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This study explores a model for detecting fall, non-fall and near-fall events as frequent experiences of near-falls are closely associated with a heightened risk of falls. Detecting near-falls can lead to more accurate predictions of falls. However, near-falls exhibit certain movement patterns similar to actual falls, making it challenging to distinguish between near-fall events and falls. We investigated the detection of fall-related activities, including falls, near-falls, and non-falls, by utilizing dynamic motion images derived from video clips. There were two primary classification approaches: a vanilla convolutional neural network (CNN) model and a transfer learning approach that utilizes InceptionV3 and DenseNet201 models as feature extractors and train conventional machine learning classifiers, such as support vector machine (SVM), K-nearest neighborhood, decision tree, and random forest, and adaptive boosting models. The vanilla CNN model achieved a high accuracy of 97.89% compared to the transfer learning approach, which reached a maximum accuracy of 95.54% for binary classification of fall and non-fall events. On the other hand, the transfer learning approach, which integrated feature from InceptionV3 and DenseNet201 into machine learning classifiers, achieved an accuracy of up to 90.14% for the three-class classification of fall, non-fall, and near-fall events. The findings of this study underscores the modelÃs robustness in detecting various fall-related activities, highlighting its potential for improving safety in at-risk populations.

Abstract Portable data acquisition devices, such as the National Instrument (NI) myDAQ, allow users to analyze electronic circuits without advanced measurement systems, making them invaluable in educational settings. Combined with NI Laboratory Virtual Instrument Engineering Workbench (LabVIEW) on a personal computer, the NI myDAQ facilitates practical experiments and analyses anytime, anywhere. However, these devices can malfunction due to misuse or operation beyond their design limitations. This research presents the design, operation, and implementation of an energy-efficient test bench for the control and measurement functionalities of the NI myDAQ, aimed at detecting device failures through a specialized hardware and software setup using the loopback method. The test bench efficiently verifies all input and output ports, providing fast and accurate fault detection, and generates detailed performance reports to aid in long-term monitoring. By automating fault detection and reducing manual intervention, this setup ensures reliable operation, making it an ideal, resource-efficient solution for educational environments where practical, portable systems are essential for hands-on learning.

Abstract An antenna array having a size of 45 $$\:\times\:$$ 40 cm2 (5.7 $$\:\times\:$$ 5 $$\:{\lambda\:}_{0}$$ 2) and consisting of four pairs of printed U-shaped dipoles positioned above a metal reflector, for 5G Sub-6 GHz base station applications, is designed and tested. The array consists of eight excitation ports, one port for each dipole. Four parasitic square patches are etched on the bottom side of the dipole arms for producing radiations in 2.2 GHz and 3.8 GHz bands. The size of the reflector and height of the dipoles are optimized in order to enhance antenna gain up to 11.5 dB at 2.2 GHz and 14.5 dB at 3.8 GHz. Beam steering up to 20 $$\:^\circ\:$$ is achieved, using phase shifted simultaneous excitation of different ports. The proposed antenna array not only fulfills 5G base station requirements but is also simple and compact as it only requires eight ports to achieve dual-band, high-gain and beam steering operation in a single design. It also offers a unique feature of dual-sector coverage per panel, which results in an increased coverage capacity of the base station without increasing the system resources.

Abstract A robust root system architecture (RSA) is vital for tree health and fruit production of perennial fruit crops across diverse environments over many years. Consequently, RSA holds significant importance for fruit crop breeding programs. However, phenotyping tree roots presents a significant challenge due to their size, complexity, and lack of an existing efficient root phenotyping system. This study aimed to address this gap by developing and validating a root phenotyping pipeline for large, complex, apple root systems. Root traits of both greenhouse and field‐grown apple trees were captured using either a camera or by imaging shaved roots with a flatbed scanner. These images were then analyzed with five root phenotyping software, RhizoVision, ImageJ, DIRT, RootGraph, and PlantCV, as well as visually ranked. Afterward, all these measurements were compared to hand‐measured root traits to evaluate their accuracy. RhizoVision accurately (p

In recent years, the Internet of Things (IoT) has become one of the most familiar names creating a benchmark and scaling new heights. IoT an indeed future of the communication that has transformed the objects (things) of the real world into smarter devices. With the advent of IoT technology, this decade is witnessing a transformation from traditional agriculture approaches to the most advanced ones. Limited research has been carried out in this direction. Thus, herein we present various technological aspects involved in IoT-based cultivation. The role and the key components of smart farming using IoT were examined, with a focus on network technologies, including layers, protocols, topologies, network architecture, etc. We also delve into the integration of relevant technologies such as cloud computing, big data analytics, and the integration of IoT-based cultivation. We explored various security issues in modern IoT cultivation and also emphasized the importance of safeguarding sensitive agricultural data. Additionally, a comprehensive list of applications based on sensors and mobile devices is provided, offering refined solutions for greenhouse management. The principles and regulations established by different countries for IoT-based cultivation systems are presented, demonstrating the global recognition of these technologies. Furthermore, a selection of successful use cases and real-world scenarios and applications were presented. Finally, the open research challenges and solutions in modern IoT-based cultivation were discussed.

Human Activity Recognition (HAR) has been employed in a number of applications including sports analytic, healthcare monitoring, surveillance, and human-computer interaction. Despite a decade of research on HAR, existing models still find it challenging under conditions like occlusion, computational efficiency, and capturing long-term temporal dependencies. To address these shortcomings, we present BiTransAct, a novel hybrid model that incorporates EfficientNet-B0 for spatial features extraction as well as Transformer Encoder to obtain the temporal relationships in video data. To evaluate the performance of our proposed model we have employed a video based dataset called SPHAR-Dataset-1.0. This dataset contains 7,759 videos with 14 diverse activities and 421,441 samples. From our experiments its established that BiTransAct consistently excels other deep learning based models like SWIN, EfficientNet, and RegNet in terms of both classification accuracy and precision. Its efficiency in handling large datasets without compromising on performance makes it stronger candidate for real-time HAR tasks. Furthermore, the features like self-attention mechanism and dynamic learning rate make BiTransAct even more robust and avoid overfitting. The results demonstrate that BiTransAct provides a scalable, efficient solution for HAR applications, with particular relevance for real-world scenarios such as video surveillance and healthcare monitoring.

Accurate sleep posture monitoring plays a pivotal role in the diagnosis and treatment of a spectrum of sleep-related disorders, such as sleep apnea, restless leg syndrome, and rapid eye movement sleep behavior disorder. These disorders can significantly affect an individual’s overall well-being and quality of life. However, the complexity of the human body and various factors affecting sleep patterns pose substantial challenges. Thus, this study introduces a sleep position determination approach that utilizes both RGB and thermal cameras to acquire visual and thermal data. This dual-source data acquisition system enables a comprehensive analysis of body positioning during sleep, including potential discomfort assessment. The methodology for this endeavor was initiated by acquiring a dataset encompassing various sleep postures, which was achieved through the deployment of RGB and thermal cameras. This dataset includes video footage of five frequently adopted sleep positions: supine, left log, right log, prone left, and prone right, involving the participation of nine individuals. Furthermore, the dataset was collected under two conditions: with and without a blanket. The proposed method begins by normalizing the database to the video frames. Next, the fine-tuned MobileNet-V2 and Inception-V3 models were employed for feature extraction. The tree-seed algorithm was used to select optimal features from the extracted data, reduce dimensionality, and improve the classification performance. Subsequently, Parallel Standard Deviation Padding Max Value (PSPMV), was applied to combine the feature vectors from the RGB and thermal datasets to enhance the accuracy. The fused vectors are then classified using ensemble machine learning models. Our method achieved an accuracy of 97.8% and 98.4% with and without blanket, using subspace KNN. After applying PSPMV fusion to blanket features, the accuracy improved to 99.2%.

Unmanned Aerial Vehicles (UAVs) have become increasingly popular in recent years, with a wide range of applications in areas such as surveying, delivery, and security. UAV technology plays an important role in human life. Integrating Artificial Intelligence (AI) techniques into UAVs can significantly enhance their capabilities and performance. After the integration of AI in UAVs, their efficiency can be improved. It can automatically detect any object and highlight those objects with detailed information using AI. In most of the security surveillance places, UAV technology is beneficial. In this paper, we comprehensively reviewed the most widely used UAV communication protocols, including Wi-Fi, Zigbee, and Long-Range Wi-Fi (LoRaWAN). The review further explores valuable insights into the strengths and weaknesses of these protocols and how cognitive abilities such as perceptions and decision-making can be incorporated into UAV systems for autonomy. This paper provides a comprehensive overview of the state-of-the-art UAV object detection in remote sensing environments, as well as its types and use cases in different applications. It highlights the potential applications of these techniques in various domains, such as wildlife monitoring, search and rescue operations, and surveillance. The challenges and limitations of these methods and open research issues are given for future research.

A sudden, unexplained decline and collapse of young apple trees on dwarfing and semi-dwarfing rootstocks has been reported across North America over the past decade. Although viruses have been detected in declining trees, no information is available on their potential causal role in the decline phenomenon. To this end, virus-inoculated apple trees were established in a high-density experimental orchard and monitored over five years. Tree decline was observed in year 4 (2022), resulting in 17% mortality, with declining trees exhibiting marked vascular tissue necrosis. However, none of the eight viruses and one viroid detected in the experimental orchard was significantly more prevalent in declining trees. Extreme temperature fluctuations in January 2022, followed by a severe water deficit in summer 2022, were recorded at the experimental orchard. Similar but distinct observations were made in a nearby commercial orchard with foliar nutrient imbalances documented in trees exhibiting symptoms of rapid decline. Together, our findings suggest that viruses are not primarily responsible for the rapid decline phenomenon and highlight the need for future work to investigate the roles of tree physiology and water stress in tree decline, as well as the potential efficacy of horticultural mitigation practices.

Over the past 20 years, there has been a surge of diabetes cases in Iraq. Blood tests administered in the absence of professional medical judgment have allowed for the early detection of diabetes, which will fasten disease detection and lower medical costs. This work focuses on the use of a Long-Short Term Memory (LSTM) neural network for diabetes classification in Iraq. Some medical tests and body features were used as classification features. The most relevant features were selected using the Binary Dragon Fly Algorithm (BDA) Binary version of the selection method because the features either selected or not. To reduce the number of features that are used in prediction, features without effects will be eliminated. This effects the classification accuracy, which is very important in both the computation time of the method and the cost of medical test that the individual will take during annual check ups.This work found out that among 11 features, only five features are most relevant to the disease. These features provide a classification accuracy up to 98% among three classes: diabetic, non diabetic and pre-diabetic.

Abstract Quantum secure metrology protocols harness quantum effects to probe remote systems with enhanced precision and security. Traditional QSM protocols require multi-partite entanglement, which limits its near-term implementation due to technological constraints. This paper proposes a QSM scheme that employs Bell pairs to provide unconditional security while offering precision scaling beyond the standard quantum limit. We provide a detailed comparative performance analysis of our proposal under multiple attacks. We found that the employed controlled encoding strategy is far better than the parallel encoding of multi-partite entangled states with regard to the secrecy of the parameter. We also identify and characterize an intrinsic trade-off relationship between the maximum achievable precision and security under the limited availability of resources. The dynamic scalability of the proposed protocol makes it suitable for large-scale network sensing scenarios.

Abstract Quantum entanglement is one of the essential resources involved in quantum information processing tasks. However, its detection for usage remains a challenge. The Bell-type inequality for relative entropy of coherence serves as an entanglement witness for pure entangled states. However, it does not perform reliably for mixed entangled states. This paper constructs a classifier by employing the relationship between coherence and entanglement for supervised machine learning methods. This method encodes multiple Bell-type inequalities for the relative entropy of coherence into an artificial neural network to detect the entangled and separable states in a quantum dataset.

The lightweight Unmanned Aerial Vehicle (UAV) flight activities are constrained, particularly in the UAV range or activity span and perseverance, by the strategic correspondence link capabilities. This paper tends to the different overlap issue of trading off a set of mission prerequisites, the UAV execution parameters, and strategic credibility; thus compromising between the communication load characterized by a crucial, communication link transmitting power necessities, power accessibility onboard UAV as a weight-restricted parameter, and the UAV security.

Researchers and practitioners in the fields of science and engineering encounter significant challenges when it comes to mitigating the proliferation of computer worms, owing to their rapid spread within computer and communication networks. This study delves into a comprehensive analysis of the mathematical model governing the hazard of worm propagation in such networks. Specifically, the mathematical framework employed herein encompasses a system of ordinary differential equations. In numerous instances, mathematical models have been employed to quantitatively investigate the propagation patterns of worms across computer networks. In this scholarly article, we present an enhanced Susceptible-Exposed-Infected-Quarantined-Vaccinated (SEIQV) model, denoted as Susceptible-Exposed-Infected-Quarantined-Patched (SEIQP), which effectively captures the dissemination dynamics of an insider threat within a network featuring air gaps. To facilitate the study, we leverage the power of feedforward neural networks that are trained using the backpropagated Levenberg-Marquardt optimization algorithm. These neural networks serve as surrogate tools, providing solutions to the SEIQP model. To evaluate the efficacy of our approach, we meticulously assess their performance across three distinct scenarios. Additionally, the stability of the mathematical model is examined by manipulating the probability of an insider threat removing a patch from the host, denoted as $\eta $ . Our empirical findings conclusively establish the effectiveness of the proposed approach in addressing the intricate challenges associated with insider threats within network environments.

This study investigated the derivatives of interlock knitted structures by changing stitch type, location, and their percentage (%) on different performance properties. In the current study, 11 different interlock knit structures were developed by using multiple combinations of knit, tuck, and miss stitches and different types of testing, i.e., physical, mechanical, thermo-physiological comfort, tactile comfort, and dimensional properties, were performed and investigated. Analysis of variance (ANOVA) was performed to determine the statistical significance of structure on the properties. Based on the results, it was concluded that stitch type, percentage, and location have a prominent influence on the abovementioned properties of knit fabrics, which is also elaborated by interaction plots. The present research used a multi-response optimization technique, i.e., principal component analysis (PCA), to identify the best structure for industrial applications like uniforms, based on the optimization of abovementioned properties. Based on the results, it was suggested that a sample with an interlock cross relief structure was found to be the best in all aspects among all samples, followed by interlock pique and double tuck, which were ranked as 2nd and 3rd, respectively. This paper highlights the possible application of different interlock knitted structures according to end use for manufacturers.

Buttermilk is a fermented drink widely used all over the world. It is produced widely both traditionally and commercially through pasteurization. Buttermilk, produced during churning of cultured cream, was supplemented with vitamin C at four different concentrations i.e., 100, 200, 300, 400 mg/100 ml, and zero concentrations (T1, T2, T3, T4 and control) and was evaluated for different parameters. Vitamin C supplementation at all concentrations had no effect on compositional attributes of buttermilk (p>0.05). The results also showed that concentration of vitamin C supplemented buttermilk in T1, T2, T3, T4, and control was 98.81, 196.14, 277.38, 382.19, and 0 mg/100 ml (p0.05) respectively. Oleic acid content in control, T1, T2, T3, T4 was 35.79, 35.75, 35.65, 35.63 and 35.19% (p>0.05). Linoleic acid content in control, T1, T2, T3, and T4 was 7.49, 7.05, 7.13, 7.10 and 7.33% (p0.05) but the variation is due to storage period. Total antioxidant capacity of T1, T2, T3, T4 and control was 35.72, 42.74, 52.93, 68.77 and 84.66% (p0.05). According to trained judges, sensory evaluation of all the treatments were performed on a 9 point hedonic scale and the results showed a non-significant difference between control and treatments. From commercial point of view, sensory results were promising as vitamin C did not affect sensory attributes of buttermilk which is quite acceptable from consumer perspective. It is therefore concluded from the results that buttermilk can be supplemented with vitamin C.

Abstract Sweetpotato, Ipomoea batatas (L.), a key food security crop, is negatively impacted by heat, drought, and salinity stress. The orange‐fleshed sweetpotato cultivar “Beauregard” was exposed to heat, salt, and drought treatments for 24 and 48 h to identify genes responding to each stress condition in leaves. Analysis revealed both common (35 up regulated, 259 down regulated genes in the three stress conditions) and unique sets of up regulated (1337 genes by drought, 516 genes by heat, and 97 genes by salt stress) and down regulated (2445 genes by drought, 678 genes by heat, and 204 genes by salt stress) differentially expressed genes (DEGs) suggesting common, yet stress‐specific transcriptional responses to these three abiotic stressors. Gene Ontology analysis of down regulated DEGs common to both heat and salt stress revealed enrichment of terms associated with “cell population proliferation” suggestive of an impact on the cell cycle by the two stress conditions. To identify shared and unique gene co‐expression networks under multiple abiotic stress conditions, weighted gene co‐expression network analysis was performed using gene expression profiles from heat, salt, and drought stress treated ‘Beauregard’ leaves yielding 18 co‐expression modules. One module was enriched for “response to water deprivation,” “response to abscisic acid,” and “nitrate transport” indicating synergetic crosstalk between nitrogen, water, and phytohormones with genes encoding osmotin, cell expansion, and cell wall modification proteins present as key hub genes in this drought‐associated module. This research lays the groundwork for exploring to a further degree, mechanisms for abiotic stress tolerance in sweetpotato.

Nutritionists recommend to consume 3 g Conjugated linoleic acid (CLA) to prevent cancers, cardiovascular diseases, diabetes, obesity and other lifestyle-related disorders. To fulfill the daily requirements of CLA, large proportions of dairy and meat products have to be included in the dietary plan that may have negative impacts such as excessive intake of fat, cholesterol, and lactose. Current investigation was aimed to determine the effect of safflower oil derived CLA on fatty acids composition, lipolysis and sensory properties of cheddar cheese. Safflower oil-derived conjugated linoleic acid (CLA) was added to cheddar cheese at four levels i.e. 1%, 2%, 3% and 4% concentrations (T1, T2, T3 & T4), cheese without CLA supplement was used as control, vacuum packaged samples were ripened at 6–8°C for 90 days. Analysis of CLA supplement revealed that concentrations of ∆9c,11 t-18:2, ∆10 t,12c-18:2, ∆9c,11c-18:2, ∆9 t, 11c-18:2 were 52.16, 44.37, 0.27 and 0.55 mg/100 g. In control, concentrations of ∆9c,11 t-18:2 and ∆10 t,12c-18:2 was 0.24% and 0.07% with a total CLA content of 0.31%. In T3, concentrations of ∆9c,11 t-18:2, ∆10 t,12c-18:2, ∆9c,11c-18:2 and ∆9 t, 11c-18:2 were 1.12, 3.82, 1.63, 2.77 (total CLA 8.34mg100g). In T4, concentrations of ∆9c,11 t-18:2, ∆10 t,12c-18:2, ∆9c,11c-18:2 and ∆9 t, 11c-18:2, were 1.47, 3.50, 3.25 and 3.44% (total CLA 11.67 mg/100 g). Supplementation of cheddar cheese at all four levels significantly increased the concentrations of lactic acid, citric acid, propionic and acetic acids. After 90 days of ripening, free fatty acids in control, T1, T2, T3 and T4 were 0.14, 0.19, 0.23, 0.29 and 0.37% with no difference in composition and sensory properties. These results suggested that CLA content of cheddar cheese can be successfully increased with safflower oil-derived CLA supplement.

For an exhaustive removal of Cr(VI), three different types of biochar were prepared, modified and evaluated for Cr(VI) removal from wastewater. Magnetic eucalyptus biochar (MBC) was synthesized via a facile pyrolysis process of eucalyptus biochar pretreated with FeCl3 firstly coupled with K2CO3 activation. As a comparison, raw biochar (BC) and FeCl3 modified biochar (FBC) were prepared. The physicochemical property of adsorbent synthesis and removal mechanism was examined using BET, SEM with EDS, zeta potential, XRD, FTIR, XPS and Vibrating sample magnetometer (VSM), and the effect of various reaction conditions and parameters were evaluated for Cr(VI) removal. Results demonstrated that compared to BC and FBC, MBC has a larger specific surface area (870.3264 m2/g), higher content of Fe mainly existed as γ-Fe2O3 and Fe3O4, and higher zeta potential (10.66). This is due to the significant alleviation of pore blockage caused by iron oxides and more efficient conversion of iron oxides with the participation of K2CO3 during modification process. Upon batch tests, the removal efficiency could achieve more than 91% for 200 mg/L Cr(VI) solution with optimum adsorbent dosage (0.01 g) at desired acid condition (pH = 2) within 9 h. The separation behavior of MBC for Cr(VI) was highly identical with elovich and Freundlich model. MBC showed better adsorption performance up to three cycles and under various co-existing ions. Besides, the removal ability of MBC gained an obvious increase of 2–5 folds and 3–10 folds at different initial Cr(VI) concentrations, compared with BC and FBC. Characterization analyses unraveled that physicochemical removal process was responsible for the efficient elimination of Cr(VI) in an aqueous solution, resulting from adsorption, electrostatic attraction, pore filling, complexation, ion exchange, redox reaction and precipitation due to the fabrication of iron oxides into biochar.

The Internet of Things (IOT)-based smart farming promises ultrafast speeds and near real-time response. Precision farming enabled by the Internet of Things has the potential to boost efficiency and output while reducing water use. Therefore, IoT devices can aid farmers in keeping track crop health and development while also automating a variety of tasks (such as moisture level prediction, irrigation system, crop development, and nutrient levels). The IoT-based autonomous irrigation technique makes exact use of farmers’ time, money, and power. High crop yields can be achieved through consistent monitoring and sensing of crops utilizing a variety of IoT sensors to inform farmers of optimal harvest times. In this paper, a smart framework for growing tomatoes is developed, with influence from IoT devices or modules. With the help of IoT modules, we can forecast soil moisture levels and fine-tune the watering schedule. To further aid farmers, a smartphone app is currently in development that will provide them with crucial data on the health of their tomato crops. Large-scale experiments validate the proposed model’s ability to intelligently monitor the irrigation system, which contributes to higher tomato yields.

Propofol infusion in anesthesia administration requires continual adjustment in the manual infusion system to regulate the hypnosis level. Hypnotic level is based on Bispectral Index Monitor (BIS) showing the cortical activity of the brain scaled between 0 to 100. The new challenging aspect of automation in anaesthesia is to estimate the concentration of hypnotic drugs in different compartments of the body including primary, rapid peripheral (muscle), slow peripheral (bones, fat) and effect site (brain) compartment based on Pharmacokinetics (PK) and Pharmacodynamics (PD) model. This paper aimed to regulate the hypnosis level with estimating the Propofol concentrations using a linear observer in feedback control strategy based on Integral Super-Twisting Sliding Mode Controller (ISTSMC). The drug concentration in plasma of the silico patients accurately estimated in nominal transient. The results show that tracking errors between the actual output in form of BIS level and linearized output nearly approaches to zero in the maintenance phase of anesthesia to ensure the controller response on sliding phase with optimum performances by achieving desired hypnotic level 50 on BIS. The robustness of control strategy is further ensured by adding measurement noise of electromagnetic environment of operation theatre distracting signal quality index of the output BIS level.

Apples (Malus domestica) are one of the most important temperate fruit crops in the world. Apple production provides, directly or indirectly, employment and improvement in the living standards to millions of people, playing a key role in rural economies. We have conducted an interview-based survey to assess the current status and challenges faced by apple production in the Batna and Khenchela provinces, the two most important apple producing regions of Eastern Algeria. The survey was conducted between March and November 2019. A total of 50 apple growers answered 40 questions, including 13 direct and 20 qualitative questions. A scale ranging from 1 (very low) to 5 (very high) was used as a quantitative measurement for 12 questions. The reliability index of the questionnaire was 0.827. The results identified the main challenges faced by apple growers in Eastern Algeria as: limited access to certified and clean planting material, limited knowledge of disease and pest identification, inadequate access to appropriate disease management, lack of storage facilities and transportation of harvested fruit, and insufficient knowledge of marketing and access to markets. The study also identified a need for the development of cost-effective plant multiplication, horticultural and disease management practices and educational programmes suitable for local conditions to enhance the productivity and economic benefits of apple orchards.

Achievement of better comfort properties in station suits of fire fighters without compromising flame retardancy is an utmost necessity. Inherently fire-resistant fibers play vital role in this scope. In this work twenty-three plain single jersey knitted fabric samples were developed by using five inherently fire-resistant fibers. The fibers used were meta-aramid Nomex, fire resistant (FR-Viscose, modacrylic (Protex), FR-polyester (Recron) and carbon fibers. All the fibers were blended in different blend ratios. Vertical flammability test was performed to investigate the flammability properties. Thermal characterization of the samples was done using thermogravimetric analysis (TGA). Thermophysiological and wearing comfort properties of all samples were evaluated. Scanning electron microscopy (SEM) was carried out for the evaluation of surface morphology of the fibers after charring. Results of vertical flammability test revealed that sample containing 100% Nomex fibers produced minimum char length. Nomex and carbon fiber blended fabric provided better moisture management along with better flame retardant behavior. Statistical tool named as Principal Component Analysis (PCA) was utilized for the optimization of all responses. All the samples were ranked as per principal component analysis. Sample containing 50/50 FR-polyester and FR-viscose fibers was found to be the top ranked, as this sample provided optimum flammability and comfort related performance.

The Vertical Take-Off and Landing (VTOL) system is a multi-variable system subjected to harsh weather conditions, which creates challenges in proving the stability of the system before takeoff, which is essential for a flight dynamics system. The presented research work is based on the experimental results of the VTOL system to investigate and prove the stability using Lyapunov theory. This is achieved by tracking the pitch along the x-axis using cascaded control and integral super twisting sliding mode control (ISTSMC) algorithms. The motor current of the propeller assembly is regulated based on proportional integral (PI) and proportional integral derivative (PID) controllers. The cascaded control shows the maximum tracking error due to high-frequency fluctuations in the controller input signal, which lead to expensive mechanical losses for the actuators. The comparison of the results shows that ISTSMC outperforms the cascaded control strategy by reducing the tracking error to less than 1% percent and reducing the high-frequency fluctuations in the controller input signal. The hardware results show a minor delay in the transient response during vertical takeoff due to the inertia of the system and the tracking error due to air friction, etc., of the external environment, compared to the simulation results obtained in MATLAB.

Conjugated linoleic acid (CLA) is perceived to protect the body from metabolic diseases. This study was conducted to determine the effect of Lactiplantibacillus plantarum (Lp. plantarum) on CLA production and sensory characteristics of cheddar cheese. Lp. plantarum can convert linoleic acid (LA) to CLA. To increase CLA in cheddar cheese and monitor the conversion of LA to CLA by Lp. plantarum, the LA content of cheese milk (3.4% fat) was increased by partially replacing fat with safflower oil (85% LA of oil) at 0, 3, 6, and 9% concentrations (T1, T2, T3, and T4). Furthermore, Lp. plantarum 108 colony-forming units (CFU)/mL (8 log CFU mL−1) was added in all treatments along with traditional cheddar cheese culture (Lactococcus lactis ssp. lactis and L. lactis ssp. cremoris). After 30 days of ripening, Lp. plantarum in T1, T2, T3, and T4 was 6.75, 6.72, 6.65, and 6.55 log CFU g−1. After 60 days of ripening, Lp. plantarum in T1, T2, T3, and T4 was 6.35, 6.27, 6.19, and 6.32 log CFU g−1. After 60 days of ripening, Lp. plantarum in T1, T2, T3, and T4 was 6.41, 6.25, 6.69, and 6.65 log CFU g−1. GC-MS analysis showed that concentrations of CLA in the 90 days’ control, T1, T2, T3, and T4 were 1.18, 2.73, 4.44, 6.24, and 9.57 mg/100 g, respectively. HPLC analysis revealed that treatments containing Lp. plantarum and LA presented higher concentrations of organic acids than the control sample. The addition of safflower oil at all concentrations did not affect cheese composition, free fatty acids (FFA), and the peroxide value (POV) of cheddar cheese. Color flavor and texture scores of experimental cheeses were not different from the control cheese. It was concluded that Lp. plantarum and safflower oil can be used to increase CLA production in cheddar cheese.

Rapid apple decline is a phenomenon characterized by a weakening of young apple trees in high density orchards, often followed by their quick collapse. The nature of this phenomenon remains unclear. In this work, we investigated the root system architecture (RSA) of declining and non-declining apple trees in two orchards in New York State. High-density orchard A consisted of 4-year-old ‘Honeycrisp’ on ‘Malling 9 Nic29’, and conventional orchard B consisted of 8-year-old ‘Fuji’ on ‘Budagovsky 9’. In both orchards, a negative correlation (−0.4–−0.6) was observed between RSA traits and decline symptoms, suggesting that declining trees have weaker root systems. Scion trunk diameter at the graft union, total root length, and the length of fine and coarse roots were significantly (p < 0.05) reduced in declining trees in both orchards. Additionally, internal trunk necrosis at, above, and below the graft union was observed in declining trees in orchard A but not in orchard B. Finally, latent viruses were not associated with decline, as their occurrence was documented in declining and non-declining trees in orchard A, but not in orchard B. Together, these results showed weakened root systems of declining trees, suggesting that these trees may experience deficiencies in water and nutrient uptake, although distinct RSA and trunk health traits between the two orchards were noticeable.

Optical coherence tomography (OCT) is widely used to detect and classify retinal diseases. However, OCT-image-based manual detection by ophthalmologists is prone to errors and subjectivity. Thus, various automation methods have been proposed; however, improvements in detection accuracy are required. Particularly, automated techniques using deep learning on OCT images are being developed to detect various retinal disorders at an early stage. Here, we propose a deep learning-based automatic method for detecting and classifying retinal diseases using OCT images. The diseases include age-related macular degeneration, branch retinal vein occlusion, central retinal vein occlusion, central serous chorioretinopathy, and diabetic macular edema. The proposed method comprises four main steps: three pretrained models, DenseNet-201, InceptionV3, and ResNet-50, are first modified according to the nature of the dataset, after which the features are extracted via transfer learning. The extracted features are improved, and the best features are selected using ant colony optimization. Finally, the best features are passed to the k-nearest neighbors and support vector machine algorithms for final classification. The proposed method, evaluated using OCT retinal images collected from Soonchunhyang University Bucheon Hospital, demonstrates an accuracy of 99.1% with the incorporation of ACO. Without ACO, the accuracy achieved is 97.4%. Furthermore, the proposed method exhibits state-of-the-art performance and outperforms existing techniques in terms of accuracy.

The apple cultivar ‘Honeycrisp’ has superior fruit quality traits, cold hardiness, and disease resistance, making it a popular breeding parent. However, it suffers from several physiological disorders, production, and postharvest issues. Despite several available apple genome sequences, understanding of the genetic mechanisms underlying cultivar-specific traits remains lacking. Here, we present a highly contiguous, fully phased, chromosome-level genome of ‘Honeycrisp’ apples, using PacBio HiFi, Omni-C, and Illumina sequencing platforms, with two assembled haplomes of 674 Mbp and 660 Mbp, and contig N50 values of 32.8 Mbp and 31.6 Mbp, respectively. Overall, 47,563 and 48,655 protein-coding genes were annotated from each haplome, capturing 96.8–97.4% complete BUSCOs in the eudicot database. Gene family analysis reveals most ‘Honeycrisp’ genes are assigned into orthogroups shared with other genomes, with 121 ‘Honeycrisp’-specific orthogroups. This resource is valuable for understanding the genetic basis of important traits in apples and related Rosaceae species to enhance breeding efforts.

Scientific applications often require high-bandwidth shared storage to perform joint simulations and collaborative data analytics. Shared memory pools provide a chance to satisfy such needs. Recently, a high-speed network such as Gen-Z utilizing persistent memory (PM) offers an opportunity to create a shared memory pool connected to compute nodes. However, there are several challenges to use scientific applications on the shared memory pool directly such as scalability, failure-atomicity, and lack of scientific metadata-based search and query. In this paper, we propose MOSIQS, a persistent memory object storage framework with metadata indexing and querying for scientific computing. We design MOSIQS based on the key idea that memory objects on PM pool can live beyond the application lifetime and can become the sharing currency for applications and scientists. MOSIQS provides an aggregate memory pool atop an array of persistent memory devices to store and access memory objects to accelerate scientific computing. MOSIQS uses a lightweight persistent memory key-value store to manage the metadata of memory objects, which enables memory object sharing. To facilitate metadata search and query over millions of memory objects resident on memory pool, we introduce Group Split and Merge (GSM), a novel persistent index data structure designed primarily for scientific datasets. GSM splits and merges dynamically to minimize the query search space and maintains low query processing time while overcoming the index storage overhead. MOSIQS is implemented on top of PMDK. We evaluate the proposed approach on many-core server with an array of real PM devices. Experimental results show that MOSIQS gains a 100% write performance improvement and executes multi-attribute queries efficiently with $2.7\times $ less index storage overhead offering significant potential to speed up scientific computing applications.

This paper presents a compact, uni-planer wide band Multiple-Input-Multiple-Output (MIMO) antenna. The designed antenna array has a compact size of (26mm x 31mm) covering a wide frequency band from 3.1GHz - 11GHz. A partial ground plane shared by two radiating patches is used. A ground stub and a single column Electromagnetic Bandgap (EBG) structure in between the two radiating patches results in very low mutual coupling in the designed antenna. The proposed MIMO-antenna was evaluated through different performance metric indicators like channel capacity loss, far-field radiation pattern, S-parameters, envelope correlation coefficient, peak gain, diversity-gain and radiation efficiency. Our designed MIMO antenna has a very high isolation amongst the MIMO antennas ( $\text{S}_{21} < -25$ dB), a high Diversity-Gain (DG $>9.995$ dB), a very low Envelope Co-relation Coefficient of (ECC < 0.001) and low Channel-Capacity-Loss (CCL < 0.1bits/s/Hz). The said antenna has an average radiation efficiency of 85.5% and a peak gain of (5.67dB) within the ultra-wideband (UWB) spectrum. A 0.8mm thick FR-4 substrate is used to fabricate the MIMO-antenna and tested in an anechoic chamber. The measured results closely match with the simulated results.

Abstract Background The NBS disease-related gene family coordinates the inherent immune system in plants in response to pathogen infections. Previous studies have identified NBS-encoding genes in Pyrus bretschneideri (‘Dangshansuli’, an Asian pear) and Pyrus communis (‘Bartlett’, a European pear) genomes, but the patterns of genetic variation and selection pressure on these genes during pear domestication have remained unsolved. Results In this study, 338 and 412 NBS-encoding genes were identified from Asian and European pear genomes. This difference between the two pear species was the result of proximal duplications. About 15.79% orthologous gene pairs had Ka/Ks ratio more than one, indicating two pear species undergo strong positive selection after the divergence of Asian and European pear. We identified 21 and 15 NBS-encoding genes under fire blight and black spot disease-related QTL, respectively, suggesting their importance in disease resistance. Domestication caused decreased nucleotide diversity across NBS genes in Asian cultivars (cultivated 6.23E-03; wild 6.47E-03), but opposite trend (cultivated 6.48E-03; wild 5.91E-03) appeared in European pears. Many NBS-encoding coding regions showed Ka/Ks ratio of greater than 1, indicating the role of positive selection in shaping diversity of NBS-encoding genes in pear. Furthermore, we detected 295 and 122 significantly different SNPs between wild and domesticated accessions in Asian and European pear populations. Two NBS genes (Pbr025269.1 and Pbr019876.1) with significantly different SNPs showed >5x upregulation between wild and cultivated pear accessions, and > 2x upregulation in Pyrus calleryana after inoculation with Alternaria alternata. We propose that positively selected and significantly different SNPs of an NBS-encoding gene (Pbr025269.1) regulate gene expression differences in the wild and cultivated groups, which may affect resistance in pear against A. alternata. Conclusion Proximal duplication mainly led to the different number of NBS-encoding genes in P. bretschneideri and P. communis genomes. The patterns of genetic diversity and positive selection pressure differed between Asian and European pear populations, most likely due to their independent domestication events. This analysis helps us understand the evolution, diversity, and selection pressure in the NBS-encoding gene family in Asian and European populations, and provides opportunities to study mechanisms of disease resistance in pear.

Abstract Background Breeding for resistance to apple scab (caused by Venturia inaequalis), the most devastating fungal disease of apples, relies on genetic resources maintained in germplasm collections. Methods To identify new sources of scab resistance, we evaluated 177 Malus accessions, including 27 primary and 13 hybrid Malus species from diverse geographical origins, in an orchard at Geneva, New York. We also screened a differential host set for 2 years to monitor for changes in the effectiveness of ten known scab resistance genes, which allowed us to confirm the presence of virulent pathogen races in the orchard. Results We found that ~ 37% of the wild Malus accessions and domesticated cultivars were resistant to apple scab in the field. Several of these accessions were unrelated to sources of previously known resistance genes and are promising for apple scab genetic research and resistance breeding. Cultivars carrying the Rvi6 (Vf) gene from Malus floribunda clone 821, e.g. ‘Liberty’ or ‘Florina’, remained resistant despite the breakdown of Rvi6. ‘Demir’, a Malus hybrid from Turkey, and ‘Chisel Jersey’, a traditional English hard cider cultivar, showed fewer symptoms than the Rvi6 resistant cultivar ‘Prima’. Races 1 to 7 and 9 of V. inaequalis were present in the orchard, but no scab was observed on the indicator host accessions for races 11 and 12. Conclusions Detailed and systematic screening of Malus germplasm identified resistant and moderately resistant donor accessions based on resistance reaction types. These accessions are promising for use in future genetic studies to identify novel sources of scab resistance alleles for apple breeding to develop cultivars with durable apple scab resistance.

Erwinia amylovora causes fire blight, the most devastating bacterial disease of apples and pears in the United States and worldwide. The model strain E. amylovora Ea1189 has been extensively used to understand bacterial pathogenesis and molecular mechanisms of bacterial-plant interactions. In this work, we sequenced and assembled the de novo genome of Ea1189, using a combination of long Oxford Nanopore Technologies and short Illumina sequence reads. A complete gapless genome assembly of Ea1189 consists of a 3,797,741-bp circular chromosome and a 28,259-bp plasmid with 3,472 predicted genes, including 78 transfer RNAs, 22 ribosomal RNAs, and 20 noncoding RNAs. A comparison of the Ea1189 genome to previously sequenced E. amylovora complete genomes showed 99.94 to 99.97% sequence similarity with 314 to 946 single nucleotide polymorphisms. We believe that the availability of the complete genome sequence of strain Ea1189 will further support studies to understand evolution, diversity and structural variations of Erwinia strains, as well as the molecular basis of E. amylovora pathogenesis and its interactions with host plants, thus facilitating the development of effective management strategies for this important disease.

Deduplication has been principally employed in distributed storage systems to improve storage space efficiency. Traditional deduplication research ignores the design specifications of shared-nothing distributed storage systems such as no central metadata bottleneck, scalability, and storage rebalancing. Likewise, inline deduplication integration poses serious threats to storage system read/write performance, consistency, and scalability. Mainly, this is due to ineffective and error-prone deduplication metadata, duplicate lookup I/O redirection, and placement of content fingerprints and data chunks. Further, transaction failures after deduplication integration often render inconsistencies in data chunks, deduplication metadata, and garbage data chunks. results in rendering inconsistencies in data chunks, deduplication metadata, and garbage data chunks. In this paper, we propose Grate, a high-performance inline cluster-wide data deduplication, complying with the design constraints of shared-nothing storage systems. In particular, Grate eliminates duplicate copies across the cluster for high storage space efficiency without jeopardizing performance. We employ a distributed deduplication metadata shard, which promises high-performance deduplication metadata and duplicate fingerprint lookup I/Os without introducing a single point of failure. The placement of data and deduplication metadata is made cluster-wide based on the content fingerprint of chunks. We decouple the deduplication metadata shard from read I/O path and replace it with a read manifestation object to further speedup read performance. To guarantee deduplication-enabled transaction consistency and efficient garbage identification, we design a flag-based asynchronous consistency scheme, capable of repairing the missing data chunks on duplicate arrival. We design and implement Grate in Ceph. The evaluation shows an average of 18% performance bandwidth improvement over the content addressable deduplication approach at smaller chunk sizes, i.e., less than 128KB while maintaining high storage space savings.

The hexaploid sweetpotato (Ipomoea batatas (L.) Lam., 2n = 6x = 90) is an important staple food crop worldwide and plays a vital role in alleviating famine in developing countries. Due to its high ploidy level, genetic studies in sweetpotato lag behind major diploid crops significantly. We built an ultra-dense multilocus integrated genetic map and characterized the inheritance system in a sweetpotato full-sib family using our newly developed software, MAPpoly. The resulting genetic map revealed 96.5% collinearity between I. batatas and its diploid relative I. trifida. We computed the genotypic probabilities across the whole genome for all individuals in the mapping population and inferred their complete hexaploid haplotypes. We provide evidence that most of the meiotic configurations (73.3%) were resolved in bivalents, although a small portion of multivalent signatures (15.7%), among other inconclusive configurations (11.0%), were also observed. Except for low levels of preferential pairing in linkage group 2, we observed a hexasomic inheritance mechanism in all linkage groups. We propose that the hexasomic-bivalent inheritance promotes stability to the allelic transmission in sweetpotato.

Abstract Background Although it is known that resistant rootstocks facilitate management of fire blight disease, incited by Erwinia amylovora, the role of rootstock root traits in providing systemic defense against E. amylovora is unclear. In this study, the hypothesis that rootstocks of higher root vigor provide higher tolerance to fire blight infection in apples is tested. Several apple scion genotypes grafted onto a single rootstock genotype and non-grafted ‘M.7’ rootstocks of varying root vigor are used to assess phenotypic and molecular relationships between root traits of rootstocks and fire blight susceptibility of apple scion cultivars. Results It is observed that different root traits display significant (p

Disease resistance: How apples fight blight Different apple varieties, such as ‘Gala’ and ‘Empire,’ defend themselves against fire blight in different ways, and studying how they do it may help in breeding varieties with better resistance. Fire blight is a devastating bacterial disease that can destroy entire orchard blocks in a single season. Breeding for increased resistance is one of the most efficient ways to combat it. Some apple varieties are naturally more resistant than others, but the underlying genetics are not well understood. Awais Khan at Cornell University in New York and co-workers investigated how the ‘Gala’ and ‘Empire’ varieties defend themselves in the first 72 h after infection. ‘Empire’ showed stronger resistance than ‘Gala,’ with many distinct resistance mechanisms. The researchers identified several resistance genes in each variety, which may eventually be used in breeding more blight-resistant apple varieties.

Apple replant disease (ARD), incited by a pathogen complex including Pythium ultimum, causes stunted growth or death of newly planted trees at replant sites. Development and deployment of resistant or tolerant rootstocks offers a cost-effective, ecologically friendly, and durable approach for ARD management. Maximized exploitation of natural resistance requires integrated efforts to identify key regulatory mechanisms underlying resistance traits in apple. In this study, miRNA profiling and degradome sequencing identified major miRNA pathways and candidate genes using six apple rootstock genotypes with contrasting phenotypes to P. ultimum infection. The comprehensive RNA-seq dataset offered an expansive view of post-transcriptional regulation of apple root defense activation in response to infection from P. ultimum. Several pairs of miRNA families and their corresponding targets were identified for their roles in defense response in apple roots, including miR397-laccase, miR398-superoxide dismutase, miR10986-polyphenol oxidase, miR482-resistance genes, and miR160-auxin response factor. Of these families, the genotype-specific expression patterns of miR397 indicated its fundamental role in developing defense response patterns to P. ultimum infection. Combined with other identified copper proteins, the importance of cellular fortification, such as lignification of root tissues by the action of laccase, may critically contribute to genotype-specific resistance traits. Our findings suggest that quick and enhanced lignification of apple roots may significantly impede pathogen penetration and minimize the disruption of effective defense activation in roots of resistant genotypes. The identified target miRNA species and target genes consist of a valuable resource for subsequent functional analysis of their roles during interaction between apple roots and P. ultimum.

Abstract Fire blight, caused by epiphytotic gram‐negative bacteria Erwinia amylovora, is the most destructive bacterial disease of apple (Malus spp.). Genetic mechanisms of fire blight resistance have mainly been studied using traditional biparental quantitative trait loci (QTL) mapping approaches. Here, we use large‐scale historic shoot and blossom fire blight data collected over multiple years and genotyping‐by‐sequencing (GBS) markers to identify significant marker–trait associations in a diverse set of 566 apple [Malus domestica (Suckow) Borkh.] accessions. There was large variation in fire blight resistance and susceptibility in these accessions. We identified 23 and 38 QTL significantly (p < .001) associated with shoot and blossom blight resistance, respectively. The QTL are distributed across all 17 chromosomes of apple. Four shoot blight and 19 blossom blight QTL identified in this study colocalized with previously identified QTL associated with resistance to fire blight or apple scab. Using transcriptomics data of two apple cultivars with contrasting fire blight responses, we also identified candidate genes for fire blight resistance that are differentially expressed between resistant and susceptible cultivars and located within QTL intervals for fire blight resistance. However, further experiments are needed to confirm and validate these marker–trait associations and develop diagnostic markers before use in marker‐assisted breeding to develop apple cultivars with decreased fire blight susceptibility.

Path planning for wheeled mobile robots on partially known uneven terrain is an open challenge since robot motions can be strongly influenced by terrain with incomplete environmental information such as locally detected obstacles and impassable terrain areas. This paper proposes a hierarchical path planning approach for a wheeled robot to move in a partially known uneven terrain. We first model the partially known uneven terrain environment respecting the terrain features, including the slope, step, and unevenness. Second, facilitated by the terrain model, we use A⋆ algorithm to plan a global path for the robot based on the partially known map. Finally, the Q-learning method is employed for local path planning to avoid locally detected obstacles in close range as well as impassable terrain areas when the robot tracks the global path. The simulation and experimental results show that the designed path planning approach provides satisfying paths that avoid locally detected obstacles and impassable areas in a partially known uneven terrain compared with the classical A⋆ algorithm and the artificial potential field method.

This paper proposes a model predictive control (MPC) method based on an interconnected model to maximize the ocean wave energy extracted by a wave-energy converter (WEC) array. In the proposed method, a formally uniform interconnected model is applied to represent the dynamics of an array consisting of an arbitrary quantity of WECs, simultaneously considering the hydrodynamic interaction among all the WEC devices. First, the WEC devices and their hydrodynamic interaction are represented in an interconnected model that describes the networked dynamics of a variety of WEC arrays with distinct spatial geometry layout of the WEC devices deployed in the sea field. Second, based on the presented model, an MPC method is applied to achieve the coordinated control of the WEC array to improve its energy conversion efficiency under the constraints of buoy position and control force. Third, a hardware-in-the-loop (HIL) platform is developed to simulate the WEC array’s physical operating conditions, and the proposed method is implemented on the platform to test its performance. The test results show that the proposed MPC method using the interconnected model has a higher energy harvesting efficiency than the classic MPC method.

Blur detection (BD) is an important and challenging task in digital imaging and computer vision applications. Accurate segmentation of homogenous smooth and blur regions, low-contrast focal regions, missing patches, and background clutter, without having any prior information about the blur, are the fundamental challenges of BD. Previous work on BD has emphasized much effort on designing local sharpness metric maps from the images. However, the smooth/blurred regions having the same patterns as sharp regions make them problematic. This paper presents a robust novel method to extract the local metric map for blurred and nonblurred regions based on multisequential deviated patterns (MSDPs). Unlike the preceding, MSDP extracts the local sharpness metric map on the images at multiple scales using different adaptive thresholds to overcome the problems of smooth/blur regions and missing patches. By using the integral values of the image along with image masking and Otsu thresholding, highly accurate segmented regions of the images are acquired. We argue/hypothesize that the local sharpness map extraction by using direct integral information of the image is highly affected by the threshold selected for distinction between the regions, whereas MSDP feature extraction overcomes the limitations substantially by using automatic threshold computation over multiple scales of the images. Moreover, the proposed method extracts the relatively accurate sharp regions from the high-dense blur and noisy images. Experiments are conducted on two commonly used SHI and DUT datasets for blur and sharp region classifications. The results indicate the effectiveness of the proposed method in terms of sharp segmented regions. Experimental results of qualitative and quantitative comparisons of the proposed method with ten comparative methods demonstrate the superiority of our method. Moreover, the proposed method is also computationally efficient over state-of-the-art methods.

Premise Apple orchards in the United States are under constant threat from a large number of pathogens and insects. Appropriate and timely deployment of disease management depends on early disease detection. Incorrect and delayed diagnosis can result in either excessive or inadequate use of chemicals, with increased production costs and increased environmental and health impacts. Methods and Results We have manually captured 3651 high‐quality, real‐life symptom images of multiple apple foliar diseases, with variable illumination, angles, surfaces, and noise. A subset of images, expert‐annotated to create a pilot data set for apple scab, cedar apple rust, and healthy leaves, was made available to the Kaggle community for the Plant Pathology Challenge as part of the Fine‐Grained Visual Categorization (FGVC) workshop at the 2020 Computer Vision and Pattern Recognition conference (CVPR 2020). Participants were asked to use the image data set to train a machine learning model to classify disease categories and develop an algorithm for disease severity quantification. The top three area under the ROC curve (AUC) values submitted to the private leaderboard were 0.98445, 0.98182, and 0.98089. We also trained an off‐the‐shelf convolutional neural network on this data for disease classification and achieved 97% accuracy on a held‐out test set. Discussion This data set will contribute toward development and deployment of machine learning–based automated plant disease classification algorithms to ultimately realize fast and accurate disease detection. We will continue to add images to the pilot data set for a larger, more comprehensive expert‐annotated data set for future Kaggle competitions and to explore more advanced methods for disease classification and quantification.

Background: The chloroplast (cp) genome is an important resource for studying plant diversity and phylogeny. Assembly of the cp genomes from next-generation sequencing data is complicated by the presence of two large inverted repeats contained in the cp DNA. Methods: We constructed a complete circular cp genome assembly for the hexaploid sweetpotato using extremely low coverage (

With the growing volumes of data, many organizations are deploying geo-distributed edge servers and building federations atop of edge servers to improve data sharing, effective collaborations, and analytics. Multiple federation file systems are designed to satisfy such needs, but due to application-specific architectures, these federations neglect some important features that can improve the overall federation performance. In this paper, we address the important challenges of federated file systems, in particular, global namespace, optimal data placement and analysis, efficient data migration across edge servers, and metadata optimizations. To further investigate these challenges, we prototyped the federation file system iStore to emulate the federation and showed the significance of the afore-mentioned key challenges in the federation. The iStore provides unified global namespace atop of geo-distributed edge servers with a generic job and resource-aware data storage and placement algorithm (JRAP), which minimizes the job execution time by considering resources at each edge server. Furthermore, to enable effective data migration, we employed direct channel file layout-aware data transfer and designed a batch-based metadata scheme for federations to reduce the metadata contention with increasing clients. We evaluated the efficacy of various big data applications from data generation to storage and analysis using the iStore on real testbed and simulation.

In this paper, a novel numerical scheme to set-membership interval state estimator design is proposed for the multiple-input-multiple-output (MIMO) linear time-varying (LTV) discrete-time systems using systems observability matrix and its past input/output values. The proposed method is more simple and efficient. First, an interval state estimator is designed that will generate a tight interval vector for the real state vector in a guaranteed way by employing interval analysis and consistency techniques for the single-input-single-output (SISO) systems. The proposed interval state estimator technique is then extended easily to the MIMO systems. Secondly, the estimation errors dynamics bounds are computed a-priori to measurements for the unknown but bounded uncertainties. Finally, the convergence of the width of the interval state vector towards a known value in finite time is provided to prove the boundedness of the interval state vector and estimation error that further quantify the accuracy of the developed technique. The performance and comparison with already existing techniques are highlighted through numerical examples.

This paper designs an interval estimator for a fourth-order nonlinear susceptible-exposed-infected-recovered (SEIR) model with disturbances using noisy counts of susceptible people provided by Public Health Services (PHS). Infectious diseases are considered the main cause of deaths among the top ten worldwide, as per the World Health Organization (WHO). Therefore, tracking and estimating the evolution of these diseases are important to make intervention strategies. We study a real case in which some uncertain variables such as model disturbances, uncertain input and output measurement noise are not exactly available but belong to an interval. Moreover, the uncertain transmission bound rate from the susceptible towards the exposed stage is not available for measurement. We designed an interval estimator using an observability matrix that generates a tight interval vector for the actual states of the SEIR model in a guaranteed way without computing the observer gain. As the developed approach is not dependent on observer gain, our method provides more freedom. The convergence of the width to a known value in finite time is investigated for the estimated state vector to prove the stability of the estimation error, significantly improving the accuracy for the proposed approach. Finally, simulation results demonstrate the satisfying performance of the proposed algorithm.

Sweetpotato is an important food security crop providing rich source of macro- and micronutrients including carbohydrates and vitamins. Here, the authors assemble of the two diploid relatives of cultivated sweetpotato and identify genes and alleles associated with carotenoid biosynthesis from breeding lines.

The use of face masks has increased dramatically since the COVID-19 pandemic started in order to to curb the spread of the disease. Additionally, breakthrough infections caused by the Delta and Omicron variants have further increased the importance of wearing a face mask, even for vaccinated individuals. However, the use of face masks also induces attenuation in speech signals, and this change may impact speech processing technologies, e.g., automated speaker verification (ASV) and speech to text conversion. In this paper we examine Automatic Speaker Verification (ASV) systems against the speech samples in the presence of three different types of face mask: surgical, cloth, and filtered N95, and analyze the impact on acoustics and other factors. In addition, we explore the effect of different microphones, and distance from the microphone, and the impact of face masks when speakers use ASV systems in real-world scenarios. Our analysis shows a significant deterioration in performance when an ASV system encounters different face masks, microphones, and variable distance between the subject and microphone. To address this problem, this paper proposes a novel framework to overcome performance degradation in these scenarios by realigning the ASV system. The novelty of the proposed ASV framework is as follows: first, we propose a fused feature descriptor by concatenating the novel Ternary Deviated overlapping Patterns (TDoP), Mel Frequency Cepstral Coefficients (MFCC), and Gammatone Cepstral Coefficients (GTCC), which are used by both the ensemble learning-based ASV and anomaly detection system in the proposed ASV architecture. Second, this paper proposes an anomaly detection model for identifying vocal samples produced in the presence of face masks. Next, it presents a Peak Norm (PN) filter to approximate the signal of the speaker without a face mask in order to boost the accuracy of ASV systems. Finally, the features of filtered samples utilizing the PN filter and samples without face masks are passed to the proposed ASV to test for improved accuracy. The proposed ASV system achieved an accuracy of 0.99 and 0.92, respectively, on samples recorded without a face mask and with different face masks. Although the use of face masks affects the ASV system, the PN filtering solution overcomes this deficiency up to 4%. Similarly, when exposed to different microphones and distances, the PN approach enhanced system accuracy by up to 7% and 9%, respectively. The results demonstrate the effectiveness of the presented framework against an in-house prepared, diverse Multi Speaker Face Masks (MSFM) dataset, (IRB No. FY2021-83), consisting of samples of subjects taken with a variety of face masks and microphones, and from different distances.