The video images captured at long range usually have low contrast floating objects of interest on a sea surface. A comparative experimental study of the statistical characteristics of reflections from floating objects and from the agitated sea surface showed the difference in the correlation and spectral characteristics of these reflections. The functioning of the recently proposed modified matched subspace detector (MMSD) is based on the separation of the observed data spectrum on two subspaces: relatively low and relatively high frequencies. In the literature the MMSD performance has been evaluated in generally and moreover using only a sea model (additive Gaussian background clutter). This paper extends the performance evaluating methodology for low contrast object detection and moreover using only the real sea dataset. This methodology assumes an object of low contrast if the mean and variance of the object and the surrounding background are the same. The paper assumes that the energy spectrum of the object and the sea are different. The paper investigates a scenario in which an artificially created model of a floating object with specified statistical parameters is placed on the surface of a real sea image. The paper compares the efficiency of the classical Matched Subspace Detector (MSD) and MMSD for detecting low-contrast objects on the sea surface. The article analyzes the dependence of the detection probability at a fixed false alarm probability on the difference between the statistical means and variances of a floating object and the surrounding sea.

Acquisition of real-time hydraulic data is an essential component for flood forecasting. However, we frequently face difficulties in obtaining discharge data using classical contact methods during high magnitude floods and for systems experiencing rapid hydro-geomorphological adjustment. Therefore, we developed low-cost, non-contact sensors and platforms that are designed to overcome these difficulties. These advances enable flood flow properties to be monitored at multiple locations across a river catchment, at low-cost, and communicated in near real-time by using an image velocimetry method. This is an optics-based approach for stream flow measurement using commercially available near-infrared digital cameras to acquire video footage in full HD (30fps). Video footage is then subjected to optical flow tracking techniques based on cross-correlation, and feature-based tracking, enabling the displacement rates of detected features (for example natural foam, seeds, woody debris, and turbulent structures) to be computed. This manual provides step by step guidance to install an image-based gauging station. It contains the list of necessary components, the calibration process of a new camera and the assembly procedure of the system.

Layer-wise 3D surface morphology information is critical for the quality monitoring and control of additive manufacturing (AM) processes. However, most of the existing 3D scan technologies are either contact or time consuming, which are not capable of obtaining the 3D surface morphology data in a real-time manner during the process. Therefore, the objective of this study is to achieve real-time 3D surface data acquisition in AM, which is achieved by a supervised deep learning-based image analysis approach. The key idea of this proposed method is to capture the correlation between 2D image and 3D point cloud, and then quantify this relationship by using a deep learning algorithm, namely, convolutional neural network (CNN). To validate the effectiveness and efficiency of the proposed method, both simulation and real-world case studies were performed. The results demonstrate that this method has strong potential to be applied for real-time surface morphology measurement in AM, as well as other advanced manufacturing processes.

Heavy metals pollution in the Straits of Malacca warrants the development of rapid, simple and sensitive assays. Enzyme-based assays are excellent preliminary screening tool with near real-time potential. The heavy-metal assay based on the protease ficin was optimized for mercury detection using Response Surface Methodology. The inhibitive assay is based on ficin action on the substrate casein and residual casein is determined using the Coomassie dye-binding assay. Heavy metals strongly inhibit the hydrolysis. A Central Composite Design (CCD) was utilized to optimize detection. The results show a marked improvement for the concentration causing 50% inhibition (IC50) for mercury, silver and copper. Compared to One-factor-at-a-time (OFAT) optimization, RSM gave an improvement of IC50 from 0.060 (95% CI, 0.0300.080) to 0.017 (95% CI, 0.0160.019), from 0.098 (95% CI, 0.0770.127) to 0.028 (95% CI, 0.0220.037) and from 0.040 (95% CI, 0.035.045) to 0.023 (95% CI, 0.0200.027), for mercury, silver and copper, respectively. A near real-time monitoring of mercury concentration in the Straits of Malacca at one location in Port Klang was carried out over a 4-h interval for a total of 24 h and validated by instrumental analysis with the result revealing an absence of mercury pollution in the sampling site.

Land surface albedo determines the splitting of downwelling solar radiation into components which are either reflected back to the atmosphere or absorbed by the surface. Land surface albedo is an important variable for the climate community and therefore was defined by the Global Climate Observing System (GCOS) as an Essential Climate Variable (ECV). Within the scope of the Satellite Application Facility for Land Surface Analysis (LSA SAF) of EUMETSAT, a near-real time (NRT) daily albedo product was developed in the last decade from observations provided by the SEVIRI instrument on board the geostationary satellites of the Meteosat Second Generation (MSG) series. In this study we present a new collection of albedo satellite products based on the same satellite data. The MSG Ten-day ALbedo (MTAL) product incorporates MSG observations over 31 days with a frequency of NRT production of 10 days. The MTAL collection is more dedicated to climate analysis studies compared to the daily albedo that was initially designed for the weather prediction community. For this reason, a homogeneous reprocessing of MTAL was done in 2018 to generate a Climate Data Record (CDR). The resulting product is called MTAL-R and has been made available to the community in addition to the NRT version of the MTAL product which has been available for several years. The retrieval algorithm behind the MTAL products comprises three distinct modules: one for atmospheric correction, one for daily inversion of a semi-empirical model of the bidirectional reflectance distribution function, and one for monthly composition that also determines surface albedo values. In this study the MTAL-R CDR is compared to ground surface measurements and concomitant albedo products collected by sensors on-board polar-orbiting satellites (SPOT-VGT and MODIS). We show that MTAL-R meets the quality requirements if MODIS or SPOT-VGT are considered as reference. This work leads to 14 years of production of geostationary land surface albedo products with a guaranteed continuity in the LSA SAF for the future years with the forthcoming third generation of European geostationary satellites.

A new method for short circuit fault location is proposed based on instantaneous signal measurement and its derivatives, and is based on the retardation phenomena. The difference between the times in which a signal is registered in two detectors is used to locate the fault. Although a description of faults in terms of a lumped circuit is useful for elucidating the methods for detecting the fault. This description will not suffice to describe the fault signal propagation hence a distributed models is needed which is given in terms of the telegraph equations. Those equations are used to derive a transmission line transfer function, and an exact analytical description of the short circuit signal propagating in the transmission line is obtained. The analytical solution was verified both by numerical simulations and experimentally.

Cognitive scientists have paid very little attention to magic as a distinctly human activity capable of creating situations or events that are considered impossible because they violate expectations and conclude with the apparent transgression of well-established cognitive and natural laws. And even though magic techniques appeal to all known cognitive processes from sensing, attention and perception to memory and decision making, the relation between science and magic has so far been mostly unidirectional, with the primary goal of unraveling how magic works. Building up from the deconstruction of a classic magic trick, we provide here a cognitive foundation for the use of magic as a unique and largely untapped research tool to dissect cognitive processes in tasks arguably more natural than those usually exploited in artificial laboratory settings. Magicians can submerge every spectator into the precise experimental protocol they have previously designed, accounting with ease for both circumstantial and social contexts. Magicians do not base the success of their experiments in statistical measures that smear out the individual in favor of an average spectator that we know never exists in the real world. They target each and everyone in the audience and, often, with a complete accomplishment. Magicians deliver their cognitive manipulations in real-time, in tight closed-loop with the audience, and in a single trial (they cannot afford to repeat the trick if it fails). Magic has also an inherent and strong social component, merging the private cognitive processes of each spectator with the group dynamics. Finally, when combined with the wide range of precise measuring and wearable technologies available today, magic paves the way for a road not taken towards real-world cognitive science. We dare to speculate that some of the mysteries of how the brain works may be trapped in the split realities present in each magic effect.

Nowadays, security is a top priority. In fact, biometrics uses cutting-edge technologies to identify terrorists and criminals. But the practice of distinguishing humans based on intrinsic physical or behavior traits goes back thousands of years. With the widespread use of computers in the late 20th century, new possibilities for digital biometrics emerged and new technologies were generously used. Among these, we remember high resolution security video cameras and drones. So, the aim of the present project is to study and explain the features of these technologies, especially the ones of the the Phantom 4 Pro+ aircraft and analyze its operating methods in order to identify human faces during live streaming of videos. For this purpose, it will be used Paul Viola and Michael Jones’ face detection algorithm, which includes Haar features and cascade classifiers to identify faces, eyes and ears of an individual.

We investigate wealth accumulation in forestry, assuming that revenues are re-invested. Three different optimization criteria are compared, two of which are based on cash flows, the third financially grounded. Direct optimization of wealth appreciation rate always yields best results. Procedures gained by maximizing internal rate of return are only slightly inferior. With external discounting interest rate, the maximization of net present value yields arbitrary results, with at worst devastating financial consequences.

Investors are currently obliged to take ESG (Environment, Social, Governance) issues into consideration as part of their fiduciary duty. As such, it becomes increasingly important to identify sustainable investments that hold financial value as well. A sector where this is especially underdeveloped is real estate. This has a lot to do with the obfuscated conceptualization of ESG. The article identifies key gaps in literature and practice, and provides a framework to further the understanding of how ESG factors can add societal and financial value in the real estate sector. A key premise of the article is that the user in the building is grossly overlooked. Drawing on insights from behavioral social science and environmental psychology, the paper explains the role of the user in improving buildings’ ESG, also taking into account the investment value. To conclude, the article makes the case that the transition to user-centered smart real estate is the solution to improving both the environmental (E) and social (S) sustainability of buildings, as well as their investment value. Therefore, practitioners and academics are encouraged to critically evaluate and contextualize the ESG framework they are using, as well as the extent to which users are considered and smart technology is employed.

Abstract Despite the lightning-fast advances in the management of SARS-CoV after 2 years of pandemic, COVID-19 continues to pose a challenge for fragile patients, who could benefit from early administration of monoclonal antibodies (mAbs) to reduce the risk of severe disease progression. We conducted a prospective study to evaluate effectiveness of mAbs against SARS-CoV-2 among patients at risk for severe disease progression, namely elderly and those with comorbidities, before the omicron variant surge. Patients were treated with either casirivimab/imdevimab, sotrovimab, and bamlanivimab/etesevimab. The rates and risk factos for clinical worsening, hospitalization, ICU admission and death (unfavourable outcomes) were evaluated. A stratified analysis according to the presence of SARS-CoV-2 IgG was also performed. Among 185 included patients, we showed low rates of unfavorable outcomes (9.2%), which were more frequent in patients with chronic kidney disease (aOR: 10.44, 95CI: 1.73-63.03; p<0.05) and basal D-dimer serum concentrations >600 ng/ml (aOR 21.74, 95CI: 1.18-397.70; p<0.05). Patients with negative SARS-CoV-2 serology at baseline showed higher C-reactive protein values compared with patients with positive serology (p <0.05) and showed a trend toward a higher admission rate to SICU and ICU compared with patients with positive serology. Our results thus showed, in a real-life setting, the efficacy of mAbs against SARS-CoV-2 before Omicron surge when the available mabs become not effective.

Most traditional theories of intelligence have little to do with the question of whether people with high intelligence can successfully address real world problems. A high IQ is correlated with many important outcomes (e.g., academic prominence, reduced crime), but it does not protect against cognitive biases, partisan thinking, reactance, confirmation bias, and even falling for discredited beliefs such as alchemy, cold fusion, and astrology. There are several newer theories that directly address the question about solving real-world problems. Prominent among them is Sternberg’s adaptive intelligence with “adaptation to the environment” as the central premise, a construct that does not exist on standardized IQ tests (e.g., Sternberg, 2019). Similarly, Stanovich and West (2014) argue that standardized tests of intelligence are not measures of rational thought—the sort of skill/ability that would be needed to address complex real-world problems. Halpern and Butler (2020) advocate for critical thinking as a better model of intelligence for addressing real-world problems than those that are based on psychometric properties of general intelligence. Yes, intelligence (i.e., critical thinking) can be enhanced and used for solving a real-world problem like Covid-19, which we use as an example of contemporary problems that need a new approach. Critical thinking may be an antidote for the chaos of the modern world.

This paper examines how housing prices are determined by macroeconomic factors in Saudi Arabia, namely, Gross Domestic Product Per capita (GDPP), Consumer Prices Index (CPI), and Unemployment Rate (UNEMP). Quarterly data for a period (2014q1 – 2019q4) were collected from publications of Saudi Arabian Monetary Authority (SAMA). Vector Autoregression Analysis (VAR) is employed to capture the dynamic effect of the variables on housing prices. Granger Causality, Variance Decomposition and Impulse response function are also used. The results show that housing prices are insignificantly and positively related to GDPP, whereas it is negatively related to both (CPI & UNEMP). Only CPI has a significant relationship. The three variables, jointly, have Granger causality on HPI. Variance decompositions show that CPI is the variable with the highest explanatory power over the variation of housing prices, followed by GDPP and the UNEMP respectively indicating that CPI is the most influential determinants for housing prices.

Real-world evidence (RWE) is increasingly involved in the early benefit assessment of medicinal drugs. It is expected that RWE will help to speed up approval processes comparable to RWE developments in vaccine research during the COVID-19 pandemic. Definitions of RWE are diverse marking the highly fluid status in this field. So far, RWE comprises information produced from data routinely collected on patient’s health status and/or delivery of health care from various sources other than traditional clinical trials. These sources can include electronic health records, claims, patient-generated data including in home-use settings, data from mobile devices as well as patient, product and disease registries. The aim of the present update was to review the current RWE developments and guidelines mainly in the U.S., the UK, Europe and Germany field during the last decade. RWE has already been included in various approval procedures of regulatory authorities reflecting its actual acceptance and growing importance in evaluating and accelerating new therapies. However, since the RWE research is still in a transition process and since a number of gaps in this field have been explored, more guidance and a consented definition are necessary to increase the implementation of real-world data.

A correct system design can be systematically obtained from a specification model of a real-time system that integrates hybrid measurements in a realistic industrial environment, this has been carried out through complete Matlab / Simulink / Stateflow models. However, there is a widespread interest in carrying out that modeling by resorting to Machine Learning models, which can be understood as Automated Machine Learning for Real-time systems that present some degree of hybridization. An induction motor controller which must be able to maintain a constant air flow through a filter is one of these systems and it is discussed in the paper as a study case of closed-loop control system. The article discusses a practical application of ML methods that demonstrates how to replace such closed loop in industrial control systems with a Simulink block generated from neural networks to show how the proposed procedure can be applied to derive complete hybrid system designs with artificial neural networks (ANN). In the proposed ANN-based method to design a real-time hybrid system with continuous and discrete components, we use a typical design of a neural network, in which we define the usual phases: training, validation, and testing. The generated output of the model is made up of reference variables values of the cyber-physical system, which represent the functional and dynamic aspects of model. They are used to feed Simulink/Stateflow blocks in the real target system.

Background: Objectives were to determine the real-life effectiveness and safety of DT with dolutegravir (50 mg/QD) plus lamivudine (300 mg/QD) in multiple-tablet regimen (MTR) in naïve PLHIV followed up for 48 weeks and to evaluate the compliance and satisfaction of patients. Material and methods: Open, single-arm, multicenter, non-randomized clinical trial from May 2019 through September 2020 with 48-week follow-up. Results: The study included 88 PLHIV (91% male) with mean age of 35.9 years; 76.1% were MSM. Mean baseline CD4 was 516.4 cells/uL, with viral load (VL) of 104,828 cop/mL, and 11.4% were in AIDS stage. DT started within 7 days of first specialist consultation in all patients and the same day in 84.1%; 3.4% had baseline resistance mutations (K103N, V106I+E138A, and V108I); 12.5% were lost to follow-up. At week 48, 86.3% had VL< 50 cop/uL by intention-to-treat analysis and 98.7% by per-protocol (PP) analysis. Virological failure (VF) was recorded in 1.1%, with no resistance mutation. One blip was detected in 5.2%, without VF. Three reported anxiety, dizziness, and cephalgia, respectively, at week 4 and one insomnia at week 24; none reported adverse events at week 48. Mean weight was 4 kg higher at 48 weeks (p=0.0001) and abdominal circumference 3 cm larger at 24 weeks (p=0.022). No forgetfulness occurred in 98.7% of patients. Patient satisfaction was 90/100 at 4, 24, and 48 weeks. Conclusion: Real-world data demonstrate that dolutegravir plus lamivudine in MTR is effective, safe, and satisfactory, moderately increasing weight and abdominal circumference and administrable on a test-and-treat strategy.

Classical laws of friction suggest that friction force is proportional to the normal load and independent of the nominal contact area. As a great improvement in this subject, it is now widely accepted that friction force is proportional to the real area in contact, and much work has been conducted based on this hypothesis. In present study, this hypothesis will be carefully revisited by measuring the friction force and real contact area in-site and real-time at both normal loading and unloading stages. Our experiments reveal that the linear relation always holds between friction force and normal load. However, for the relation between friction force and real contact area, the linearity holds only at the loading stage while fails at the unloading stage. This study may improve our understanding of the origin of friction.

The accurate calculation of real contact area between rough surfaces is a key issue in tribology. In this paper, based on the geometrical information of total contact area and the number of contact spots with respect to surface separation, a new method is proposed to determine the relation between real contact area and normal load. The contact of rough surfaces is treated as an accumulation of incremental multi-plateaus indentations with varying average contact radius. Comparisons with direct finite element calculations and some other theoretical predictions demonstrate the efficiency of this approach.

Operational weather and also flood forecasting has been performed successfully for decades and is of great socioeconomic importance. Up to now, forecast products focus on atmospheric variables, such as precipitation, air temperature and, in hydrology, on river discharge. Considering the full terrestrial system from groundwater across the land surface into the atmosphere, a number of important hydrologic variables are missing especially with regard to the shallow and deeper subsurface (e.g. groundwater), which are gaining considerable attention in the context of global change. In this study, we propose a terrestrial monitoring/forecasting system using the Terrestrial Systems Modeling Platform (TSMP) that predicts all essential states and fluxes of the terrestrial hydrologic and energy cycles from groundwater into the atmosphere. Closure of the terrestrial cycles provides a physically consistent picture of the terrestrial system in TSMP. TSMP has been implemented over a regional domain over North Rhine-Westphalia and a continental domain over European in a real-time forecast/monitoring workflow. Applying a real-time forecasting/monitoring workflow over both domains, experimental forecasts are being produced with different lead times since the beginning of 2016. Real-time forecast/monitoring products encompass all compartments of the terrestrial system including additional hydrologic variables, such as plant available soil water, groundwater table depth, and groundwater recharge and storage.

Rapid growth of IoT applications and their interference in our daily lives led to many different IoT devices which generates enormous data. The IoT devices’ resources are very limited, so storing and processing IoT data in the devices is very inefficient. Several resources of cloud-computing are efficiently used to handle some IoT resources issues. While using resources in the cloud centers cause some other issues, like latency in the IoT applications, which are time-critical. Thus, the technology of edge cloud has evolved recently. This technology permits storage and data processing at the network edge. This paper studies edge computing in-depth for timeless sensitive devices in IoT. In-depth, cutting-edge IoT computing systems (ECAs-IoT) are evaluated and characterized in this paper according to numerous criteria, such as information placement, improvisation facilities, reliability, and data visualization. Moreover, according to distinctive properties, the paper aims at comparing each structure in detail. The paper also highlights the significant limitations of the new ECAs-IoT and recommends solutions to them. The studies also introduce and propose solutions to some of the most important restrictions of the current ECAs-IoT. Consequently, in the edge computing domain, this survey outlines the IoT implementations. Lastly, with the use of IoT implementations for ECAs-IoT, the paper suggests four distinct scenarios.

Scientists and astronomers have attached Scientists and astronomers have attached great importance to the task of discovering new exoplanets, even more so if they are in the habitable zone. To date, more than 4300 exoplanets have been confirmed by NASA, using various discovery techniques, including planetary transits, in addition to the use of various databases provided by space and ground-based telescopes. This article proposes the development of a deep learning system for detecting planetary transits in Kepler Telescope lightcurves. The approach is based on related work from the literature and enhanced to validation with real lightcurves. A CNN classification model is trained from a mixture of real and synthetic data, and validated only with real data and different from those used in the training stage. The best ratio of synthetic data is determined by the perform of an optimisation technique and a sensitivity analysis. The precision, accuracy and true positive rate of the best model obtained are determined and compared with other similar works. The results demonstrate that the use of synthetic data on the training stage can improve the transit detection performance on real light curves.

White Spot Syndrome Virus (WSSV) has emerged as one of the most prevalent and lethal viruses globally, and infects both shrimps and crabs in the aquatic environment. This study aimed to investigate the occurrence of WSSV in different ghers of Bangladesh and the virulence of the circulating phylotypes. We collected 360 shrimp (Penaeus monodon) and 120 crab (Scylla sp.) samples from the South-East (Cox’s Bazar) and South-West (Satkhira) coastal regions of Bangladesh. The VP28 gene-specific PCR assays and sequencing revealed statistically significant (p < 0.05, Kruskal Wallis test) differences in the prevalence of WSSV in shrimps and crabs between the study areas (Cox’s Bazar and Satkhira), and over the study periods (2017-2019). The mean Log load of WSSV varied from 8.40 (Cox’s Bazar) to 10.48 (Satkhira) per gram of tissue. The mean values for salinity, dissolved oxygen, temperature and pH were 14.71±0.76 ppt, 3.7±0.1 ppm, 34.11±0.38˚C and 8.23±0.38, respectively in the WSSV-positive ghers. The VP28 gene-based phylogenetic analysis showed an amino-acid substitution (E→G) at 167th position in the isolates from Cox’s Bazar (referred to as phylotype BD2) compared to the globally circulating one (BD1). Shrimp PL artificially challenged with BD1 and BD2 phylotypes with filtrates of tissue containing 0.423 X 109 copies of WSSV per mL resulted a median LT50 value of 73 hrs and 75 hrs, respectively. The in-vivo trial showed higher mean Log WSSV copies (6.47±2.07 per mg tissue) in BD1 challenged shrimp PL compared to BD2 (4.75±0.35 per mg tissue). Crabs infected with BD1 and BD2 showed 100% mortality within 48 hrs and 62 hrs of challenge, respectively with mean Log WSSV copies of 12.06±0.48 and 9.95±0.37 per gram tissue, respectively. Moreover, shrimp antimicrobial peptides (AMPs) penaeidin and lysozyme expression was lower in BD1 challenged group compared to BD2 challenged shrimps. These results collectively demonstrated that relative virulence properties of WSSV based on mortality rate, viral load and expression of host immune genes in artificially infected shrimp PL could be affected by single aa substitution in VP28.

Timeworn telecommunication are progressively being substituted by a new one that run over IP networks, which is recognized as voice over internet protocol (VoIP). VoIP has a number of qualities (e.g., inexpensive call rate), which make it progressively widespread in the telecommunication domain. However, VoIP faces plentiful obstacles that slow its growth. One of the major obstacles is poorly utilizing the network bandwidth. A number of techniques have been offered to handle this obstacle, including packet multiplexing techniques. This paper designs an original multiplexing techniques, called packet multiplexing and carrier header (PM-CH), to decrease the quantity of the bandwidth consumed by VoIP. PM-CH protect the bandwidth by multiplexing the packets in a header and using the Timestamp field in the RTP header. The achievement of the PM-CH technique was examined depends on connection capacity and payload shortening. Simulation outcomes show that the PM-CH technique outperforms the contrast technique in the two factors. For instance, the PM-CH technique’s connection capacity outperforms the comparable technique by 58.9% when the connection bandwidth is 1000 kbps. Consequently, the PM-CH technique attains its objective of reducing the unexploited bandwidth caused by VoIP.

The real estate market is exposed to many fluctuations in prices, because of existing correlations with many variables, some of which cannot be controlled or might even be unknown. Housing prices can increase rapidly (or in some cases, also drop very fast), yet the numerous listings available online where houses are sold or rented are not likely to be updated that often. In some cases, individuals interested in selling a house (or apartment) might include it in some online listing, and forget about updating the price. In other cases, some individuals might be interested in deliberately setting a price below the market price in order to sell the home faster, for various reasons. In this paper we aim at developing a machine learning application that identifies opportunities in the real estate market in real time, i.e., houses that are listed with a price substantially below the market price. This program can be useful for investors interested in the housing market. We have focused in a use case considering real estate assets located in the Salamanca district in Madrid (Spain) and listed in the most relevant Spanish online site for home sales and rentals. The application is formally implemented as a regression problem, that tries to estimate the market price of a house given features retrieved from public online listings. For building this application, we have performed a feature engineering stage in order to discover relevant features that allows attaining a high predictive performance. Several machine learning algorithms have been tested, including regression trees, $k$-nearest neighbors, support vector machines and neural networks, identifying advantages and handicaps of each of them.

Real-time strategy (RTS) game has proposed many challenges for AI research for its large state spaces, enormous branch factors, limited decision time and dynamic adversarial environment. To tackle above problems, the method called Adversarial Hierarchical Task Network planning (AHTN) has been proposed and achieves favorable performance. However, the HTN description it used cannot express complex relationships among tasks and impacts of environment on tasks. Moreover, the AHTN cannot handle task failures during plan execution. In this paper, we propose a modified AHTN planning algorithm named AHTNR. The algorithm introduces three elements essential task, phase and exit condition to extend the HTN description. To deal with possible task failures, the AHTNR first uses the extended HTN description to identify failed tasks. And then a novel task repair strategy is proposed based on historical information to maintain the validity of previous plan. Finally, empirical results are presented for the μRTS game, comparing AHTNR to the state-of-the-art search algorithms for RTS games.

This work presents the results of an experimental socio-economic study conducted in two shanty towns of Messina as part of a systemic urban regeneration and fight against poverty program called Capacity. The study has shown that the development of a positive attitude towards the future and the confidence in others are associated with the development of the riskiest option, which is the one that can give the highest pay-off. The paper also illustrates the expected and unexpected outcomes of projects for individuals and the community, as well as the economic benefits for the public administration and the society of a strategy that reduces the reliance on social welfare measures as well as the local control exercised by organized crime.

In a safety-critical system typically not all provided services have the same criticality, which we call mixed-criticality systems. Criticality arithmetic, also called SIL arithmetic, is an approach to lower the development effort of a service by providing redundancy with tasks that are developed for a lower criticality level. In this paper we present ATMP-CA, which is a derivation of the multi-core scheduler ATMP. ATMP-CA is able to take into account the knowledge about the use of criticality arithmetic. ATMP-CA has a modified core allocation and procedure for utility optimisation, considering the context of the replicated tasks. We conducted experiments that show that ATMP-CA is able to provide the services using criticality arithmetic, while the reference schedulers were not.

An outbreak of winter disease, complicated by severe respiratory syndrome, occurred in January 2020 in a high production dairy cow herd located in a hilly area of the Calabria region. Of the 52 animals belonging to the farm, 5 (9.6%) died with severe respiratory distress, death occurring 3-4 days after the appearance of the respiratory signs (caught and gasping breath). Microbiological analysis revealed absence of pathogenic bacteria whilst Real-time PCR identified the presence of RNA from Bovine Coronavirus (BCoV) in several organs: lungs, small intestine (jejunum), mediastinal lymph nodes, liver and placenta. Since being the only pathogen identified, BCoV was hypothesized to be the cause of the lethal pulmonary infection. Like the other CoVs, BCoV is able to cause different syndromes. Its role in calfhood diarrhoea and in mild respiratory disease is well known: we report instead the involvement of this virus in a severe and fatal respiratory disorder, with symptoms and disease evolution resembling that of Severe Acute Respiratory Syndromes (SARS).

The aim of the study was to determine the presence Fusarium species and mycotoxins in winter wheat grain in Poland. Grain samples from different locations in Poland in 2009 and 2010 were analysed for the content of biomass of Fusarium species and mycotoxins. In 2009 biomass of F. graminearum and F. poae was present in all samples, F. culmorum in 82% of samples, F. avenaceum in 55% of samples. F. sporotrichioides, F. tricinctum and F. equiseti were found only in individual samples. F. langsethiae was not detected. In 2010, five Fusarium species were detected with the exception of F. sporotrichioides. The highest content of biomass was found for F. graminearum followed by F. avenaceum, F. poae and F. langsethiae. The amount of F. culmorum biomass was very low. The most frequently occurring species was F. poae and F. graminearum. In 2009, deoxynivalenol was detected in all samples. In 2010, the average content of deoxynivalenol was lower than in 2009. Nivalenol was detected at very low concentration in both years. Significant correlations between content of F. graminearum biomass and deoxynivalenol concentration in grain and between content of F. poae biomass and nivalenol concentration in grain in 2009 were found. The most important finding of this study was that main Fusarium species infecting wheat kernels in Poland in both years was F. graminearum. The amount of biomass of F. graminearum was the highest in both years. It was present in the most samples. The other frequently detected species was F. poae, which in 2010 appeared in more samples than F. graminearum. However, the amount of F. poae biomass was lower. F. culmorum, species that was previously dominating as wheat pathogen in Poland, was found less frequently than F. graminearum. The amount of biomass of this species was the lowest in 2010.

In this paper, the author obtains an analytic exact form of Heaviside function, which is also known as Unit Step function and constitutes a fundamental concept of the Operational Calculus.In particulat, this function is explicitly expressed in a very simple manner by the aid of purely algebraic representations. The novelty of this work is that the proposed explicit formula is not performed in terms of non – elementary special functions, e.g. Dirac delta function or Error function and also is neither the limit of a function, nor the limit of a sequence of functions with point wise or uniform convergence. Hence, it may be much more appropriate and useful in the computational procedures which are inserted into Operational Calculus techniques and other engineering practices.

When a rotary machine is running, from which the acquired vibro-acoustic signals enable to reveal its operation status and health condition. The study proposed a DSP-based adaptive angular-velocity Vold-Kalman filtering order tracking (AV2KF_OT) algorithm with an online real-time nature for signal interpretation and machine condition monitoring. Theoretical derivation and numerical implementation of computation schemes are briefly introduced. An online real-time monitoring system based on the AV2KF_OT algorithm, which was implemented through both a digital signal processor and a user interface coded by using LabVIEW, was developed. Two experimental tasks were applied to justify the proposed technique, including (i) the detection of startup on the fluid-induced whirl performed through a journal-bearing rotor rig, and (ii) the separation of close orders from the measured signals of a multifunction transmission-element ball-bearing bench.

This research presents alternative solutions for an Energy Efficiency Management System (EEMS) serving as a framework for optimizing the energy consumption algorithm and lowering energy consumption. First, a monitoring Wireless Sensor and Actuator Network (WSAN) is used for sensing, measuring, gathering data, and modeling all the dynamic disturbance parameters of the rooms in the building. Second, integrated software for metering and controlling the processes of digital data flow is used. Third, an alternative solution is proposed to reduce energy consumption. The primary benefits of this system are real-time monitoring; rapid, alternative solutions; and the ability to make a prudent decision on how to lower energy consumption. The system shows instant and accumulated solutions for short and long-term time planning. The solutions identified can be implemented in the same buildings under the same circumstances. The universities of Majmaah and Philadelphia have buildings with similar infrastructure. The system was applied to the buildings at Philadelphia University. The results were generalized to both universities. After implementation, the energy consumption of the EEMS using WSAN (based on the monitoring was reduced up to 23% when compared to that of the initial state.

This paper deals with the problem of real-time management of Smart Grids. For this sake, the energy management is integrated with the power system through a telecommunication system. The use of Multiagent Systems leads the proposed algorithm to find the best-integrated solution, taking into consideration the operating scenario and the system characteristics. The proposed technique is tested with the help of an academic microgrid, so the results may be replicated.

This paper presents a methodology to design sensor fusion parameters using real performance indicators of navigation in UAVs based on PixHawk flight controller and peripherals. This methodology and the selected performance indicators allows to find the best parameters for the fusion system of a determined configuration of sensors and a predefined real mission. The selected real platform is described with stress on available sensors and data processing software, and the experimental methodology is proposed to characterize sensor data fusion output and determine the best choice of parameters using quality measurements of tracking output with performance metrics not requiring ground truth.

Real estate market analysis and place-based decision-making can both benefit from understanding house price development. Although considerable amounts of interest have been devoted to housing price modelling, the assessment of house price fluctuation still requires further comparing studies. Housing price prediction is challenging as contributing factors are quite dynamic and subject to a variety of regulating elements. The future understanding of the housing market trends not only provides sufficient customers’ investment trust potential but also enables the financial support to progress more realistic in advance. In this study, a comprehensive data-informed framework is developed to investigate and anticipate real estate house prices using historical data by combining explanatory features. We examined about 500 houses in the Boston area as a case study and discussed how the increase in housing prices could vary by each of the contributing components. Fourteen Machine Learning (ML) regressors imply to the dataset and lead to a comparative study of the accuracy of all the models. ML-based regressors forecast real estate home prices as a function of thirteen influencing factors. The most informative features were also selected by conducting the Permutation Feature Importance technique on all the features The study provides a comprehensive tool for evaluating the robustness and efficiency of ML models for housing price predictions. The results highlighted Random Forest as the best model has an R2 equals to 0.88 and Voting Regressor as the second highest rated model has R2 equals to 0.87. Results of multivariate exploratory data analysis also implied that the average number of rooms and percentage of the lower status of the population have the most significant impact on the price range predictions.

Today's major challenge for smart Microgrids is to ensure the security of communications in a large number of changing data sets that are vulnerable to attacks by denial of services in constant evolution. The Internet Protocol Traceback defines a set of methods that help identify the source of an attack with minimal requirements for memory and processing. However, the concept of Traceback is not yet being used in smart Microgrids. As a result, the main challenge of this article is to incorporate a new Traceback approach into the cybernetic system of a smart mesh Microgrid, which can be tested using a network simulator (NS-3) based on delay, debit, and packet loss rate parameters. In fact, the simulation results show the efficacy of this approach compared to others existing in the literature. Furthermore, using the proposed Traceback technique and the mesh nodes, we were able to create a smart meshed Microgrid. Moreover, using the Traceback approach given for merging Intel Galileo Gen.1 nodes with the Compex WLE200NX.11a/b/g/n to establish a secure test bench, which is deployed as a prototype at the Sfax Digital Research Center in Tunisia, we were able to create an intelligent Microgrid. In fact, by identifying all attack vectors and revealing their origins, we could boost the efficiency of our operation by 100%.

RT-PCR tests have become the gold standard for detecting the SARS-CoV-2 virus in the context of the COVID-19 pandemic. Because of the extreme number of cases in periodic waves of infection, there is a severe financial and logistical strain on diagnostic laboratories. For this reason, alternative implementations, and validations of academic protocols, that employ the lowest cost and most widely available equipment and reagents found in different regions, is essential. In this study, we report an alternative implementation of the EUA 2019-nCoV CDC assay which uses a previously characterized duplex PCR reaction for the N1 and RNAse P target regions and an additional uniplex reaction for the N2 target region. Taking advantage of the Abbott m2000 Sample Preparation System and NEB Luna Universal Probe One-Step RT-qPCR kit, some of the most widely available and lowest cost nucleic acid extraction and amplification platforms, this modified test shows a state-of-the-art analytical and clinical sensitivities and specificities, when compared with the Seegene Allplex-SARS-CoV-2 assay. This implementation has the potential to be verified and implemented by diagnostic laboratories around the world to guarantee low-cost RT-PCR tests that can take advantage of widely available equipment and reagents.

Proposal: Pineal region cysts (PCs) may affect the tectum and aqueduct and cause deep central vein congestion and endocrine dysfunction. In addition to headaches, PC often causes a broad range of symptoms, leading to prolonged diagnosis and therapy. The aims of this study are to reveal parameters that might explain the ambiguity of the symptoms and to identify factors association with the respiration driven neurofluid preload system. Methods: This retrospective study included 28 paediatric patients (mean age 11.6 years) who received surgical treatment for pineal region cysts and 18 patients (mean age 11.3 years) who were followed conservatively. Multiple clinical patient characteristics, such as symptoms, time to neuroimaging diagnosis, cyst size, ventricular indices, head circumference and postoperative outcome, were analysed. Four patients were investigated for CSF dynamics with real-time MRI. The mean follow-up time was 1.6 years. Results: The most common early onset symptoms were headaches (92%), blurred vision (42.8%), sleep disturbances (39.3%) and vertigo (32.1%). Tectum contact was observed in 82% of patients, and MRI examinations revealed that imaging flow void signals were absent in 32.1% of patients. The mean cyst diameters were 13.7 mm for the axial axis and 15.6 mm for the longitudinal axis. Together with a postoperative flow void signal, 4 patients recovered their respiration-driven CSF upward flow, which was not detectable before OP. After operation in 92.1% of patients, the leading symptoms improved without any mortality or morbidity. Conclusion: Despite proximity to the tectum and aqueduct with frequently absent aqueductal flow void signals, hydrocephalic ventricular enlargement was never detected. Data from real-time MRI depicted a reduced preoperative filling of the ventricular CSF compartments, indicating a diminished fluid preload, which recovered postoperatively.

Abstract: Windtech device is a novel tool for measuring the sensation of the ‘cold’. Cold poses numerous challenges for industrial operations, human survival, and living convenience. The impact of the cold is not possible to be quantified just based on temperatures; however other factors such as wind speed, humidity, irradiance have to be taken into consideration. Efforts have been made to develop combined indices such as wind chill temperature (WCT), AccuWeather RealFeel®, and others. The presented article discusses these along with the industrial standards that emphasize on the quantification of the ‘cold’. The following article introduces the Windtech device and its operating principle involving ‘heated temperature’, where the ‘heated temperature’ is affected by environmental parameters including ambient temperature, humidity, wind velocity, and irradiance. The discussed Windtech device is calibrated for operation according to the ISO 11079:2007 standard.

Peanut (Arachis hypogaea) contains allergenic proteins, which make it harmful to the sensitised population. The presence of peanut in foods must be indicated on label, to prevent accidental consumption by allergic population. In this work, we use chloroplast markers for specifically detection of peanut by real-time PCR, in order to increase the assay sensitivity. Binary mixtures of raw and processed peanut flour in wheat were performed at concentrations ranging from 100000 to 0.1 mg/kg. DNA isolation from peanut, mixtures and other legumes was carried out following three protocols for obtaining genomic and chloroplast-enrich DNA. Quantity and quality of DNA was evaluated, obtaining better results for protocol 2. Specificity and sensitivity of the method has been assayed with specific primers for three chloroplast markers (mat k, rpl16 and trnH-psbA) and Ara h 6 peanut allergen-coding region was selected as nuclear low-copy target and TaqMan probes. Efficiency and linear correlation of calibration curves were within the adequate ranges. Mat k chloroplast marker yielded the most sensitive and efficient detection for peanut. Moreover, detection of mat K in binary mixtures of processed samples was possible up to 10 mg/kg even after boiling, and autoclave 121°C 15 min, with acceptable efficiency and linear correlation. Applicability of the method has been assayed in several commercial food products.

The specific demands inherent to supply chains built upon large IoT systems, make a must the design of a coordinated framework for cyber resilience provisioning intended to guaranteeing trusted supply chains of ICT systems, built upon distributed, dynamic, potentially insecure and heterogeneous ICT infrastructures. As such, the proposed solution is envisioned to deal with the whole supply chain system components, from the IoT ecosystem to the infrastructure connecting them, addressing security and privacy functionalities related to risks and vulnerabilities management, accountability and mitigation strategies as well as security metrics and evidence-based security assurance. In this paper we present FISHY, as a preliminary designed architecture, designed to orchestrate both existing and beyond state-of-the-art security appliances in composed ICT scenarios and also leveraging capabilities of programmable network and IT infrastructure through seamless orchestration and instantiation of novel security services, both in real-time and proactively. The paper also includes a thorough business analysis to go far beyond the technical benefits of a potential FISHY adoption as well as three real-world use cases where to strongly support the envisioned benefits of a FISHY adoption.

Climate change and development have increased urban flooding, requiring modernization of stormwater infrastructure. Retrofitting standard passive systems with controllable valves/pumps is promising, but requires real-time control (RTC). One method of automating RTC is reinforcement learning (RL), a general technique for sequential optimization and control in uncertain environments. The notion is that an RL algorithm can use inputs of real-time flood data and rainfall forecasts to learn a policy for controlling the stormwater infrastructure to minimize measures of flooding. In real-world conditions, rainfall forecasts and other state information, are subject to noise and uncertainty. To account for these characteristics of the problem data, we implemented Deep Deterministic Policy Gradient (DDPG), an RL algorithm that is distinguished by its capability to handle noise in the input data. DDPG implementations were trained and tested against a passive flood control policy. Three primary cases were studied: (i) perfect data, (ii) imperfect rainfall forecasts, and (iii) imperfect water level and forecast data. Rainfall episodes (100) that caused flooding in the passive system were selected from 10 years of observations in Norfolk, Virginia, USA; 85 randomly selected episodes were used for training and the remaining 15 unseen episodes served as test cases. Compared to the passive system, all RL implementations reduced flooding volume by 70.5% on average, and performed within a range of 5%. This suggests that DDPG is robust to noisy input data, which is essential knowledge to advance the real-world applicability of RL for stormwater RTC.

In the study, a new correction method was applied to reduce error during detection on mixed pesticide residue in apples by using Raman spectra. Combined with self-built pesticide residues detection system by Raman spectroscopy and the application of surface enhancement technology, rapid real-time qualitative and quantitative analysis of deltamethrin and acetamiprid residues in apples can be applied effectively. In quantitative analysis, compared with the intensity value of characteristic peaks of single pesticide with same concentration, the intensity value of characteristic peaks of the two pesticides decreased after mixing the pesticides, which interferes the results severely. By comparing the difference in the intensity of characteristic peaks of single and mixed pesticides, a correction method is proposed to eliminate the influence of pesticides mixture. Characteristic peak intensity values of gradient concentration pesticide from 10-1 g•kg-1 to 10-6 g•kg-1 and Lagrangian interpolation are applied in the correction method. And a smooth surface is applied to describe the correction ratio of characteristic peak intensity. Through detecting the characteristic peak intensity values of the mixed pesticide, correction ratio will be obtained. Then real values of the peak intensity of pesticides and the content of each component of the mixed pesticide will be acquired by the correction method. Correlation coefficient of model validation exceeds 0.88 generally and Root Mean Square Error also decreases obviously after correction, which proved the reliability of the method.

In modern Commercial Off-The-Shelf (COTS) multicore systems, cores can produce several simultaneous memory requests. The processing of such requests over the memory controller negatively impacts the interference delay triggered by running parallel tasks on the platform. In this paper, we propose a software-based testing approach for analyzing memory interference delay, when cores are exposed to extensive read/write requests that access in parallel their Cache Coherent Interconnect. The hardware targeted in this work is the well-known LayerScape QorIQ LS2085A, which can be approached as a potential successor to the Freescale QorIQ P4080. The test analysis was conducted based on a bare-metal operating system that we developed to guarantee a deterministic execution environment at all time points. Our testing was accomplished using a set of carefully designed synthetic benchmarks as well as TACLeBench benchmarks.

Given a stream of depth images with a known cuboid reference object present in the scene, we propose a novel approach for accurate camera tracking and volumetric surface reconstruction in real-time. Our contribution in this paper is threefold: (a) utilizing a priori knowledge of the cuboid reference object, we keep drift-free camera tracking without explicit global optimization; (b) we improve the fineness of the volumetric surface representation by proposing a prediction-corrected data fusion strategy rather than simple moving average, which enables accurate reconstruction of high-frequency details such as sharp edges of objects and geometries of high curvature; (c) we introduce a benchmark dataset CU3D containing both synthetic and real-world scanning sequences with ground-truth camera trajectories and surface models for quantitative evaluation of 3D reconstruction algorithms. We test our algorithm on our dataset and demonstrate its accuracy compared with other state-of-the-art algorithms. We release both our dataset and code as opensource1 for other researchers to reproduce and verify our results.

The purpose of this work is to present an adaptive sliding mode luenberger state observer with improved disturbance rejection capability and better tracking performance under dynamic conditions. The sliding hyperplane is altered by incorporating the estimated disturbance torque with the stator currents. Also, the effects of parameter detuning on the speed convergence is observed and compared with the conventional disturbance rejection mechanism. The entire drive system is first built in simulink environment. Then, the simulink model is integrated with RT-Lab blocksets and implemented in a relatively new real-time environment using OP4500 real-time simulator. Real-time simulation and testing platforms have succeeded offline simulation and testing tools due to their reduced development time. The real-time results validate the improvement in the proposed state observer and also correspond to the performance of the actual physical model.

Recently, there has been a growing interest in the production of electricity from renewable energy sources (RES). The RES investment is characterized by uncertainty, which is long-term, costly, depend on feed-in-tariff and support schemes. In this paper, we address the real option valuation (ROV) of a solar power plant investment. The real option framework is investigated. This framework considers the renewable certificate price, furthermore the cost of delay between establishing and operating the solar power plant. The optimal time of launching the project and assess the value of deferred option are discussed. The new three stage numerical methods are constructed, the Lobatto3C-Milstein (L3CM) methods. The numerical methods are integrated with concept of Black-Scholes option pricing theory, and applied in option valuation for solar energy investment with uncertainty. The numerical results of L3CM, finite difference and Monte Carlo methods are compared to show the efficiency of our methods. Our data set refers to the Arab Republic of Egypt.

Acute myeloid leukemia (AML) is a hematological malignancy that predominantly affects the elderly. Prognosis declines with age. For those who cannot tolerate intensive chemotherapy, historically established treatment options have been hypomethylating agents (HMAs), low dose cytarabine (LDAC), and best supportive care (BSC). As the standard of care evolves for those unfit for intensive chemotherapy, there is a need to understand established treatment pathways, clinical outcomes and healthcare resource utilization in Canada. The CURRENT study was a retrospective chart review of AML patients not eligible for intensive chemotherapy who initiated first-line treatment between 1 January 2015 and 31 December 2018. Data were collected from 170 Canadian patients treated at six hematology centers, of whom 118 received systemic therapy and 52 received BSC as first-line treatment. Median overall survival was 8.58 months and varied from 2.96 months for BSC to 13.31 months for HMAs. Over 80% of patients had at least one outpatient visit, and 67% of patients receiving systemic therapy and 71% of those receiving BSC had at least one admission to hospital, during their first line of therapy. A total of 96 (81.4%) patients receiving first line systemic therapy and 39 (75.0%) of those receiving first line BSC had at least one red blood cell or platelet transfusion. These findings highlight the unmet need for novel therapies for patients ineligible for intensive chemotherapy.

Purpose: ETV is indicated for treating obstructions of major CSF pathways. The outcome evaluation often yields success rates of only +- 70% for shunt independency. Hence, compromised CSF absorption seems to occur more often than expected. We searched for parameters suitable to assess the involved CSF dynamics. Material and Methods: This was a prospective study in 58 paediatric patients (7.7 yrs. mean age) between 2000 and 2020 with aqueductal stenosis (11/58), obstruction of the aqueduct due to tumor growth (22/58),and connatal hydrocephalus (9/58). The average follow-up interval was 4.7 years. Head circumferences, Evans- and fronto-occipital horn ratios before and 3 months after ETV were obtained as Delta-indices. Furthermore ETV success score (ETVSS), the patency of the aqueduct pre- and postoperatively as well as of the stoma were assessed by flow void signs on MRI. Evaluation on MRI also included the shape of the floor of the 3^rd ventricle and whether or not the septum pellucidum showed signs of perforation. Four patients were analysed pre- and postoperatively via real-time MRI. At least the educational status regarding protected or unprotected education was analyzed. Results:The prevalence of a bowing of the floor of the 3^rd ventricle was 72%, and the ETVSS was 71.0%. In 26 children a septal perforations or an open aqueduct prior to ETV (19) could be identified. Mean ER and FOHR were reduced by 0.03 and 0.05 , respectively. Maintained open (flow void on postop MRI) or perforation could successfully be carried out during endoscopic surgery in 44 patients (79%). The disproportionate increase of head circumference abated in 79.4% of patients. New shunt insertion occurred in 16 patients (27.5%). Intraoperatively upward CSF flow was detected in all cases. Statistical analyses(ANOVA) showed significant results for unprotected education, postoperative ER and FOHR but not for open stoma. Conclusion: The identification of flow through the stoma on postoperative MRI seems to be a necessary but not sufficient condition for ETV success. In our study, ventricular volumes were used as parameters to determine success rates as well as unprotected education. Furthermore, enabling upward CSF flow driven by inspiration seems crucial for successful ETV.

Cellular Nonlinear Networks (CNN) are a concept introduced in 1988 by Leon Chua and Lin Yang as a bio-inspired architecture, capable of massively parallel computation. Later on, CNN have been enhanced by incorporating designs that incorporate memristors to profit from their processing and memory capabilities. In addition, Stochastic Computing (SC) can be used to optimize the quantity of required processing elements; thus it provides a lightweight approximate computing framework, quite accurate and effective, though. In this work, we propose utilization of SC in designing and implementing a memristor-based CNN. As a proof of the proposed concept, an example of application is presented. This application combines Matlab and a FPGA in order to create the CNN. The implemented CNN has then been used to perform three different real-time applications on a 512x512 gray-scale and a 768x512 color image: storage of the image, edge detection, and image sharpening. It has to be pointed out that the same CNN has been used for the three different tasks, with the sole change of some programmable parameters. Results show an excellent capability with significant accompanying advantages, like the low number of needed elements further allowing for a low cost FPGA-based system implementation, something confirming the system’s ability for real time operation.

Background: Observational health data has the potential to be a rich resource to inform clinical practice and regulatory decision making. However, the lack of standard data quality processes makes it difficult to know if these data are research ready. The EHDEN COVID-19 Rapid Col-laboration Call presented the opportunity to assess how the newly developed open-source tool Data Quality Dashboard (DQD) informs the quality of data in a federated network. Methods: 15 Data Partners (DPs) from 10 different countries worked with the EHDEN taskforce to map their data to the OMOP CDM. Throughout the process at least two DQD results were collected and compared for each DP. Results: All DPs showed an improvement in their data quality between the first and last run of the DQD. The DQD excelled at helping DPs identify and fix conformance is-sues but showed less of an impact on completeness and plausibility checks. Conclusions: This is the first study to apply the DQD on multiple, disparate databases across a network. While study-specific checks should still be run, we recommend that all data holders converting their data to the OMOP CDM use the DQD as it ensures conformance to the model specifications and that a database meets a baseline level of completeness and plausibility for use in research.

The aim is to evaluate a program for direct oral anticoagulants (DOACs) management in nonvalvular atrial fibrillation (NVAF) patients, according to patient profiles, appropriateness of dosing, patterns of crossover, effectiveness and safety. This is an observational and longitudinal retrospective study in a cohort of patients attended in daily clinical practice in a single regional hospital in Spain with a systematic follow-up plan for up to 3 years for patients initiating dabigatran, rivaroxaban or apixaban between JAN/2012-DEC/2016. We analyzed 490 episodes of treatment (apixaban 2.5 mg: 9.4%, apixaban 5 mg: 21.4%, dabigatran 75 mg: 0.6%, dabigatran 110 mg: 12,4%, dabigatran 150 mg: 19.8%, rivaroxaban 15 mg: 17.8% and rivaroxaban 20 mg: 18.6%) in 445 patients. 13.6% of patients on dabigatran, 9.7% on rivaroxaban, and 3.9% on apixaban, switched to other DOACs or changed dosing. Apixaban was the most frequent DOAC switched to. The most frequent reasons for switching were toxicity (23.8%), bleeding (21.4%) and renal deterioration (16.7%). Inappropriateness of dose was found in 23.8% of episodes. Patients taking apixaban 2.5 mg were older, had higher CHA2DS2VASc score and lower creatinine clearance. Patients taking dabigatran 150 mg and rivaroxaban 20 mg were younger, had lower CHA2DS2VASc and higher creatinine clearance. Rates of stroke/transient ischemic attack (TIA) were 1.64/0.54 events/100 patients-years, while rates of major, clinically relevant non-major (CRNM) bleeding and intracranial bleeding where 2.4, 5, and 0.5 events/100 patients-years. Gastrointestinal and genitourinary bleeding were the most common type of bleeding events (BE). On multivariable analysis, prior stroke (RR: 4.2; CI: 1.5-11.8; p=0.006) and age (RR: 1.2; CI: 1.1-1.4; p=0.006) were independent predictors of stroke/TIA. Concurrent platelet inhibitors (RR: 7.1; CI: 2.3-21.8; p=0.001), male gender (RR: 2.1; CI: 1.2-3.7; p=0.0012) and age (RR: 1.1; CI: 1.02-1.13; p=0.005) were independent predictors of BE. This study complements the scant data available on the use of DOACs in NVAF patients in Spain, confirming a good safety and effectiveness profile

Predictive maintenance (PM) algorithms are widely applied for detecting operational anomalies on industrial processes to trigger maintenance before a possible breakdown; however, much less focus has been devoted to the use of such PM predictions as feedback in automated process control mechanisms. They usually integrate preventive solutions to protect the machines, usually causing downtimes. The premise of this study is to develop a holistic adaptive process scheduling mechanism that incorporates PM analysis as a safety component to optimize the operation mode of an industrial process toward preventing breakdowns while maintaining its availability and operational state, thereby reducing downtimes. As PM is largely a data-driven approach; hence, relies on the setup, we first compare different PM approaches and identify a one-class support vector machine (OCSVM) as the best performing option for the anomaly detection on our setup. Then, we propose a novel pipeline to integrate maintenance predictions into a real-time adaptive process scheduling mechanism. It schedules for the most suitable operation, i.e., optimizing for machine health and process efficiency, according to the abnormal readings. To demonstrate the pipeline on action, we implement our approach on a small-scale conveyor belt system utilizing our Internet of Things (IoT) framework. The results show that our PM-based adaptive process control provides an efficient process with less or no downtime. We also conclude that a PM approach does not provide sufficient efficiency without its integration into an autonomous planning process.

With the development of designing and manufacturing level for micro-electromechanical system (MEMS) gyroscopes, the control circuit system becomes a key point to determine their internal performances. Nevertheless, phase delay of electron components may result in some serious hazards. This paper describes a real-time circuit phase delay correction system for MEMS vibratory gyroscopes. A detailed theoretical analysis is provided to clarify the influences of circuit phase delay on the in-phase and quadrature (IQ) coupling characteristics and zero rate output (ZRO) utilizing force-to-rebalance (FTR) closed-loop detection and quadrature correction system. By deducing the relationship between amplitude-frequency, phase-frequency of MEMS gyroscope and the phase relationship of the whole control loop, a real-time correction system is proposed to automatically adjust the phase reference value of phase-locked loop (PLL) and thus compensate for the real-time circuit phase delay. The experimental results show that the correction system can accurately measure and compensate the circuit phase delay in real time. Furthermore, the unwanted IQ coupling can be eliminated and the ZRO is decreased by 755% to 0.095°/s. This correction system realizes a small angle random walk of 0.978°/√h, and a low bias instability of 9.458°/h together with a scale factor nonlinearity of 255 ppm at room temperature. Besides, the thermal drift of ZRO is reduced to 0.0034°/s/°C at a temperature range from -20°C to 70°C.

In this paper, we propose a multi-zone service control scheme to maximize the performance of each service zone when a large number of cellular service zones and D2D (Device-to-Device) service zones are composed to 5G cellular network. This paper also improves performance of service zone by dividing traffic into real-time traffic and non-real-time traffic in order to minimize traffic interference. Real-time traffic and non-real-time traffic have a significant impact on communication performance. We propose a new self-detection traffic interference control technique to improve the QoS and throughput of D2D and C2D communication in a cellular network, STICS (Self-detecting Traffic Interference Control Scheme). The proposed STICS scheme distinguishes between short-term traffic congestion process and long-term traffic congestion process according to traffic characteristics to detect and control traffic. When the proposed scheme is applied to the 5G-based cellular network environment, it is expected that the traffic type will be efficiently classified by self-detecting the traffic according to the flow. Such classified traffic is less sensitive to communication between the D2D and C2D links, thereby reducing traffic overload. We evaluate the performance of the proposed scheme through simulation and show that the proposed scheme is more efficient than other comparison schemes.

In this paper, we study the Parisian time of a reflected Brownian motion with drift on a finite collection of rays. We derive the Laplace transform of the Parisian time using a recursive method, and provide an exact simulation algorithm to sample from the distribution of the Parisian time. The paper is motivated by the settlement delay in the real-time gross settlement (RTGS) system. Both the central bank and the participating banks in the system are concerned about the liquidity risk, and are interested in the first time that the duration of settlement delay exceeds a predefined limit, we reduce this problem to the calculation of the Parisian time. The Parisian time is also crucial in the pricing of Parisian type options; to this end, we will compare our results with the existing literature.

Real estate industry is related to the national economy and people's livelihood，characterized by a high degree of financial intensity. The enterprises in this industry need certain financial ability and large shareholder controlling ability to support their survival. However，due to the multiple adverse impacts of current state policies，banks and private capital，the credit crunch，the sudden decrease in withdrawn funds and the limitation of internal financing，the problem of capital restraint of real estate enterprises has become more and more serious. From the perspective of corporate governance，this paper studies the interaction among financial constraints，ownership concentration and corporate performance under different shareholding states by analyzing the quantitative characteristics of equity structure，and looks for the appropriate range of the largest shareholder holding ratio，which has considered the financial performance and risk. It is found that raising the ownership concentration can effectively ease the financing constraints and improve the performance of enterprises，both of which are significant under the state of high ownership concentration， while the financial constraints play a significant intermediary effect under the State of absolute holding， while in the decentralized state of ownership，there is a significant regulatory effect，and the interaction of the three will be different due to the size of the enterprise.

The calculation of the release of liquid hazardous chemicals storage tanks is an important part of the quantitative risk assessment of accidents. This paper mainly establishes a continuous real–time release model based on the instantaneous mass flow Q_m model. Meanwhile, the software function module was analyzed, and programming software was developed using C# language for model solving. A series of experiments for repeated leakage tests was designed and the discharges through three small holes with different heights for 200 s were observed. The results show that the continuous real–time leakage model is effective, and the deviation between theoretical and experimental release amounts are within a reasonable range. The higher the liquid level above the leak hole is, and the smaller the height of the leak hole from the ground is, the greater the flow rate at the leak orifice is and the smaller discharge rate change is. Therefore, the deviation between the theoretical release amount M_t and the experimental average release amount Ma is greater while the height of the leak hole from the ground is smaller, which indicates that the smaller the distance from the leak orifice to the ground, the greater the influence of the empirical discharge coefficient C₀ on the release amount M.

Future cyber-physical systems may extend over broad geographical areas, like cities or regions, thus requiring the deployment of large real-time networks. A strategy to guarantee predictable communication over such networks is to synthesize an offline time-triggered communication schedule. However, this synthesis problem is computationally hard (NP-complete), and existing approaches do not scale satisfactorily to the required network sizes. This article presents a segmented offline synthesis method which substantially reduces this limitation, being able to generate time-triggered schedules for large hybrid (wired and wireless) networks. We also present a series of algorithms and optimizations that increase the performance and compactness of the obtained schedules while solving some of the problems inherent to segmented approaches. We evaluate our approach on a set of realistic large-size multi-hop networks, significantly larger than those considered in the existing literature. The results show that our segmentation reduces the synthesis time up to two orders of magnitude.

This paper proposes an innovative mechatronic piezo-actuated module to control vibrations in modern machine tools. Vibrations represent one of the main issues that compromise seriously the quality of the workpiece. The active vibration control (AVC) device is composed by a host part integrated with sensors and actuators synchronized by a regulator, able to make a self-assessment and adjust to the environmental alteration. This study presents the mechatronic model based on the kinematic and dynamic analysis of the AVC device. To ensure a real time performance, a H2-LQG controller has been developed and validated by simulations involving machine tool, PZT actuator and controller models. The Hardware-in-the-loop (HIL) architecture is adopted to control and attenuate the vibrations. A set of experimental tests has been performed to validate the AVC module on a commercial machine tool. The feasibility of the real time vibration damping is demonstrated and the simulation accuracy is evaluated.

The alteration of natural land cover to impervious surfaces during development increases stormwater runoff. Stormwater Control Measures (SCMs) are used to manage water quantity and enhance water quality by restoring the hydrologic cycle altered by development. Often, SCMs have an outflow pipe to handle overflows or to manage the release of water detained when infiltration is not possible. Traditionally, these are static controls (e.g. a small orifice is used to restrict the volume of outflow), however, these systems can be improved by instituting real-time controls (RTC). RTC improve the functionality of SCMs by dynamically controlling outflows to adjust to environmental conditions. A major impediment to the widespread implementation of RTC is the high cost of installation and operation. This study utilized machine learning methods to develop a forecasting approach for the implementation of low-cost RTC that were implemented on a programmable gate of the outlet structure of a multi-stage basin in southeastern Pennsylvania. The goals were to decrease the peak flow exiting the basin during rain events, increase the volume of water detained, decrease the number of overtopping events, maintain healthy vegetation in the basin, and protect the downstream vegetation from erosion. Multiple popular data science algorithms were evaluated including multiple linear regression and long short-term memory. These algorithms were used with a dataset, which consisted of four years of historical sensor data, collected in 5-minute intervals, to train models to predict water levels to optimize operations. The accuracy of 30 models with three different methods of handling missing values were compared. A long short-term memory model configured with a 30-minute lead time produced the best results. Having an approximate same lag time of 30 minutes for the contributing drainage area of the SCM provided a sufficient RTC functioning period to improve the performance of the outlet structure.

Fusarium head blight (FHB) is a wheat disease caused by fungi of the genus Fusarium. The aim of the study was to find relationships between the weather conditions in the experimental years and the locations and the amount of F. culmorum DNA and trichothecene genotypes, as well as the proportions between them. A three-year field experiment (2017, 2018 and 2019) was established at two locations (Poznań, Radzików). F. culmorum DNA was detected in all grain samples in an average amount of 20124 pg per 1 g of wheat DNA. The average amount of DNA from the 3ADON genotype was 4879 pg/μg and the amount of DNA from the NIV genotype was 3330 pg/μg. In the three experimental years, a large variability was observed in the coefficients of correlation between DNA concentrations and the FHB index, FDK, ergosterol, and the corresponding toxins. There were significant correlations between disease incidence, fungal biomass (quantified as the total amount of fungal DNA or DNA trichothecene genotypes) and toxins (DON, 3AcDON and NIV) concentrations. The 3ADON trichothecene genotype dominates over the NIV genotype (ratio 1.5); however, this varied greatly depending on environmental conditions.

Classical and optimal control architectures for motion mechanics with fusion of noisy sensors use different algorithms and calculations to perform and control any number of physical demands, to varying degrees of accuracy, precision, and cost. Their performances are tested for the purpose of comparison through the means of a Monte Carlo simulation that simulates how different parameters might vary under noise, representing real-world imperfect sensors. We find that improvements in one figure of merit often come at a cost in the performance in the others, especially depending on the presence of noise in the system sensors. If sensor noise is negligible, open-loop optimal control performs the best. However, in the overpowering presence of sensor noise, using a control law inversion patching filter performs as the best replacement, but has significant computational strain.

Considering the significant advancements in autonomous vehicle technology, research in this field is of interest to researchers. To drive vehicles autonomously, controlling steer angle, gas hatch, and brakes need to be learned. The behavioral cloning method is used to imitate humans’ driving behavior. We created a dataset of driving in different routes and conditions and using the designed model, the output used for controlling the vehicle is obtained. In this paper, the Learning of Self-driving Vehicles Based on Real Driving Behavior Using Deep Neural Network Techniques (LSV-DNN) is proposed. We designed a convolutional network which uses the real driving data obtained through the vehicle’s camera and computer. The response of the driver is during driving is recorded in different situations and by converting the real driver’s driving video to images and transferring the data to an excel file, obstacle detection is carried out with the best accuracy and speed using the Yolo algorithm version 3. This way, the network learns the response of the driver to obstacles in different locations and the network is trained with the Yolo algorithm version 3 and the output of obstacle detection. Then, it outputs the steer angle and amount of brake, gas, and vehicle acceleration. The LSV-DNN is evaluated here via extensive simulations carried out in Python and TensorFlow environment. We evaluated the network error using the loss function. By comparing other methods which were conducted on the simulator’s data, we obtained good performance results for the designed network on the data from KITTI benchmark, the data collected using a private vehicle, and the data we collected.

In cognitive radio systems, identifying spectrum opportunities is fundamental to efficiently use the spectrum. Spectrum occupancy prediction is a convenient way of revealing opportunities based on previous occupancies. Studies have demonstrated that usage of the spectrum has a high correlation over multidimensions which includes time, frequency, and space. Accordingly, recent literature uses tensor-based methods to exploit the multidimensional spectrum correlation. However, these methods share two main drawbacks. First, they are computationally complex. Second, they need to re-train the overall model when no information is received from any base station for any reason. Different than the existing works, this paper proposes a method for dividing the multidimensional correlation exploitation problem into a set of smaller sub-problems. This division is achieved through composite two-dimensional (2D)-long short-term memory (LSTM) models. Extensive experimental results reveal a high detection performance with more robustness and less complexity attained by the proposed method. The real-world measurements provided by one of the leading mobile network operators in Turkey validate these results.

This study aims to explore the effect of real-time visual feedback (VF) information of the pres-sure of center (COP) provided by intelligent insoles on balance training in a one leg stance (OLS) and tandem stance (TS) posture. Thirty healthy female college students were randomly assigned to the visual feedback balance training group (VFT), non-visual feedback balance training group (NVFT), and control group (CG). The balance training includes: OLS, tandem Stance (dominant leg behind, TSDL), tandem stance (non-dominant leg behind, TSNDL). The training lasted 4 weeks, the training lasts 30 minutes at an interval of 1 days. There was a sig-nificant difference in the interaction effect between Groups*Times of the COP parameters (p<0.05) for OLS. There was no significant difference in the interaction effect between Groups*Times of the COP parameters (p>0.05) for TS. The main effect of the COP parameters was a significant difference in Times (p<0.05). The COP displacement, velocity, radius, and area in VFT significantly decreased after training (p < 0.05). Therefore, the visual feedback technology of intelligent auxiliary equipment during balance training can enhance the benefit of training. The use of smart wearable devices in OLS balance training may improve the visual and physical balance integration ability.

Alnus glutinosa is important woody plant in Lithuanian forest ecosystems. Knowledge of fluorescence properties of black alder pollen is necessary for scientific and practical purposes. By the results of the study we aimed to evaluate possibilities of identifying Alnus glutinosa pollen fluorescence properties by modeling ozone effect and applying two different fluorescence-based devices. To implement experiments, black alder pollen was collected in a typical habitat during the annual flowering period in 2018-2019. There were three groups of experimental variants, which differed in the duration of exposure to ozone, conditions of pollen storage before the start of the experiment, and the experiment start time. Data for pollen fluorescence analysis were collected using two methods. The microscopy method was used in order to evaluate the possibility of employing image analysis systems for investigation of pollen fluorescence. The second data collection method is related to the automatic device identifying pollen in real-time, which uses the fluorescence method in the pollen recognition process. Data were assessed employing image analysis and principal component analysis (PCA) methods. Digital images of ozone-exposed pollen observed under the fluorescence microscope showed the change of the dominant green colour towards the blue spectrum. Meanwhile, the automatic detector detects more pollen whose fluorescence is at the blue light spectrum. It must be noted that assessing pollen fluorescence several months after exposure to ozone, no effect of ozone on fluorescence remains.

Fast 3D reconstruction with semantic information on road scenes is of great requirements for autonomous navigation. It involves issues of geometry and appearance in the field of computer vision. In this work, we propose a method of fast 3D semantic mapping based on the monocular vision. At present, due to the inexpensive price and easy installation, monocular cameras are widely equipped on recent vehicles for the advanced driver assistance and it is possible to acquire semantic information and 3D map. The monocular visual sequence is used to estimate the camera pose, calculate the depth, predict the semantic segmentation, and finally realize the 3D semantic mapping by combination of the techniques of localization, mapping and scene parsing. Our method recovers the 3D semantic mapping by incrementally transferring 2D semantic information to 3D point cloud. And a global optimization is explored to improve the accuracy of the semantic mapping in light of the spatial consistency. In our framework, there is no need to make semantic inference on each frame of the sequence, since the mesh data with semantic information is corresponding to sparse reference frames. It saves amounts of the computational cost and allows our mapping system to perform online. We evaluate the system on naturalistic road scenes, e.g., KITTI and observe a significant speed-up in the inference stage by labeling on the mesh.

In factory automation systems, hybrid wired/wireless networks are often deployed to connect devices of difficult reachability such as those mounted on mobile equipment. A widespread version of these networks makes use of Access Points (APs) to implement wireless extensions of Real--Time Ethernet (RTE) networks via the IEEE 802.11 Wireless LAN (WLAN). Unfortunately, APs may introduce random delays in packet forwarding, mainly related to the their internal behaviors (e.g. code processing times) that negatively impact on the whole performance of the automation systems. Consequently, the knowledge of these delays represent a crucial design information. This paper presents an original and effective method to measure the delays introduced by APs, exploiting a hybrid loop-back link and a simple set-up with moderate instrumentation requirements. The method, which requires an initial calibration by means of a reference AP, has been successfully tested on some commercial APs. As it will be shown, the proposed measurement procedure is general and, as such, can be profitably adopted in even different scenarios.

A programmable DC power supply with Real-time Digital Simulator (RTDS)-based photovoltaic (PV) Power Hardware-In-the-Loop (PHIL) simulators have been used to improve the control algorithm and reliability of PV Inverter. This paper proposes a supervisory control algorithm for PV PHIL simulator with non-RTDS device that is an alternative solution of high cost PHIL simulator. However, when such a simulator with conventional algorithm which is used in RTDS is connected to a PV inverter, the output is in the transient state and it makes it impossible to evaluate the performance of the PV Inverter. Therefore proposed algorithm controls the voltage and current target values according to the constant voltage (CV) and constant current (CC) modes to overcome the limitation of the Computing Unit, DC power supply and also uses a multi-rate system to account for the characteristics of each component of simulator. A mathematical models of a PV system, programmable DC power supply, isolated DC measurement device and Computing Unit are integrated to form a real-time processing simulator. Performance tests using a PV PHIL simulator which is applied proposed algorithm connected a PV inverter are carried out and proved superiority and utility of this method against conventional methods.

Objective: To perform a systematic review of real-world outcomes for anti-CGRP-mAbs. Methods: Following the PRISMA guidelines, we searched PubMed for real-world data of Erenumab, Galcanezumab, Fremanezumab, or Eptinezumab in patients with migraine. Results: We identified 104 publications (73 retrospective), comprising 8 pharmaco-epidemiologic and 63 clinic-based studies, 30 case reports and 3 other articles. None of the clinic-based studies provided follow-up data over more than one year in more than 200 patients. Findings suggest reductions in health insurance claims and days with sick-leave as well as better treatment adherence with anti-CGRP-mAbss. Effectiveness, reported in 59 clinic-based studies, was comparable to randomized controlled trials. A treatment pause was associated with an increase in migraine frequency and switching to another antibody resulted in a better response in some of the patients. Adverse events and safety issues were addressed in 70 papers including 22 single case reports. Conclusion: Real-world data on anti-CGRP-mAbs are limited by retrospective data collection, small patient numbers and short follow-up periods. The majority of papers seem to support good effectiveness and tolerability of anti-CGRP-mAbs in the real-world setting. There is an unmet need for large prospective real-world studies providing long-term follow-up of patients treated with anti-CGRP-mAbs.

Objectives: Human herpes viruses can cause life-threatening diseases in immunocompromised children, especially leukemic patients. Therefore, the aim of this study is to detect the human herpes viruses (HHV1-7) and to investigate its clinical significance in Middle Eastern Pediatric Leukemia Patients by using 2 Independent PCR assays. Methods: Detection of human herpes virus DNA has been done in blood samples of 200 pediatric leukemia patients in addition to 90 blood donors as a control group using multiplex PCR assays. When a ‘‘positive’’ result was observed, real-time PCR was performed to measure the viral load. Results: The most frequent herpes virus infection in Middle Eastern Pediatric Leukemia cases was CMV, followed by EBV, then HHV6, VZV, HHV7, HSV1, and HSV2, where they were 92/200 (46%), 76/200 (38%), 72/200 (36%), 48/200 (24%), 12/200 (6%), 8/200 (4%), and 2/200 (1%) respectively. Also, there was a statistically significance difference between leukemic patients and their controls regarding CMV, EBV, HHV6, and VZV (P <0.05). Correlation between percentage of co-infection, and clinical parameters for the 7 herpes viruses has been studied, and there is an increase in absolute neutrophilic count (ANC), total leukocyte count (TLC) and duration of fever and neutropenia in age group 6-11 years for HHV6/CMV, then in age group 12-18 years especially for EBV/CMV and CMV/HHV6. Also, our results show that multiplex PCR assay is close to single PCR assay in relation to specificity and sensitivity which in turn prove its validity for early diagnosis of herpes viral infection. Conclusions: Adopting multiplex PCR technique is helpful in screening of virus infections. It will save time, effort, cost effective and will assist in rapid diagnosis. However, the clinical relevance of the virus infection needs to be evaluated by quantitative real-time PCR which in turn will help patient's management by using appropriate antiviral treatment.

African swine fever (ASF) is one of the major threats to pig production, and real-time PCR (qPCR) protocols are integral part of ASF laboratory diagnosis. With the pandemic spread of ASF, commercial kits have risen on the market. In Germany, the kits have to go through an approval process and thus, general validation can be assumed. However, they were never compared to each other. In this study, 12 commercial PCR kits were compared to an OIE recommended method. Samples representing different matrices, genome loads, and genotypes were included in a panel that was tested under diagnostic conditions. The comparison included user-friendliness, internal controls, and the time required. All qPCRs were able to detect ASFV genome in different matrices across all genotypes and disease courses. With one exception, there were no significant differences when comparing the overall mean. The overall specificity was 100 % [95 % CI 87.66 - 100], and the sensitivity was between 95 % and 100 % [95 % CI 91.11 - 100]. As can be expected, variability concerned samples with low genome load. Concluding, all tests were fit for purpose. The test system can therefore be chosen based on compatibility and prioritization of the internal control system.

Private vehicle travel is the most basic mode of transportation, and the effective control of the real-world fuel consumption rate of light-duty vehicles plays a vital role in promoting sustainable economic development as well as achieving a green low-carbon society. Therefore, the impact factors of individual carbon emission must be elucidated. This study builds five different models to estimate real-world fuel consumption rate of light-duty vehicles in China. The results reveal that the Light Gradient Boosting Machine (LightGBM) model performs better than the linear regression, Naïve Bayes regression, Neural Network regression, and Decision Tree regression models, with mean absolute error of 0.911 L/100 km, mean absolute percentage error of 10.4%, mean square error of 1.536, and R squared (R2) of 0.642. This study also assesses a large number of factors, from which three most important factors are extracted, namely, reference fuel consumption rate value, engine power and light-duty vehicle brand. Furthermore, a comparative analysis reveals that the vehicle factors with greater impact on real-world fuel consumption rate are vehicle brand, engine power, and engine displacement. Average air pressure, average temperature, and sunshine time are the three most important climate factors.

Wearable cardiac sensors pave the way to advanced cardiac monitoring applications based on heart rate variability (HRV). In real-life settings, heart rate (HR) measurements are subject to mo-tion artifacts that can be timely removed from the recordings. This leads to frequent data loss in the HR signal, especially for commercial devices based on photoplethysmography (PPG). The cur-rent study had two main goals: (i) to provide a white-box quality index that estimates the amount of missing samples in any piece of HR signal; and (ii) to quantify the impact of data loss on feature extraction in a PPG-based HR signal. This was done by comparing real-life recordings from com-mercial sensors featuring both PPG (Empatica E4) and ECG (Zephyr BioHarness 3). After an out-lier rejection process, our quality index was used to isolate portions of ECG-based HR signal that could be used as benchmark, to validate the output of Empatica E4 at the signal level and at the feature level. Our results showed high accuracy for estimating the mean HR, poor accuracy for short-term HRV features and moderate accuracy for longer-term HRV features. Levels of error could be substantially reduced by using our quality index to identify time windows with few or no missing data.

The deterioration of gait can be used as a biomarker for ageing and neurological diseases. Continuous gait monitoring and analysis are essential for early deficit detection and personalized rehabilitation. The use of mobile and wearable inertial sensor systems for gait monitoring and analysis have been well explored with promising results in the literature. However, most of these studies focus on the technologies for gait characteristic assessment, few of them have considered the data acquisition bandwidth of the sensing system. Inadequate sampling frequency will sacrifice signal fidelity, thus leading to an inaccurate estimation especially for spatial gait parameters. In this work, we developed an inertial sensor based in-shoe gait analysis system for real-time gait monitoring and investigated the optimal sampling frequency to capture all the information on walking patterns. An exploratory validation study was performed using an optical motion capture system on four healthy adult subjects, where each person underwent five walking sessions, giving a total of 20 sessions. Percentage mean absolute errors (MAE%) obtained in stride time, stride length, stride velocity, and cadence while walking were 1.19%, 1.68%, 2.08%, and 1.23%, respectively. In addition, an eigen-analysis based graphical descriptor from raw gait cycle signals was proposed as a new gait metric that can be quantified by principal component analysis to differentiate gait patterns, which has great potential to be used as a powerful analytical tool for gait disorder diagnostics.

This study reports the likely real-world effects of fleet replacement with electric vehicles (EVs) and higher efficiency EURO6 vehicles on the exhaust emissions of NOx, PM, and CO2 in the seven boroughs of the West Midlands (WM) region, UK. National fleet composition data, local EURO distributions and traffic compositions were used to project vehicle fleet compositions for different roads in each borough. A large dataset of real-world emission factors including over 90,000 remote-sensing measurements, obtained from remote sensing campaigns in five UK cities, was used to parameterise the emission profiles of the studied scenarios. Results show that adoption of the fleet electrification approach would have the highest emission reduction potential on urban roads in WM boroughs. It would result in maximum reductions ranging from 35.0-37.9%, 44.3-48.3%, 46.9-50.3% for NOx, PM, and CO2, respectively. In comparison, the EURO6 replacement fleet scenario would lead to reductions ranging from 10.0-10.4%, 4.0-4.2%, and 6.0-6.4% for NOx, PM, and CO2, respectively. The studied mitigation scenarios have higher efficacies on motorways than on rural and urban roads because of the differences in traffic fleet composition. The findings presented will help policymakers choose climate and air quality mitigation strategies.

Urban accessibility represents one of the great challenges of the contemporary city, which is required to adopt sustainable development models in line with the UN Agenda 2030 objectives, recently confirmed by the health emergency. Urban accessibility and walkability are topics closely related to those aiming at a livable, healthy and inclusive city, based on a system of high-quality public spaces and on a network of services and infrastructures. However, these principles collide with the fragmentation of many urban contexts, built following vehicular accessibility needs. Within this framework, the city of Cagliari represents an interesting case study as it is affected by the disposal of public properties which appear as “enclaves” in the historic urban fabric. This research aims to evaluate if and in which terms the abandoned assets can facilitate the development of the 15-minutes city, as a city reducing the need to move over a certain time and space and therefore granting a more equal access to urban services to a wide range of citizens. This is done by proposing indexes defined as porosity, crossing and attractiveness, which constitute a combined index to improve the pedestrian accessibility in the “central places” of the contemporary city, where the walkability can also become a possible “free choice” for a new healthy lifestyle. These indexes were calculated for the most significant large disused public buildings in the historic center to guide future scenarios towards a 15 minutes city.

Background. To determine the effectiveness and safety of meropenem in routine clinical practice, in terms of clinical and microbiological response. Methods. A real-world, observational, descriptive, longitudinal study with daily monitoring of clinical history records was conducted on all patients who were medically prescribed meropenem during a period between October 2015 and March 2016 at a university hospital in Bucaramanga (Colombia). Results. The study evaluated 84 patients with an average age of 63.2 years, mostly older adults with multiple comorbidities, of whom 54.8% were men. A positive clinical or microbiological response was obtained in 98.8% of the patients. At the end of the treatments, significant improvements in dysthermia (0% vs 29% at the beginning, p = 0.000), tachycardia (13% vs 47%, p = 0.049) and leukocytosis (39% vs 15% at the beginning, p = 0.008) were evidenced. The improvement in the indicator that combines all the SIRS criteria was also significant (p = 0.000). The treatment was well tolerated, although we identified some non-serious and expected adverse reactions. Conclusions. Generic meropenem proved to be effective and well tolerated for different types of infection in routine clinical practice. The results are consistent with the findings of the clinical studies with the innovator drug.

Previously, various techniques have been proposed for reducing the lift-off effect on the thickness measurement of the non-magnetic films, including the peak-frequency feature and phase feature in the Dodd-Deed analytical formulation. To realise a real-time feedback response on the thickness monitoring, the phase term in the Dodd-Deeds formulation must be taken off the integration. Previous methods were based on the slow change rate of the phase term when compared to the rest of the term – the magnitude term. However, the change rate of the phase term is still considerable for a range of working frequencies. In this paper, a high-frequency feature has been found. That is, the ratio between the imaginary and real part of the phase term is proportional to the integral variable under high frequencies. Based on this proportion relationship, the phase term has been taken out; and a thickness algorithm has been proposed. By combing the measured impedance from the custom-built sensor (three coils), the thickness of the metallic film can be reconstructed. Experiments have been carried out for the verification of the proposed scenario. Results show that the thickness of the metal film can be reconstructed with a small error of less than 2 %, and immune to a reasonable range of lift-offs.

A 'little brother' of pain, itch is an unpleasant sensation that creates a specific urge to scratch. To date, various machine-learning based image classifiers (MBICs) have been proposed for quantitative analysis of itch-induced scratch behaviour of laboratory animals in an automated, non-invasive, inexpensive and real-time manner. In spite of MBICs' advantages, the overall performances (accuracy, sensitivity and specificity) of current MBIC approaches remains inconsistent, with their values varying from ~50% to ~99%, for which the reasons underlying have yet to be investigated further, both computationally and experimentally. To look into the variation of the performance of MBICs in automated detection of itch-induced scratch, this article focuses on the experimental data recording step, and reports here for the first time that MBICs' overall performance is inextricably linked to the sharpness of experimentally recorded video of laboratory animal scratch behaviour. This article furthermore demonstrates for the first time that a linearly correlated relationship exists between video sharpness and overall performance (accuracy and specificity, but not sensitivity) of MBICs, and highlight the primary role of experimental data recording in rapid, accurate and consistent quantitative assessment of laboratory animal itch.

Millimeter wave wide-band imaging is widely studied for a variety of applications. However real-time millimeter wave wide-band imaging at frequencies above 30GHz for moving targets in a large field of view has not been realized commercially. A 2D sparse array with transmitter multiplexing is a promising solution to this problem. In this article, a method combining compressed sensing and orthogonal coded multiplexing was proposed, and the imaging performance was analyzed for different reconstruction algorithms and observation matrices by imaging simulation for a continuous object. Also the influence on the dynamic range of the original signal introduced by orthogonal coded multiplexing was studied. This work demonstrated that the proposed method was effective in reconstructing the image with a real-time capability. It is shown that different algorithms and matrices resulted in distinct performances, while the evaluation parameter selection also played a role. This work provided useful instructions for both the hardware and software design of a real-time 3D millimeter wave imaging system in the future.

BACKGROUND Epidemiological sciences have been evolving at an exponential rate paralleled only by the comparable growth within the discipline of data science. Digital epidemiological studies are playing a vital role in medical science analytics for the past few decades. To date, there are no published attempts at deploying the use of real-time analytics in connection with the disciplines of Dentistry or Medicine. AIMS AND OBJECTIVES We deployed a real-time statistical analysis in connection with topics in Dental Anatomy and Dental Pathology represented by the maxillary sinus, posterior maxillary teeth, related oral pathology. The purpose is to infer the digital epidemiology based on a continuous stream of raw data retrieved from Google Trends database. MATERIALS AND METHODS Statistical analysis was carried out via Microsoft Excel 2016 and SPSS version 24. Google Trends database was used to retrieve data for digital epidemiology. Real-time analytics and the statistical inference were based on encoding a programming script using Python high-level programming language. A systematic review of the literature was carried out via PubMed-NCBI, the Cochrane Library, and Elsevier databases. RESULTS The comprehensive review of databases of the literature, based on specific keywords search, yielded 491813 published studies. These were distributed as 488884 (PubMed-NCBI), 1611 (the Cochrane Library), and 1318 (Elsevier). However, there was no single study attempting real-time analytics. Nevertheless, we succeeded in achieving an automated real-time stream of data accompanied by a statistical inference based on data extrapolated from Google Trends. CONCLUSION Real-time analytics are of considerable impact when implemented in biological and life sciences as they will tremendously reduce the required resources for research. Predictive analytics, based on artificial neural networks and machine learning algorithms, can be the next step to be deployed in continuation of the real-time systems to prognosticate changes in the temporal trends and the digital epidemiology of phenomena of interest.

This paper investigates the impact of price and real exchange rate volatility on Foreign Direct Investment (FDI) inflows in a panel of 10 Latin American and Caribbean countries, observed between 1990 and 2012. Both price and exchange rate volatility series are estimated through the Generalized Autoregressive Conditional Heteroscedasticity model (GARCH). Our results, obtained employing the Fixed Effects estimator, confirm the theory of hysteresis and option value, in so far it is found a statistically significant negative effect of exchange rate volatility on FDI. Price volatility, instead, turns out to be positive but insignificant. Moreover, we show that human capital and trade openness are key for attracting foreign capital. From the policy perspective, our analysis suggests the importance of stabilization policies as well as the one of government credibility in promoting trade openness and human capital formation.

In this article, we introduce the concept of soft intervals, soft ordering and sequences of soft real numbers, and some of their structural properties are studied. The notion of soft Lebesgue measure on the soft real numbers has been introduced. Also, a correspondence relationship has been established between the soft Lebesgue measure and the classical Lebesgue measure. Furthermore, we have studied some exciting results and relations between the soft Lebesgue measure and the Lebesgue measure of soft real sets.

Purpose: To meet the challenges of the precision medicine era, quality assessment of shared sarcoma care becomes pivotal. The MDT approach is the most important parameter for succesfull outcome. Because of all MDTs disciplines surgery is the key step to render sarcoma patients disease free, defining the spectrum of a sarcoma surgeon is critical. To the best of the authors knowledge, a comprehensive interoperable digital platform to assess the scope of sarcoma surgery and the experience of a sarcoma surgeon in its full complexity is lacking. Methods: A web-based real-world data (RWD) registry on sarcoma surgery has been created to assess the clinical exposure, tumor characteristics, and surgical settings and techniques applied for both resections and reconstructions of sarcomas and thereby the surgical exposure of an individual surgeon over time. Results: During 10 years, there were 723 sarcoma board/MDT meetings discussing 3130 patients. A total of 1094 patients underwent 1250 surgical interventions on mesenchymal tumors by one single sarcoma surgeon. These included 615 deep soft tissue tumors (197 benign, 102 intermediate, 281 malignant, 27 simulator, 7 metastasis, 1 blood), 116 superficial soft tissue tumors (45 benign, 12 intermediate, 40 malignant, 18 simulator, 1 blood) and 519 bone tumors (129 benign, 112 intermediate, 182 malignant, 18 simulator, 46 metastasis, 14 blood and 18 sequelae of 1st treatment). Detailed types of resections and reconstructions were analyzed. Conclusion: A web-based RWD sarcoma surgeon registry with transparent real-time descriptive analytics is feasible and enables large scale definition of the surgical complexity and ultimately quality of sarcoma care.

Dielectric spectroscopy (DS) is a promising cell screening method that can be used for diagnostic and drug discovery purposes. The primary challenge of using DS in physiological buffers is the electrode polarization (EP) that overwhelms the impedance signal within a large frequency range. These effects further amplify with miniaturization of the measurement electrodes. In this study, we present a microfluidic system and the associated equivalent circuit models for real-time measurements of cell membrane capacitance and cytoplasm resistance in physiological buffers with 10s increments. The current device captures several hundreds of biological cells in individual microwells through gravitational settling and measures the system’s impedance using microelectrodes covered with dendritic gold nanostructures. Using PC-3 cells (a highly metastatic prostate cancer cell line) suspended in cell growth media (CGM), we demonstrate stable measurements of cell membrane capacitance and cytoplasm resistance in the device for over 15 minutes. We also describe a consistent application of the equivalent circuit model, starting from the reference measurements used to determine the system parameters. The circuit model is tested using devices with varying dimensions, and the obtained cell parameters between different devices are nearly identical. Further analyses of the impedance data have shown that accurate cell membrane capacitance and cytoplasm resistance can be extracted using a limited number of measurements in the 5 MHz to 10 MHz range. This will potentially reduce the timescale required for real-time DS measurements below 1s. Overall the new microfluidic device can be used for dielectric characterization of biological cells in physiological buffers for various cell screening applications.

Viral entry is the first stage in the virus replication cycle and, for enveloped viruses, is mediated by virally encoded glycoproteins. Viral glycoproteins have different receptor affinities and triggering mechanisms. We employed vesicular stomatitis virus (VSV), a BSL-2 enveloped virus that can incorporate non-native glycoproteins, to examine the entry efficiencies of diverse viral glycoproteins. To compare glycoprotein-mediated entry efficiencies of: VSV G, SARS-CoV-2 S, EBOV GP, LASV GP, and CHIKV E we produced recombinant VSV (rVSV) viruses that produce the five glycoproteins. The rVSV virions encoded a nano luciferase-PEST (NLucP) reporter gene, which we used in combination with the live-cell substrate Endurazine™ to monitor viral entry kinetics in real time. Our data indicate that rVSV particles with glycoproteins that require more post-internalization priming typically demonstrate delayed entry in comparison to VSV G. In addition to determining the time required for each virus to complete entry, we also used our system to evaluate viral cell surface receptor preferences, monitor fusion, and elucidate endocytosis mechanisms. This system can be rapidly employed to examine diverse viral glycoproteins and their entry requirements.

Currently, entire world is crumbled due to COVID-19 caused by novel SARS-CoV-2. Globally, over 5 million people are infected by SARS-CoV-2 with 6% fatality rate. The surface spike (S) protein plays a key role in the pathogenesis of SARS-CoV-2 by mediating viral entry through human angiotensin converting enzyme 2 (hACE2) receptors on the host cell and there is a big global race to find viral neutralizing antibodies and vaccine against S protein of SARS-CoV-2. Since SARS-CoV-2 evolved into 10 different clades in a very short span, a study on sipke protein mutation is essential to have effective vaccine coverage globally. Based on the mutation analysis of S protein from 166 Indian SARS-CoV-2 genome, a total of 40 different SNPs comprising of 14 synonymous and 26 non-synonymous mutations were observed, and notably, Indian S protein diverged into two major clusters, D614 and G614, with 11 different types. Majority of Indian strains fall in A2a and O clusters. Alarmingly, we have observed six SNPs at RBD and notably two of them at RBM (S438F and S494P). S494P SNP, similar to Bat–SARS like-CoV, may indicate a low ACE2 binding affinity. Interestingly 38% of Indian strains harbor a characteristic D614G SNP which was found predominantly in A2a cluster, mostly comprising USA and European strains with high disease severity. The association of disease severity with D614G SNP is well-correlated in states with high death rate except Maharashtra. Notably, more than 50% of D614G mutation were observed in Northern part of India and 14% in Southern part but not in Kerala and Tamil Nadu strains. Highly conserved motif, D614 (608-VAVLYQDVNCT-618) in upstream and also few downstream, of S1/S2 furin cleavage site may indicate specific key role in efficient interaction with host proteases in pathogenesis. Further studies are warranted to clarify the impact of SD614G SNP association to disease severity . Interestingly, C2367T (Y789Y) synonymous SNP is observed in 37% of Indian strains and notably similar SNPs with degeneracy bases were observed which is a key indication for the possibility of misdiagnosis by Real-Time PCR and revised strategies are needed for the precise diagnosis. Circulation of high number of signature SNPs [D614G and C2367T (Y789Y)] in certain states may be an early indication of emergence of community transmission in India. Further large genome sequence data from India will aid in deep understanding on the diversity of circulating SASR-Cov-2 and its impact on disease severity, origin of imported cases to India, community spread, effect on diagnosis and vaccine coverage.

Scrub typhus is a life-threatening infectious disease and always creating a diagnostic dilemma in terms of rapid turnaround time and accuracy, qRT PCR can become a very good option to achieve the desired result with the molecular level of accuracy and boost up the rapid patient management. This study was performed to evaluate the performance of qRT PCR in comparison to commonly used IgM ELISA and Weil-Felix tests to diagnose scrub typhus, as well as to look for the demographic and clinical profile of the disease in North-East India. It was a hospital-based prospective study conducted in a tertiary care hospital of north-east India, over a period of 1 year, in which all the samples from suspected scrub typhus cases were screened by Weil-Felix test as per institute’s diagnostic protocol after which IgM ELISA for Scrub Typhus was performed. All the IgM positive samples and 20 highly suspected but ELISA negative samples were subjected to qRT PCR, targeting 56 kDa type-specific gene of O. tsutsugamushi. Statistical analysis was done by MS-Excel for Windows v2013® and MedCalc® v17.9 for Windows (MedCalc Software, Acacialaan 22, B-8400 Ostend, Belgium). In this study, we have successfully evaluated the performance of qRT PCR kit for diagnosis of scrub typhus. Out of 54 samples tested, 24 IgM ELISA positive samples and 3 IgM ELISA negative samples have shown the presence of bacterial DNA with quantification of DNA copies. It has also been observed that 21 out of 27 PCR positive samples (77.8%) were detected within the 1st 7 days of illness. All the demographic, as well as clinical data, were also analysed. The performance of the commercial qRT PCR kit used in our study is satisfactory, which provides the extra advantage of quantification of DNA copies and increases diagnostic accuracy within the 1st week of fever.

More than 179,000 individuals have fallen ill of the Coronavirus disease (COVID-19) caused by the SARS-CoV-2 virus, which first emerged in China less than four months ago in December 2019. As of today, there exist no approved treatments against COVID-19. Vaccines are being developed, but they will take time, at least one year, to reach the population. Drug repositioning represents a fast track because already approved medicines have been broadly tested through multiple trials. We developed a repositioning strategy that mostly leads to candidates that are commonly used. The advantages are that they will facilitate proof of concept in humans through a “real-world evidence” approach and should be rapidly available to the population. We focus on the established research results that the unfolded protein response (UPR) and autophagy pathways of the host cells are essential to the life cycle of previously known coronaviruses. We performed the relevant bioinformatics analysis to understand and confirm if SARS-CoV-2 may interact with these druggable pathways. Based on these considerations, we prioritized two additional druggable pathways, which are important to the viral life cycle and tightly connected to UPR/autophagy signaling: the mitochondrial permeability transition pores (MPTP) and NLRP-3 inflammasome pathways. These four important pathways are perturbed in most major common diseases and have therefore been targeted by numerous broadly prescribed drugs. We have identified 97 approved drugs that are known to modulate these four identified pathways, and which represent, therefore, interesting repositioning candidates. Although it is indisputable that these drugs should never be used for immediate self-medication against COVID-19, we notice that some of them could also be prescribed to individuals who have COVID-19 comorbidities (e.g., hypertension). It is debated if these comorbidities are linked to the pathology itself (e.g., hypertension) or the drugs used to treat the pathology (e.g., sartans). Therefore, relevant preclinical tests and massive electronic health records (i.e., real-world evidence) must be used to pre-screen them and check the COVID-19 prognosis of individuals taking these drugs.

We hypothesized possibility that inline registered reticulorumen pH can be as biomarker of cows reproduction and health status. Aim of this study was to evaluate the relationship of reticulorumen pH with biomarkers from automatic milking system (AMS) and some blood parameters and determinate reticulorumen pH as biomarker of cows reproduction and health status. According to cows reproductive status the cows were classified as belonging to the following four groups: 15-30 d. postpartum; 1-34 d. after insemination; 35 d. after insemination (non-pregnant); 35 d. after insemination (pregnant). According reticulorumen pH assay experimental animals were divided into four classes: 1) pH<6.22 (5.3% of cows), 2) pH - 6.22-6.42 (42.1% of cows), 3) pH - 6.42-6.62 (21.1% of cows), 4) pH >6.62 (10.5% of cows). Rumination time, body weight, milk yield, milk fat – protein ratio, milk lactose, milk somatic cell count (SCC), milk electrical conductivity of all quarters of udder were registered with the help of Lely Astronaut® A3 milking robots. The pH, temperature of the contents of cow reticulorumens and cow activity were measured using specific smaX-tec boluses. Blood gas parameters were analyzed using a blood gas analyzer (EPOC, Canada). We found that pregnant cows has higher reticulorumen pH during insemination time, comparing with non-pregnant. Cows with lower reticulorumen pH has lowest milk fat – protein ratio, and lactose concentration, and highest SCC. Cows with lowest reticulorumen pH has lowest blood pH. With increase reticulorumen pH, increases blood potasium and hematocrit, decreases CO2, saturation and sodium.

Scheduling of real time tasks are very important aspect in systems as processes should complete its task at a specific time. There is a need of high energy efficiency and low response time in large data stream so for this energy efficient resources and optimized frameworks are needed. Both hard real time and mixed critically systems are targeted. Soft deadline can be handled while hard deadlines are difficult to cater. Different algorithms are used to schedule tasks like rate monotonic, earliest deadline first, deadline monotonic etc.

A portable urea sensor for use in the fast flow condition was fabricated using porous polytetrafluoroethylene (PTFE) membranes coated with amine-functionalized parylene, parylene-A, by vapor deposition. To generate a specific electrochemical sensor signal from urea, the urea-hydrolyzing enzyme urease was immobilized on the parylene-A-coated PTFE membranes via chemical crosslinking using glutaraldehyde. The urease-immobilized membranes were assembled in a polydimethylsiloxane (PDMS) fluidic chamber, and a screen-printed carbon three-electrode system was used for electrochemical measurements. The success of urease immobilization was confirmed using fluorescence microscopy, scanning electron microscopy, and Fourier-transform infrared spectroscopy. The optimum concentration of urease for immobilization on the parylene-A-coated PTFE membranes was determined to be 48 mg/mL, and the optimum number of membranes in the PDMS chamber was found to be 8. Using these optimized conditions, we fabricated the urea biosensor and monitored urea samples under various flow rates ranging from 0.5 to 10 mL/min in the flow condition using chronoamperometry. To test the applicability of the sensor for physiological samples, we used it for monitoring urea concentration in the waste peritoneal dialysate of a patient with chronic renal failure, at a flow rate of 0.5 mL/min.

To elucidate dynamic developmental processes in plants, live tissues and organs have to be visualized frequently and for long time periods. The development of roots is studied in depth at a cellular resolution not only to comprehend the basic processes fundamental to maintenance and pattern formation but also study stress tolerance adaptation in plants. Despite technological advancements, maintaining continuous access to samples and simultaneously preserving their morphological structures and physiological conditions without causing damage presents hindrances in the measurement, visualization and analyses of growing organs including plant roots. We propose a preliminary system which integrates the optical real-time visualization through light microscopy with a liquid culture which enables us to image at the tissue and cellular level horizontally growing Brachypodium roots every few minutes and up to 24 hours. We describe a simple setup which can be used to track the growth of the root as it grows including the root tip growth and osmotic stress dynamics. We demonstrate the system’s capability to scale down the PEG-mediated osmotic stress analysis and collected data on gene expression under osmotic stress.

Autonomous navigation of spacecraft necessitates innovative technologies, methods, and algorithms, particularly when orbiting in proximity of other space objects. Optimization methods are useful for autonomous spacecraft navigation, guidance, and control, but their performance is hampered by noisy multi-sensor technologies and poorly modeled system equations, and real-time on-board utilization is generally computationally burdensome. Some proposed methods use noisy sensor data to learn the optimal guidance and control solutions real-time (online), where non-iterative instantiations are preferred to reduce computational burdens. This study aims to highlight efficacy and limitations of several common methods for optimizing guidance and control while proposing a few more, where all methods are applied to the full, nonlinear, coupled equations of motion including cross-products of motion from the transport theorem. Five disparate types of optimum guidance and control algorithms are presented and compared to a classical benchmark. Comparative analysis is based on tracking errors (both states and rates), fuel usage, and computational burden. Real-time optimalization with singular switching plus nonlinear transport theorem decoupling proves superior by matching open-loop solutions to the constrained optimization problem (in terms of state and rate errors and fuel usage), while robustness is validated in the utilization of mixed, noisy state and rate sensors and uniformly varying mass and mass moments of inertia. State tracking errors are reduced one-hundred ten percent. Rate tracking errors are reduced one-hundred thirteen percent. Control utilization (e.g., fuel) is reduced eighty four percent, while computational burden in reduced ten percent simultaneously.

A new treatment option for cluster headache (CH) prevention is needed. Monoclonal antibodies (mABs) against calcitonin gene-related peptide (CGRP) ligands are used as a preventative treatment for migraine. Considering the CGRP’s role in the CH attack’s ignition and upkeep, fremanezumab and galcanezumab have been evaluated for CH preventative treatment. However, only high-dose (300 mg) galcanezumab was proven for episodic CH prevention. We herein report 3 cases of migraine and comorbid CH with previous failures of preventive treatments. The 2 cases were treated with fremanezumab and the one with non-high-dose galcanezumab. All 3 cases showed good results not only on migraine but also on CH attacks. Our report suggested the efficacy of CGRP-mABs for CH prevention. Our cases differed from the cases in the phase 3 trials of CGRP-mABs for CH prevention in the following 2 points. First, the patients had both migraine and comorbid CH. Second, the combined use of CGRP-mABs with preventative drugs for CH, such as verapamil and/or prednisolone, was performed. Future accumulation of real-world data may prove the efficacy of CGRP-mABs for CH prevention.

Quantitative differential phase contrast (qDPC) imaging has become an important method of optical measurement and life science research in microscopy because of its high reconstruction resolution and non-invasive, high-contrast and quantitative imaging of biological samples. Despite the continuous development of the principle and algorithm, the frame rate of the existing qDPC algorithm is still much lower than that of camera acquisition, so it is hardly applied to real-time image the fast-moving biological samples. In this paper, based on color-coded multiplexing strategy, a compact real-time quantitative phase imaging system is designed to realize multi-mode imaging. The system employs a programmable LED array to illuminate directly, and the phase reconstruction algorithm is deployed in the graphics processing unit (GPU) of the laptop to accelerate the calculation. The system can achieve high-speed quantitative phase imaging of non-stained biological samples, and the frame rate can reach 60fps. The device has the advantages of compact structure, low cost and portability. Thus, it is suitable for mobile medical applications.

In the leading/trailing edge’s adaptive machining of the near-net-shaped blade, a small portion of the theoretical part is retained for securing aerodynamic performance by manual work. However, this procedure is time-consuming and depends on the human experience. In this paper, we defined retained theoretical leading/trailing edge as the reconstruction area. To accelerate the reconstruction process, an anchor-free neural network model based on Transformer was proposed, named LETR (Leading/trailing Edge Transformer). LETR extracts image features from an aspect of mixed frequency and channel domain. We also integrated LETR with the newest meta-Acon activation function. We tested our model on the self-made dataset LDEG2021 on a single GPU and got an mAP of 91.9\%, which surpassed our baseline model, Deformable DETR by 1.1\%. Furthermore, we modified LETR’s convolution layer and named the new model after GLETR (Ghost Leading/trailing Edge Transformer) as a lightweight model for real-time detection. It is proved that GLETR has fewer weight parameters and converges faster than LETR with an acceptable decrease in mAP (0.1\%) by test results.

Early diagnosis and treatment have always been highly desired in the fight against cancer, and detection of circulating tumor DNA (ctDNA) has recently been touted as highly promising for early cancer screening. Consequently, the detection of ctDNA in liquid biopsy gains much attention in the field of tumor diagnosis and treatment, which has also attracted research interest from the industry. However, traditional gene detection technology is difficult to achieve low cost, real-time and portable measurement of ctDNA. Electroanalytical biosensors have many unique advantages such as high sensitivity, high specificity, low cost and good portability. Therefore, this review aims to discuss the latest development of biosensors for minimal-invasive, rapid, and real-time ctDNA detection. Various ctDNA sensors are reviewed with respect to their choices of receptor probes, detection strategies and figures of merit. Aiming at the portable, real-time and non-destructive characteristics of biosensors, we analyze their development in the Internet of Things, point-of-care testing, big data and big health.