This paper uses panel data to find the determinants of tracking errors in exchange traded funds (ETFs) in the Hong Kong stock market. A comparison of tracking errors between physical and synthetic ETFs also indicates that the synthetic ETFs have higher tracking errors. The magnitude of tracking errors is found to be negatively related to size but positively related to dividend yield, trading volumes of funds, and market risk. However, this study also finds that expense ratio has a negative impact on tracking error, which is not consistent with previous studies, and which this paper addresses.

In recent years, demand has been increasing for target detection and tracking from aerial imagery via drones using onboard powered sensors and devices. We propose a very effective method for this application based on a deep learning framework. A state-of-art embedded hardware system empowers small flying robots to carry out the real-time onboard computation necessary for object tracking. Two types of embedded modules were developed: one is designed using a Jetson TX or AGX Xavier, and the other is based on an Intel Neural Compute Stick. These are suitable for real-time onboard computing power on small flying drones with limited space. A comparative analysis of current state-of-art deep-learning-based multi-object detection algorithms was carried out utilizing the designated GPU-based embedded computing modules to obtain detailed metric data about frame rates as well as the computation power. We also introduce an effective target tracking approach for moving objects. The algorithm for tracking moving objects is based on the extension of simple online and real-time tracking. It was developed by integrating a deep-learning-based association metric approach (Deep SORT), which uses a hypothesis tracking methodology with Kalman filtering and a deep-learning-based association metric. In addition, a guidance system that tracks the target position using a GPU-based algorithm is introduced. Finally, we demonstrate the effectiveness of the proposed algorithms by real-time experiments with a small multi-rotor drone.

The study uses annual time series data from the South Africa Reverse Bank (SARB) from 1980 to 2020 to examine the effectiveness of fiscal policy on economic growth in South Africa. The Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) unit root tests, as well as the Johansen Co-integration test, Granger causality test, and Vector Auto-Regression (VAR) method, were used in the study. Real GDP per capita (RGDP) is used as proxy of economic growth and gross fixed capital formation (GFCF), government expenditure (GEXP) and government deficit (GOVD) as the proxies of fiscal policy. The ADF test results show that all variables are stationary at the first difference, with the exception of GFCF and GEXP, which are stationary at I(0), whereas the PP test results show that all variables are stationary at I(1), with the exception of GEXP, which is stationary at I(0). At Maximum Eigenvalue, the four variables are not cointegrated. The findings of the Granger causality test demonstrated a unidirectional causation from GOVD to RGDP, as well as a bidirectional causality from RGDP to GFCF and GEXP. Error Correction Model Estimated using VAR shows that GFCF, GEXP have positive effect on RGDP whereas GOVD has a negative effect on RGDP in the short run. The findings also presented that the VAR's residuals are homoscedastic, which means they are normally distributed and have no serial correlation.

This paper presents the results of an experimental investigation about target detecting with passive sonar in Persian Gulf. Detecting propagated sounds in the water is one of the basic challenges of the researchers in sonar field. This challenge will be complex in shallow water (like Persian Gulf) and noise less vessels. Generally, in passive sonar the targets are detected by sonar equation (with constant threshold) which increase the detection error in shallow water. Purpose of this study is proposed a new method for detecting targets in passive sonars using adaptive threshold. In this method, target signal (sound) is processed in time and frequency domain. For classifying, Bayesian classification is used and prior distribution is estimated by Maximum Likelihood algorithm. Finally, target was detected by combining the detection points in both domains using LMS adaptive filter. Results of this paper has showed that proposed method has improved true detection rate about 27% compare other the best detection method.

Traditional camera sensors rely on human eyes for observation. However, the human eye 1 is prone to fatigue when observing targets of different sizes for a long time in complex scenes, and 2 human cognition is limited, which often leads to judgment errors and greatly reduces the efficiency. 3 Target recognition technology is an important technology to judge the target category in camera 4 sensor. In order to solve this problem, a small size target detection algorithm for special scenarios was 5 proposed by this paper. Its advantage is that this algorithm not only has higher precision for small 6 size target detection, but also can ensure that the detection accuracy of each size is not lower than the 7 existing algorithm. In this paper, a new down-sampling method was proposed, which could better 8 preserve the context feature information. The feature fusion network was improved to effectively 9 combine shallow information and deep information. A new network structure was proposed to 10 effectively improve the detection accuracy of the model. In terms of accuracy, it is better than: YOLOX, 11 YOLOXR, YOLOv3, scaled YOLOv5, YOLOv7-Tiny and YOLOv8.Three authoritative public data sets 12 were used in this experiment: a) On Visdron data sets (small size targets), DC-YOLOv8 is 2.5% more 13 accurate than YOLOv8. b) On Tinyperson data sets (minimal size targets), DC-YOLOv8 is 1% more 14 accurate than YOLOv8. c) On PASCAL VOC2007 data sets (Normal size target), DC-YOLOv8 is 0.5% 15 more accurate than YOLOv8.

A common reactor type in the chemical and process industry is the fixed-bed reactor. Accurate modeling can be achieved with particle-resolved Computational Fluid Dynamic (CFD) simulations. However, the underlying bed morphology plays a paramount role. Synthetic bed-generation methods are much more flexible and faster than image-based approaches. In this study, we look critically at the two different bed generation methods: Discrete Element Method (DEM) (in the commercial software STAR-CCM+) and the rigid-body model (in the open-source software Blender). The two approaches are compared in terms of synthetically generated beds with experimental data of overall and radial porosity, particle orientation, as well as radial velocities. Both models show accurate agreement for the porosity. However, only Blender shows similar particle orientation than the experimental results. The main drawback of the DEM is the long calculation time and the shape approximation with composite particles.

This paper presents an optimized energy management strategy for Li-ion power batteries used on electric vehicles (EVs) at low temperatures. Under low-temperature environments, EVs suffer a sharp driving range loss resulted from the energy and power capability reduction of the battery. Simultaneously, because of Li plating, battery degradation becomes an increasing concern as temperature drops. All these factors could greatly increase the total vehicle operation cost. Prior to battery charging and vehicle operating, preheating battery to a battery-friendly temperature is an approach to promote energy utilization and reduce total cost. Based on the proposed LiFePO4 battery model, the total vehicle operation cost under certain driving cycles is quantified in the present paper. Then given a certain ambient temperature, a target temperature of preheating is optimized under the principle of minimizing total cost. As for the preheating method, a liquid heating system is also implemented on an electric bus. Simulation results show that the preheating process becomes increasingly necessary with a decreasing ambient temperature; however, the preheating demand declines as driving range grows. Vehicle tests verify that the preheating management strategy proposed in this paper is able to save total vehicle operation cost.

In this paper, by means of the Banach fixed point theorem and the Krasnoselskii's fixed point theorem, we investigate the existence of solutions for some fractional neutral functional integro-differential equations involving infinite delay. This paper deals with the fractional equations in the sense of Caputo fractional derivative and in the Banach spaces. Our results generalize the previous works on this issue. Also, an analytical example is presented to illustrate our results.

The Internet-of-Things (IoT) enables enterprises to obtain profits from data but triggers data protection questions and new types of cyber risk. Cyber risk regulations for the IoT however do not exist. The IoT risk is not included in the cyber security assessment standards, hence, often not visible to cyber security experts. This is concerning, because companies integrating IoT devices and services need to perform a self-assessment of its IoT cyber security posture. The outcome of such self-assessment needs to define a current and target state, prior to creating a transformation roadmap outlining tasks to achieve the stated target state. In this article, a comparative empirical analysis is performed of multiple cyber risk assessment approaches, to define a high-level potential target state for company integrating IoT devices and/or services. Defining a high-level potential target state represent is followed by a high-level transformation roadmap, describing how company can achieve their target state, based on their current state. The transformation roadmap is used to adapt IoT risk impact assessment with a Goal-Oriented Approach and the Internet of Things Micro Mart model.

We introduce the notion of generalized contraction on dualistic partial metric spaces. A fixed point theorem for mappings satisfying above mentioned contraction is obtained. Some consequences of our result are obtained. We construct an example to demonstrate the effectiveness of our result among the corresponding results in partial metric spaces. Our results provide substantial generalizations and improvements of several well known results existing in the comparable literature. We discuss an application of our fixed point results to show the existence of solution of functional equations.

The world is currently experiencing the fourth industrial revolution driven by the newest wave of digitisation in the manufacturing sector. The term Industry 4.0 (I4.0) represents at the same time: a paradigm shift in industrial production, a generic designation for sets of strategic initiatives to boost national industries, a technical term to relate to new emerging business assets, processes and services, and a brand to mark a very particular historical and social period. I4.0 is also referred to as Industrie 4.0 the New Industrial France, the Industrial Internet, the Fourth Industrial Revolution and the digital economy. These terms are used interchangeably in this text. The aim of this article is to discuss major developments in this space in relation to the integration of new developments of IoT and cyber physical systems in the digital economy, to better understand cyber risks and economic value and risk impact. The objective of the paper is to map the current evolution and its associated cyber risks for the digital economy sector and to discuss the future developments in the Industrial Internet of Things and Industry 4.0.

This paper develops a novel dynamic correction method for the reliability assessment of large oil-immersed power transformers. First, with the transformer oil-paper insulation system (TOPIS) as the target of evaluation and the winding hot spot temperature (HST) as the core point, an HST-based static ageing failure model is built according to the Weibull distribution and Arrhenius reaction law, in order to describe the transformer ageing process and calculate the winding HST for obtaining the failure rate and life expectancy of TOPIS. A grey target theory based dynamic correction model is then developed, combined with the data of Dissolved Gas Analysis (DGA) in power transformer oil, in order to dynamically modify the life expectancy calculated by the built static model, such that the corresponding relationship between the state grade and life expectancy correction coefficient of TOPIS can be built. Furthermore, the life expectancy loss recovery factor is introduced to correct the life expectancy of TOPIS again. Lastly, a practical case study of an operating transformer has been undertaken, in which the failure rate curve after introducing dynamic corrections can be obtained for the reliability assessment of this transformer. The curve shows a better ability of tracking the actual reliability level of transformer, thus verifying the validity of the proposed method and providing a new way for transformer reliability assessment. This contribution presents a novel model for the reliability assessment of TOPIS, in which the DGA data, as a source of information for the dynamic correction, is processed based on the grey target theory, thus the internal faults of power transformer can be diagnosed accurately as well as its life expectancy updated in time, ensuring that the dynamic assessment values can commendably track and reflect the actual operation state of the power transformers.

Traffic congestion is one of the foremost problems confronted by the urban and suburban tenants of today. Traffic congestion increases vehicle emissions and degrades air quality. Urban planners and policy makers have consequently been always investigating choices to alleviate traffic congestion and to enhance air quality. Teleworking is one option that has received significant consideration and has been studied in the recent past. The aim of the study is to explore the relationship between teleworking, air quality and traffic in Switzerland. The analysis relies on panel individual and household level data over the period 2002-2013. We examine five main air pollutants; the sulphur dioxide (SO₂), the ground-level ozone (O₃) the nitrogen dioxide (NO₂), the carbon monoxide (CO) and the particulate matter less than 10 microns (PM₁₀). Based on the fixed effects estimates, teleworking reduces traffic volume by 1.9 per cent. Furthermore, the reduction observed on air pollution is higher for NO₂, CO and PM₁₀ ranging between 3.3-3.7 per cent, followed by O₃ at 2.3 per cent and SO₂ at 2.1 per cent. According to instrumental variable (IV) approach and the two stage least squares (2SLS) method, the effect is higher ranging between 2.6-4.1 per cent. The respective reduction on traffic becomes 2.7 per cent. Overall, the main concluding remark of the study is that teleworking can be a promising tool for urban planning and development, focusing at the traffic volume reduction, and the air quality improvement. Additional policy implications of teleworking and its beneficial effects for the society are further discussed.

Purpose: To compare the therapeutic efficacy and safety of dorzolamide/timolol fixed-combination in newly diagnosed primary open angle glaucoma patients. Methods: In this prospective, interventional case series, newly diagnosed primary open angle glaucoma (POAG) patients that had not been treated for glaucoma were included. Patients were started on Cosopt twice a day (BID) for 1 month and then switched to three times a day (TDS) for additional 1 month. Patients underwent comprehensive ophthalmic examination, diurnal intraocular pressure (IOP), blood pressure (BP) and 24-hours heart rate (HR) measurements at baseline, month 1( BID), and month 2( TDS). IOP, systolic and diastolic pressures were measured at 8:00 AM,12:00 AM, 4:00 PM, 8:00 PM and 12:00 PM. Throughout the study, all adverse events were recorded and monitored by the investigators. Results: In 31 POAG patients that completed the study ,mean baseline IOP was 23.1±3.15 mmHg . IOP was decreased significantly 16.5 ± 2.21 at 1 month (P < 0.0001) and 13.9 ± 2.23 mmHg at 1 and 2 month follow up. (P < 0.0001) IOP was significantly lower in month 2 compared to month 1 (P = 0.0004). While Cosopt BID significantly reduced the mean 24-hour systolic BP and mean 24-hour HR from baseline (P < 0.0001), the mean 24-hour systolic BP and HR remained unchanged 2ith Cosopt TDS compared to BID (P = 0.62). Conclusions: Cosopt TDS has a superior IOP-lowering effect than Cosopt BID in POAG patients with comparable safety profile.

The Internet-of-Things (IoT) triggers new types of cyber risks. Therefore, the integration of new IoT devices and services requires a self-assessment of IoT cyber security posture. By security posture this article refers to the cybersecurity strength of an organisation to predict, prevent and respond to cyberthreats. At present, there is a gap in the state-of-the-art, because there are no self-assessment methods for quantifying IoT cyber risk posture. To address this gap, an empirical analysis is performed of 12 cyber risk assessment approaches. The results and the main findings from the analysis is presented as the current and a target risk state for IoT systems, followed by conclusions and recommendations on a transformation roadmap, describing how IoT systems can achieve the target state with a new goal-oriented dependency model. By target state, we refer to the cyber security target that matches the generic security requirements of an organisation. The research paper studies and adapts four alternatives for IoT risk assessment and identifies the goal-oriented dependency modelling as a dominant approach among the risk assessment models studied. The new goal-oriented dependency model in this article enables the assessment of uncontrollable risk states in complex IoT systems and can be used for a quantitative self-assessment of IoT cyber risk posture.

BACKGROUND: Manufacturing costs, along with operational performance, are among the major factors determining the selection of the propulsion system for unmanned aerial vehicles (UAVs), especially for aerial targets and cruise missiles. OBJECTIVES: In this paper, the design requirements and operating parameters of small turbofan engines for single-use and reusable UAVs are analysed to introduce alternative materials and technologies for manufacturing their compressor blades, such as sintered titanium, a new generation of aluminium and an alloy based on titanium aluminides. METHODS: To assess the influence of severe plastic deformation (SPD) on the hardening efficiency of the proposed materials, the alloys in the coarse-grained and submicrocrystalline states were studied. Changes in physical and mechanical properties of materials were taken into account. The thermodynamic analysis of the compressor was performed in a finite element analysis system (ANSYS) to determine the impact of gas pressure and temperature on the aerodynamic surfaces of compressor blades of all stages. RESULTS: Based on thermal and structural analysis, the stress and temperature maps on compressor blades and vanes were obtained, taking into account the physical and mechanical properties of advanced materials and technologies of their processing. The safety factors of the components were established based on the assessment of their stress-strength reliability. Thanks to nomograms, the possibility of using the new materials and the technologies was confirmed in view of the permissible operating temperature and safety factors of blades. CONCLUSIONS: The proposed alternative materials and production technologies for the compressor blades and vanes meet the design requirements of the turbofan at lower manufacturing costs.

Renewable energy sources will play a central role in the sustainable energy systems of the future. Scenario analyses of such hypothesized energy systems require sound knowledge of the techno-economic potential of renewable energy technologies. Although there have been various studies concerning the potential of offshore wind energy, higher spatial resolution, as well as the future design concepts of offshore wind turbines, has not yet been addressed in sufficient detail. Here, we aim to overcome this gap by applying a high spatial resolution to the three main aspects of offshore wind potential analysis, namely ocean suitability, the simulation of wind turbines and cost estimation. A set of constraints is determined that reveal the available areas for turbine placement across Europe’s maritime boundaries. Then, turbine designs specific to each location are selected by identifying turbines with the cheapest levelized cost of electricity (LCOE), restricted to capacities, hub heights and rotor diameters of between 3-20 MW, 80-200 m and 80-280 m, respectively. Ocean eligibility and turbine design are then combined to distribute turbines across the available areas. Finally, LCOE trends are calculated from the individual turbine costs, as well as the corresponding capacity factor obtained by hourly simulation with wind speeds from 1980 to 2017. The results of cost-optimal turbine design reveal that the overall potential for offshore wind energy across Europe will constitute nearly 8.6 TW and 40.0 PWh at roughly 7 €_ct kWh^-1 average LCOE by 2050. Averaged design parameters at national level are provided in an appendix.

For improving the tracking accuracy and model switching speed of maneuvering target tracking in nonlinear systems, a new algorithm named interacting multiple model fifth-degree spherical simplex-radial cubature filter (IMM5thSSRCKF) is proposed in this paper. The new algorithm is a combination of the interacting multiple model (IMM) filter and fifth-degree spherical simplex-radial cubature filter (5thSSRCKF). The proposed algorithm makes use of Markov process to describe the switching probability among the models, and uses 5thSSRCKF to deal with the state estimation of each model. The 5thSSRCKF is an improved filter algorithm, which utilizes the fifth-degree spherical simplex-radial rule to improve the filtering accuracy. Finally, the tracking performance of the IMM5thSSRCKF is evaluated by simulation in a typical maneuvering target tracking scenario. Simulation results show that the proposed algorithm has better tracking performance and quicker model switching speed when disposing maneuver models compared with IMMUKF, IMMCKF and IMM5thCKF.

Vascular cell adhesion molecule-1 (VCAM-1) is closely associated with tumor progression and metastasis. However, the relevance and role of VCAM-1 in lung cancer have not been clearly elucidated. In this study, we found that VCAM-1 was highly overexpressed in lung cancer tissue compared with that of normal lung, and high VCAM-1 expression correlated with poor survival of lung cancer patients. VCAM-1 knockdown reduced invasion in A549 human lung cancer cells, and competitive blocking experiments targeting the Ig-like domain 6 of VCAM-1 (VCAM-1-D6) demonstrated that the VCAM-1-D6 domain was critical for VCAM-1-mediated A549 cell invasion. Next, we developed a human monoclonal antibody specific to human and mouse VCAM-1-D6 (VCAM-1-D6 huMab), which was isolated from a human synthetic antibody library using phage display technology. Finally, we showed that VCAM-1-D6 huMab had a nanomolar affinity for VCAM-1-D6 and that it potently suppressed invasion in A549 and NCI-H1299 lung cancer cell lines. Taken together, these results suggest that VCAM-1-D6 is a novel therapeutic target in VCAM-1-mediated lung cancer invasion and that our newly developed VCAM-1-D6 huMab will be a useful tool for inhibiting VCAM-1-expressing lung cancer cell invasion.

Autonomous aerial refueling technology is an effective solution to extend flight duration of unmanned aerial vehicles, and also a great challenge due to its high risk. For autonomous probe-and-drogue refueling tasks, relative navigation to provide relative position between the receiver aircraft and the refueling drogue is the first and essential step, and vision-based method is the most frequently used. A new monocular vision navigation sensor with image processing strategy consisting of the drogue detection method and the tracking method is developed for autonomous aerial refueling in this paper. In the drogue detection method, thresholding and mathematical morphology method are adopted to eliminate image interference, and contours extraction method is applied to obtain all contours, which are then subsequently checked to achieve target contour of drogue. In the tracking method, a rectangle of interest (ROI) of current frame image is determined by positioning results of last frame, and then processed by the previous drogue detection method. Finally, the proposed image processing strategy in monocular vision navigation sensor is validated using real flight images, which are captured from an autonomous aerial refueling testbed using a micro six-rotor aircraft as receiver aircraft.

Inflammatory bowel disease (IBD) is a chronic and relapsing disease caused by a dysregulated immune response to host intestinal microbiota that occurs in genetically predisposed individuals. IBD encompasses two major clinical entities: ulcerative colitis (UC), which is limited to the colonic mucosa, and Crohn disease (CD), which might affect any segment of the gastrointestinal tract. Despite the prevalence of IBD is increasing worldwide, therapy remains suboptimal, largely because the variability of causative mechanisms, raising the need to develop individualized therapeutic approaches targeted to each individual patient. In this context, patients-derived intestinal organoids represent an effective tool for advancing our understanding on IBD’ s pathogenesis. Organoid 3D culture systems offer a unique model for dissecting epithelial mechanisms involved IBDs and test individualized therapy, although the lack of a functional immune system and a microbiota, two driving components of the IBD pathogenesis, represent a major barrier for their exploitation in clinical medicine. In this review we have examined how to improve the translational utility of intestinal organoids in IBD and how co-coltures of 3D or 2D organoids and immune cells and/or intestinal microbiota might help to overcome these limitations.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has resulted in the current COVID-19 pandemic. Worldwide this disease has infected around 1.5 million individuals with a mortality rate ranging from 5 to 10%. It has also imposed extreme challenges on global health, economy, and social behavior. Due to the unavailability of therapeutics, several efforts are going on in the drug discovery to control the SARS-CoV-2 viral infection. The main protease (M^Pro) plays a critical role in viral replication and maturation, thus can serve as the primary drug target. To understand the structural evolution of M^Pro, we have performed phylogenetic and SSN analysis, that depicted divergence of Coronaviridae M^Pro in five clusters specific to viral hosts. This clustering was also corroborated with the comparison of M^Pro structures. Furthermore, it has been observed that backbone and binding site conformations are conserved despite variation in some of the residues. This conservation can be exploited to repurpose available viral protease inhibitors against SARS-CoV-2 M^Pro. In agreement with this, we performed screening of the custom-made library of ~7100 molecules including active ingredients present in the Ayurvedic anti-tussive medicines, anti-viral phytochemicals and synthetic anti-virals against SARS-CoV-2 M^Pro as the primary target. We identified several natural molecules that strongly binds to SARS-CoV-2 M^Pro among which top seven molecules are d-Viniferin, Myricitrin, Taiwanhomoflavone A, Lactucopicrin 15-oxalate, Nympholide A, Biorobin and Phyllaemblicin B. Most of the predicted lead molecules are from Vitis vinifera, also reported for anti-tussive and/or antiviral activities. These molecules also showed strong binding with other main targets RdRp and hACE-2. We anticipate that our approach for identification of multi-target-directed ligand will provide new avenues for drug discovery against SARS-CoV-2 infection.

We develop an interactive likelihood (ILH) for sequential Monte-Carlo (SMC) methods for image-based multiple target tracking applications. The purpose of the ILH is to improve tracking accuracy by reducing the need for data association. In addition, we integrate a recently developed deep neural network for pedestrian detection along with the ILH with a multi-Bernoulli filter. We evaluate the performance of the multi-Bernoulli filter with the ILH and the pedestrian detector in a number of publicly available datasets (2003 PETS INMOVE, AFL, and TUD-Stadtmitte) using standard, well-known multi-target tracking metrics (OSPA and CLEAR MOT). In all datasets, the ILH term increases the tracking accuracy of the multi-Bernoulli filter.

The world's growing population is highly dependent on animal agriculture. Animal products provide nutrient-packed meals that help to sustain individuals of all ages in communities across the globe. As the human demand for animal proteins grows, the agricultural industry must continue to advance its efficiency and quality of production. One of the most commonly farmed livestock is poultry and their significance is felt on a global scale. Current poultry farming practices result in the premature death and rejection of billions of chickens on an annual basis before they are processed for meat. This loss of life is concerning regarding animal welfare, agricultural efficiency, and economic impacts. The best way to prevent these losses is through the individualistic and/or group level assessment of animal on a continuous basis. On large-scale farms, such attention to detail was generally considered to be inaccurate and inefficient, but with the integration of Artificial Intelligence (AI) assisted technology individualized and per-herd assessments of livestock are possible and accurate. Various studies have shown cameras linked with specialized systems of AI can properly analyze flocks for health concerns, thus improving the survival rate and product quality of farmed poultry. Building on the recent advancements, this review explores the aspects of AI in the detection, counting and tracking of the poultry in commercial and research-based applications.

With the increasing of copper consumption, the in-use stock of copper tends to increase. This paper used the “average use life method” to quantify the amount of copper in-use stock, and calculated the average age of in-use stock. It was indicated that the total in-use stock had an overall smooth trends, and reached its peak in 2007 was about 68.9 Mt (million tons), in addition, in-use stock per capita reached its peak ,234 kg/capita in 2001. The results demonstrated that during the period 1992-2002, the average age of copper in-use stock was continually decreased, but gradually increased since the year 2003. The fixed assets depreciation method used in this paper is applied to analyze depreciated copper in-use stock, and to analyze the relationship with economic indicator (GDP). It is demonstrated that it was inconsistent with the theory of Environmental Kuznets Curve (EKC) before 2000, this might be the demand for copper in the service sector is greater than the reduced strength of alternatives. Finally, scenario analysis of future copper in-use stock and depreciated copper in-use stock per capita in the U.S. were presented. The corresponding average age of the in-use stock will have a slight rise in the next decade.

The Doubly-Fed Induction Generator (DFIG) has significant features in comparison with Fixed Speed Wind Turbine (FSWT), which has popularized its application in power system. Due to partial rated back-to-back converters in the DFIG, Fault Ride-Through (FRT) capability improvement is one of the great subjects regarding new grid code requirements. To enhance the FRT capability of the DFIG, many studies have been carried out. Fault current limiting devices as one of the techniques are utilized to limit the current level and protect switches of the back-to-back converter from over-current damage. In this paper, a review is done based on fault current limiting characteristic of the proposed fault current limiting devices Therefore, Fault Current Limiters (FCLs) and Series Dynamic Braking Resistors (SDBRs) are mainly taken into account. Operation of all configurations including their advantages and disadvantages is explained. Impedance type and the fault current limiting devices’ location are two important factors, which significantly affect the DFIG behaviour in the fault condition. These two factors are basically studied by the simulation and their effects on the key parameters of the DFIG are investigated. Finally, future works in respect to the FCL application in the FRT improvement of the DFIG have also been discussed.

The effects of the surface waves generated by the wind have a significant effect on the currents. A wave current coupled model plays an important role in the design of offshore structures. The interaction between fluids such as incompressible ocean waves and current and offshore structures is significant with many real-time applications in offshore engineering. These coupled models can be applied to Offshore Floating Production Operating and offloading (FPSO), Wind or current turbines and offshore pipelines. The complex issues related to the design are analyzed by using Computational Fluid Dynamics, which requires an investigation of the multiphase flow between wave and current and the structure which is considered restrictive due to the computational cost. If viscous effects are neglected then the single-phase flow models have been recommended, where wave-current interaction have been modelled successfully. Models have been developed where velocities and pressure are computed and the results can be verified with the experimental results available in the literature. In this study the existing numerical methods, mesh types are discussed along with their coupling methods. Here single-phase and multiphase models with small and medium movement are reviewed and their applications are highlighted.

The aim of this document is to provide practical guidance on the choice of target difference used in the sample size calculation of a randomised controlled trial (RCT). Guidance is provided with a definitive trial, one that seeks to provide a useful answer, in mind and not those of a more exploratory nature. The term “target difference” is taken throughout to refer to the difference that is used in the sample size calculation (the one that the study formally “targets”). Please see the glossary for definitions and clarification with regards other relevant concepts. In order to address the specification of the target difference, it is appropriate, and to some degree necessary, to touch on related statistical aspects of conducting a sample size calculation. Generally the discussion of other aspects and more technical details is kept to a minimum, with more technical aspects covered in the appendices and referencing of relevant sources provided for further reading.The main body of this guidance assumes a standard RCT design is used; formally, this can be described as a two-arm parallel-group superiority trial. Most RCTs test for superiority of the interventions, that is, whether or not one of the interventions is superior to the other (See Box 1 for a formal definition of superiority, and of the two most common alternative approaches). Some common alternative trial designs are considered in Appendix 3. Additionally, it is assumed in the main body of the text that the conventional (Neyman-Pearson) approach to the sample size calculation of an RCT is being used. Other approaches (Bayesian, precision and value of information) are briefly considered in Appendix 2 with reference to the specification of the target difference.

In recent years, many algorithms based on end-to-end deep networks have been proposed to deal with the target detection problem of videos. However, the deep network models usually consume a lot of computing resources during the procedure of analysis of videos with complex dynamic backgrounds. In this paper, a new method of object detection based on information theory is presented. Firstly, each frame in a video is converted into an effective information map by using the Harris corner detection method. Secondly, the sensitive areas in the frame are extracted by using the context information and the effective information maps of the consecutive video frames. The sensitive areas in the video frame are the candidate areas where the target objects would be appeared at high probabilities. Thirdly, the information entropy features of each sensitive area are extracted to form the feature matrix, based on which, an SVM model is trained for selecting the target areas from the sensitive areas. Finally, the locations of the objects are detected based on the target areas in the video with a complex dynamic background. As a lightweight video detection framework, the method presented in this paper can save a lot of computing resources. Experimental results show that this method can achieve good results in the benchmark of CDnet 2014.

Targeted enrichment of ultraconserved elements (UCE) has emerged as a promising tool for inferring evolutionary history in many taxa, with utility ranging from phylogenetic and phylogeographic questions at deep time scales to population level studies at shallow time scales. However, the methodology can be daunting for beginners. Our goal is to introduce UCE phylogenomics to a wider audience by summarizing recent advances in arthropod research, and to familiarize readers with background theory and steps involved. We define terminology used in association with the UCE approach, evaluate current laboratory and bioinformatic methods and limitations, and, finally, provide a roadmap of steps in the UCE pipeline to assist phylogeneticists in making informed decisions as they employ this powerful tool. By facilitating increased adoption of UCE in phylogenomics studies that deepen our comprehension of the function of these markers across widely divergent taxa, we aim to ultimately improve understanding of the arthropod tree of life.

The concept of Design Fatigue Factors (DFFs) was introduced for providing desired level of safety in structural fatigue design, often associated with damage calculated from S-N curves. Calculation of fatigue damage from S-N curves can be affected by multiple factors, e.g. types of weld class, corrosion condition, loading conditions, stress concentration on different geometries etc. Each of them can be subject to different level of uncertainties. This study intends to recalibrate the DFFs from a detailed reliability analysis by investigating the probabilistic models derived from the database of S-N curves that has been most frequently used in offshore wind industry. The results of such study indicate that the DFFs can be reduced substantially for the corrosive environmental fatigue models from current standards to the same level of target reliability.

Radar transmit signal design is a critical factor for the radar performance. In this paper, we investigate the problem of radar signal waveform design under the small signal power conditions for detecting a doubly spread target, whose impulse response can be modeled as a random process, in a colored noise environment. The doubly spread target spans multiple range bins (range-spread) and its impulse response is time-varying due to fluctuation (hence also Doppler-spread), such that the target impulse response is both time-selective and frequency-selective. Instead of adopting the conventional assumption that the target is wide-sense stationary uncorrelated scattering,we assume that the target impulse response is both wide-sense stationary in range and in time to account for the possible correlation between the impulse responses corresponding to close range intervals. The locally most powerful detector, which is asymptotically optimal for small signal cases, is then derived for detecting such targets. The signal waveform is optimized to maximizing the detection performance of the detector or equivalently maximizing the Kullback-Leibler divergence. Numerical simulations validate the effectiveness of the proposed waveform design for the small signal power conditions and performance of optimum waveform design are shown in comparison to the frequency modulated waveform.

Covid-19 infections are rapidly spreading worldwide with more than 100000 death and thus understanding the molecular mechanism of tropism of human cells is an urgent need for drug design. We have described here a bioinformatics approach to predict the functional aspects of non-structural nsp16 protein of Corona virus. The covid-19 7098 AA large polyprotein was degraded into sixteen proteins and last nsp16 protein was found an RlmE type rRNA methyltransferase. Nsp16 has no similarity to bacterial RlmABCD but has 25 percent similarity to the bacterial RlmE protein which methylates the U2551 2-hydroxy group of Ribose. The nsp16 proteins of different corona viruses like covid-19, bat-coronavirus, SARS and MERS have strong homology. Mrm2 and Dim1 like yeast and mammalian rRNA methyltransferases have 26-33 percent homologies but not with 2-O-capping MTase as reported previously. Rrp8 MTases also has no similarity to nsp16. We postulated that mitochondrial rRNA methylation of bronchial cells were mediated by the nsp16 protein causing inhibition of protein synthesis due to poor assembly of aminoacyl-tRNA or mRNA and peptidyl transferase at the PTC. This is one of the new molecular mechanism of corona virus cellular tropism and different than ACE-2 mediated blockage of cellular signalling to inhibit aldesterone biosynthesis with abnormal Na⁺ ions in cells. We also designed primers based on nsp16 cDNA sequence (nt 20659-21552, accession no MT121215) specific for Covid-19 diagnosis by RT-PCR.

This paper considers the detection of fluctuating target in heavy-tailed clutter through the use of dynamic programming based on track-before-detect (DP-TBD) in radar systems. The clutter is modeled in terms of K-distribution, which can be widely used to describe non-Gaussian clutter received from high-resolution radars and radars working at small grazing angle. Swerling type 1 is considered to describe the target fluctuation between scans. Conventional TBD techniques suffer from significant performance loss in heavy-tailed environments due to the more frequent occurrences of target-like outliers. In this paper, we resort to DP-TBD algorithm based on prior information, which can enhance the detection performance by using the environment and target fluctuating information during the integration process of TBD. Under non-Gaussian background, the expressions of the likelihood ratio merit function for Swerling type 1 target are derived first. However, the closed analytical form of the merit function is difficult to be obtained. In order to reduce the complexity of evaluating the merit function and the computational load, an efficient approximation method as well as a two-stage detection approach is proposed and used in the integration process. Finally, several numerical simulations of the new strategy and the comparisons are presented to verify that the proposed algorithm can improve the detection performance, especially for fluctuating target in heavy-tailed clutter.

This paper considers target characterization and recognition in radar images with keypoint-based local descriptor. Most of the preceding works rely on the global features or raw intensity values, and hence produce the limited recognition performance. Moreover, the global features are sensitive to the real-world sources of variability, such as aspect view, configu-ration, and incidence angle changes, clutter, articulation, and occlusion. Keypoint-based local descriptor was developed as a powerful strategy to address invariance to contrast change and geometric distortion. This property inspires us to investigate whether the family of local features are relevant for radar target recognition. Most of the preceding works typically devote to finding the correspondences between a collected image and a reference one. The representative applications include image register and change detection. Little work was pursued to target recognition in SAR images. This is because the huge number of local descriptors resulting from radar images make the computational cost and memory consumption unacceptable. To handle the problems, this paper develops two families of methods. The proposed methods are used to achieve target recognition by means of local descriptors. Our first solver refers to building multiple linear regression models, and addresses the problem by the theory of sparse representation. The second scheme rebuilds a new feature by the feature quantization skill, from which the inference can be drawn. Multiple comparative studies are pursued to verify the performance of detectors and descriptors popularly used. The source code was publicly released on https://ganggangdong.github.io/homepage/.

Nucleoid-associated proteins (NAPs) play an architectural role by bending, bridging, and wrapping the DNA along with a regulatory role of controlling various transcriptional units in the cell. Previews reviews have highlighted the role of HU and its paralog IHF plays in intracellular function as a transcriptional regulator, nucleoid bending protein and sometimes also moonlights in other functions. This review highlights along with the canonical functions of HU and IHF which affects genes responsible for translational machineries, cell wall biosynthesis, aerobic respiration and virulence ; other non-canonical roles which HU plays outside the cellular milieu, notably in acting as an adhesin and playing role in host-cell adhesion, its role in biofilm architecture and its association with cationic low complexity region, resembling histone like H1 proteins. HU and IHF thus has evolved as a hub protein performing a vast type of functions which makes it a important drug target for antibacterial therapy.

In this review, we discuss the insulin resistance (IR) and its development in the insulin target tissues that leads to diabetes. Also, we highlight the use of induced pluripotent stem cells (iPSCs) to understand the mechanisms underlying the development of IR. IR is associated with several metabolic disorders, including type 2 diabetes (T2D). The development of IR in insulin target tissues involves genetic and acquired factors. Persons at genetic risk for T2D tend to develop IR several years before glucose intolerance. Although there are currently several mouse models for both IR and T2D that had provided a lot of information about the disease, these models cannot recapitulate all the aspects of this complex disease as seen in each individual. Patient-specific iPSCs can overcome the hurdles faced with the classical mouse models for studying IR. iPSC technology can generate cells genetically identical to IR individuals, which can help in distinguishing between genetic and acquired defects in insulin sensitivity. Combining the technologies of the genome editing and iPSCs may provide important information about the inherited factors underlying the development of different forms of IR. Further studies are required to fill the gaps in understanding the pathogenesis of IR and diabetes.

Water is an indispensable natural resource that is often prodigiously threatened by anthropomorphic activities. This study evaluated the physicochemical properties of water and selected heavy metals in edible muscles of a piscivorous fish (Protopterus annectens) from Nyabarongo and Nyabugogo rivers of Rwanda. Edibility health risk was evaluated using the target hazard quotient method. Water samples were taken in triplicate from Ruliba station and Kirinda bridge on Nyabarongo river and Giticyinyoni on Nyabugogo river. Fish samples were obtained from the sampling stations on Nyabarongo river. All samples were analyzed following standard methods and analytical results indicated that the average temperature, pH, total dissolved solids and electrolytic conductivity of water from the rivers were within WHO acceptable limits. The statistical mean concentrations of the ionic components of the water samples were 1.61 ± 0.03, 0.53 ± 0.002, 0.24 ± 0.02 and 0.051 ± 0.01 mg/L for Fe, Mn, Cu and Pb respectively at Ruliba station and 0.63 ± 0.02, 0.02 ± 0.002, 0.09 ± 0.01, 0.06 ± 0.002 and 0.75 ± 0.02 mg/L for Fe, Mn, Zn, Cr and Pb respectively at Kirinda bridge. Water from Giticyinyoni had 1.57 ± 0.02, 0.49 ± 0.03, 0.29 ± 0.058, 0.43 ± 0.058, 0.15 ± 0.00 and 0.59 ± 0.058 mg/L of Fe, Mn, Cu, Zn, Cr and Pb respectively. Zinc, Cu, Cr and Cd were below detection limits in samples from Ruliba station and Kirinda bridge (Nyabarongo river). Edible muscles of P. annectens from Nyabarongo river contained 272.8 ± 0.36, 292.2 ± 0.25, 8.8 ± 0.36, 135.2 ± 0.15, 148.0 ± 0.21 and 432. 0 ± 0.50 mgkg^-1 for Fe, Mn, Cu, Zn, Cr and Pb at Ruliba station and 336.0 ± 0.70, 302.6 ± 1.22, 6.4 ± 0.26, 44.7 ± 0.20, 138.2 ± 0.17 and 302.4 ± 1.50 mgkg^-1 for Fe, Mn, Cu, Zn, Cr and Pb respectively at Kirinda bridge. Health risk assessment indicated that consumption of the edible muscles of P. annectens may lead to deleterious health effects as reflected by values of target hazard quotients being greater than one. Therefore, the Rwandese government should lay strategies to reduce pollution of the rivers. Further research should evaluate the heavy metal content of metabolically active organs of P. annectens from Nyabarongo river as well as the microbiological profile of water from the rivers.

In this paper, we established the existence of PC-mild solutions for non local fractional impulsive functional integro-differential equations with finite delay. The proofs are obtained by using the techniques of fixed point theorems, semi-group theory and generalized Bellman inequality. In this paper, we have used the distributed characteristic operators to define the mild solution of the system. Results obtained here improve and extend some known results.

The exploration and identification of targets and pathways for Atopic dermatitis (AD) treatment and diagnosis are critical for AD control. The conventional target exploration approach such as the literature review is not satisfying in terms of efficiency and accuracy. Recently, the bioinformatic approach is drawing attention for its unique advantage of high-volume data analysis for target and pathway exploration; Open Targets Platform is the targets source for this study to extract top 200 high-rank proteins from 3122 AD associated proteins. STRING, Cytoscape, CytoHubba, ClueGo, and CluePedia function had been applied for data analysis. The KEGG Mapper search & colour pathway was the pathway map resource for identified pathways; 23 key hub genes (VDR, KIT, BCL2L11, NFKBIA, KRAS, IL13, JAK2, STAT3, IL21, IL4R, REL, PDGFRB, FOXP3, RARA, RELB, EGFR, IL21R, MYC, CREBBP, NR3C1, IL2, JAK1, and KITLG). Additionally, 8 correlated pathways and the biological process had been identified; Through this study, a viable approach for target and pathway exploration had been presented. The identified AD targets and pathways will be tested for upcoming research for traditional Chinese medicinal herb interactions

Since smoke usually occurs before a flame arises, fire smoke detection is especially significant for early warning systems. In this paper, a DSATA(Depthwise Separability And Target Awareness) algorithm based on depthwise separability and target awareness is proposed. Existing deep learning methods with convolutional neural networks pretrained by abundant and vast datasets are always used to realize generic object recognition tasks. In the area of smoke detection, collecting large quantities of smoke data is a challenging task for small sample smoke objects. The basis is that the objects of interest can be arbitrary object classes with arbitrary forms. Thus, deep feature maps acquired by target-aware pretrained networks are used in modelling these objects of arbitrary forms to distinguish them from unpredictable and complex environments. In this paper, this scheme is introduced to deal with smoke detection. The depthwise separable method with a fixed convolution kernel replacing the training iterations can improve the speed of the algorithm to meet the enhanced requirements of real-time fire spreading for detecting speed. The experimental results demonstrate that the proposed algorithm can detect early smoke, is superior to the state-of-the-art methods in accuracy and speed, and can also realize real-time smoke detection.

Cyclic STAT3 decoy (CS3D) is a second-generation, double-stranded oligodeoxynucleotide (ODN) that mimics a genomic response element for signal transducer and activator of transcription 3 (STAT3), an oncogenic transcription factor. CS3D competitively inhibits STAT3 binding to target gene promoters, resulting in decreased expression of proteins that promote cellular proliferation and survival. Previous studies have demonstrated antitumor activity of CS3D in preclinical models of solid tumors. However, prior to entering human clinical trials, the efficiency of generating the CS3D molecule and its stability in biological fluids should be determined. CS3D is synthesized as a single-stranded ODN and must have its free ends ligated to generate the final cyclic form. In this study, we report a ligation efficiency of nearly 95 percent. The ligated CS3D demonstrated a half-life of 7.9 hours in human serum, indicating adequate stability for intravenous delivery. These results provide requisite biochemical characterization of CS3D that will inform upcoming clinical trials.

Viruses are the major causes of acute and chronic infectious diseases in the world. According to the World Health Organization, there is an urgent need for better control of viral diseases. Re-purposing existing antiviral agents from one viral disease to another could play a pivotal role in this process. Here we identified novel activities of obatoclax and emetine against herpes simplex virus type 2 (HSV-2), human immunodeficiency virus 1 (HIV-1), echovirus 1 (EV1), human metapneumovirus (HMPV) and Rift Valley fever virus (RVFV) in cell cultures. Moreover, we demonstrated novel activities of emetine against influenza A virus (FluAV), niclosamide against HSV-2, brequinar against HIV-1, and homoharringtonine against EV1. Our findings may expand the spectrum of indications of these safe-in-man agents and reinforce the arsenal of available antiviral therapeutics pending the results of further in vivo tests.

Global crop yields are estimated to be reduced by 30–40% per year on account of plant pests and pathogens. Agricultural insect pests raise concerns about constraining global food security and climate changes contributing to the rise of infestation. The current management relies on plant breeding, associated or not with transgenes and chemical pesticides. Both approaches face serious technology obsolescence on the field due to resistance breakdown or development of insecticide resistance. The need for new Modes of Action (MoA) approaches in managing crop health grows each year, driven by market demands to reduce economic losses and phytosanitary requirements to meet the consumer perception. Disabling pest genes by sequence-specific expression silencing is considered a promising tool in the development of environment and health respectful biopesticides. The specificity conferred by long dsRNA-base solutions give support to minimizing effects on off-targeted genes in the insect pest genome and the target gene in non-target organisms (NTOs). In this review, we summarize the current status of gene silencing by RNA interference (RNAi) for agricultural control. More specifically, we focus on the engineering, development and application of gene silencing to control Lepidoptera by the employment of non-transforming dsRNA technologies. Despite some delivery and stability drawbacks of topical applications, we reviewed works showing convincing proof-of-concept results that point to imminent innovative solutions. Considerations about the regulamentation of the ongoing research on dsRNA-based pesticides to produce commercialized products for exogenous application are discussed. Academic and industry initiatives reveal a worthy effort to accomplish controlling Lepidoptera pests with this new mode of action to provide more sustainable and reliable technologies to field management. New data on genomics of this taxon encourage the increment of a customized target genes portfolio. As a case of study, we illustrate how dsRNA and associated methodologies could be applied to control an important Lepidopteran coffee pest.

In contrast to physical sustainable measures carried out in homes, such as insulation, the installation of a Home Energy Management System (HEMS) has no direct and immediate energy-saving effect. A HEMS gives insight into resident behaviour regarding energy use. When this is linked to the appropriate feedback, the resident is in a position to change his or her behaviour. This should result in reduced gas and/or electricity consumption. The aim of our study is to contribute towards the effective use of home energy management systems (HEMS) by identifying types of homeowners in relation to the use of HEMS. The research methods used were a literature review and the Q-method. A survey using the Q-method was conducted among 39 owners of single-family homes in various Rotterdam neighbourhoods. In order to find shared views among respondents, a principal component analysis (PCA) was performed. Five different types of homeowner could be distinguished: the optimists, the privacy-conscious, the technicians, the sceptics, and the indifferent. Their opinions vary as regards the added value of a HEMS, what characteristics a HEMS should have, how much confidence they have in the energy-saving effect of such systems, and their views on the privacy and safety of HEMS. The target group classification can be used as input for a way in which local stakeholders, e.g. a municipality, can offer HEMS that is in line with the wishes of the homeowner.

In the framework of a multiplicative metric space. We have discuss some Properties of convex structures in pseudo multiplicative metric space and have applied these properties toiobtain alfixed point results in complete pseudo. It would be interesting to prove some further results in such metric spaces with completeness property.

The purpose of this article is to characterize the Caristi type mapping by the absolute derivative. The equivalences of the Caristi mapping with contraction mapping is discussed too. In addition, it was shown that the contraction mapping can be characterized by its absolute derivative.

Causal relationships lie at the very core of our scientific description of biophysical phenomena. Nevertheless, observable facts such as chaotic dynamics, symmetry breaks, long-range collisionless neural interactions, zero-value energy singularities, particle/wave duality, ecological approaches to visual perception elude cause effect inductive explanations. We illustrate how simple topological claims, seemingly useless and far away from scientific inquiry (e.g., “given at least some wind on Earth, there must at all times be a cyclone or anticyclone somewhere”; “if one stirs to dissolve a lump of sugar in a cup of coffee, it appears there is always a point without motion”; “at any moment, there is always a pair of antipodal points on the Earth’s surface with equal temperatures and barometric pressures”) describe a real world, made of projections and mappings unsupplied with causality. Unavoidable, necessary, passive, spontaneous changes in systems’ shapes, dimensions and symmetries incarnate biophysical quantifiable counterparts of abstract mathematical concepts. Real features arising from topological mechanisms do not require causal powers to be produced, because are independent of exerted physical forces, energetic fields’ actions and thermodynamic constraints. Providing examples that shed novel interpretative light on phenomena such as double slit experiments, cellular mechanisms and brain function, we conclude as follows: countless scientific matters can be accessed through methodological weapons which not just break causality, replace temporal correlations and probabilistic a priori knowledge of previous cases, but also permit augments in available information devoid of entropy increases and energy losses.

Pristimerin (PM) is a naturally occurring quinonemethide triterpenoid compound that isolated from the Celastraceae and Hippocrateaceae families. Its anticancer effects have attracted a great deal of attention, but the mechanisms of action remain obscure. In this study, we screened for the active compounds of Pristimerin using a drug-likeness approach. Potential protein targets of Pristimerin were predicted by PharmMapper and Coremine database. Candidate protein targets were then uploaded to GeneMANIA and GO pathway analysis. Finally, compound-target, target-pathway, and compound-target-pathway networks were constructed using Cytoscape 3.3. The results showed that Pristimerin had good drug ability and identified 13 putative protein targets. Network analysis revealed that these targets are associated with cancer, inflammation and other physiological processes. In summary, Pristimerin is predicted to target a variety of proteins and pathways to form a network that exerts systemic pharmacological effects.

All countries are facing decisions about which groups to prioritise for COVID-19 vaccination after the first vaccine product has been licensed, at which time supply shortages are inevitable. Here we define the key target populations and their size in China for a phased introduction of COVID-19 vaccination with evolving goals, accounting for the risk of illness and transmission. Essential workers (47.2 million) like healthcare workers could be prioritized for vaccination to maintain essential services. Subsequently, older adults, individuals with underlying health conditions and pregnant women (616.0 million) could be targeted to reduce severe COVID-19 outcomes. Then it could be further extended to target adults without underlying health conditions and children (738.7 million) to reduce symptomatic infections and/or to stop virus transmission. The proposed framework could assist Chinese policy-makers in the design of a vaccination program, and could be generalized to inform other national and regional COVID-19 vaccination strategies.

The lean supply chain of construction engineering projects is to achieve the maximum satisfaction of the owners' needs in order to effectively achieve the goal of supply chain management. This paper explores an effective method of lean supply chain cost management for construction engineering projects with target cost management, so that each participating unit on the supply chain node can fully utilizes its core competencies to minimize internal consumption and waste, and achieve the optimal overall efficiency of the supply chain. According to the requirements of the goal planning theory of the construction project company, establish a lean supply chain cost planning system for the construction project, realize the basic model of the lean supply chain cost management of the construction project, and set the target cost from the lean project of the construction project. The technical decomposition is established by the process of cost decomposition and cost pressure transmission and sub-target cost planning.

Desmoplastic small round cell tumor (DSRCT) is an extremely rare, aggressive sarcoma affecting adolescents and young adults with male predominance. Generally, it originates from serosal surface of abdominal cavity. The hallmark characteristic of DSRCT is the EWSR1-WT1 gene fusion. This translocation up-regulates the expression of PDGFRα, VEGF and other proteins related to tumor and vascular cell proliferation. Current management of DSRCT includes a combination of chemotherapy, radiation and aggressive cytoreductive surgery plus intra-peritoneal hyperthermic chemotherapy (HIPEC). Despite advances in multimodal therapy, outcomes remain poor since the majority of patients present disease recurrence and die within 3 years. The dismal survival makes DSRCT an orphan disease with urgent need of new drugs. The treatment of advanced and recurrent disease with tyrosine kinase inhibitors, such as pazopanib, sunitinib, and mTOR inhibitors have been evaluated in small studies. Recent works using comprehensive molecular profiling of DSRCT identified potential therapeutic targets. In this review, we aim to describe the current studies conducted to better understand DSRCT biology and to explore the new therapeutic strategies under investigation in preclinical models and in early phase clinical trials.

There are dozens of approved, investigational and experimental antiviral agents. Many of these agents cause serious side effects, which can be revealed only after drug administration. Identification of the side effects prior to drug administration is challenging. Here we describe an ex vivo approach for studying immuno- and neuro-modulatory properties of antiviral agents, which could be associated with potential side effects of these therapeutics. The approach combines drug toxicity/efficacy tests and transcriptomics, which is followed by cytokine and metabolite profiling. We demonstrated the utility of this approach with several examples of antiviral agents. We also showed that the approach can utilize different immune stimuli and cell types. It can also include other omics techniques, such as genomics and epigenomics, to allow identification of individual markers associated with adverse reactions to antivirals with immuno- and neuro-modulatory properties.

In this paper, we prove some random fixed point theorems for generalized random $\alpha-\psi-$contractive mappings in a Polish space and, as some applications, we show the existence of random solutions of second order random differential equation.

COVID-19 pandemic will continue to pose a major public health threat until vaccination-mediated herd immunity is achieved. Most projections predict vaccine will reach a large subset of the population late in 2021 or early 2022. In the meantime, countries are exploring options to remove strict lockdown measures and allow for society and the economy to return to normal function. One possibility is to expand on existing COVID-19 testing strategies by including large-scale rapid point of care diagnostic tests (POCTs). Currently, there is significant variability in performance and features of available POCTs, making selection and procurement of appropriate test for specific use case difficult. In this review, we have used the World Health Organization’s (WHO) recently published Target Product Profiles (TPPs) for specific use cases of COVID-19 diagnostic tests to screen for top-performing POCTs on the market. Several top-performing POCTs, based on clinical sensitivity/specificity, the limit of detection, and time to results, that meet WHO TPP criteria for direct detection of SARS-CoV-2 (acute infection), or indirect diagnosis of past infection (host antibodies) are highlighted here.

Unmanned surface vehicles have the advantages of maneuverability, concealment, wide activity area and low cost of use. Therefore, they have broad application prospects. This makes unmanned surface vehicles a research hotspot at home and abroad, and the sensing technology is the basis for the unmanned surface vehicles to perform tasks. The perception technology based on optical vision has the advantages of convenient application, relatively low cost, easy data acquisition and large amount of information, and has been widely studied by scholars at home and abroad. This paper mainly discusses the research of optical vision in unmanned surface vehicles from five aspects: Firstly, the water surface image preprocessing based on unmanned surface vehicles, mainly including water surface image stabilization research and defogging enhancement research; two water boundary detection; It is the use of light vision target detection; the fourth is the surface target tracking method. Finally, the light vision research of unmanned surface vehicles is summarized and forecasted.

In the present study, Electrocoagulated Metal Hydroxide Sludge (EMHS) was analyzed as adsorbent material to remove both fluoride ion (F-) and arsenic V (As(V)) from aqueous effluents. This material was generated during an electrocoagulation process using Aluminum anode. It was characterized by using specific surface areas and the surface morphology was studied by scanning electron microscopy (SEM). Adsorbent fixed-beds are generally studied to remove different class of contaminants. EMHS was evaluated using a continuous flow rate column test with an experimental design. The effect of initial concentration of F- (2.5–10 mg L⁻¹) and the Empty Bed Contact Time (EBCT (0.4–0.8 min)) was studied following a central composite design methodology. The experimented parameters had a significant influence on saturation time, breakthrough volume, and breakthrough time. A response surface analysis was a tool for analyzing the adsorption study, showing interactions that are complicated to identify by others methods. The results, here reported, revealed that EMHS is an efficient and promising adsorbent material in order to remove F- and As(V) from water contaminated by these pollutants.

The aim of our study was to describe our experience using a Spectra Optia® automated apheresis system in children with sickle cell disease (SCD). We used automated red blood cell exchange (RCE) to treat acute and chronic complications in 75 children with SCD who had a median age of 10 years [7-13]. We analysed 649 exchange sessions. Peripheral venous access was limited in a number of the children, thus requiring a femoral central double‐lumen venous catheter (CVC). We recommend the use of heparin locking, with 500 units in each lumen of a CVC. This method was well tolerated, with few complications during the procedures. For preoperative prevention, all of the patients had achieved a post-RCE HbS level of <30% since this is a mandatory condition imposed by the anaesthesiologist. With a post-RCE Hb level of approximately 10-11 g/dL, a blood exchange volume of ≥32 mL/kg, and an interval between each RCE procedure of ≤30 days, it was able to maintain the residual HbS level below 30%. Despite a target pre‐exchange HbS level of 47%, we did not encounter a single stroke recurrence. Erythrocytapheresis is useful and safe for children with SCD.

Protein-DNA interactions are critical for the successful functioning of all natural systems. The key role in these interactions is played by processes of protein search for specific sites on DNA. Although it has been studied for many years, only recently microscopic aspects of these processes became more clear. In this work, we present a review on current theoretical understanding of the molecular mechanisms of the protein target search. A comprehensive discrete-state stochastic method to explain the dynamics of the protein search phenomena is introduced and explained. Our theoretical approach utilizes a first-passage analysis and it takes into account the most relevant physical-chemical processes. It is able to describe many fascinating features of the protein search, including unusually high effective association rates, high selectivity and specificity, and the robustness in the presence of crowders and sequence heterogeneity.

Liquid biopsy, based on the circulating tumor cells (CTCs) and cell-free nucleic acids has potential applications at multiple points throughout the natural course of cancer, from diagnosis to follow-up. The advantages of doing ctDNA assessment vs. tissue-based genomic profile are the minimal procedural risk, the possibility to serial testing in order to monitor disease-relapse and response to therapy over time and to reduce hospitalization costs during the entire process. However some critical issues related to ctDNA assays should be taken in consideration. The sensitivity of ctDNA assays depends on the assessment technique and genetic platforms used, on tumor-organ, stage, tumor heterogeneity, tumor clonality. The specificity is usually very high, whereas the concordance with tumor-based biopsy is generally low. In patients with renal cell carcinoma (RCC) qualitative analyses of ctDNA have been performed with interesting results regarding selective pressure from therapy, therapeutic resistance, exceptional treatment response to everolimus and mutations associated with aggressive behavior. Quantitative analyses showed variations of cfDNA levels at different tumor stage. Compared to CTC assay, ctDNA is more stable than cells and easier to isolate. Splice variants, information at single-cell level and functional assays along with proteomics, transcriptomics and metabolomics studies can be performed only in CTCs.

In this article, we show the existence of fixed point for the derivative of interval-valued functions. Meanwhile, the fixed point inquiry will utilize the common fixed point methods under the condition of compatibility of the hybrid composite mappings in the sense of the Hausdorff metric. Some examples are given to support the usability of the result of this research .

The vertebrate annexin superfamily (AnxA) consists of 12 members of a calcium (Ca²⁺) and phospholipid binding protein family which share a high structural homology. In keeping with this hallmark feature, annexins have been implicated in the Ca²⁺-controlled regulation of a broad range of membrane events. In this review, we identify and discuss several themes of annexin actions that hold a potential therapeutic value, namely the regulation of the immune response and the control of tissue homeostasis, and that repeatedly surface in the annexin activity profile. Our aim is to identify and discuss those annexin properties which might be exploited from a translational science and specifically clinical point of view.

Microturbines can be used not only in models and education but also to propel UAVs. However, their wider adoption is limited by their relatively low efficiency and durability. Validated simulation models are required to monitor their performance, improve their lifetime, and design engine control systems. This study aims at developing a numerical model of a micro gas turbine for prediction and prognostics of engine performance. To build a reliable zero-dimensional model, the available compressor and turbine maps were scaled to the available test bench data with the least squares method, to meet the performance of the engine achieved during bench and flight tests. A steady-state aeroengine model was implemented in GSP and compared with experimental operating points. The selected flight data was then used as input for the transient engine model. The exhaust gas temperature (EGT) and fuel flow were chosen as the two key parameters to validate the model, comparing the numerical predicted values with the experimental ones. The observed difference between the model and the flight data was lower than 3% for both EGT and fuel flow.

The vertebrate annexin superfamily (AnxA) consists of 12 calcium (Ca2+) and phospholipid binding proteins which share a high structural homology. In keeping with this hallmark feature, annexins have been implicated in the Ca2+-controlled regulation of membrane events. In this review, we discuss several themes of potential therapeutic value, namely the regulation of the immune response and the control of tissue homeostasis, that repeatedly surface in the annexin action profile. Our aim is to identify and discuss those annexin properties which might be exploited from a translational science and specifically clinical point of view.

The development of new target stations for radioisotope production based on a dedicated 70~MeV commercial cyclotron is described. Currently known as the South African Isotope Facility (SAIF), this initiative will free the existing separated-sector cyclotron (SSC) at iThemba LABS (near Cape Town) to mainly pursue research activities in nuclear physics and radiobiology. It is foreseen that the completed SAIF facility will realize a three-fold increase in radioisotope production capacity compared to the current programme based on the SSC.

We are in a new chapter of crop and livestock improvement with the emergence of genome editing. This latest generation of molecular tools can be used to make targeted changes in a genome including insertions, deletions, and mutations. With new advances comes new risks for unintended changes and impacts, thus the need for appropriate risk assessment for product development and to inform regulatory measures. Though CRISPR/Cas has arisen as the predominant technology, there are multiple types of genome editing tools each with pros and cons depending on the organism and desired outcome. Furthermore, each editing tool differs in specificity as they may edit non-intended sites, referred to as off-target edits. The consensus of the agricultural editing community is to avoid off-target editing through design and detection, instead of determining whether off-target editing in each case is detrimental. The design of a targeting component, the tool chosen, and the identification of the edit(s) made are the critical factors in avoiding off-target edits and confirming intended edits in final products that are released commercially. The limited amount of head-to-head comparisons of genome editing tools in diverse crops and livestock make it difficult to develop broad conclusions and best practices, which is further compounded by the diversity of techniques, targets, and processes. Developers and breeders should consult the literature and test as needed to determine which editing technology will be the most effective for their purposes, especially as more tools with altered efficiency and specificity become available. Yet, the lack of off-target edits in studies that employed careful design of targeting components followed by wide testing for on- and off-target edits bodes well for the use of genome editing with proper precautions of target selection and screening.

This paper deals with the study of the existence and non existence of solutions of a three parameter's family of nonlinear fractional differential equation with mixed-integral boundary value conditions. We consider the $\alpha$-Riemann-Liouville fractional derivative, with $\alpha \in (1,2]$. In order to deduce the existence and non existence results, we first study the linear equation, by deducing the main properties of the related Green's functions. We obtain the optimal set of parameters where the Green's function has constant sign. After that, by means of the index theory, the nonlinear boundary value problem is studied. Some examples, at the end of the paper, are showed to illustrate the applicability of the obtained results.

Drug design is used for different applications of bioinformatics tools analyze DNA, genome, and sequence target region of a small organic molecule in order to understand the molecules of disease. Bioinformatics tools are identified a newly wide research field and minimize future risks through web servers and data mining. Clinical sample test performed with the bioinformatics tools as the biomedical detective. A particular structure and configuration of protein obliging in Drug design concluded Bioinformatics. This review bioinformatics tools and webserver will discuss functions of small organic molecules according to clinical pharmacology.

Research on healthy ageing shows that lifespan reductions are often caused by mitochondrial dysfunction. Thus, it is very interesting that the deletion of mitochondrial matrix peptidase LonP1 was observed to abolish embryogenesis, while deletion of the mitochondrial matrix peptidase ClpP prolonged survival. To unveil the targets of each enzyme, we documented the global proteome of LonP1^+/- mouse embryonal fibroblasts (MEF), for comparison with ClpP^-/- depletion. Proteomic profiles of LonP1^+/- MEF generated by label-free mass spectrometry were further processed with the STRING webserver Heidelberg for protein interactions. ClpP was previously reported to degrade Eral1 as a chaperone involved in mitoribosome assembly, so ClpP deficiency triggers accumulation of mitoribosomal subunits and inefficient translation. LonP1^+/- MEF also showed Eral1 accumulation, but no systematic effect on mitoribosomal subunits. In contrast to ClpP^-/- profiles, several components of the respiratory complex I membrane arm were accumulated, whereas the upregulation of numerous innate immune defense components was similar. Overall, LonP1 as opposed to ClpP appears to have no effect on translational machinery, instead it shows enhanced respiratory dysfunction; this agrees with reports on the human CODAS syndrome caused by LonP1 mutations.

MicroRNAs (miRNAs) are small and non-coding RNAs displaying aberrant expression in the tissue and plasma of cancer patients in comparison to healthy individuals. In past decades, accumulating data proposed that miRNAs could be diagnostic and prognostic biomarkers in cancer patients. It has been identified that miRNAs can act either as oncogenes through silencing of tumor inhibitors or tumor suppressor via targeting oncoproteins. MiR-144 is located in chromosomal region 17q11.2 that was widely destroyed in many types of cancers. Several studies revealed that miR-144 has different target genes including rapamycin, zonula occludens1, SFRP1, and ANO1. MiR-144 acts as a tumor suppressor or oncogene by targeting specific genes. In this review, we define the role of miR‐144 and its targets in different cancers and provide understanding in tumor proliferation, migration, and apoptosis.

This paper presents an extended Kalman filter derivation for loosely coupled GPS (Global Positioning System)/INS (Inertial Navigation System) integration based on quaternion attitude representation using the Earth-Centered Earth (ECEF) Frame. In this loosely coupling integration, both the position and velocity estimates from GPS receiver are used as the measurements to extended Kalman filter, and then they are integrated with inertial measurements from inertial measurement units (IMU) to estimate the attitude, position and velocity of an air vehicle. The velocity estimates which have centimeter level estimation error from the GPS receiver are used to improve the filter performance. For attitude estimation, the global attitude parameterization is given by a quaternion and a multiplicative quaternion-error approach is used to guarantee a normalization constraint of quaternion in the filter. Simulation results are shown to obtain the estimation of the attitude, position, velocity, biases and scale factors and to evaluate the performance of the EKF with the measurement combination composed of the two different t

The safe and reliable operation of autonomous vehicles in complex and dynamic environments is heavily dependent upon exteroceptive and proprioceptive sensors. A LiDAR system generates accurate 3D point clouds, which are crucial for the detection, classification, and tracking of multiple targets. LiDAR data, however, presents significant challenges due to its density, noise, and varying sampling rates. In this study, various clustering and MTT techniques for LiDAR point clouds will be identified and classified within the context of autonomous driving to assess the state-of-the-art methods’ key challenges and their performances. We have categorized clustering and MTT methods used in AV applications, identified research gaps and challenges and analyzed existing algorithms and their challenges in detail. Researchers and practitioners in the field of autonomous driving will find this review to be a valuable resource.

The paper aims to investigate on the determinants of the capital structure of the Italian SMEs and it is based on a large panel of Italian manufacturing SMEs observed in the years 2001-2013. The analysis achieves a comparative investigation between the capital structure of the firms belonging in the Southern region, the so-called Italian Mezzogiorno that is the slow growing area of the country, and the firms of the Center-northern region of Italy, that is the more developed area of the country. Fixed effect panel techniques applied to the several standard leverage models allows to reaffirm some distinctive features showing differences of the financial behavior of regional firms.

We analyze the modular geometry of the variable exponent Lebesgue space L^p(.). We show that L^p(.) possesses a modular uniform convexity property. Part of the novelty is that the property holds even in the case supp(x) = ∞ . We present specific applications to fixed point theory. xÆΩ

Ability of quantitative structure – property / activity relationships (QSPRs/QSARs) to serve for epistemological processes in natural sciences is discussed. Some weirdness of QSPR/QSAR state-of-art are listed. There are some contradictions in the research results in this area. Sometimes, these should be classified as paradoxes or weirdness. These points often are ignored. Here these are listed and briefly commented. In addition, hypothesises on the future evolution of the QSPR/QSAR theory and practice are suggested.

Humankind has entered a new era wherein a main characteristic is the convergence of various technologies providing services exerting major impact upon all aspects of human activity, be it social interactions as well as interactions with the natural environment. Fixed networks are about to play a major role in this convergence, since they form, along with mobile networks, the backbone that provides access to a broad gamut of services, accessible from any point of the globe. It is for this reason that we introduce a forward-looking approach for fixed networks, particularly focused on Fixed 6th Generation (F6G) networks. First, we adopt a novel classification scheme for the main F6G services, comprising six categories. This classification is based on the key service requirements, namely latency, capacity and connectivity. F6G networks differ from those of previous generations (F1G-F5G) in that they concurrently support multiple key requirements. We then propose concrete steps towards transforming the main elements of fixed networks, such as optical transceivers, optical switches etc. such that they satisfy the new F6G service requirements. Our study categorizes the main networking paradigm of optical switching into two categories namely ultra-fast and ultra-high capacity switching, tailored to different service categories. With regard to the transceiver physical layer, we propose: a) use of all-optical processing to mitigate performance barriers of analog to digital and digital to analog converters (ADC/DAC); b) exploitation of optical multi-band transmission, space division-multiplexing and adoption of more efficient modulation formats.

Periodontitis is a chronic inflammatory disease affecting the tissues that surround and support the teeth. In the U. S., approximately 65 million people are affected by this condition. Its occurrence is also associated with many important systemic diseases such as cardiovascular disease, rheumatoid arthritis, and Alzheimer’s disease. Among the most important etiologies of periodontitis is Porphyromonas gingivalis, a keystone bacterial pathogen. Keystone pathogens can orchestrate inflammatory disease by remodeling a normally benign microbiota causing imbalance between normal and pathogenic microbiota (dysbiosis). The important characteristics of P. gingivalis causing dysbiosis are its virulence factors that cause effective subversion of host defenses to its advantage [1], allowing other pathogens to grow. However, the mechanisms involving these processes are poorly understood. However, various microbial strategies target host sialoglycoproteins for immune dysregulation. In addition, the enzymes that break down sialoglycoproteins/sialoglycans are the “sialoglycoproteases”, resulting in exposed terminal sialic acid. This process could lead to pathogen-toll like receptor (TLR) interactions mediated through sialic acid receptor–ligand mechanisms. By assessing the function of P. gingivalis sialoglycoproteases, could pave the way to designing carbohydrate analogues and sialic acid mimetics to serve as drug targets.

In this work, we extend the fundamental results of Schu to the class of monotone asymptotically nonexpansive mappings in modular function spaces. In particular, we study the behavior of the Fibonacci-Mann iteration process defined by $$x_{n+1} = t_n T^{\phi(n)}(x_n) + (1-t_n)x_n,$$ for $n \in \mathbb{N}$, when $T$ is a monotone asymptotically nonexpansive self-mapping.

In this paper, the fixed-time stabilization problem for a class of uncertain chained system is addressed by using a novel nonsingular recursive terminal sliding mode control approach. A fixed-time controller and an adaptive law are designed to guarantee the uncertain chained form system both Lyapunov stable and fixed-time convergent within the settling time. The advantage of the controller based on the sliding mode is that the settling time does not depend on the system initial state. Furthermore, we use RBF neural network to estimate the uncertainty of the system. Finally, the simulation results demonstrate the performance of the control laws.

The purpose of this paper is to prove strong convergent theorems for Browder's type iterations and Halpern's type iterations of a family of nonexpansive mappings in a complete geodesic space with curvature bounded above by a positive number. Moudafi's viscosity type methods are also discussed without the nice projection property.

The combination of non-steroidal anti-inflammatory drugs (NSAIDs) with non-opioid analgesics is common in clinical practice for the treatment of acute pain conditions like post-operative and post-traumatic pain. Besides that, and despite the very satisfactory results achieved by oral analgesics, parenteral analgesia is a key tool in the treatment of painful conditions when the enteral routes are not convenient. The parenteral ready-to-use fixed-dose combinations of NSAIDs with non-opioid analgesics like paracetamol or metamizole could play a central role in the treatment of painful conditions since they are able to gather into only one formulation the advantages of multimodal and parenteral analgesia. Surprisingly, only very recently a parenteral ready-to-use fixed-dose combination of Ibuprofen/Paracetamol was launched to the market. This review aims to review the current availability of NSAID-based Parenteral Fixed-dose combinations with paracetamol and metamizole in the European and American markets, and how the combination of such drugs could play a central role in a multimodal analgesia strategy. Also, it is explored how the parenteral formulations of NSAIDs, paracetamol, and metamizole could serve as starting elements for the development of new parenteral ready-to-use fixed-dose combinations. With this review, we concluded that, despite the well-recognized utility of combining NSAIDs with non-opioid analgesics, there are in the literature several randomized clinical trial studies unable to demonstrate clear advantages concerning their efficacy and safety. In the future, it is deemed necessary to perform clinical trials specifically designed to assess the efficacy and safety of these fixed-dose formulations to generate solid evidence of their clinical advantages.

About 50% of colorectal cancer patients develop liver metastases. Patients with metastatic colorectal cancer have 5-year survival rates below 20% despite new therapeutic regimens. Tumor heterogeneity has been linked with poor treatment response and clinical outcome, but was so far mainly studied via bulk genomic analyses. In this study we performed spatial proteomics via MALDI mass spectrometry imaging on six patient-matched CRC primary tumor and liver metastases to characterize interpatient, intertumor and intratumor hetereogeneity. We found several peptide features that were enriched in vital tumor areas of primary tumors and liver metastasis and tentatively derived from tumor cell specific proteins such as annexin A4 and prelamin A/C. Liver metastases of colorectal cancer showed higher heterogeneity between patients than primary tumors while within patients both entities show similar intratumor heterogeneity sometimes organized in zonal pattern. Together our findings give new insights into the spatial proteomic heterogeneity of primary CRC and patient-matched liver metastases.

In this paper, we study a three step iterative scheme to approximate fixed points of Suzuki's generalized non-expansive mappings. We establish some weak and strong convergence results for such mappings in uniformly convex Banach spaces. Further, we show numerically that iterative scheme (1.8) converges faster than some other known iterations for Suzuki's generalized non-expansive mappings. To support our claim, we give an illustrative example and approximate fixed points of such mappings using Matlab program. Our results are new and generalize several relevant results in the literature.

The standard of care for metastatic breast cancer (MBC) is systemic therapies with imbrication of focal treatment in case of symptomatology onset. Recently, thanks to implementation of radiological and metabolic exams and development of new target therapies, oligometastatic and oligoprogressive disease presentations are even more common, leading to a change of paradigm of focal treatments. In fact, acknowledgement of behaviour of disease in these setting of patients is carrying aim of radiotherapy towards modalities with radical intent. The aim of this literature review is to analyse available clinical data regarding disease behaviour, imaging, radiotherapy and chemo-radiotherapy integration outcomes for understanding bone metastasis from breast cancer and the potential impact of targeting it.

In order to meet the national energy saving goals set in the Vietnam National Energy Efficiency Program in the period of 2019 – 2030 (VNEEP), the Vietnamese government has adopted a series solutions and policies to improve energy efficiency. The Vietnam’s 63 provinces will be as main actor for the national achievement in energy efficiency. Thus, understanding the province’s potentiality of energy efficiency is useful for the harmonious and sustainable development between the economy and energy systems. In this study, provincial and national data from General Statistic Office are analyzed in terms of the energy efficiency levels. With the trends of economic development and energy consumption in both national and regional levels, the Lorenz curve between Vietnamese energy consumption and GDP is investigated. The Lorenz coefficient shows the energy allocation is nether reasonable nor balanced. By using clustering method, the 63 provinces of Vietnam clustered into 7 groups that the provinces in the cluster has the similar indexes of energy efficiency i.e. ability, responsibility, potential and difficulty. The energy consumption and GDP are predicted in the period of 2019 – 2025. Based on the difference of GDP development and energy consumption levels, the target of energy efficiency for each province through clustering is set. The results show that 33 provinces included in the cluster 1, 2, 3, 4 and 6 are heavy contribution. Among them, the provinces in the cluster 2 and 3 need to focus on the industry sector in their energy saving policy. The cluster 7 included the under-developed provinces can learn development’s experiences of the provinces in the cluster 1, 2, 3 and 4 to find the best way of their future development.

In this paper, we consider with a class of system of differential equations whose argument transforms are involution. In this an initial value problem for a differential equation with involution is reduced to an initial value problem for a higher order ordinary differential equation. Than either two initial conditions are necessary for a solution, the equation is then reduced to a boundary value problem for a higher order ODE.

The advancement of precision medicine critically depends on the robustness and specificity of the carriers used for the targeted delivery of effector molecules in the human body. Numerous nanocarriers have been explored in vivo, to ensure the precise delivery of molecular cargos via tissue-specific targeting, including the endocrine part of the pancreas, thyroid, and adrenal glands. However, even after reaching the target organ, the cargo-carrying vehicle needs to enter the cell and then escape from lysosomal destruction. Most of artificial nanocarriers suffer from intrinsic limitations that either prevent them from completing the specific delivery of the cargo. In this respect, extracellular vesicles (EVs) seem to be the natural tool for payload delivery due to their versatility and low toxicity. However, EV-mediated delivery is not selective and usually short-ranged. By inserting the viral membrane fusion proteins into exosomes, it is possible to increase the efficiency of membrane recognition and also ease the process of membrane fusion. This review describes the molecular details of the viral-assisted interaction between the target cell and extracellular vesicles. We also discuss the question of the usability of viral fusion proteins in developing extracellular vesicle-based nanocarriers with higher efficacy of payload delivery. Finally, this review specifically highlights the role of Gag and RNA binding proteins in RNA sorting into extracellular vesicles.

Nanoscale imaging with the ability to identify cellular organelles and protein complexes has been a highly challenging subject in secondary ion mass spectrometry (SIMS) of biological samples. This is because only a few isotopic tags can be used successfully to target specific proteins or organelles. To address this, we have generated gold-nanoprobes, in which gold nanoparticles are conjugated to nanobodies. The nanoprobes were well suited for specific molecular imaging using NanoSIMS at subcellular resolution. They demonstrated to be highly selective to different proteins of interest, and sufficiently sensitive for SIMS detection. The nanoprobes offer the possibility of correlating the investigation of cellular isotopic turnover to the positions of specific proteins and organelles, thereby enabling an understanding of functional and structural relations that are currently obscure.

The 2030 Agenda for Sustainable Development, the SDGs, are high on the agenda for most countries of the world. In its publication of the SDGs, the UN has provided the goals and target descriptions that, if implemented at a country level, would lead towards a sustainable future. The IAEG (InterAgency Expert Group of the SDGs) was tasked with disseminating indicators and methods to countries that can be used to gather data describing the global progress towards sustainability. However 2030 Agenda leaves it to countries to adopt the targets with each government setting its own national targets guided by the global level of ambition but taking into account national circumstances. At present, guidance on how to go about this is scant, but it is clear that the responsibility is with countries to implement and that it is actions at a country level that will determine the success of the SDGs. SDG reporting by countries takes on two forms 1) global reporting using prescribed indicator methods and data; 2) National Voluntary Reviews where a country reports on its own progress in more detail but is also able to present data that are more appropriate for the country. For the latter, countries need to be able to adapt the global indicators to fit national priorities and context, thus the global description of an indicator could be reduced to describe only what is relevant to the country. Countries may also, for the National Voluntary Review, use indicators that are unique to the country but nevertheless contribute to measurement of progress towards the global SDG target. Importantly, for those indicators that relate to the security of natural resources security (e.g. water) indicators, there are no prescribed numerical targets/standards or benchmarks. Rather countries will need to set their own benchmarks or standards against which performance can be evaluated. This paper presents a procedure that would enable a country to describe national targets with associated benchmarks that are appropriate for the country. The procedure focusses on those SDG targets that are natural resource-security focussed e.g. extent of water-related ecosystems (6.6), desertification (15.3) etc., because the selection of indicator methods and benchmarks is based on the location of natural resources, their use and present state and how they fit into national strategies.

For the in-house validation of a droplet digital PCR method, a factorial experimental design was implemented. This design serves different purposes. On the one hand, it is an efficient design in relation to the workload involved in achieving a desirable level of reliability of variance estimates. On the other hand, it allows a partitioning of total variance into different components, thus providing information regarding the dominant sources of random variation. The statistical modelling reflects the actual measurement mechanism, establishing relationships between nominal target DNA copies per well, the range of variation of copy numbers per droplet, probability of detection values, and estimated numbers of copies.

Huge effort has been devoted to developing drugs targeting integrins over 30 years, because of the primary roles of integrins in the cell-matrix milieu. Five αv-containing integrins, in the 24 family members, have been a central target of fibrosis. Currently, a small molecule against αvβ1 is undergoing a clinical trial for NASH-associated fibrosis as a rare reagent aiming at fibrogenesis. Latent TGFβ activation, a distinct talent of αv-integrins, has been intriguing as therapeutic target. None of the αv-integrin inhibitors, however, has been in the clinical market. αv-integrins commonly recognize an Arg-Gly-Asp (RGD) sequence, and thus the pharmacophore of inhibitors for the 5-integrins is based on the same RGD structure. The RGD preference of the integrins, at the same time, dilutes ligand specificity, as the 5-integrins share ligands containing RGD sequence such as fibronectin. With the inherent little specificity in both drugs and targets, “disease specificity” has become less important for the inhibitors than blocking as many αv-integrins. In fact, an almighty inhibitor for αv-integrins, pan-αv, was in a clinical trial. On the contrary, approved integrin inhibitors are all specific to target integrins, which are expressed in cell-type specific manner: αIIbβ3 on platelets, α4β1, α4β7 and αLβ2 on leukocytes. Herein, “disease specific” integrins would serve as attractive targets. α8β1 and α11β1 are selectively expressed in hepatic stellate cells (HSCs) and distinctively induced upon culture activation. The exceptional specificity to activated HSCs reflects rather “pathology specific” nature of these new integrins. The monoclonal antibodies against α8β1 and α11β1 in preclinical examinations may illuminate the road to the first medical reagents.

In the present work we study the existence of solutions for hybrid nonlinear fractional integrodifferential equations. We developed an algorithm by using the operator theoretical techniques in order to obtain the approximate solutions. The main results depend on the Dhage iteration method that were incorporated with the modern hybrid fixed point theorems. The approximate solutions were obtained by using Lipschitz conditions and weaker form of mixed partial continuity. Further, we provide some examples to explain the hypotheses and the related results.

It is known that the class of p-normed spaces (0 < p ≤ 1) is an important generalization of usual normed spaces with rich topological and geometrical structure, but the most of tools and general principles in nature with nonlinearity have not been developed yet, thus the main goal of this paper is to develop tools for nonlinear analysis under the framework of p-vector spaces. In particular, we first develop the general fixed point theorems which provide solutions to answer Schauder conjecture since 1930’s in the affirmative for p-vector spaces when p = 1 (which is just general topological vector spaces); then the one best approximation result for upper semi-continuous mappings is given, which is used as a powerful tool to establish fixed points for non-self set-valued mappings with either inward or outward set conditions; and finally we establish comprehensive existence results of solutions for Birkhoff-Kellogg Problems, and the general principle of nonlinear alternative by including Leray-Schauder alternative and related results. The results given in this paper not only include the corresponding results in the existing literature as special cases, but also expected to be useful tools for the study of nonlinear problems arising from theory to practice.

MicroRNAs (MiRNAs) are a class of non-coding single-stranded RNA molecules of approximately 20-24 nucleotides in plants that play an important regulatory role in a variety of biological processes such as plant growth and development and response to various abiotic stresses. For example Drought, Salt, Cold, High temperature, Heavy metals and Nutrition. MiRNAs affect gene expression by manipulating the cleavage, translational expression or DNA methylation of target mRNAs. This review describes the current progress made on the way miRNAs are produced and regulated and the way miRNA/target gene is used in plant responses to abiotic stresses. Studying the molecular mechanism of action of miRNAs downstream target genes can optimize the genetic manipulation of crop growth and development conditions that provide a more theoretical basis for improving crop production.

Background and objectives: Induction chemotherapy (ICT) before definitive chemoradiation (CRT) gives high response rates in LA-SCCHN. However, pre-ICT gross tumour volume (GTV) for radiotherapy (RT) planning is still recommended. As ¹⁸F-FDG PET/CT has an advantage of biological tumour information comparing to standard imaging methods, we aimed to evaluate the feasibility of ¹⁸F-FDG PET/CT-based post-ICT GTV delineation for RT planning in LA-SCCHN and to assess the prognostic value of PET parameters: maximum standardized uptake value (SUV_max), metabolic tumour volume (MTV) and total lesion glycolysis (TLG). Methods: 47 LA-SCCHN patients were treated with 3 cycles of ICT (docetaxel, cisplatin, and 5-fluorouracil) followed by CRT (70 Gy in 35 fractions with weekly cisplatin). Pre- and post-ICT PET/CT examinations were acquired. Planning CT was co-registered with post-ICT PET/CT and RT target volumes were contoured according to post-ICT PET. Post-ICT percentage decrease of SUV_max, MTV and TLG in primary tumour and metastatic regional lymphnodes (LN) was counted. Loco-regional failure patterns, 3-year progression free (PFS) and overall survival (OS) were evaluated. Results: 3-year PFS and OS rates for study population were 67% and 61% respectively. 31.9% of patients progressed loco-regionally. All progresses were localised in high-to-intermediate dose (60–70 Gy) RT volumes and none in low dose (50 Gy) volumes. Decrease of SUV_max ≥74% (p = 0.03), MTV ≥ 68% (p = 0.04), TLG ≥ 76% (p = 0.02) in primary tumour, and LN TLG decrease ≥74% (p = 0.03) were associated with PFS. Decrease of primary tumour SUV_max ≥ 74% (p = 0.04), MTV ≥ 69% (p = 0.04), TLG ≥ 74% (p = 0.02) and LN TLG ≥ 73% (p = 0.02) were prognostic factors for OS. Conclusions: According to our results, 18F-FDG PET/CT-based post-ICT GTV delineation is feasible strategy without negative impact on loco-regional control and survival. Percentage decrease of metabolic PET parameters SUV_max, MTV and TLG has a prognostic value in LA-SCCHN.

Despite UAV multi-target detection exhibits considerable developmental potential worldwide, it suffers distinct challenges compared with traditional tasks in this field. These challenges include insufficient feature extraction capabilities for small targets, limited capabilities for multi-dimensional feature fusion, as well as constraints on hardware computation parameters. Especially in mission scenarios such as disaster detection, these challenges will be further amplified. Consequently, this paper introduces SSE-YOLO, an innovative YOLO framework algorithm specially designed to address these challenges. To enhance the model's feature extraction capability, we employ the SPDConv module to replace the original Conv in the backbone section, utilizing depth-separable convolution instead of traditional convolution pooling. Concurrently, we eliminate the SPPF module at the bottom and address a new Separate Kernel Attention Pyramid Pooling (SKAPP) module, substantially enhancing the feature fusion capability at the model's core. Moreover, to address the challenge of multi-dimensional feature fusion, we replace the Concat module of the neck and head with E-BiFPN, transmitting feature information from the backbone to the lower network through four CBS blocks, which effectively resolves the issue of lost contextual information in the model. Meanwhile, SSE-YOLO undergoes ablation experiments on the VisDrone2019 dataset to evaluate its effectiveness against alternative methods, and experimental results illustrate the model's exceptional precision in detecting UAV targets. In comparison to models with comparable experimental accuracy, SSE-YOLO requires remarkably fewer parameters. On the VisDrone2019-DET-test-dev dataset, SSE-YOLO enhances mAP by 17.3%, with a 42.5% reduction in the parameter amounts. Therefore, the proposed method effectively tackles the challenge of reconciling low parameters and high accuracy, presenting a novel pathway for deep learning-based UAV multi-target detection.

Portfolio weights solely based on risk avoid estimation error from the sample mean, but they are still affected from the misspecification in the sample covariance matrix. To solve this problem, we shrink the covariance matrix towards the Identity, the Variance Identity, the Single-index model, the Common Covariance, the Constant Correlation and the Exponential Weighted Moving Average target matrices. By an extensive Monte Carlo simulation, we offer a comparative study of these target estimators, testing their ability in reproducing the true portfolio weights. We control for the dataset dimensionality and the shrinkage intensity in the Minimum Variance, Inverse Volatility, Equal-risk-contribution and Maximum Diversification portfolios. We find out that the Identity and Variance Identity have very good statistical properties, being well-conditioned also in high-dimensional dataset. In addition, the these two models are the best target towards to shrink: they minimise the misspecification in risk-based portfolio weights, generating estimates very close to the population values. Overall, shrinking the sample covariance matrix helps reducing weights misspecification, especially in the Minimum Variance and the Maximum Diversification portfolios. The Inverse Volatility and the Equal-Risk-Contribution portfolios are less sensitive to covariance misspecification, hence they benefit less from shrinkage.

Although natural products are an important source of drugs and drug leads, identification and validation of their target proteins have proven difficult. Here, we report the development of a systematic strategy for target identification and validation employing drug affinity responsive target stability (DARTS) and mass spectrometry imaging (MSI) without modifying or labeling natural compounds. Through a validation step using curcumin, which targets aminopeptidase N (APN), we successfully standardized the systematic strategy. Using label-free voacangine, an antiangiogenic alkaloid molecule as the model natural compound, DARTS analysis revealed vascular endothelial growth factor receptor 2 (VEGFR2) as a target protein. Voacangine inhibits VEGFR2 kinase activity and its downstream signaling by binding to the kinase domain of VEGFR2, as was revealed by docking simulation. Through cell culture assays, voacangine was found to inhibit the growth of glioblastoma cells expressing high levels of VEGFR2. Specific localization of voacangine to tumor compartments in a glioblastoma xenograft mouse was revealed by MSI analysis. The overlap of histological images with the MSI signals for voacangine was intense in the tumor regions and showed colocalization of voacangine and VEGFR2 in the tumor tissues by immunofluorescence analysis of VEGFR2. The strategy employing DARTS and MSI to identify and validate the targets of a natural compound as demonstrated for voacangine in this study is expected to streamline the general approach of drug discovery and validation using other biomolecules including natural products.

In this paper, a high-performance antenna array system model is presented to analyze moving-object-skin-returns and track them in the presence of stationary objects using frequency modulated continuous wave (FMCW). The main features of the paper are bonding the aspects of antenna array and electromagnetic (EM) wave multi-skin-return modeling and simulation (M&S) with the aspects of algorithm and measurement/tracking system architecture. The M&S aspect models both phase and amplitude of the signal waveform from a transmitter to the signal processing in a receiver. In the algorithm aspect, a novel scheme for FMCW signal processing is introduced by combining time- and frequency-domain methods, including a vector moving target indication filter and a vector direct current canceller in time-domain, and a constant false alarm rate detector and a mono-pulse digital beamforming angle tracker in frequency-domain. In addition, unlike previous designs of using M×N fast Fourier transform (FFT) for an M×N array, only four FFTs are used, which tremendously saves time and space in hardware. With the presented model, the detection of the moving-target-skin-return in stationary objects under a noisy environment is feasible. Therefore, to track long range and high-speed objects, the proposed technique is promising. Using a scenario having 1) a target with 17 dBm2 radar cross section (RCS) at about 40 km range with 5.93 Mach speed and 11.6 dB post processing signal to noise ratio, and 2) a strong stationary clutter with 37 dBm2 RCS located at the proximity of the target, it demonstrates that the root-mean-square errors of range, angle and Doppler measurements are about 26 meters, 0.68 degree and 1100 Hz, respectively.

Pancreatic cancer leads the most common lethal tumor in America. This lethality is related to limited treatment options. Conventional treatments involve a non-specific use of chemotherapeutical agents like 5-FU, capecitabine, gemcitabine, cisplatine, oxaliplatine, or irinotecan, that produce several side effects. This review we focus on the use of targeted nanoparticles as an alternative to the standard treatment for the pancreatic cancer. The principal objective of the use of nanoparticles is the reduction in side effects that conventional treatments produce, mostly because of their nonspecificity. Currently, several molecular markets of pancreatic cancer cells have been studied to target nanoparticles and improve the actual treatment. Therefore, properly functionalizated nanoparticles with specific aptamers or antibodies can be used to recognize pancreatic cancer cells and once cancer is recognized, these nanoparticles can attack the tumor by drug delivery, hyperthermia, or gene therapy.