrics have the advantage of being simple, 8 easy to read transformations of the data packed with useful information. Obvious difficulties arise 9 when crop/tree and soil have similar spectral responses as in case of grass filled areas in vineyards. 10 In this case grass and canopy are close in terms of NDVI values and thresholding techniques will 11 generally fail. Radiometric approaches could be integrated or replaced by a geometric approach that 12 is based on terrain data like Digital Surface Models (DSMs). These models are one of the ouputs 13 of orthorectification engines usually present in data acquired by using unmanned platforms. In 14 this paper we present two approaches based on DSM that are able to segment crop/tree from soil 15 while over gradient terrain. The DSM data are processed through a two dimensional data slicing or 16 reduction technique. Each slice is separately processed as a one dimensional time series to derive the 17 terrain and tree structures separately, here interpreted as object probability densities. In particular 18 the first approach is a Cartesian grid rasterization (CARSCAN) of the terrain and the second is its 19 immediate generalisation or radial grid rasterization of the DSM model (FANSCAN). The FANSCAN 20 recovers information from the original image at greater frequencies on the Fourier plane. These 21 approaches enable the identification of crop/tree from soil in case of slopes or hilly terrain without 22 any constraint on the displacement / direction of plant/tree row. The proposed algorithm uses pure 23 DSM information even if it is possible to fuse its output with other classifiers.

This paper proposes several LabVIEW applications to accomplish the data acquisition, processing, features extraction and real-time classification of the electroencephalographic (EEG) signal detected by the embedded sensor of the NeuroSky Mindwave Mobile headset. The LabVIEW applications are aimed at the implementation of a Brain-Computer Interface system, which is necessary to people with neuromotor disabilities. It is analyzed a novel approach regarding the preparation and automatic generation of the EEG dataset by identifying the most relevant multiple mixtures between selected EEG rhythms (both time and frequency domains of raw signal, delta, theta, alpha, beta, gamma) and extracted statistical features (mean, median, standard deviation, route mean square, Kurtosis coefficient and others). The acquired raw EEG signal is processed and segmented into temporal sequences corresponding to the detection of the multiple voluntary eye-blinks EEG patterns. The main LabVIEW application accomplished the optimal real-time artificial neural networks techniques for the classification of the EEG temporal sequences corresponding to the four states: 0 - No Eye-Blink Detected; 1 - One Eye-Blink Detected; 2 – Two Eye-Blinks Detected and 3 – Three Eye-Blinks Detected. Nevertheless, the application can be used to classify other EEG patterns corresponding to different cognitive tasks, since the whole functionality and working principle could estimate the labels associated with various classes.

Background: The efficacy of sunscreen is evaluated by SPF values, which are quantitatively determined in laboratories on the backs of human subjects according to a standardized procedure. However, SPF cannot be directly translated to sunburn protection under real-life situations because actual efficacy depends on various factors related to human behaviors and environmental conditions. This study clinically evaluated the efficacy of two sunscreen sprays (SPF 30 and SPF 70) under natural sunlight exposure on healthy subjects at the beach. Methods: Twenty subjects were divided into two cells for the two sunscreen sprays (SPF 70 and SPF 30) in a single-center, actual usage test. The primary endpoint of the study was sunburn protection on the dorsal arms and the secondary endpoint was protection on the face and neck. Subjects stayed at the beach for 4 h after application of the sunscreens with normal beach activities. Subjects’ behavior at the beach, the amounts of sunscreen applied and reapplied, and environmental conditions were all recorded. Results: There was no significant sunburn for a majority of the subjects in either cell. However, neither sunscreen completely blocked the sunburn, especially for the face/neck area. We found that the SPF 70 sunscreen was more effective than the SPF 30 sunscreen. Conclusion: Modern sunscreen sprays, applied liberally, are effective in providing sunburn protection for the body in a beach setting.

This research proposed in this paper focuses on gathering evidence from devices with Windows 10 operating systems in order to discover and collect artifacts left by cloud storage applications that suggest their use even after the deletion of the Google client application. We show where and what type of data remnants can be found using our analysis which can be used as evidence in a digital forensic investigations.

Multi-floor environments are usually ignored while designing an autonomous robot for indoor cleaning applications. However, for efficient operation in such environments, the ability of a robotic platform to traverse staircases is crucial. Staircase detection and localization is highly important for planning the traversal on staircases. This paper describes a deep learning approach using Convolutional Neural Networks (CNNs) based Robot Operation System (ROS) to staircase detection and localization. We use an object detection network to detect staircases in images. We also localize these staircases using a contour detection algorithm to detect the target point, a point close to the center of the first step, and the angle of approach to the target point. Experiments are performed with data obtained from images captured on different types of staircases at different view points/angles. Results show that the approach is very accurate in identifying the presence of the staircase in the working environment and is also able to locate the target point with good accuracy.

A copula is a useful tool for constructing bivariate and/or multivariate distributions. In this article, we consider a new modified class of (Farlie-Gumbel-Morgenstern) FGM bivariate copula for constructing several dierent bivariate Kumaraswamy type copulas and discuss their structural properties, including dependence structures. It is established that construction of bivariate distributions by this method allows for greater flexibility in the values of Spearman's correlation coefficient rho,  and Kendall's tau . For illustrative purposes, one representative data set is utilized to exhibit the applicability of these proposed bivariate copula models.

Regardless of country and age, the importance of history education is always being emphasized. Although the importance of history education is being emphasized in Korea, there are many difficulties in getting students to understand history properly through school classes alone, and it is also difficult to attract students to participate in classes. The effectiveness of education using games has been proven 20 years ago, and the demand for game-based education is gradually increasing in the current education world, which is becoming more open. In this paper, based on the effects proven through research on the existing game-based education, the improvement of historical thinking ability, experiential history learning, and the problems of game-based education introduced in the ESN report and the discomfort of teachers who participated in the education were improved. A plan was suggested to select and use games suitable for basic education. In this thesis, we selected a history game with a clear historical and periodic background and without distortion of history, and experimented with teaching using games focusing on historical thinking and empirical history learning. The learning achievement of textbook-based education was compared.

General Variable Neighborhood Search (GVNS) is a well known and widely used metaheuristic for efficiently solving many NP-hard combinatorial optimization problems. Quantum General Variable Neighborhood Search (qGVNS) is a novel, quantum inspired extension of the conventional GVNS. Its quantum nature derives from the fact that it takes advantage and incorporates tools and techniques from the field of quantum computation. Travelling Salesman Problem (TSP) is a well known NP-Hard problem which has broadly been used for modelling many real life routing cases. As a consequence, TSP can be used as a basis for modelling and finding routes for Geographical Systems (GPS). In this paper, we examine the potential use of this method for the GPS system of garbage trucks. Specifically, we provide a thorough presentation of our method accompanied with extensive computational results. The experimental data accumulated on a plethora of symmetric TSP instances (symmetric in order to faithfully simulate GPS problems), which are shown in a series of figures and tables, allow us to conclude that the novel qGVNS algorithm can provide an efficient solution for this type of geographical problems.

Various tissue resident stem cells are receiving attention from basic scientists and clinicians as they hold certain promise for regenerative medicine. This paper is intended to clarify and facilitate the understanding, development and adoption of regenerative medicine in general and specifically of therapies based on unmodified, autologous adipose-derived regenerative cells (UA-ADRCs). To this end, results of landmark experiments on stem cells and stem cell therapy performed in the labs of the authors are summarized, the most intriguing of which are the following: (i) vascular associated mesenchymal stem cells (MSCs) can be isolated from different organs (adipose tissue, heart, skin, bone marrow and skeletal muscle) and differentiated into ectoderm, mesoderm and endoderm, providing significant support for the hypothesis of the existence of a small, ubiquitously distributed, universal vascular associated stem cell with full pluripotency; (ii) the orientation and differentiation of MSCs are driven by signals of the respective microenvironment; and (iii) these stem cells irrespective of the tissue origin exhibit full pluripotent differentiation potential without any prior genetic modification or the need for culturing. They can be obtained from a small amount of adipose tissue when using the appropriate technology for isolating the cells, and can be harvested from and re-applied to the same patient at the point of care without the need for complicated processing, manipulation, culturing, expensive equipment, or repeat interventions. These findings demonstrate the potential of UA-ADRCs for triggering the development of an entire new generation of medicine for the benefit of patients and of healthcare systems.

Ti64 alloy plays a significant role in the biomedical applications such as bioimplants for its excellent biocompatibility. Its usage can be further extended by improving the surface hardness and wear resistance. In this respect, laser surface glazing (LSG), an advanced surface modification technique, is very useful which can produce thin hardened surface layer and strong metallurgical bonding. Investigation of temporal and spatial temperature distributions of laser glazed surface of materials are essential because temperature plays significant role in achieving required surface properties. Therefore, in this study, a 3D Finite element analysis has been developed to perform transient thermal analysis of LSG for Ti64 alloy. The model investigated temperature distribution, depth of modified zone and heating and cooling. The results show that the peak temperature is attained 2095 K for 300 W laser power, 0.2 mm beam width and 0.15 ms residence time. Since this temperature is above the melting point (1933 K) of Ti64 alloy, the melt depth is calculated 22.5 μm. Furthermore, from the simulation results, the average heating and cooling rates are estimated 1.19×10⁷ Ks^-1 and 2.71×10⁶ Ks^-1 respectively which indicate the presence of hard phases in the modified zone.

The energy crisis and environmental problems are becoming more and more severe due to the long-term consumption of fossil fuels. Therefore, energy conversion devices with high energy density and environmental friendliness (such as fuel cells, metal-air batteries, etc.) have been ex-pected to be reliable alternatives to traditional fossil energy. However, due to the inevitable use of precious metals as the electrode catalysts for these devices, the popularization of these alternatives is seriously hindered. Transition metal nitrides (TMNs) exhibit similar surface and adsorption properties to noble metals since the atomic distance between metal atoms increases and the d-band center of metal atoms downshift after the nitrogen atoms enter the metal lattice. TMNs have become one of the best electrode materials to replace noble metal electrocatalysts in energy storage and conversion devices. In this review, the latest development in the electrocatalytic ap-plication of TMNs nanocrystalline is covered. Firstly, we briefly discuss the structure and activity origin of TMNs and introduce the common physical and chemical methods for the preparation of TMNs. Subsequently, we illustrate the applications of unary TMNs and multivariate TMNs in oxygen reduction reaction (ORR) and oxygen evolution reaction (OER). Finally, we summarize the challenges and problems of TMNs encountered at the present stage, and expect its future de-velopment.

A huge number of applications available for Android-based smartphone devices have emerged over the past years. Due to which a huge number of malicious applications has been growing explosively. Many approaches have been proposed to ensure the security and quality of application in the markets. Usually, Machine Learning approaches are utilized in the classification process of malicious application detection. Calculating accurate results of characterizing applications behaviors, or other features, has a direct effect on the results with Machine Learning calculations. Android applications emerge so quickly. The behavior of current applications has gotten progressively malicious. The extraction of malware-infected features from applications is thus become a difficult task. According to our knowledge, a ton of features have been extricated in existing work however no survey has overviewed the features built for identifying malicious applications efficiently. In this paper, we will in general give an extensive review of such sort of work that identifies feature applications by describing various practices of uses with various kinds of features. In this survey we have discussed the following dimensions: extraction and selection of feature methods if any, methods of detection and evaluation performed. In light of our review, we notice the issues of investigating malware-affected features from applications, give the scientific categorization and demonstrate the future headings.

This paper presents a comprehensive analysis of the scholarly footprint of ChatGPT, an AI language model, using bibliometric and scientometric methods. The study aims to understand the evolution of research output, citation patterns, collaborative networks, application domains, and future research directions related to ChatGPT. By analyzing data from the Scopus database, 533 relevant articles were identified for analysis. The findings reveal the prominent publication venues, influential authors, and countries contributing to ChatGPT research. Collaborative networks among researchers and institutions are visualized, highlighting patterns of co-authorship. The application domains of ChatGPT, such as customer support and content generation, are examined. Moreover, the study identifies emerging keywords and potential research areas for future exploration. The methodology employed includes data extraction, bibliometric analysis using various indicators, and visualization techniques such as Sankey diagrams. The analysis provides valuable insights into ChatGPT's influence in academia and offers researchers guidance for further advancements. This study stimulates discussions, collaborations and innovations to enhance ChatGPT's capabilities and impact across domains.

It is since long known, that SAW devices, resonators as well as delay lines, can be used as passive wireless sensors for physical quantities like temperature and pressure as well as gas sensors or ID-Tags. The sensors are robust, work passively without battery, can be applied at high temperatures and provide a high resolution. Nevertheless, if the devices should be readout wirelessly in an industrial environment, several constraints have to be taken into account, especially when more than one quantity or device needs to be measured at the same time. The paper addresses the challenges that have to be tackled when establishing multi-sensor-wireless-readout for industrial applications. Major issues here are the legal ISM-band regulations, as well as sampling time and costs, which impose severe restrictions to any system design. We describe several design approaches and their constraints. We have successfully designed sensors based on reflective delay lines that allow the parallel readout of four independent temperature sensors in the 2.45 GHz ISM-band. These devices have been fabricated, positively tested and demonstrate the applicability of SAW sensors for industrial applications.

The morbidity and mortality caused by invasive fungal infections is increasing across the globe due to developments in transplant surgery, the use of immunosuppressive agents, and the emergence of drug-resistant fungal strains, which has led to a challenge in terms of treatment due to the limitations of three classes of drugs. Hence, it is imperative to establish effective strategies to identify and design new antifungal drugs. Drug repurposing is an effective way of expanding the application of existing drugs. In the last years, various existing drugs have been shown to be useful in the prevention and treatment of the invasive fungi. In this review, we summarize the currently used antifungal agents. In addition, the most up to date information on the effectiveness of existing drugs with antifungal activity is discussed. Moreover, the antifungal mechanisms of existing drugs are highlighted. These data will provide valuable knowledge to stimulate further investigation and clinical application in this field.

This work explores an additive-manufacturing-enabled combination-of-function approach for design of modular products. AM technologies allow the design and manufacturing of nearly free-form geometry, which can be used to create more complex, multi-function or multi-feature parts. The approach presented here replaces sub-assemblies within a modular product or system with more complex single parts that are designed and manufactured using AM technologies. This approach can increase the reliability of systems and products by reducing the number of interfaces, as well as allowing the optimization of the more complex parts during the design. The smaller part count and the ability of users to replace or upgrade the system or product parts on-demand should reduce user risk, life-cycle costs, and prevent obsolescence for the user of many systems. This study presents a detailed review on the current state-of-the-art in modular product design in order to demonstrate the place, need and usefulness of this AM-enabled method for systems and products that could benefit from it. A detailed case study is developed and presented to demonstrate the concepts.

Comments are exploited by product vendors to measure satisfaction of consumers. With the advent of Natural Language Processing (NLP), comments on Google Play can be processed to extract knowledge on applications such as their reputation. Proposals in that direction are either informal or interested merely on functionality. Unlike, this work aims to determine reputation of Android applications in terms of confidentiality, integrity, availability and authentication (CIAA). This work proposes a model of assessing app reputation relying on sentiment analysis and text analysis of comments. While assuming that comments are reliable, we collect Google Play applications subject to comments which include security keywords. An in-depth analysis of keywords based on Naive Bayes classification is made to provide polarity of any comment. Based on comment polarity, reputation is evaluated for the whole application. Experiments made on real applications including dozens to billions of comments, reveal that developers lack to make efforts to guarantee CIAA services. A fine-grained analysis shows that not security reputed applications can be reputed in specific CIAA services. Results also show that applications with negative security polarities display in general positive functional polarities. This result suggests that security checking should include careful comment analysis to improve security of applications.

The continued application of organochlorine pesticides (OCPs) in indoor residual spraying has posed significant threat to human health in Northern part of South Africa, despite its ban. In this study, we investigated the occurrence and spatial distribution of DDTs and its metabolites in surface soil (30 samples) collected in and around the spray homesteads; demarcated into three concentric zones A, B, C in Tshilamusi Vhembe district, South Africa. DDTs were the most abundant of all the OCPs chemicals found in soil samples. The concentrations of DDT range from 12.19 to 65.69 µg/kg, with the highest occurring at zone A which is the zone of application. DDTs and all its metabolites considered in this study were found in appreciable concentrations in soil of Zones B and C, far from spray sites. The results showed a strong correlation between distance and concentration levels of DDTs and its metabolites. However, there is a need for proper monitoring of OCPs chemicals in other environmental matrices as well as relevant agencies enforcing strict adherence to regulations on consumption of OCPs.

A graphene-containing LDH was prepared by re-hydration of the oxides produced by the calcination of an organic LDH. While the memory effect is a widely recognized effect on oxides produced by inorganic LDHs, it is unprecedented from the calcination/re-hydration of organic ones. Different temperatures (400, 600 and 1100 °C) were tested, on the basis of thermogravimetric data. Water instead of a carbonate solution was used for the re-hydration, with CO₂ available from water itself and/or air to induce a slower process with an easier and better intercalation of the carbonaceous species within the layers. The samples were characterized by X-ray Powder Diffraction (XRPD), IR and Raman spectroscopy and scanning electron microscopy (SEM). XRPD indicate the presence of carbonate LDH mixed with a layered phase with a larger d-spacing. IR confirmed that the prevailing anion is carbonate, coming from the water used for the re-hydration and/or air. Raman data indicated the presence of low-ordered graphenic species moieties and SEM the absence of separated graphene of graphitic sheets, suggesting an intimate mixing of the carbonaceous phase with reconstructed LDH. Organic LDHs gave better memory effect after calcination at 400 °C. Conversely, the graphenic species are observed after rehydration of the sample calcined at 600 °C with a reduced memory effect, demonstrating the interference of the carbonaceous phase with LDH reconstruction and the bonding with LDH layers to form a graphene-LDH nanocomposite.

Financial statement analysis is an analysis that uses analytical procedures to evaluate a company's financial health, risks, performance, and future growth potential. This article aims to find out the application of analysis of financial statements of PT. Astra Internasional, Tbk towards the decision-making of novice investors in the Indonesian capital market. This article is qualitative descriptive with literature studies. The results of this article can be concluded that the analysis of the financial statements of PT. Astra Internasional, Tbk can help novice investors in the Indonesian capital market in making investment decisions.

Student attendance serves many other important purposes aside from monitoring. In certain universities, the attendance of students in a course is also used as one of the requirements for students to be allowed to sit for the final examination. Traditionally, among most Malaysian Institutions of Higher Learning (IHL), attendance recording is usually done using pen and paper, or uses simple web-based system that is time consuming and difficult for faculty periodic monitoring. To address the identified drawbacks, this research aims to develop a Smart Attendance for Faculty Monitoring System using the Bluetooth Low Energy (BLE) technology to assist faculty in recording, managing and monitoring students’ attendance and class schedules effectively. The system is developed for Android-based devices using an agile methodology consists of iteration and incremental approaches. Thus, to evaluate the effectiveness of the system, a survey was conducted on 140 respondents involving lecturers and students of Kolej Universiti Poly-Tech MARA (KUPTM). Respondents were selected using purposive sampling. The descriptive analysis showed that 87.9% of the respondents strongly agreed that the system is effective in assisting lecturers to record attendance, manage class schedules and student attendance as well as to assist faculty in monitoring students’ absenteeism.

With the recent developments in augmented reality (AR) technologies comes an increased interest in the use of smart glasses for hands-on training. Whether this interest is turned into market success or not depends at the least on whether the interaction with smart AR glasses satisfies users, an aspect of AR use that so far has received little attention. With this contribution, we seek to change this. The objective of the article, therefore, is to investigate user satisfaction in AR applied to three cases of practical use. User satisfaction of AR can be broken down into satisfaction with the interaction and satisfaction with the delivery device. A total of 142 participants from three different industrial sectors contributed to this study, namely, aeronautics, medicine, and astronautics. In our analysis, we investigated the influence of different factors, such as age, gender, level of education, level of Internet knowledge, and the roles of the participants in the different sectors. Even though users were not familiar with the smart glasses, results show that general computer knowledge has a positive effect on user satisfaction. Further analysis using two-factor interactions shows that there is no significant interaction between the different factors and user satisfaction. The results of the study affirm that the questionnaires developed for user satisfaction of smart glasses and the AR application performed well, but leave room for improvement.

Despite the existence of pasteurization and its advocacy worldwide, most marketed milk in a developing country like Nigeria are sold raw by the locals. It costs hundreds of millions of Naira to set up a conventional pasteurizing plant in Nigeria. Boiled milk ends up curdling which results in to a loss of nutrients, so it is not beneficial to treat milk by this method. This paper hence presents the re-designing and fabrication of the contact area of a solar milk pasteurizer

Blockchain-based currencies or cryptocurrencies have become a global phenomenon known to most people as a disruptive technology, and a new investment vehicle. However, due to their decentralized nature, regulating these markets has presented regulators with difficulties in finding a balance between nurturing innovation, and protecting consumers. The growing concerns about illicit activity have forced regulators to seek new ways of detecting, analyzing, and ultimately policing public blockchain transactions. Extensive research on machine learning, and transaction graph analysis algorithms has been done to track suspicious behaviour. However, having a macro view of a public ledger is equally important before pursuing a more fine-grained analysis. Benford’s law, the law of first digit, has been extensively used as a tool to discover accountant frauds (many other use cases exist). The basic motivation that drove our research presented in this paper was to test he applicability of the well established method to a new domain, in this case the identification of anomalous behavior using Benford’s law conformity test to the cryptocurrency domain. The research focused on transaction values in all major cryptocurrencies. A suitable time-period was identified that was long enough to sport sufficiently large number of observations for Benford’s law conformity tests and was also situated long enough in the past so that the anomalies were identified and well documented. The results show that most of the cryptocurrencies that did not conform to Benford’s law had well documented anomalous incidents, the first digits of aggregated transaction values of all well known cryptocurrency projects were conforming to Benford’s law. Thus the proposed method is applicable to the new domain.

The study aims to investigate Gen Z's intentions to utilize the Waze mobile application as a solution for sustainable traffic management in Meto Manila. The extended Technology Acceptance Model (TAM) was used to define user behavior and technology usage to determine the factors influencing Filipino drivers' application acceptance of Waze. The study’s data results have revealed that the System Quality, Perceived Location Accuracy, Perceived Usefulness, and Perceived Ease of Use affect Filipinos' intentions to use traffic navigation applications, particularly Waze. The study utilized a survey and was given to 300 Filipino drivers traveling within the National Capital Region to accurately measure the factors and their connections to usage intention and actual use. The analytical approach used for this investigation was partial least squares structural equation modeling (PLS-SEM). The study's results can be applied as a theoretical framework for future researchers of Waze mobile applications, Waze route accuracy, and Waze overall application performance, as well as the features of the Waze application, to evaluate how the use of the Waze mobile application could help in the effective management of traffic situations in Metro Manila. The study was able to assess the effectiveness of the Waze navigation app as a platform for efficiently managing traffic situations in Metro Manila. Moreover, this study also highlighted the application performance of Waze and its acceptance by Filipino drivers in Metro Manila. Furthermore, the study implores a critical review of Waze's application performance. The study recommends that similar future studies must consider samples from a more diverse geographic background to obtain a more accurate representation of Filipinos traveling. Future researchers are also advised to adopt a qualitative research design to gain more detailed insights of Filipinos regarding their experience with the Waze application.

This paper proposes a position control method for low cost EGR valve system in automotive application. Generally, position control system using in automotive application has many restrictions such as cost and space, the mechanical structure of actuator implies high friction and large difference between static friction and coulomb friction. This large friction difference occurs the vibrated position control result when the controller uses conventional linear controller such as P, PI. In this paper, low cost position control method which can apply under the condition of high difference friction mechanical system. Proposed method is verified by comparing conventional control result of experiments.

AI is leveraging all aspects of life. Medical services are not untouched. Especially in the field of medical image processing and diagnosis. Big IT and Biotechnology companies are investing millions of dollars in medical and AI research. The recent outbreak of SARS COV-2 gave us a unique opportunity to study for a non interventional and sustainable AI solution. Lung disease remains a major healthcare challenge with high morbidity and mortality worldwide. The predominant lung disease was lung cancer. Until recently, the world has witnessed the global pandemic of COVID19, the Novel coronavirus outbreak. We have experienced how viral infection of lung and heart claimed thousands of lives worldwide. With the unprecedented advancement of Artificial Intelligence in recent years, Machine learning can be used to easily detect and classify medical imagery. It is much faster and most of the time more accurate than human radiologists. Once implemented, it is more cost-effective and time-saving. In our study, we evaluated the efficacy of Microsoft Cognitive Service to detect and classify COVID19 induced pneumonia from other Viral/Bacterial pneumonia based on X-Ray and CT images. We wanted to assess the implication and accuracy of the Automated ML-based Rapid Application Development (RAD) environment in the field of Medical Image diagnosis. This study will better equip us to respond with an ML-based diagnostic Decision Support System(DSS) for a Pandemic situation like COVID19. After optimization, the trained network achieved 96.8% Average Precision which was implemented as a Web Application for consumption. However, the same trained network did not perform like Web Application when ported to Smartphone for Real-time inference, which was our main interest of study. The authors believe, there is scope for further study on this issue. One of the main goals of this study was to develop and evaluate the performance of AI-powered Smartphone-based Real-time Applications. Facilitating primary diagnostic services in less equipped and understaffed rural healthcare centers of the world with unreliable internet service.

Deep learning has shown remarkable results in every field, especially in the biomedical field, due to its ability to exploit large-scale datasets. A convolutional neural network (CNN) is a widely used deep learning approach to solve medical imaging problems. Over the past few years, many studies have focused on CNN-based techniques for brain tumor diagnosis. There are, however, still some critical challenges that CNNs face towards clinic application. This study presents a comprehensive review of current literature that involves CNN architectures for brain tumor classification. We compare the key achievements in the performance evaluation metrics of the applied classification algorithms. In addition, this review assesses the clinical effectiveness of the included studies to elaborate on the limitations and directions of this area for future work. No review focusing on the clinical effectiveness of previous works in this field has been published. We believe that this study has the potential to elevate the application of CNN-based deep learning methods in clinical practice and also can be a quick reference for biomedical researchers who are interested in this field.

Reuse of patterns is a self-evident approach for managing interoperability concerns. Although patterns for resolving interoperability barriers exist in the literature, no study exists on adoption of interoperability patterns by Geographic Information Systems (GIS) practitioners in industry. Thus there is limited understanding of pattern re-usability, yet the advantages offered by interoperability patterns provide a reasonably sound justification for their usage. This paper examines the adoption of proven interoperability best practices in the GIS industry. An empirical study that involved the use of semi-structured interviews was employed to gather data from GIS developers on domain interoperability best practices. Results indicated that industry and communities of practice have been converging on the technical level to ensure interoperability of GIS concerns. Semantic interoperability and related patterns are least understood, yet semantic barriers still exist. This is partly due to the complexity associated with the top-down approach used to develop semantic interoperability solutions. Therefore, this study proposes research into resolving barriers in the adoption of interoperability patterns that reduce complexity while solving semantic interoperability barriers.

Plant proteins being considered to become the most important protein source of the future, they must be able to replace the animal-derived proteins currently in use as technofunctional food ingredients. This poses challenges because plant proteins are oftentimes storage proteins with a high molecular weight and low water solubility. One promising approach to overcome these limitations is the glycation of plant proteins. The covalent bonding between the proteins and different carbohydrates created via the initial stage of the Maillard reaction can improve the technofunctional characteristics of these proteins without the involvement of potentially toxic chemicals. However, compared to studies with animal-derived proteins, glycation studies on plant proteins are currently still underrepresented in literature. This review provides an overview of the existing studies on the glycation of the major groups of plant proteins with different carbohydrates using different preparation methods. Emphasis is put on the reaction conditions used for glycation as well as the modifications to physicochemical properties and technofunctionality. Different applications of these glycated plant proteins in emulsions, foams, films, and encapsulation systems are introduced. Another focus lies on the reaction chemistry of the Maillard reaction and ways to harness it for controlled glycation and to limit the formation of undesired advanced glycation products. Finally, challenges related to the controlled glycation of plant proteins to improve their properties are discussed.

(1) In recent ten years, with the fast development of digital and engineering manufacturing technology, additive manufacturing has already been more and more widely used in the field of dentistry, from the first personalized surgical guides to the latest personalized restoration crowns and root implants. (2) Especially, the bioprinting of teeth and tissue is of great potential to realize organ regeneration and finally improve the life quality. (3) In this review paper, we firstly presented the workflow of additive manufacturing technology. Then we summarized main applications and recent research progresses of additive manufacturing in dentistry. (4) Lastly, we sketched out some challenges and future directions of additive manufacturing technology in dentistry.

The effect of retrogression re-aging treatment (RRA) on corrosion behavior of 7055 Al-Zn-Mg alloy were studied. Results provides the corrosion resistance could be greatly improved by RRA. After the RRA treatment, the isolated precipitates occurred on grain boundaries (GBs) and the low angle grain boundaries (LAGBs) presented a larger fraction compared to the single-stage peaking aging. The samples after RRA treatment also show better corrosion resistance than the single-stage peak aging. The results of electrochemical impedance spectroscopy (EIS) show that impedance spectrum is consisted of semi-infinite layer diffusion impedance and stagnant Weber impedance. The semi-infinite layer diffusion impedance corresponded a limited retention layer on the electrode surface. In corrosion process, Weber impedance corresponded to stagnant layer of corrosion products generated by the anode branches. The RRA sample has the high Rf and low Cf, Cp values, and the corrosion current density of the RRA samples is ten times less than the single-stage peak aging samples with the 10% hardness losing.

This paper presents an Application Based Active Learning (ABAL) methodology on Power Electronics (PE) and Electric Machines (EM) as a hybrid laboratory course for the undergraduate students to design and implement the real-world engineering problems. The ABAL is a type of active learning which is a branch of Learner-centered teaching (LCT). The DC/DC converter along with the speed control of DC separately excites the motor. In addition, a DC/AC converter is designed to control the speed of an induction motor. The results are then investigated on a hardware platform under the ABAL experimental methodology. This paper also discusses the problem identification selection of the equipment, circuit design, hardware mounting and critical analysis of the results acquired from the hybrid laboratory. The ABAL methodology was evaluated based on student satisfaction, feedback, grades and interest to solve the real-world problem rather than cramming the engineering concepts and fulfill so-called lab routine and tasks

Students are often portrayed as future leaders. Their participation in climate change mitigation would improve when they access climate information and gain a high level of climate change awareness. This study was initiated to assess the Congolese students’ awareness of climate change by focusing on their sources of information on climate change, knowledge about the causes and impacts of climate change and activities that can raise awareness on climate change. Using a convenience sampling technique, we collected data through individual interviews conducted among 1,278 students from 13 universities across the Democratic Republic of Congo (DRC). The interview results showed that all students irrespective of their disciplines were concerned about climate change, a phenomenon strongly driven by human activities, such as deforestation (78%), urbanisation (30%), agriculture activities (30%) and transport services (26%). The students’ perceptions of climate change impacts included increase in temperature (82%), decrease in the number of rainy days (66%), proliferation of pests (60%) and increase in the number of malaria patients (39%). The primary sources of information that significantly affected students’ awareness of climate change included environment-related university courses and television broadcasts. The awareness-raising and mitigation actions related to climate change recommended by the students included educating people about good waste management (56%), planting trees (65%) and using the taxes paid by mining companies for the restoration of degraded ecosystems. The students believed that in DRC, all layers of the society (educational institutions, civil society organisations, community members and businesses) are important in building resilience to climate change. This study can guide teachers to focus their educational efforts on shaping pro-environmental behaviour in students.

In this short note, the described step-by-step derivations of the industrial energy demand function from the production function framework and provided researchers with two specifications. Then we applied these theoretical specifications to the time series data as empirical analysis. We concluded that theories should be considered at the beginning of the empirical analyses but the data also should be allowed to speak freely. Hence, the main suggestion of this short note is that it would be a better strategy to consider the combination of theory-driven and data-driven approaches in the empirical analyses.

Social media HPV vaccination interventions show promise for increasing HPV vaccination rates. An important consideration for the implementation of effective interventions into real-world practice in the translation potential, or external validity, of the intervention. To this end, we conducted a systematic literature review to describe the current body of evidence regarding the external validity of social media HPV vaccination-related interventions. Constructs related to external validity were based on the RE-AIM (reach, effectiveness, adoption, implementation, maintenance) framework. Seventeen articles published between 2006 and 2020 met inclusion criteria. Three researchers independently coded each article using a validated RE-AIM (reach, effectiveness/efficacy, adoption, implementation, maintenance) framework. Discrepant codes were discussed with a fourth reviewer to gain consensus. Of these 17 studies, three were pilot efficacy studies, 10 were RCTs to evaluate effectiveness, one was a population-based study, and three did not explicitly state which type of study was conducted. Reflecting this distribution of study types, across all studies the mean level of reporting RE-AIM dimensions varied with reach recording 90.8%, effectiveness (72.1%), adoption (40.3%), implementation (45.6%), and maintenance (26.5%). This review suggests that while the current HPV vaccination social media-driven interventions provide sufficient information on internal validity (reach and effectiveness), few have aimed to gather data on external validity needed to translate the interventions into real world implementation. Our data suggest that implementation research is needed to move HPV vaccination-related interventions into practice. Included in this review are recommendations for enhancing the design and reporting of these HPV vaccination social media-related interventions.

Composite materials are widely used in the dental field in clinics as biomaterials. For example, it has been used as a biomaterial to repair caries and restore masticatory function, and as a cement to adhere the restoration to the tooth substrate. In order to demonstrate its function, dental biomaterials are measured their mechanical strength. From such basic research, we explained the potential of dental biomaterials, especially flexural strength and modulus of elasticity. Mechanical properties of dental biomaterials similar to those of the tooth, thermal stimulation, and aesthetic elements in the oral cavity. In this part, we will introduce the commercialized products of composites suitable for the characteristics and tooth quality, and provide the reader with the characteristics from the flexural characteristics of the composite materials used in clinical dentistry. In clinical performance, it might be advisable to delay polishing when composite biomaterials are used for luting materials, filling materials and core build-up materials since improved the flexural strength and the flexural modulus of elasticity were displayed after 1-day storage. And it is thought that flexural strength or characteristics is a significant important mechanical property of oral biomaterials.

Currently, most middleware application developers have two choices when designing or implementing API services: They can either stick with REST or explore the emerging GraphQL technology. Although REST is widely considered the standard method for API development, GraphQL is believed to overcome the main drawbacks of REST, especially data fetching issues. Yet, doubts remain as there are no experimental studies with convincing results to evaluate the performance of the two services. In this paper, we propose a research methodology to evaluate the performance of REST and GraphQL API services, which includes two main contributions. The first contribution is that the performance evaluation of the two services is conducted in the real operation of a massive and intensively accessible management information system. The second contribution is the fair and independent performance evaluation results obtained for both API services. The performance evaluation was studied using basic measures of QoS, including response time, throughput, CPU load, and memory usage. The results show that REST is still faster in response time and throughput, while GraphQL is very efficient in resource utilization, i.e., CPU load and memory utilization. Therefore, GraphQL is the right choice when data requirements change frequently and resource utilization is the most important consideration. REST is used when some data is frequently accessed called by multiple requests.

Raman Spectroscopy has long been anticipated to augment clinical decision making, such as classifying oncological samples. Unfortunately, the complexity of Raman data has thus far inhibited its routine use in clinical settings. Traditional machine learning models have been used to help exploit this information, but recent advances in deep learning have the potential to improve the field. However, there are a number of potential pitfalls with both traditional and deep learning models. We conduct a literature review to ascertain the recent machine learning methods used to classify cancers using Raman spectral data. We find that while deep learning models are popular, and ostensibly outperform traditional learning models, there are many methodological considerations which may be leading to an over-estimation of performance: primarily, small sample sizes which compound upon sub-optimal choices regarding sampling and validation strategies. Amongst several recommendations is a call to collate large benchmark Raman datasets, similar to those that have helped transform digital pathology, which researchers can use to develop and refine deep learning models.

Regardless of country and age, the importance of history education is always being emphasized. Although the importance of history education is being emphasized in Korea, there are many difficulties in getting students to understand history properly through school classes alone, and it is also difficult to attract students to participate in classes. The effectiveness of education using games has been proven 20 years ago, and the demand for game-based education is gradually increasing in the current education world, which is becoming more open. In this paper, based on the effects proven through research on the existing game-based education, the improvement of historical thinking ability, experiential history learning, and the problems of game-based education introduced in the ESN report and the discomfort of teachers who participated in the education were improved. A plan was suggested to select and use games suitable for basic education. In this thesis, we selected a history game with a clear historical and periodic background and without distortion of history, and experimented with teaching using games focusing on historical thinking and empirical history learning. The learning achievement of textbook-based education was compared.

Liposomes have been used for several decades for the encapsulation of drugs and bioactives in cosmetics and cosmeceuticals. On the other hand, the use of these phospholipid vesicles in food applications is more recent and has been increasing significantly in the last ten years. Although in different stages of technological maturity - in the case of cosmetics, many products are on the market - processes to obtain liposomes suitable for the encapsulation and delivery of bioactives are highly expensive, especially aiming at scaling-up. Among the bioactives proposed for cosmetics and food applications, vitamins are the most frequently used. Despite the differences between the administration routes (oral for food and mainly dermal for cosmetics), some challenges are very similar (e.g., stability, bioactive load, average size, increase of drug bioaccessibility and bioavailability). In the present work, a systematic review of the technological advancements in the nanoencapsulation of vitamins using liposomes and related processes was performed; challenges and future perspectives were also discussed in order to underline the advantages of these drug loaded biocompatible nanocarriers for cosmetics and food applications.

Visualization is an essential tool for analysis of data and communication of findings in the sciences, and the Earth System Science (ESS) are no exception. However, within ESS specialized visualization requirements and data models --- particularly for those data arising from numerical models --- often make general-purpose visualization packages difficult, if not impossible, to effectively use. This paper presents VAPOR: a domain-specific visualization package that targets the specialized needs of ESS modelers, particularly those working in research settings where highly interactive exploratory visualization is beneficial. We specifically describe VAPOR’s ability to handle ESS simulation data from a wide variety of numerical models, as well as a multi-resolution representation that enables interactive visualization on very large data while using only commodity computing resources. We also describe VAPOR’s visualization capabilities, paying particular attention to features for geo-referenced data and advanced rendering algorithms suitable for time-varying, 3D data. Finally, we illustrate VAPOR's utility in the study of a numerically simulated tornado. Our results demonstrate both ease-of-use and the rich capabilities of VAPOR in such a use case.

Gravity played an important role in the development of life on earth. The effect of gravity on a living organisms can be investigated by controlling the magnitude of gravity. Most reduced gravity experiments are conducted on the lower earth orbit (LEO) in the International Space Station (ISS). However, running experiments in ISS face challenges such as high cost, extreme condition, lack of direct accessibility, and long waiting period. Therefore, researchers have developed various ground-based devices and methods to perform reduced gravity experiments. However, the search for life outside the earth requires more intensive research. Advancements in conventional methods and the development of new tools are required for this purpose. The advantages of Lab-on-a-Chip (LOC) devices make them an attractive option for simulating microgravity. This paper briefly reviews the advancement of LOC technologies for simulating microgravity in a earth-based laboratory.

Low soil nitrogen status of savanna soils in Nigeria contributes to the persistent Striga hermonthica (Del.) Benth. infestation that limits maize production. The application of nitrogen fertilizer to Striga-resistant hybrids may reduce Striga infection and increase grain yields. This study assessed the performance of maize hybrids at low (30 kg ha-1) and high (120 kg ha-1) nitrogen application under natural infestation with Striga at Kafin Madaki and Tudun Wada in 2014 and 2015. Results showed that the application of nitrogen at 120 kg ha-1 reduced number of Striga plants by 59% compared to application at 30 kg N ha-1 in Kafin Madaki and by 21% in Tudun Wada. Compared to 30 kg N ha-1, the 120 kg N ha-1 rate also reduced Striga damage rating by 22% in Kafin Madaki and by 33% in Tudun Wada across the hybrids. Hybrids 8338-1 (5.3) and OBASUPER 1 (4.3) were the only entries with Striga damage rating greater than 4.5 (SDR > 4.5) when averaged across the nitrogen levels at both locations. Grain yield was 86 and 98% higher in Kafin Madaki and Tudun Wada, respectively when N was applied at 120 kg N ha-1 than at 30 kg N ha-1. The hybrids M1124-3 and M1227-14 produced grain yields that were significantly higher than those of the other hybrids in all locations. The hybrid 8338-1 produced the lowest grain yield across locations. Our results showed that, the application of 120 kg N ha-1 to Striga resistant maize hybrids will reduce Striga infection and increase grain yield.

This article discuss the estimation of electrical stresses input that is required to determine lifetime degradation of the HV cables installed within overhead transmission lines (OHTL’s). Abort from lightning effects, these electrical stresses are mainly generated due to repetitive switching operation of the transmission line that produces transient overvoltage (TOV) stress and high frequency stress. The transient overvoltage (TOV) due to switching-off the transmission line at the interface points between the cable and OHTL is estimated using artificial neural network (ANN). PSCAD software is used to build the transmission line model for 749 different case in order to build the required database for ANN training. Two different algorithm input to train ANN with one hidden layer and one output layer using different number of nodes are modeled in MATLAB. The results performance is continuously compared and evaluated till acceptable Regression is achieved to insure the error is less than +/-1.5%. The high frequency due to switching-on the transmission line is also recorded and analyzed. The results show that this new method is efficient, accurate and useful as there is no cable monitoring system is available for each HV cable installed within the transmission line.

Optimizing the nitrogen (N) timings and rate can improve nutrient uptake, and nutrient-efficiencies, especially of N in wheat under the changing climate scenario. Climatic stress in the form of high temperature and drought resulted in the decreased crop physiology and, ultimately, grain yield. Taking the example of rainfed wheat, we quantified the impact of N application rates as full and split-dose at three variable sites of rainfed Pothwar, Pakistan by conducting field experiments for two years (2013-14 and 201-15). Treatments include, T₁ = Control (No fertilizer applied), full dose of N applied at the time of crop sowing, i.e. T₂ = 50 kg N ha^-1, T₃ = 100 kg N ha^-1 and T₄ = 150 kg N ha^-1 and split application of N at different timings during different stages of the crop called as split application of N, i.e. T₅: Application of 50 kg N ha^-1 (15 kg N ha^-1 (Sowing) : 20 kg N ha^-1 (Tillering) :15 kg N ha^-1 (Anthesis), T₆: Application of 100 kg N ha^-1 (30 kg N ha^-1 (Sowing): 40 kg N ha^-1 (Tillering) : 30 kg N ha^-1 (Anthesis) and T₇: Application of 150 kg N ha^-1(45 kg N ha^-1 (Sowing) : 60 kg N ha^-1 (Tillering) : 45 kg N ha^-1 (Anthesis). Three study sites include viz. Islamabad (High rainfall with optimum temperature), University Research Farm (URF)-Koont (Medium rainfall with moderate temperature), and Talagang (low rainfall with high temperature). Results showed that the highest stomatal conductance (0.80 mole m^-2 sec^-1), net photosynthetic rate (20.07 μmole m^-2s^-1), transpiration rate (9.58 mmole m^-2s^-1), intercellular CO₂ concentration (329.25 μmole CO₂ mol^-1 air), SPAD values (58.86 %) and proline contents (35.42 μg g^-1) were obtained for split application of N (T₆ = Split N₁₀₀) compared to control and full dose of N treatments. Among sites, these physiological traits remained highest at Islamabad and lowest at Talagang, while among years, maximum values of the measured parameters were obtained in 2013-14. A similar trend was observed for crop total N, N efficiencies, and agronomic traits of the crop. Our results suggest that optimum N application rate and its suitable timings can help to harvest real benefits of N as in our findings, split dose resulted in the maximum performance of the crop from physiological parameters to the agronomic traits of the rainfed wheat crop.

The space shuttle, a revolutionary spacecraft that has played a significant role in human space exploration, was composed of various advanced materials that were carefully selected to meet the extreme demands of spaceflight. This review paper provides a comprehensive examination of the materials historically employed in the construction of space shuttles and explores the latest trends shaping the field. The evolution of space shuttle materials is traced from the inception of the space program to contemporary missions, highlighting key milestones, challenges, and breakthroughs. Emphasis is placed on the critical role that materials play in the overall performance, safety, and sustainability of space shuttles. The paper begins by elucidating the diverse requirements that materials must fulfill in the harsh and complex environment of space, encompassing extreme temperatures, radiation exposure, and mechanical stresses. A detailed analysis of the materials utilized in the fabrication of various shuttle components, such as thermal protection systems, structural elements, and propulsion systems, is presented. Special attention is given to the challenges posed by re-entry and the strategies employed to mitigate heat-related issues. Furthermore, the review explores recent innovations and emerging materials that are reshaping the landscape of space shuttle design. Advancements in nanotechnology, composite materials, and additive manufacturing are discussed in the context of their potential applications for enhancing shuttle performance and reducing mission costs. The paper also addresses the importance of sustainability in space exploration, exploring materials with lower environmental impact and improved recyclability. The review concludes with a forward-looking perspective on the future of materials, considering ongoing research, development, and the potential incorporation of cutting-edge technologies. Insights are provided into how the evolving landscape of materials science may influence the design and manufacturing processes of the next generation of space vehicles. This paper provides an overview of the materials used in the construction of the space shuttle, including their properties, applications, and challenges. The materials used in the space shuttle are critical in ensuring the safety, performance, and longevity of the spacecraft, and understanding their characteristics and performance in space is crucial for the advancement of aerospace engineering.

Background: ED overcrowding is described as one of the main issues in emergency departments (EDs) of any hospital. In Saudi Arabia, the ministry of health applied new telemedicine technology to serve patients by using the mobile application which include Sehha application and 937 medical call center. The main aim of this study is to determine the role of different telemedicine services in reducing non-urgent visits to EDs in Saudi Arabia. Methods: A cross-section study was conducted during August 2020 - May 2021 among 319 patients were using two telemedicine services in Saudi Arabia, including medical call center, and Sehha smart phone application. The primary endpoint of this study aims to determine the role of different telemedicine services in reducing non-urgent visits to EDs in Saudi Arabia. Results: This study analyzed the data from 319 patients who completed the survey provided by the Saudi Ministry of Health that concerning on information related to their health status, and ED visits. Among patients that had the intention to visit the ED (N=159), 53 of them did not go to EDs after using telemedicine services (p-value < 0.01). Regarding medical call center and Sehha application, 9.6% and 24.4%, respectively of the patients used these telemedicine services had changed their mind concerning visiting ED after taking the medical advice (p-values < 0.01).Conclusion: The implemented telemedicine services in Saudi Arabia, namely Sehha application and medical call center showed to be effective in reducing ED overload by providing medical advices to less- and non-urgent patients and deal with their minor medical issues.

An early hypothesis in robot-assisted stroke therapy was that sensorimotor stimulation through robotic assistance is beneficial for recovery. Despite 25 years of upper-limb rehabilitation robotics research, this hypothesis remains untested barring a few studies with small sample sizes. This review aims to provide a critical summary of the current state of this hypothesis by collating evidence from rehabilitation robotics and other related therapeutic approaches. The review starts with a causal model to expose the various direct and indirect routes through which robotic assistance can aid sensorimotor recovery. The indirect routes include the influence of robotic assistance on therapy intensity, patient motivation, and active participation. The direct route is through changes in cortical networks through Hebbian(-like) learning and changes in peripheral tissue properties. There is currently mixed evidence for the direct causal effect of robotic assistance on recovery from the upper-limb rehabilitation robotics literature. However, evidence from the neuromuscular electrical stimulation literature provides some support for this causal role. Based on the data at hand, we hypothesize that the enhanced movement-related sensory feedback from robotic assistance has a direct, possibly small, causal role in the sensorimotor recovery of the upper-limb, and this effect might be detectable with high-intensity therapy. Large, high-intensity, controlled studies are warranted to support or refute the role of robotic assistance on recovery, which is both scientifically and practically important.

Newly characterising 245 Italian and Albanian HCV-2 NS5B sequences collected between 2001 and 2016 was used to reconstruct the origin and dispersion pathways of HCV-2c. The tree of a subset of these sequences aligned with 247 publicly available sequences was reconstructed in spatio-temporal scale using the Bayesian approach, and the effective replication number (Re) was estimated using the birth-death model. Our findings show that HCV-2c was the most prevalent subtype in Italy and Albania, and that GT2 originated in Guinea Bissau in the XVI century and spread to Europe in the XX century. The HCV-2c subtype had two internal nodes respectively dating back to the 1930s and 1950s having as most probable locations Ghana and Italy, respectively. Phylodynamic analysis revealed an exponential increase in the effective number of infections and Re in both Italy between the 1950s and 1980s, and Albania between the 1990s and the early 2000s. It seems very likely that HCV-2c reached Italy from Africa at the time of the second Italian colonisation (1936-1941), but did not reach Albania until the period of dramatic migration to Italy in the 1990s.

The object of this research is designing of new robot-to-robot communication system working in the middle of highway/roads to support mobile safety for approaching vehicles. The result of research project directs to a group of safety robot devices which induce a vehicle on a bypass route, as a vehicle guidance method using the same, and a vehicle safety guidance system. According to an embodiment of the present invention, a safety device includes a detector configured to detect a vehicle approaching the safety device, and an image projector configured to project an image onto a road surface approaching the vehicle when the detector recognizes an approach of the vehicle. It can include. According to the present invention, when it is necessary to guide the vehicle to the bypass route, the driver of the vehicle can grasp the detour route in time and move the vehicle to the next lane.

Tuberculosis (TB) remains a global healthcare crisis with an estimated 10 million new cases and 1.4 million deaths per year TB is caused by infection with the major human pathogen Mycobacte-rium tuberculosis, which is difficult to rapidly diagnose and treat. There is an urgent need for new methods of diagnosis, sufficient in vitro models which capably mimic all physiological condi-tions of the infection, and high-throughput drug screening platforms. Microfluidic-based tech-niques provide single-cell analysis which reduces experimental time, the cost of reagents, and have been extremely useful for gaining insight into monitoring microorganisms. This review out-lines the field of microfluidics and discusses the use of this novel technique so far in M. tuberculo-sis diagnostics, research methods, and drug discovery platforms. The practices of microfluidics have promising future applications for diagnosing and treating TB.

The growth of demand for metallic minerals has faced with the need for new techniques and improving technologies for all mine life cycle operations. Nowadays, the exploitation of old tailings and mine wastes facilities could represent a solution to this demand, with economic and environmental advantages. W-Sn Panasqueira Mine has been operating for more than 100 years. Its first processing plant “Rio” was located near Zêrere river being the mineral processing residues deposited on the top hillside on the margin of this river in Cabeço do Pião tailings dam. The lack of maintenance and monitoring of this enormous structure in the last twenty years represents high risks to the environment and population of the surrounding region. The re-mining of the tailings by hydrometallurgical methods was considered, in order to satisfy these two enounced conditions - metals demand and environmental risk, aiming for the sale of the metal to pay the environmental intervention. Field samples campaign allowed collecting data and results from laboratory tests driving to use regression optimization. The re-mining solution was studied, taking into account the technical, economic, social, and environmental aspects.

Demonstrating biosimilarity entails comprehensive analytical evaluations, clinical pharmacolo-gy profiling, and efficacy testing for at least one medical indication in patients. These require-ments are stipulated by the U.S. Biologics Price Competition and Innovation Act (BPCIA). The costliest element—efficacy testing—can be waived if other compliance benchmarks are satisfied, including comparing functional pharmacodynamic (PD) biomarkers, even when they do not di-rectly correlate with clinical outcomes. Most biological drugs, such as monoclonal antibodies (mAbs), lack identifiable PD biomarkers. The FDA has employed various 'omics' technologies to identify potential PD biomarkers, including proteomics, glycomics, transcriptomics, genomics, epigenomics, and metabolomics. Although these efforts provide a robust scientific basis for estab-lishing biosimilarity, they are neither practical nor necessarily superior to existing functional biomarkers, such as receptor binding and mode-of-action outcomes. As we report for the first time, these functional biomarkers can effectively serve as PD indicators for all FDA-licensed bio-logical drugs. We recommend that the FDA consider officially listing these functional biomarkers to expedite and reduce the cost of biosimilar development, thereby increasing the accessibility of biological drugs. PD surrogates, like the receptor binding and pharmacokinetic profiles, are more robust and offer a rational solution to finding PD markers to compare for establishing biosimi-larity.

Canopy characteristics are crucial for accurately and safely determining the pesticide quantity and volume of water used for spray applications in vineyards. The inevitably high degree of intra-plot variability makes it difficult to develop a global solution for the optimal volume application rate. Here, the design procedure of, and the results obtained from, a variable rate application (VRA) sprayer are presented. Prescription maps were generated after detailed canopy characterization, using a multispectral camera embedded on an unmanned aerial vehicle, throughout the entire growing season in Torrelavit (Barcelona) in four vineyard plots of Chardonnay (2.35 ha), Merlot (2.97 ha), and Cabernet Sauvignonn (4.67 ha). The maps were obtained by merging multispectral images with information provided by DOSAVIÑA®, a decision support system, to determine the optimal volume rate. They were then uploaded to the VRA prototype, obtaining actual variable application maps after the application processes were complete. The prototype had an adequate spray distribution quality and exhibited similar results in terms of biological efficacy on powdery mildew compared to conventional (and constant) application volumes. The VRA results demonstrated an accurate and reasonable pesticide distribution, with potential for reduced disease damage even in cases with reduced amounts of plant protection products and water.

During recent decades, contextual computing applications have emerged in the field of healthcare and particularly in the field of telemonitoring of patients suffering from chronic obstructive pulmonary disease (COPD). According to WHO rankings, chronic obstructive pulmonary disease (COPD) is the third leading cause of death worldwide. Various research works are therefore carried out to improve the health of patients and the monitoring of patients in the comfort of their home environment. To this end, several telemonitoring systems are designed to COPD patients. These systems are connected to health center. Emergency physicians follow the patients subscribed to these systems remotely. These systems focus mainly on prediction, decision-making and the requirements of the healthcare profession, and do not address the quality control aspects of services or QoS based on service level agreements (SLAs). This situation can be dangerous for patients in case of extreme exacerbation of COPD patients. For example, the unavailability of the monitoring system can lead to the death of the patient because the emergency physician could not have access to the patient's data in real time in the context of COPD Patient Monitoring. In addition, Remote medical monitoring platforms are manipulating large volumes of data and the risks of data lost or data quality are real. It is therefore important to have the mechanisms to continuously improve the quality of service of these monitoring platforms in general and COPD patients particularly. In this article, we propose an ontology that uses SLA information from COPD monitoring platforms with dynamic data from the patient context. The purpose of this article is to propose a dynamic mechanism model for evaluating SLA violations. This solution allows retrieving knowledge from the main items of the SLA document based on XML and the COPD patient context data dynamically from a COPD SLA ontology. These data retrieved in real time allow the calculation of SLO-based metrics and display a SLA template available on the supplier and consumer interfaces. The information of the SLA violation control Interface changes dynamically depending the context-aware system and SLA document data. The SLA parties can dynamically control their Key Performance Indicators (KPI) Target.

An evaluation of emerging issues in One Health (OH) in Sub-Saharan Africa was undertaken to map the existing OH initiatives in Sub-Saharan African (SSA) countries. Desk review, expert opinions survey, limited interviews and wider consultations with selected OH stakeholders were conducted. The strengths, weaknesses, opportunities and threats to OH initiatives were identified. OH influence, interest and impacts were evaluated. One Health is transiting from multidisciplinary to transdisciplinary concepts and OH viewpoint should move from ‘proxy for zoonoses’, to include issues of climate change, nutrition and food safety, social sciences, geography, policy and planning, economics, welfare and well-being, antimicrobial resistance (AMR), vector-borne diseases, toxicosis and pesticides issues. While the identified major strengths should be boosted, the weaknesses should be addressed.OH Networks in SSA were spatially and temporally spread across SSA and stakeholders were classified as key, latent, marginal and OH defenders. Imbalance in stakeholders’ representation led to hesitation in buying-in from stakeholders who are outside the main networks. Theory of change, monitoring and evaluation frameworks, and tools to standardized evaluation of OH policies is needed for sustained future of OH and the future OH engagement should be outputs and outcomes-driven and not activity-driven.National roadmap for OH implementation and institutionalization is necessary and proofs of concepts in OH should be verified and scaled-up. Dependence on external funding is unsustainable and must be addressed. Necessary policy and legal instrument to support OH nationally and sub-nationally should be implemented taking cognizance of contemporary issues like urbanization, endemic poverty and other emerging issues. Utilizing current technologies and OH approach to address ongoing pandemic of COVID-19 and other emerging diseases is desirable. Finally, OH implementation should be anticipatory and not reactive to significantly benefit budgeting and contain disease outbreaks in animal sources before the risk of spillover to human can be envisaged.

Diagnosis of Awake Bruxism (AB) is problematic due to the inability to use continuous recordings during daytime activities. Recently, a new semi-instrumental approach was suggested, viz., an Ecological Momentary Assessment (EMA), with the use of a smartphone application. With the application subjects are requested to report, at least 12 times per day, the status of their masticatory muscle activity (relaxed muscles, jaw bracing without tooth contact, teeth contact, teeth clenching or teeth grinding). The aim of the present study was to compare the EMA to the assessment of AB as defined by a single point self-report. The most frequent condition recorded by the EMA was relaxed muscles (ca. 60%) and the least frequent one - Teeth grinding (0.6 %). The relaxed muscle condition also showed the lowest coefficient of variance over a 7day period of report. Additionally, only the relaxed muscles and the Jaw bracing conditions presented an acceptable ability to discriminate between AB positive and AB negative subjects, as defined by single point self-report questions. The combination between self-report and EMA may have a potential to promote our ability to diagnose AB. We suggest to re-consider the conditions of Teeth contact and Teeth grinding while using EMA to evaluate AB.

Most smallscale farmers still use the traditional way of agricultural crop farming, and relay mostly use of chemical fertilizers (CF). Recently CF have become expensive and could in some way have a negative impact on soil quality after long term application. However, co-application of biogas slurry (BGS) with CF could help reduce farming costs while improving dry matter yields, grain yields, primary macronutrient uptake of (Nitrogen) N, (Phosphorus) P, (Potasium) K, (Calcium) Ca and (Magnesium) Mg and soil concentration of pH, (organic carbon) OC, N, P, K, Ca and Mg after crop harvest. The study was a field experiment conducted in 2016-2017 and 2017-2018 growing seasons. The field experiment was arranged in a randomized complete block design with four replicates. The treatments were based on percentages of recommended N rates of 120 kg N ha-1 for maize production. The BGS/CF treatments were (i) 0/0, (ii) 0/120, (iii) 24/96, (iv) 48/72, (v) 72/48, (vi) 96/24, (vii) 120/0 kg N ha-1. The spreading of the combination of BGS/CF treatments was performed by hand and incorporated into the top soil (0-10 cm) in each experimental plots. BGS/CF (48/72) treatment resulted into higher dry matter yield in 2016-2017, which was higher than all other treatment combinations, while in the 2017-2018 season, treatment of (0/120) resulted into higher dry matter than all other treatment combinations. The 48/72 and 0/120 treatments resulted into similar grain yield in 2016-2017 season which were higher than all other treatments. Treatments of 48/72, 72/48 and 120/0 had higher N, P, K, Ca and Mg uptake than 0/0, 0/120, 24/96 and 96/24 treatments in both seasons. Soil pH, total N, K, and Mg were high from the treatment of (120/0) than all other treatments in 2016-2017 while in the 2017-2018 season, treatment of (48/72) had higher OC, P and K after maize harvest. The findings of his study show that co-application of BGS/CF at 48/72 and 72/48 have maize yield benefits compared to the two resources, BGS/CF (120/0) and BGS/CF (0/120), applied separately in soil especially in the arid and semi-arid regions.

Calcium carbonate cements have been synthesized by mixing amorphous calcium carbonate and vaterite powders with water to form a cement paste and study how mechanical strength is created during the setting reaction. In-situ XRD was used to monitor the transformation of ACC and vaterite phases into calcite and a rotational rheometer was used to monitor the strength evolution. There are two characteristic time scales of the strengthening of the cement paste. The short timescale of the order 1 hour is controlled by smoothening of the vaterite grains, allowing closer and therefore adhesive contacts between the grains. The long timescale of the order 10-50 hours is controlled by the phase transformation of vaterite into calcite. This transformation is, unlike in previous studies using stirred reactors, found to be mainly controlled by diffusion in the liquid phase. The evolution of shear strength with solid volume fraction is best explained by a fractal model of the paste structure.

Over the last few years, Earth Observation (EO) data has shifted towards increased use to produce official statistics, particularly in the agriculture sector. National statistics offices worldwide, including in Asia and the Pacific, are expanding their use of EO data to produce agricultural statistics such as crop classification, yield estimation, irrigation mapping, and crop loss estimation. The advances in image classification, such as pixel-based and phenology-based classifications, and machine learning create new opportunities for researchers to analyze EO data applied to agriculture statistics. However, it requires the ground truth (GT) data because classification result mainly depends on the quality of GT. Therefore, in this study, we introduced a random sampling approach to design and collect GT data using EO imagery and ancillary data. As a result of data collection, GT data improve the algorithms and validates classification results. Nevertheless, despite the importance of GT data, they are rarely disseminated as a data product in themselves. Thus, this results in an untapped opportunity to share GT data as a global public good, and improved use of survey and census data as a source of GT data.

Objective: To highlight opportunities for future nutrition intervention research within early childhood and education care (ECEC) settings, with a focus on generating evidence that has applicability to real-world policy and practice. Methods: An overview of opportunities to progress the field was developed by authors using a collaborative writing approach and informed by recent research in the field. The group developed a list of recommendations aligned with the reach, effectiveness, adoption, implementation and maintenance (RE-AIM) framework. Pairs of authors drafted individual sections of the manuscript, which was then reviewed by a separate pair. The first and senior author consolidated all sections of the manuscript and sought critical input on draft iterations of the manuscript. Results: Interventions that employ digital platforms (Reach) in ECEC settings, as well as research in the family day care setting (Effectiveness) were identified as areas of opportunities. Research understanding the determinants of and effective strategies for dissemination (Adoption), implementation of nutrition programs, in addition to de-implementation (Implementation) of inappropriate nutrition practices, is warranted. For maintenance, there is a need to better understand sustainability and sustainment of interventions, in addition to undertaking policy-relevant research. Conclusions: The ECEC setting is prime for innovative and practical nutrition intervention research.

To keep its business reliable, Google is concerned to ensure quality of apps on the store. One crucial aspect concerning quality is security. Security is achieved through Google Play protect and anti-malware solutions. However, they are not totally efficient since they rely on application features and application execution threads. Google provides additional elements to enable consumers to collectively evaluate applications providing their experiences via reviews or showing their satisfaction through rating. The latter is more informal and hides details of rating whereas the former is textually expressive but requires further processing to understand opinions behind. Literature lacks approaches which mine reviews through sentiment analysis to extract useful information to improve security aspects of provided applications. This work goes in this direction and in a fine-grained way, investigates in terms of confidentiality, integrity, availability and authentication (CIAA). While assuming that reviews are reliable and not fake, the proposed approach determines review polarities based on CIAA-related keywords. We rely on the popular classifier Naive Bayes to classify reviews into positive, negative and neutral sentiment. We then provide an aggregation model to fusion different polarities to obtain application global and CIAA reputations. Quantitative experiments have been conducted on 13 applications including e-banking, live messaging and anti-malware apps with a total of 1050 security-related reviews and 7.835.322 functionality-related reviews. Results show that 23% of applications (03 apps) have a reputation greater than 0.5 with an accent on integrity, authentication and availability, while the remaining 77% has a polarity under 0.5. Developers should make lot of efforts in security while developing codes and that more efforts should be made to improve confidentiality reputation. Results also show that applications with good functionality-related reputation generally offer bad security-related reputation. This situation means that even if the number of security reviews is low, it does not mean that security aspect is not a consumer preoccupation. Unlike, developers put much more time to test whether applications works without errors even if they include possible security vulnerabilities. A quantitative comparison against well-known rating systems reveals effectiveness and robustness of CIAA-RepDroid to repute apps in terms of security. CIAA-RepDroid can be associated to existing rating solutions to recommend developers exact CIAA aspects to improve within source codes.

In systems biology, biological phenomena are often modeled by ODE and distributed in the de facto standard file format SBML. The primary analyses performed with such models are dynamic simulation, steady-state analysis, and parameter estimation. These methodologies are mathematically formalized, and libraries for such analyses have been published. Several tools exist to create, simulate, or visualize models encoded in SBML. However, setting up and establishing analysis environments is a crucial hurdle for non-modelers. Therefore, easy access to perform fundamental analyses of ODE models is a significant challenge. We developed SBMLWebApp, a web-based service to execute SBML-based simulations, steady-state analysis, and parameter estimation directly in the browser without the need for any setup or prior knowledge to address this issue. SBMLWebApp visualizes the result and numerical table of each analysis and provides a download of the results. SBMLWebApp allows users to select and analyze SBML models directly from the BioModels Database. Taken together, SBMLWebApp provides barrier-free access to an SBML analysis environment for simulation, steady-state analysis, and parameter estimation for SBML models. SBMLWebApp is implemented in Java™ based on an Apache Tomcat® web server using COPASI, the SBSCL, and LibSBMLSim as simulation engines. SBMLWebApp is licensed under MIT with source code available from https://github.com/TakahiroYamada/SBMLWebApp. The program runs online at http://simulate-biology.org.

Since SARS-CoV-2 has spread all over the world, one of the key questions is how long immunity works for protecting hosts from infection. However, it was difficult to distinguish reinfection from re-detected positive cases caused by other reasons. We confirmed 23 re-detected positive cases in Sapporo City in 2020, and among them, the case with 205-days interval from initial onset to the second confirmation date was found. We calculated the probability of observing more than one case with over d-days interval of re-detection, pobs, to find out whether the case with 205-days interval had the similar epidemiological characteristic with others or not. We found that 5th percentile and its 95% confidence interval of pobs was 118 (45, 216) for Sapporo City data and 92 (86, 98) for KCDC data. This result indirectly suggested the case with 205-days interval was reinfection.

Fumaric acid esters (FAEs) are small molecules with anti-oxidative, anti-inflammatory and immune-modulating effects. Dimethyl fumarate (DMF) is the best characterised FAE and is approved and registered for the treatment of psoriasis and Relapsing-Remitting Multiple Sclerosis (RRMS). Psoriasis and RRMS share an immune-mediated aetiology, driven by severe inflammation and oxidative stress. DMF, as well as monomethyl fumarate and diroximel fumarate, are commonly prescribed first-line agents with favourable safety and efficacy profiles. The potential benefits of FAEs against other diseases that appear pathogenically different but share the pathologies of oxidative stress and inflammation are currently investigated.

Porous tantalum (Ta) is a promising biomaterial and has been applied in orthopedics and dentistry for nearly two decades. The high porosity and interconnected pore structure of porous Ta promise fine bone ingrowth and new bone formation within the inner space, which further guarantee rapid osteointegration and bone-implant stability in long term. Porous Ta has high wettability and surface energy that can facilitate adherence, proliferation and mineralization of osteoblasts. Meanwhile, low elastic modulus and high friction coefficient of porous Ta can effectively avoid stress shield effect, minimize marginal bone loss and ensure primary stability. Accordingly, the satisfactory clinical application of porous Ta based implants or prostheses are mainly derived from its excellent biological and mechanical properties. With the advent of additive manufacturing, personalized porous Ta based implants or prostheses have shown their clinical value in the treatment of individual patient who need specially designed implant or prosthesis. In addition, many modification methods have been introduced to enhance the bioactivity and antibacterial property of porous Ta with promising in vitro and in vivo research results. In any case, choosing suitable patients is of great importance to guarantee surgical success after porous Ta insertion.

Existing implementations of game design patterns have largely been confined to theoretical or research settings. Weaknesses in these implementations have prevented game design patterns from being properly evaluated as an educational and practical development tool. This paper examines these weaknesses, describes a method of developing and applying patterns that overcome the weaknesses, and evaluates use of the method for game design education and practice. Weaknesses in existing pattern implementations are: omission of design problems, presumption of functional completeness at the level of pattern languages, narrow topical focus, and lack of a concise, repeatable method for pattern production. Several features of the proposed method were specifically built to address these weaknesses, namely the pattern template, the process for connecting patterns into a language and assessing the language’s scope, a rubric for assessing pattern confidence and interconnectivity confidence, and pattern-building exercises. This method was applied in a classroom setting. Results, as assessed by the evaluation of student work, suggest that creating patterns/pattern languages is an effective pedagogical approach. De-signs produced using designer-created patterns closely align with existing design theory and are clearly understood by students. The above results may indicate that the path to gaining wider acceptance of pattern theory as a design framework within game design is not to produce a universal pattern language but to facilitate the creation of case-specific languages, by students and professional designers, that use a shared ontology and thus can be combined easily to solve the diverse sets of problems faced by these groups.

This work presents the dynamics of forest clearing in the Brazilian Amazon during the period 2006–2019 in which includes the approval of the new Brazilian Forest Code in 2012. The study was carried out in the Brazilian Amazon, Pará State and in the municipality of Novo Progresso (Pará State). The analysis was based on deforestation and fire hotspot datasets issued by the Brazilian Institute for Space Research (INPE), produced based on optical and thermal sensors onboard different satellites. Deforestation data was also used to assess greenhouse gas (GHG) emissions from the slash-and-burn practices. The work showed a good correlation between the occurrence of fires in the newly deforested area in the municipality of Novo Progresso and the slash-and-burn practices. The same trend was also observed in the Pará State, suggesting a common practice along the deforestation arch. The study indicated positive coefficients of determination of 0.72 and 0.66 between deforestation and fire occurrences for the municipality of Novo Progresso and Pará State, respectively. The increased number of fire occurrences in the primary forest suggests possible ecosystem degradation. Deforestation reported for 2019 surpassed 10,000 km2, a significant intensification comparatively higher than the previous ten years which was on an average of 6,760 km2. The steady increase of deforestation in the Amazon after 2012 has been a worldwide concern because of the forest loss itself as well as the massive GHG emitted in the Brazilian Amazon (295 million tons of net CO2 equivalent in the year 2019). Better correlation of deforestation and fires occurrences reported from satellite images confirmed the slash-and-burn practice and the secondary effect of deforestation, which degrades primary forest surrounding the deforested areas.

The outbreak of SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) has caused severe damage to the world. With the support of classic evolutionary theories and population genetics principles, many studies on the origin of SARS-CoV-2 have revealed encouraging results but meanwhile are still under debate. We are concerned with the validity of applying classic evolutionary theories and formula to the evolution of RNA viruses. We have raised several factors like the RNA replication feature and the RNA modification systems of the hosts, which might jeopardize the validity of the application of classic methods to analyze the SARS-CoV-2 data.

Research focused on understanding wider systemic factors driving behavioral change is limited with a dominant focus on the role of individual farmer and psychosocial factors for farming practice change, including reducing fertilizer application in agriculture. Adopting a wider systems perspective, the current study examines change and the role that supporting services have on fertilizer application rate change. A total of 238 sugarcane growers completed surveys reporting on changes in fertilizer application along with factors that may explain behavior change. Logistic regressions and negative binomial count-data regressions were used to examine whether farmers had changed fertilizer application rates and if they had, how long ago they made the change, and to explore the impact of individual and system factors in influencing change. Approximately one in three sugarcane growers surveyed (37%) had changed the method they used to calculate fertilizer application rates for the cane land they owned/managed at some point. Logistic regression results indicated growers were less likely to change the basis for their fertilizer calculation if they regarded maintaining good relationships with other local growers as being extremely important, they had another source of off-farm income, and if they had not attended a government-funded fertilizer management workshop in the five years preceding the survey. Similar drivers promoted early adoption of fertilizer practice change; namely, regarding family traditions and heritage as being unimportant, having sole decision-making authority on farming activities and having attended up to 5 workshops in the five years prior to completing the survey. Results demonstrated the influence of government-funded services to support practice change.

Malignant gliomas are heterogeneous neoplasms. Glioma stem-like cells (GSCs) are undifferentiated and self-renewing cells that develop and maintain these tumors. These cells are the main population that resist current therapies. Genomic and epigenomic analyses has identified various molecular subtypes. Bone morphogenetic protein 4 (BMP4) reduces the number of GSCs through differentiation and induction of apoptosis, thus increasing therapeutic sensitivity. However, the short half-life of BMP4 impedes its clinical application. We have previously reviewed BMP4 signaling in central nervous system development and glioma tumorigenesis and its’ potential as a treatment target in human gliomas. Recent advances in understanding both adult and pediatric malignant gliomas highlight critical roles of BMP4 signaling pathways in the regulation of tumor biology, and indicate its’ potential as a therapeutic molecule. Furthermore, significant progress has been made on synthesizing BMP4 biocompatible delivery materials, which can bind to and markedly extend BMP4 half-life. Here, we review current research associated with BMP4 in brain tumors, especially in pediatric malignant gliomas. We also summarize BMP4 delivery strategies, with a focus on biocompatible BMP4 binding peptide amphiphile nanostructures as promising novel delivery platforms for treatment of these devastating tumors.

Abstract: Like other plants, every PV power plant will one day reach the end of its service life. Calculations show that 20,400 tons of PV waste will be generated worldwide by 2030 and 60.2 million tons by 2050, not including the mass of the support structure. Such large amounts of waste pose a potential threat to the environment and people if not properly managed. The paper analysed the options for dealing with PV waste, namely reuse, recycling and landfilling. For recycling as the best option in terms of environmental protection and circular economy, an overview of recycling technologies and the percentage of achievable recycling for the materials contained in each PV system component is given. In addition, the current situation of legislation and recycling of PV modules in Europe was examined with special reference to the Balkan countries. There are a small number of factories for recycling PV modules in Europe, but none in the Balkan countries. The main reason for this is the small amount of PV waste in these countries and consequently the economic unprofitability. For this reason, PV modules (after dismantling the aluminum frame and cables) are mostly disposed of as non-hazardous waste in landfills in these countries. Finally, the main barriers to faster implementation of PV module reuse and recycling are listed, along with guidelines for their removal. The cost of recycling Si modules is about ten times higher than the cost of disposal. To change this ratio in favor of recycling, cheaper recycling methods need to be developed and taxes on landfill disposal need to be increased.