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This paper presents a three-axis inductive tactile force sensor utilizing a magnetorheological elastomer (MRE) marker for inducing inductance change in square-shaped coils, in response to an input force. The sensor consists of copper coils on a printed circuit board (PCB) and a patterned elastomer layer between the marker and coils. The proposed configuration of the coils reduces the mechanical cross-talk between the applied three-axis forces, thus decoupling the force axes. Finite element method (FEM) based analyses are carried out to optimize the sensing gap between the coils and marker. To observe the effect of the elastomer material on the sensor performance, two types of silicone rubbers, Ecoflex-30 and RTV-528 are used to make the patterned elastomer and the marker. The sensor with Ecoflex-30 elastomer can measure forces up to 25 N in normal and 2.5 N in shear directions whereas the sensor with RTV-528 elastomer has a higher force range of up to 30 N for normal and 6 N for shear directions but has a lower sensitivity. A resolution of 12.71 mN, 7.96 mN and 6.61 mN is achieved with Ecoflex-30 whereas for RTV-528 the force resolution is 76.52 mN, 11.7 mN and 23.98 mN for normal, shear and angular shear directions respectively. The hysteresis error for the proposed sensors is 7.2% and 5.3% for Ecoflex-30 and RTV-528 elastomers, respectively. The proposed inductive tactile force sensor can be used in a wide range of robotic surgical applications for sensing multi-directional forces with better resolution and low hysteresis error.

Background: The aim of this study was to investigate the phenotypic features (adherence, biofilm formation, virulence, antibiotic susceptibility) and the genetic background of antibiotic resistance in nosocomial ESCAPE strains consecutively isolated from surgical wound infections in hospitalized patients. Methods: 86 bacterial strains consecutively isolated from various wound infections were analysed by their antibiotic resistance (antibiotic susceptibility testing and PCR for certain antibiotic resistance genes), virulence, biofilm formation and cellular adherence. Results: The bacterial isolates were identified as: Enterobacterales (n = 39) including Escherichia coli (n = 9), Klebsiella pneumoniae (n = 14) Proteus mirabilis (n = 7), followed by Staphylococcus aureus (n = 26) and Enterococcus faecalis (n = 20). Compared to other isolates, S. aureus strains exhibited the highest capacity to produce soluble virulence factors and to develop biofilms in vitro, with significant differences between methicillin resistant and methicillin susceptible isolates. Among enterobacterial isolates, K. pneumoniae strains expressed the highest capacity to develop biofilms. The assessment of bacterial adherence to HeLa cells revealed that all bacterial strains adhered to the cellular substrata, showing various adherence patterns. E. faecalis strains exhibited a low soluble virulence factors profile, a lower capacity to adhere to epithelial cells and to develop biofilms. Conclusions: The present study could contribute to the understanding of the pathology of infected wounds, depending on the etio-logical agents, providing data with positive impact on the therapeutic management of surgical wounds infections.

Amputees with lower limb loss need special care during daily life activities to make the movement natural as before amputation. No such work exists covering the main aspects from causes of amputation to the psycho-social impact of the amputees after using the prosthetic device. This review presents for lower limb prosthesis; the study of lower limb amputation, design & development, control strategies & machine learning algorithms, the psycho-social impact of prosthetic users, and design trends in patents. Research articles, review papers, magazines, letters, study reports, surveys, and patents, etc. have been used as sources for this review. Traumatic injuries and different diseases have been found as common causes of amputation. Design & development section illustrates design mechanisms, the categories of passive, active, & semi-active prostheses, an overview of a subset of commercially available prosthetic devices, and 3D printing of the accessories. The control section provides information about control techniques, sensors used, machine learning algorithms, and their key outcomes. Quality of life, phantom limb pain, and psycho-social impact of prosthetic users have been summarized for different countries that are believed to attract the interest of the readers. We have also developed an open-source database “FAKH-50” for patents to emphasize the design trends and advancements in lower limb prostheses from 1970 to 2020. Overall trend analysis determined is in the descending order as the knee (48%) > ankle (28%) > foot (22%) > hip (2%) patents in the current version of our database. The forthcoming section highlights the challenges and prospects of the domain. A mutual observation demands the design of a bio-compatible, lightweight, and economic prosthesis to track the normal human gait by eliminating phantom limb pain. This will empower the amputees to live a quality life in society. This work may be beneficial for researchers, technicians, clinicians, and amputees.

Zinc oxide (ZnO) nanoparticles (NPs) have been investigated for various skin therapies in recent years. These NPs can improve the healing and modulate inflammation in the wounds, but the mechanisms involved in such changes are yet to be known. In this study, we have designed a facile ZnO nano-coated dressing with improved antimicrobial efficiency against typical wound pathogens involved in biofilm and chronic infections. ZnO NPs were obtained by hydrothermal method and characterized by X-ray diffraction, scanning electron microscopy, transmission electron microscopy, and Fourier-transform infrared spectroscopy. Antibacterial and antibiofilm effects were evaluated against laboratory and clinical isolates of significant Gram-negative (Pseudomonas aeruginosa and Escherichia coli) and Gram-positive (Staphylococcus aureus and Enterococcus faecalis) opportunistic pathogens, by quantitative methods. Our results have shown that the developed dressings have a high antibacterial efficiency after 6–24 h of contact when containing 0.6 and 0.9% ZnO NPs and this effect is similar against reference and clinical isolates. Moreover, biofilm development is significantly impaired for up to three days of contact, depending on the NPs load and microbial species. These results show that ZnO-coated dressings prevent biofilm development of main wound pathogens and represent efficient candidates for developing bioactive dressings to fight chronic wounds.

Efficient antibiotics to cure Pseudomonas aeruginosa persistent infections are currently insufficient and alternative options are needed. A promising lead is to design therapeutics able to modulate key phenotypes in microbial virulence and thus control the progression of the infectious process without selecting resistant mutants. In this study, we developed a nanostructured system based on Fe3O4 nanoparticles (NPs) and eugenol, a natural plant-compound which has been previously shown to interfere with microbial virulence when utilized in subinhibitory concentrations. The obtained functional NPs are crystalline, with a spherical shape and 10–15 nm in size. The subinhibitory concentrations (MIC 1/2) of the eugenol embedded magnetite NPs (Fe3O4@EUG) modulate key virulence phenotypes, such as attachment, biofilm formation, persister selection by ciprofloxacin, and the production of soluble enzymes. To our knowledge, this is the first report on the ability of functional magnetite NPs to modulate P. aeruginosa virulence and phenotypic resistance, our data highlights the potential of these bioactive nanostructures to be used as anti-pathogenic agents.

There are several well-known long-term complications following prosthetic valve replacement. Pannus formation is one of the rare complications whose estimated incidence varies between 0.3% and 1.3% per patient-year [1]. The exact etiology of pannus formation is unknown but histopathologically, pannus formation is due to fibroelastic hyperplasia that variably occurs after valve implantation [2]. The rarity and acute coronary syndrome like clinical presentation makes pannus formation a clinically important diagnosis during initial presentation. Workup preferably includes Transthoracic Echocardiography (TEE) with or without CT angiography. Management includes urgent or emergent surgical excision of the pannus with or without re-replacement of the aortic valve [1]. We present a 66-year-old female who presented with typical angina symptoms along with diffuse ischemic EKG changes five years after aortic valve replacement surgery with Trifecta Bioprosthesis due to severe aortic stenosis with Non-ST Elevation Myocardial Infarction (NSTEMI). Coronary angiography done on admission showed left main ostial stenosis of 90-95 % in proximity to the aortic valve prosthesis. A Transthoracic Echocardiogram (TTE) revealed an ejection fraction of 30-35 % with global hypokinesia but did not suggest any valve dysfunction. CT angiogram was done the next day which revealed a noncalcified plaque (pannus) greater than 75% occlusion in the left main in proximity of the aortic valve. Despite expedited care and surgical evaluation, unfortunately the patient decompensated in the surgical operating room and did not survive.

Experimentation and analysis in brain-computer interface (BCI) has increasingly been receiving quite some consideration as a substitute communication possibility for patients who are severely paralyzed in the last few years. To measure brain activities using optical signals a fairly new and non-invasive brain imaging tool can be put to test know as Functional near-infrared spectroscopy (fNIRS). Comparability low cost, safety, portability and wear ability are some of the main advantages of imaging of brain using this non-invasive modality. We propose in this paper to apply this relatively new non-invasive fNIRS technique to make an image of brain activities during four different mental tasks. These tasks include Mental Arithmetic (MA), Motor Imagery (i.e. Left-Hand and Right-Hand Motor Imagery) and Rest. fNIRS data used is an open access dataset which was collected by Continuous-wave imaging system (NIR Scout NIRx GmbH, Berlin, Germany) with the sampling frequency of 10 Hz. The research we have done in this paper Data synchronization is performed before the data is preprocessed. After the preprocessing and signal analysis of data our results shows hemodynamic behavior of multiple patterns during the tasks performed. These unique patterns of hemodynamic behavior can be used to differentiate and distinguish different task. We were able to compare, differentiate and distinguish the brain signal activities captured while performing 4 different tasks using 3 different classifiers i.e. Linear Discriminant Analysis (LDA), Support Vector Machine (SVM) and K Nearest Neighbor (KNN). The average classification accuracy of above 90% is achieved by using K Nearest Neighbors (KNN).

Analysis of visual cues for fruit classification and sorting allows to automate the visual inspection and packaging process in agricultural applications that is performed so far by human workers. Challenges for automated multi-class sorting systems are similarity in color and shape of different fruit varieties and variation among the same category of fruit. A major constraint in using well known deep neural networks for fruit classification arises because deep neural networks require large training datasets for achieving high accuracies which are generally not available in case of agricultural products especially various fruits and vegetable varieties. A thorough analysis is required to find an appropriate combination of various handcrafted features that could give precise and accurate classification results for small datasets. This paper investigates the use of various handcrafted visual features for fruit classification using traditional machine learning techniques. Different color, shape and texture features are analyzed by comparing the results obtained from six supervised machine learning techniques including K nearest neighbors, Support Vector Machines, Naive Bayes, Linear Discriminant Analysis, Decision Trees and Feed forward back propagation neural network. We propose a novel combination of Hue, Color-SIFT, Discrete Wavelet Transform and Haralick features in fruit classification problem that outperforms other handcrafted visual features. This feature combination is found to be invariant to rotation and illumination effects and works well with intra class variations providing good results for identifying subcategories of fruits along with high classification accuracies obtained for difficult fruit categories that are visually similar. It is found that Color-SIFT features alone work very well for fruit classification problem by outperforming other individual handcrafted features. Our approach is trained and tested on publicly available Fruits 360 dataset. Out of different classifiers best results are obtained using Back Propagation Neural Network, SVM and KNN classifier with classification accuracies between 99% and 100%.

Estimation of on-tree mango maturity is essential for the prediction of harvest time. Dry matter (DM) is a useful index in deciding mango maturity, and post-harvest quality. Existing NIR based maturity meters employ machine learning regressors to predict a maturity index value (such as DM, oBrix, or etc.) and then impose a hard threshold on predicted value to estimate maturity state of the fruit. In this paper, a new approach for non-destructive hand-held fruit maturity meter is investigated. The developed approach directly classifies the maturity state (mature/immature) using a classifier trained on maturity labels assigned through standard DM thresholds for investigated mango varieties. To develop the hardware of the device, a commercial-off-the-shelf development kit of NIR micro-spectrometer in the spectral range of 400–1100 nm was used with an embedded computational hardware, a micro-halogen lamp, a lithium battery, and a display. The application software (developed in C++) is designed to collect interactance spectra, remove noise, reduce dimensionality, and classify maturity state. Performance of the developed approach is evaluated by on-tree test samples of mango fruit of different season. Comparison of both the literature reported indirect maturity estimation and proposed direct maturity classification is conducted. The test results show that the maximum accuracy achieved using indirect maturity estimation using hard thresholds is 55.9%. Whereas direct maturity classification using KNN achieved 88.2% accuracy in predicting the maturity state (mature/immature) of the test mangoes. Overall results show that the developed DM mango maturity method has considerable potential to detect maturity state of mangoes in practical situations.

The study proposed the classification and recognition of hand gestures using electromyography (EMG) signals for controlling the upper limb prosthesis. In this research, the EMG signals were measured through an embedded system by wearing a band of MYO gesture control. In order to observe the behavior of these change movements, the EMG data was acquired from 10 healthy subjects (five male and five females) performing four upper limb movements. After extracting EMG data from MYO, the supervised classification approach was applied to recognize the different hand movements. The classification was performed with a 5-fold cross-validation technique under the supervision of Quadratic discriminant analysis (QDA), support vector machine (SVM), random forest, gradient boosted, ensemble (bagged tree), and ensemble (subspace K-Nearest Neighbors) classifier. The execution of these classifiers shows the overall accuracy of 83.9% in the case of ensemble (bagged tree) which is higher than other classifiers. Additionally, in this research an embedded system-based classification approach of hand movement was used for designing an upper limb prosthesis. This approach is different than previous techniques as MYO is used with an external Bluetooth module and different libraries that make its movement and performance boundless. The results of this study also inferred the operations which were easy for hand recognition and can be used for developing a powerful, efficient, and flexible prosthetic design in the future.

The design objective of the 4G and beyond networks is not only to provide high data rate services but also ensure a good subscriber experience in terms of quality of service. However, the main challenge to this objective is the growing size and heterogeneity of these networks. This paper proposes a genetic-algorithm-based approach for the self-optimization of interference mitigation parameters for downlink inter-cell interference coordination parameter in Long Term Evolution (LTE) networks. The proposed algorithm is generic in nature and operates in an environment with the variations in traffic, user positions and propagation conditions. A comprehensive analysis of the obtained simulation results is presented, which shows that the proposed approach can significantly improve the network coverage in terms of call accept rate as well as capacity in terms of throughput.