The COVID-19 crisis has emphasized the importance of digital transformation of SMEs and has served as an accelerator. Digital transformation empowers SMEs to rethink the way they make decisions and apply technology in meaningful and sustainable ways. The purpose of this study is to explore the role of digital transformation, fostered by COVID-19, and its potential contribution to the sustainability of SMEs. This paper examines a new conceptual framework designed on resource-based theory perspectives by using a survey data of 246 SMEs in Latvia and employing mediation analysis. The main findings of the study are that digital orientation and digital capability have direct positive effects on digital transformation; higher levels of digital orientation and capability, mediated by digital transformation, lead to higher revenue; as well as a more sophisticated business model in the case of digital orientation. Even if we cannot confirm that higher digital capability, mediated by digital transformation, leads to a more sophisticated business model, we found statistically significant direct effects from digital capability to digital transformation and from digital transformation to business model. These findings can be useful for policymakers, managers and practitioners to clarify how digital orientation, digital capability intermediated through digital transformation affect sustainability of SMEs.

In this study, a full-scale pilot testing was performed with side-by-side operation of a conventional enhanced biological phosphorus removal (EBPR) process and a side-stream EBPR (S2EBPR) process. A comparison of the performance, activities and population dynamics of key functionally relevant populations between the two configurations were carried out. The results demonstrated that, with the same influent wastewater characteristics, S2EBPR configuration showed more effective and stable orthophosphate (PO₄-P) removal performance (up to 94% with average effluent concentration down to 0.1 mg P/L) than conventional EBPR, especially when the mixers in side-stream reactor were operated intermittently. Mass balance analysis illustrated that both denitrification and EBPR performance have been enhanced in S2EBPR configuration through diverting primary effluent to anoxic zone and producing additional carbon (~40%) via fermentation in side-stream reactor. Microbial characterization showed that there was no significant difference in the relative abundances of Ca. Accumulibacter (~5.9%) and Tetrasphaera (~16%) putative polyphosphate-accumulating organisms (PAOs) between the two configurations. However, lower relative abundance of known GAOs was observed in S2EBPR configuration (1.1%) than the conventional one (2.7%). A relatively higher PAO activity and increased degree of dependence on glycolysis pathway than TCA cycle was observed in S2EBPR configuration using P release and uptake batch test. Adequate anaerobic solid retention time (SRT) and conditions that generate continuous and slow feeding/production of volatile fatty acid (VFA) with higher composition percentage of propionate in the side-stream reactor of S2EBPR process likely provide a competitive advantage for PAOs over GAOs.

Results-Based Funding (RBF) for Reducing Emissions from Deforestation and Forest Degradation (REDD+) has become an important instrument for channeling financial resources to forest conservation activities. At the same time, much literature on conservation funding is ambiguous about the effectiveness of existing RBF schemes. Many effectiveness evaluations follow a simplified version of the principal-agent model, although in practice the relation between aid providers and funding recipients is much more complex. As a consequence, intermediary steps of conservation funding are often not accounted for effectiveness studies. This research paper aims to provide a nuanced understanding of conservation funding by analyzing the allocation of financial resources for one of the largest RBF schemes for REDD+ in the world: the Brazilian Amazon Fund. As part of this analysis, this study has built a dataset of information on Amazon Fund projects at unprecedented detail in order to accurately reconstruct the allocation of financial resources across different stakeholders (i.e. governments, NGOs, research institutions), geographies and activities. The results show that stakeholders seem to hold preferences with respect to the type of activities that they support, thereby suggesting that project owners exert much influence on how deforestation reduction is to be attained. There are evidences that governmental organizations lack financial additionality of their projects, which renders the growing share of funding to this type of stakeholder particularly worrisome. By contrast, the geographical distribution of financial resources seemed to follow a more focused rationale as financial support tends to concentrate in areas where deforestation threats are highest. Overall, the allocation of the financial resources from the Amazon Fund reflects an arbitrary support of different projects that adopt very diverging theories of change that are not primarily concerned with attaining further deforestation reductions. As projects owners exert influence on funding effectiveness to some extent, the Amazon Fund may either seek to regulate the allocation of financial resources more actively or adopt funding effectiveness evaluations that account for this influence more comprehensively.

Multidisciplinary Design Optimization (MDO) is the most active field in the design of current complex system engineering, which is possessed with such two difficulties as subsystem information exchange and analytical and computational complexity of systems. Therefore, an improved collaborative optimization algorithm based on ANN (artificial neural network) response surface was proposed dependent on the consistency constraint algorithm and concurrent subspace algorithm. As an optimization method with secondary structure, it satisfied only local constraints in discipline layer, but provided a coordinated mechanism for interdisciplinary conflict in system layer. Finally, it was applied in the multidisciplinary design optimization of autonomous underwater vehicle (AUV). As shown from the result, the MDO convergence stability and reliability of low resistance, low noise and high maneuvering performance of the AUV shape can be ensured by the improved collaborative optimization algorithm, thus verifying the effectiveness of the algorithm.

Hang-ju was one of five officinal varieties of Flos chrysanthemum for its edible and potable usage. Besides Flos Chrysanthemum (FL), there were also Bud Chrysanthemum (BC) and Fetal Chrysanthemum (FC) at the early and late stage of buds, respectively, in the consumption market of Hang-ju with higher prices. Whether the quality and efficiency of BC and FC was superior to FL or merely consumption misunderstandings? Three commercial products of Hongxinju, a representive cultivar of Hang-ju were studied with a GC-MS based metabolomics approach, complemented with morphology, contents of moisture and protein and the anti-oxidant activity, to reveal the metabolic alterations of violate components in Hongxinju in different flowering stage and at different processing periods. It revealed that most of the violate components were increased from fresh FC to FL, and the low-boiling fractions, inflammatory methyl arachidonate and air-polluting component of ethylbenzene were declined while the representative components with pungent flavor and cool nature of a-curcumene and (Z,Z,Z)-9,12,15-octadecatrienoic acid, vision improving carotenol of rhodopin and high-boiling fractions were elevated after processed in final FL compared with that in BC and/or FC. Though the content of protein and anti-oxidative capacity of final BC and FC were nearly equal to those of FL, in comprehensive consideration of the representative components related with the efficiency in heat cooling and vision improving, as well as the representative components related with inflammation and air-pollution, final FL was recommended other than BC and FC in the practice of medicine with the yield and quality integrated into account.

Interfaces between layers in 3D-printed elements produced by extrusion-based material deposition were investigated on both macro- and micro-scales. On the macro-scale, compression and bend tests were performed on two 3D-printable cement-based compositions (3PCs), namely C1 and C2. The influences of binder composition and time interval between layers on layer-interface strength were critically analyzed. In the context of additive manufacturing, the optimized composition C2, containing pozzolanic additives, exhibited mechanical performance superior to that of the mixture with Portland cement as the sole binder. In particular, Mixture C2 showed a less pronounced decrease in interface tensile strength. Even for time intervals between depositions of two layers as long as 1 day the loss in corresponding flexural strength was below 25%, as compared with C2 specimens tested in the perpendicular direction. In contrast, the decrease in flexural strength measured for C1 specimens amounted to over 90% for the same set of parameters. Higher porosity at the interfaces of the printed concrete layers was identified as the cause for the lower interface strengths of C1. Microscopic observations supported the findings of the macroscopic investigations. While a pronounced recovery (“self-healing”) of the porous, discontinuous interlayers was observed with increasing age for Mixture C2, in case of C1 the filling products grown in the porous interlayer were found to be non-strengthening.

Background: Metabolic flux analysis (MFA) is an indispensable tool in metabolic engineering. The simplest variant of MFA relies on an overdetermined stoichiometric model of the cell’s metabolism under the pseudo-steady state assumption, to evaluate the intracellular flux distribution. Despite its long history, the issue of model error in the overdetermined MFA, particularly misspecifications of the stoichiometric matrix, has not received much attention. Method: We evaluated the performance of statistical tests from linear least square regressions, namely Ramsey RESET test, F-test and Lagrange multiplier test, in detecting model misspecifications in the overdetermined MFA, particularly missing reactions. We further proposed an iterative procedure using the F-test to correct such an issue. Result: Using Chinese hamster ovary and random metabolic networks, we demonstrated that: (1) a statistically significant regression does not guarantee high accuracy of the flux estimates, (2) the removal of a reaction with a low flux magnitude can cause disproportionately large biases in the flux estimates, (3) the F-test could efficiently detect missing reactions, and (4) the proposed iterative procedure could robustly resolve the omission of reactions. Conclusion: Our work demonstrated that statistical analysis and tests could be used to systematically assess, detect and resolve model misspecifications in the overdetermined MFA.

With the rapid development of satellite remote sensing technology, the volume of image datasets in many application areas is growing exponentially and the demand for Land-Cover and Land-Use change remote sensing data is growing rapidly. It is thus becoming hard to efficiently and intelligently retrieve the change information that users need from massive image databases. In this paper, content-based image retrieval is successfully applied to change detection and a content-based remote sensing image change information retrieval model is introduced. First, the construction of a new model framework for change information retrieval in a remote sensing database is described. Then, as the target content cannot be expressed by one kind of feature alone, a multiple-feature integrated retrieval model is proposed. Thirdly, an experimental prototype system that was set up to demonstrate the validity and practicability of the model is described. The proposed model is a new method of acquiring change detection information from remote sensing imagery and so can reduce the need for image pre-processing, deal with problems related toseasonal changes as well as other problems encountered in the field of change detection. Meanwhile, the new model has important implications for improving remote sensing image management and autonomous information retrieval.

This study aims to examines three elements shape leadership in Leader-Member Exchange (LMX) theory as a relationship and process. LMX quality is important for the company, because it relates to employee behavior and attitudes, including improving employee performance. The research method applied literature review using description logic and systematics. In this article the theory will be observed specially the effect of LMX on employee performance and antecedents of LMX. The results of the study found that the effect of LMX quality on performance is determined by the characteristics of the task as antecedent LMX in the company.

Cryptography has been used from time immemorial for preserving the confidentiality of data/information in storage or in transit. Thus, cryptography research has also been evolving from the classical Caesar cipher to the modern cryptosystems based on modular arithmetic to the contemporary cryptosystems based on quantum computing. The emergence of quantum computing imposes a major threat on the modern cryptosystems based on modular arithmetic whereby, even the computationally hard problems which constitute for the strength of the modular arithmetic ciphers could be solved in deterministic time. This threat triggered post-quantum cryptography research in order to design and develop post-quantum algorithms that can withstand quantum computing attacks. This paper provides a review of the various post-quantum cryptography and, in specific, code-based cryptography research dimensions. The research directions that are yet to be explored in code-based cryptography research is another key contribution of this paper.

The thymus hosts the development of a specific type of adaptive immune cells called T cells. T cells orchestrate the adaptive immune response through recognition of antigen by the highly variable T-cell receptor (TCR). T-cell development is a tightly coordinated process comprising lineage commitment, somatic recombination of Tcr gene loci and selection for functional, but non-self-reactive TCRs, all interspersed with massive proliferation and cell death. Thus, the thymus produces a pool of T cells throughout life capable of responding to virtually any exogenous attack while preserving the body through self-tolerance. The thymus has been of considerable interest to both immunologists and theoretical biologists due to its multiscale quantitative properties, bridging molecular binding, population dynamics and polyclonal repertoire specificity. Here, we review mathematical modelling strategies that were reported to help understand the flexible dynamics of the highly dividing and dying thymic cell populations. Furthermore, we summarize the current challenges to estimating in vivo cellular dynamics and to reaching a next-generation multiscale picture of T-cell development.

Wetlands are among the most bio-diverse and highest productivity ecosystems on earth, making their monitoring a high priority to conservation, protection and management interests. Although visual interpretation of satellite images is generally precise for monitoring wetlands, recent works have emphasized computerized classification methods because of the reduction in analyst time. However, it is difficult to automatically identify wetland solely based on spectral characteristics due to the complexity of wetland ecosystems. The ability to extract wetland information rapidly and accurately is the basis and the key to wetland mapping at a large scale. Here we propose an operational method to map China wetlands based on Landsat TM data and ancillary data. On the basis of theoretical analysis of wetland automatic classification, we developed a revised multi-layer wetland classification scheme and a rule-based classification model. In the latter, supervised classification (SVM and decision tree) and unsupervised classification (ISODATA) methods were tested. Four Landsat TM images, representing various wetland eco-regions in China (i.e. the Sanjiang Plain in the northeast China, the North China Plain, the Zoige Plateau in the southwest China and the Pearl River Estuary in southeast China), were automatically classified. The overall classification accuracies were 86.57%, 96.00%, 84.51% and 88.30%, respectively, which we considered to be satisfactory accuracy. Our results indicate that issues such as the resolution of geographic data and the understanding of wetland samples should be carefully addressed in the future.

The importance of predicting the exposure to environmental hazards is highlighted by issues like global climate change, public health problems caused by environment stresses, and property damages and depreciations. Several approaches have been used to assess potential exposure and achieve optimal results under various conditions, for example, for different scales, groups of people, or certain points in time. Micro-simulation tools are becoming increasingly important in human exposure assessment, where each person is simulated individually and continuously. This paper describes an agent-based model (ABM) framework that can dynamically simulate human exposure levels, along with their daily activities, in urban areas that are characterized by environmental stresses such as air pollution and heat stress. Within the framework, decision making processes can be included for each individual based on rule-based behavior to achieve goals under changing environmental conditions. The ideas described in this paper are implemented in a free and open source NetLogo platform. A simplified modeling scenario of the ABM framework in Hamburg, Germany, further demonstrates its utility in various urban environments and individual activity patterns, and portability to other models, programs and frameworks. The prototype model can potentially be extended to support environmental incidence management by exploring the daily routines of different groups of citizens and compare the effectiveness of different strategies. Further research is needed to fully develop an operational version of the model.

High-Intensity Functional Training (HIFT) is a novel exercise intervention that may test body systems in a balanced and integrated fashion by challenging individuals’ abilities to complete mechanical work. However, research has not previously determined if physical work capacity is unique to traditional physiologic measures of fitness. Twenty-five healthy men and women completed a six-week HIFT intervention with physical work capacity and various physiologic measures of fitness assessed pre- and post-intervention. At baseline, these physiologic measures of fitness (e.g., aerobic capacity) were significantly associated with physical work capacity and this relationship was even stronger at post-intervention assessment. Further, there were significant improvements across these physiologic measures in response to the delivered intervention. However, the change in these physiologic measures failed to predict the change in physical work capacity induced via HIFT. These findings point to the potential utility of HIFT as a unique challenge to individuals’ physiology beyond traditional resistance or aerobic training. Elucidating the translational impact of increasing work capacity via HIFT may be of great interest to health and fitness practitioners ranging from strength/conditioning coaches to physical therapists.

In the current study, the failure behavior of retrofitted steel structures was studied experimentally and theoretically with steel/CFRP double strap joints (DSJs) under quasi-static tensile loading. A series of DSJs with different bonding lengths are also considered and examined to experimentally assess the effective bond length. To predict the failure load values of the tested specimens, a new stress-based criterion, namely the point stress (PS) criterion is proposed. Although some theoretical predictive modelling for the strength between steel/CFRP joints under various loading conditions has been presented, in this work by using the new proposed approach, one can calculate rapidly and conveniently the failure loads of the steel/CFRP specimens. Furthermore, to assess the validity of the new proposed criterion, further experimental data on steel/CFRP DSJs available in the open literature are predicted using the PS criterion. Finally, it was found that a good agreement exists between the experimental results and the theoretical predictions based on the PS criterion.

Lithium batteries and an increasing focus on CO₂ reduction have become an integral part of daily life and business for many people. Boron and boron compounds have been widely studied together in the history and development of lithium batteries. With a broad examination of battery components and systems but a boron-centric approach to raw materials, this review seeks to summarize past and recent studies on the following: which boron compounds are studied in lithium battery, in which parts of lithium batteries, what improvements are offered for battery performance, and what improvement mechanisms can be explained. The uniqueness of boron and its extensive application beyond batteries contextualizes the interesting similarity with studies on batteries. The paper predominantly focuses on lithium-ion batteries (LIBs) but also mentions other lithium batteries. At the end, the article aims to predict prospective trends for future studies that may lead to the successful and extensive use of boron compounds on a commercial scale.

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.

The aim of this research was to investigate the mass, substances and energy flow through two-stroke low speed Diesel engines. For this reason, a zero-dimensional model of the combustion in the engine was developed with a calculated amount and composition of exhaust gases. Due to the large amount of oxygen in the exhaust gases, a ratio of real air consumption and stoichiometric amount of air required for combustion of injected fuel was set. The calculated ratio showed that the engine consumes four times more air than needed for combustion in AFR_stoich. In this work it is called the Air Consumption Factor or Ratio and has not been mentioned in scientific literature before. Air Consumption Ratio is defined as a factor of dry or humid air. To be more comprehensive, a modified diagram of composition of the flow in and out of a two-stroke fuel injection engine and the cylinder was drawn.

As a new kind of highly compact and efficient micro-channel heat exchanger, printed circuit heat exchanger (PCHE) is a promising candidate satisfying the heat exchange requirements of liquefied natural gas (LNG) vaporization at low and high pressure. The effects of airfoil fin arrangement on heat transfer an flow resistance were numerically investigated using supercritical liquefied natural gas (LNG) as a working fluid. The thermal properties of supercritical LNG were tested by utilizing a REFPROF software database. Numerical simulation was performed using FLUENT. The inlet temperature of supercritical LNG was 121 K,and its pressure was 10.5MPa. The reference mass flow rate of LNG was set 1.22 g/s for the vertical pitch L_v = 1.67 mm and the staggered pitch L_s = 0 mm, with the Reynolds number of about 3750. The SST k-ω model with enhanced wall treatment was selected by comparing with the experimental data. The airfoil fin PCHE had better thermal-hydraulic performance than that of the straight channel PCHE. Moreover, the airfoil fins with staggered arrangement displayed better thermal performance than that of the fins with parallel arrangement. The thermal-hydraulic performance of airfoil fin PCHE was improved with increasing L_s and L_v. Moreover, L_v affected on the Nusselt number and pressure drop of airfoil fin PCHE more obviously. In conclusion, a sparser staggered arrangement of fins showed a better thermal-hydraulic performance in airfoil fin PCHE.

In this work, a hybrid component Fault Detection and Diagnosis (FDD) approach for industrial sensor systems is established and analyzed, to provide a hybrid schema that combines the advantages and eliminates the drawbacks of both model-based and data-driven methods of diagnosis. Moreover, it shines the light on a new utilization of Random Forest (RF) together with model-based diagnosis, beyond its ordinary data-driven application. RF is trained and hyperparameter tuned using three-fold cross validation over a random grid of parameters using random search, to finally generate diagnostic graphs as the dynamic, data-driven part of this system. This is followed by translating those graphs into model-based rules in the form of if-else statements, SQL queries or semantic queries such as SPARQL, in order to feed the dynamic rules into a structured model essential for further diagnosis. The RF hyperparameters are consistently updated online using the newly generated sensor data to maintain the dynamicity and accuracy of the generated graphs and rules thereafter. The architecture of the proposed method is demonstrated in a comprehensive manner, and the dynamic rules extraction phase is applied using a case study on condition monitoring of a hydraulic test rig using time-series multivariate sensor readings.

Emotion detection from the text is an important and challenging problem in text analytics. The opinion-mining experts are focusing on the development of emotion detection applications as they have received considerable attention of online community including users and business organization for collecting and interpreting public emotions. However, most of the existing works on emotion detection used less efficient machine learning classifiers with limited datasets, resulting in performance degradation. To overcome this issue, this work aims at the evaluation of the performance of different machine learning classifiers on a benchmark emotion dataset. The experimental results show the performance of different machine learning classifiers in terms of different evaluation metrics like precision, recall ad f-measure. Finally, a classifier with the best performance is recommended for the emotion classification.

Non-invasive, electroencephalography (EEG)-based brain-computer interfaces (BCIs) on motor imagery movements translate the subject’s motor intention into control signals through classifying the EEG patterns caused by different imagination tasks, e.g. hand movements. This type of BCI has been widely studied and used as an alternative mode of communication and environmental control for disabled patients, such as those suffering from a brainstem stroke or a spinal cord injury (SCI). Notwithstanding the success of traditional machine learning methods in classifying EEG signals, these methods still rely on hand-crafted features. The extraction of such features is a difficult task due to the high non-stationarity of EEG signals, which is a major cause for the stagnating progress in classification performance. Remarkable advances in deep learning methods allow end-to-end learning without any feature engineering, which could benefit BCI motor imagery applications. We developed three deep learning models: 1) a long short-term memory (LSTM); 2) a proposed spectrogram-based convolutional neural network model (pCNN); and 3) a recurrent convolutional neural network (RCNN), for decoding motor imagery movements directly from raw EEG signals without (manual) feature engineering. Results were evaluated on our own, publicly available, EEG data collected from 20 subjects and on an existing dataset known as 2b EEG dataset from "BCI Competition IV". Overall, better classification performance was achieved with deep learning models compared to state-of-the art machine learning techniques, which could chart a route ahead for developing new robust techniques for EEG signal decoding. We underpin this point by demonstrating the successful real-time control of a robotic arm using our CNN based BCI.

In only a few months after initial discovery in Wuhan, China, SARS-CoV-2 and the associated COVID-19 disease has become a global pandemic causing significant mortality and morbidity. In the absence of vaccines and effective therapeutics, reliable serological testing can be a key element of public health policy to control further spread of the disease and gradually ease quarantine measures. However, prior to launch of large-scale seroprevalence studies to assess herd immunity, it is critical to understand the limits and potential of current SARS-CoV-2 serological tests on the market. In this study, we provide an overview of serological testing and conduct a systematic review of independent evaluations of SARS-CoV-2 serological tests performance. Our findings show significant variability in the accuracy of marketed tests and highlight several lab-based and point-of-care rapid diagnostic tests with high performance level in detecting SRAS-CoV-2 specific antibodies. The findings of this review highlight the need for ongoing independent evaluations of commercialized COVID-19 diagnostic tests.

The actions of many drugs involve enzyme inhibition. This is exemplified by the inhibitors of monoamine oxidases (MAO) and the cholinsterases (ChE) that have been used for several pharmacological purposes. This review describes key principles and approaches for the reliable determination of enzyme activities and inhibition as well as some of the methods that are in current use for such studies with these two enzymes. Their applicability and potential pitfalls arising from their inappropriate use are discussed. Since inhibitor potency is frequently assessed in terms of the quantity necessary to give 50% inhibition (the IC50 value), the relationships between this and the mode of inhibition is also considered, in terms of the misleading information that it may provide. Incorporation of more than one functionality into the same molecule to give a multi-target-directed ligands (MTDLs) requires careful assessment to ensure that the specific target effects are not significantly altered and that the kinetic behaviour remains as favourable with the MTDL as it does with the individual components. Such factors will be considered in terms of recently developed MTDLs that combine MAO and ChE inhibitory functions.

In this paper, the effect of an exterior shading element (Iwan) on energy consumption in four different climatic regions, and for different geographical directions, has been investigated numerically and experimentally. By applying different materials and techniques and creating various elements and spaces, architects make hard climatic conditions more tolerable for residents. Iwan is one of the cooling elements which is used in different forms and dimensions in the Islamic architecture. In the present research, Iwan has been introduced as a climatic element in traditional and contemporary architectures and its role in reducing the energy consumption in buildings has been studied. In this respect, first, the thermal loads of a building without Iwan are computed by means of EnergyPlus software. Then, four different forms of Iwan are added to the above-mentioned structure along the four principal geographical directions, and the effect of Iwan on the reduction of thermal loads is analyzed for four different climates. Finally, the design parameters of Iwan, in terms of depth and form, that can help reduce the thermal loads in different climatic conditions are presented. The results show that the best position for using an Iwan is the south direction and the use of Iwan in temperate & humid, hot & humid, cold & mountainous and hot & dry climates could reduce the energy consumption in buildings by 32%, 26%, 14%, and 29%, respectively.

New hybrid nanostructured electrodes for supercapacitors made by combination of electrical double layer and faradaic supercapacitors based-nanomaterials within a single hybrid composite has a great potential on expanding the range of use of these devices and increase their electrochemical performance. In this work, we developed several hybrid nanostructured composites with combinations of such types of materials with potential applicability as electrodes in supercapacitors. In particular, these composites were obtained by easy, cost-effective and scalable procedures, and were composed by NiCo2O4 nanocores as the main faradaic-based nanomaterial and either the conductive polymer polyaniline (PANI), multiwall carbon nanotubes (MWCNTs), or reduced graphene oxide (r-GO) as the electrical double layer-based carbonaceous-based nanomaterials in order to enable the combination of both type of energy storage processes within a single nanostructured device. These constructions allowed us to obtain specific capacitance as large as 1760 F/g, 900 F/g and 734 F/g at a current density of 1 A/g for NiCo2O4/PANI, NiCo2O4/MWCNT, and NiCo2O4/r-GO hybrid nanocomposite electrodes, respectively. Besides, the stability of NiCo2O4/MWCNTs and NiCo2O4/r-GO-based electrodes was outstanding, with capacity losses below 10% after long periods of operation (> 500 cycles).

Infiltration based stormwater best management practices bring considerable economic, social and ecological benefits. Controlling stormwater quantity and quality are primarily important to prevent urban flooding and minimizing loads of pollutants to the receiving waters. However, there have been growing concerns about how the traditional design approach contributes to the failure of infiltration based BMP’s that have caused flooding, ponding, prolonged movement of surface water, and frequent clogging, etc. Many of these problems were due to the fact that the current design approaches of stormwater BMP’s only focus on surface hydrology and give little or no attention to the underline subsoil permeability rate and other constraints during the design and sizing process. As a result, we are exhibiting many newly constructed infiltration based BMP’s are failing to function well. This paper presents and demonstrates a new paradigm shift in designing infiltration-based stormwater BMP’s by combining subsurface hydrology and undelaying native soil constraints to establish acceptable criteria for sizing infiltration based BMPs.

Lattice and code cryptography can replace existing schemes such as Elliptic Curve Cryptography because of their resistance to quantum computers. In support of public key infrastructures, the distribution, validation and storage of the cryptographic keys is then more complex to handle longer keys. This paper describes practical ways to generate keys from physical unclonable functions, for both lattice and code based cryptography. Handshakes between client devices containing the PUFs and a server are used to select sets of addressable positions in the PUFs, from which streams of bits called seeds are generated on demand. The public and private cryptographic key pairs are computed from these seeds together with additional streams of random numbers. The method allows the server to independently validate the public key generated by the PUF, and act as a certificate authority in the network. Technologies such as High performance computing, and graphic processing units can further enhance security by preventing attackers to make this independent validation when only equipped with less powerful computers.

Almost all single-family detached house in Canada consume huge electricity for space heating and domestic hot water (DHW) purposes. There are many possibilities to design an energy-efficient house. A solar water heating system can be used for domestic water and space heating. Water temperature can be kept constant always by connecting a heat pump or oil burner because solar energy is intermittent. Proper and optimized solar photovoltaic and collector design, tank design, heat pump selection, house insulation, total demand calculation in each section are essential. Energy-Efficient house design has been proposed with water heating and space heating system and compared with the existing system, solar PV based systems, and solar collector based system. The tracking and non-tracking based solar thermal collector based and the solar photovoltaic based system has been compared in this paper and investigate the suitable one for practically applicable and acceptable by the people. Simulation has been done by using the PolySun software. It found that by implementing the proposed PV based system with tracking is highly suitable considering lower cost, high output power, flexibility, easy installation.

Wireless communication has proliferated due to the colossal augmentation of smart phones, mobile phones, tablets and laptops etc. Multimedia applications such as live videos, audios, still images, animated graphics and live communications all requires accompaniments of quality of service (QoS) as well as reliable end-to-end transfer of data. Information about the availability of resources in channel is must in order to improve the QoS. QoS can be determined in terms of capacity of channel, available bandwidth (ABW), and bulk transfer capacity (BTC). Bandwidth availability is a key check for improving the QoS in a network. The performance of a multimedia application is directly affected by the bandwidth availability. One of the most important QoS characteristic is ABW at a wireless route and it can be demarcated as least unused capacity of links instituting a network route. Since now there have been many bandwidth estimation techniques are available in the literature to increase the network performance. Bandwidth estimation techniques have been arranged into three leading classifications: i) Active probing bandwidth estimation techniques, ii) Passive bandwidth estimation technique and iii) Model based bandwidth estimation techniques. Each of these techniques are briefly discussed in this paper.

Meshfree particle method, which is always regarded as a pure Lagrangian approach, is easily represented complicated domain topologies, moving boundaries, and multiphase media. Solving acoustic problems with the mesfree particle method forms a branch of the acoustic wave modeling field, namely, particle-based computational acoustics (PCA). The aim of this paper is to improve the accuracy of using the PCA method to solve two-dimensional acoustic problems, and realize the particle representation with a hybrid meshfree and finite-difference time-domain (FDTD) method for acoustic boundary conditions at both the plane and curved surface. As a widely used Lagrangian meshfree method, the smoothed particle hydrodynamics (SPH) based on the support domain and the kernel function has developed rapidly in recent years. The traditional SPH method is easily implements parallel processing and has been applied in sound wave simulation. As a corrective method with higher accuracy than SPH, the acoustic propagation and scattering in the time domain is simulated with the corrective smoothed particle method (CSPM). Moreover, a hybrid meshfree-FDTD boundary treatment technique is utilized to represent different acoustic boundaries in the Lagrangian approach. In this boundary treatment technique, the parameter value of virtual particles is obtained with the FDTD method, which concerns truncation errors based on the Tayler series expansion. Soft, rigid, and Mur’s absorbing boundary conditions are developed to simulate sound waves in finite and infinite domain. Results of modeling acoustic propagation and scattering show that CSPM is accurate and convergence with exact solutions, and different acoustic boundaries are validated to be effective in the computation.

Mapping vegetation species is critical to facilitate related quantitative assessment, and for invasive plants mapping their distribution is important to enhance monitoring and controlling activities. Integrating high resolution multispectral remote sensing (RS) image and lidar (light detection and ranging) point clouds can provide robust features for vegetation mapping. However, using multiple source of high-resolution RS data for vegetation mapping at large spatial scale can be both computationally and sampling intensive. Here we designed a two-step classification workflow to decrease computational cost and sampling effort, and to increase classification accuracy by integrating multispectral and lidar data to derive spectral, textural, and structural features for mapping target vegetation species. We used this workflow to classify kudzu, an aggressive invasive vine, in the entire Knox County (1,362 km2) of Tennessee, the United States. Object-based image analysis was conducted in the workflow. The first-step classification used 320 kudzu samples and extensive coarsely labeled samples (based on national land cover) to generate an overprediction map of kudzu using random forest (RF). For the second step, 350 samples were randomly extracted from the overpredicted kudzu and labeled manually for the final prediction using RF and support vector machine (SVM). Computationally intensive features were only used for the second-step classification. SVM had constantly better accuracy than RF, and the Producer’s Accuracy, User’s Accuracy, and Kappa for the SVM model on kudzu was 0.94, 0.96, and 0.90, respectively. SVM predicted 1010 kudzu patches covering 1.29 km2 in Knox County. We found the sample size of kudzu used for algorithm training impacted the accuracy and number of kudzu predicted. The proposed workflow could also improve sampling efficiency and specificity. Our workflow had much higher accuracy than the traditional method conducted in this research, and could be easily implemented to map kudzu in other regions or other vegetation species.

Is it true that the nuclear technology applied to electric energy generation offers a clean, safe, reliable and affordable i.e. sustainable alternative? Yes it is, but its impact on the environment strongly depends on the implementation bearing residual risks due to a human factor, technical failures or natural catastrophes. A full response is therefore difficult and can first be given when the wicked multi-disciplinary problems get well formulated and “solved”. These problems have multi-dimensional nature lying at the interface between: necessary R&D effort, the industrial deployment and the technology impact in view of the environmental sustainability including the management of produced hazardous waste. This enormous complexity indicates that just a description of the problem might represent a problem. The paper proposes a holistic approach to assess the nuclear energy systems potential with respect to sustainable performance applying Multi-criteria decision analysis with a suitable objective tree and a multi-level criteria structure and examines the trading-off techniques for ranking of the alternatives. The framework proposes a multi-criteria and multi-stakeholders treatment which can be used as a pre-decisional support towards an implementation of nuclear fuel cycles adapted to national preferences and priorities. Proposed approach addresses some aspects of the environmental footprint of nuclear energy systems. Advanced nuclear fuel cycles, previously investigated by the NEA/OECD expert group WASTEMAN, are analyzed as a case study. Sustainability facets of waste management, resource utilization and economics are in focus.

The stormwater drainage problem is one of the major challenges facing in Shire Endaslasse town, Ethiopia. In a town, Street flooding and overtopping drainage system problems are occurring during the rainy season. This causes ponding which poses difficulties in ease of transportation and it hinders the day-to-day activity of the people. So, the study focuses on the performance of the stormwater drainage system in Shire Endaslasse town using Arc GIS and SWMM5.1. For this study, the primary data were collected by field surveys and interviews with the council body. Simulation results for storm events show that in some of the drainage systems in different regions of Shire Endaslasse town have flooded. During the field observation, the drainage structures are filled with solid wastes, inadequate inlet and outlet structures and some of the top element of the manhole have been broken this may cause a problem of aesthetic and healthy at large it may increase flood risk. The flooding risk in the drainage systems is very high due to the drainage system is undersized to cope with the current rainfall rates, but also is very limited to face the upcoming predicted rainfall.

Latin binomials, popularised in the eighteenth century by the Swedish naturalist Linnaeus, have stood the test of time in providing a stable, clear and memorable system of nomenclature across biology. However, relentless and ever-deeper exploration and analysis of the microbial world has created an urgent unmet need for huge numbers of new names for Archaea and Bacteria. Manual creation of such names remains difficult and slow and typically relies on expert-driven nomenclatural quality control. Keen to ensure the legacy of Linnaeus lives on in the age of microbial genomics and metagenomics, we propose an automated approach, employing combinatorial concatenation of roots from Latin and Greek to create linguistically correct names for genera and species that can be used off the shelf as needed. As proof of principle, we document over a million new names for Bacteria and Archaea. We are confident that our approach provides a road map for how to create new names for decades to come.

Artificial sweeteners are widely used food ingredients in beverages and drinks to lower calorie intake which in turn prevent lifestyle diseases such as obesity. Epidemiological evidences suggest that an overdose of artificial sweeteners could result to adverse effects after consumption. Thus, our study aims to systematically explore the potential adverse effects of eight commercial artificial sweeteners, including acesulfame-K, alitame, aspartame, sodium cyclamate, dulcin, neotame, saccharin and sucralose on cardiac performances of zebrafish (Danio rerio) and Daphnia as model animals. Embryonic zebrafish and Daphnia were exposed to eight artificial sweeteners at 100 ppb concentrations and their cardiac performance (heart rate, ejection fraction, fractional shortening, stroke volume, cardiac output and heartbeat regularity) were measured and compared. Saccharin significantly increased the heart rate of zebrafish larvae while a significant decrease was observed in Daphnia. Significant increase was also noted in zebrafish heart rate variability after incubation in acesulfame K, dulcin, sodium cyclamate, and sucralose. However, a significant increase in Daphnia was only observed after incubation in dulcin. Based on Principal Component Analysis (PCA) and hierarchical clustering results, several artificial sweetener samples were species-specific to zebrafish and Daphnia. Our study demonstrates the potential adverse physiological effects of artificial sweeteners in cardiovascular systems of zebrafish larvae and Daphnia.

This paper presents the systematic development and performance characterization of a non-proprietary 3D-printable ultra-high-performance fiber-reinforced concrete (UHPFRC) for digital construction. Several fresh and hardened properties of the developed 3D-printable UHPFRC matrix (without fiber) and composite (with 2% volume fraction of steel fibers) were evaluated and compared to that of conventionally mold-cast UHPFRC. Additionally, the effects of testing direction on the compressive and flexural strengths of the printed UHPFRC were investigated. The fresh properties of the UHPFRC developed in this study satisfied the criteria for extrudability, buildability, and shape-retention-ability, which are relevant for ensuring printability. The printed UHPFRC exhibited superior flexural performance to the mold-cast UHPFRC due to alignment of the short fibers in the printing direction. The high compressive and flexural strengths, along with the deflection-hardening behavior, of the developed UHPFRC can enable the production of thin 3D-printed components with significant reduction or complete elimination of conventional steel bars.

The interest on the use of renewable energy resources is increasing, especially towards wind and hydro powers, which should be efficiently converted into electric energy via suitable technology tools. To this aim, data--driven control techniques represent viable strategies that can be employed for this purpose, due to the features of these nonlinear dynamic processes working over a wide range of operating conditions, driven by stochastic inputs, excitations and disturbances. Some of the considered methods, such as fuzzy and adaptive self--tuning controllers, were already verified on wind turbine systems, and similar advantages may thus derive from their appropriate implementation and application to hydroelectric plants. These issues represent the key features of the work, which provides some guidelines on the design and the application of these control strategies to these energy conversion systems. The working conditions of these systems will be also taken into account in order to highlight the reliability and robustness characteristics of the developed control strategies, especially interesting for remote and relatively inaccessible location of many installations.

While the Internet of Things (IoT) technology has been widely recognized as the essential part of Smart Cities, it also brings new challenges in terms of privacy and security. Access control (AC) is among the top security concerns, which is critical in resource and information protection over IoT devices. Traditional access control approaches, like Access Control Lists (ACL), Role-based Access Control (RBAC) and Attribute-based Access Control (ABAC), are not able to provide a scalable, manageable and efficient mechanism to meet the requirements of IoT systems. Another weakness in today's AC is the centralized authorization server, which can be the performance bottleneck or the single point of failure. Inspired by the smart contract on top of a blockchain protocol, this paper proposes BlendCAC, which is a decentralized, federated capability-based AC mechanism to enable an effective protection for devices, services and information in large scale IoT systems. A federated capability-based delegation model (FCDM) is introduced to support hierarchical and multi-hop delegation. The mechanism for delegate authorization and revocation is explored. A robust identity-based capability token management strategy is proposed, which takes advantage of the smart contract for registering, propagating and revocating of the access authorization. A proof-of-concept prototype has been implemented on both resources-constrained devices (i.e., Raspberry PI node) and more powerful computing devices (i.e., laptops), and tested on a local private blockchain network. The experimental results demonstrate the feasibility of the BlendCAC to offer a decentralized, scalable, lightweight and fine-grained AC solution for IoT systems.

In this changing world, we all are surrounded by the surmountable risk of getting injured. Amongst various risk factors, major burns are the most distressing and catastrophic. Burn wounds are not easy to heal via natural healing process and ultimately ended up with scar formation. If the degree of burn is high then the loss of tissue and its function is very common. To fasten-up the natural burn wound healing; zinc, an essential trace element is found to be very much effective. But due to its’ particle size limitation, less contact with wounded cells and tissues, and high inherent toxicity restrict its use. Needlessly, zinc is an element with dual action i.e. both antimicrobial and wound healing it is a prime choice to apply its aptitude in burn wound healing. To overcome the documented limitations zinc has converted to nanoparticle form. Zinc oxide nanoparticles, in particular, have attained ample of interest due to their unique properties and potential antimicrobial activity along with wound healing activity which makes it promising for the healing of topical burn wounds. Plant mediated green synthesis of nano-metal oxide particles is gaining a lot of significance due to its simplicity, eco-friendliness and extensive antimicrobial activity and recommended as an appealing substitute to not only physical methods but also chemical methods avoiding the use of the high rate of toxic chemicals and extreme surroundings. This study includes ZnO NPs role in burn wound healing with Phyto-mediated synthesis methods to provide evidence of their potential applications. Additionally, it provides an overview of traditional methods used for the synthesis of ZnO nanoparticles and characterization techniques to obtain information concerning the size, shape and optical properties along with toxicity and safety concern of ZnO NPs and its biomedical applications.

Nature-based solutions (NbS) - working with and enhancing nature to address societal challenges - are increasingly being featured in climate change adaptation policy and plans. While there is growing evidence that NbS can reduce vulnerability to climate change impacts in general, there is a lack of understanding on the mechanisms through which this can be achieved, particularly in the Global South. To address this, we analyse 85 nature-based interventions in rural areas across the Global South, and factors mediating their effectiveness, based on a systematic map of peer-reviewed studies encompassing a wide diversity of ecosystems, climate impacts, and intervention types. We develop and apply an analytical framework of people’s social-ecological vulnerability to climate change, in terms of six pathways of vulnerability reduction: social and ecological exposure, sensitivity, and adaptive capacity. Most cases (95%) report a reduction in vulnerability, primarily by lowering ecosystem sensitivity to climate impacts (73% of interventions), followed by reducing social sensitivity (52%), reducing ecological exposure (36%), increasing social adaptive capacity (31%), increasing ecological adaptive capacity (19%) and/or reducing social exposure (14%). An analysis of mediating factors shows that social dimensions are equally important as technical factors in NbS to achieving equitable and effective outcomes. Attention to the distinct social and ecological pathways through which vulnerability is reduced helps to harness the multiple benefits of working with nature in a warming world.

Introduction: Exams are a relatively stressful period for all students, especially undergraduate medical students. Exams bring anxiety and stress for the students. Some students experience such high stress that it hinders their academic productivity and reduces their exam performance. The aim of this study is to assess the effects of pre-exam anxiety on the academic performance of medical students. Methods: Two hundred and twenty-five final year medical students completed Westside Test Anxiety (WTA) Scale one month before their exams. Grade Point Average (GPA) of these students was noted when the results were announced. Data was processed and analyzed using SPSS v 22.0Results: The mean anxiety score on WTA scale was 3.46 ± 0.87. All students (100%) who scored ≤ 2.50 GPA were highly-extremely highly anxious. In the 2.51-3.00 GPA group, 46% were highly-extremely highly anxious, 32% were moderately anxious, and 21% had low to normal anxiety. In the 3.01-3.50 GPA group, 30% were highly-extremely highly anxious, 30% were moderately anxious, and 39% had low to normal anxiety. In the 3.51-4.00 GPA group, 29% were highly-extremely highly anxious, 23% were moderately anxious, and 47% had low to normal anxiety. The correlation coefficient between GPA and test anxiety of students was -.314 which shows inverse relationship.Conclusion: Pre-exam anxiety and stress imparts negative effects on the exam performance of final year medical students. Poor academic performance was associated with high to extremely high pre-exam anxiety while high achievers had relatively lower anxiety levels.

In this paper, a new design of a helium-assisted hybrid drone is proposed for flight time enhancement. As is widely known, most of the drones with a VTOL (vertical take-off and landing) feature have a short operation time, limiting their capability to carry out sustainable operations for the given missions. Thus, with the clear goal of enhancing the flight time, this study aims to develop a hybrid drone system, where a helium balloon is used to provide a lifting force for this purpose. The proposed design for the hybrid drone has several benefits including easiness to manufacture and relatively small size when compared to other types of hybrid drones. Various analyses are conducted for the design of the hybrid drone system including the balloon shape and size, buoyant force, flight time, and connector design. Since stability and performance are one of the most important issues for the new design, the pole location analysis is conducted based on the control theory. This rigorous analysis provides that the proposed hybrid drone design is stable as well as robust against swinging motions. To validate the effectiveness of the proposed design and flight time enhancement, simulations were conducted and experimental results are also provided using the manufactured hybrid drone system. Through the real experiments, it is proved that the hybrid drone can increase the flight time more than 2.5 times while guaranteeing stable motions.

This paper presents a brief account and rationale for implementation of competency-based learning in any form of management and leadership development programmes in South Africa. The fact that competency-based learning (CBL), also known as outcomes-based learning has been scrapped from the schooling system in South Africa is unfortunate as this method enhances critical thinking skills and practical problem-solving skills. This paper presents CBL as a model of meta-cognitive approach to learning that integrates both theory and practice into experiential learning through six dimensions, namely, active learning, constructive learning, cumulative learning, goal-oriented learning, learner-centred and curriculum design strategies. In the advent of the fast-paced global economy, managers and leaders need to seek those business schools that value real-time practical approach to curriculum for relevance and to maximize shareholder value through human resource development. This brief presentation ends by recommending an approach through CBL that fosters three critical pillars of leadership development, namely: intellectual, behavioural and emotional agenda.

This paper attempts at applying adaptive network-based fuzzy inference system (ANFIS) for analysis of the resonant frequency of a microstrip rectangular patch antenna with two equal size slots which are placed on the patch vertically. The resonant frequency is calculated as the position of slots is shifted to the right and left sides on the patch. As a result , the antenna resonates at more than one frequency . Commonly, machine algorithms based on artificial neural networks are employed to recognize the whole resonant frequencies. However ,they fail to estimate the resonant frequencies correctly as in some cases variations are not very sensible and the resonant frequencies overlap each other . It can be concluded that artificial neural networks could be replaced in such designs by the adaptive network-based fuzzy Inference system due to its high approximation capability and much faster convergence rate.

Bangladeshi construction industry suffers a lot of safety and accidental issues than other developing countries in the world. Among many of these, accident of construction project goes far beyond and shape a horrific figure of death for every year. The aims of this study is that analysis and discussion of causes of accident at construction project in Bangladesh. A widespread statistical data collection and data analysis take place to identify the causes and design the questionnaire. The questionnaire-based survey was used to elicit the attitude of four stakeholders as workers, owners, consultants, and contractors towards passive causes of fatal accident at construction site. These study also identify 77 passive causes under 14 major groups and ranked them based on Relative Importance Index (RII). The top 5 major group of causes are (1) Management related, (2) Consultant related, (3) Technology related, (4) Labour related and (5) Contractor related. The top 5 passive causes are: (1) Unaware of safety-related issue, (2) Lack of personal protective equipment, (3) Lack of safety eliminating/ avoiding design, (4) Unfit equipment, (5) Lack of knowledge and training on equipment.

There is increasing evidence that the impact of climate change on the productivity of grasslands will at least partly depend on their biodiversity. A high level of biodiversity may confer stability to grassland ecosystems against environmental change, but there are also direct effects of biodiversity on the quantity and quality of grassland productivity. To explain the manifold interactions, and to predict future climatic responses, models may be used. However, models designed for studying the interaction between biodiversity and productivity tend to be structurally different from models for studying the effects of climatic impacts. Here we review the literature on the impacts of climate change on biodiversity and productivity of grasslands. We first discuss the availability of data for model development. Then we analyse strengths and weaknesses of three types of model: ecological, process-based and integrated. We discuss the merits of this model diversity and the scope for merging different model types.

This paper studies the optimization method of pump hydraulic performance based on the response surface method. A parametric model of impeller and diffuser is established. Three-dimensional optimization is carried out on the basis of the initial model obtained by one-dimensional design method. We select the pump hydraulic efficiency and the head as objective function and constraint function. Response surface models are constructed to analyze the relationship between the objectives and the design variables, and the global optimization of hydraulic performance is realized. According to the internal flow characteristics of pump, this paper proposes the strategy of two steps optimization, which aims at meridional plane and blade shape, respectively, to solve the problem of large numbers of design parameters and computational cost. The optimization results show that the hydraulic efficiency of pump increased by 3.7%, and the head is nearly the same.

The potential advantages of plant-based interventions in decreasing the incidence and lessening the morbidity and mortality of COVID-19 are yet to be widely acknowledged. Nevertheless, recent publications propose that these interventions could be effective. Our cardiology center conducted an interventional study in which we incorporated plant-based foods and provided supplementation to 3470 elderly COVID-19 patients with multiple comorbidities. We pay close attention to the precise selection of food items, the application of appropriate processing methods, and the provision of nourishing meals to our patients. Our outcome was highly successful as we achieved a zero mortality rate, and none of our patients experienced worsening conditions or required hospitalization. In this review article we present the mechanisms how plant-based and supplement interventions can mitigate COVID-19 disease severity and mortality. And experts have validated our hypotheses very recently. The measures and methods used to address the ongoing SARS-CoV-2 pandemic through plant-based interventions and supplements are effective and may help prevent or manage future pandemics.

Object-Based Image Analysis (OBIA) has been successfully used to map slums. In general, the occurrence of uncertainties in producing geographic data is inevitable. However, most studies concentrated solely on assessing the classification accuracy and neglecting the inherent uncertainties. Our research analyses the impact of uncertainties in measuring the accuracy of OBIA-based slum detection. We selected Jakarta as our case study area, because of a national policy of slum eradication, which is causing rapid changes in slum areas. Our research comprises of four parts: slum conceptualization, ruleset development, implementation, and accuracy and uncertainty measurements. Existential and extensional uncertainty arise when producing reference data. The comparison of a manual expert delineations of slums with OBIA slum classification results into four combinations: True Positive, False Positive, True Negative and False Negative. However, the higher the True Positive (which lead to a better accuracy), the lower the certainty of the results. This demonstrates the impact of extensional uncertainties. Our study also demonstrates the role of non-observable indicators (i.e., land tenure), to assist slum detection, particularly in areas where uncertainties exist. In conclusion, uncertainties are increasing when aiming to achieve a higher classification accuracy by matching manual delineation and OBIA classification.

Education plays an essential role in the acquisition of knowledge and skills. School is the place where children’ attitudes, interests, and habits are developed. More involved and supportive a parent is towards their adolescent the higher self-esteem they will gain allowing the adolescent to feel confident and focused during school. Parents therefore enroll their children in schools that can give them expected quality of lives. The main core objective for this study was to determine the parental influence on students’ academic performance - A case study of Debre Berhan General Secondary School. A total of 118 students were included in the present study using simple random sampling technique from the total students (2367). Cross-sectional design was appropriate for this study. Primary source of data was employed collected from students. Descriptive and inferential statistics were applied. For statistical analysis SPSS 20 was used at 5% level of significance. Descriptive statistics results indicate that the average mark of students was with minimum and maximum value of 47 and 94 respectively. Multiple regression models showed that Income level, Rate of money given to satisfy basic and educational materials, Parent’s occupation, Family size, Parent’s educational level, and Parent’s way of giving motivation were found to be a significant effect on academic performance of students at 5% level of significance at study area. However, sex of students, age of students and grade level of students were not found to be significant effect to academic performance at study area. Overall model is statistically significance for the data. All the assumption of regression analysis was satisfied. Normality, Constant variance, absence of multicolinearity, linearity and absence of autocorrelation were satisfied due to formal tests and diagnostic plots. Study recommended the need for programs related to improvement of mother and father education for caring child in appropriate success of their education and also parents should allocate time to visit schools and establish good relationship and discussion about their children’s academic progress in according to way of motivation to learn actively in school.

In this study, the on-going research into the improvement of micro-gas turbine propulsion system performance and the suitability for application as propulsion systems for small tactical UAVs (<600 kg) is investigated. The study is focused around the concept of converting existing micro turbojet engines to turbofan with the use of a continuously variable gearbox, thus maintaining a single spool configuration and relative design simplicity. This is an effort to reduce the initial engine development cost, whilst improving propulsive performance. The BMT 120 KS micro turbojet engine is selected for performance evaluation of the conversion process using gas turbine performance software, GasTurb13. The preliminary design of a matched low-pressure compressor (LPC) for the proposed engine is then performed using meanline calculation methods. According to the analysis carried out, an improvement in the converted micro gas turbine engine performance in terms of thrust and specific fuel consumption is achieved. Furthermore, with the introduction of a CVT gearbox, fan speed operation may be adjusted independently of the core, allowing increased thrust generation or better fuel consumption. Therefore, enabling a wider gamut of operating conditions and enhances the performance and scope of tactical UAV.

Smoothed particle hydrodynamics (SPH) is regarded as a pure Lagrangian approach, which can solve fluid dynamics problems without the creation of mesh. In this paper, a paralleled SPH solver is developed to solve particle-based computational acoustics (PCA). The aim of this paper is to study the feasibility of using SPH to solve acoustic problems and to improve the efficiency of solving processes by paralleling some procedures on GPU during calculating. A stand SPH code running serially in a CPU is proposed to solve wave equation. This is a wave propagating in a two-dimensional domain. After finishing the computation, the results are compared with the theoretical solutions and they agree well. So its feasibility is verified. There are two main methods for searching neighbor particles: all-pair search method and linked-list search method. Both methods are used in different codes to simulate an identical problem and their runtimes are compared to investigate their searching efficiencies. The runtime results show that linked-list search method has a higher efficiency, which can save a lot of searching time when simulating problems with huge amounts of particles. Furthermore, the percentages of different procedures’ runtimes in a simulation are also discussed to find the most consuming one. Then, some codes are modified to run in different GPUs and their runtimes are compared with those of serial ones on a CPU. Runtime results show that the paralleled algorithm can be more than 80 times faster than the serial one. The result shows that GPU paralleled SPH computing can achieve desirable accuracy and speed in solving acoustic problems.

Deep reinforcement learning (DRL) has achieved significant results in many Machine Learning (ML) benchmarks. In this short survey we provide an overview of DRL applied to trading on financial markets, including a short meta-analysis using Google Scholar, with an emphasis on using hierarchy for dividing the problem space as well as using model-based RL to learn a world model of the trading environment which can be used for prediction. In addition, multiple risk measures are defined and discussed, which not only provide a way of quantifying the performance of various algorithms, but they can also act as (dense) reward-shaping mechanisms for the agent. We discuss in detail the various state representations used for financial markets, which we consider critical for the success and efficiency of such DRL agents. The market in focus for this survey is the cryptocurrency market.

Cash crops are agricultural crops intended to be sold for profit as opposed to subsistence crops, meant to support the producer, or to support livestock. Since cash crops are intended for future sale, they translate into large financial value when considered on a wide geographical scale, so their production directly involves financial risk. At a national level, extreme weather events including destructive rain or hail, as well as drought, can have a significant impact on the overall economic balance. It is thus important to map such crops in order to set up insurance and mitigation strategies. Using locally generated data -such as municipality-level records of crop seeding- for mapping purposes implies facing a series of issues like data availability, quality, homogeneity etc. We thus opted for a different approach relying on global datasets. Global datasets ensure homogeneity and availability of data, although sometimes at the expense of precision and accuracy. A typical global approach makes use of spaceborne remote sensing, for which different land cover classification strategies are available in literature at different levels of cost and accuracy. We selected the optimal strategy in the perspective of a global processing chain. Thanks to a specifically developed strategy for fusing unsupervised classification results with environmental constraints and other geospatial inputs including ground-based data, we managed to obtain good classification results despite the constraints placed. The overall production process was composed using ``good-enough" algorithms at each step, ensuring that the precision, accuracy, and data-hunger of each algorithm was commensurate to the precision, accuracy, and amount of data available. This paper describes the tailored strategy developed on the occasion as a cooperation among different groups with diverse backgrounds, a strategy which is believed to be profitably reusable in other, similar contexts. The paper presents the problem, the constraints and the adopted solutions; it then summarizes the main findings including that efforts and costs can be saved on the side of Earth Observation data processing when additional ground-based data are available to support the mapping task.

Neisseria meningitidis causes most cases of bacterial meningitis. Meningococcal meningitis is a public health burden to both developed and developing countries throughout the world. There are a number of vaccines (polysaccharide-based, glycoconjugate, protein-based and combined conjugate vaccines) that are approved to target five of the six disease-causing serogroups of the pathogen. Immunization strategies have been effective at helping to decrease the global incidence of meningococcal meningitis. Researchers continue to enhance these efforts through discovery of new antigen targets that may lead to a broadly protective vaccine and development of new methods of homogenous vaccine production. This review describes current meningococcal vaccines and discusses some recent research discoveries that may transform vaccine development against N. meningitidis in the future.

Mereotopology is a concept rooted in analytical philosophy. The phase-field concept is based on mathematical physics and finds applications in materials engineering. The two concepts seem to be disjoint at a first glance. While mereotopology qualitatively describes static relations between things like x isConnected y (topology) or x isPartOf y (mereology) by first order logic and Boolean algebra, the phase-field concept describes the geometric shape of things and its dynamic evolution by drawing on a scalar field. The geometric shape of any thing is defined by its boundaries to one or more neighboring things. The notion and description of boundaries thus provides a bridge between mereotopology and the phase-field concept. The present article aims to relate phase-field expressions describing boundaries and especially triple junctions to their Boolean counterparts in mereotopology and contact algebra. An introductory overview on mereotopology is followed by an introduction to the phase-field concept already indicating first relations to mereo- topology. Mereotopological axioms and definitions are then discussed in detail from a phase-field perspective. A dedicated section introduces and discusses further notions of the isConnected relation emerging from the phase-field perspective like isSpatiallyConnected, isTemporallyConnected, isPhysicallyConnected, isPathConnected and wasConnected. Such relations introduce dynamics and thus physics into mereotopology as transitions from isDisconnected to isPartOf can be described.

2S-hesperidin is a flavanone (flavonoid) found in high concentrations in citrus fruits. It has an antioxidant and anti-inflammatory effect, improving performance in animals. This study investigated the effects of chronic intake of an orange extract (2S-hesperidin) or placebo on aerobic-anaerobic and metabolic performance markers in amateur cyclists. A double-blind, randomized, placebo-controlled trial was carried out between late September and December 2018. Forty amateur cyclists were randomized into two groups: one taking 500mg/day 2S-hesperidin and other taking 500 mg/day placebo (microcellulose) for 8 weeks. All participants completed the study. Performance and metabolic aerobic-anaerobic markers were measured using incremental and rectangular tests by indirect calorimetry. The anaerobic power was determined using Wingate tests. After 8 weeks supplementation, there was a significant increase in the incremental test in estimated functional threshold power (FTP) (3.23%; p≤0.05) and maximum power (2.68%; p≤0.05) with 2S-Hesperdin compared to placebo. In the rectangular test, there was a significant decrease in VO₂ (-8.26%; p≤0.01) and VO₂R (-8.88%; p≤0.01) at VT2 in placebo; however, there were no significant differences between groups. In the Wingate test, there was a significant increase (p≤0.05) in peak and relative power in both groups, but without significant differences between groups. Supplementation with an orange extract (2S-hesperdin) 500mg/day improves estimated FTP and maximum power performance in amateur cyclists.

Recent media reports commented about a possible antidiabetics’ misuse issue related to molecules promoted as a weight-loss treatment in non-obese people. We here evaluated available pharmacovigilance misuse/abuse signals related to semaglutide, a glucagon-like peptide-1 (GLP-1) analogue, in comparison to other GLP-1 receptor agonists (albiglutide; dulaglutide; exenatide; liraglutide; lixisenatide; tirzepatide) and the phentermine-topiramate combination. To that aim, we analysed the Food and Drug Administration-FDA Adverse Events Reporting System (FAERS) dataset, performing a descriptive analysis of adverse event reports (AER) and calculating related pharmacovigilance measures, including the reporting odds ratio (ROR) and the proportional reporting ratio (PRR). During January 2018-December 2022, a total of 31,542 AER involving the selected molecules were submitted to FAERS; most involved dulaglutide (n=11,858; 37.6%) and semaglutide (n=8,249; 26.1%). In comparing semaglutide vs the remaining antidiabetics, the AER ‘drug abuse’, ‘drug withdrawal syndrome’, ‘prescription drug used without a prescription’ and ‘intentional product use issue’ respective PRR values were 4.05, 4.05, 3.60 and 1.80 (all<0.01). The same comparisons of semaglutide vs the phentermine-topiramate combination were not associated with any significant differences. To our best, this is the first study documenting the misusing/abusing potential of semaglutide in comparison with other novel antidiabetics and the phentermine-topiramate combination. Current findings will need to be confirmed by further empirical investigations to fully understand the safety profile of those molecules.

The purpose of the article is to determine the peculiarities of using of teaching elements of research-based training at the Institute of Human Sciences of Borys Grinchenko Kyiv University. Based on the focus group methodology, the authors identify the key methodological characteristics of research-based training, which have been put into basis of analysis of educational programs for the purpose of determining the application of tasks that contribute to the development of research skills of students. The study used a method of focus group. Its purpose was to obtain the necessary information from the participants to describe the methodological basis and justification of methods, forms, indicators, etc. of research-based training system among people who are competent, have experience in this field. After that, the method of "theoretical sampling" was used, which enabled to formulate generalized characteristics according to the results of focus groups. The practical value of the study is determination of the methodological characteristics of research-based training which is the basis for the application of tasks by university teachers that promote the development of research competence of students. The research is one of the first attempts to determine the methodological characteristics of research-based training in Ukraine.

The study aims to analyze the performance criteria of the GR4J model to reproduce high water flows in the Ouémé watershed at Bonou's outlet which has been vulnerable to climate change in recent decades. The methodology focused on the use of daily climatological and hydrometric data extracted from files of National Directorate of Meteorology, and General Directorate of Water; they were supplemented by those of SIEREM/HSM dataset over the period 1961-2015. The rainfall was regionalized using Thiessen method. The performance of the GR4J model was assessed with NSE, RMSE and KGE criteria. The results indicate that the study area is marked by rainfall variabilities and detection of two breakpoints (1968 and 1987) which divide the series into three sub-periods; these discontinuities have repercussions on the streamflow. It's found that GR4J model overestimates the streamflow during the low water period and underestimates them in high water. However, the efficiency and performance criteria NSE, RMSE and KGE calculated on high water flow rates are better in calibration than in validation. The KGE values are range between 83-85% in calibration and 56-68% during validation, which gives to GR4J model the efficiency and performance to reproduce high flow rates in the study area

The purpose of this study is to ascertain whether implementation difficulty can be used in slot allocation model as a new mechanism for slightly weakening grandfather right. According to which, a linear integer programming model is designed to compare and analyze displacement, implementation difficulty and priority with different weights. Test results show that the implementation difficulty can be significantly reduced without causing excessive displacement and disruption of existing priorities, by weight setting while declared capacity is cleared. In addition to this, whether the movements are listed in order of descending priority or not have great impact on displacement and implementation difficulty within slot allocation model. Capacity is surely a key factor affecting displacement and implementation difficulties. This study contributes to propose a new mechanism for slightly weakening grandfather right, which can help decision makers to upgrade slot allocation policies.

This paper reports a novel fabric phase sorptive extraction-high performance liquid chromatography-ultra violet detection (FPSE-HPLC-UV) method for the simultaneous extraction and analysis of four phenyltin derivatives that include triphenyltin hydroxide, triphenyltin acetate, triphenyltin chloride and tetraphenyltin in environmental water (agricultural waste water and municipal waste water) and canned food samples. The selected analytes were well resolved by Waters Nova pack C18 column (3.9 x 150 mm, 4 µm particle size) in isocratic elution mode within 15 minutes. The new microextraction media has been analytically evaluated using phenyltin derivatives as model compounds. The factors affecting the extraction efficiency of FPSE have been evaluated and the optimum extraction conditions were determined. Under these optimum conditions, the limits of detection (LODs) for sol-gel C18 coated fabric phase sorptive extraction (FPSE) media in combination with HPLC-UV for the analysis of the phenyltin derivatives were in the range of 10-100 ng/mL with high precision (low relative standard deviation) at 10 ng/mL concentration with good absolute recoveries. To the best of our knowledge, this is the first FPSE extraction procedure applied to environmental water and canned food samples for the simultaneous determination of phenyltin derivatives and could be readily adopted as a rapid and robust green analytical tool for routine environmental and food analytical laboratories.

At present European buildings typically consume two to five times more energy than predicted at the design stage. An important cause of this performance gap is the discrepancies between the design specification and the As-Built condition. Such discrepancies are mainly due to the gaps in knowhow between design, production and construction professionals. Design is more and more contained into a virtual environment and loses touch with the physical production and construction sites. As the construction sector enters the Industry 4.0 era, Building Information Modelling (BIM) based Mixed Reality can intertwine virtual and real worlds to bridge the knowhow gaps.

Participation by local communities in wildlife conservation projects have long been advocated since it is socially just and is effective to reach conservation and development goals. Socio–economic variables that drive participation and impact of participation have been studied, but the contextual process that stir up local community participation remains understudied. In this paper, we studied factors facilitating community participation in three wildlife conservation projects in Northeast India. Through ethnographic fieldwork at these sites we identified conservation actors and examined interactions between them.We found common modes of participation at these sites and these were related to gaining material incentives, providing labour, attending consultative workshops. Levels of interaction and coercion were found to be different in three sites. Three critical factors that drive participation were: (1) trigger, (2) negotiation and (3) sustenance. Trigger factors kickstart participation through establishment of a crisis narrative and facilitation by external actors. Negotiation factors emerge from day–to–day interaction between local community and external actors and involve effective entry stage activities, income opportunity, mediating voices within the community and intra–community dynamics. Sustenance factors affect the long term participation by community in the conservation project and involve tangible/intangible results, capability development of locals, funding and availability to critical information. In our paper we argue that investment of time and fund to understand the stakeholders and their concept of participation, periodic feedback sessions, capacity development of locals for self–mobilization, innovative information dissemination and securing long term funding are necessary for effective local community participation.

Quantitative and qualitative fuzzy measures have been proposed to hesitant fuzzy sets (HFSs) from different points. However, few of the existing HFSs fuzzy measures refer to the grey relational analysis (GRA) theory. Actually, the GRA theory is very useful in the fuzzy measure domain, which has been developed for such the intuitionistic fuzzy sets. Therefore, in this paper, we apply the GRA theory to the HFSs and interval-valued hesitant fuzzy sets (IVHFS) domain. We propose the HFSs grey relational degree, HFSs slope grey relational degree, HFSs synthetic grey relational degree and IVHFSs grey relational degree, which describe the closeness, the variation tendency and both the closeness and variation tendency of HFSs and closeness of IVHFSs, respectively, greatly enriching the fuzzy measures of HFSs. Furthermore, with the help of the TOPSIS method, we develop the grey relational based MADM methodology to solve the HFSs and IVHFSs MADM problems. Finally, combined with two practical MADM examples about energy policy selection with HFSs information and emergency management evaluation with IVHFSs information, we obtain the most desirable decision results, and compared with the previous methods, the validity, effectiveness and accuracy of the proposed grey relational degree for HFSs and IVHFSs are demonstrated in detail.

Developments in technology have enabled envisioning the derivation of materials and products from renewable biomass, as an alternative to finite fossil-based resource consumption. Therefore, bioeconomy is regarded as an opportunity for sustainable economic growth. Countries are formulating strategies in accordance with their goals to attain from bioeconomy. Proper measurement, monitoring and reporting of the outcomes of these strategies is crucial for long-term success. This study aims to critically evaluate the national methods used for the measurement, monitoring and reporting of bioeconomy contribution to the total economy. For this purpose, research and surveys have been conducted on selected countries (Argentina, Germany, Malaysia, the Netherlands, South Africa and the United States). The results reveal that the bioeconomy targets set up in the strategies often reflect country’s priorities and comparative advantages. However, comprehensive approaches to measure and monitor bioeconomy progress are frequently lacking. Most countries only measure the contribution to gross domestic product (GDP), turnover and employment of the sectors included in their bioeconomy definition, which may provide an incomplete picture. In addition, this study identifies the mismatch between the targets and measurement methods, as the environmental and social impacts of bioeconomy are often foreseen, but not measured. It is concluded that existing global efforts towards a sustainable bioeconomy monitoring can be strengthened and leveraged to measure progress towards sustainable goals.

Increasingly, there is a focus on utilising renewable energy resources in a bid to fulfil increasing energy requirements and mitigate the climate change impacts of fossil fuels. While most renewable resources are free, the technology used to usefully convert such resources is not and there is an increasing focus on improving the conversion economy and efficiency. To this end, advanced control technologies can have a significant impact and is already a relatively mature technology for wind turbines. Though hydroelectric plants can use simple regulation systems, significant benefits have been shown to accrue from the appropriate use of the same control methods designed for wind turbine plants. This represents the key point of the paper. In fact, to date, the application communities connected with wind and hydraulic energies have had little communication, resulting in little cross fertilisation of control ideas and experience, particularly from the more mature wind area to hydrodynamic systems. Therefore, this paper examines the models and the application of control technology across both domains, both from a comparative and contrasting point of view, with the aim of identifying commonalities in models and control objectives, as well as potential solutions. Key comparative reference points include the articulation of the exployed models, specification of control objectives, development of high--fidelity simulators, and development of solution concepts. Not least, in terms of realistic system requirements are the set of physical and constraints under which such renewable energy systems must operate, and the need to provide reliable and robust control solutions, which respect the often remote and relatively inaccessible location of many onshore and offshore deployments.

Injuries of repetitive efforts constitute one of the prime causes of absenteeism in the workplace, have bear a considerable cost for the public health system and can cast doubt on the sustainability of a company. The objective of this paper is to build, in the researchers, the needed knowledge to choose a set of relevant scientific articles about repetitive strain injuries in the poultry meat industry, aiming identify characteristics in those scientific publications that have the potential to contribute on the topic of this paper. The research is characterized as exploratory-descriptive, draws on primary and secondary data sources. The study involves the application of a method for selection and analysis of the selected articles. To this end, the method utilized was the Knowledge Development Process – Constructivist (Proknow-C) as theoretical intervention instrument. Within the process development, it was obtained a portfolio of 16 articles aligned to the research and scientifically recognized with the main periodicals, papers, authors and keywords. The ProKnow-C process allowed identify opportunities in the literature about injuries in the poultry meat industry and showed opportunities for research future. This paper, under the constructivist perspective, presents a structured process to build, in the researcher, the necessary knowledge to identification, selection and analysis of relevant scientific articles relating to research context and, for these articles, find prominences and opportunities for a research theme without similar publications.

Since the outbreak of the novel coronavirus disease (COVID-19) at the end of 2019, the clinical presentation of the disease showed a great heterogeneity with a diverse impact between different subpopulations. Emerging evidence from different parts of the world showed significantly poor outcome among males compared to female patients. A better understanding of the molecular mechanisms behind this difference might be a fundamental step for a more effective and targeted response to the outbreak. For that reason, here we try to investigate the molecular basis of the gender variations in mortality rates related to COVID-19 infection. To achieve this, we used our in-house pipeline to process publicly available lung transcriptomic data from 141 females compared to 286 males. After excluding Y specific genes, our results showed a shortlist of 73 genes that are differentially expressed between the two groups. Our results showed downregulation of a group of genes that are involved in the regulation of hydrolase activity including (AGTR1, CHM, DDX3X, FGFR3, SFRP2, and NLRP2), which is also believed to be essential for lung immune response and antimicrobial activity in the lung tissues in males compared to females. In contrast, our results showed an upregulation of angiotensin II receptor type 1 (AGTR1), a member of the renin-angiotensin system (RAS) that plays a role in angiotensin-converting enzyme 2 (ACE2) activity modulation. Interestingly, recent reports and experimental animal models highlight an important role of this receptor in SARS-Coronavirus lung damage as well as pulmonary edema, suggesting a possible role of its blockers like losartan and olmesartan as potential therapeutic options for COVID-19 infection. Finally, our results also showed a differential expression of different genes that are involved in the immune response including the NLRP2 and PTGDR2, further supporting the notion of the sex-based immunological differences. Taken together, our results provide an initial evidence of the molecular mechanisms that might be involved in the differential outcomes observed between both genders during the COVID-19 outbreak. This might be essential for the discovery of new targets and more precise therapeutic options to treat COVID-19 patients from different clinical and epidemiological characteristics with the aim of improving their outcome.

The aim of the present study is to investigate both the performance and preferences of males and females Computer Science (CS) graduates. In order to attain the above goal, a quantitative case study was conducted regarding 89 degrees, acquired from 2006 to 2012, from the Department of Computer Science and Technology, University of Peloponnese, Greece. The analysis of the data revealed that in terms of performance, no significant differences between the mean grades of males and females exist, in almost most of the courses included in the curriculum of the aforementioned CS department. Any statistically significant differences in performances were present in almost equal number of courses in favor of males and females. It seems also, that females performed better in the courses they selected more than males. Regarding preferences, in CS courses, it seems that gender differences are existent. Males preferred more than females did core programming courses and advanced topics of Software Systems, computer networks, computer engineering, robotics and mathematics, whereas females preferred more the study of algorithms and security issues, computer fractals, data management, computer architecture, and mobile communication. In addition, females preferred courses in reference with humanities and social sciences, CS terminology, and career opportunities. Yet, females did not select any of programming lab-based courses, computer engineering, computer network issues and robotics.

Islamic banks (IBs) have been criticized as not being genuinely Islamic, and the methods in measuring their performances have been debatable. While the literature on IBs performance has been emerging, such studies precisely assess its recent development remains absent. Therefore, we aim to evaluate the development of scholarly articles that measure IBs’ performances. We employ bibliometric analysis and sample related articles from the Scopus database. We find that the development of IBs performance literature may be understood by 111 peer-reviewed journal articles, 4 conference papers, 1 book, and 1 book series. We analyze these materials based on publication sources, country and institution affiliation, keywords association, and cluster dendrogram. Our model that quantifies the keywords association and cluster dendrogram provides a novelty in assessing IBs performance literature development. Future studies may replicate our model to cluster and identify the keyword associations from the unstructured data sources.

: The cyber-physical gap (CPG) is the difference between the 'real' state of the world and the way the system perceives it. This discrepancy often stems from the limitations of sensing and data collection technologies and capabilities, and is an inevitable issue in any cyber-physical system (CPS). Ignoring or misrepresenting such limitations during system modeling, specification, design, and analysis can potentially result in systemic misconceptions, disrupted functionality and performance, system failure, severe damage, and potential detrimental impacts on the system and its environment. We propose CPG-Aware Modeling & Engineering (CPGAME), a conceptual model-based approach for capturing, explaining, and mitigating the CPG, on top of and in sync with the conventional system model, and as an inherent systems engineering activity. This approach enhances the systems engineer’s ability to cope with CPGs, mitigate them by design, and prevent erroneous decisions, actions, and hazardous implications. CPGAME is a generic, conceptual approach, specified and demonstrated with Object Process Methodology (OPM). OPM is a holistic conceptual modeling paradigm for multidisciplinary, complex, dynamic systems, which is also ISO-19450. We analyze the 1979 Three Miles Island 2 nuclear accident as a prime example of the disastrous consequences of unmitigated CPGs in complex systems.

While lidar-based forest inventory methods have been widely demonstrated, prediction of tree diameters with lidar is not well understood. The performance metrics typically used in studies for prediction of diameters can be difficult to interpret and may not support comparative inferences between sampling designs or study areas. We evaluate a variety of lidar and k nearest neighbor (k-NN) strategies for prediction of tree diameter distributions using two indices which are easier to interpret and compare. The indices are based on the coefficient of determination (R²), and root mean square deviation (RMSD). These indices facilitate comparisons with alternative (non-lidar) inventory strategies, and with other project areas. We evaluate k nearest neighbors (k-NN) dbh density (relative frequency by dbh class) prediction strategies with lidar for 190 training plots distribute across the 800 km² Savannah River Site in South Carolina, USA. We evaluate the performance of k-NN with respect to distance metrics, number of neighbors, predictor sets, and response sets. Amongst the examined strategies we found Mahalanobis distance with k = 3 neighbors performed best according to a number of criteria.

There is increasing demand from stakeholders for tools to support outcome-based approaches in environmental management. For such tools to be useful, understanding user requirements is key. In Scotland, UK, stakeholders were engaged in the development of an Environmental Decision Support System (EDSS) to support the management of land and freshwater resources for multiple policy outcomes. A structured participatory engagement process was employed to determine stakeholder requirements, establish development principles to fulfil these requirements and road-test prototypes. The EDSS specification that emerged from this bottom-up process was spatially-explicit, free at the point of use, and touch and mobile device compatible. This application, which is under development, does not closely resemble most existing published EDSS. We suggest that there is a mismatch between the way scientists typically conceptualise EDSS and the kinds of applications that are likely to be useful to decision-makers on the ground. Interactive mobile and web-based geospatial information services have become ubiquitous in our daily lives, but their importance is not reflected in current literature on EDSS. The current focus in environmental management on adaptive, stakeholder-centred strategies based on outcomes offers an opportunity to make better use of these new technologies to aid decision-making processes.

Over the past few decades, aircraft Ground Handling Operations (GHO) have been investigated by numerous researchers. Some aspects of GHO have been more focused on than others due to their importance in the GH processes. For instance, GHO performance has been tackled from different perspectives while workers and vehicles allocation suffer from the lack of research in the area. This paper is a literary review of the research that has been conducted in different areas of GHO. It sheds light on GHO performance, resource allocation and scheduling, operations scheduling, vehicle routing, and workers and vehicles allocation.

Gene editing tools such as CRISPR-Cas9 have created unprecedented opportunities for genetic studies in plants and animals. We designed a course-based undergraduate research experience (CURE) to train introductory biology students in the concepts and implementation of gene editing technology as well as develop their soft skills in data management and scientific communication. We present two versions of the course that can be implemented with twice-weekly meetings over a five-week period. In the remote-learning version, students perform homology searches, design guide RNAs and primers, and learn the principles of molecular cloning. This version is appropriate when access to laboratory equipment or in-person instruction is limited, such as closures that have occurred in response to the Covid-19 pandemic. In the in-person version, students design guide RNAs, clone CRISPR-Cas9 constructs, and perform genetic transformation of the model plant Arabidopsis thaliana. The highly parallel nature of the CURE makes it possible to target dozens to hundreds of genes, depending on the number of course sections available. Applying this approach in a sensitized mutant background enables focused reverse genetic screens for genetic suppressors or enhancers. The course can be readily adapted to other organisms or projects that employ gene editing.

Deepfake technologies are known for the creation of forged celebrity pornography, face and voice swaps, and other fake media content. Despite the negative connotations the technology bears, the underlying machine learning algorithms have a huge potential that could be applied to not just digital media, but also to medicine, biology, affective science, and agriculture, just to name a few. Due to the ability to generate big datasets based on real data distributions, deepfake could also be used to positively impact non-human animals such as livestock. Generated data using Generative Adversarial Networks, one of the algorithms that deepfake is based on, could be used to train models to accurately identify and monitor animal health and emotions. Through data augmentation, using digital twins, and maybe even displaying digital conspecifics where social interactions are enhanced, deepfake technologies have the potential to increase animal health, emotionality, sociality, animal-human and animal-computer interactions and thereby animal welfare, productivity, and sustainability of the farming industry.

The influence and importance of research and development (R&D) for business sustainability have gained increasing interests, especially in the high-tech sector. However, the efforts of R&D might cause complex and mixed impacts on the financial results considering the associated expenses. Thus, this study aims to examine how R&D efforts may influence business to improve its financial performance considering the dual objectives: the gross and the net profitability. This research integrated a rough-set-based soft computing technique and multiple criteria decision-making (MCDM) methods to explore this complex and yet valuable issue. A group of public listed companies from Taiwan, all in the semiconductor sector, was analyzed as a case study. Initially, more than 30 variables were considered, and the adopted soft computing technique retrieved 14 core attributes—for the dual profitability objectives—to form the evaluation model. The importance of R&D for pursuing superior financial prospects is confirmed, and the empirical case demonstrates how to guide an individual company to plan for improvements to achieve its long-term sustainability by this hybrid approach.

Structure-based design and synthesis of two biphenyl-N-acyl-β-D-glucopyranosylamine derivatives as well as their assessment as inhibitors of human liver glycogen phosphorylase (hlGPa, a pharmaceutical target for type 2 diabetes) is presented. X-ray crystallography revealed the importance of structural water molecules and that the inhibitory efficacy correlates with the degree of disturbance caused by the inhibitor binding to a loop crucial for the catalytic mechanism. The in silico derived models of the binding mode generated during the design process corresponded very well with the crystallographic data.

List-only entity linking is the task of mapping ambiguous mentions in texts to target entities in a group of entity lists. Different from traditional entity linking task, which leverages rich semantic relatedness in knowledge bases to improve linking accuracy, list-only entity linking can merely take advantage of co-occurrences information in entity lists. State-of-the-art work utilizes co-occurrences information to enrich entity descriptions, which are further used to calculate local compatibility between mentions and entities to determine results. Nonetheless, entity coherence is also deemed to play an important part in entity linking, which is yet currently neglected. In this work, in addition to local compatibility, we take into account global coherence among entities. Specifically, we propose to harness co-occurrences in entity lists for mining both explicit and implicit entity relations. The relations are then integrated into an entity graph, on which Personalized PageRank is incorporated to compute entity coherence. The final results are derived by combining local mention-entity similarity and global entity coherence. The experimental studies validate the superiority of our method. Our proposal not only improves the performance of list-only entity linking, but also opens up the bridge between list-only entity linking and conventional entity linking solutions.

The exposure to extreme temperatures in workplaces involves physical hazards for workers. A poorly acclimated worker may have lower performance and vigilance and may therefore be more exposed to accidents and injuries. Due to the incompatibility of the existing standards implemented in some workplaces and the lack of thermoregulation in many protective equipment, thermal stress remains one of the most frequent physical hazards in many work sectors. However, many of these problems can be overcome with the use of smart textile technologies that enable intelligent thermoregulation in personal protective equipment. Smart textiles can detect, react and adapt to many external stimuli. Interconnected sensors and actuators that interact and react to existing risks can provide the wearer with increased safety, protection and comfort. Thus, the skills of smart protective equipment can contribute to the reduction of errors and the number and severity of accidents in the workplace, and thus promote improved performance, efficiency and productivity.This review provides an overview and opinions of authors on the current state of knowledge on these types of technologies by reviewing and discussing the state of the art of commercially available systems and the advances made in previous research works.

Cycling reliability is crucial for Si-based materials due to severe volume change during cycles that results in the fast capacity fading. Though the binder only occupies a very low amount of the total mass of anodes, it is proved to perform a key parameter to improve the cycle performance of Si-based anodes. Because they are eco-friendly and cost saving, water-based binders styrene-butadiene rubber (SBR) and carboxymethyl cellulose (CMC) are regarded as the better binder to substitute Poly (vinylidene difluoride) (PVDF) as the binder for Si-based anode. In this study, the anodes are fabricated by simply mixing the active materials (naso Si, graphite and conductive additive) together and using the mixture of SBR and CMC as a binder. The results showed that the retention capacities of the anodes are more than 440 mAh/g after 400 cycles. It indicates that it is an easy and simple way to make high performance anodes.

In this systematic survey, an overview of non-conventional and soft-actuators is presented. The review is performed by using well-defined performance criteria with a direction to identify the exemplary applications in robotics. In addition to this, initial guidelines to measure the performance and applicability of soft actuators are provided. The meta-analysis is restricted to four main types of soft actuators: shape memory alloys (SMA), fluidic elastomer actuators (FEA), dielectric electro-activated polymers (DEAP) and shape morphing polymers (SMP). In exploring and comparing the capabilities of these actuators, the focus was on seven different aspects: compliance, topology, scalability-complexity, energy efficiency, operation range, performance and technological readiness level. The overview presented here provides a state-of-the-art summary of the advancements and can help researchers to select the most convenient soft actuators using the comprehensive comparison of the performance criteria.

Studies have demonstrated the positive effects of acupuncture on athletic performance. The aim of this study was to determine the acute effects of acupuncture on heart rate, the perceived exertion scale and lactate levels in recreational athletes. Fifteen competitive males engaged in HIIT. The characteristic was 29.86±2.51 years old, heart rate reserve 59.00±3.33, lactate 3.17±0.50 mM/DL. The subjects were submitted to two exercise sessions. Both training sessions consisted of 10 burpees, 12 thrusters and 14 box jumps (75 cm high) for 12 minutes. Activity intensity was between 85 and 95% of maximum heart rate. Acupuncture points: ST36, L3, LI11. The student’s t-test was adopted, Shapiro-Wilk test was applied for normality, and Pearson correlation. There was a positive correlation of r = 0.69 between lactate levels and heart rate. Lactate: Lac1 15.00±1.18 – Lac2 19.59± 1.46 p= 0.0001*; Heart 1rate: HRF 163.71±7.27 – HRF2 177.60±6.99 p=0.0001*; Blood pressure: SBP1 174.86±1.57 – SBP2 180.86±1.77 p= 0.0001*; PES1: 19.4±1.14; PES2 16.8±0.84 p= 0.0001*; weight1 – 182,57±12,05; weight 2 206,43±11,39 p=0.0325*. Acupuncture increased lactate accumulation, heart rate and blood pressure, suggesting that the exertion reached after acupuncture is higher than without acupuncture. The acupuncture technics improved the athlete performance.

This is a quantitative research based on secondary sources of data. The study examines the influence of Chief Executive Officer’s (CEO) compensation on a firm's performance. The objectives of the study were to determine if CEO compensation and firm size do significantly influence a firm’s performance. In other to elicit information to examine the relationship between the variables, the convenience sampling technique, with the combination of both the cross-sectional and time-series data (panel data) were used since they provide greater precision and guard against having an illusory sample. 10 banks quoted on the Nigerian Stock Exchange were sampled for easy accessibility of data. The least square regression technique was used to test the hypotheses of the study. Two hypotheses were tested using panel least square (EViews 8) and from the research work, we summarize the following results; there is a significant relationship between CEO compensation and firm performance in the Nigerian banking industry. In addition, firm size does significantly influence firm performance in the Nigerian banking industry. The study recommends that there should be proper compensation review as this will increase the productivity of the executives. Since increased pay is necessary for the efficiency of the workers, it is advised to ensure a considerable pay as this will ensure for efficiency in the organization. In addition, since the core goal of setting up any business is to make a profit, business organisations should sort out ways at maximising profit and this could include cutting down expenses such as cutting down excessive employees’ pay (CEOs pay especially) and setting apposite pay package for employees. Therefore, policymakers (board of directors) should make an effort to align CEO’s paywith the firm’s capability to pay.

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.

The availability of new self-sensing cement-based strain sensors allows the development of dense sensor networks for Structural Health Monitoring (SHM) of reinforced concrete structures. These sensors are fabricated by doping cement-matrix materials with conductive fillers, such as Multi Walled Carbon Nanotubes (MWCNTs), and can be embedded into structural elements made of reinforced concrete prior to casting. The strain sensing principle is based on the multifunctional composites outputting a measurable change in their electrical properties when subjected to a deformation. Previous work by the authors was devoted to material fabrication, modeling and applications in SHM. In this paper, we investigate the behavior of several sensors fabricated with and without aggregates and with different MWCNTs content. The strain sensitivity of the sensors, in terms of fractional change in electrical resistivity for unit strain, as well as their linearity are investigated through experimental testing under both static and dynamically varying compressive loadings. Moreover, the responses of the sensors when subjected to destructive compressive tests are evaluated. Overall, the presented results contribute to improving the scientific knowledge on the behavior of smart concrete sensors and to furthering their understanding for SHM applications.

In order to fully perform their functions and be durable, mortars for renders and plasters are requested to have a set of characteristics that can vary with the type of exposure to external environmental actions and the type of substrate. Generally, they need moderate strength, high deformability, some water protection capability, good adhesion to the substrate, and compatibility with the pre-existent materials. The presence of water and its movement inside the pore structure of the mortars are among the most significant causes of degradation. Moreover, several authors consider that the main factors for durability and good performance of lime-based mortars are mostly related with the good quality of the binder and the use of adequate aggregates. This paper intends to study the effect of ageing on the properties and durability of air lime mortars, using aggregates of different mineralogy. For this purpose, different mortars compositions are exposed to an accelerated weathering test under defined conditions. The obtained characteristics are discussed and compared with the results obtained with the same mortars tested in laboratory conditions. The effects of the aggregate type on the durability of mortars seem to be linked to their effects on the mortars porous structure.

Hospital-based programs democratize mRNA therapeutics by facilitating the processes to translate a novel RNA idea from the bench to the clinic. Because mRNA is essentially biological software, therapeutic RNA constructs can be rapidly developed. The generation of small batches of clinical grade mRNA to support IND applications and first-in-man clinical trials, as well as personalized mRNA therapeutics delivered at the point-of-care, is feasible at a modest scale of cGMP manufacturing. Advances in mRNA manufacturing science and innovations in mRNA biology, are increasing the scope of mRNA clinical applications.

Decision tables have been used for many years in data processing and business applications to simulate complex rule sets. Several computer languages have been developed based on rule systems and they are easily programmed in several current languages. Land management and river-reservoir models simulate complex land management operations and reservoir management in highly regulated river systems. Decision tables are a precise yet compact way to model the rule sets and corresponding actions found in these models. In this study, we discuss the suitability of decision tables to simulate management in the river basin scale Soil and Water Assessment Tool (SWAT+) model. Decision tables are developed to simulate automated irrigation and reservoir releases. A simple auto irrigation application of decision tables was developed using plant water stress as a condition for irrigating corn in Texas. Sensitivity of the water stress trigger and irrigation application amounts were shown on soil moisture and corn yields. In addition, the Grapevine Reservoir near Dallas, Texas was used to illustrate the use of decision tables to simulate reservoir releases. The releases were conditioned on reservoir volumes and flood season. The release rules as implemented by the decision table realistically simulated flood releases as evidenced by a daily NSE (Nash-Sutcliffe Efficiency) of 0.52 and a percent bias of -1.1%. Using decision tables to simulate management in land, river and reservoir models was shown to have several advantages over current approaches including: 1) mature technology with considerable literature and applications, 2) ability to accurately represent complex, real world decision making, 3) code that is efficient, modular and easy to maintain, and 4) tables that are easy to maintain, support, and modify.

A car-roof photovoltaic has an enormous potential to change our society. With this technology, 70% of the personal car can run by the solar energy collected by the solar panel on its car-roof. Unfortunately, it is not a simple extension of the conventional photovoltaic technology. This paper list what we need to do for realizing the future that majority of the personal cars run by renewable solar energy, after clarification of the difference from the conventional photovoltaic technology. In addition to the technological development, the standardization of this innovative technology will be important, and the list was made highlighting the standardization.

Since their discovery in 1993, numerous microRNAs (miRNAs) have been identified in humans and other eukaryotic organisms, and their role as key regulators of gene expression is still being elucidated. It is now known that miRNAs not only play a central role in the processes that ensure normal development and physiology, but they are often dysregulated in various diseases. In this review, we present an overview of the role of miRNAs in normal renal development and physiology, in maladaptive renal repair after injury, and in the pathogenesis of renal parenchymal diseases. In addition, we describe methods used for their detection and their potential as therapeutic targets. Continued research on renal miRNAs will undoubtedly improve our understanding of diseases affecting the kidneys and may also lead to new therapeutic agents.

Prediction of stock groups values has always been attractive and challenging for shareholders. This paper concentrates on the future prediction of stock market groups. Four groups named diversified financials, petroleum, non-metallic minerals and basic metals from Tehran stock exchange are chosen for experimental evaluations. Data are collected for the groups based on ten years of historical records. The values predictions are created for 1, 2, 5, 10, 15, 20 and 30 days in advance. The machine learning algorithms utilized for prediction of future values of stock market groups. We employed Decision Tree, Bagging, Random Forest, Adaptive Boosting (Adaboost), Gradient Boosting and eXtreme Gradient Boosting (XGBoost), and Artificial neural network (ANN), Recurrent Neural Network (RNN) and Long short-term memory (LSTM). Ten technical indicators are selected as the inputs into each of the prediction models. Finally, the result of predictions is presented for each technique based on three metrics. Among all algorithms used in this paper, LSTM shows more accurate results with the highest model fitting ability. Also, for tree_based models, there is often an intense competition between Adaboost, Gradient Boosting and XGBoost.

This study assessed the hydrological performance and runoff water quality of 12 green roof (GR) modular systems located at the Universidad de los Andes campus (Bogotá, Colombia). Based on 223 rainfall events spanning a 3-year period, average rainfall retention was 85% (SD = 25%). T-tests, Welch Test, multiple linear regressions and correlation analysis were performed in order to assess the potential effect of air temperature, substrate type, vegetation cover, relative humidity, antecedent dry weather period (ADWP), rainfall duration and rainfall maximum intensity. In some cases, GR design variables (i.e. growing media and type of vegetation) were found to be significant for describing rainfall retention efficiencies and, depending on the GR type, some hydrological variables were also correlated with the rainfall retention. Rainfall and GR runoff were monitored for Total Kjeldahl Nitrogen (TKN), Nitrates, Nitrites, Ammonia, Total Phosphorus (TP), Phosphates, pH, Total Dissolved Solids (TDS), Total Suspended Solids (TSS), Color, Turbidity, Biological Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Total Coliforms, metals and Poliaromatic Hydrocarbons (PAHs). The results obtained confirmed that GR systems have the ability to neutralize pH, but are source of the rest of the aforementioned parameters, excluding PAHs (with concentrations below detection limits), Ammonia, TSS, Se and Li, where differences with reference values (rainfall and plastic panel runoff) were not statistically significant. Substrate type, event size and rainfall regime are relevant variables for explaining runoff water quality.

Coronavirus, also known as COVID-19, has been declared a pandemic by the World Health Organization (WHO). At the time of conducting this study, it had recorded over 1.6 million cases while more than 105,000 have died due to it, with these figures rising on a daily basis across the globe. The burden of this highly contagious respiratory disease is that it presents itself in both symptomatic and asymptomatic patterns in those already infected, thereby leading to an exponential rise in the number of contractions of the disease and fatalities. It is therefore crucial to expedite the process of early detection and diagnosis of the disease across the world. The case-based reasoning (CBR) model is an effective paradigm that allows for the utilization of cases’ specific knowledge previously experienced, concrete problem situations or specific patient cases for solving new cases. This study therefore aims to leverage the very rich database of cases of COVID-19 to solve new cases. The approach adopted in this study employs the use of an improved CBR model for state-of-the-art reasoning task in classification of suspected cases of Covid19. The CBR model leverages on a novel feature selection and semantic-based mathematical model proposed in this study for case similarity computation. An initial population of the archive was achieved with 68 cases obtained from the Italian Society of Medical and Interventional Radiology (SIRM) repository. Results obtained revealed that the proposed approach in this study successfully classified suspected cases into their categories at an accuracy of 97.10%. The study found that the proposed model can support physicians to easily diagnose suspected cases of Covid19 base on their medical records without subjecting the specimen to laboratory test. As a result, there will be a global minimization of contagion rate occasioned by slow testing and as well reduce false positive rates of diagnosed cases as observed in some parts of the globe.

High levels ofmeat consumption are increasingly being criticised for ethical, environmental, 2 and social reasons. Plant-based meat substitutes have been identified as healthy sources of protein in 3 comparison to meat. This alternative offers several social, environmental and health benefits and may 4 play a role in reducing meat consumption. However, there has been a lack of research on how specific 5 meat substitute attributes can influence consumers to replace or partially replace meat in their diets. 6 Research demonstrates that in many countries consumers are highly attached to meat.They consider 7 it as an essential and integral element of their daily diet. For these consumers which are not interested 8 in vegan or vegetarian alternatives to meat, so-called meathybrids could be a low-threshold option 9 for a more sustainable food consumption behaviour. In meathybrids only a fraction of the meat 10 product (e.g. 20% to 50%) is replaced with plant-based proteins. In this paper, the results of an online 11 survey with 500 German consumers are presented with focus on preferences and attitudes relating 12 to meathyrids. The results show that more than fifty percent of consumers substitute meat at least 13 occasionally. Thus, about half of the respondents reveal an eligible consumption behaviour in respect 14 to sustainability and healthiness to a certain degree. Concerning the determinants of choosing either 15 meathybrid or meat it becomes evident that the highest effect is exerted by the health perception. The 16 healthier meathybrids are perceived, the higher is the choice probability. Thus, this egoistic motive 17 seems to outperform altruistic motives like animal welfare or environmental concerns when it comes 18 to choice for this new product category.

Soccer is the most played sport worldwide, with over 265 million participants. It is an incredibly demanding sport, with many different technical skills and physical loads placed on the body. This makes post-match recovery strategies amongst high level soccer players of great importance. The aim of this review is to summarize the existing literature on stretching for post-match recovery, examining its relation to injury prevention, Delayed Onset Muscle Soreness (DOMS), and performance. Scientific evidence of the highest quality and relevance was extracted and reviewed. Despite its common practice, evidence does not support static stretching as a modality to improve recovery post-match amongst soccer players. Larger trials with important outcome measures are needed to determine if a post-match stretching regimen to facilitate recovery exists.

Tree species composition is an important key element for biodiversity and sustainable forest management, and hyperspectral data provide detailed spectral information, which can be used for tree species classification. There are two main challenges for using hyperspectral imagery: a) Hughes phenomena, meaning by increasing the number of bands in hyperspectral imagery, the number of required classification samples would increase exponentially, and b) in a more complex environment, such as riparian mixed forest, focusing on spectral variability per pixel may not be adequate for definability of tree species. Therefore, the focus of this study is to assess spectral-spatial dimensionality reduction of airborne hyperspectral imagery by using minim noise fraction (MNF) transformation, and object-based image analysis (OBIA). An airborne prism experiment (APEX) hyperspectral imagery was used. A study area was a riparian mixed forest located along the Salzach river, and six tree species including Picea abies, Populus (canadensis and balsamifera), Fraxinus excelsior, Alnus incana, and Salix alba were selected. A machine learning algorithm random forest (RF) was used to train and apply a prediction model for classification. Using a spectral dimensionality reduced APEX, a pixel-level classification was also done. According to a confusion matrix, the object-level classification of MNF-derived components achieved the overall accuracy of 85 %, and kappa coefficient of 0.805. The performance of classes according to producer’s accuracy varied between 80% for Fraxinus excelsior, Alnus incana, and Populus canadensis to 90% for Salix alba and Picea abies. Comparison the results to a pixel-level classification, showed a better performance of object-level classification (an overall accuracy of 63% and Kappa coefficient of 0.559 were achieved for pixel-level classification). The performance of classes using pixel-based classification varied 45 % for Alnus incana to 80% for Picea abies. In general, Spectral-spatial complexity reduction using MNF transformation and object-level classification yielded a statistically satisfactory results.

Impervious surface mapping with high-resolution remote sensing imagery has attracted increasing interest as it can provide detailed information for urban structure and distribution. Previous studies have suggested that the combination of LiDAR data and high-resolution imagery for impervious surface mapping performs better than using only high-resolution imagery. However, due to the high cost of the acquisition of LiDAR data, it is difficult to obtain the multi-sensor remote sensing data acquired at the same acquisition time for impervious surface mapping. Consequently, real landscape changes between multi-sensor remote sensing data at different acquisition times would lead to the error of misclassification in impervious surface mapping. This issue has mostly been neglected in previous works. Furthermore, the observation differences generated from multi-sensor data, including the problems of misregistration, missing data in LiDAR data, and shadow in high-resolution images would also challenge the final mapping result in the fusion of LiDAR data and high-resolution images. In order to conquer these problems, we propose an improved impervious surface mapping method incorporating both LiDAR data and high-resolution imagery at different acquisition times in consideration of real landscape changes and observation differences. In the proposed method, a multi-sensor change detection by supervised multivariate alteration detection is employed to obtain changed areas and misregistration areas. The no-data areas in the LiDAR data and the shadow areas in the high-resolution imagery are extracted by independent classification yielded by its corresponding single sensor data. Finally, an object-based post-classification fusion is proposed to take advantage of independent classification results with single-sensor data and the joint classification result with stacked multi-sensor data. Experiments covering the study site in Buffalo, NY, USA demonstrate that our method can accurately detect landscape changes and obviously improve the performance of impervious surface mapping.