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To understand how to improve interactions with dog-like robots, we evaluated the importance of “dog-like” framing and physical appearance on interaction, hypothesizing multiple interactive benefits of each. We assessed whether framing Aibo as a puppy (i.e., in need of development) versus simply a robot would result in more positive responses and interactions. We also predicted that adding fur to Aibo would make it appear more dog-like, likable, and interactive. Twenty-nine participants engaged with Aibo in a 2 × 2 (framing × appearance) design by issuing commands to the robot. Aibo and participant behaviors were monitored per second, and evaluated via an analysis of commands issued, an analysis of command blocks (i.e., chains of commands), and using a T-pattern analysis of participant behavior. Participants were more likely to issue the “Come Here” command than other types of commands. When framed as a puppy, participants used Aibo’s dog name more often, praised it more, and exhibited more unique, interactive, and complex behavior with Aibo. Participants exhibited the most smiling and laughing behaviors with Aibo framed as a puppy without fur. Across conditions, after interacting with Aibo, participants felt Aibo was more trustworthy, intelligent, warm, and connected than at their initial meeting. This study shows the benefits of introducing a socially robotic agent with a particular frame and importance on realism (i.e., introducing the robot dog as a puppy) for more interactive engagement.

The misuse of antibiotics in the cattle sector can lead to milk contamination, with concomitant effects on the dairy industry and human health. Biosensors can be applied in this field; however, the influence of the milk matrix on their activity has been poorly studied in light of the preanalytical process. Herein, aptamer-conjugated gold nanoparticles (nanoaptasensors) were investigated for the colorimetric detection in raw milk of four antibiotics used in cattle. The effect of milk components on the colorimetric response of the nanoaptasensors was analyzed by following the selective aggregation of the nanoparticles, using the absorption ratio A520/A720. A preanalytical strategy was developed to apply the nanoaptasensors to antibiotic-contaminated raw milk samples, which involves a clarification step with Carrez reagents followed by the removal of cations through dilution, chelation (EDTA) or precipitation (NaHCO3). The colorimetric signals were detected in spiked samples at concentrations of antibiotics as low as 0.25-fold the maximum residue limits (MRLs) for kanamycin (37.5 μg/L), oxytetracycline (25 μg/L), sulfadimethoxine (6.25 μg/L) and ampicillin (1 μg/L), according to European and Chilean legislation. Overall, we conclude that this methodology holds potential for the semiquantitative analysis of antibiotic residues in raw milk obtained directly from dairy farms.

Hospitals, especially their emergency services, receive a high number of wrist fracture cases. For correct diagnosis and proper treatment of these, images obtained from various medical equipment must be viewed by physicians, along with the patients medical records and physical examination. The aim of this study is to perform fracture detection by use of deep learning on wrist Xray images to support physicians in the diagnosis of these fractures, particularly in the emergency services. Using SABL, RegNet, RetinaNet, PAA, Libra R_CNN, FSAF, Faster R_CNN, Dynamic R_CNN and DCN deep learning based object detection models with various backbones, 20 different fracture detection procedures were performed on Gazi University Hospitals dataset of wrist Xray images. To further improve these procedures, five different ensemble models were developed and then used to reform an ensemble model to develop a unique detection model, wrist fracture detection_combo (WFD_C). From 26 different models for fracture detection, the highest detection result obtained was 0.8639 average precision (AP50) in the WFD-C model. Huawei Turkey R&D Center supports this study within the scope of the ongoing cooperation project coded 071813 between Gazi University, Huawei and Medskor. Code is available at https://github.com/fatihuysal88/wrist-d, This paper is accepted at Sensors, MDPI, 2022, 22, 1285. Section: "Sensing and Imaging"

Video tracking involves detecting previously designated objects of interest within a sequence of image frames. It can be applied in robotics, unmanned vehicles, and automation, among other fields of interest. Video tracking is still regarded as an open problem due to a number of obstacles that still need to be overcome, including the need for high precision and real-time results, as well as portability and low-power demands. This work presents the design, implementation and assessment of a low-power embedded system based on an SoC-FPGA platform and the honeybee search algorithm (HSA) for real-time video tracking. HSA is a meta-heuristic that combines evolutionary computing and swarm intelligence techniques. Our findings demonstrated that the combination of SoC-FPGA and HSA reduced the consumption of computational resources, allowing real-time multiprocessing without a reduction in precision, and with the advantage of lower power consumption, which enabled portability. A starker difference was observed when measuring the power consumption. The proposed SoC-FPGA system consumed about 5 Watts, whereas the CPU-GPU system required more than 200 Watts. A general recommendation obtained from this research is to use SoC-FPGA over CPU-GPU to work with meta-heuristics in computer vision applications when an embedded solution is required.

Microfluidic systems are of paramount importance in various fields such as medicine, biology, and pharmacy. Despite the plethora of methods, accurate dosing and mixing of small doses of liquid reagents remain challenges for microfluidics. In this paper, we present a microfluidic device that uses two micro pumps and an alternating drive pattern to fill a microchannel. With a capacitive sensor system, we monitored the fluid process and controlled the micro pumps. In a first experiment, the system was set up to generate a 1:1 mixture between two fluids while using a range of fluid packet sizes from 0.25 to 2 µL and pumping frequencies from 50 to 100 Hz. In this parameter range, a dosing accuracy of 50.3 ± 0.9% was reached, validated by a gravimetric measurement. Other biased mixing ratios were tested as well and showed a deviation of 0.3 ± 0.3% from the targeted mixing ratio. In a second experiment, Trypan blue was used to study the mixing behavior of the system. Within one to two dosed packet sets, the two reagents were reliably mixed. The results are encouraging for future use of micro pumps and capacitive sensing in demanding microfluidic applications.

Florescence information monitoring is essential for strengthening orchard management activities, such as flower thinning, fruit protection, and pest control. A lightweight object recognition model using cascade fusion YOLOv4-CF is proposed, which recognizes multi-type objects in their natural environments, such as citrus buds, citrus flowers, and gray mold. The proposed model has an excellent representation capability with an improved cascade fusion network and a multi-scale feature fusion block. Moreover, separable deep convolution blocks were employed to enhance object feature information and reduce model computation. Further, channel shuffling was used to address missing recognition in the dense distribution of object groups. Finally, an embedded sensing system for recognizing citrus flowers was designed by quantitatively applying the proposed YOLOv4-CF model to an FPGA platform. The mAP@.5 of citrus buds, citrus flowers, and gray mold obtained on the server using the proposed YOLOv4-CF model was 95.03%, and the model size of YOLOv4-CF + FPGA was 5.96 MB, which was 74.57% less than the YOLOv4-CF model. The FPGA side had a frame rate of 30 FPS; thus, the embedded sensing system could meet the demands of florescence information in real-time monitoring.

In this paper, an adaptation method for adjusting the scaling parameters of an unscented Kalman filter (UKF) is proposed to improve the estimation performance of the filter in dynamic conditions. The proposed adaptation method is based on a sequential algorithm that selects the scaling parameter using the user-defined distribution of discrete sets to more effectively deal with the changing measurement distribution over time and avoid the additional process for training a filter model. The adaptation method employs regularized optimal transport (ROT), which compensates for the error of the predicted measurement with the current measurement values to select the proper scaling parameter. In addition, the Sinkhorn–Knopp algorithm is used to minimize the cost function of ROT due to its fast convergence rate, and the convergence of the proposed ROT-based adaptive adjustment method is also analyzed. According to the analysis results of Monte Carlo simulations, it is confirmed that the proposed algorithm shows better performance than the conventional algorithms in terms of the scaling parameter selection in the UKF.

Polymer microtips are 3D microstructures manufactured on the end face of an optical fiber by using the photopolymerization process. Such micro-optic elements made on a multi-mode optical fiber were previously tested as a transducer of refractive index sensor. These studies were an inspiration to investigate the possibility of using this type of transducer to measure the presence of volatile organic compounds in the air. The experimental results of microtips polymerized with UV and VIS were reported. It was possible to detect the presence of five different volatile compounds in the air due to the sensitivity of the transducer to the refractive indices changes. These changes were induced by the vapors condensed on the microtip surface. The measured time responses have shown that the return loss decreases rapidly as the microtip is inserted inside a glass vial filled with the tested compound. Moreover, correlations between calculated dynamic ranges and refractive indices and volumes of the volatile compounds inside the vials were negligible. Therefore, this type of sensor can be categorized as a condensed material threshold sensor. This sensor can be used in warning systems for monitoring leakages of pipelines carrying volatile chemicals.

This paper presents the details of our research and the activities involved. Japan is one of the most advanced countries in medicine worldwide. However, in terms of technology, knowledge sharing, and successor development, Japanese medicine lags behind other developed countries, and these matters require addressing. The country is also facing a shortage of doctors, among other things, and this medical problem will surely become critical in the near future. In this study, we aim to help solve such problems from the medical engineering viewpoint, analyze and create systems based on the experience of doctors from the engineering viewpoint, and make it easy for patients to understand orthodox and general statistical analysis methods. We perform a visualization and quantitative medical data analysis and examine diagnostic support. We consider sleep apnea syndrome (SAS), and orthostatic dysregulation (OD) in children in this study. This research aims to detect SAS early, identify people with pre-SAS who are likely to become SAS in the near future, and identify OD. We analyze and identify these diseases through statistics and a multivariate analysis and create a dedicated analysis system for them. Our research and system development will allow specialists to make informed diagnoses, reproduce empirical rules, improve work efficiency, and improve patients’ health awareness. This research has only looks at two diseases, but these methods can be expected to be applied to other diseases.

Today, solar energy is taking an increasing share of the total energy mix. Unfortunately, many operational photovoltaic plants suffer from a plenitude of defects resulting in non-negligible power loss. The latter highly impacts the overall performance of the PV site; therefore, operators need to regularly inspect their solar parks for anomalies in order to prevent severe performance drops. As this operation is naturally labor-intensive and costly, we present in this paper a novel system for improved PV diagnostics using drone-based imagery. Our solution consists of three main steps. The first step locates the solar panels within the image. The second step detects the anomalies within the solar panels. The final step identifies the root cause of the anomaly. In this paper, we mainly focus on the second step comprising the detection of anomalies within solar panels, which is done using a region-based convolutional neural network (CNN). Experiments on six different PV sites with different specifications and a variety of defects demonstrate that our anomaly detector achieves a true positive rate or recall of more than 90% for a false positive rate of around 2% to 3% tested on a dataset containing nearly 9000 solar panels. Compared to the best state-of-the-art methods, the experiments revealed that we achieve a slightly higher true positive rate for a substantially lower false positive rate, while tested on a more realistic dataset.

The sophistication of ship detection technology in remote sensing images is insufficient, the detection results differ substantially from the practical requirements, mainly reflected in the inadequate support for the differentiated application of multi-scene, multi-resolution and multi-type target ships. To overcome these challenges, a ship detection method based on multiscale feature extraction and lightweight CNN is proposed. Firstly, the candidate-region extraction method, based on a multiscale model, can cover the potential targets under different backgrounds accurately. Secondly, the multiple feature fusion method is employed to achieve ship classification, in which, Fourier global spectrum features are applied to discriminate between targets and simple interference, and the targets in complex interference scenarios are further distinguished by using lightweight CNN. Thirdly, the cascade classifier training algorithm and an improved non-maximum suppression method are used to minimise the classification error rate and maximise generalisation, which can achieve final-target confirmation. Experimental results validate our method, showing that it significantly outperforms the available alternatives, reducing the model size by up to 2.17 times while improving detection performance be improved by up to 5.5% in multi-interference scenarios. Furthermore, the robustness ability was verified by three indicators, among which the F-measure score and true–false-positive rate can increase by up to 5.8% and 4.7% respectively, while the mean error rate can decrease by up to 38.2%.

Emulsion gel, a novel oral delivery carrier, provides the possibility to co-load hydrophilic and lipophilic nutrients simultaneously. In this study, duo-induction methods of laccase and glucono-δ-lactone (L&GDL) or laccase and transglutaminase (L&MTG) were used to prepare the soy protein isolate-sugar beet pectin (SPI-SBP) emulsion gel. The textural data of the emulsion gel was normalized to analyze the effect of different induction methods on the gel property of the SPI-SBP emulsion gels. The characterization studies showed the structure of L&MTG emulsion gel was denser with a lower swelling ratio and reduced degree of digestion, compared with L&GDL emulsion gel. Moreover, the release profiles of both β-carotene and riboflavin co-loaded in the SPI-SBP emulsion gels were correlated to the digestion patterns of the gel matrix; the controlled-release of encapsulated functional factors was regulated by a gel network induced by different induction methods, mainly due to the resulting porosity of the structure and swelling ratio during digestion. In conclusion, SPI-SBP emulsion gels have the capability of encapsulating multiple functional factors with different physicochemical properties.

One of the main methods for link quality prediction is to predict the physical layer parameters first, and then evaluate the link quality based on the mapping models between such parameters and packet reception ratio (PRR). However, existing methods often ignore the temporal correlations of physical layer parameter series and rarely consider the influence of link fluctuations, which lead to more errors under moderate and sudden changed links with larger fluctuations. In view of these problems, this paper proposes a more effective link quality prediction method RNN-LQI, which adopts Recurrent Neural Network (RNN) to predict the Link Quality Indicator (LQI) series, and then evaluates the link quality according to the fitting model of LQI and PRR. This method accurately mines the inner relationship among LQI series with the help of short-term memory characteristics of RNN and effectively deals with link fluctuations by taking advantage of the higher resolution of LQI in the transitional region. Compared with similar methods, RNN-LQI proves to be better under different link qualities. Especially under moderate and sudden changed links with larger fluctuations, the prediction error reduces at least by 14.51% and 13.37%, respectively. Therefore, the proposed method is more suitable for low power wireless links with more fluctuations.

It is becoming increasingly common to collect multiple related neuroimaging datasets either from different modalities or from different tasks and conditions. In addition, we have non-imaging data such as cognitive or behavioral variables, and it is through the association of these two sets of data—neuroimaging and non-neuroimaging—that we can understand and explain the evolution of neural and cognitive processes, and predict outcomes for intervention and treatment. Multiple methods for the joint analysis or fusion of multiple neuroimaging datasets or modalities exist; however, methods for the joint analysis of imaging and non-imaging data are still in their infancy. Current approaches for identifying brain networks related to cognitive assessments are still largely based on simple one-to-one correlation analyses and do not use the cross information available across multiple datasets. This work proposes two approaches based on independent vector analysis (IVA) to jointly analyze the imaging datasets and behavioral variables such that multivariate relationships across imaging data and behavioral features can be identified. The simulation results show that our proposed methods provide better accuracy in identifying associations across imaging and behavioral components than current approaches. With functional magnetic resonance imaging (fMRI) task data collected from 138 healthy controls and 109 patients with schizophrenia, results reveal that the central executive network (CEN) estimated in multiple datasets shows a strong correlation with the behavioral variable that measures working memory, a result that is not identified by traditional approaches. Most of the identified fMRI maps also show significant differences in activations across healthy controls and patients potentially providing a useful signature of mental disorders.

Recent technological developments pave the path for deep learning-based techniques to be used in almost every domain of life. The precision of deep learning techniques make it possible for these to be used in the medical field for the classification and detection of various diseases. Recently, the coronavirus (COVID-19) pandemic has put a lot of pressure on the health system all around the world. The diagnosis of COVID-19 is possible by PCR testing and medical imagining. Since COVID-19 is highly contagious, diagnosis using chest X-ray is considered safe in various situations. In this study, a deep learning-based technique is proposed to classify COVID-19 infection from other non-COVID-19 infections. To classify COVID-19, three different pre-trained models named EfficientNetB1, NasNetMobile and MobileNetV2 are used. The augmented dataset is used for training deep learning models while two different training strategies have been used for classification. In this study, not only are the deep learning model fine-tuned but also the hyperparameters are fine-tuned, which significantly improves the performance of the fine-tuned deep learning models. Moreover, the classification head is regularized to improve the performance. For the evaluation of the proposed techniques, several performance parameters are used to gauge the performance. EfficientNetB1 with regularized classification head outperforms the other models. The proposed technique successfully classifies four classes that include COVID-19, viral pneumonia, lung opacity, and normal, with an accuracy of 96.13%. The proposed technique shows superiority in terms of accuracy when compared with recent techniques present in the literature.

The acoustic emission (AE) method is a popular and well-developed method for passive structural health monitoring of metallic and composite structures. The current study focuses on the analysis of one of its processes, sound source or signal propagation. This paper discusses the principle of plate wave signal sensing using piezoelectric transducers, and derives an analytical expression for the response of piezoelectric transducers under the action of stress waves, to obtain an overall mathematical model of the acoustic emission signal from generation to reception. The acoustic emission caused by fatigue crack extension is simulated by a finite element method, and the actual acoustic emission signal is simulated by a pencil lead break experiment. The results predicted by the mathematical model are compared with the experimental results and the simulation results, respectively, and show good agreement. In addition, the presence of obvious S0 mode Lamb waves is observed in the simulation results and experimental results, which further verifies the correctness of the analytical model prediction.

The rapid growth and adaptation of medical information to identify significant health trends and help with timely preventive care have been recent hallmarks of the modern healthcare data system. Heart disease is the deadliest condition in the developed world. Cardiovascular disease and its complications, including dementia, can be averted with early detection. Further research in this area is needed to prevent strokes and heart attacks. An optimal machine learning model can help achieve this goal with a wealth of healthcare data on heart disease. Heart disease can be predicted and diagnosed using machine-learning-based systems. Active learning (AL) methods improve classification quality by incorporating user–expert feedback with sparsely labelled data. In this paper, five (MMC, Random, Adaptive, QUIRE, and AUDI) selection strategies for multi-label active learning were applied and used for reducing labelling costs by iteratively selecting the most relevant data to query their labels. The selection methods with a label ranking classifier have hyperparameters optimized by a grid search to implement predictive modelling in each scenario for the heart disease dataset. Experimental evaluation includes accuracy and F-score with/without hyperparameter optimization. Results show that the generalization of the learning model beyond the existing data for the optimized label ranking model uses the selection method versus others due to accuracy. However, the selection method was highlighted in regards to the F-score using optimized settings.

Considering the consistent reduction in battery range due to the operation of the Heating Ventilation and Air Conditioning (HVAC) system, this study deals with the CO2 measurement inside the cabin of an electric crane and aims to reduce the energy consumption through the control of the air recirculation. A control strategy was defined and tested through an experimental set-up where the presence of a driver was simulated as a source of CO2. The cabin was placed inside a climatic wind tunnel and the benefits of this control strategy on the HVAC system energy consumption were assessed, both in the heating and the cooling modes. In addition, we discussed the optimal position of the CO2 sensor inside the cabin by comparing the results obtained from some sensors placed around the cabin occupant with the ones logged by three sensors in the breathing zone. Finally, an investigation of the uncertainty of the indirect measurement of the leakage flow and its dependence on the number of CO2 sensors installed in the cabin was made through the Monte Carlo method.

Target-following mobile robots have gained attention in various industrial applications. This study proposes an ultra-wideband-based target localization method that provides highly accurate and robust target tracking performance for a following robot. Based on the least square approximation framework, the proposed method improves localization accuracy by compensating localization bias and high-frequency deviations component by component. Initial calibration method is proposed to measure the device-dependent localization bias, which enables a compensation of the bias error not only at the calibration points, but also at the any other points. An iterative complementary filter, which recursively produces optimal estimation for each timeframe as a weighted sum of previous and current estimation depending on the reliability of each estimation, is proposed to reduce the deviation of the localization error. The performance of the proposed method is validated using simulations and experiments. Both the magnitude and deviation of the localization error were significantly improved by up to 77 and 51%, respectively, compared with the previous method.

Blood pressure measurements are one of the most routinely performed medical tests globally. Blood pressure is an important metric since it provides information that can be used to diagnose several vascular diseases. Conventional blood pressure measurement systems use cuff-based devices to measure the blood pressure, which may be uncomfortable and sometimes burdensome to the subjects. Therefore, in this study, we propose a cuffless blood pressure estimation model based on Monte Carlo simulation (MCS). We propose a heterogeneous finger model for the MCS at wavelengths of 905 nm and 940 nm. After recording the photon intensities from the MCS over a certain range of blood pressure values, the actual photoplethysmography (PPG) signals were used to estimate blood pressure. We used both publicly available and self-made datasets to evaluate the performance of the proposed model. In case of the publicly available dataset for transmission-type MCS, the mean absolute errors are 3.32 ± 6.03 mmHg for systolic blood pressure (SBP), 2.02 ± 2.64 mmHg for diastolic blood pressure (DBP), and 1.76 ± 2.8 mmHg for mean arterial pressure (MAP). The self-made dataset is used for both transmission- and reflection-type MCSs; its mean absolute errors are 2.54 ± 4.24 mmHg for SBP, 1.49 ± 2.82 mmHg for DBP, and 1.51 ± 2.41 mmHg for MAP in the transmission-type case as well as 3.35 ± 5.06 mmHg for SBP, 2.07 ± 2.83 mmHg for DBP, and 2.12 ± 2.83 mmHg for MAP in the reflection-type case. The estimated results of the SBP and DBP satisfy the requirements of the Association for the Advancement of Medical Instrumentation (AAMI) standards and are within Grade A according to the British Hypertension Society (BHS) standards. These results show that the proposed model is efficient for estimating blood pressures using fingertip PPG signals.

In this study, the authors present the accurate imaging of the behavior of simultaneous operations of multiple low radar cross-section (RCS) aerial targets. Currently, the popularity of low RCS targets is increasing day by day, and detection and identification of these targets have become critical issues. Micro-Doppler signatures are key components for detecting and identifying these low RCS targets. For this, an innovative approach is proposed along with the smooth pseudo-Wigner–Ville distribution (SP-WVD) and adaptive filter bank to improve the attenuation of cross-term interferences to generate more accurate images for the micro-Doppler signatures/patterns of simultaneous multiple targets. A C-band (5.3 GHz) radio-frequency (RF) sensor is designed and used to acquire the micro-Doppler signatures of aerial rotational, flapping, and motional low RCS targets. Digital pipelined-parallel architecture is designed inside the Xilinx field-programable gate array (FPGA) for fast sensor data collection, data preprocessing, and interface to the computer (imaging algorithm). The experimental results of the proposed approach are validated with the results of the classical short-term Fourier transform (STFT), continuous wavelet transform (CWT), and smooth pseudo-Wigner Ville distribution (SP-WVD). Realistic open-field outdoor experiments are conducted covering different simultaneous postures of (i) two-/three-blade propeller/roto systems, (ii) flapping bionic bird, and (iii) kinetic warhead targets. The associated experimental results and findings are reported and analyzed in this paper. The limitations and possible future research studies are also discussed in the conclusion.

Realising a low-complexity Farrow channelisation algorithm for multi-standard receivers in software-defined radio is a challenging task. A Farrow filter operates best at low frequencies while its performance degrades towards the Nyquist region. This makes wideband channelisation in software-defined radio a challenging task with high computational complexity. In this paper, a hybrid Farrow algorithm that combines a modulated Farrow filter with a frequency response interpolated coefficient decimated masking filter is proposed for the design of a novel filter with low computational complexity. A design example shows that the HFarrow filter bank achieved multiplier reduction of 50%, 70% and 64%, respectively, in comparison with non-uniform modulated discrete Fourier transform (NU MDFT FB), coefficient decimated filter bank (CD FB) and interpolated coefficient decimated (ICDM) filter algorithms. The HFarrow filter bank is able to provide the same number of sub-band channels as other algorithms such as non-uniform modulated discrete Fourier transform (NU MDFT FB), coefficient decimated filter bank (CD FB) and interpolated coefficient decimated (ICDM) filter algorithms, but with less computational complexity.

In the Literature, we can find several research works to help in the digital crime fight in order to prove integrity and authenticity of a published document, image or video. Among all the crimes, fake news certainly is among the most recurrent ones and needs to be mitigated. There are several Blockchain-based applications in order to make use of the benefits derived from technology, but little is found to verify the authenticity of Web content records as well as the history of all updates that have taken place in each Web content. Such kind of solution has become important nowadays as a way to cover the gap in the combat against fake news, for example. The purpose of this paper is to present BlockProof, a framework for verifying web content authenticity and integrity that offers a solution for content providers to register Web content, regardless of whether the page has dynamic or static content, in addition to enabling the consultation of the history of all records made for a given URL. We understand that such kind of solution may be useful to data producers/providers to provide evidence that they are in compliance with the fight against fake news, for instance.

This paper proposes an optimization framework for terrain large scale optical sensor placement to improve border protection. Compared to the often used, maximal coverage of an area approach, this method minimizes the undetected passages in the monitored area. Border protection is one of the most critical areas for sensor placement. Unlike traditional border protection solutions, we do not optimize for 2D but for 3D to prevent transit. Additionally, we consider both natural and built environmental coverings. The applied environmental model creates a highly inhomogeneous sensing area for sensors instead of the previously used homogeneous one. The detection of each sensor was provided by a line-of-sight model supplemented with inhomogeneous probabilities. The optimization was performed using a bacterial evolutionary algorithm. In addition to maximizing detection, minimizing the number of the applied sensors played a crucial role in design. These two cost components are built on each other hierarchically. The developed simulation framework based on ray tracing provided an excellent opportunity to optimize large areas. The presented simulation results prove the efficiency of this method. The results were evaluated by testing on a large number of intruders. Using sensors with different quantities and layouts in the tested 1×1×1 km environment, we reduced the probability of undetected intrusion to below 0.1% and increased the probability of acceptable classification to 99%.

This study aimed to fabricate food-grade Pickering emulsions stabilized by chitosan-phytic acid-β-cyclodextrin (CS-PA-CD) nanoparticles. The CS-PA-CD nanoparticles were characterized with FITR, XRD, and TGA to prove its successfully crosslinking, then characterized by DLS system and scanning electron microscopy showing the smallest average particle size was 434.2 ± 2.5 nm and it increased with the ratio of PA-CD to CS increasing. Pickering emulsions stabilized by CS-PA-CD nanoparticles was prepared and it showed the best stability at around pH 6. The particle concentration higher than 1.0% (w/v) and the oil fraction above 0.5% (v/v) could reach the emulsion stability. In addition, the Pickering emulsions were stable at various temperature (30–70 °C) and influenced by the certain change of ionic strength (0–500 mM). These CS-PA-CD Pickering emulsions showed great application in the formation of functional foods and pharmaceutical industries.

Semantic segmentation is used to enable a computer to understand its surrounding environment. In image processing, images are partitioned into segments for this purpose. State-of-the-art methods make use of Convolutional Neural Networks to segment a 2D image. Compared to that, 3D approaches suffer from computational cost and are not applicable without any further steps. In this work, we focus on semantic segmentation based on 3D point clouds. We use the idea to project the 3D data into a 2D image to accelerate the segmentation process. Afterward, the processed image gets re-projected to receive the desired result. We investigate different projection views and compare them to clarify their strengths and weaknesses. To compensate for projection errors and the loss of geometrical information, we evolve the approach and show how to fuse different views. We have decided to fuse the bird’s-eye and the spherical projection as each of them achieves reasonable results, and the two perspectives complement each other best. For training and evaluation, we use the real-world datasets SemanticKITTI. Further, we use the ParisLille and synthetic data generated by the simulation framework Carla to analyze the approaches in more detail and clarify their strengths and weaknesses. Although these methods achieve reasonable and competitive results, they lack flexibility. They depend on the sensor used and the setup in which the sensor is used.

Automatic feature extraction from images of speech articulators is currently achieved by detecting edges. Here, we investigate the use of pose estimation deep neural nets with transfer learning to perform markerless estimation of speech articulator keypoints using only a few hundred hand-labelled images as training input. Midsagittal ultrasound images of the tongue, jaw, and hyoid and camera images of the lips were hand-labelled with keypoints, trained using DeepLabCut and evaluated on unseen speakers and systems. Tongue surface contours interpolated from estimated and hand-labelled keypoints produced an average mean sum of distances (MSD) of 0.93, s.d. 0.46 mm, compared with 0.96, s.d. 0.39 mm, for two human labellers, and 2.3, s.d. 1.5 mm, for the best performing edge detection algorithm. A pilot set of simultaneous electromagnetic articulography (EMA) and ultrasound recordings demonstrated partial correlation among three physical sensor positions and the corresponding estimated keypoints and requires further investigation. The accuracy of the estimating lip aperture from a camera video was high, with a mean MSD of 0.70, s.d. 0.56 mm compared with 0.57, s.d. 0.48 mm for two human labellers. DeepLabCut was found to be a fast, accurate and fully automatic method of providing unique kinematic data for tongue, hyoid, jaw, and lips.

Internet of Things (IoT) technology is now widely used in energy, healthcare, services, transportation, and other fields. With the increase in industrial equipment (e.g., smart mobile terminals, sensors, and other embedded devices) in the Internet of Things and the advent of Industry 4.0, there has been an explosion of data generated that is characterized by a high volume but small size. How to manage and protect sensitive private data in data sharing has become an urgent issue for enterprises. Traditional data sharing and storage relies on trusted third-party platforms or distributed cloud storage, but these approaches run the risk of single-node failure, and third parties and cloud storage providers can be vulnerable to attacks that can lead to data theft. To solve these problems, this paper proposes a Hyperledger Fabric blockchain-based secure data transfer scheme for enterprises in the Industrial Internet of Things (IIOT). We store raw data in the IIoT in the InterPlanetary File System (IPFS) network after encryption and store the Keyword-index table we designed in Hyperledger Fabric blockchain, and enterprises share the data by querying the Keyword-index table. We use Fabric’s channel mechanism combined with our designed Chaincode to achieve privacy protection and efficient data transmission while using the Elliptic Curve Digital Signature Algorithm (ECDSA) to ensure data integrity. Finally, we performed security analysis and experiments on the proposed scheme, and the results show that overall the data transfer performance in the IPFS network is generally better than the traditional network, In the case of transferring 5 MB file size data, the transmission speed and latency of IPFS are 19.23 mb/s and 0.26 s, respectively, and the IPFS network is almost 4 times faster than the TCP/IP network while taking only a quarter of the time, which is more advantageous when transferring small files, such as data in the IIOT. In addition, our scheme outperforms the blockchain systems mainly used today in terms of both throughput, latency, and system overhead. The average throughput of our solution can reach 110 tps (transactions are executed per second), and the minimum throughput in experimental tests can reach 101 tps.

This work presents a rigorous and generic sensitivity analysis of silicon nitride on silicon dioxide strip waveguide for virus detection. In general, by functionalizing the waveguide surface with a specific antibodies layer, we make the optical sensor sensitive only to a particular virus. Unlike conventional virus detection methods such as polymerase chain reaction (PCR), integrated refractive index (RI) optical sensors offer cheap and mass-scale fabrication of compact devices for fast and straightforward detection with high sensitivity and selectivity. Our numerical analysis includes a wide range of wavelengths from visible to mid-infrared. We determined the strip waveguide’s single-mode dimensions and the optimum dimensions that maximize the sensitivity to the virus layer attached to its surface at each wavelength using finite difference eigenmode (FDE) solver. We also compared the strip waveguide with the widely used slot waveguide. Our theoretical study shows that silicon nitride strip waveguide working at lower wavelengths is the optimum choice for virus detection as it maximizes both the waveguide sensitivity (Swg) and the figure of merit (FOM) of the sensor. The optimized waveguides are well suited for a range of viruses with different sizes and refractive indices. Balanced Mach–Zehnder interferometer (MZI) sensors were designed using FDE solver and photonic circuit simulator at different wavelengths. The designed sensors show high FOM at λ = 450 nm ranging from 500 RIU−1 up to 1231 RIU−1 with LMZI = 500 µm. Different MZI configurations were also studied and compared. Finally, edge coupling from the fiber to the sensor was designed, showing insertion loss (IL) at λ = 450 nm of 4.1 dB for the design with FOM = 500 RIU−1. The obtained coupling efficiencies are higher than recently proposed fiber couplers.

The As2S3-Cu interface was studied by dielectric spectroscopy measurements on Cu-As2S3-Cu thin film heterostructure samples to assess the charge carriers’ contribution to the electrical properties of such an interface. Three-dimensional printed masks ensured good reproducibility during the PLD deposition of heterostructure samples. The samples were tested for electrical conductivity and AC photoconductivity by dielectric spectroscopy measurements. DC bias voltages and light were applied to the samples. The electrical capacity of the thin film heterostructure can be modified electrically and optically. We observed long-term photoconductivity with a time dependency that was not exponential, and a quick change of the electrical capacity, indicating the potential of the heterostructure cells as photodetector candidates.

In the present study, the effectiveness of a multilayer film of polylactide (PLA), fully bio-based and compostable, was ascertained to develop a novel sustainable packaging solution for the preservation of fresh pork meat. To this end, the multilayer PLA films were first characterized in terms of their thermal characteristics, structure, mechanical performance, permeance to water and aroma vapors and oxygen, and optical properties and, for the first time, compared with two commercial high-barrier multilayer packaging films. Thereafter, the multilayers were thermosealed to package fillets of fresh pork meat and the physicochemical changes, lipid oxidation levels, and microbiological counts were monitored in the food samples during storage under refrigeration conditions. Results showed that the meat fillets packaged in PLA developed a redder color and showed certain indications of dehydration and oxidation, being more noticeably after 11 days of storage, due to the higher water vapor and oxygen permeance values of the biopolymer multilayer. However, the pH changes and bacterial growth in the cold-stored fresh pork meat samples were minimal and very similar in the three tested multilayer films, successfully accomplishing the requirements of the food quality and safety standards at the end of storage.

Uncontrolled hemorrhage remains a leading cause of death in both emergency and military medicine. Tourniquets are essential to stopping hemorrhage in these scenarios, but they suffer from subjective, inconsistent application. Here, we demonstrate how tourniquet application can be automated using sensors and computer algorithms. The auto-tourniquet self-tightens until blood pressure oscillations are no longer registered by the pressure sensor connected to the pneumatic pressure cuff. The auto-tourniquet’s performance in stopping the bleed was comparable to manual tourniquet application, but the time required to fully occlude the bleed was longer. Application of the tourniquet was significantly smoother, and less variable, for the automatic tourniquet compared to manual tourniquet application. This proof-of-concept study highlights how automated tourniquets can be integrated with sensors to provide a much more consistent application and use compared to manual application, even in controlled, low stress testing conditions. Future work will investigate different sensors and tourniquets to improve the application time and repeatability.

Deep learning based medical image registration remains very difficult and often fails to improve over its classical counterparts where comprehensive supervision is not available, in particular for large transformations—including rigid alignment. The use of unsupervised, metric-based registration networks has become popular, but so far no universally applicable similarity metric is available for multimodal medical registration, requiring a trade-off between local contrast-invariant edge features or more global statistical metrics. In this work, we aim to improve over the use of handcrafted metric-based losses. We propose to use synthetic three-way (triangular) cycles that for each pair of images comprise two multimodal transformations to be estimated and one known synthetic monomodal transform. Additionally, we present a robust method for estimating large rigid transformations that is differentiable in end-to-end learning. By minimising the cycle discrepancy and adapting the synthetic transformation to be close to the real geometric difference of the image pairs during training, we successfully tackle intra-patient abdominal CT-MRI registration and reach performance on par with state-of-the-art metric-supervision and classic methods. Cyclic constraints enable the learning of cross-modality features that excel at accurate anatomical alignment of abdominal CT and MRI scans.

Video surveillance systems process high volumes of image data. To enable long-term retention of recorded images and because of the data transfer limitations in geographically distributed systems, lossy compression is commonly applied to images prior to processing, but this causes a deterioration in image quality due to the removal of potentially important image details. In this paper, we investigate the impact of image compression on the performance of object detection methods based on convolutional neural networks. We focus on Joint Photographic Expert Group (JPEG) compression and thoroughly analyze a range of the performance metrics. Our experimental study, performed over a widely used object detection benchmark, assessed the robustness of nine popular object-detection deep models against varying compression characteristics. We show that our methodology can allow practitioners to establish an acceptable compression level for specific use cases; hence, it can play a key role in applications that process and store very large image data.

Bread is a high glycemic index (GI) food with high amounts of readily digestible carbohydrates. Fucoidan refers to a group of sulfated polysaccharides isolated from brown seaweed that has been gaining traction for its many functional properties, including its ability to inhibit starch hydrolases. In this study, fucoidan was added into bread to lower the glycemic index of bread. Fucoidan fortification at 3.0% reduced the starch digestion rate of baked bread by 21.5% as compared to control baked bread. This translated to a 17.7% reduction in the predicted GI (pGI) with 3.0% of fucoidan. Fucoidan was retained in the bread after baking. Although the in vitro bioavailability of fucoidan was negligible, the in vitro bioaccessibility of fucoidan was high, at 77.1–79.8%. This suggested that although fucoidan may not be absorbed via passive diffusion, there is potential for the fucoidan to be absorbed via other modes of absorption. Thus, there is a potential for the use of fucoidan as a functional ingredient in bread to reduce the glycemic potential of bread.

We developed a high spatially-resolved ion-imaging system using focused electron beam excitation. In this system, we designed a nanometric thin sensor substrate to improve spatial resolution. The principle of pH measurement is similar to that of a light-addressable potentiometric sensor (LAPS), however, here the focused electron beam is used as an excitation carrier instead of light. A Nernstian-like pH response with a pH sensitivity of 53.83 mV/pH and linearity of 96.15% was obtained. The spatial resolution of the imaging system was evaluated by applying a photoresist to the sensing surface of the ion-sensor substrate. A spatial resolution of 216 nm was obtained. We achieved a substantially higher spatial resolution than that reported in the LAPS systems.

Cold atmospheric plasma (CAP) is a non-thermal technology that could be applied for food decontamination from both biological (microorganisms) and chemical (pesticides, food allergens, mycotoxins) contaminants, thanks to the production of reactive species (RS). However, RS could also promote the onset and the progress of food lipid oxidation, which may limit the quality and acceptability of the final products. The aim of this work was to assess the oxidation degree of pistachio kernels after treatment in a surface dielectric barrier discharge (SDBD). Two different operative conditions for CAP generation were investigated, resulting in the production of high (800 ppm) or low (300 ppm) concentrations of ozone. Limited amounts of hydroperoxides (3.00–4.22 mEq O2/kg), thiobarbituric acid reactive substances (TBARS, 0.072–0.600 mg TEP/g oil), and phytosterol oxidation products (POPs, 14.43–17.20 μg/g) were observed in lipids of both control and plasma processed pistachios. Plasma treatments did not significantly affect the total fatty acid composition and the amounts of identified unsaponifiable matter constituents (4-desmethylsterols, 4,4-dimethylsterols, 4-methylsterols), except for an unexpected significant increase of γ-tocopherol content in extracted oils. These findings contribute to gaining further knowledge for the scale-up of CAP technology to industrial processing.

This study aimed to investigate the functional and quality improvement of retorted Korean ginseng chicken soup that was hydrolyzed using a single extract from Cordyceps militaris (CM) mushroom, or in combination with bromelain, flavorzyme, or a mix of both. A total of 36 fat-trimmed breast meat from commercial broilers were hydrolyzed with one of six treatments, (1) flavorzyme as a positive control (PC), (2) no addition as negative control (NC), (3) crude CM extract (CME), CM extract prepared with either (4) bromelain (CMB), (5) flavorzyme (CMF), or (6) bromelain:flavorzyme mixture (CMBF) in a water bath at 55 °C for 2.5 h, and subsequently retorted at 121.1 °C, 147.1 kPa for 1 h. The highest antioxidant activity was observed in the CMB treatment (40.32%), followed by CMBF (34.20%), and CME (32.97%). The suppression of malondialdehyde ranged between 28 and 83%. The water-holding-capacity of the treated samples increased, ranging between 59.69 and 62.98%, and significantly tenderized the meat. The shear force decreased from 23.05 N in negative control to 11.67 N in the CMB samples. The predominant nucleotides across the samples were 5′-IMP and hypoxanthine, and the lowest was adenosine. The intensification of the taste properties was due to the increase of umami substances, both by 5′-nucleotides (5′-IMP, 5′-GMP) and free amino acids (FAAs), whereas the highest improvement was observed in the CMB group. Therefore, the hydrolyzation of Korean ginseng chicken soup using CM extract, prepared using bromelain, improves functional and quality profiles.

The aroma characteristics of six red jujube cultivars (Jinchang—‘JC’, Junzao—‘JZ’, Huizao—‘HZ’, Qiyuexian—‘QYX’, Hetiandazao—‘HTDZ’, and Yuanzao—‘YZ’), cultivated in Xinjiang Province, China, were studied by E-nose and GC-IMS. The presence of acetoin, E-2-hexanol, hexanal, acetic acid, and ethyl acetate played an important role in the classification results. JC, JZ, HZ, and YZ were different from others, while QYX and HTDZ were similar to each other. HZ had the most abundant specific VOCs, including linalool, nonanoic acid, methyl myristoleate, 2-acetylfuran, 1-octen-3-one, E-2-heptenal, 2-heptenone, 7-octenoic acid, and 2-pentanone. HZ had higher intensity in jujube ID, floral, sweet, and fruity attributes. Correlation analysis showed that jujube ID (identity) might be related to phenylacetaldehyde and isobutanoic acid that formed by the transamination or dehydrogenation of amino acids; meanwhile, the sweet attribute was correlated with amino acids, including threonine, glutamic acid, glycine, alanine, valine, leucine, tyrosine, phenylalanine, lysine, histidine, and arginine.

The cross-linking degree between myosin affected the surimi gel properties in the hairtail. In this study, the effects of phlorotannin extracts (PE) derived from brown algae (Sargassum horneri) with different concentrations (0.05%, 0.3%, 1% w/w) on the hairtail surimi gel-forming properties were investigated in comparison with the commercial phloroglucinol (PG). The breaking forces of surimi gel with 1% PE and 0.05% PG were increased by 14.80% and 2.73%, respectively. The increase in deformation was 9.66% with 1% PE compared with the control added with water, but there was no increase in deformation of surimi gel with 0.05% PG. The improved surimi gel structure with PE as a bridge for the three-dimensional network forming of protein was observed in the microstructure. Moreover, PE could significantly shorten the water relaxation time (p < 0.05), reduce free water content (p < 0.05), and increase the hydrogen proton density of the hairtail surimi according to the results of NMR, dielectric properties, and MRI map, respectively. Our findings suggest that the extracts from the brown algae could be a potential economical gel structure enhancer to improve the myosin network.

Detecting pedestrians in autonomous driving is a safety-critical task, and the decision to avoid a a person has to be made with minimal latency. Multispectral approaches that combine RGB and thermal images are researched extensively, as they make it possible to gain robustness under varying illumination and weather conditions. State-of-the-art solutions employing deep neural networks offer high accuracy of pedestrian detection. However, the literature is short of works that evaluate multispectral pedestrian detection with respect to its feasibility in obstacle avoidance scenarios, taking into account the motion of the vehicle. Therefore, we investigated the real-time neural network detector architecture You Only Look Once, the latest version (YOLOv4), and demonstrate that this detector can be adapted to multispectral pedestrian detection. It can achieve accuracy on par with the state-of-the-art while being highly computationally efficient, thereby supporting low-latency decision making. The results achieved on the KAIST dataset were evaluated from the perspective of automotive applications, where low latency and a low number of false negatives are critical parameters. The middle fusion approach to YOLOv4 in its Tiny variant achieved the best accuracy to computational efficiency trade-off among the evaluated architectures.

The extraction of molecules with high added value plays an important role in the recovery of food waste. This work aimed to valorize tomato pomace, a by-product composed of skin and seeds, through extraction of carotenoids, especially lycopene and β-carotene. The tomato pomace was dried using three different methods (freeze-drying, heat drying, and non-thermal air-drying) to reduce its weight, volume, and water activity and to concentrate the carotenoid fraction. These drying approaches were compared considering the extractive potential. Three solvent mixtures were compared, a traditional one (n-hexane:acetone) and two green deep eutectic solvent mixtures (ethyl acetate:ethyl lactate and menthol:lactic acid) in combination with different drying procedures. The extract obtained using ethyl acetate:ethyl lactate with non-thermal air-drying showed the highest contents of lycopene and β-carotene (75.86 and 3950.08 µg/g of dried sample, respectively) compared with the other procedures.

The Pacific oyster is a commercially important mollusc and, in contrast to most other shellfish species, frequently consumed without prior heat treatment. Oysters are rich in many nutrients but can also cause food allergy. Knowledge of their allergens and cross-reactivity remains very limited. These limitations make an optimal diagnosis of oyster allergy difficult, in particular to the Pacific oyster (Crassostrea gigas), the most cultivated and consumed oyster species worldwide. This study aimed to characterise IgE sensitisation profiles of 21 oyster-sensitised patients to raw and heated Pacific oyster extract using immunoblotting and advanced mass spectrometry, and to assess the relevance of recombinant oyster allergen for improved diagnosis. Tropomyosin was identified as the major allergen recognised by IgE from 18 of 21 oyster-sensitised patients and has been registered with the WHO/IUIS as the first oyster allergen (Cra g 1). The IgE-binding capacity of oyster-sensitised patients’ IgE to purified natural and recombinant tropomyosin from oyster, prawn, and dust mite was compared using enzyme-linked immunosorbent assay. The degree of IgE binding varied between patients, indicating partial cross-sensitisation and/or co-sensitisation. Amino acid sequence alignment of tropomyosin from these three species revealed five regions that contain predicted IgE-binding epitopes, which are most likely responsible for this cross-reactivity. This study fully biochemically characterises the first and major oyster allergen Cra g 1 and demonstrates that the corresponding recombinant tropomyosin should be implemented in improved component-resolved diagnostics and guide future immunotherapy.

Fermentation of plant-based substrates with edible fungi enhances the nutrient profile and digestibility, but it has been scarcely applied to edible seeds, which are rich in healthy lipids. In this study, chia and sesame seeds were solid-state fermented with Pleurotus ostreatus, followed by drying and milling. Fermentation led to increased content of lipid and protein in both seeds' products, and a change in fatty acid profile in favor of increased polyunsaturated fatty acids. Then, the samples were subjected to in vitro digestion. Lipolysis, determined by nuclear magnetic resonance, was higher in sesame than in chia products, and the fermented counterparts had increased values compared to the controls. In terms of physical properties, fermentation showed reduced particle size and increased matrix degradation and decreased viscosity of the digestion medium, which were related to increased lipolysis. In conclusion, applying solid-state fermentation on chia and sesame seeds could be a recommendable approach., The authors would like to thank the following institutions and project for funding this research: The predoctoral grant awarded to Andrea Asensio Grau (Grant number ACIF/2017/008) by Conselleria de Educació i Investigació de la Generalitat Valenciana and the European Union (“El Fondo Social Europeo (FSE) invierte en tu futuro”); the post-doctoral grant awarded to Joaquim Calvo-Lerma (Grant number APOSTD 2019-102) by Conselleria de Educació I Investigació de la Generalitat Valenciana; and the project PID2019-107723RB-C22 funded by the Ministry of Science and Innovation MCIN/AEI/10.1309/501100011033.

Weight-bearing exoskeletons are robots that need to carry loads and interact with humans frequently. Therefore, the actuators of these exoskeletons are supposed to be capable of outputting sufficient force with high compliance and little weight. A series–parallel elastic actuator (SPEA) is designed, in this work, to meet the demanding requirements of an exoskeleton robot called PALExo. A gas spring is installed in parallel with an electric cylinder to adjust the force output range of the actuator according to the needs of the exoskeleton. A series elastic module (SEM) is installed in series with the electric cylinder and gas spring to improve the compliance of the actuator, the stiffness of which is variable to adapt to the different stiffness requirements of the exoskeleton’s legs in the standing phase and swinging phase. A force controller combining dynamic compensation and a cascade control with an inner velocity loop and a disturbance observer is designed for the SPEA. The performance of the force controller is verified by experiments and the results demonstrate that the controller has good adaptability to the stiffness of the SEM.

Food-derived hypotensive peptides have attracted attention in the field of active peptide research in recent years. In this study, based on ACE inhibition rate and using the Box–Behnken central combination design principle to optimise the process of ACE inhibitor peptides prepared by double-enzyme hydrolysis. The amino acid sequences of ACE inhibitor peptides were determined by liquid chromatography mass spectrometry (LC-MS/MS), and their binding to ACE was studied by molecular docking. The optimal processing conditions were 1:1 alkaline protease: compound protease, pH was 8.43, enzymolysis temperature was 44.32 °C, and enzymolysis time was 3.52 h. Under these conditions, the ACE inhibition rate reached 65.12%, and the inhibition rate after separation and purification was 80.68% (IC50 = 0.9 mg/mL). Three novel peptides with ACE inhibitory activity were detected by LC-MS/MS, with sequences LVYP (Leu-Val-Tyr-Pro), VYPW(Val-Tyr-Pro-Trp) and YPWT(Tyr-Pro-Trp-Thr). Molecular docking revealed that the three novel peptides all established hydrogen bonds with the S1(Tyr523, Glu384, Ala354) and S2 (His353) pockets of ACE. Among them, LVYP, VYPW and YPWT, respectively, formed eleven hydrogen bonds, six hydrogen bonds and nine hydrogen bonds with ACE. The study revealed that these peptides have the potential for the development of novel ACE inhibitor drugs and provide a new avenue for high-value utilisation of mushrooms scraps.

Peptide Phe-Thr-Gly-Met-Leu (FTGML) is a bioactive oligopeptide with tyrosinase inhibitory activity derived from gelatin hydrolysate of grass carp scales. Previous studies have shown that FTGML addition can effectively inhibit mushroom tyrosinase activity in vitro, and also has some effect on the inhibition of melanogenesis in zebrafish in vivo, but the underlying mechanism is not fully understood. In this study, we used FTGML to treat B16F10 melanoma cells, and found a significant inhibition of tyrosinase activity and melanin synthesis. Interestingly, the treatment showed a strong correlation between antioxidant activity and anti-melanin, which was associated with FTGML reducing the involvement of reactive oxygen species in melanin synthesis. Furthermore, FTGML reduced melanogenesis in B16F10 cells by downregulating the cAMP-PI3K/Akt and MAPK pathways (p38 and JNK). These results suggested that FTGML can reduce melanin production in mouse B16F10 melanoma cells through multiple pathways.

Arthrospira platensis and Arthrospira maxima are prokaryotic microalgae commercially marketed as spirulina. The pigments extracted from these algae are widely used for cosmetic and nutraceutical applications. This work aimed to evaluate the influence of three light-emitting lamps (white, orange and blue) on the growth and biomass composition of two strains of A. platensis (M2 and M2M) and one of A. maxima. The obtained data show strain- and light-dependent responses of the microalgae. In addition, white and orange lights led to a similar overall effect by increasing the levels of chlorophyll a and carotenoids. However, exposure to orange light resulted in the highest dry weight (5973.3 mg L−1 in M2M), whereas white light stimulated an increase in the carbohydrate fraction (up to 42.36 g 100 g−1 in A. maxima). Conversely, blue light led to a constant increase in the concentration of phycocyanin (14 g 100 g−1 in A. maxima) and a higher content of proteins in all strains. These results provide important environmental information for modulating the growth of different spirulina strains, which can be used to address the synthesis of biochemical compounds of strategic importance for the development of new nutraceutical foods.

Real-time systems are widely used in industry, including technological process control systems, industrial automation systems, SCADA systems, testing, and measuring equipment, and robotics. The efficiency of executing an intelligent robot’s mission in many cases depends on the properties of the robot’s sensor and control systems in providing the trajectory planning, recognition of the manipulated objects, adaptation of the desired clamping force of the gripper, obstacle avoidance, and so on. This paper provides an analysis of the approaches and methods for real-time sensor and control information processing with the application of machine learning, as well as successful cases of machine learning application in the synthesis of a robot’s sensor and control systems. Among the robotic systems under investigation are (a) adaptive robots with slip displacement sensors and fuzzy logic implementation for sensor data processing, (b) magnetically controlled mobile robots for moving on inclined and ceiling surfaces with neuro-fuzzy observers and neuro controllers, and (c) robots that are functioning in unknown environments with the prediction of the control system state using statistical learning theory. All obtained results concern the main elements of the two-component robotic system with the mobile robot and adaptive manipulation robot on a fixed base for executing complex missions in non-stationary or uncertain conditions. The design and software implementation stage involves the creation of a structural diagram and description of the selected technologies, training a neural network for recognition and classification of geometric objects, and software implementation of control system components. The Swift programming language is used for the control system design and the CreateML framework is used for creating a neural network. Among the main results are: (a) expanding the capabilities of the intelligent control system by increasing the number of classes for recognition from three (cube, cylinder, and sphere) to five (cube, cylinder, sphere, pyramid, and cone); (b) increasing the validation accuracy (to 100%) for recognition of five different classes using CreateML (YOLOv2 architecture); (c) increasing the training accuracy (to 98.02%) and testing accuracy (to 98.0%) for recognition of five different classes using Torch library (ResNet34 architecture) in less time and number of epochs compared with Create ML (YOLOv2 architecture); (d) increasing the training accuracy (to 99.75%) and testing accuracy (to 99.2%) for recognition of five different classes using Torch library (ResNet34 architecture) and fine-tuning technology; and (e) analyzing the effect of dataset size impact on recognition accuracy with ResNet34 architecture and fine-tuning technology. The results can help to choose efficient (a) design approaches for control robotic devices, (b) machine-learning methods for performing pattern recognition and classification, and (c) computer technologies for designing control systems and simulating robotic devices.

Established Internet of Things (IoT) platforms suffer from their inability to determine whether an IoT app is secure or not. A security analysis system (SAS) is a protective shield against any attack that breaks down data privacy and security. Its main task focuses on detecting malware and verifying app behavior. There are many SASs implemented in various IoT applications. Most of them build on utilizing static or dynamic analysis separately. However, the hybrid analysis is the best for obtaining accurate results. The SAS provides an effective outcome according to many criteria related to the analysis process, such as analysis type, characteristics, sensitivity, and analysis techniques. This paper proposes a new hybrid (static and dynamic) SAS based on the model-checking technique and deep learning, called an HSAS-MD analyzer, which focuses on the holistic analysis perspective of IoT apps. It aims to analyze the data of IoT apps by (1) converting the source code of the target applications to the format of a model checker that can deal with it; (2) detecting any abnormal behavior in the IoT application; (3) extracting the main static features from it to be tested and classified using a deep-learning CNN algorithm; (4) verifying app behavior by using the model-checking technique. HSAS-MD gives the best results in detecting malware from malicious smart Things applications compared to other SASs. The experimental results of HSAS-MD show that it provides 95%, 94%, 91%, and 93% for accuracy, precision, recall, and F-measure, respectively. It also gives the best results compared with other analyzers from various criteria.