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Accordingly, we demonstrated that the giant magnetoimpedance effect of Co-rich microwires can be tailored by controlling the magnetic anisotropy of Co-rich microwires, using appropriate thermal treatment.Ergonomics evaluation through measurements of biomechanical parameters in real time has a great potential in reducing non-fatal occupational injuries, such as work-related musculoskeletal disorders. Assuming a correct posture guarantees the avoidance of high stress on the back and on the lower extremities, while an incorrect posture increases spinal stress. Here, we propose a solution for the recognition of postural patterns through wearable sensors and machine-learning algorithms fed with kinematic data. Twenty-six healthy subjects equipped with eight wireless inertial measurement units (IMUs) performed manual material handling tasks, such as lifting and releasing small loads, with two postural patterns correctly and incorrectly. Measurements of kinematic parameters, such as the range of motion of lower limb and lumbosacral joints, along with the displacement of the trunk with respect to the pelvis, were estimated from IMU measurements through a biomechanical model. Statistical differences were found for all kinematic parameters between the correct and the incorrect postures (p less then 0.01). Moreover, with the weight increase of load in the lifting task, changes in hip and trunk kinematics were observed (p less then 0.01). To automatically identify the two postures, a supervised machine-learning algorithm, a support vector machine, was trained, and an accuracy of 99.4% (specificity of 100%) was reached by using the measurements of all kinematic parameters as features. Meanwhile, an accuracy of 76.9% (specificity of 76.9%) was reached by using the measurements of kinematic parameters related to the trunk body segment.Scene recognition is an essential part in the vision-based robot navigation domain. The successful application of deep learning technology has triggered more extensive preliminary studies on scene recognition, which all use extracted features from networks that are trained for recognition tasks. In the paper, we interpret scene recognition as a region-based image retrieval problem and present a novel approach for scene recognition with an end-to-end trainable Multi-column convolutional neural network (MCNN) architecture. The proposed MCNN utilizes filters with receptive fields of different sizes to have Multi-level and Multi-layer image perception, and consists of three components front-end, middle-end and back-end. The first seven layers VGG16 are taken as front-end for two-dimensional feature extraction, Inception-A is taken as the middle-end for deeper learning feature representation, and Large-Margin Softmax Loss (L-Softmax) is taken as the back-end for enhancing intra-class compactness and inter-class-separability. Extensive experiments have been conducted to evaluate the performance according to compare our proposed network to existing state-of-the-art methods. Experimental results on three popular datasets demonstrate the robustness and accuracy of our approach. To the best of our knowledge, the presented approach has not been applied for the scene recognition in literature.Despite the growing interest in pulsed electric field modes in membrane separation processes, there are currently not many works devoted to studying the effect of the surface properties and composition of ion-exchange membranes on their efficiency in these modes. In this paper, we have shown the effect of increasing mass transfer using different kinds of ion-exchange membranes (heterogeneous and homogeneous with smooth, undulated, and rough surfaces) during electrodialysis in the pulsed electric field modes at underlimiting and overlimiting currents. It was found that the maximum increment in the average current is achieved when the average potential corresponds to the right-hand edge of the limiting current plateau of the voltammetric curve, i.e., at the maximum resistance of the system in the DC mode. For the first time, the development of electroconvective vortices was visualized in pulsed electric field modes and it was experimentally shown that even at relatively low frequencies, a non-uniform concentration field is preserved at the time of a pause, which stimulates the rapid development of electroconvection when pulses are switched on again. In the case of relatively high pulse frequencies, the electroconvective vortices formed during a pulse lapse do not completely decay during a pause; they only slightly decrease in size.In the traditional single polarimetric persistent scatterers interferometric (PSI) technology, the amplitude dispersion index (ADI) is usually used to select persistent scatterer candidates (PSC). Obviously, based on single polarimetric information, it is difficult to use the statistical characteristics for comprehensively describing the temporal stability of scatterers, which leads to a decrease in persistent scatterer (PS) density. Considering that the temporal polarimetric stationarity of PS, the paper is based on complex Wishart distribution and proposes the polarimetric stationarity omnibus test (PSOT) for identifying PSC. The nonstationary pixels can be removed by the preset significance threshold, which reduces the subsequent processing error and the calculation cost. Then, the exhaustive search polarimetric optimization (ESPO) method is selected for improving the phase quality of PSCs while suppressing the sidelobe of the strong scatterer effectively. For validating the effectiveness of the proposed method, we select a time-series quad-polarimetric ALOS PALSAR-1 images in an urban area as experimental data and mainly perform five group experiments for detailed analysis, including the PSOT+ESPO, ADI+ESPO, ADI+HH, ADI+HV, and ADI+VV. The results show that the proposed PSOT+ESPO method has a better performance on both PSC selection and interferometric phase optimization aspects than that of other methods. Specifically, compared to the last four methods, both the PSCs and PSs identified by the proposed PSOT+ESPO are more concentrated in the high-coherence region. The PSs with the standard deviation (STD) less than 5mm in the PSOT+ESPO method account for 94% of all PSs, which is greater than that of the ADI+ESPO, ADI+HH, ADI+HV, and ADI+VV methods, respectively.In this study, we developed a modified glassy carbon electrode (GCE) with graphene oxide, multi-walled carbon nanotube hybrid nanocomposite in chitosan (GCE/GO-MWCNT-CHT) to achieve simultaneous detection of four nucleobases (i.e., guanine (G), adenine (A), thymine (T) and cytosine (C)) along with uric acid (UA) as an internal standard. The nanocomposite was characterized using TEM and FT-IR. The linearity ranges were up to 151.0, 78.0, 79.5, 227.5, and 162.5 µM with a detection limit of 0.15, 0.12, 0.44, 4.02, 4.0, and 3.30 µM for UA, G, A, T, and C, respectively. Compared to a bare GCE, the nanocomposite-modified GCE demonstrated a large enhancement (~36.6%) of the electrochemical active surface area. Through chronoamperometric studies, the diffusion coefficients (D), standard catalytic rate constant (Ks), and heterogenous rate constant (Kh) were calculated for the analytes. Moreover, the nanocomposite-modified electrode was used for simultaneous detection in human serum, human saliva, and artificial saliva samples with recovery values ranging from 95% to 105%.The treatment of invasive fungal infections remains challenging and the emergence of new fungal pathogens as well as the development of resistance to the main antifungal drugs highlight the need for novel therapeutic strategies. Although in vitro antifungal susceptibility testing has come of age, the proper evaluation of therapeutic efficacy of current or new antifungals is dependent on the use of animal models. Mammalian models, particularly using rodents, are the cornerstone for evaluation of antifungal efficacy, but are limited by increased costs and ethical considerations. To circumvent these limitations, alternative invertebrate models, such as Galleria mellonella, have been developed. Larvae of G. mellonella have been widely used for testing virulence of fungi and more recently have proven useful for evaluation of antifungal efficacy. This model is suitable for infection by different fungal pathogens including yeasts (Candida, Cryptococcus, Trichosporon) and filamentous fungi (Aspergillus, Mucorales). Antifungal efficacy may be easily estimated by fungal burden or mortality rate in infected and treated larvae. The aim of the present review is to summarize the actual data about the use of G. mellonella for testing the in vivo efficacy of licensed antifungal drugs, new drugs, and combination therapies.Plant-based diets have been linked to both health benefits and a lower climate impact. However, plant-based diets may represent both healthy and unhealthy dietary practices. The present study aimed to develop a nationally adapted healthy plant-based diet based on the global EAT-Lancet reference diet. Development took place in a series of steps. First, the original EAT-Lancet reference diet was evaluated based on food availability, i.e., using Danish food data (Model 1). Then, the model was further modified to reflect national food based dietary guidelines (FBDG) and characteristics of current consumption pattern, e.g., by including processed food, discretionary foods and beverages in the diet (Model 2). The contents of macronutrients, vitamins and minerals, except for vitamin D and iodine, were found to be sufficient for Model 2, according to the recommended nutrient density to be used for planning diets for groups of individuals aged 6-65 years. In addition, the study gave an insight into the nutrients and foods to be aware of in planning a predominantly plant-based diet, thereby providing directions for future revisions of sustainable FBDGs. These include a stronger emphasis on the intake of legumes, nuts and seeds, fruit and vegetables including dark green vegetables, whole-grain products and vegetable oils as well as lowering meat intake.Campylobacter jejuni, a zoonotic pathogen that frequently colonizes poultry, possesses two Microbial Surface Components Recognizing Adhesive Matrix Molecule(s) (MSCRAMMs) termed CadF and FlpA that bind to the glycoprotein fibronectin (FN). Previous to this study, it was not known whether the CadF and FlpA proteins were functionally redundant or if both were required to potentiate host cell binding and signaling processes. We addressed these questions by generating a complete repertoire of cadF and flpA mutants and complemented isolates, and performing multiple phenotypic assays. Both CadF and FlpA were found to be necessary for the maximal binding of C. jejuni to FN and to host cells. In addition, both CadF and FlpA are required for the delivery of the C. jejuni Cia effector proteins into the cytosol of host target cells, which in turn activates the MAPK signaling pathway (Erk 1/2) that is required for the C. jejuni invasion of host cells. These data demonstrate the non-redundant and bi-functional nature of these two C. jejuni FN-binding proteins. Taken together, the C. jejuni CadF and FlpA adhesins facilitate the binding of C. jejuni to the host cells, permit delivery of effector proteins into the cytosol of a host target cell, and aid in the rewiring of host cell signaling pathways to alter host cell behavior.