Evidence of a New Crystalline Phase of Prednisolone Obtained from the Study of the Hydration-Dehydration Mechanisms of the Sesquihydrate

The dehydration of prednisolone sesquihydrate is studied and characterized by different physico-chemical analysis methods. The meticulous study of this dehydration led to the highlighting of a new solid form (form 3), metastable, never identified before. In a second step, the rehydration of anhydrous forms 1 and 2 of prednisolone is studied, in particular by Dynamic Vapor Sorption. It is then demonstrated that neither of the two forms is sensitive to humidity. By means of solid-gas equilibria, the sesquihydrate can only be obtainable from the isomorphic anhydrous form. Finally, a classification of the sesquihydrate is made, taking into account, in particular, the activation energy determined during dehydration.

Event-Based Circular Detection for AUV Docking Based on Spiking Neural Network

In this paper, a circular objects detection method for Autonomous Underwater Vehicle (AUV) docking is proposed, based on the Dynamic Vision Sensor (DVS) and the Spiking Neural Network (SNN) framework. In contrast to the related work, the proposed method not only avoids motion blur caused by frame-based recognition during docking procedure but also reduces data redundancy with limited on-chip resources. First, four coplanar and rectangular constrained circular light sources are constructed as the docking landmark. By combining asynchronous Hough circle transform with the SNN model, the coordinates of landmarks in the image are detected. Second, a Perspective-4-Point (P4P) algorithm is utilized to calculate the relative pose between AUV and the landmark. In addition, a spatiotemporal filter is also used to eliminate noises generated by the background. Finally, experimental results are demonstrated from both software simulation and experimental pool, respectively, to verify the proposed method. It is concluded that the proposed method achieves better performance in accuracy and efficiency in underwater docking scenarios.

The Effect of Chitosan Type on Biological and Physicochemical Properties of Films with Propolis Extract

The aim of the research was to determine the influence of chitosan type and propolis extract concentration on biological and physicochemical properties of chitosan-propolis films in terms of their applicability in food packaging. The films were prepared using three types of chitosan: from crab shells, medium and high molecular weight and propolis concentration in the range of 0.75-5.0%. The prepared polysaccharide films were tested for antimicrobial properties, oxygen transmission rate (OTR) and water vapor transmission rate (WVTR). Moreover, sorption tests and structural analysis were carried out. Microbiological tests indicated the best antimicrobial activity for the film consisting of high molecular weight chitosan and 5.0% propolis extract. Both the type of chitosan and propolis concentration affected transmission parameters-OTR and WVTR. The best barrier properties were recorded for the film composed of high molecular weight chitosan and 5.0% propolis extract. The results of sorption experiments showed a slight influence of chitosan type and a significant effect of propolis extract concentration on equilibrium moisture content of tested films. Moreover, propolis extract concentration affected monolayer water capacity (Mm) estimated using the Guggenheim, Anderson and de Boer (GAB) sorption model. The obtained results indicate that chitosan films with an addition of propolis extract are promising materials for food packaging applications, including food containing probiotic microorganisms.

Numerical Modelling of a Distributed Acoustic Sensor Based on Ultra-Low Loss-Enhanced Backscattering Fibers

In this study, a distributed acoustic sensor (DAS) was numerically modeled based on the non-ideal optical components with their noises and imperfections. This model is used to compare the response of DAS systems to standard single-mode fibers and ultra-low loss-enhanced backscattering (ULEB) fibers, a fiber with an array of high reflective points equally spaced along its length. It is shown that using ULEB fibers with highly reflective points improves the signal-to-noise ratio and linearity of the measurement, compared with the measurement based on standard single-mode fibers.

Water Uptake as a Crucial Factor on the Properties of Cryogels of Gelatine Cross-Linked by Dextran Dialdehyde

We investigated the water sorption properties of macroporous cryogels of gelatine (Gel) and dextran dialdehyde (DDA) prepared via cryogelation at 260 K and following the freeze drying processes. Water vapour sorption isotherms for aerogels were studied at 293 K by two independent methods: static-gravimetric and dynamic vapour sorption (DVS) over a water activity range of 0.11-1.0. Experimental data were fitted by use of the Brunauer-Emmett-Teller (BET) and Guggenheim-Anderson-de Boer (GAB) models. The BET model (for a water activity range of 0.1 ≤ p/po ≤ 0.5) was used to calculate the sorption parameters of the studied cryogels (the monolayer capacity, surface area and energy of interaction). In comparison with BET, the GAB model can be applied for the whole range of water activities (0.1 ≤ p/po ≤ 0.95). This model gave an almost perfect correlation between the experimental and calculated sorption isotherms using nonlinear least squares fitting (NLSF). Confocal Laser Scanning Microscopy (CLSM) was used to confirm the structural differences between various DDA:Gel cryogel compositions. Thermogravimetric analysis and DSC data for aerogels DDA:Gel provided information regarding the bonded water loss, relative remaining water content of the material and the temperature of decomposition. Estimation of the amount of bound water in the cryogels after the freeze drying process as well as after the cycle of treatment of cryogels with high humidity and drying was performed using DSC. The results of the DSC determinations showed that cryogels with higher gelatin content had higher levels of bonded water.

Increased Comfort of Polyester Fabrics

The hydrophilicity of fibers is directly related to the comfort of a fabric and represents one of the most important aspects of a textile. Therefore, polyester (PES) modification has focused on an increase in moisture content and a subsequent improvement of the user’s experience. Based on the glycerol hygroscopic properties, the main objective has been the enhancement of the hydrophilicity of polyester by glycerol treatments. Furthermore, microwave irradiation and alkaline treatment have been applied, in order to increase glycerol adhesion. Treated PES samples were characterized by performing moisture content, negative ion, water diffusion and water vapor resistance analyses. The effect of different treatment conditions such as bath ratio (1/10 or 1/15), temperature (40, 60 or 100 °C), time (2 or 5 min) and microwave radiation intensity (300 or 500 W) was evaluated. The moisture content of treated PES results indicated that by decreasing the bath ratio and increasing the time and temperature the moisture gain can reach almost 14%, which can be easily related to increases in the weight of the fiber. The treatment with alkali was done and led to the highest moisture increase. Treatment with 500 W microwave irradiation led to higher glycerol retention after rinsing. Different experimental conditions were applied to the glycerol-treated PES fabrics, and a clear improvement in moisture content was obtained increasing the comfort. The results were compared with the ones obtained for cotton and wool, where the moisture is higher than non treated PES.

A New Spiking Convolutional Recurrent Neural Network (SCRNN) With Applications to Event-Based Hand Gesture Recognition

The combination of neuromorphic visual sensors and spiking neural network offers a high efficient bio-inspired solution to real-world applications. However, processing event- based sequences remains challenging because of the nature of their asynchronism and sparsity behavior. In this paper, a novel spiking convolutional recurrent neural network (SCRNN) architecture that takes advantage of both convolution operation and recurrent connectivity to maintain the spatial and temporal relations from event-based sequence data are presented. The use of recurrent architecture enables the network to have a sampling window with an arbitrary length, allowing the network to exploit temporal correlations between event collections. Rather than standard ANN to SNN conversion techniques, the network utilizes a supervised Spike Layer Error Reassignment (SLAYER) training mechanism that allows the network to adapt to neuromorphic (event-based) data directly. The network structure is validated on the DVS gesture dataset and achieves a 10 class gesture recognition accuracy of 96.59% and an 11 class gesture recognition accuracy of 90.28%.

Enhanced Distributed Fiber Optic Vibration Sensing and Simultaneous Temperature Gradient Sensing Using Traditional C-OTDR and Structured Fiber with Scattering Dots

We present results demonstrating several beneficial effects on distributed fiber optic vibration sensing (DVS) functionality and performance resulting from utilizing standard single mode optical fiber (SMF) with femtosecond laser-inscribed equally-spaced simple scattering dots. This modification is particularly useful when using traditional single-wavelength amplitude-based coherent optical time domain reflectometry (C-OTDR) as sensing method. Local sensitivity is increased in quasi-distributed interferometric sensing zones which are formed by the fiber segments between subsequent pairs of the scattering dots. The otherwise nonlinear transfer function is overwritten with that of an ordinary two-beam interferometer. This linearizes the phase response to monotonous temperature variations. Furthermore, sensitivity fading is mitigated and the demodulation of low-frequency signals is enabled. The modification also allows for the quantitative determination of local temperature gradients directly from the C-OTDR intensity traces. The dots’ reflectivities and thus the induced attenuation can be tuned via the inscription process parameters. Our approach is a simple, robust and cost-effective way to gain these sensing improvements without the need for more sophisticated interrogator technology or more complex fiber structuring, e.g., based on ultra-weak FBG arrays. Our claims are substantiated by experimental evidence.

Chemico-calorimetric analysis of amorphous granules manufactured via continuous granulation process

The current study explores the first case of the implementation of solution calorimetry (SolCal) in order to determine the amorphous content of crystalline benzoyl-methoxy-methylindol-acetic acid (BMA)-a model poorly soluble drug, in the amorphous granules prepared via single-step continuous twin-screw dry granulations (TSG). Amorphous magnesium aluminometasilicate (Neusilin®) (US2) was used as a novel inorganic carrier via a TwinLab 10 mm twin-screw extruder. The BMA/US2 blends were processed at 180 °C and varying drug: carrier ratios of 1:4, 1:2.5 and 1:1 (w/w). Physico-chemical characterisation conducted via SEM, DSC and XRPD showed amorphous state of the drug in all granulated formulations. Reverse optical microscopy revealed a meso-porous structure of US2 in which the drug particles are adsorbed and/or entrapped within the porous network of the carrier. This phenomenon can be the underlying reason for the increase of the amorphous content in the extruded granules. Solution calorimetry (SolCal) study revealed amorphous content of the drug in all formulations quite precisely, whereas the dynamic vapour sorption (DVS) analysis complemented the results from SolCal. Furthermore, an attempt has been made for the first time to interrelate the findings from the SolCal to that of the release of the drug from the amorphous granules. It can be concluded that SolCal can be used as a novel technique to precisely quantify and interrelate the amorphous content to its physico-chemical performances such as drug release from the granulated formulations processed via TSG.

Detection of Leak-Induced Pipeline Vibrations Using Fiber-Optic Distributed Acoustic Sensing

In the presented work, the potential of fiber-optic distributed acoustic sensing (DAS) for detection of small gas pipeline leaks (<1%) is investigated. Helical wrapping of the sensing fiber directly around the pipeline is used to increase the system sensitivity for detection of weak leak-induced vibrations. DAS measurements are supplemented with reference accelerometer data to facilitate analysis and interpretation of recorded vibration signals. The results reveal that a DAS system using direct fiber application approach is capable of detecting pipeline natural vibrations excited by the broadband noise generated by the leaking medium. In the performed experiment, pipeline vibration modes with acceleration magnitudes down to single μg were detected. Simple leak detection approach based on spectral integration of time-averaged DAS signals in frequency domain was proposed. Potential benefits and limitations of the presented monitoring approach were discussed with respect to its practical applicability. We demonstrated that the approached is potentially capable of detection and localization of gas pipeline leaks with leak rates down to 0.1% of the pipeline flow volume and might be of interest for monitoring of short- and medium-length gas pipelines.

Modification of Pea Starch and Dextrin Polymers with Isocyanate Functional Groups

Pea starch and dextrin polymers were modified through the unequal reactivity of isocyanate groups in isophorone diisocyanate (IPDI) monomer. The presence of both urethane and isocyanate functionalities in starch and dextrin after modification were confirmed by Fourier transform infrared spectroscopy (FTIR) and ¹³C nuclear magnetic resonance (¹³C NMR). The degree of substitution (DS) was calculated using elemental analysis data and showed higher DS values in modified dextrin than modified starch. The onsets of thermal degradation and temperatures at maximum mass losses were improved after modification of both starch and dextrin polymers compared to unmodified ones. Glass transition temperatures (T_g) of modified starch and dextrin were lower than unmodified control ones, and this was more pronounced in modified dextrin at a high molar ratio. Dynamic water vapor sorption of starch and dextrin polymers indicated a slight reduction in moisture sorption of modified starch, but considerably lower moisture sorption in modified dextrin as compared to that of unmodified ones.

Training Deep Spiking Neural Networks Using Backpropagation

Deep spiking neural networks (SNNs) hold the potential for improving the latency and energy efficiency of deep neural networks through data-driven event-based computation. However, training such networks is difficult due to the non-differentiable nature of spike events. In this paper, we introduce a novel technique, which treats the membrane potentials of spiking neurons as differentiable signals, where discontinuities at spike times are considered as noise. This enables an error backpropagation mechanism for deep SNNs that follows the same principles as in conventional deep networks, but works directly on spike signals and membrane potentials. Compared with previous methods relying on indirect training and conversion, our technique has the potential to capture the statistics of spikes more precisely. We evaluate the proposed framework on artificially generated events from the original MNIST handwritten digit benchmark, and also on the N-MNIST benchmark recorded with an event-based dynamic vision sensor, in which the proposed method reduces the error rate by a factor of more than three compared to the best previous SNN, and also achieves a higher accuracy than a conventional convolutional neural network (CNN) trained and tested on the same data. We demonstrate in the context of the MNIST task that thanks to their event-driven operation, deep SNNs (both fully connected and convolutional) trained with our method achieve accuracy equivalent with conventional neural networks. In the N-MNIST example, equivalent accuracy is achieved with about five times fewer computational operations.

Evaluation of Event-Based Algorithms for Optical Flow with Ground-Truth from Inertial Measurement Sensor

In this study we compare nine optical flow algorithms that locally measure the flow normal to edges according to accuracy and computation cost. In contrast to conventional, frame-based motion flow algorithms, our open-source implementations compute optical flow based on address-events from a neuromorphic Dynamic Vision Sensor (DVS). For this benchmarking we created a dataset of two synthesized and three real samples recorded from a 240 × 180 pixel Dynamic and Active-pixel Vision Sensor (DAVIS). This dataset contains events from the DVS as well as conventional frames to support testing state-of-the-art frame-based methods. We introduce a new source for the ground truth: In the special case that the perceived motion stems solely from a rotation of the vision sensor around its three camera axes, the true optical flow can be estimated using gyro data from the inertial measurement unit integrated with the DAVIS camera. This provides a ground-truth to which we can compare algorithms that measure optical flow by means of motion cues. An analysis of error sources led to the use of a refractory period, more accurate numerical derivatives and a Savitzky-Golay filter to achieve significant improvements in accuracy. Our pure Java implementations of two recently published algorithms reduce computational cost by up to 29% compared to the original implementations. Two of the algorithms introduced in this paper further speed up processing by a factor of 10 compared with the original implementations, at equal or better accuracy. On a desktop PC, they run in real-time on dense natural input recorded by a DAVIS camera.

Continuous twin-screw granulation for enhancing the dissolution of poorly water soluble drug

The article describes the application of a twin-screw granulation process to enhance the dissolution rate of the poorly water soluble drug, ibuprofen (IBU). A quality-by-design (QbD) approach was used to manufacture IBU loaded granules via hot-melt extrusion (HME) processing. For the purpose of the study, a design of experiment (DoE) was implemented to assess the effect of the formulation compositions and the processing parameters. This novel approach allowed the use of, polymer/inorganic excipients such as hydroxypropyl methylcellulose (HPMC) and magnesium aluminometasilicate (Neusilin(®)-MAS) with polyethylene glycol 2000 (PEG) as the binder without requiring a further drying step. IBU loaded batches were processed using a twin screw extruder to investigate the effect of MAS/polymer ratio, PEG amount (binder) and liquid to solid (L/S) ratios on the dissolution rates, mean particle size and the loss on drying (LoD) of the extruded granules. The DoE analysis showed that the defined independent variables of the twin screw granulation process have a complex effect on the measured outcomes. The solid state analysis showed the existence of partially amorphous IBU state which had a significant effect on the dissolution enhancement in acidic media. Furthermore, the analysis obtained from the surface mapping by Raman proved the homogenous distribution of the IBU in the extruded granulation formulations.

Interactions between carboxymethyl konjac glucomannan and soy protein isolate in blended films

To elucidate biopolymer interactions between carboxymethyl konjac glucomannan (CMKGM) and soy protein isolate (SPI) in different ratios on physicochemical properties of the blended films, biodegradable CMKGM/SPI films were prepared and characterized. The results showed that CMKGM and SPI are highly compatible in blended film formation, and that Maillard reactions and hydrogen bonds interactions between CMKGM and SPI occurred. The water adsorption of the CMKGM/SPI films progressively decreased with increasing CMKGM level, the surface wettability of the blended films was improved with increasing CMKGM content; the CMKGM/SPI blend films had enhanced tensile strength (TS) and elongation at break (EAB) compared to pure CMKGM and SPI films; the oxygen permeability of blend films was decreased; the roughness was decreased with increasing CMKGM content. Moreover, the CMKGM/SPI film was biocompatible and biodegradable.

Energy-efficient scheduling for hybrid tasks in control devices for the Internet of Things

In control devices for the Internet of Things (IoT), energy is one of the critical restriction factors. Dynamic voltage scaling (DVS) has been proved to be an effective method for reducing the energy consumption of processors. This paper proposes an energy-efficient scheduling algorithm for IoT control devices with hard real-time control tasks (HRCTs) and soft real-time tasks (SRTs). The main contribution of this paper includes two parts. First, it builds the Hybrid tasks with multi-subtasks of different function Weight (HoW) task model for IoT control devices. HoW describes the structure of HRCTs and SRTs, and their properties, e.g., deadlines, execution time, preemption properties, and energy-saving goals, etc. Second, it presents the Hybrid Tasks' Dynamic Voltage Scaling (HTDVS) algorithm. HTDVS first sets the slowdown factors of subtasks while meeting the different real-time requirements of HRCTs and SRTs, and then dynamically reclaims, reserves, and reuses the slack time of the subtasks to meet their ideal energy-saving goals. Experimental results show HTDVS can reduce energy consumption about 10%-80% while meeting the real-time requirements of HRCTs, HRCTs help to reduce the deadline miss ratio (DMR) of systems, and HTDVS has comparable performance with the greedy algorithm and is more favorable to keep the subtasks' ideal speeds.