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.

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.

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.

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.

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.

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.

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.

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.

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.

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Although various vaccines are now commercially available, they have not been able to stop the spread of COVID-19 infection completely. An excellent strategy to quickly get safe, effective, and affordable COVID-19 treatment is to repurpose drugs that are already approved for other diseases as adjuvants along with the ongoing vaccine regime. The process of developing an accurate and standardized drug repurposing dataset requires a considerable level of resources and expertise due to the commercial availability of an extensive array of drugs that could be potentially used to address the SARS-CoV-2 infection. To address this bottleneck, we created the CoviRx platform. CoviRx is a user-friendly interface that provides access to the data, which is manually curated for COVID-19 drug repurposing data. Through CoviRx, the data curated has been made open-source to help advance drug repurposing research. CoviRx also encourages users to submit their findings after thoroughly validating the data, followed by merging it by enforcing uniformity and integ-rity-preserving constraints. This article discusses the various features of CoviRx and its design principles. CoviRx has been designed so that its functionality is independent of the data it dis-plays. Thus, in the future, this platform can be extended to include any other disease X beyond COVID-19. CoviRx can be accessed at www.covirx.org.

Hypoplastic left heart syndrome (HLHS) is a severe congenital heart disease (CHD) affecting 1 in 5,000 newborns. We constructed the interactome of 74 HLHS-associated genes identified from a large-scale mouse mutagenesis screen, augmenting it with 408 novel protein-protein interactions (PPIs) using our High-precision Protein-Protein Interaction Prediction (HiPPIP) model. The interactome is available on a webserver with advanced search capabilities (http://severus.dbmi.pitt.edu/wiki-HLHS). 364 genes including 73 novel interactors were differentially regulated in tissues/iPSC-derived cardiomyocytes of HLHS patients. Novel PPIs facilitated the identification of TOR signaling and endoplasmic reticulum stress modules. 60.5% of the interactome consisted of housekeeping genes that may harbor large-effect mutations and drive HLHS etiology but show limited transmission. Network proximity of diabetes, Alzheimer’s disease, and liver carcinoma-associated genes to HLHS genes suggested a mechanistic basis for their comorbidity with HLHS. Interactome genes showed tissue-specificity for sites of extracardiac anomalies (placenta, liver and brain). The HLHS interactome shared significant overlaps with the interactomes of ciliopathy and microcephaly-associated genes, with the shared genes respectively enriched for genes involved in intellectual disability and/or developmental delay, and neuronal death pathways. This supported the increased burden of ciliopathy variants and prevalence of neurological abnormalities observed among HLHS patients with developmental delay and microcephaly respectively.

The objective of this pilot study was to compare glucose concentrations in capillary blood (CB) samples analysed in a laboratory by a validated method and glucose concentrations measured in the interstitial fluid (ISF) by continuous glucose monitoring under different physical activity levels in a postprandial state in healthy and active subjects without diabetes. Ten healthy, active subjects (26±4 years, 67±11 kg bodyweight (BW), 11±3 h) were included in the study. Within 14 days, they underwent six tests consisting of a) resting fasted (R/Fast), b) resting after intake of 1 g glucose/kg BW (R/Glc) and c) running for 60 minutes at moderate (65/Glc) and d) high (85/Glc) intensity after intake of 1 g glucose/kg BW. Data were collected in the morning, following a standardised dinner before test day. Sensor-based glucose concentrations were compared to simultaneous capillary blood glucose concentrations. Pearson’s r correlation coefficient was highest for R/Glc (.92, p<.001) compared to R/Fast (.45, p<.001), 65/Glc (.60, p<.001) and 85/Glc (.70, p<.001). Mean absolute relative deviation (MARD) and standard deviation (SD) was smallest for resting fasted and similar between all other conditions (R/Fast: 8±6%, R/Glc: 17±12%, 65/Glc: 22 ± 24%, 85/Glc: 18±17%). However, Bland-Altman plot analysis showed a higher range between lower and upper limits of agreement (95% confidence interval) of paired data under exercising compared to resting conditions. Under resting fasted conditions, both methods produce similar outcomes. Under resting postprandial and exercising conditions, respectively, there are differences between both methods. However, further data in healthy subjects need to be gathered considering physical activity and nutrition status.

Spatial planning processes generally consider three levels of planning, which are applied to three types of territory: state, county and city. As the coastal areas are of a significant natural, cultural, economic and social value, as well as are characterized by a diverse range of involved society with specific interests and needs, there is a necessity for an innovative and new approach to sustainable development planning in accordance with the modern age of growth, as well as to work with local communities in specific areas. Planning of a small populated area like village territory is more diverse and subject to the wishes and needs of the population. Small territory planning involves a very narrow circle of individuals or communities that identify spatial development needs for the future, including socio-economic, cultural, and environmental and climate change scenarios. In order to assess the development opportunities and needs of the area, it is necessary to monitor the area by regularly updating data. As it is well known, methodically derived data (facts) provide objectivity and transparency. Nowadays, when information about the present and the past is circulating very fast, it is possible to analyze the current situation, to forecast the future using databases, and to show several constructed realities (scenarios) using the geographic information system (GIS). Therefore, it is crucial to explore and find out the local needs-based planning approach to the development of village in coastal areas.

The awareness of the existence in plants of bioactive compounds namely phytochemicals (PHYs), having health properties is progressively expanding. Therefore, their massive introduction in the normal diet, in food supplements, and their use as natural therapeutics to treat several diseases are increasingly emphasized by several sectors. Particularly, most PHYs possessing antifungal, antiviral, anti-inflammatory, antibacterial, antiulcer, anti-cholesterol, hypoglycemic, immunomodulatory, and antioxidant properties have been isolated from plants. Additionally, their secondary modification with new functionalities, to further improve their intrinsic beneficial effects, is extensively investigated. Unfortunately, although the idea of exploiting PHYs as therapeutics is amazing, its realization is far from simple, and the possibility of exploiting them as effective orally administrable drugs is almost utopic. Most PHYs are insoluble in water and, when introduced orally, they scarcely reach the site of action in therapeutic concentrations. Degradation by enzymatic and microbial digestion, occurring in the mouth, stomach, and intestine, as well as fast metabolism and rapid excretion via the kidney, biliary, or lung, strongly limit their in vivo activity. To overcome these drawbacks, several nanotechnological approaches have been used and many PHYs-loaded delivery systems with dimensions of nanometers have been developed. This paper, also by reporting various recent case studies, reviews the foremost nano-suspension and nano-emulsion-based techniques developed for formulating the most relevant PHYs in more bioavailable nanoparticles (NPs), suitable or promising for clinical application. Also, the acute and chronic toxic effects due to the exposure to NPs reported so far, the possible nanotoxicity which could derive by their massive employment, as well as the ongoing actions to improve the knowledge in the field were discussed. The state of the art concerning the actual clinical application of both PHYs and the nanotechnologically engineered PHYs was also reviewed.

This paper proposed a new evaluation model based on analytic hierarchy process to quantitatively evaluate the capability of multi-satellite cooperative remote sensing observation. The analytic hierarchical process model is a combination of qualitative and quantitative analysis of systematic decision analysis method. According to the objective of the remote sensing cooperative observation mission, we decompose the complex problem into several levels and a number of factors, compare and calculate various factors in pairs, and obtain the combination weights of different schemes. The model can be used to evaluate the observation capability of resource satellites. Taking the optical remote sensing satellites such as China’s resource satellite series and GF-4 as examples, this paper verifies and evaluates the model for three typical tasks: point target observation, regional target observation and moving target continuous observation. The results show that the model can provide quantitative reference and model support for comprehensive evaluation of the collaborative observation capability of remote sensing satellites.

Paper-based microfluidic analysis devices (μPADs) have attracted attention as a cost-effective platform for point-of-care testing (POCT), food safety, and environmental monitoring. Recently, three-dimensional (3D)-μPADs have been developed to improve the performance of μPADs. For accurate diagnosis of diseases, however, 3D-μPADs need to be developed to simultaneously detect multiple biomarkers. Here, we report a 3D-μPADs platform for the detection of multiple biomarkers that can be analyzed and diagnosed with a smartphone. The 3D-μPADs were fabricated using a 3D digital light processing printer and consisted of a sample reservoir (300 µL) connected to 24 detection zones (of 4 mm in diameter) through 8 microchannels (of 2 mm in width). With the smartphone application, eight different biomarkers related to various diseases were detectable in concentrations ranging from normal to abnormal conditions: glucose (0–20 mmol/L), cholesterol (0–10 mmol/L), albumin (0–7 g/dL), alkaline phosphatase (0–800 U/L), creatinine (0–500 µmol/L), aspartate aminotransferase (0–800 U/L), alanine aminotransferase (0–1000 U/L), and urea nitrogen (0–7.2 mmol/L). These results suggest that 3D-µPADs can be used as a POCT platform for simultaneous detection of multiple biomarkers.

Electrospinning is a simple versatile method to generate nanofibres. Remarkable progress has been made in the development of electrospinning process. The production of nanofibers is affected by many parameters, which influence the final material properties. Electrospun fibers have a wide range of applications such as energy storage and biomedical scaffolds. Polyacrylonitrile (PAN) has been widely used in the production of carbon nanofibers. PAN has received increasing interest in recent years, due to its excellent characteristics, such as spinnability, biocompatibility, and commercial viability, opening new applications in the biotechnological field. This paper provides an overview of the electrospinning process, its main parameters, and operation modes that affect nanofibers fabrication. A review of their biological applications is proposed. A focus is made on PAN fibers formation, functionalization, and application as scaffolds to allow cell growth. Overall, nanofibre scaffolds, made of stabilized PAN, appear as a powerful tool in medical applications needing controlled cell culture.

Connected and Autonomous Vehicle (CAV) combines technologies of autonomous vehicle (AV) and connected vehicles (CV) to develop quicker, more reliable and safer traffic. Artificial Intelligence (AI) based CAV solutions play significant roles in sustainable city. The convergence imposes stringent security requirements for CAV safety and reliability. In practice, vehicles are developed with increased automation and connectivity. Increased automation increases the reliance on the sensor-based technologies and decreases the reliance on driver; increased connectivity increases the exposures of vehicles vulnerability and increases the risk for an adversary to implement a cyber-attack. Much work has been dedicated to identifying the security vulnerabilities and recommending mitigation techniques associated with different sensors, controllers, and connection mechanisms, respectively. However, there is an absence of comprehensive and in-depth studies to identify how the cyber-attacks exploit the vehicles vulnerabilities to negatively impact the performance and operations of CAV. In this survey, we set out to thoroughly review the security issues introduced by AV and CV technologies, analyze how the cyber-attacks impact the performance of CAV, and summarize the solutions correspondingly. The impact of cyber-attacks on the performance of CAV is elaborated from both viewpoints of intra-vehicle system and inter-vehicle system. We pointed out that securing the perception and operations of CAV would be the top requirement to enable CAV to be applied safely and reliably in practice. Also, we suggested to utilize cloud and new AI methods to defend against smart cyber-attacks on CAV.

Pulmonary thromboembolism is a very common cardiovascular disease, with a still high mortality rate. Despite the clear guidelines, this disease still represents a great challenge both in diagnosis and treatment. Heterogeneous clinical picture, often without pathognomonic signs and symptoms, represents a huge differential diagnostic problem even for experienced doctors. The decision on the therapeutic regimen also represents a major dilemma in the group of patients who are hemodynamically stable at initial presentation and have signs of right ventricular (RV) dysfunction proven by echocardiography and positive biomarker values (pulmonary embolism of intermediate-high risk). Studies have shown conflicting results about the benefit of using fibrinolytic therapy in this group of patients until hemodynamic decompensation, due to the risk of major bleeding. The latest recommendations give preference to new oral anticoagulants (NOACs) compared to vitamin K antagonists (VKA), except for certain categories of patients (patients with antiphospholipid syndrome, mechanical valves, pregnancy). When using oral anticoagulant therapy, special attention should be paid to drug-drug interactions, which can lead to many complications, even to the death of the patient. Special population groups such as pregnant women, obese patients, patients with antiphospholipid syndrome and cancer represent a great therapeutic challenge in the application of anticoagulant therapy. In these patients, not only the effectiveness of the drugs must be taken into account, but great attention must be paid to their safety and possible side effects, which is why a multidisciplinary approach is emphasized in order to provide the best therapeutic option.

Thrombosis has threatened human health in past decades. Bacillus nattokinase is a potential thrombolytic drug without side-effect and low cost and has been introduced into the consumer market as a functional food or dietary supplement. This review firstly summarizes the biodiversity sources and fermentation process of nattokinase, and systematically expounds the structure, cata-lytic mechanism and enzymatic properties of nattokinase; In view of the problems of low fermen-tation yield, insufficient activity and stability of nattokinase, this review discusses the heterologous expression of nattokinase in different microbial hosts, and summarizes the protein and genetic en-gineering progress of nattokinase-producing strains; Finally, this review summarizes the clinical application of nattokinase.

Nanotechnology is concerned with the creation of particles and materials at nanoscale levels. Nanocomposite is a combination or matrix, in which different materials combine to develop new properties of the materials ensuring that one of the materials have size in range of 1-100 nm. Nanocomposites are classified according to the types of reinforcement materials and matrix materials used in their construction. Nanocomposites are divided into three types based on the matrix material: ceramic matrix nanocomposites (CMNC), polymer matrix nanocomposites (PMNC), and metal matrix nanocomposites (MMNC) (MMNC). The synthetic methods of nanocomposites are frequently classified in to two classes. These are top-down and bottom up approaches. The type of synthetic technique used is determined by the nanocomposites' intended properties. There are different wet methods for synthesis of metal nanoparticles and nanocomposites among these co-precipitations, sol gel and hydrothermal methods are cost effective as compared to other methods. The synthesized nanocomposites can be characterized using different techniques to get insight into the morphology, particle size, phase, composition, thermal stability, optical, magnetic, electrical and thermal properties. Also, nanocomposites are used in medical, pharmaceutical industry, food packaging, to electronics and energy industry. Thus this review focuses on the main ideas about synthesis, characterization and application of nanocomposites.

This research assesses the classification of Arabic coffee into three primary variations (light, medium, and dark) using simulated data based on actual measurements of color information, antioxidant laboratory tests, and chemical composition tests. Two types of simulated data were generated, with the standard deviation of the measures varied. Multiple classifiers were used for classification. The findings demonstrate the use of color information in accurately distinguishing Arabic coffee types. The different CIE color values, as well as the excellent connection between color information and coffee classes, lead to flawless classification results. However, depending primarily on antioxidant information results in lower classification performance, with variability amongst classifiers due to increasing data complexity. Chemical composition information, on the other hand, possesses outstanding discriminatory strength, allowing faultless classification on its own. Certain characteristics, such as crude protein and crude fiber, show strong correlations and are critical in the classification of coffee types. Based on the findings, it is recommended to create a mobile application that uses image recognition to analyze coffee color while providing chemical composition information. End users, such as consumers, will be able to make informed judgments regarding their coffee preferences owing to this application.

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.

Background: Depressive symptoms often occurred during the adolescence period. This situation affected their psychological well-being, which is an important element for a positive youth development. Thus, there is an urgent need to prevent such mental health problems and promote psychological health outcomes among adolescents. Mobile health has been created as a platform to promote mental health to adolescents to ensure ongoing safety and effectiveness. Despite the positive evidence to support the mobile health, tailored mobile health to promote mental health and prevent psychological symptoms among adolescents in Thailand is lacking.This study aimed to assess the feasibility and acceptability of delivering digital technology intervention prototype called “MU-My-Mind Mobile Application (MU-My-mind App)” among Thai adolescents and evaluate the efficacy of this application in improving psychological health outcomes. Materials and Methods: This quasi-experimental research was conducted in secondary school students in Thailand. A convenience sample was recruited and participants were asked to download the MU-My-Mind App on their smart phone. Health outcomes were also evaluated by self-administered questions and focus group interview at baseline and post-intervention. Feasibility and acceptability were assessed. Quantitative data were analyzed by descriptive statistics and pair t-test. Qualitative data were analyzed by thematic analysis. Results: Thirty students completed the MU-My-Mind application. Quantitative data suggested significant improvements on mindfulness, psychological well-being, depression, anxiety and stress. Qualitative data derived into six themes: a) overall feedback of the program, b) learning to manage stress and challenges, c) elevating relaxation responses and calmness, d) heightened mindfulness and concentration, e) increased awareness and management of emotions, and f) altering perspectives and managing thoughts. Overall, data suggested the acceptability and feasibility of the intervention. Conclusion: The “MU My Mind Mobile Application” prototype was demonstrated feasible and well acceptable among Thai adolescents to promote their psychological health outcome. However, there are some limitation and suggestion emerged during this pilot testing. Future research should be explored in a larger sample size with comparison control group and over a longer duration with a range of diverse samples to ensure the efficacy of this application.

Gene expression data is key for the functional annotation of single nucleotide polymorphisms (SNPs) identified in genome-wide association studies (GWAS). Expression and splicing quantitative trait loci (e/sQTLs) in normal colon tissue, such as those from the University of Barcelona and University of Virginia RNA sequencing project (BarcUVa-Seq) and the Genotype-Tissue Expression project (GTEx), are required to gain biological insight of colon-related diseases risk loci. Moreover, transcriptome-wide association studies (TWAS) rely on reference gene expression imputation panels in the tissue of interest to nominate susceptibility genes. Also, it is of high interest to study the relationships between genes in a network framework. For facilitating these analyses, we have updated and expanded the scope of the Colon Transcriptome Explorer (CoTrEx) to the version 2.0. This web-based resource provides exhaustive visualization and analysis of transcriptome-wide gene expression profiles of normal colon tissue from BarcUVa-Seq and GTEx. In addition to the integration of new datasets, CoTrEx 2.0 provides additional e/sQTLs sets, as well as gene expression prediction models and regulatory and co-expression networks. It is freely available at https://barcuvaseq.org/cotrex/. Overall, it is of high interest for researchers aiming to investigate the genetic susceptibility to colon-related complex traits and diseases.

Poor medication adherence among older adults is a widespread problem worldwide. As the population ages, the design of smartphone medication management apps is critical to improving medication adherence among older adults. Taking the design of an elderly medication reminder APP as an example, this study proposes a sustainable design research method that integrates the KANO model, Analytic Hierarchy Process (AHP), Quality Function Deployment (QFD), and PUGH decision matrix. The method collects user demands through in-depth interviews, and applies the KANO model to classify these demands. The hierarchical structure of user needs is established by using AHP, and the priority is sorted according to the weight and importance determined by the judgment matrix. QFD is used to translate user needs into design requirements, and the house of quality matrix identifies key design requirements. Finally, design alternatives are evaluated using Pugh's concept selection method. The results of this study demonstrate that the integration of KANO-AHP-QFD-PUGH can be effective as a sustainable optimal design approach for the user experience of a medication reminder application for the elderly. This integrated method not only facilitates innovative optimization and sustainability of application design methods but also provides valuable theoretical and practical insights for future drug-assisted design for elderly users.

Background: Artificial intelligence can help improve the quality of healthcare by analyzing vast amounts of data and providing more effective and personalized treatment plans. Researchers are working on developing AI-powered solutions that can help improve the outcomes of patients. Objective: To explore the potential of AI in improving healthcare outcomes and patient experience. Results: The study suggests that AI can improve healthcare efficiency and patient outcomes but cannot fully replace human healthcare professionals. AI can assist healthcare professionals in their work, leading to better resource utilization and improved patient care. However, there is still a need for human healthcare professionals to oversee AI systems and provide empathy and personalized care to patients. Conclusion: While there is immense potential for AI in healthcare, it is not yet feasible to replace human healthcare workers. Instead, it should be viewed as a tool that can help improve the efficiency and effectiveness of human healthcare.

This paper presents a system based on pedestrian dead reckoning for localization of networked mobile users, which relies only on sensors embedded in the devices and device- to-device connectivity. The user trajectory is reconstructed by measuring step by step the user displacements. Though step length can be estimated rather accurately, heading evaluation is extremely problematic in indoor environments. Magnetometer is typically used, however measurements are strongly perturbed. To improve the location accuracy, this paper proposes a cooperative system to estimate the direction of motion based on a machine learning approach for perturbation detection and filtering, combined with a consensus algorithm for performance augmentation by cooperative data fusion at multiple device. A first algorithm filters out perturbed magnetometer measurements based on a-priori information on the Earth's magnetic field. A second algorithm aggregates groups of users walking in the same direction, while a third one combines the measurements of the aggregated users in a distributed way to extract a more accurate heading estimate. Extensive indoor experiments show that the heading error is highly reduced by the proposed approach thus leading to noticeable enhancements in localization performance.

For Convolutional Neural Network (CNN), Depthwise Separable CNN (DSCNN) is a preferred architecture for Application Specific Integrated Circuit (ASIC) implementation on edge devices. It can benefit from a multi-mode approximate multiplier proposed in this work. The proposed approximate multiplier uses two 4-bit multiplication operations to implement a 12-bit multiplication operation by reusing the same multiplier array. With this approximate multiplier, sequential multiplication operations are pipelined in a modified DSCNN to fully utilize the PE array in the convolutional layer. This Approximate (A-DSCNN) was implemented on TSMC 40-nm CMOS process with a supply voltage of 0.9 V. At the clock frequency of 200 MHz, the design achieves 4.78 GOPs/mW while occupying 1.24 mm x 1.24 mm silicon area. Compared to conventional DSCNN implemented in a similar process node, the chip area and power consumption were reduced by 53% and 25%, while the throughput was improved by 17%.

With the development of industrialization, traditional manufacturing technologies are no longer able to meet the production needs of modern society. Additive manufacturing has successfully solved the problems existing in traditional manufacturing technology. Selective laser melting (SLM) is a powder bed melting technology that produces metal parts by selectively melting metal powders on a platform using a laser beam. It is widely used in additive manufacturing. This article introduces the characteristics and current development status of SLM technology, summarizes the main application fields and common defects of SLM, and finally explores and prospects the future development trend of SLM.

The paper summarizes the current knowledge about the structure, species, and content differ-ences of β-D-glucans as a storage and functional component, especially of cereal grains. The pa-per also provides comprehensive information on the biosynthesis of this plant cell wall polysac-charide, genetic and non-genetic factors influencing the content variability and indicates the pos-sibilities of breeding modern varieties with a defined content of this metabolite. The final part of the text is also devoted to the influence of significant technological processes applied to cere-als containing β-D-glucans. The chapter discusses the influence of these processes on the content and structure of the polysaccharide and the resulting changes in its physicochemical properties. Part of the discussed issue are the possibilities of effective application of β-D-glucans in different food products.

This paper discusses the development process regarding a group of four Augustana College seniors' research. The project is a mobile app for Android built-in Android Studio called Augustana Health and Dining. This application will improve the dining experience at Augustana College, provide additional health metrics for personal use and promote healthier lifestyles. The app features the menus of various on-campus dining areas, with calories, allergens, and other essential food information. It also includes a profile section that will display campus meal credits and a calorie counting metric that can be used to track calories consumed to display this information to users so they can make more informed choices on their meals and health.