Search

Gait analysis identifies the posture during movement in order to provide the correct actions for a normal gait. A person’s gait may differ from others and can be recognized by specific patterns. Healthy individuals exhibit normal gait patterns, while lower limb amputees exhibit abnormal gait patterns. To better understand the pitfalls of gait, it is imperative to develop systems capable of capturing the gait patterns of healthy individuals. In this research, spatio-temporal parameters were computed using the concepts of static and dynamic equilibrium to analyze the gait cycle. A relationship was also developed among static equilibrium, dynamic equilibrium, speed, and body states. A sensing unit was installed on the designed metal-based leg mounting assembly on the lateral side of the leg. An algorithm was proposed based on two variables: the position of the leg in space and the angle of the knee joint measured by using an inertial measurement unit (IMU) sensor and a rotary encoder. It was acceptable to satisfy the static conditions when the body was in a fixed orientation, whether lying down or standing. While walking and running, the orientation was determined by the position and knee angle variables, which fulfill the dynamic condition. High speed reveals a rapid change in orientation, while slow speed reveals a slow change in orientation. The proposed encoder-based feedback system successfully determined the flexion at 47°, extension at 153°, and all seven gait cycle phases were recognized within this range of motion. Computed spatio-temporal parameters may help individuals avoid slipping or falling.

Gait recognition provides the opportunity to identify different walking styles of people without physical intervention. However, covariates such as changing clothes and carrying conditions may influence recognition accuracy. Our objective was to identify the walking patterns of people for different covariates through analyzing images from publicly available data set CASIA-B on gait. On the dataset, the proposed method was evaluated by using GEI (gait energy image) as inputs for normal walking, changing clothes, and carrying conditions in a multi-view environment. A support vector machine (SVM) and a histogram of oriented gradients (HOG) were applied to classify images of the human gait in order to meet the objectives. Observations show that, under consideration of the mean of the individual accuracies, the accuracy of recognition is in the following order: clothing > normal walk > carrying at a 90° angle. Measurement accuracy of 87.9% was achieved for the coat-wearing people and measurement accuracy of 83.33% was achieved for all the mentioned covariates. The accuracy of the clothing covariate stated as 87.9% is a useful for people especially for different season like winter.

Weeds affects crops health as it shares water and nutrients from the soil, as a result it decreases crop yield. Manual weedicide spray through bag-pack is hazardous to human health. Localized autonomous weedicide spray through aerial spraying units can help save water, weedicide chemical and effect less on human health. Such systems require multi-spectral cues to classify crop, weed, and soil surface. Our focus in this paper is on the detection of weeds in the sugar beet crop, using air-borne multispectral camera sensors, which is considered as an alternative crop to sugarcane to obtain sugar in Pakistan. We developed a new framework for weed identification; a patch-based classification approach as appose to semantic segmentation that is more realistic for real-time intelligent aerial spraying systems. Our approach converts 3-class pixel classification problem into a 2-class crop-weed patch classification problem which in turns improves crop and weed classification accuracy. For classification, we developed a new VGG-Beet convolutional neural network (CNN), which is based on generic VGG16 (visual graphics group) CNN model with 11 convolutional layers. For experiments, we captured a sugar beet dataset with 3-channel multispectral sensor with a ground sampling distance (GSD) of 0.2 cm/pixel and a height of 4 meters. For better comparison, we used two publicly available sugar beet crop aerial imagery datasets, captured using a 5-channel multispectral sensor and a 4-Channel multispectral sensor with a ground sampling distance of 1cm and a height of 10 meters. We observed that patch-based method is more robust to different lighting conditions. To produce low cost weed detection system usage of Agrocam sensor is recommended, for higher accuracy Red Edge and Sequoia multispectral sensors with more channels should be deployed. We observed higher crop-weed accuracy and lower testing time for our patch-based approach as compared to U-Net and Deeplab based semantic segmentation networks.

Navigating an autonomous vehicle through a circular ramp to join a multilane highway having high-density traffic is a very challenging task. Occupant and vehicle safety are the major considerations while establishing merging scenarios. This article proposes and experimentally implements safe and tactical navigational procedures for merging and leaving the highway scenario using the rendezvous guidance (RG) technique and its modified form. Test scenarios have been established for navigating AVs to merge and leave the highway on a multilane highway with or without the presence of any other vehicle in the zone. In addition, the navigation with enhanced performance of the modified RG algorithm is also established and compared to the conventional RG algorithm. The results indicate that time is reduced considerably by using the modified RG technique. The experimental and simulation results are compared, and their results are the same.

Artificial immune systems (AIS) are intelligent algorithms derived from the principles inspired by the human immune system. In this study, electroencephalography (EEG) signals for four distinct motor movements of human limbs are detected and classified using a negative selection classification algorithm (NSCA). For this study, a widely studied open source EEG signal database (BCI IV–Graz dataset 2a, comprising nine subjects) has been used. Mel frequency cepstral coefficients (MFCCs) are extracted as selected features from recorded EEG signals. Dimensionality reduction of data is carried out by applying two hidden layered stacked auto-encoder. Genetic algorithm (GA) optimized detectors (artificial lymphocytes) are trained using negative selection algorithm (NSA) for detection and classification of four motor movements. The trained detectors consist of four sets of detectors, each set is trained for detection and classification of one of the four movements from the other three movements. The optimized radius of detector is small enough not to mis-detect the sample. Euclidean distance of each detector with every training dataset sample is taken and compared with the optimized radius of the detector as a nonself detector. Our proposed approach achieved a mean classification accuracy of 86.39% for limb movements over nine subjects with a maximum individual subject classification accuracy of 97.5% for subject number eight.

Abstract Quantum systems evolve in time in one of two ways: through the Schrödinger equation or wavefunction collapse. So far, deterministic control of quantum many-body systems in the lab has focused on the former, due to the probabilistic nature of measurements. This imposes serious limitations: preparing long-range entangled states, for example, requires extensive circuit depth if restricted to unitary dynamics. In this work, we use mid-circuit measurement and feed-forward to implement deterministic non-unitary dynamics on Quantinuum’s H1 programmable ion-trap quantum computer. Enabled by these capabilities, we demonstrate a constant-depth procedure for creating a toric code ground state in real-time. In addition to reaching high stabilizer fidelities, we create a non-Abelian defect whose presence is confirmed by transmuting anyons via braiding. This work clears the way towards creating complex topological orders in the lab and exploring deterministic non-unitary dynamics via measurement and feed-forward.

High levels of air pollutants pose significant health risks, increasing the chances of respiratory infections, lung cancer, and heart complications, particularly for those already susceptible to illness. Modern societal advancements have worsened air quality degradation, with daily activities such as transportation, industrial processes, and domestic operations releasing harmful contaminants. This study addresses the urgent need for air quality monitoring and forecasting, especially in developing nations like India, where machine learning-based prediction technologies play a crucial role in understanding environmental aspects. Our study focuses on analyzing and predicting air quality using data from two distinct areas in Kolkata—Victoria and Rabindra. Thorough pre-processing and data analysis have been conducted to identify essential features and detect undermining patterns in the data. Utilizing five classic machine learning algorithms, we predict the air quality category by categorizing the predicted AQI into six AQI classes, emphasizing extensive hyper-parameter tuning for each model. The Rabindra dataset yields the best-performing Support Vector Machine (SVC) model with a 97.98% accuracy, while the best prediction accuracy for Victoria location is 93.29% from Random Forest Classifier (RFC). This study offers valuable insights into the effectiveness of machine learning algorithms for air quality prediction, with the novelty of the study lying in the focus on hyper-parameter tuning to achieve the highest accuracy.

IntroductionNon-melanoma skin cancer comprising Basal cell carcinoma (BCC), Squamous cell carcinoma (SCC), and Intraepidermal carcinoma (IEC) has the highest incidence rate among skin cancers. Intelligent decision support systems may address the issue of the limited number of subject experts and help in mitigating the parity of health services between urban centers and remote areas.MethodIn this research, we propose a transformer-based model for the segmentation of histopathology images not only into inflammation and cancers such as BCC, SCC, and IEC but also to identify skin tissues and boundaries that are important in decision-making. Accurate segmentation of these tissue types will eventually lead to accurate detection and classification of non-melanoma skin cancer. The segmentation according to tissue types and their visual representation before classification enhances the trust of pathologists and doctors being relatable to how most pathologists approach this problem. The visualization of the confidence of the model in its prediction through uncertainty maps is also what distinguishes this study from most deep learning methods.ResultsThe evaluation of proposed system is carried out using publicly available dataset. The application of our proposed segmentation system demonstrated good performance with an F1 score of 0.908, mean intersection over union (mIoU) of 0.653, and average accuracy of 83.1%, advocating that the system can be used as a decision support system successfully and has the potential of subsequently maturing into a fully automated system.DiscussionThis study is an attempt to automate the segmentation of the most occurring non-melanoma skin cancer using a transformer-based deep learning technique applied to histopathology skin images. Highly accurate segmentation and visual representation of histopathology images according to tissue types by the proposed system implies that the system can be used for skin-related routine pathology tasks including cancer and other anomaly detection, their classification, and measurement of surgical margins in the case of cancer cases.

Abstract Robustness of modern information confidentiality algorithm depends on its individual components. Modern block ciphers highly depend on two components namely confusion and diffusion. These two main characteristics in any modern cipher are achieved by substitution and permutation boxes. In this article, a decision-making algorithm is utilized for the selection of optimum substitution box (S-box), which plays a significant role in the field of information confidentiality. For this purpose, an interval-based Pythagorean fuzzy technique for order of preference by similarity to the ideal solution (IVPF–TOPSIS) method is introduced to get the desired nonlinear confusion component of block cipher namely S-box. In this manner, we performed cryptographic analyses of standard S-boxes namely nonlinearity (NL), strict avalanche criterion (SAC), bit-independent criterion (BIC), absolute indicator (ABI), the sum of square and absolute indicator (SSAI), algebraic degree (AD), algebraic immunity (AI), transparency order (TO), composite algebraic immunity (CAI), robustness (RB), signal to noise ratio (SNR), confusion coefficient of variance (CCV). With these cryptographic characteristics, we have used interval-valued based Pythagorean fuzzy TOPSIS multi-criteria decision-making technique to classify standard S-boxes suitable for construction of modern block ciphers.

Pulsed laser deposition (PLD) is a simple and extremely versatile technique to grow thin films and nanomaterials from a wide variety of materials. Compared to traditional fabrication methods, PLD is a clean physical vapour deposition approach that avoids complicated chemical reactions and by-products, achieving a precise stochiometric transfer of the target material onto the substrate and providing control over the film thickness. Halide perovskite materials have attracted extensive attention due to their excellent photoelectric and photovoltaic properties. In this paper, we present an overview of the fundamental and practical aspects of PLD. The properties and preparation methods of the halide perovskite materials are briefly discussed. Finally, we will elaborate on recent research on the preparation of perovskite solar cells by PLD, summarize the advantages and disadvantages of the PLD preparation, and prospect the all-vacuum PLD-grown solar cells in a full solar cell structure.

Abstract The global orange industry constantly faces new technical challenges to meet consumer demands for quality fruits. Instead of traditional subjective fruit quality assessment methods, the interest in the horticulture industry has increased in objective, quantitative, and non-destructive assessment methods. Oranges have a thick peel which makes their non-destructive quality assessment challenging. This paper evaluates the potential of short-wave NIR spectroscopy and direct sweetness classification approach for Pakistani cultivars of orange, i.e., Red-Blood, Mosambi, and Succari. The correlation between quality indices, i.e., Brix, titratable acidity (TA), Brix: TA and BrimA (Brix minus acids), sensory assessment of the fruit, and short-wave NIR spectra, is analysed. Mix cultivar oranges are classified as sweet, mixed, and acidic based on short-wave NIR spectra. Short-wave NIR spectral data were obtained using the industry standard F-750 fruit quality meter (310–1100 nm). Reference Brix and TA measurements were taken using standard destructive testing methods. Reference taste labels i.e., sweet, mix, and acidic, were acquired through sensory evaluation of samples. For indirect fruit classification, partial least squares regression models were developed for Brix, TA, Brix: TA, and BrimA estimation with a correlation coefficient of 0.57, 0.73, 0.66, and 0.55, respectively, on independent test data. The ensemble classifier achieved 81.03% accuracy for three classes (sweet, mixed, and acidic) classification on independent test data for direct fruit classification. A good correlation between NIR spectra and sensory assessment is observed as compared to quality indices. A direct classification approach is more suitable for a machine-learning-based orange sweetness classification using NIR spectroscopy than the estimation of quality indices.

Recently, fiber-reinforced polymers (FRP) have begun to be used for steel structure reinforcement, following decades of successful utilization for the reinforcement of concrete structures. However, rehabilitation of tubular joints with a crack at the interface of mating members using FRP has rarely been investigated. A tubular KT-joint having a semi-elliptical crack subjected to axial tensile load is explored in this study. The joint was simulated using the fracture tool of ANSYS Structural, and the effect of crack size, location, and FRP reinforcement on stress intensity factor (SIF) was evaluated. The numerical simulations show that FRP reinforcement reduces the SIF, decreases the likelihood of crack growth, and may increase the fatigue life. A 4–12% reduction per millimeter thickness of unidirectional FRP was recorded.

The increasing mortality rate in bike-related road accidents due to head injuries necessitates stronger helmets. This paper presents a cost-effective machine setup to evaluate helmet behavior during impacts. By employing unique methods to measure impact duration and post-impact energy absorption, this project eliminates the need for high-speed cameras. The results indicate that the developed machine enables scenario-based impact tests directly on helmets, providing valuable insights into collision effects. Validation confirms its effectiveness. The low-cost impact testing machine offers a practical solution for studying helmet performance and addressing the rising concerns surrounding bike accidents.

Atmospheric balloons are the cheapest source utilized by scientists to investigate various research areas. Thermal analyses of atmospheric balloons is usually performed to attain the required thermal equilibrium in the shortest timeframe. In this paper, a mathematical model was developed that numerically solved material properties (i.e., ε (emissivity of balloon surface), αV (balloon absorptivity averaged over the visible and near infrared band), εIR (absorptivity averaged over infrared band), αIR (balloon absorptivity averaged over infrared band), etc.) as well as geometric properties (i.e., AC (cross-sectional area), AS (surface area), M (balloon mass), etc.) by incorporating these values in the heat balance equation. An optimized code was engendered in MATLAB, which simultaneously solved input parameters and delivered the required thermal equilibrium in the shortest timeframes. This may serve as a guide to new material generation.

In this paper, the model predictive control technique is proposed to control the voltage balancing for the subway train 1500 V DC system for variable loads. This paper compares the conventional neutral point clamped converter (NPC) using the control technique of a PI controller with model predictive control in variable load conditions. MPC enhances the stability of the system during variable loads in comparison with the conventional technique. Consequently, the suggested control technique using MPC can maintain the DC bus output voltage dynamics at variable loads for the subway. Simulation results are provided to demonstrate the accuracy of the DC bus output voltage dynamics for the proposed control method.

Exposure to load and offshore environment degrades the load-bearing capacity of tubular joints, necessitating reinforcement of these joints. Reinforcement is sometimes required for lifespan enhancement or qualification based on new requirements. Available reinforcement techniques include welded rings inside/outside the chord, doubler/collar plate at the brace-chord interface, grout filling, and clamp installation on the joints with/without cement. While these techniques increase the load-bearing capacity of damaged tubular joints, various practical limitations exist. Clamping may require heavy machinery, whereas welding stiffeners involves hot work and may not be permitted sometimes. Fiber-reinforced polymers (FRPs) have immense potential for reinforcing steel structures and are a viable alternative for rehabilitating tubular joints due to their exceptional mechanical and physical characteristics, offering competitive advantages over other methods. FRP reinforcement is becoming more feasible and economical for underwater joints. FRP reinforcement can be either precured, pre-impregnated, or wet layup. Aside from the significance of joint rehabilitation, a document covering the well-known options was lacking. This paper summarizes the advantages and limitations of these reinforcement methods, particularly FRP reinforcement. Possible research directions in FRP reinforcement of tubular joints are also discussed.

Agrochemicals, which are very efficacious in protecting crops, also cause environmental pollution and pose serious threats to farmers' health upon exposure. In order to cut down the environmental and human health risks associated with agrochemical application, there is a need to develop intelligent application equipment that could detect and recognize crops/weeds, and spray precise doses of agrochemical at the right place and right time. This paper presents a machine-learning based crop/weed detection system for a tractor-mounted boom sprayer that could perform site-specific spraying on tobacco crop in fields. An SVM classifier with a carefully chosen feature combination (texture, shape, and color) for tobacco plant has been proposed and 96% classification accuracy has been achieved. The algorithm has been trained and tested on a real dataset collected in local fields with diverse changes in scale, orientation, background clutter, outdoor lighting conditions, and variation between tobacco and weeds. Performance comparison of the proposed algorithm has been made with a deep learning based classifier (customized for real-time inference). Both algorithms have been deployed on a tractor-mounted boom sprayer in tobacco fields and it has been concluded that the SVM classifier performs well in terms of accuracy (96%) and real-time inference (6 FPS) on an embedded device (Raspberry Pi 4). In comparison, the customized deep learning-based classifier has an accuracy of 100% but performs much slower (0.22 FPS) on the Raspberry Pi 4.

Wireless communication systems play a very crucial role in modern society for entertainment, business, commercial, health and safety applications. These systems keep evolving from one generation to next generation and currently we are seeing deployment of fifth generation (5G) wireless systems around the world. Academics and industries are already discussing beyond 5G wireless systems which will be sixth generation (6G) of the evolution. One of the main and key components of 6G systems will be the use of Artificial Intelligence (AI) and Machine Learning (ML) for such wireless networks. Every component and building block of a wireless system that we currently are familiar with from our knowledge of wireless technologies up to 5G, such as physical, network and application layers, will involve one or another AI/ML techniques. This overview paper, presents an up-to-date review of future wireless system concepts such as 6G and role of ML techniques in these future wireless systems. In particular, we present a conceptual model for 6G and show the use and role of ML techniques in each layer of the model. We review some classical and contemporary ML techniques such as supervised and un-supervised learning, Reinforcement Learning (RL), Deep Learning (DL) and Federated Learning (FL) in the context of wireless communication systems. We conclude the paper with some future applications and research challenges in the area of ML and AI for 6G networks.

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.

In Pakistan most of the local hospitals have limited resources and budget deficits to address day-to-day routine activities. This is further intensified by inefficient management of hospital resources. Hence, it is essential to efficiently manage hospital resource as it will lead to health care costs reduction and increased patient's satisfaction. The focus of this research is to demonstrate the use of the tool in healthcare system, specifically in Pakistan, and balance the resources of a hospital, which will reduce the waiting time for the Outdoor Patients Departments. The resources at the Outdoor Patients Department including X-ray, Labs, Ultrasound and the ECG sections were included in the computer-based simulation model with the aim to find the bottleneck departments as they are the main cause of waiting time in the hospital. These bottlenecks are anticipated due to the lack of an optimized model, which has been addressed in this simulation. Based on the simulation study an optimized model is proposed to the hospital administration where the resources are readjusted resulting in the utilization of underutilized departments and decreasing the waiting time of the stations particularly the bottleneck stations. The motivation behind this research is to aid hospitals in better management of resources, which can ultimately improve customers (patients) satisfaction.

Intelligent and safe overtaking maneuvering is always a challenging task for autonomous vehicles. This paper proposes and experimentally implements a novel approach of overtaking maneuvering using modified form of Rendezvous Guidance (RG) algorithm for trajectory planning and obstacle avoidance, considering driver safety and comfort during autonomous overtaking. The simulations for all possible scenarios are conducted to ensure the effectiveness of proposed modified RG algorithm. These scenarios involved presence and absence of obstacle vehicle in overtaking lane alongside leading vehicle in driving lane. In addition, the enhanced performance of modified RG algorithm is established over conventional RG algorithm by comparative analysis. The results indicate that overtaking maneuvering period could be decreased by 10% using a modified RG algorithm and vehicle will cover less distance to complete overtaking. The efficacy of proposed method is justified by performing experiments using mobile robots. The experimental results and simulation results of modified RG algorithm are compared, and their plots are almost identical.

Intelligent and safe overtaking maneuvering is always a challenging task for autonomous vehicles. This paper proposes and experimentally implements a novel approach of overtaking maneuvering using modified form of Rendezvous Guidance (RG) algorithm for trajectory planning and obstacle avoidance, considering driver safety and comfort during autonomous overtaking. The simulations for all possible scenarios are conducted to ensure the effectiveness of proposed modified RG algorithm. These scenarios involved presence and absence of obstacle vehicle in overtaking lane alongside leading vehicle in driving lane. In addition, the enhanced performance of modified RG algorithm is established over conventional RG algorithm by comparative analysis. The results indicate that overtaking maneuvering period could be decreased by 10% using a modified RG algorithm and vehicle will cover less distance to complete overtaking. The efficacy of proposed method is justified by performing experiments using mobile robots. The experimental results and simulation results of modified RG algorithm are compared, and their plots are almost identical.

Context: Automated acceptance testing validates a product's functionality from the customer's perspective. Text-based automated acceptance tests (AATs) have gained popularity because they link requirements and testing. Objective: To propose and evaluate a cost-effective systematic reuse process for automated acceptance tests. Method: A systematic approach, method engineering, is used to construct a systematic reuse process for automated acceptance tests. The techniques to support searching, assessing, adapting the reusable tests are proposed and evaluated. The constructed process is evaluated using (i) qualitative feedback from software practitioners and (ii) a demonstration of the process in an industry setting. The process was evaluated for three constraints: performance expectancy, effort expectancy, and facilitating conditions. Results: The process consists of eleven activities that support development for reuse, development with reuse, and assessment of the costs and benefits of reuse. During the evaluation, practitioners found the process a useful method to support reuse. In the industrial demonstration, it was noted that the activities in the solution helped in developing an automated acceptance test with reuse faster than creating a test from scratch i.e., searching, assessment and adaptation parts. Conclusion: The process is found to be useful and relevant to the industry during the preliminary investigation.

This paper presents a multi-axis low-cost soft magnetic tactile sensor with a high force range for force feedback in robotic surgical systems. The proposed sensor is designed to fully decouple the output response for normal, shear and angular forces. The proposed sensor is fabricated using rapid prototyping techniques and utilizes Neodymium magnets embedded in an elastomer over Hall sensors such that their displacement produces a voltage change that can be used to calculate the applied force. The initial spacing between the magnets and the Hall sensors is optimized to achieve a large displacement range using finite element method (FEM) simulations. The experimental characterization of the proposed sensor is performed for applied force in normal, shear and 45° angular direction. The force sensitivity of the proposed sensor in normal, shear and angular directions is 16 mV/N, 30 mV/N and 81 mV/N, respectively, with minimum mechanical crosstalk. The force range for the normal, shear and angular direction is obtained as 0–20 N, 0–3.5 N and 0–1.5 N, respectively. The proposed sensor shows a perfectly linear behavior and a low hysteresis error of 8.3%, making it suitable for tactile sensing and biomedical applications. The effect of the material properties of the elastomer on force ranges and sensitivity values of the proposed sensor is also discussed.

Genus Trichogramma Westwood is being utilized in various biological control programme worldwide. Trichogramma spp. are egg parasitoids which lay their eggs inside the host eggs. More than 240 species are known, of which, 45 species are recorded from India. It is quite difficult to identify and differentiate among the species of Trichogramma due to smaller size. In present study, we hypothesized a methodology to identify the Trichogramma species based on potential morphometric characteristics. The males and females of five different species of Trichogramma namely, T. brassicae, T. chilotraeae, T. danaidiphaga, T. danausicida and T. dendrolimi were morphometrically analysed. A total of 33 characters in males and 27 in females were taken into consideration for morphometric identification and analysis using principal component method. It was observed that few characters of male like body length, length of flagellar hair, maximum marginal hair length of fore wing, genitalia characters including aedeagus length, distance between chelate structure and gonoforceps, and others were identified as important morphometric characters. Similarly, in females, ovipositor length, body and head length, eye width, antennal club width and number of setae in forewings were found important for taxonomic identification. Male and female of each species are clearly identified from high definition microscopic images.

This paper presents analysis, design and experimentation of a high voltage DC power supply (HVDCPS) with power factor correction based on LLC resonant converter. For power factor correction improvement, the proposed topology has an input rectifier with two filter capacitors, two inductors with a bus capacitor (Cbus) and a resonant tank. To prevent the reverse current flow towards the source diodes (D9 & D10) are employed. A couple of power switches are inserted in a single leg that makes a half-bridge network. To form an LLC resonance circuit, a capacitor and two inductors are connected to the primary winding of the high voltage transformer (HVT). To rectify the high frequency and high voltage, a full-bridge rectifier is inserted to secondary side of high voltage transformer (HVT). The secondary diodes always get on and off under zero current switching (ZCS) due to discontinuous conduction mode of proposed topology. It is found that due to power factor correction, less cost, lower losses and smaller size, the proposed topology achieves several major improvements over the conventional high voltage power supply. To obtain zero voltage switching (ZVS) the converter operate in a narrow frequency range. The output voltage can be varied or regulate through pulse width modulation of power switches. Due to ZVS and ZCS, the proposed topology has minimum switching losses and therefore higher efficiency. To verify the feasibility of the proposed topology a prototype is being implemented and verified by simulation & experimental results for 1.5KV prototype of the proposed topology. The results make sure the achievement, good efficiency and successful operation of the proposed topology.

[This corrects the article DOI: 10.1371/journal.pone.0239008.].

Order parameters are key to our understanding of phases of matter. Not only do they allow one to classify phases, but they also enable the study of phase transitions through their critical exponents which identify the universal long-range physics underlying the transition. Topological phases are exotic quantum phases which are lacking the characterization in terms of order parameters. While probes have been developed to identify such phases, those probes are only qualitative, in that they take discrete values, and, thus, provide no means to study the scaling behavior in the vicinity of phase transitions. In this paper, we develop a unified framework based on variational tensor networks (infinite projected entangled pair states) for the quantitative study of both topological and conventional phase transitions through entanglement order parameters. To this end, we employ tensor networks with suitable physical and/or entanglement symmetries encoded and allow for order parameters detecting the behavior of any of those symmetries, both physical and entanglement ones. On the one hand, this gives rise to entanglement-based order parameters for topologically ordered phases. These topological order parameters allow one to quantitatively probe the behavior when going through topological phase transitions and, thus, to identify universal signatures of such transitions. We apply our framework to the study of the toric code model in different magnetic fields, which along some special lines maps to the (2+1)D Ising model. Our method identifies 3D Ising critical exponents for the entire transition, consistent with those special cases and general belief. However, we, in addition, also find an unknown critical exponent β^{*}≈0.021 for one of our topological order parameters. We take this—together with known dualities between the toric code and the Ising model—as a motivation to also apply our framework of entanglement order parameters to conventional phase transitions. There, it enables us to construct a novel type of disorder operator (or disorder parameter), which is nonzero in the disordered phase and measures the response of the wave function to a symmetry twist in the entanglement. We numerically evaluate this disorder operator for the (2+1)D transverse field Ising model, where we again recover a critical exponent hitherto unknown in the (2+1)D Ising model, β^{*}≈0.024, consistent with the findings for the toric code. This shows that entanglement order parameters can provide additional means of characterizing the universal data both at topological and conventional phase transitions and altogether demonstrates the power of this framework to identify the universal data underlying the transition.

The developments and applications of wireless body area networks (WBANs) for healthcare and remote monitoring have brought a revolution in the medical research field. Numerous physiological sensors are integrated in a WBAN architecture in order to monitor any significant changes in normal health conditions. This monitored data are then wirelessly transferred to a centralized personal server (PS). However, this transferred information can be captured and altered by an adversary during communication between the physiological sensors and the PS. Another scenario where changes can occur in the physiological data is an emergency situation, when there is a sudden change in the physiological values, e.g., changes occur in electrocardiogram (ECG) values just before the occurrence of a heart attack. This paper presents a centralized approach for the detection of abnormalities, as well as intrusions, such as forgery, insertions, and modifications in the ECG data. A simplified Markov model-based detection mechanism is used to detect changes in the ECG data. The features are extracted from the ECG data to form a feature set, which is then divided into sequences. The probability of each sequence is calculated, and based on this probability, the system decides whether the change has occurred or not. Our experiments and analyses show that the proposed scheme has a high detection rate for 5% as well as 10% abnormalities in the data set. The proposed scheme also has a higher true negative rate with a significantly reduced running time for both 5% and 10% abnormalities. Similarly, the receiver operating characteristic (ROC) and ROC convex hull have very promising results.

The Internet of Vehicles (IoVs) is an emerging paradigm aiming to introduce a plethora of innovative applications and services that impose a certain quality of service (QoS) requirements. The IoV mainly relies on vehicular ad-hoc networks (VANETs) for autonomous inter-vehicle communication and road-traffic safety management. With the ever-increasing demand to design new and emerging applications for VANETs, one challenge that continues to stand out is the provision of acceptable QoS requirements to particular user applications. Most existing solutions to this challenge rely on a single layer of the protocol stack. This paper presents a cross-layer decision-based routing protocol that necessitates choosing the best multi-hop path for packet delivery to meet acceptable QoS requirements. The proposed protocol acquires the information about the channel rate from the physical layer and incorporates this information in decision making, while directing traffic at the network layer level. Key performance metrics for the system design are analyzed using extensive experimental simulation scenarios. In addition, three data rate variant solutions are proposed to cater for various application-specific requirements in highways and urban environments.

Minimum quantity lubricant (MQL) is an advanced technique in machining to achieve sustainability, productivity, higher precision, economic benefits, and a reduction in carbon footprints. The present research work aims to investigate the effect of the cutting process parameters of the end milling of AA5005H34 material under dry and MQL cutting environments. The key performance indicators of machining include the surface roughness profile, the material removal rate, and tool wear. Surface roughness parameters are measured with the help of the Mitutoyo surface roughness tester, and the cutting tool wear is measured according to the ISO 8688-2:1989 standard using a scanning electron microscope (SEM). Sixteen experiments are designed based on the Taguchi orthogonal array mixture design. Single responses are optimized based on signal to noise ratios, while for multi-response optimization composite desirability function coupled with principal component analysis is applied. Analysis of variance (ANOVA) results revealed that the feed rate followed by spindle speed, axial depth of the cut, width of the cut, and cutting environment are the most significant factors contributing to the surface roughness profile, material removal rate, and tool wear. The optimized parameters are obtained as cutting speed of 3000 rev/min, feed rate of 350 mm/min, axial depth of cut of 2 mm, and width of cut of 6 mm under an MQL environment.

We construct and study quantum trimer models and resonating SU(3)-singlet models on the kagome lattice, which generalize quantum dimer models and the resonating valence bond wave functions to a trimer and SU(3) setting. We demonstrate that these models carry a Z_{3} symmetry which originates in the structure of trimers and the SU(3) representation theory, and which becomes the only symmetry under renormalization. Based on this, we construct simple and exact parent Hamiltonians for the model which exhibit a topological ninefold degenerate ground space. A combination of analytical reasoning and numerical analysis reveals that the quantum order ultimately displayed by the model depends on the relative weight assigned to different types of trimers—it can display either Z_{3} topological order or form a symmetry-broken trimer crystal, and in addition possesses a point with an enhanced U(1) symmetry and critical behavior. Our results accordingly hold for the SU(3) model, where the two natural choices for trimer weights give rise to either a topological spin liquid or a system with symmetry-broken order, respectively. Our work thus demonstrates the suitability of resonating trimer and SU(3)-singlet ansatzes to model SU(3) topological spin liquids on the kagome lattice.

Welding distortion is a critical issue as it leads to severe deterioration of structural integrity of welded work piece and dimensional precision. This study aims at studying the effects of shielded metal arc welding (SMAW) parameters on the evolution of mechanical properties, including tensile strength, impact toughness, and hardness, along with angular distortion on a welded joint from SA 516 grade 70. Such parameters are analyzed and optimized by employing the Taguchi method and Grey relational analysis. SA 516 grade 70 is commercially used for fabrication of storage tanks, boilers and pressure vessels. SMAW is investigated with three levels of root gap, groove angle, electrode diameter, and pre-heat temperature, which were varied on a butt joint in flat (1 G) position to determine their effects on response variables at room temperature. Nine experiments were designed using a Taguchi L9 orthogonal array, welded according to American Society of Mechanical Engineers (ASME) section IX, and samples were prepared and tested as per ASTM A 370. The Taguchi method and Grey relational analysis were employed to observe the most significant parameters and optimal levels that synergically yield improved responses. Results are validated by conducting confirmatory experiments that show good agreement with optimum results.

Tuberous sclerosis complex (TSC) is an autosomal dominant, variably expressed multisystem disease. The predominant pulmonary features of TSC are identical to those of lymphangioleiomyomatosis (LAM). Pneumothorax, multifocal micronodular pneumocyte hyperplasia, and chylothorax are rare complications of TSC. We report a young male with pneumothorax, lung nodules, and chylous effusion who developed empyema thoracis after esophageal rupture. Hospital course was complicated by respiratory failure. Family opted to transfer to hospice care. Chylothorax is a rare complication of TSC with few scattered reports mostly in female patients. Patients with TSC are usually managed by multispecialists and it is important to be aware of the rare pulmonary manifestations of this disease. A male patient with TSC having lung nodules presenting with chylothorax and empyema thoracis from Boerhaave syndrome makes our case unique.

Histoplasma duboisii, a variant of Histoplasma capsulatum that causes “African histoplasmosis,” can be resistant to itraconazole, requiring intravenous amphotericin B treatment. Rarely, these patients do not respond to intravenous antifungal therapy, and in such cases, patients may progress to develop secondary hemophagocytic lymphohistiocytosis (HLH). We present a case of a 34-year-old male patient with sickle cell disease who presented with a 5-month history of an enlarging painless axillary mass, persistent low grade fevers, night sweats, weight loss, and anorexia. An excisional biopsy of the right axillary lymph node revealed yeast and granulomas consistent with histoplasma infection. He was started on oral itraconazole. After 4 weeks of therapy, laboratory evaluation revealed worsening anemia, thrombocytopenia, and transaminitis. Due to failure of oral therapy, he was admitted for intravenous amphotericin B treatment. During his hospital course anemia, thrombocytopenia, and transaminitis all worsened. A bone marrow biopsy was done that was consistent with HLH. His clinical status continued to deteriorate, developing multiorgan failure and disseminated intravascular coagulation. He unfortunately had a cardiorespiratory arrest after eight days of admission and passed away.

Metabolic alkalosis secondary to citrate toxicity from plasma exchange is very uncommon in patients with normal renal function. In patients with advanced renal disease this can be a fatal event. We describe a case of middle-aged woman with Goodpasture’s syndrome treated with plasma exchange who developed severe metabolic alkalosis. High citrate load in plasma exchange fluid is the underlying etiology. Citrate metabolism generates bicarbonate and once its level exceeds the excretory capacity of kidneys, the severe metabolic alkalosis ensues. Our patient presented with generalized weakness, fever, and oliguria and developed rapidly progressive renal failure. Patient had positive serology for antineutrophilic cytoplasmic antibodies myeloperoxidase (ANCA-MPO) and anti-glomerular basement membrane antibodies (anti-GBM). Renal biopsy showed diffuse necrotizing and crescentic glomerulonephritis with linear glomerular basement membrane staining. Patient did not respond to intravenous steroids. Plasma exchange was started with fresh frozen plasma but patient developed severe metabolic alkalosis. This metabolic alkalosis normalized with cessation of plasma exchange and initiation of low bicarbonate hemodialysis. ANCA-MPO and anti-GBM antibodies levels normalized within 2 weeks and remained undetectable at 3 months. Patient still required maintenance hemodialysis.

One new species, Oligosita ferozepurensis is described based on specimens collected from Ferozepur, Punjab, India.

A 66-year-old male with newly diagnosed untreated acquired immunodeficiency syndrome (AIDS) presented with chronic nonspecific complaints of weakness, fatigue, myalgia, and weight loss. His initial EKG showed complete heart block necessitating temporary pacemaker placement. He had no previous history of cardiac disease. He was also found to have a persistent lactic acidosis and imaging studies showed abdominal lymphadenopathy. The patient underwent biopsy of these lymph nodes and was found to have diffuse large B-cell lymphoma. The hospital course was complicated by respiratory failure requiring mechanical ventilator support and cardiac arrest. Patient remained critically ill; he was not a candidate for chemotherapy and, after a month of hospitalization, he died. Lactic acidosis and heart block as an initial presentation of non-Hodgkin lymphoma in an AIDS patient are an unusual and unique presentation.

Abstract this study was Conducted to estimate the antibacterial activity of the Ziziphus leaves and honey compared with the activity of anumber of antibiotics and other types of honey, the aqueous extract and alcoholic extract were prepared. The results showed that the aqueous extract of the Ziziphus leaves was more effective than the alcoholic extract of the plant on the bacteria studied. It was found that increasing concentration of extract was accompanied by an increase in the effectiveness of inhibitory activity and the concentration 200 mg/cm3 had the best inhibitory effect on all bacteria studied Regarding the impact of Ziziphus honey was found to be effective inhibitory effect than in other species, and 100 mg/cm3 was the most effective. When comparing the effectiveness of the Ziziphus extracts and honey with some antibiotics it was found that the concentration of 200 mg/cm3 of aqueous extract of Ziziphus leaves may have superior the effect on the bacteria studied compared to antibiotics Tetracycline, Rifampin, Doxycycline, Gentamicin. And was comparable in its effect of other antibiotics, Ziziphus honey Display superior to most antibiotics studied depending on the type of bacteria studied.

The design and fabrication of a Polyvinylidene fluoride (PVDF) based, mouse (or rodent) whisker mimicking, tactile sensor is presented. Unlike previous designs reported in the literature, this sensor mimics the mouse whisker not only mechanically, but it also makes macro movements just like a real mouse whisker in a natural environment. We have developed a mathematical model and performed finite element analysis using COMSOL, in order to optimise the whisker to have the same natural frequency as that of a biological whisker. Similarly, we have developed a control system that enables the whisker mimicking sensor to vibrate at variable frequencies and conducted practical experiments to validate the response of the sensor. The natural frequency of the whisker can be designed anywhere between 35 and 110 Hz, the same as a biological whisker, by choosing different materials and physical dimensions. The control system of this sensor enables the whisker to vibrate between 5 and 236 Hz.

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.

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.