Miracle-dz/UniFAR-FTUs huggingface dataset:
Current use and future potential of (physiologically based) pharmacokinetic modelling of radiopharmaceuticals: a review

Rationale: Physiologically based pharmacokinetic (PBPK) and population pharmacokinetic (PK) modelling approaches are widely accepted in non-radiopharmaceutical drug development and research, while there is no major role for these approaches in radiopharmaceutical development yet. In this review, a literature search was performed to specify different research purposes and questions that have previously been answered using both PBPK and population PK modelling for radiopharmaceuticals. Methods: The literature search was performed using the databases PubMed and Embase. Wide search terms included radiopharmaceutical, tracer, radioactivity, physiologically based pharmacokinetic model, PBPK, population pharmacokinetic model and nonlinear mixed-effects model. Results: Eight articles and twenty articles were included for this review based on this literature search for population PK modelling and PBPK modelling, respectively. Included population PK analyses showed to have an added value to develop predictive models for a population and to describe individual variability sources. Main purposes of PBPK models appeared related to optimizing treatment (planning), or more specifically: to find the optimal combination of peptide amount and radioactivity, to optimize treatment planning by reducing the number of measurements, to individualize treatment, to get insights in differences between pre-therapeutic and therapeutic scans or to understand inter-patient differences. Other main research subjects were regarding radiopharmaceutical comparisons, selecting ligands based on their peptide characteristics and gaining a better understanding of drug-drug interactions. Conclusions: The use of PK modelling approaches in radiopharmaceutical research remains scarce, but can be expanded to obtain a better understanding of PK and whole-body distribution of radiopharmaceuticals in general. PK modelling of radiopharmaceuticals has great potential for the nearby future and could contribute to the evolving research of radiopharmaceuticals.
Data-dependent Haar-like transform for signal and image compression

Exploiting the geometrical interpretation of the Haar transform as a rotation of the coordinate system by an angle (pi) /4, a new Haar-like transform is proposed which rotates the co-ordinate system by an angle which depends on the data. This data dependent transform gives better compression ratios for both signals and images than the Haar transform.
FLOODWALL: A Real-Time Flash Flood Monitoring and Forecasting System Using IoT

In recent years, flooding has become a major problem, encountered in many places all over the world, causing damage to property ranging from human life to economic losses. Floods cannot be prevented and eliminated, but the catastrophic damage caused by them can be mitigated. Floods can be predicted in advance with the help of emerging technologies, such as the Internet of Things (IoT). Using such technologies, the people can be warned in advance and evacuated from affected areas to safe places, along with their valuable possessions. In this context, a real-time application is required that can provide an early flood warning based on the seamless data received from IoT devices about various parameters. In this work, we have developed an IoT-based prototype to collect hydrological data of rivers, such as water flow, water level, and water discharge. The proposed system is also able to collect meteorological data, such as temperature, humidity, wind speed, and wind direction. Furthermore, the collected data have been analyzed and classified by using the long short-term memory (LSTM) model with water discharge, water level, rainfall, and temperature as input parameters. The LSTM model then classified the flood events either into “no alert,” “yellow alert,” “orange alert,” or “red alert. ” As rivers have dynamic geographical features, measuring the total discharge of water becomes a complex task. To address this vulnerability, a novel approach for determination of water discharge using water flow, sectional area width, and sectional average depth is proposed. In addition, the difficulties in measuring the total amount of rainfall due to the erratic behavior of the weather conditions of the unique geographical location have been addressed. The system is found to accurately predict the flood event state with F1-score of 97% for “no alert,” 97% for “yellow alert,” 96% for “orange alert,” and 98% for “red alert. ”
Warp-knitted vamp fabric quality detection algorithm based on improved MatchNet

Abstract To solve the problem of low efficiency and high cost of conventional manual inspection of warp-knitted vamp fabrics, an algorithm for quantity statistics and quality inspection of warp-knitted vamps based on an improved MatchNet model is proposed. The noise in the warp-knitted vamp images was removed using median filtering and the filtering speed was accelerated using the mean division method. To extract the vamp image from the cloth image, an adaptive thresholding watershed algorithm was designed to remove the background texture information from the warp-knitted vamp and extract the vamp features. Based on the MatchNet model, the feature extraction network for warp-knitted vamps was redesigned and the model was optimised using the AdaDelta algorithm to improve the model training efficiency. A sampler was used to generate a sample set of warp-knitted vamp data with a balanced number of positive and negative samples to train the improved MatchNet model. A warp-knitted vamp fabric detection platform was built to verify the effectiveness and real-time performance of the improved algorithm. The MatchNet model and the improved MatchNet model algorithm were applied to three different vamps to perform comparison test verification. The results show that the average detection accuracy of the improved Match Net algorithm reached 97.7%, and the detection speed is improved by 3–4 times compared with that of the the conventional manual inspection method.
Peak detection in fiber Bragg grating using a fast phase correlation algorithm

Fiber Bragg grating sensing principle is based on the exact tracking of the peak wavelength location. Several peak detection techniques have already been proposed in literature. Among these, conventional peak detection (CPD) methods such as the maximum detection algorithm (MDA), do not achieve very high precision and accuracy, especially when the Signal to Noise Ratio (SNR) and the wavelength resolution are poor. On the other hand, recently proposed algorithms, like the cross-correlation demodulation algorithm (CCA), are more precise and accurate but require higher computational effort. To overcome these limitations, we developed a novel fast phase correlation algorithm (FPC) which performs as well as the CCA, being at the same time considerably faster. This paper presents the FPC technique and analyzes its performances for different SNR and wavelength resolutions. Using simulations and experiments, we compared the FPC with the MDA and CCA algorithms. The FPC detection capabilities were as precise and accurate as those of the CCA and considerably better than those of the CPD. The FPC computational time was up to 50 times lower than CCA, making the FPC a valid candidate for future implementation in real-time systems.
History-dependent attachment of Pseudomonas aeruginosa to solid–liquid interfaces and the dependence of the bacterial surface density on the residence time distribution

This study investigates how the recent history of bacteria affects their attachment to a solid–liquid interface. We compare the attachment from a flowing suspension of the bacterium, Pseudomonas aeruginosa PAO1, after one of two histories: (a) passage through a tube packed with glass beads or (b) passage through an empty tube. The glass beads were designed to increase the rate of bacterial interactions with solid–liquid surfaces prior to observation in a flow cell. Analysis of time-lapse microscopy of the bacteria in the flow cells shows that the residence time distribution and surface density of bacteria differ for these two histories. In particular, bacteria exiting the bead-filled tube, in contrast to those bacteria exiting the empty tube, are less likely to attach to the subsequent flow cell window and begin surface growth. In contrast, when we compared two histories defined by different lengths of tubing, there was no difference in either the mean residence time or the surface density. In order to provide a framework for understanding these results, we present a phenomenological model in which the rate of bacterial surface density growth, dN(t)/dt , depends on two terms. One term models the initial attachment of bacteria to a surface, and is proportional to the nonprocessive cumulative residence time distribution for bacteria that attach and detach from the surface without cell division. The second term for the rate is proportional to the bacterial surface density and models surface cell division. The model is in surprisingly good agreement with the data even though the surface growth process is a complex interplay between attachment/detachment at the solid–liquid interface and cell division on the surface.
Transfer function analysis of ultraviolet observations of NGC 5548

A general linear transfer function relating an input series (UV continuum flux) to an output series (emission-line flux) is presented. The methodology for fitting the transfer function is briefly described. The autocorrelation structure of IUE observations of NGC 5548 in different spectral bands is investigated, and appropriate univariate autoregressive moving average models given. The results of extensive transfer function fitting, using respectively the λλ 1337 and 1350 continuum variations as input series, are presented. There is little evidence for a dead time in the response of the emission-line variations, which are presumed to be driven by the continuum
Antimicrobial Susceptibility of Salmonella Isolated from Chickens and Humans in Wau, South Sudan

Background Salmonella infections are a public health problem across the globe. In South Sudan, there is little information regarding the prevalence and antibiotic resistance patterns of Salmonella. Therefore, this study assessed the prevalence and antimicrobial susceptibility of Salmonella isolates from chickens and humans in South Sudan. Fecal samples were collected and cultured on Xylose Lysine Deoxycholate Agar for the isolation of Salmonella and confirmed using biochemical tests and PCR through the amplification of the invA gene. A total of 417 fecal samples were examined, of which 270 (64.7%) were chicken cloacal swabs while 147 (35.3%) were humans' stool specimens. Results Eleven (11) Salmonella isolates were isolated from humans while nine were from chickens. All 11 isolates from humans were susceptible to sulfamethoxazole-trimethoprim, chloramphenicol, streptomycin, cefotaxime, nalidixic acid, and gentamicin. However, 4 (36.7%) isolates showed resistance to ciprofloxacin, 2 (18.9%) to ampicillin, and 1 (9.1%) to tetracycline. All chicken isolates were susceptible to chloramphenicol, streptomycin, sulfamethoxazole-trimethoprim, ciprofloxacin, cefotaxime, nalidixic acid, and gentamicin but showed resistance to tetracycline 2 (22.2%) and ampicillin 1 (11.1%). Conclusion Antimicrobial resistant isolates were isolated in both chickens and humans. Further, MDR isolates were found in both chicken and human samples, and this is a public health concern. This, therefore, calls for concerted efforts to educate producers and consumers on public health, food safety, food hygiene in food production, and enhancement of surveillance programmes on zoonotic bacteria and antimicrobial susceptibility.
Characteristics of Background Color Shifts Caused by Optical See-Through Head-Mounted Displays

Abstract Optical see-through head-mounted displays (OST-HMDs) have been increasingly used in many applications as Augmented Reality (AR) support devices. However, problems still exist that prevent their use as general-purpose devices. One of these issues is the color blending problem. This is the problem in which light from the background overlaps with light from the OST-HMD and shifts the color of OST-HMD’s light from its intended display intensity and color. Though color compensation methods exist, in order to properly compensate for light shifts, we need to know how the background color will affect the light that eventually hits the user’s eye when combined with the OST-HMD image. In this paper, we study how background colors shift as a result of passing through the OST-HMD’s optics in order to better inform the development of color compensation methods. We measured the background color objectively for three off-the-shelf OST-HMDs and evaluated results. We found that all three OST-HMDs shift background color to a perceptible degree and that the degree of shift depends on the original background color. We also investigated how the degree of shift differs between different areas on the OST-HMD screens and from different measuring angles. The results showed that the background color shift depends on both the area and angle measured for some OST-HMDs
Knowledge-Driven Automatic Solution for Service Composition based on ELECTRE III and Quality-Aware Service Selection for the Wildfire Prediction

Wildfire prediction gained much attention in recent years, but, it remains a computational challenge, since it requires the collection of several environmental parameters in real time, from diverse environmental data sources. Moreover, since the globalization of environmental Web Services accessing the environmental data sources, services can be functionally similar, but with different qualities of services. Therefore, environmental data sources compete to provide these functionally similar services with different levels of qualities re-lated to the services (QoS) and also to the data sources and their related data (QoDS). Furthermore, automating and dynamically composing services, while taking prior domain knowledge into account remains an ongoing challenge, since the service composition process is data-driven in the context of wildfire prediction. Subsequently, we present an approach to fulfill the requirement of ranking and selecting the optimal services based on the data received from previous services calls and their related quality dimensions. Our contribution is an automatic, knowledge-driven approach that relies on the ELECTRE III
Elaboration and Characterization of Natural Deep Eutectic Solvents (NADESs): Application in the Extraction of Phenolic Compounds from pitaya

In this paper, natural deep eutectic solvents (NADESs) with lactic acid, glycine, ammonium acetate, sodium acetate, and choline chloride were prepared with and without the addition of water. NADES formation was evaluated using FTIR and Raman, where hydrogen bonds were identified between the hydroxyl group of lactic acid and the amino and carboxyl groups of glycine. Acetate and ammonium ions were also identified as forming bonds with lactic acid. The addition of water did not cause changes in the vibrational modes of the FTIR and Raman spectra but contributed to a reduction in NADES viscosity and density. Viscosity ranged from 0.335 to 0.017 Pa s−1, and density ranged from 1.159 to 0.785 g mL−1. The best results for the extraction of phenolic compounds from pitaya (dragon fruit) were achieved with an organic solvent (450. 41 mg 100 g−1 dry bases-db) in comparison to NADESs lactic acid:glycine (193.18 mg 100 g−1 db) and lactic acid:ammonium acetate (186.08 mg 100 g−1 db). The antioxidant activity of the extracts obtained with the NADESs was not statistically different from that of the extract obtained with organic solvents.
The Role of Intermolecular Interaction on Aggregation-Induced Emission Phenomenon and OLED Performance

In this work, the role of intermolecular interaction on the aggregation-induced emission (AIE) phenomenon and organic light-emitting diodes’ (OLEDs) performance was investigated. During the research, a group of compounds consisting of the (-CH=C(CN)(COOR)) moiety with identical energy parameters was designed using the DFT approach and successfully synthesized. The optical, electrochemical, and aggregation-induced emission properties were studied. The aggregation-induced emission of compounds has been studied in the mixture of MeCN (as a good solvent) and water (as a poor solvent) with different water fractions ranging from 0% to 99%. Moreover, the time dependence on the AIE feature was also evaluated. Thanks to their molecular structures, almost identical behavior of these substances in dilute solutions was observed. For molecules that exhibit the strong AIE phenomenon, emission efficiency increases rapidly during aggregation. What is also very interesting is it has been shown that by introducing an appropriate substituent, one can control the degree of intermolecular interactions and “control” the length of the emitted wave. Finally, OLEDs were fabricated by the spin-coating/evaporation hybrid method. Devices showed green–blueish electroluminescence (CIE coordinates: 0.107, 0.165) with maximum luminance reaching 25 cd m−2 and EQE reaching 2%.
S POTTING E XPRESSIVITY B OTTLENECKS AND F IXING T HEM O PTIMALLY

Machine learning tasks are generally formulated as optimization problems, where one searches for an optimal function within a certain functional space. In practice, parameterized functional spaces are considered, in order to be able to perform gradient descent. Typically, a neural network architecture is chosen and fixed, and its parameters (connection weights) are optimized, yielding an architecturedependent result. This way of proceeding however forces the evolution of the function during training to lie within the realm of what is expressible with the chosen architecture, and prevents any optimization across possible architectures. Costly architectural hyper-parameter optimization is often performed to compensate for this. Instead, we propose to adapt the architecture on the fly during training. We show that the information about desirable architectural changes, due to expressivity bottlenecks when attempting to follow the functional gradient, can be extracted from the backpropagation. To do this, we propose a new mathematically wellgrounded method to detect expressivity bottlenecks on the fly and solve them by adding suitable neurons when and where needed. Thus, while the standard approach requires large networks, in terms of number of neurons per layer, for expressivity and optimization reasons, we are able to start with very small neural networks and let them grow appropriately. As a proof of concept, we show results on the MNIST dataset, matching large neural network accuracy, with competitive training time, while removing the need for standard architectural hyper-parameter search.
Essential Oil Composition of Aerial Part of Pluchea ovalis (Pers.) DC., Silver Nanoparticles Synthesis, and Larvicidal Activities against Fall Armyworm

In this study, essential oil of the aerial part of Pluchea ovalis (POEO) was isolated and employed for the synthesis of AgNPs (POEO-AgNPs). Then, larvicidal activities of POEO and PEO-AgNPs were evaluated against the larvae of fall armyworm (FAW), Spodoptera frugiderda (J. E. Smith); (Lepidoptera: Noctuidae). The potential medicinal values of P. ovalis and the lack of scientific reports on the applications of essential oils and nanoparticles of the plant species from the ecology of Ethiopia motivated the authors to carry out this research activity. The hydrodistillation technique was used for the isolation of POEO. Characterization of samples was done using gas chromatography/mass spectrometry (GC/MS), ultraviolet–visible spectroscopy (UV–Vis), scanning electron microscope-energy dispersive X-ray spectroscopy (SEM-EDX), Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), and zeta nanoanalyzer instruments. GC/MS analysis showed that sesquiterpenes (91.27%) are the dominant chemical constituents of POEO. The characteristic UV–Vis spectra absorption of POEO-AgNPs is observed at 428 nm. SEM imaging reveals that POEO-AgNPs have a dominantly spherical shape. A strong peak of EDX at 3.0 keV shows the existence of Ag element in POEO-AgNPs. XRD analysis determines the diffraction peaks of POEO-AgNPs at 2θ of 38.2°, 44.1°, 64.6°, and 77.8° which are indexed to (111), (200), (220), and (311), respectively. The average particle size and surface potential of POEO-AgNPs are 132 nm and −64.7 mV, respectively. POEO-AgNPs were stored at room temperature and 4 °C and showed good stability for about 6 months without aggregation or dissolution. The larvicidal activity was tested at 500, 250, and 125 µg/mL of POEO solution and 100% (full strength = 0.083 g/mL), 50%, and 25% POEO-AgNPs solution against 2nd instar larvae of S. frugiderda for 3 consecutive days. LC50 and LC90 are determined as 154.88 and 11,749.00 µg/mL for POEO and 69.18 and 1318.26% for POEO-AgNPs solutions, respectively. This finding will benefit the applications of POEO and POEO-AgNPs for a sustainable eco-friendly crop pest management method.
Voronoi Filters for Simulation Enrichment

The simulation of complex deformation problems often requires enrichment techniques that introduce local high‐resolution detail on a generally coarse discretization. The use cases include spatial or temporal refinement of the discretization, the simulation of composite materials with phenomena occurring at different scales, or even codimensional simulation. We present an efficient simulation enrichment method for both local refinement of the discretization and codimensional effects. We dub our method Voronoi filters, as it combines two key computational elements. One is the use of kinematic filters to constrain coarse and fine deformations, and thus provide enrichment functions that are complementary to the coarse deformation. The other one is the use of a centroidal Voronoi discretization for the design of the enrichment functions, which adds high‐resolution detail in a compact manner while preserving the rigid modes of coarse deformation. We demonstrate our method on simulation examples of composite materials, hybrid triangle‐based and yarn‐level simulation of cloth, or enrichment of flesh simulation with high‐resolution detail.
A Hybrid Genetic Algorithm for Service Caching and Task Offloading in Edge-Cloud Computing

—Edge-cloud computing is increasingly prevalent for Internet-of-Thing (IoT) service provisioning by combining both benefits of edge and cloud computing. In this paper, we aim to improve the user satisfaction and the resource efficiency by service caching and task offloading for edge-cloud computing. We propose a hybrid heuristic method to combine the global search ability of the genetic algorithm (GA) and heuristic local search ability, to improve the number of satisfied requests and the resource utilization. The proposed method encodes the service caching strategies into chromosomes, and evolves the population by GA. Given a caching strategy from a chromosome, our method exploits a dual-stage heuristic method for the task offloading. In the first stage, the dual-stage heuristic method pre-offloads tasks to the cloud, and offloads tasks whose requirements cannot be satisfied by the cloud to edge servers, aiming at satisfying as many tasks’ requirements as possible. The second stage re-offloads tasks from the cloud to edge servers, to get the utmost out of limited edge resources. Experimental results demonstrate the competitive edges of the proposed method over multiple classical and state- of-the-art techniques. Compared with five existing scheduling algorithms, our method achieves 11.3% to 23.7% more accepted tasks and 1.86% to 18.9% higher resource utilization.