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Introduction: The objective of the present initiative was to build capacity of health care providers in private sector along with standardisation of care during antenatal period for common antenatal problems of GDM and iron deficiency anaemia in private sector. Methods: A pilot project for all levels of health care providers including doctors, nurses, counsellors and laboratory technicians of 34 private facilities in six districts of Jharkhand was planned. Training modules for GDM and anaemia based on government of India guidelines were developed. End line evaluation included data collection and descriptive analysis of quantitative data quality scores from assessment standards on GDM and anaemia in pregnancy. Results: Knowledge assessment of health care providers and doctors through baseline and end line knowledge assessment survey questionnaire showed that 100% health care providers who were trained scored 85% or more in knowledge assessment questionnaires as seen by baseline and end line questionnaire results. All project hospitals (n = 34) in Jharkhand achieved quality standards of care in intervention period for gestational diabetes mellitus and anaemia in pregnancy. They achieved total score more than 80% and exceeded target of 80% of the quality standards. Conclusion: A systematic strengthening of private health care facilities through a blended tele-mentoring and onsite support is possible. [ABSTRACT FROM AUTHOR]

Introduction: Pulmonary fibrosis is a characteristic of various interstitial lung diseases (ILDs) with differing etiologies. Clinical trials in progressive pulmonary fibrosis (PPF) enroll patients based on previously described clinical criteria for past progression, which include a clinical practice guideline for PPF classification and inclusion criteria from the INBUILD trial. In this study, we compared the ability of past FVC (forced vital capacity) progression and baseline biomarker levels to predict future progression in a cohort of patients from the PFF Patient Registry. Methods: Biomarkers previously associated with pathobiology and/or progression in pulmonary fibrosis were selected to reflect cellular senescence (telomere length), pulmonary epithelium (SP-D, RAGE), myeloid activation (CXCL13, YKL40, CCL18, OPN) and fibroblast activation (POSTN, COMP, PROC3). Results: PFF or INBUILD-like clinical criteria was used to separate patients into past progressor and non-past progressor groups, and neither clinical criterion appeared to enrich for patients with greater future lung function decline. All baseline biomarkers measured were differentially expressed in patient groups compared to healthy controls. Baseline levels of SP-D and POSTN showed the highest correlations with FVC slope over one year, though correlations were low. Conclusions: Our findings provide further evidence that prior decline in lung function may not predict future disease progression for ILD patients, and elevate the need for molecular definitions of a progressive phenotype. Across ILD subtypes, certain shared pathobiologies may be present based on the molecular profile of certain biomarker groups observed. In particular, SP-D may be a common marker of pulmonary injury and future lung function decline across ILDs. [ABSTRACT FROM AUTHOR]

The challenge of developing an Android malware detection framework that can identify malware in real-world apps is difficult for academicians and researchers. The vulnerability lies in the permission model of Android. Therefore, it has attracted the attention of various researchers to develop an Android malware detection model using permission or a set of permissions. Academicians and researchers have used all extracted features in previous studies, resulting in overburdening while creating malware detection models. But, the effectiveness of the machine learning model depends on the relevant features, which help in reducing the value of misclassification errors and have excellent discriminative power. A feature selection framework is proposed in this research paper that helps in selecting the relevant features. In the first stage of the proposed framework, t-test, and univariate logistic regression are implemented on our collected feature data set to classify their capacity for detecting malware. Multivariate linear regression stepwise forward selection and correlation analysis are implemented in the second stage to evaluate the correctness of the features selected in the first stage. Furthermore, the resulting features are used as input in the development of malware detection models using three ensemble methods and a neural network with six different machine-learning algorithms. The developed models' performance is compared using two performance parameters: F-measure and Accuracy. The experiment is performed by using half a million different Android apps. The empirical findings reveal that malware detection model developed using features selected by implementing proposed feature selection framework achieved higher detection rate as compared to the model developed using all extracted features data set. Further, when compared to previously developed frameworks or methodologies, the experimental results indicates that model developed in this study achieved an accuracy of 98.8%. [ABSTRACT FROM AUTHOR]

Modern optical transport networks are extremely complicated systems that make managing and distributing management information accurate and recognisable difficult. The complexity of network management operations is increased by a variety of optical technologies and service delivery regulations. This study suggests a unique technique for analysing security as well as resource allocation in optical communication networks. Graph networks based on reinforcement beam propagation and wavelength multiplexing are used to allocate network resources. Next, software defined fuzzy logistic vector spatial networks are used to do a security study of the network. The accuracy, packet delivery ratio, routing, modulation, and spectrum assignment of the experimental investigation are all assessed. With the help of mobile edge computing resources as well as evaluation of functionality of nearby user equipment, causes causing this delay are anticipated. An effective communication network is developed to improve service quality, and a cognitive agent model is built to evaluate resource allocation. [ABSTRACT FROM AUTHOR]

The use of tobacco products is one of the established contributors toward the development and spread of oral cancer. Additionally, recent research has indicated oral microbiome, infections with Human papilloma virus (HPV), Epstein–Barr virus (EBV), Candida as significant contributing factors to this disease along with lifestyle habits. Deregulation of cellular pathways envisaging metabolism, transcription, translation, and epigenetics caused by these risk factors either individually or in unison is manifold, resulting in the increased risk of oral cancer. Globally, this cancer continues to exist as one of the major causes of cancer-related mortalities; the numbers in the developing South Asian countries clearly indicate yearly escalation. This review encompasses the variety of genetic modifications, including adduct formation, mutation (duplication, deletion, and translocation), and epigenetic changes evident in oral squamous cell carcinoma (OSCC). In addition, it highlights the interference caused by tobacco products in Wnt signaling, PI3K/Akt/mTOR, JAK-STAT, and other important pathways. The information provided also ensures a comprehensive and critical revisit to non-tobacco-induced OSCC. Extensive literature survey and analysis has been conducted to generate the chromosome maps specifically highlighting OSCC-related mutations with the potential to act as spectacles for the early diagnosis and targeted treatment of this disease cancer. [ABSTRACT FROM AUTHOR]

Postpartum haemorrhage (PPH) is the loss of blood above 500 ml during vaginal or caesarean deliveries. It is difficult to find a PPH in an earlier stage, so pregnant women are exposed to excess blood loss that makes them suffer and die. Antenatal practices help in identifying risk factors, and modern technology is used to overcome the risk. Still, the morbidity rate and the mortality arise due to the unpredicted and unexpected cause. PPH is still a significant cause of maternal morbidity and mortality worldwide. The novelty of this research work is to alert medical practitioner about excessive bleeding of pregnant women during childbirth. We are proposing an automation system using wearable devices to prevent pregnant women from the PPH. These devices measure parameters like temperature, pulse rate, blood pressure, and sweat rate of pregnant women. Fuzzy neural technique-based rules are used for each parameter to predict the risk in developing PPH and to evaluate the performance of proposed system for reducing mortality and morbidity rates. Our findings of experiment are carried on metrics of HPPH (high-level postpartum haemorrhage), NPPH (normal-level postpartum haemorrhage), and MPPH (medium-level postpartum haemorrhage) for 15 patients. Fuzzy output value of 1 indicates patient state with NPPH, 0 indicates patient state with HPPH, and values in between 0 and 1 indicate MPPH. Based on the sensitivity of the predicted values, medical attention is taken from doctors or nurses in nearby locations using Internet of Things infrastructure. [ABSTRACT FROM AUTHOR]

The certification of wine quality is essential to the wine industry. The main goal of this work is to develop a machine learning model to forecast wine quality using the dataset. We utilised samples from the red wine dataset (RWD) with eleven distinct physiochemical properties. With the initial RWD, five machine learning (ML) models were trained and put to the test. The most accurate algorithms are Random Forest (RF) and Extreme Gradient Boosting (XGBoost). Using these two ML approaches, the top three features from a total of eleven features are chosen, and ML analysis is performed on the remaining features. Several graphs are employed to demonstrate the feature importance based on the XGBoost model and RF. Wine quality was predicted using relevant characteristics, often referred to as fundamental elements, that were shown to be essential during the feature selection procedure. When trained and tested without feature selection, with feature selection (RF), and with key attributes, the XGBoost classifier displayed 100% accuracy. In the presence of essential variables, the RF classifier performed better. Finally, to assess the precision of their predictions, the authors trained an RF classifier, validated it, and changed its hyperparameters. To address collinearity and decrease the quantity of predictors without sacrificing model accuracy, we have also used cluster analysis. [ABSTRACT FROM AUTHOR]

Due to the severe underwater environment, research into Underwater Wireless Sensor Networks is at its most undeveloped stage. Due to the restricted sensor batteries and significant variations in water parameters, like temperature, turbulence, flooding, pH, salinity, and others, this research is not as simple as it would seem. To increase node battery life and provide load balancing, several routing protocols are implemented. In this article, we propose the Effective Load Balancing with Sensor Injection routing method, which uses hop distance calculations to enable sensors to connect with sinks through other intermediary sensors. In order to communicate among sensors, localization-based routing protocol is employed. With the use of weight factor calculations, each node determines the minimal distance needed for connection to another node and calculates distance from the lowest hop node. This algorithm balances the load using the sensor injection approach. When weight factor of any node becomes high, it sends request to Network Management System (NMS) for load balancing. NMS directs Autonomous Underwater Vehicle for new sensor injection as neighbour of high factor node. Thus, by using this sensor injection method network lifetime gets increased. Simulation is carried out on NS 2.30 and improved results are obtained in comparison to existing schemes w.r.t., number of dead nodes and alive nodes at every round. [ABSTRACT FROM AUTHOR]

Diabetes causes DR. Diabetes duration influences retinopathy development. The retinal vein weakening may have no side effects or a little eyesight impairment at initially. Blindness may occur. DR intervention and treatment need early clinical indications. Thus, frequent eye examinations must guide patients to a doctor for a full eye inspection and therapy to avoid irreversible vision loss. This work develops a machine learning-based optical image-based data classification method for diabetic retinopathy identification. OCT analyses the retinal picture and the ensemble pulse coupled filtering and green histogram channel equalization-based adaptive filtering segment this picture for blood vessel characterization. CenterResnet-50 classifies images for color fundus detection. Classification accuracy, sensitivity, specificity, AUC, and ROC curves were examined for various optical retina pictures. The proposed method has 98% classification accuracy, 67% sensitivity, 73% specificity, and 63% AUC. [ABSTRACT FROM AUTHOR]

Nanotechnology and photonics, two of the most promising fields of the 21st century, come together in nanophotonics. Its primary advantage is that it may be used to build a number of innovative features based on local electromagnetic interaction. Computed tomography (CT) imaging considerably aids in the identification, prognosis, and assessment of therapy response in kidney cancer. This paper presents a unique approach for nuclear image-based kidney tumour identification using deep learning-based segmentation and classification. In addition to CT-features-based gene mutation identification, segmentation-free volume estimate, autonomous kidney localization, and diagnosis of cancer, we developed deep learning approaches. The plan is to boost the contrast of the picture using a convolutional network-based active contour normalisation and then categorise the segmented image with a stacked Monte Carlo Markov encoder neural network. In the experimental study, we look at how different sets of nuclear images perform in terms of training accuracy, Jaccard index, root mean square error (RMSE), NSE, average precision, and F-measure. Training accuracy of 95% was achieved using the suggested method, along with a Jaccard index of 62%, RMSE of 58%, NSE of 61%, average precision of 65%, and F-measure of 71%. [ABSTRACT FROM AUTHOR]

Background: Death from unexpected circulatory arrest within 60 min of onset of symptom is known as sudden cardiac death (SCD). In spite of the advancement in treatment and prevention strategies, SCD remains the most common cause of death worldwide especially in the young. Main body: This review focuses on highlighting how different cardiovascular diseases contribute to SCD. We discuss the clinical symptoms that the patient experience prior to sudden cardiac arrest and the treatment strategies including pharmacological and surgical treatment. Conclusions: We conclude that since there are many causes of SCD and very few treatment options, prevention strategies, early detection, and resuscitation of those at greatest risk is important. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the impact of the lowest instrumented vertebra (LIV) on Distal Junctional kyphosis (DJK) incidence in adult cervical deformity (ACD) surgery. Methods: Prospectively collected data from ACD patients undergoing posterior or anterior–posterior reconstruction at 13 US sites was reviewed up to 2-years postoperatively (n = 140). Data was stratified into five groups by level of LIV: C6-C7, T1-T2, T3-Apex, Apex-T10, and T11-L2. DJK was defined as a kyphotic increase > 10° in Cobb angle from LIV to LIV-1. Analysis included DJK-free survival, covariate-controlled cox regression, and DJK incidence at 1-year follow-up. Results: 25/27 cases of DJK developed within 1-year post-op. In patients with a minimum follow-up of 1-year (n = 102), the incidence of DJK by level of LIV was: C6-7 (3/12, 25.00%), T1-T2 (3/29, 10.34%), T3-Apex (7/41, 17.07%), Apex-T10 (8/11, 72.73%), and T11-L2 (4/8, 50.00%) (p < 0.001). DJK incidence was significantly lower in the T1-T2 LIV group (adjusted residual = −2.13), and significantly higher in the Apex-T10 LIV group (adjusted residual = 3.91). In covariate-controlled regression using the T11-L2 LIV group as reference, LIV selected at the T1-T2 level (HR = 0.054, p = 0.008) or T3-Apex level (HR = 0.081, p = 0.010) was associated with significantly lower risk of DJK. However, there was no difference in DJK risk when LIV was selected at the C6-C7 level (HR = 0.239, p = 0.214). Conclusion: DJK risk is lower when the LIV is at the upper thoracic segment than the lower cervical segment. DJK incidence is highest with LIV level in the lower thoracic or thoracolumbar junction. [ABSTRACT FROM AUTHOR]

Introduction: Porto-mesenteric venous thrombosis (PMVT) is a significant complication that occurs more frequently after laparoscopic sleeve gastrectomy (SG) than other bariatric procedures and presents later than other venous thromboembolic (VTE) events often 2 weeks after the operation. The common current practice in bariatric surgery of perioperative chemoprophylaxis until discharge may not adequately prevent PMVT. Therefore, a 30-day post-discharge chemoprophylaxis (PDC) might reduce the incidence of PMVT. The objective of this study is to determine whether 30-day PDC with rivaroxaban 10 mg daily following SG can reduce the incidence of PMVT. Methods: In a retrospective cohort study, 292 consecutive patients undergoing SG by a single surgeon were either prescribed rivaroxaban 10 mg daily for 30 days upon discharge (group A) or did not receive any PDC (group B). Primary outcome was PMVT and secondary outcome was bleeding. Patients on chronic anticoagulation therapy were excluded from the study. Results: PMVT events differences were significant between the groups while bleeding events were not. Group A had zero PMVT events, while group B had four (p =.045). There were 4 bleeding events in group A and 7 bleeding events on group B (p =.341). Conclusion: A 30-day PDC regimen of rivaroxaban 10 mg daily is both safe and effective. This study demonstrated zero PMVT events without an increased risk of bleeding using this regimen. [ABSTRACT FROM AUTHOR]

HER2-positive breast cancer is an aggressive subtype of breast cancer with five-year survival rates of 30% for the advanced stage. The development of anti-HER2 treatments has led to a paradigm shift in the management and clinical outcomes of advanced HER2-positive breast cancer patients. The standard first-line treatment consists of taxane-based chemotherapy plus dual anti-HER2 therapies with trastuzumab and pertuzumab. The antibody-drug conjugate (ADC) ado-trastuzumab emtansine (T-DM1) has been a second-line therapeutic standard, but the second-line treatment approach is rapidly evolving. Given a substantial advantage of another ADC, Fam-trastuzumab deruxtecan (T-DXd), compared to T-DM1 in a recent randomized trial in the second-line setting, T-DXd is currently the preferred second-line option. Optimal third-line treatment strategies are still not established, and multiple approaches have been used including combinations based on capecitabine, trastuzumab, or both with oral anti-HER2 tyrosine kinase inhibitors. Tucatinib plus capecitabine and trastuzumab, lapatinib plus trastuzumab, neratinib or lapatinib plus capecitabine are some of the FDA approved combinations. Another newer agent approved for third- or later-line therapy in the metastatic setting is margetuximab, an Fc-engineered anti-HER2 monoclonal antibody, in combination with chemotherapy. Other novel agents currently under clinical trials are the drugs that indirectly target HER2, including immune cell cycle inhibitors, PI3K/mTOR inhibitors, and immunotherapy agents. [ABSTRACT FROM AUTHOR]

During medical picture transmission, the most pressing concern is security. Medical images must be encrypted since they are extremely sensitive. Watermarking, digital fingerprinting/signature, and encoding are some of the available image security techniques. Images and movies, for example, must be highly encrypted and decoded without losing any content information. Medical photos, for example, require extra protection, and protecting medical images is a critical issue when medical images and related patient information are transferred over public networks. This research work proposes a visual encryption strategy to secure medical pictures before being transmitted or stored in the cloud. This technique makes such pictures of unauthorized people unavailable and also maintains confidentiality, a prime safety requirement. The process made use of a pixel shuffling-based encryption technique and a secret key created from the image. In this research, we encrypted the medical image using modified Arnold Map Encryption and generated secret key values. Therefore, the image is encrypted, and henceforth it is decrypted as well. So this work gave us the encrypted image and decrypted image/original image as well. The modified Arnold Map Encryption tries to add more randomness, thus increasing the entropy of the image and thus makes it harder to decrypt. The modified Arnold Map Encryption is also compared to other algorithms such as Hyper Chaotic, Secure Hash Algorithm-13 (SHA-13), Ten Logistic Maps, Bakers Map, HenonMap, Cross Chaos Map, and 2D Logistic Map and shows better results in terms of encryption speed and Number of Pixel Change Rate (NPCR) value. [ABSTRACT FROM AUTHOR]

The work aim to develop an automatic recognition model using IoT and machine learning (ML) techniques for the classification of therapeutic plants to enrich the traditional medicinal system. Ayurveda, the oldest Indian system of medicine is still practiced today as it widely promotes herbs as medicines for treating different health conditions and presents many advantages, such as low cost, availability in abundance, and minimal side effects. While many countries have accepted conventional medicine as the best alternative to synthetic drugs, the lack of knowledge and unsupported evidence has raised concerns and reduced its usage. Herein, an intelligent system is proposed using Raspberry Pi 3 Model B + (RPi) and the RPi camera to identify real-time images of Indian medicinal herbs and reveal their respective medicinal properties. Four ML models are developed in the work, of which one model is proposed to identify the details of a captured medicinal leaf on the RPi user interface. The proposed model predicts an accuracy (top-1) of 98.98% on a custom leaf dataset of 25 different medicinal species, containing 1500 leaf images, by combining two feature extraction techniques, namely scale invariant feature transform (SIFT) and histogram of oriented gradients. Bag of Visual Words is obtained by applying k-means clustering on SIFT descriptors as a feature selection and assessed using a support vector machine classifier. The suggested model integrated into RPi shows a real-time top-3 accuracy of 99%. The designed system has the advantages of being built solely for medicinal herbs, with reduced camera cost, and even works efficiently in remote areas. [ABSTRACT FROM AUTHOR]

Autonomous underwater vehicle networks is a significant resource for aquatic life maintenance and monitoring underwater pollution. It is necessary to study the underwater localization algorithms which self-localize themselves. The proposed work concentrates only on the range-free localization model with a goal is to use only very few sensing nodes in the network with low cost and high battery power to self-localize themselves. Few beacon nodes have been used to localize the sensor nodes in the definite position of the network. Compressive sensing theory based on hop count information for localizing the sensor nodes has also been used for localization. This research aims to collect the connectivity information of all the nodes in the network with compressive sensing theory and improve the localization accuracy. The analysis presents that the proposed method works well with location accuracy. Compressive Sensing Node Localization reduces cost and enhances sensors' energy efficiency compared to other underwater localization algorithms. [ABSTRACT FROM AUTHOR]

Introduction: Partial meal replacement (PMR) offers potential glycemic and weight control benefits in type 2 diabetes mellitus (T2DM) patients. We evaluated the clinical impact of PMR (diabetes-specific nutritional supplement [DSNS]) in overweight/obese Indian patients with T2DM. Methods: PRIDE, a 12-week, phase IV, open-label, multicenter study randomized (1:1) newly diagnosed T2DM patients (≤ 1 year) to either DSNS plus standard of care (SOC; diabetes treatment with dietary counseling) group (PMR) or SOC alone group (SOC). The primary endpoint was mean change in glycated hemoglobin (HbA1c) from baseline to week 12. Secondary endpoints were changes in glucose profiles, body weight, waist circumference, lipid profile, and factors impacting quality-of-life (QoL) at week 6 and 12 from baseline. Safety was assessed throughout the study. Results: Of the 176 patients enrolled, 171 (n = 85 in PMR group; n = 86 in SOC group) were included in the modified intent-to-treat population. The mean reduction in HbA1c at week 12 from baseline in PMR group was significant compared to the SOC group (− 0.59 vs. − 0.21%, p = 0.002). At week 12, the PMR group showed significant reduction in mean body weight (− 2.19 vs. − 0.22 kg; p = 0.001) and waist circumference (− 2.34 vs. − 0.48 cm; p = 0.001) compared to SOC group. Mean fasting plasma glucose and post-prandial glucose significantly reduced from baseline at week 6 and 12 in each group (p < 0.05). No significant change was observed in lipid profile. QoL parameters (treatment adherence, general well-being, and energy fulfilment) in the PMR were significantly better than SOC group (p < 0.05). Patients were satisfied with the taste of DSNS. No serious adverse events were reported. Conclusions: DSNS is an encouraging option for PMR strategy, as it significantly improved HbA1c, body weight, waist circumference, and overall well-being among overweight/obese Indian T2DM patients. Trial Identification No.: CTRI/2019/10/021595. [ABSTRACT FROM AUTHOR]

Background: The risk of mortality for the mother and the newborn is aggravated during birth in low- and middle-income countries due to preventable causes, which can be addressed with increased quality of care practices. One such practice is intrapartum fetal heart rate (FHR) monitoring, which is crucial for the early detection of fetal ischemia, but is inadequately monitored in low- and middle-income countries. In India, there is currently a lack of sufficient data on FHR monitoring. Methods: An assessment using facility records, interviews and observation was conducted in seven facilities providing tertiary, secondary or primary level care in aspirational districts of three states. The study sought to investigate the frequency of monitoring, devices used for monitoring and challenges in usage. Results: FHR was not monitored as per standard protocol. Case sheets revealed 70% of labor was monitored at least once. Only 33% of observed cases were monitored every half hour during active labor, and none were monitored every 5 min during the second stage of labor. More time was observed for monitoring with a Doppler compared with a stethoscope, as providers reported fluctuation in readings. Reportedly, low audibility and a perceived need of expertise were associated with using a stethoscope. High case load and the time required for monitoring were reported as challenges in adhering to standard monitoring protocols. Conclusion: The introduction of a standardized device and a short refresher training on the World Health Organization and skilled birth attendant protocols for FHR monitoring will improve usage and compliance. [ABSTRACT FROM AUTHOR]

Mobile ad hoc network (MANET) is an infrastructure-less, self-motivated, arbitrary, self-configuring, rapidly changing, multi-hop network that is self-possessing wireless bandwidth-conscious links without centrally managed router support. In such a network, wireless media is easy to snoop. It is firm to the surety to access any node, easier to insertion of bad elements or attackers for malicious activities in the network. Therefore, security issues become one of the significant considerations for such kind of networks. The deployment of an effective intrusion detection system is important in order to provide protection against various attacks. In this paper, a Digitally Signed Secure Acknowledgement Method (DSSAM) with the use of the RSA digital signature has been proposed and simulated. Three different parameters are considered, namely secure acknowledgment, node authentication, and packet authentication for study. This article observes the DSSAM performance and compares it with two existing standard methods, namely Watchdog and 2-ACK under standard Dynamic Source Routing (DSR) routing environment. In the end, it is noticed that the rate of detection of malicious behaviour is better in the case of the proposed method. However, associated overheads are high. A trade-off between performance and overhead has been considered. [ABSTRACT FROM AUTHOR]

Keywords: Portal vein thrombosis; Sleeve gastrectomy complication; Chemoprophylaxis EN Portal vein thrombosis Sleeve gastrectomy complication Chemoprophylaxis 1930 1931 2 06/05/23 20230601 NES 230601 Dear Editor, We would like to thank Professor Hany and his coauthors for their thoughtful and well-referenced critique of our study [[1]]. Portal vein thrombosis, Sleeve gastrectomy complication, Chemoprophylaxis. [Extracted from the article]

The aim of this work is to design, characterize, and manufacture a new class of passive actuators that are composed of natural chitosan biopolymers and 3D printed carbon fiber composites (CFRP). These actuators leverage variation in the physical properties of chitosan films with respect to their water content to produce programmable deformation. To control the actuator and its performance, various fundamental physicochemical material characterizations such as microstructural, thermal, hygroscopic swelling for chitosan films, and mechanical analyses were conducted for both materials to validate their properties for the intended application. The morphological behavior confirmed that there was good miscibility with no phase separation in chitosan film, and 3D printed CFRP possesses consistent accuracy irrespective of its minor defects. The swelling ratio of chitosan films was determined as a means of measuring the tunable hygroscopic expansion. According to thermal and mechanical analysis, both polymers possess strength and stiffness desirable for actuator design. Initial built prototypes of small-scale actuators were tested to guide the manner in which the passive actuation behavior could be directed for functional transformations. The results were integrated into CAD software to simulate a large-scale prototype that was subsequently fabricated. The experimental and simulated motions of the large-scale actuator were compared, and the overall performance was discussed with respect to structural integrity and response time. [ABSTRACT FROM AUTHOR]

One of the major applications of the Heterogeneous Network (HetNet) is in the healthcare system. Deploying HetNet in healthcare systems enables patients, physicians, and other stakeholders to communicate easily with each other. Due to the large growth in the network's subscribers, the security of the stored health data became one of the major concerns because unauthorized access to this data may lead to very serious complications, and unreliable transmission of data may lead to fatal risks to the patient's life. Therefore, taking data integrity into consideration, user authentication has become one of the main factors. However, significant research work has been performed at HetNet's physical layer to secure communication, but the result of this leads to an increase in hardware components. The increasing hardware components not only costs money but also power consumption. Therefore, this paper presents an alternate way of securing communication in HetNet at the network layer. However, resolving security problems at the network layer increases computational complexity. Nevertheless, earlier, some encryption techniques like identity-based encryption (IBE), symmetric key encryption (SKE), and public-key encryption (PKE) have been utilized for securing data. Due to their own disadvantages, this paper utilizes an attribute-based encryption (ABE) authentication scheme for securing health data in medical applications. With the help of this method, access to the intruders is denied which results in reduced communication overhead. This authentication scheme helps protect the essential information against attacks by the intruders. It includes a third party server that helps to authenticate and store patient's information. The whole security technique has been written in the form of HLPSL (high-level protocol specification language) codes, and the results are then validated with the help of AVISPA (automated validation of Internet security protocols and applications) tool. [ABSTRACT FROM AUTHOR]

The spin-polarized electronic structure, magnetic moments, and spectra of optical conductivity of the ternary ErSn1.1Ge0.9 and TmSn1.1Ge0.9 compounds have been calculated within the GGA+U method, taking into account strong electron correlations in the 4f-shell of Er and Tm, as well as non-stoichiometry of compositions. The calculation results pointed to antiferromagnetic ordering for both intermetallics with effective magnetic moments that precisely reproduce the published experimental values. Optical properties of these compounds were measured using the ellipsometric method in the energy range of 0.078–5.64 eV. It has been shown that the main peculiarities of experimental curves are described by calculated densities of electronic states. The good agreement of magnetic moments and interband optical conductivities, which are calculated from first principles, with the experimental data indicates the importance of accounting for the strong electron correlations and non-stoichiometry of compositions in calculations of electronic structure of the RSn1.1Ge0.9-type compounds containing heavy rare-earth metals. [ABSTRACT FROM AUTHOR]

The production of enzymes involved in lignin degradation (laccase, ligninase), carbon cycling (β-glucosidase), and phosphorous cycling (phosphomonoesterase) by white rot fungi (Pleurotus sajor-caju) was studied. In the presence of chlorpyrifos, carbofuran, and their mixture, laccase activity was highest on the 7th day, i.e., 192.5 ± 0.31 U ml− 1, 213.6 ± 0.31 U ml− 1, and 164.6 ± 0.31 U ml− 1, respectively, compared to the control which produced maximum laccase on the 14th day (126.9 ± 0.15 U ml− 1). Phosphomonoesterase activity in the presence of chlorpyrifos, carbofuran, and their mixture was 31.5 ± 0.25, 24.1 ± 0.15, and 29.2 ± 0.35 µg PNP min−1 ml−1, respectively, which was more than the control on the 21st day (11.63 ± 0.21 µg PNP min−1 ml−1). β-Glucosidase production increased with the days of incubation in the presence of pesticides than in the control. β-Glucosidase activity on the 21st day in the presence of chlorpyrifos, carbofuran, and their mixture was 32.4 ± 0.1, 24.2 ± 0.3, and 28.4 ± 0.25 µg PNP min−1 ml−1, respectively, as compared to control (15.3 ± 0.6 µg PNP min−1 ml−1). Thus, chlorpyrifos, carbofuran, and their mixture were found to have a positive effect on the production of laccase, β-glucosidase, and phosphomonoesterase by P. sajor-caju, which can use these pesticides as a source of their nutrition, thereby improving the health of pesticide-polluted soils. [ABSTRACT FROM AUTHOR]

Background: With the advent of next-generation sequencing techniques, culture-independent metagenome approaches have now made it possible to predict possible presence of genes in the environmental bacteria most of which may be non-cultivable. Short reads obtained from the deep sequencing can be assembled into long contigs some of which include plasmids. Plasmids are the circular double stranded DNA in bacteria and known as one of the major carriers of antibiotic resistance genes. Objective: Metagenomic analyses, especially focused on plasmids, could help us predict dissemination mechanisms of antibiotic resistance genes in the environment. However, with the availability of a myriad of metagenomic assemblers, the selection of the most appropriate metagenome assembler for the plasmid metagenome study might be challenging. Therefore, in this study, we compared five open source assemblers to suggest most effective way of plasmid metagenome analysis. Methods: IDBA-UD, MEGAHIT, SPAdes, SOAPdenovo2, and Velvet are compared for conducting plasmid metagenome analyses using two water samples. Results: Our results clearly showed that abundance and types of antibiotic resistance genes on plasmids varied depending on the selection of assembly tools. IDBA-UD and MEGAHIT demonstrated the overall best assembly statistics with high N50 values with higher portion of longer contigs. Conclusion: These two assemblers also detected more diverse plasmids. Among the two, MEGAHIT showed more memory efficient assembly, therefore we suggest that the use of MEGAHIT for plasmid metagenome analysis may offer more diverse plasmids with less computer resource required. Here, we also summarized a fundamental plasmid metagenome work flow, especially for antibiotic resistance gene investigation. [ABSTRACT FROM AUTHOR]

Fuel injection timing for early injection mode at higher loads in a gasoline direct-injection engine is critical for mixture formation, combustion process and emissions. In the present work, the effect of fuel injection timing on cycle-to-cycle spray variations and macroscopic spray characteristics is quantitatively analyzed at wide-open throttle condition with stoichiometric air–fuel ratio. A Mie scattering technique was employed to visualize the liquid phase of the fuel dispersion with early injection timings at 180° before top dead center (BTDC), 210° BTDC and 240° BTDC of compression with fuel pressure of 5 MPa. A quantitative analysis using proper orthogonal decomposition revealed that cycle-to-cycle spray variations were not significant. Comparing different injection timings, results, however, showed that spray variations were slightly higher at injection timing of 240° BTDC compared to injection timings at 210° BTDC and 180° BTDC. Concerning macroscopic characteristics, it was found that the spray tip penetration length was longer at 210° BTDC compared to injection timings at 240° BTDC and 180° BTDC. It was also observed that the spray areas were comparable for different injection timings until about 0.9 ms after the start of injection, but it was enhanced at later time stamps for injection at 240° BTDC compared to injections at 210° BTDC and 180° BTDC. [ABSTRACT FROM AUTHOR]

Edge computing is an innovative solution to cloud computing’s IoT application support issues. Due to network instability and increased attack surfaces, edge-assisted IoT security must be improved. Network security protects a network against threats to its availability. Network security must also handle unlawful access and abuse of network-accessible resources. This paper presents a unique strategy to enhance edge computing-based data privacy via secure data transfer and deep learning-based optimization using Internet of Things. Collaboration and hybrid federated server-based stochastic vector networks protect edge network privacy. After firefly grey optimization, the trust-based multiple encryption technique secures data transfer. Network data privacy and optimization experiments explored throughput, network security analysis, PDR, latency, and energy usage. For R2L attack-based dataset, suggested method has 55% energy usage, current SDN 51%, and FLEC 53%. For probe attack-based dataset, suggested approach has 62% energy usage, current SDN 55%, and FLEC 59%. [ABSTRACT FROM AUTHOR]

The increased antibiotic resistance among microorganisms has resulted into growing interest for investigating the wastewater treatment plants (WWTPs) as they are reported to be the major source in the dissemination of antibiotic resistance genes (ARGs) and heavy metal resistance genes (HMRGs) in the environment. In this study, we investigated the prevalence and persistence of ARGs and HMRGs as well as bacterial diversity and mobile genetic elements (MGEs) in influent and effluent at the WWTP in Gwangju, South Korea, using high-throughput sequencing based metagenomic approach. A good number of broad-spectrum of resistance genes (both ARG and HMRG) were prevalent and likely persistent, although large portion of them were successfully removed at the wastewater treatment process. The relative abundance of ARGs and MGEs was higher in effluent as compared to that of influent. Our results suggest that the resistance genes with high abundance and bacteria harbouring ARGs and MGEs are likely to persist more through the treatment process. On analyzing the microbial community, the phylum Proteobacteria, especially potentially pathogenic species belonging to the genus Acinetobacter, dominated in WWTP. Overall, our study demonstrates that many ARGs and HMRGs may persist the treatment processes in WWTPs and their association to MGEs may contribute to the dissemination of resistance genes among microorganisms in the environment. [ABSTRACT FROM AUTHOR]

Particulate matter levels and physiological parameters of 150 school going children were monitored continually for 3 years (2013-2016) at three agriculturally active sites. Percent changes in physiological parameters like forced vital capacity, peak expiratory flow, etc. were estimated using mixed effect model with adjustment of covariates such as BMI. Results show that the increase in fine PM levels were much more in rice seasons than in wheat seasons. During the burning episodes, severe adverse effects on physiological parameters of the selected subjects were observed due to enhanced PM2.5 levels. Significant changes were observed in FVC (− 5.27 to − 7.53%) and PEF (− 4.89 to − 7.12%) in comparison to FEV1 and FEF25-75%. Respiratory health in terms of FVC and PEF corresponded very well with the body mass indices of the human subjects for different PM levels in the ambient air. The subjects having lower BMI level were affected more than those with normal and high BMI on exposure to same level of fine particulate matter. It has been concluded that the trends of fall in respiratory parameters were alarming especially for the subjects with lower and higher BMI during crop residue burning episodes. [ABSTRACT FROM AUTHOR]

Polycrystalline RAgSn (R=Ho, Er) compounds, synthesized by arc melting method are found to crystallize in cubic crystal structure with the space group Pm3 m. The dc and ac magnetic susceptibility data show that the compounds are antiferromagnetic at low temperatures. The absence of magnetic hysteresis reveals the soft magnetic nature. Magnetocaloric effect has been estimated from magnetization data that show moderate value for these compounds. The structure of the electronic states and corresponding inter-band transitions calculated for these compounds within the LSDA+U method are found in good agreement with experimental optical conductivity. The theoretical calculations also confirm antiferromagnetic ordering in RAgSn (R=Ho, Er). The values of the effective magnetic moments of holmium and erbium are found to be close to the experimental ones. [ABSTRACT FROM AUTHOR]

Congenital scoliosis is not normally associated with pain in young children. We are presenting two cases of young patients with congenital scoliosis and moderate to severe pain. There were no spinal cord abnormalities found in these patients. The magnetic resonance imaging and CT scan revealed disc degeneration with Modic changes at the apex of the congenital scoliosis. We hypothesized that the mechanical instability resulting from poor spinal element formations associated with congenital scoliosis was responsible for causing the disc degeneration and endplate changes. Modic changes have been reported to be associated with pain in degenerative conditions of the spine. Both patients underwent a posterior spinal fusion and instrumentation, which relieved their pain immediately. [ABSTRACT FROM AUTHOR]

GdCuSi has been studied by structural, magnetic, magneto-thermal, and magneto-transport measurements. The compound crystallizes in the NiIn-type hexagonal structure. Magnetic measurements show antiferromagnetic ordering at T = 14.2 K and a magnetic anomaly near 5 K, which are confirmed by different measurements. Magnetocaloric effect (MCE) has been estimated from both magnetization and heat capacity data and it shows a change in sign below T. Similar to MCE, magnetoresistance (MR) data also show sign change below T. The compound shows large MCE and MR near its ordering temperature. The sign change in MCE and MR is attributed to the non-collinear antiferromagnetic structure of the compound. The theoretical calculations suggest competition of ferromagnetic and antiferromagnetic interactions, which causes non-collinear magnetic structure in this compound. [ABSTRACT FROM AUTHOR]

Feasibility of near-infrared (NIR) spectroscopy for developing multi-species model for plantation timber was explored for estimation of holocellulose in un-extracted milled wood samples. Six commonly planted species of Eucalyptus tereticornis, E. camaldulensis, E. grandis, Leucaena leucocephala, Dalbergia sissoo and Populus deltoides from a wide range of locations and varying age groups were taken for the present study. Few samples of E. hybrid between E. tereticornis and E. camaldulensis were also included in the study to make the model useful for practical application. NIR models were evaluated using partial least squares regression (PLSR-1-full cross-validation, PLSR-2-cross-validation which leaves more than one out) and by dividing the samples into calibration and prediction (test) sets and interchanging them from calibration to prediction sets. The predictive ability of the model was assessed by calculating four ratios of multivariate statistics for individual species model and combined species models. A final combined model for all the species having component range of 76.14-63.03 % and standard deviation of 2.586 % was developed in the spectral range of 7502-4246 cm wave number using 1st derivative plus multiplicative scatter correction using factor of nine by removing samples with outliers found in all the PLSR-2 evaluation steps and in most of the models. The model remained stable even when 30 % of the samples were left out with no outlier detected. [ABSTRACT FROM AUTHOR]