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Introduction: Magnetic resonance imaging (MRI) is a common procedure in tertiary care neonatal intensive care units (NICUs). MRIs aid in detailing structural anatomy and are increasingly utilized for prognostication. Keeping babies calm and motion-free in the MRI suite is challenging, and various approaches have been adopted to obtain the best image quality. We share our experience of intervention bundle for procedural sedation with the novel use of buccal midazolam in our NICU for babies undergoing MRI. Methods: This single-center quality improvement project comprised two epochs. Epoch 1 from April 2018 to December 2020 provided baseline data regarding sedation use and helped identify causes for suboptimal images and the adverse event rate. Following the implementation of an interventional bundle comprising specific midazolam dose recommendations tailored to background risk factors and streamlining the procedural sedation process, similar comparative data were collected in epoch 2 (May 2021 to December 2022) after a washout period. Results: Of 424 patients, 238 and 108 had MRI done under either procedural sedation protocol or feed and wrap technique in epoch 1 and 2, respectively. After excluding babies whose MRIs were performed under sedative infusions, 30 (13%) babies had adverse events in epoch 1, while only 8 (7%) events occurred in epoch 2. There was also a 37% improvement in the documentation of procedural sedation between the two epochs. Conclusion: Procedural sedation with buccal midazolam under neonatologist supervision is safe, efficient, and effective in babies undergoing MRI in this single-center study. More extensive studies may be warranted to assess the suitability of this sedation modality for broader use.

Our ability to share memories constitutes a social foundation of our world. When exposed to another person's memory, individuals can mentally reconstruct the events described, even if they were not present during the related events. However, the extent to which the neuronal connectivity patterns elicited by the mental reconstruction of an event mirror those present in the brains of individuals who experienced the original event remains unclear. Through two independent fMRI experiments, we explore the Functional Connectivity (FC) patterns at different timescales associated with these cognitive processes using the innovative Multiscale Functional Connectivity (MFC) technique. This study aims to shed light on how our brains construct mental representations of scenes in a movie compared to the verbal transmission of the same scenes. Our results demonstrated that the Default Mode Network (DMN) plays a crucial role in these experiments and exhibits unique FC patterns across different timescales, yet remarkably consistent among participants. In addition, we found significant connectivity patterns within the temporal cortex, including significant contributions of the temporal pole and the fusiform gyrus, which exhibited a pivotal role in cooperation with the DMN in both experiments.

A novel truss deployable mechanism composed of scissors links is proposed in this paper. First, the geometry analysis of the general single modular unit of truss deployable antenna and the construction of the planar double scissors link mechanism module is conducted. The entire antenna is prepared by connecting the single module in a circular configuration. Using the Gruebler's method, the mobility of the double scissors link truss deployable module is proved equals to 1. Furthermore, based on the Newtonian approach, kinematic characteristics of the double scissors link truss deployable mechanism are examined. The velocities and accelerations of the antenna components obtained analytically are validated through simulations in MATLAB, SolidWorks and ADAMS. In addition to this, the forces, torques and bearing stresses involved in various struts and joints are calculated to ensure that the operation is within the yield limits. The dynamic characteristics of the antenna i.e., acceptable natural frequency is calculated by developing a 10 DoF vibration model. The analytical equations are solved using MATLAB, and the results are validated through simulations in ANSYS Workbench. The novel antenna assembly gives the natural frequency response of 0.0652 Hz with a 1.6 % error. The proposed double scissors link truss deployable mechanism shows storage ratio of 1 and 10.2 along vertical and radial directions respectively. The dynamic response and storage ratio of novel assembly is validated with results in literature. The research lays the foundation for the kinematics and dynamics analysis of other space deployable mechanisms.

Exogenous application of sodium nitroprusside (SNP) has previously been reported to trigger plant tolerance against a variety of environmental stresses. The present study was planned to investigate the possible role/s of exogenously applied SNP (50 or 100 μM) in alleviating cadmium (Cd)-induced effects on physio-biochemical processes, yield attributes, and grain quality traits of three fragrant rice cultivars, viz., Meixiangzhan-2 (MXZ), Guixiangzhan (GXZ), and Xiangyaxiangzhan (XYXZ) under 50 mg Cd kg−1 of soil. The results revealed that foliar spray of SNP (50 or 100 μM) on Cd-stressed rice plants reduced oxidative stress (lower hydrogen peroxide (H2O2), malondialdehyde (MDA), and electrolyte leakage (EL)) and improved the photosynthetic apparatus through higher chlorophyll contents, gas exchange attributes, and intact chloroplast configurations, and reduced Cd concentration in the leaves and grains of aromatic rice cultivars. The reduced levels of cellular ROS, MDA, and EL were related to the endogenous NO-mediated improvement in the activity of anti-oxidative enzymes and those involved during the ascorbate–glutathione cycle. However, among the different SNP levels, the foliar spraying of 50 μM of SNP was recorded to be the best treatment for fragrant rice growth, which increased grain yield by 42.06%, 46.03%, and 31.21%, and the quality trait of 2-acetyl-1-pyrroline (2-AP) content by 43.12%, 55.84%, and 35.72% in MXZ, GXZ, and XYXZ respectively, suggesting that GXZ is more responsive to SNP than MXZ and XYXZ fragrant rice cultivars. Collectively, our results deduced that cultivating the GXZ fragrant rice cultivar along with foliar application of 50 μM of SNP could sustain the grain yield and quality features of aromatic rice cultivation in heavy metal (especially Cd)-polluted soils.

Abstract Background Deep neck space infections are uncommon in infants. Retropharyngeal abscess (RPA) is a deep space neck infection that can present with subtle signs and symptoms. Delay in diagnosis can lead to life-threatening complications. Here we describe life-threatening complication of retropharyngeal abscess. Case presentation We report a life-threatening complication of retropharyngeal abscess in 10-month old Asian infant weighing 8.2 kg. The patient presented with fever, right-sided neck swelling, hoarseness of voice, and respiratory distress. The clinical and radiological findings were suggestive of airway obstruction complicated by retropharyngeal abscess. The patient was urgently taken to the operating room and underwent (grade 3) intubation. After stabilization, the patient underwent endoscopic trans-oral incision and drainage, during which 5 cc pus was aspirated. Antibiotics were prescribed for 2 weeks following the procedure based on the sensitivity result of the pus culture. Conclusion In retropharyngeal abscess, a delay in diagnosis can result in life-threatening complications. This report highlights the importance of prompt recognition of a threatened airway and the management of retropharyngeal abscess by emergency physicians. If an emergency physician suspects RPA with airway obstructions, the airway should be immediately secured in a secure environment by otolaryngologist and an anesthesiologist.

Globally, A leading cause of death is heart disease and a serious public health concern. The anomalies in heart sound appears before the heart disease symptoms. The sounds are type of auscultation, which is a process dealing with sounds in a body that generates due to mechanical vibrations of organs, Auscultation is a potential method in medical science to detect abnormalities in heart sounds and in case of suspicion The patient follows up with a referral for other evaluations, such as an electrocardiogram. In medical sciences early detection of symptoms are of major importance, this research work is a good step toward the detection of abnormalities in heart before symptom appearing by processing the phonocardiogram (PCG) signal. In this paper PCG signals are classified by utilizing the features of Mel frequency cepstral coefficients (MFCC) through Cartesian Genetic Programming - Artificial Network (CGP-ANN) Classifier. The diagnostic accuracy of proposed methodology is found 99.50% which is more than other classifiers like Support Vector Machine (SVM) and Convolutional Neural Network (CNN). The accuracy of model as compared to other models can prove the performance superiority of the proposed system.

Introduction. We aimed to determine the burden of respiratory disease by examining clinical profiles and associated predictors of morbidity and mortality of patients admitted to a Pediatric Intensive Care Unit (PICU) in Pakistan, a resource limited country. We also stratified the respiratory diseases as defined by the Pediatric Advanced Life Support (PALS) Classification. Methods. A retrospective study was conducted on children aged 1 month to 18 years who were diagnosed with respiratory illness at the PICU in a tertiary hospital in Karachi, Pakistan. Demographics, essential clinical details including immunization status, and the outcome in terms of mortality or survival were recorded. Predictors of mortality and morbidity including prolonged intubation and mechanical ventilation in the PICU were analyzed using the chi-square test or Fischer’s exact test as appropriate. Results. 279 (63.8% male; median age 9 months, IQR 4–36 months) patients were evaluated of which 44.2% were malnourished and 23.3% were incompletely immunized. The median length of stay in the PICU was 3 days (IQR 2–5 days). Pneumonia was the principal diagnosis in 170 patients (62%) and accounted for most deaths. 76/279 (27.2%) were ventilated, and 67/279(24.0%) needed inotropic support. A high Pediatric Risk of Mortality (PRISM) III score, pneumothorax, and lower airway disease were significantly associated with ventilation support. The mortality rate of patients was 14.3%. Predictors of mortality were a high PRISM III score (OR 1.179; 95% CI 1.024–1.358, P=0.022) and a positive blood culture (OR 4.305; 95% CI 1.062-17.448, P=0.041). Conclusion. Pneumonia is a significant contributor of respiratory diseases in the PICU in Pakistan and is the leading cause of morbidity and mortality. A high PRISM III score, pneumothorax, and lower airway disease were predictors for ventilation support. A high PRISM III score and a positive blood culture were predictors of patient mortality in our study.

BackgroundIntraventricular hemorrhage (IVH) is a severe condition with poor outcomes and high mortality. IRRAflow® (IRRAS AB) is a new technology introduced to accelerate IVH clearance by minimally invasive wash-out. The IRRAflow® system performs active and controlled intracranial irrigation and aspiration with physiological saline, while simultaneously monitoring and maintaining a stable intracranial pressure (ICP). We addressed important aspects of the device implementation and intracranial lavage.MethodTo allow versatile investigation of multiple device parameters, we designed an ex vivo lab setup. We evaluated 1) compatibility between the IRRAflow® catheter and the Silverline f10 bolt (Spiegelberg), 2) the physiological and hydrodynamic effects of varying the IRRAflow® settings, 3) the accuracy of the IRRAflow® injection volumes, and 4) the reliability of the internal ICP monitor of the IRRAflow®.ResultsThe IRRAflow® catheter was not compatible with Silverline bolt fixation, which was associated with leakage and obstruction. Design space exploration of IRRAflow® settings revealed that appropriate settings included irrigation rate 20 ml/h with a drainage bag height at 0 cm, irrigation rate 90 ml/h with a drainage bag height at 19 cm and irrigation rate 180 ml/h with a drainage bag height at 29 cm. We found the injection volume performed by the IRRAflow® to be stable and reliable, while the internal ICP monitor was compromised in several ways. We observed a significant mean drift difference of 3.16 mmHg (variance 0.4, p = 0.05) over a 24-hour test period with a mean 24-hour drift of 3.66 mmHg (variance 0.28) in the pressures measured by the IRRAflow® compared to 0.5 mmHg (variance 1.12) in the Raumedic measured pressures.ConclusionBolting of the IRRAflow® catheter using the Medtronic Silverline® bolt is not recommendable. Increased irrigation rates are recommendable followed by a decrease in drainage bag level. ICP measurement using the IRRAflow® device was unreliable and should be accompanied by a control ICP monitor device in clinical settings.

Cadmium (Cd) stress causes serious disruptions in plant metabolism, physio-biochemical processes, crop yield, and quality characters. Nitric oxide (NO) improves the quality features and nutritional contents of fruit plants. However, how NO confers Cd toxicity in fragrant rice plants, is sparse. Hence, the present study investigated the effects of 50 µM NO donor sodium nitroprusside (SNP) on physio-biochemical processes, plant growth attributes, grain yield, and quality traits of fragrant rice under Cd stress (100 mg kg−1 soil). The results revealed that Cd stress diminished rice plant growth, impaired photosynthetic apparatus and antioxidant defense system, and deteriorated the grain quality traits. However, foliar application of SNP mitigated Cd stress by improving plant growth and gas exchange attributes. Higher electrolyte leakage (EL) was accompanied with elevated levels of malondialdehyde (MDA) and hydrogen peroxide (H2O2) under Cd stress; however, exogenous application of SNP reduced them. The activities and relative expression levels of enzymatic antioxidants; superoxide dismutase (SOD), peroxidase (POD), catalase (CAT) and ascorbate peroxidase (APX) and non-enzymatic antioxidants, glutathione (GSH) contents were reduced by Cd stress, while SNP application regulated their activity and transcript abundances. SNP application improved fragrant rice grain yield and 2-acetyl-1-pyrroline content by 57.68 % and 75.54 % respectively, which is concomitant with higher biomass accumulation, photosynthetic efficiency, photosynthetic pigment contents, and an improved antioxidant defense system. Collectively, our results concluded that SNP application regulated the fragrant rice plant physio-biochemical processes, yield traits and grain quality characters under Cd-affected soil.

Background: Patients admitted to critical care need timely interventions after evaluating all clinical parameters. This study aims to assess the impact of the comprehensive rounding checklist on physician compliance and patient-related outcomes in a multidisciplinary pediatric intensive care unit (PICU). Subjects and Methods: This prospective observational study was conducted in two phases over period of 2 months at multidisciplinary PICU from Pakistan. A structured rounding checklist of 35 components encompassing patient care was introduced and implemented in the PICU. During the preimplementation phase, a nurse took notes of relevant patient clinical details missed by the physician during the round, included them in the rounding checklist. And during the implementation phase, the nurse actively intervened when a physician did not discuss a particular component during the round. All components discussed were check marked. Results: The checklist was completed for 812 rounds (162 patients) in preimplementation and 2348 rounds (590 patients) in the postimplementation phase. Most frequently missed components in the preimplementation phase were checking for surgical site infection (8.9%), discharge plan (7.5%), deep vein thrombosis (DVT) prophylaxis (7.3%), and ventilator-associated pneumonia bundles (7.0%). Whereas, during the postimplementation phase, resident teaching (8.0%), discharge plan (7.2%), and DVT prophylaxis (6.5%) were most frequently missed. Hospital length of stay decreased from 4.5 ± 3.7 to 4.1 ± 2.9 days (P = 0.05), and overall mortality decreased from 19% to 11.4% (P < 0.05). Conclusions: Introducing the checklist with a prompter, improved physician compliance and patient-related outcomes in our PICU.

The world population is rapidly increasing, which in turn increases food needs. On the other hand, food production is the main cause of water withdrawal. Precision agriculture, based on the Internet of Underground Things (IoUT), has recently been proposed to reduce water withdrawals. IoUT comprises sensors and communication devices that are partially or fully submerged beneath the ground surface. However, the current technology used in IoUT is challenged by inefficient spectral and energy efficiency. This paper proposes a deep learning (DL)-based index modulation technique to increase spectral efficiency without raising the system’s bit error rate. The article decreases the peak-to-average power ratio (PAPR) and enhances energy efficiency by employing the X-transform time-domain synchronous index modulation (TDS-IM) orthogonal frequency division multiplexing (OFDM) in underground communication. Unlike the radio frequency channel, the underground channel is environment-dependent. Therefore, in this paper, we propose a DL receiver detector to eliminate such environmental dependency and simplify the system’s complexity. This proposal can establish new identification parameters, achieving accurate estimation of the modulated symbols even in harsh communication channels. The results of the simulation indicate the superior performance of the proposed scheme in terms of both spectral and energy efficiency compared to the benchmarks, as well as its ability to improve the system’s bit error rate.

Abstract Signal decomposition (SD) approaches aim to decompose non-stationary signals into their constituent amplitude- and frequency-modulated components. This represents an important preprocessing step in many practical signal processing pipelines, providing useful knowledge and insight into the data and relevant underlying system(s) while also facilitating tasks such as noise or artefact removal and feature extraction. The popular SD methods are mostly data-driven, striving to obtain inherent well-behaved signal components without making many prior assumptions on input data. Among those methods include empirical mode decomposition and variants, variational mode decomposition and variants, synchrosqueezed transform and variants and sliding singular spectrum analysis. With the increasing popularity and utility of these methods in wide-ranging applications, it is imperative to gain a better understanding and insight into the operation of these algorithms, evaluate their accuracy with and without noise in input data and gauge their sensitivity against algorithmic parameter changes. In this work, we achieve those tasks through extensive experiments involving carefully designed synthetic and real-life signals. Based on our experimental observations, we comment on the pros and cons of the considered SD algorithms as well as highlighting the best practices, in terms of parameter selection, for the their successful operation. The SD algorithms for both single- and multi-channel (multivariate) data fall within the scope of our work. For multivariate signals, we evaluate the performance of the popular algorithms in terms of fulfilling the mode-alignment property, especially in the presence of noise.

Introduction: Retinopathy of prematurity (ROP) is a multifactorial disease and a preventable cause of blindness in childhood. Hyperoxia and hypoxia can cause retinal neovascularization resulting in retinal detachment and blindness if left untreated. Besides oxygen treatment, other reasons for ROP development are well known. We prospectively adopt various strategies to keep oxygen saturation (SpO2) within targets, between 91 and 95% for those on supplemental oxygen. By adapting this, we postulated that the incidence of severe ROP might be reduced. Methods: 2018–2019 provided pre-intervention and 2020 post-intervention data for the project. For all babies (≤32 weeks, ≤1,500 g with FiO2 >0.21), target SpO2 between 91 and 95% was measured as a percentage of time spent within and outside target SpO2 during 1–4 weeks of life. Results: 112 and 60 preterm neonates were screened for ROP during the pre- and post-intervention phase. Twenty neonates (18.3%) during pre-intervention and 16 (26.7%) in the post-intervention phase developed severe ROP requiring treatment. Despite a statistically significant increase of 10 percent points in time spent within target SpO2 (91–95%) in the post-intervention phase (p < 0.05), the incidence of severe ROP did not decline. Using a multivariate model, odds of ROP development decreased with gestational age (25%) while increasing with PDA requiring treatment (4.33 times) and glucose ≥10 mg/dL (4.15 times), considering one variable at a time, keeping others constant. Conclusion: Our QI project showed successful attainment of maximum time; the SpO2 remained within targets during supplemental oxygen; however, the incidence of severe ROP had not declined. Factors other than SpO2 might be responsible for a high incidence of ROP in our neonatal intensive care unit.

Hypernatremia is a potentially serious condition in both term and preterm babies, which can lead to severe and permanent neurological damage. There are many physiological changes in sodium homeostasis that occur soon after birth. Understanding this physiological process, early anticipation of hypernatremia and familiarization with the neonatal management of hypernatremia can prevent mortality and long-term morbidity associated with this condition. This review aims to provide a practical and understandable approach to the diagnosis and management of hypernatremia in neonates.

Copper (Cu2+) toxicity can inhibit plant growth and development. It has been shown that silicon (Si) can relieve Cu2+ stress. However, it is unclear how Si-nanoparticles (SiNPs) relieve Cu2+ stress in wheat seedlings. Therefore, the current study was conducted by setting up four treatments: CK, SiNP: (2.5 mM), Cu2+: (500 µM), and SiNP+Cu2+: (2.5 mM SiNP+500 µM Cu2+) to explore whether SiNPs can alleviate Cu2+ toxicity in wheat seedlings. The results showed that Cu2+ stress hampered root and shoot growth and accumulated high Cu2+ concentrations in roots (45.35 mg/kg) and shoots (25.70 mg/kg) of wheat as compared to control treatment. Moreover, Cu2+ treatment inhibited photosynthetic traits and chlorophyll contents as well as disturbed the antioxidant defense system by accumulating malondialdehyde (MDA) and hydrogen peroxidase (H2O2) contents. However, SiNPs treatment increased root length and shoot height by 15.1% and 22%, respectively, under Cu2+ toxicity. Moreover, SiNPs application decreased MDA and H2O2 contents by 31.25% and 19.25%, respectively. SiNPs increased non-enzymatic compounds such as ascorbic acid-glutathione (AsA-GSH) and enhanced superoxide dismutase (SOD), peroxidase (POD), catalase (CAT), and ascorbic peroxidase (APX) activities by 77.5%, 141.7%, 68%, and 80%, respectively. Furthermore, SiNPs decreased Cu2+ concentrations in shoots by 26.2%, as compared to Cu2+ treatment alone. The results concluded that SiNPs could alleviate Cu2+ stress in wheat seedlings. The present investigation may help to increase wheat production in Cu2+ contaminated soils.

ObjectiveTo determine the performance of a commercially available risk analytic tool (IDO2) to estimate the risk for SVO2 < 40% in patients admitted in cardiac intensive care unit (CICU).MethodsMedical and T3 records of all patients (aged 1 day to 12 years, weight >2 kg) who received care in the CICU between October 1st, 2019 and October 1st, 2020, had SvO2 lab(s) drawn during CICU course and whose data was transmitted to T3, were included. The average IDO2 Index was computed in the 30-min period immediately prior to each SvO2 measurement and used as a predictor score for SvO2 < 40%.ResultsA total of 69 CICU admissions from 65 patients, median age 9.3 months (interquartile range 20.8) were identified. Surgical and medical patients were 61 (88%) and 8 (12%) respectively; 4 (5.7%) patients had single ventricle physiology. Tetralogy of Fallot n = 23 (33.3%) and ventricular septal defects 17 (24.6%) were major cardiac diagnosis. Sixty-one (89.9%) of the admissions were successfully discharged from the hospital. Of the 187-total included SvO2 labs, 17 (9%) were

Background To determine the rate of conversion of abstracts presented at conferences into full-text articles published in peer-reviewed journals in the field of pediatric critical care medicine (PCCM) in a developing country. Methods We retrospectively reviewed PCCM abstracts from Pakistan presented at national and international pediatric and critical care conferences over 10 years (January 2010 to March 2020). Data included abstract characteristics, such as presentation (poster/oral), presenter (fellow/resident), time of meeting (month and year), type of meeting, study design and topic; and publication characteristics, such as journal name, time (month and year) and first author. The primary outcome was publication rate of PCCM abstracts presented in meetings and time (months) from presentation to publication. Results A total of 79 PCCM abstracts were presented in 20 meetings during the study period. There were 65 poster presentations (82.28%), of which 63 (79.74%) were presented at international critical care conferences and all presenters were PCCM fellows. In total, 64 (81%) abstracts were descriptive observational studies (retrospective: 50, 63.29%) and prospective (14, 17.72%). Only one was an interventional randomized controlled trial. The publication rate of PCCM abstracts was 63.3% (50/79) and the mean time to publication was 12.39±13.61 months. The publication rate was significantly correlated to the year of publication (P

In the past decades, researchers/scientists have paid attention to the physical layer of underwater communications (UWCs) due to a variety of scientific, military, and civil tasks completed beneath water. This includes numerous activities critical for communication, such as survey and monitoring of oceans, rescue, and response to disasters under the sea. Till the end of the last decade, many review articles addressing the history and survey of UWC have been published which were mostly focused on underwater sensor networks (UWSN), routing protocols, and underwater optical communication (UWOC). This paper provides an overview of underwater acoustic (UWA) physical layer techniques including cyclic prefix orthogonal frequency division multiplexing (CP-OFDM), zero padding orthogonal frequency division multiplexing (ZP-OFDM), time-domain synchronization orthogonal frequency division multiplexing (TDS-OFDM), multiple input multiple output orthogonal frequency division multiplexing (MIMO-OFDM), generalized frequency division multiplexing (GFDM), unfiltered orthogonal frequency division multiplexing (UF-OFDM), continuous phase modulation orthogonal frequency division multiplexing (CPM-OFDM), filter bank multicarrier (FBMC) modulation, MIMO, spatial modulation technologies (SMTs), and orthogonal frequency division multiplexing index modulation (OFDM-IM). Additionally, this paper provides a comprehensive review of UWA channel modeling problems and challenges, such as transmission loss, propagation delay, signal-to-noise ratio (SNR) and distance, multipath effect, ambient noise effect, delay spread, Doppler effect modeling, Doppler shift estimation. Further, modern technologies of the physical layer of UWC have been discussed. This study also discusses the different modulation technology in terms of spectral efficiency, computational complexity, date rate, bit error rate (BER), and energy efficiency along with their merits and demerits.

The purpose of the research paper is to discuss the perceptions of university teachers and students towards digital classroom pedagogy, learning, and assessment methods in higher education in Pakistan amidst the COVID-19 pandemic outbreak. The researchers used the mixed method approach. The data was collected from a large-scale public-sector university with nine campuses across the province of Punjab, Pakistan. The instruments used for the collection of the data included Google Survey Form for students (n=92), university teachers' interviews via WhatsApp (n=15), and university students’ views extracted from social media platforms i.e.official Facebook page of the university (n=20). The data obtained from multiple sources was analyzed using the corpus-based techniques for Sentiment Lexicon Generation (Darwich et.al, 2019). The findings of this study have revealed that 92% of students were interested in face-to-face classes and 82% of students had a preference for a face-to-face assessment. It has also been observed that the students also expressed their views negatively on social media regarding online teaching and assessment. They poured out their frustration against the university administration for conducting online assessments amidst COVID-19 qualms, chaos, and uncertainty. In contrast to this analysis, it has been perceived that both teachers and students struggled and successfully adapted themselves to the online environment. Moreover, it is interpreted from the university teachers’ views that the quality of digital pedagogy, learning, and assessment can be enhanced through mentoring, observation, novel techniques and strategies, teamwork, and self-directed learning. The study has recommended that teacher education programs in Pakistan can organize digital pedagogical and assessment trainingfor teachers to meet the drastic changes in pedagogy and assessment amidst COVID-19.

Whales communicate using whistle vocalizations that are essentially underwater acoustic frequency-modulated tones. Inevitable environmental noise decreases recognition accuracy of these sounds during wide range detection. In this paper, we propose a robust time - frequency analysis method that combines resonance sparse signal decomposition (RSSD) and spectrogram ridge extraction. We apply RSSD to extract whistle components from the raw signal, and then we segment the ridge regions of the whistle spectrograms. By applying a partial derivative method, we extract the whistle spectrogram ridge representing an accurate trace of the whistle vocalization. From these results, we extract ridge features and use an SVM or a random forest to identify the whale species. We evaluated our method using experiments with samples for four whale species. Compared with direct ridge extraction directly without RSSD, our proposed method achieved better extraction of frequency characteristics of the vocalizations. Our proposed method achieved an accuracy rate of over 98% for sounds from four species when using five training samples.

Empirical mode decomposition (EMD) is a data driven method for nonstationary data analysis which has proven to be extremely useful in diverse applications in biomedical engineering. In its original formulation, the EMD method works collectively on a batch of data and hence is not suitable for online applications that demand continuous data processing capability in real-time. To cater for such applications, few complete field-programmable gate array (FPGA) based designs for EMD computations have emerged in recent years, yet they have found limited practical applications. The main reason could be that existing complete FPGA based architectures invariably work on integer data sets thus ignoring the sensitivity EMD to truncation errors. Moreover, these architectures mostly implement simplistic linear interpolation technique for sifting process within EMD, thereby compromising its accuracy. To that end, we develop a complete FPGA-based architecture for EMD which works on fixed point data formats thereby alleviating the main source of error in existing EMD based architectures. We also implement well established and accurate cubic spline interpolation technique in addition to giving provision to linear interpolation within the sifting process in EMD, which further improves its accuracy. Our proposed pipeline design ensures that despite implementing computationally expensive EMD related tasks, the proposed architecture exhibits lowest execution time among all existing EMD architectures. We provide examples of computation of EMD decomposition through proposed architecture for both synthetic and real world biomedical signals to demonstrate the prowess of our work.

Vessel segmentation has come a long way in terms of matching the experts at detection accuracy, yet there is potential for further improvement. In this regard, the accurate detection of vessels is generally more challenging due to the high variations in vessel contrast, width, and the observed noise level. Most vessel segmentation strategies utilize contrast enhancement as a preprocessing step, which has an inherent tendency to aggravate the noise and therefore, impede accurate vessel detection. To alleviate this problem, we propose to use the state-of-the-art Probabilistic Patch-Based (PPB) denoiser within the framework of an unsupervised retinal vessel segmentation strategy based on the Frangi filter. The PPB denoiser helps preserve vascular structure while effectively dealing with the amplified noise. Also, the modified Frangi filter is evaluated separately for tiny and large vessels, followed by individual segmentation and linear recombination of the binarized outputs. This way, the performance of the modified Frangi filter is significantly enhanced. The performance evaluation of the proposed method is evaluated on two recognized open-access datasets, viz: DRIVE and STARE. The proposed strategy yields competitive results for both preprocessing modalities, i.e., Contrast Limited Adaptive Histogram Equalization (CLAHE) and Generalized Linear Model (GLM). The performance observed for CLAHE over DRIVE and STARE datasets is (Sn = 0.8027, Acc = 0.9561) and (Sn = 0.798, Acc = 0.9561), respectively. For GLM, it is observed to be (Sn = 0.7907, Acc = 0.9603) and (Sn = 0.7860, Acc = 0.9583) over DRIVE and STARE datasets, respectively. Furthermore, based on the conducted comparative study, it is established that the proposed method outperforms various notable vessel segmentation methods available in the existing literature.

This paper aims at to analyze the causes of matrimonial failure in George Eliot’s Middlemarch among the central couples: Casaubon and Dorothea and Dr. Lydgate and Rosamond _ by applying Social Exchange Theory and its principles (rewards and costs, Comparison level, relational resources, trust and commitment and marital solidarity), through which their causes of failure of conjugal relationship are meticulously analyzed, and not fulfilling of these cause failure and fragility in central marriages in the novel.

The red imported fire ant (Solenopsis invicta) is one of the deadliest invasive ant species that threatens the world by disrupting biodiversity, important functions within a natural ecosystem, and community structure. They are responsible for huge economic losses in the infested countries every year. Synthetic insecticides, especially indoxacarb, have been broadly used to control S. invicta for many years. However, the biochemical response of S. invicta to indoxacarb remains largely undiscovered. Here, we used the sublethal doses of indoxacarb on the S. invicta collected from the eight different cities of Southern China. The alteration in the transcriptome profile of S. invicta following sublethal dosages of indoxacarb was characterized using high-throughput RNA-seq technology. We created 2 libraries, with 50.93 million and 47.44 million clean reads for indoxacarb treatment and control, respectively. A total of 2018 unigenes were regulated after insecticide treatment. Results indicated that a total of 158 differentially expressed genes (DEGs) were identified in the indoxacarb-treated group, of which 100 were significantly upregulated and 58 were downregulated, mostly belonging to the detoxification enzymes, such as AChE, CarE, and GSTs. Furthermore, results showed that most of these DEGs were found in several KEGG pathways, including steroid biosynthesis, other drug metabolizing enzymes, glycerolipid metabolism, chemical carcinogenesis, drug-metabolizing cytochrome P450, glutathione metabolism, glycerophospholipid metabolism, glycolysis/gluconeogenesis, and metabolism of xenobiotics. Together, these findings indicated that indoxacarb causes significant alteration in the transcriptome profile and signaling pathways of S. invicta, providing a foundation for further molecular inquiry.

Selecting the most suitable local invariant feature detector for a particular application has rendered the task of evaluating feature detectors a critical issue in vision research. Although the literature, offers a variety of comparison works focusing on performance evaluation of image feature detectors under several types of image transformations, the influence of the scene content on the performance of local feature detectors has received little attention so far. This paper, aims to bridge this gap with a new framework for determining the type of scenes which maximize and minimize the performance of detectors in terms of repeatability rate. The results are presented for several state-of-the-art feature detectors that have been obtained using a large image database of 20482 images under JPEG compression, uniform light and blur changes with 539 different scenes captured from real-world scenarios. These results provide new insights into the behavior of feature detectors.

Currently, medical media technologies have become a center of attention due to emerging trends in miniaturized wearable devices from factories to health corner stores everywhere. Due to the power-constrained nature of these portable devices, it is challenging to adopt them during critical medical operations and diagnoses. Maximizing energy efficiency and, hence, extending the battery life is vital. In addition, conventional approaches with constant transmission power are inappropriate option for green and smart healthcare. Thus, this paper first proposes a transmission power control (TPC)-based energy-efficient algorithm (EEA) for when a subject is in different postures, i.e., standing, walking, and running, in wireless body sensor networks. Second, a hardware platform was developed on the Intel Galileo board to test and compare the proposed EEA and conventional adaptive TPC (ATPC) in terms of energy and channel reliability or packet loss ratio (PLR). Experimental results revealed that the proposed EEA obtained energy savings of 42.5% with an acceptable PLR compared with that of the traditional ATPC method.

Time division synchronization orthogonal frequency division multiplexing (TDS-OFDM) has been attractive due to its fast synchronization and efficient spectral efficiency over conventional cyclic prefix orthogonal frequency division multiplexing (OFDM) and zero padding OFDM. However, inter-block interference (IBI) affects its performance because of delay over multipath channels. To evade IBI, dual pseudo-random noise (DPN) sequences have been introduced that causes to reduce spectral and energy efficiency. But, DPN is unprepared for underwater acoustic (UWA) communication because of battery-based nature and limited bandwidth. To overcome these issues, this paper exploits compressive sensing theory algorithm for obtaining the time-varying channel state information by utilizing sparse property of UWA channels. In this paper, the IBI free region is utilized to estimate accurate UWA channel impulse response and mitigate its interference. Look-ahead backtracking orthogonal matching pursuit-based sparse channel estimation technique is proposed for underwater TDS-OFDM in a real sparse time-varying multipath channel (channel taps are randomly distributed). Furthermore, Doppler-shift of UWA channel is estimated and compensated by PN sequence in time domain. The performance of the proposed technique is evaluated and demonstrated through numerical computation of bit error rate (BER) and mean square error (MSE) using Monte Carlo iterations. Simulation analysis confirms the superiority of the proposed scheme not only in terms of BER and MSE over the conventional ones but also achieve high energy and spectral efficiency.

Research on the development of dissolving microneedles (DMNs) has focused on bolus drug delivery, with little attention on sustained release. Here, we evaluated the sustained release, absorption pattern, and effective drug permeation of a novel donepezil-loaded DMN patch through an in vivo investigation on rats. The applications of DMN patches to the shaved skin of rats for 1 week and 1 h were compared with oral donepezil administration to assess their sustained release capabilities. We used a validated liquid chromatography–tandem mass spectrometry method to quantify donepezil in the plasma. We found that the microneedle arrays effectively delivered donepezil across the skin, with dissolution observed within 1 h of application. Furthermore, skin irritation test showed that the patches produced no irritation response. The DMN arrays also effectively increased drug permeation and demonstrated sustained release and absorption of donepezil from DMN patches. These patches allow extended dosing intervals, reduced gastrointestinal adverse effects, and convenient self-administration to mitigate poor drug compliance, making them beneficial for the treatment of elderly patients with Alzheimer’s disease.

Strigolactones (SLs) represent an important new plant hormone class marked by their multifunctional roles in plants and rhizosphere interactions, which stimulate hyphal branching in arbuscular mycorrhizal fungi (AMF) and seed germination of root parasitic plants. SLs have been broadly implicated in regulating root growth, shoot architecture, leaf senescence, nodulation, and legume–symbionts interaction, as well as a response to various external stimuli, such as abiotic and biotic stresses. These functional properties of SLs enable the genetic engineering of crop plants to improve crop yield and productivity. In this review, the conservation and divergence of SL pathways and its biological processes in multiple plant species have been extensively discussed with a particular emphasis on its interactions with other different phytohormones. These interactions may shed further light on the regulatory networks underlying plant growth, development, and stress responses, ultimately providing certain strategies for promoting crop yield and productivity with the challenges of global climate and environmental changes.

Abstract Background Strigolactones (SLs) play important roles in controlling root growth, shoot branching, and plant-symbionts interaction. Despite the importance, the components of SL biosynthesis and signaling have not been unequivocally explored in soybean. Results Here we identified the putative components of SL synthetic enzymes and signaling proteins in soybean genome. Soybean genome contains conserved MORE AXILLARY BRANCHING (MAX) orthologs, GmMAX1s, GmMAX2s, GmMAX3s, and GmMAX4s. The tissue expression patterns are coincident with SL synthesis in roots and signaling in other tissues under normal conditions. GmMAX1a, GmMAX2a, GmMAX3b, and GmMAX4a expression in their Arabidopsis orthologs’ mutants not only restored most characteristic phenotypes, such as shoot branching and shoot height, leaf shape, primary root length, and root hair growth, but also restored the significantly changed hormone contents, such as reduced JA and ABA contents in all mutant leaves, but increased auxin levels in atmax1, atmax3 and atmax4 mutants. Overexpression of these GmMAXs also altered the hormone contents in wild-type Arabidopsis. GmMAX3b was further characterized in soybean nodulation with overexpression and knockdown transgenic hairy roots. GmMAX3b overexpression (GmMAX3b-OE) lines exhibited increased nodule number while GmMAX3b knockdown (GmMAX3b-KD) decreased the nodule number in transgenic hairy roots. The expression levels of several key nodulation genes were also altered in GmMAX3b transgenic hairy roots. GmMAX3b overexpression hairy roots had reduced ABA, but increased JA levels, with no significantly changed auxin content, while the contrast changes were observed in GmMAX3b-KD lines. Global gene expression in GmMAX3b-OE or GmMAX3b-KD hairy roots also revealed that altered expression of GmMAX3b in soybean hairy roots changed several subsets of genes involved in hormone biosynthesis and signaling and transcriptional regulation of nodulation processes. Conclusions This study not only revealed the conservation of SL biosynthesis and signaling in soybean, but also showed possible interactions between SL and other hormone synthesis and signaling during controlling plant development and soybean nodulation. GmMAX3b-mediated SL biosynthesis and signaling may be involved in soybean nodulation by affecting both root hair formation and its interaction with rhizobia.

Autophagy is a highly conserved degradation mechanism in eukaryotes, executing the breakdown of unwanted cell components and subsequent recycling of cellular material for stress relief through vacuole-dependence in plants and yeast while it is lysosome-dependent in animal manner. Upon stress, different types of autophagy are stimulated to operate certain biological processes by employing specific selective autophagy receptors (SARs), which hijack the cargo proteins or organelles to the autophagy machinery for subsequent destruction in the vacuole/lysosome. Despite recent advances in autophagy, the conserved and diversified mechanism of autophagy in response to various stresses between plants and animals still remain a mystery. In this review, we intend to summarize and discuss the characterization of the SARs and their corresponding processes, expectantly advancing the scope and perspective of the evolutionary fate of autophagy between plants and animals.

We propose objective and robust measures for the purpose of classification of “vaginal vs. cesarean section” delivery by investigating temporal dynamics and complex interactions between fetal heart rate (FHR) and maternal uterine contraction (UC) recordings from cardiotocographic (CTG) traces. Multivariate extension of empirical mode decomposition (EMD) yields intrinsic scales embedded in UC-FHR recordings while also retaining inter-channel (UC-FHR) coupling at multiple scales. The mode alignment property of EMD results in the matched signal decomposition, in terms of frequency content, which paves the way for the selection of robust and objective time-frequency features for the problem at hand. Specifically, instantaneous amplitude and instantaneous frequency of multivariate intrinsic mode functions are utilized to construct a class of features which capture nonlinear and nonstationary interactions from UC-FHR recordings. The proposed features are fed to a variety of modern machine learning classifiers (decision tree, support vector machine, AdaBoost) to delineate vaginal and cesarean dynamics. We evaluate the performance of different classifiers on a real world dataset by investigating the following classifying measures: sensitivity, specificity, area under the ROC curve (AUC) and mean squared error (MSE). It is observed that under the application of all proposed 40 features AdaBoost classifier provides the best accuracy of 91.8% sensitivity, 95.5% specificity, 98% AUC, and 5% MSE. To conclude, the utilization of all proposed time-frequency features as input to machine learning classifiers can benefit clinical obstetric practitioners through a robust and automatic approach for the classification of fetus dynamics.

Two novel image denoising algorithms are proposed which employ goodness of fit (GoF) test at multiple image scales. Proposed methods operate by employing the GoF tests locally on the wavelet coefficients of a noisy image obtained via discrete wavelet transform (DWT) and the dual tree complex wavelet transform (DT-CWT) respectively. We next formulate image denoising as a binary hypothesis testing problem with the null hypothesis indicating the presence of noise and the alternate hypothesis representing the presence of desired signal only. The decision that a given wavelet coefficient corresponds to the null hypothesis or the alternate hypothesis involves the GoF testing based on empirical distribution function (EDF), applied locally on the noisy wavelet coefficients. The performance of the proposed methods is validated by comparing them against the state of the art image denoising methods.

Electric power industry is extensively using gas-insulated systems for the transmission and distribution of electrical energy. In high-voltage technology, the gas insulation is dominated by Sulphur-hexafluoride (SF(6)) since the early 60th. It is a greenhouse gas and has long atmospheric lifetime. This paper proposes comparatively more environmental friendly R410 gas as an alternative to SF(6) for high-voltage applications. To validate the performance and theoretical concepts, it has been analysed experimentally. Buffer gases air and carbon dioxide were mixed with the base gas R410A to reduce the liquification temperature and global warming potential. Dielectric strength of R410A was tested for DC as well as AC voltage, results were compared with that of SF(6) and other alternatives available in literature, and the results were comparable. Insulation self-recoverability test of R410A was performed and the synergistic effect of the mixed gases was calculated. Consequently, this work has discovered that R410A is the suitable alternative to SF(6) for high-voltage applications.

Torque density is one of the major limiting factors in machine design. In this paper, we propose the hybrid excited partitioned stator switched flux machine3 (HE-PSSFM3). In HE-PSSFM3, armature winding is positioned on the outer stator whereas the permanent magnet (PM) and field winding are placed at the inner stator, while the rotor is free from excitation sources and armature winding. Moreover, concentrated field winding is replaced by toroidal winding. The power splitting ratio between two stators/rotor pole combinations is analytically optimized and are validated through genetic algorithm (GA) in order to enhance average torque and flux regulation capability. The electromagnetic characteristics of the improved and initial design are evaluated and compared with existing designs, i.e., HE-PSSFM1 and HE-PSSFM2. The proposed HE-PSSFM3 has achieved high average torque, i.e., 2.0015 Nm, at same armature and field current densities of 5 A/mm2. The results show that the average torques of the proposed design are 35% and 15% greater than HE-PSSFM1 and HE-PSSFM2, respectively. Furthermore, the analysis of various parameters such as flux linkage, flux regulation, electromagnetic performances, cogging torque, back EMF, electromagnetic torque, and torque ripples are investigated using two dimensional (2D) finite element analysis (FEA). Moreover, the simulation results of the proposed design are validated through GA and analytical modeling.

Herbal drugs are widely used for the auxiliary treatment of diseases. The pharmacokinetics of a drug may be altered when it is coadministered with herbal drugs that can affect drug absorption. The effects of herbal drugs on absorption must be evaluated. In this study, we investigated the effects of Rumex acetosa (R. acetosa) extract on fexofenadine absorption. Fexofenadine was selected as a model drug that is a substrate of P-glycoprotein (P-gp) and organic anion transporting polypeptide 1A2 (OATP1A2). Emodine—the major component of R. acetosa extract—showed P-gp inhibition in vitro and in vivo. Uptake of fexofenadine via OATP1A2 was inhibited by R. acetosa extract in OATP1A2 transfected cells. A pharmacokinetic study showed that the area under the plasma concentration–time curve (AUC) of fexofenadine was smaller in the R. acetosa extract coadministered group than in the control group. R. acetosa extract also decreased aqueous solubility of fexofenadine HCl. The results of this study suggest that R. acetosa extract could inhibit the absorption of certain drugs via intervention in the aqueous solubility and the drug transporters. Therefore, R. acetosa extract may cause drug interactions when coadministered with substrates of drug transporters and poorly water-soluble drugs, although further clinical studies are needed.

A novel signal denoising method is proposed whereby goodness-of-fit (GOF) test in combination with a majority classifications-based neighbourhood filtering is employed on complex wavelet coefficients obtained by applying dual tree complex wavelet transform (DT-CWT) on a noisy signal. The DT-CWT has proven to be a better tool for signal denoising as compared to the conventional discrete wavelet transform (DWT) owing to its approximate translation invariance. The proposed framework exploits statistical neighbourhood dependencies by performing the GOF test locally on the DT-CWT coefficients for their preliminary classification/detection as signal or noise. Next, a deterministic neighbourhood filtering approach based on majority noise classifications is employed to detect false classification of signal coefficients as noise (via the GOF test) which are subsequently restored. The proposed method shows competitive performance against the state of the art in signal denoising.

Whale sounds may mix several elements including whistle, click, and creak in the same vocalization, which may overlap in time and frequency, so it leads to conventional signal separation techniques challenging to be applied for the signal extraction. Unlike conventional signal separation techniques which are based on the frequency bands, such as WT and EMD, tunable-Q wavelet transform (TQWT) can separate the objected signal into particular components with different structures according to its oscillation property and eliminate in-band noise using the basis pursuit method. Considering the characteristics of oscillatory and transient impulse, we propose a novel signal separation method for whale whistle and click extraction. The proposed method is performed by the following two steps: first, TQWT is used to construct the dictionary for sparse representation. Secondly, the whale click and whistle construction are performed by designing the basis pursuit denoising (BPD) algorithm. The proposed method has been compared with one of the popular signal decomposition techniques, i.e., the EMD method. The experimental results show that the proposed method has a better performance of click and whistle signal separation in comparison with the EMD algorithm.

The integral image, an intermediate image representation, has found extensive use in multi-scale local feature detection algorithms, such as Speeded-Up Robust Features (SURF), allowing fast computation of rectangular features at constant speed, independent of filter size. For resource-constrained real-time embedded vision systems, computation and storage of integral image presents several design challenges due to strict timing and hardware limitations. Although calculation of the integral image only consists of simple addition operations, the total number of operations is large owing to the generally large size of image data. Recursive equations allow substantial decrease in the number of operations but require calculation in a serial fashion. This paper presents two new hardware algorithms that are based on the decomposition of these recursive equations, allowing calculation of up to four integral image values in a row-parallel way without significantly increasing the number of operations. An efficient design strategy is also proposed for a parallel integral image computation unit to reduce the size of the required internal memory (nearly 35% for common HD video). Addressing the storage problem of integral image in embedded vision systems, the paper presents two algorithms which allow substantial decrease (at least 44.44%) in the memory requirements. Finally, the paper provides a case study that highlights the utility of the proposed architectures in embedded vision systems.

A novel scheme to perform the fusion of multiple images using the multivariate empirical mode decomposition (MEMD) algorithm is proposed. Standard multi-scale fusion techniques make a priori assumptions regarding input data, whereas standard univariate empirical mode decomposition (EMD)-based fusion techniques suffer from inherent mode mixing and mode misalignment issues, characterized respectively by either a single intrinsic mode function (IMF) containing multiple scales or the same indexed IMFs corresponding to multiple input images carrying different frequency information. We show that MEMD overcomes these problems by being fully data adaptive and by aligning common frequency scales from multiple channels, thus enabling their comparison at a pixel level and subsequent fusion at multiple data scales. We then demonstrate the potential of the proposed scheme on a large dataset of real-world multi-exposure and multi-focus images and compare the results against those obtained from standard fusion algorithms, including the principal component analysis (PCA), discrete wavelet transform (DWT) and non-subsampled contourlet transform (NCT). A variety of image fusion quality measures are employed for the objective evaluation of the proposed method. We also report the results of a hypothesis testing approach on our large image dataset to identify statistically-significant performance differences.

Malonylated isoflavones are the major forms of isoflavonoids in soybean plants, the genes responsible for their biosyntheses are not well understood, nor their physiological functions. Here we report a new benzylalcohol O-acetyltransferase, anthocyanin O-hydroxycinnamoyltransferase, anthranilate N-hydroxycinnamoyl/benzoyltransferase, deacetylvindoline 4-O-acetyltransferase (BAHD) family isoflavone glucoside malonyltransferase GmIMaT1, and GmIMaT3, which is allelic to the previously characterized GmMT7 and GmIF7MaT. Biochemical studies showed that recombinant GmIMaT1 and GmIMaT3 enzymes used malonyl-CoA and several isoflavone 7-O-glucosides as substrates. The Km values of GmIMaT1 for glycitin, genistin, and daidzin were 13.11, 23.04, and 36.28 μM, respectively, while these of GmIMaT3 were 12.94, 26.67, and 30.12 μM, respectively. Transgenic hairy roots overexpressing both GmIMaTs had increased levels of malonyldaidzin and malonylgenistin, and contents of daidzin and glycitin increased only in GmIMaT1-overexpression lines. The increased daidzein and genistein contents were detected only in GmIMaT3-overexpression lines. Knockdown of GmIMaT1 and GmIMaT3 reduced malonyldaidzin and malonylgenistin contents, and affected other isoflavonoids differently. GmIMaT1 is primarily localized to the endoplasmic reticulum while GmIMaT3 is primarily in the cytosol. By examining their transcript changes corresponding to the altered isoflavone metabolic profiles under various environmental and hormonal stresses, we probed the possible functions of GmIMaTs. Two GmIMaTs displayed distinct tissue expression patterns and respond differently to various factors in modifying isoflavone 7-O-glucosides under various stresses.

Spatial Modulation Technologies (SMTs) are schemes that reduce inter-carrier interference (ICI), inter-channel interference, inter-antenna synchronization (IAS), and system complexity for multiple-input multiple-output (MIMO) communication systems. Moreover, high spectral and energy efficiency have rendered SMTs attractive to underwater acoustic (UWA) MIMO communication systems. Consequently, this paper focuses on SMTs such as spatial modulation (SM), generalized spatial modulation (GSM), and fully generalized spatial modulation (FGSM) in which one constant number and one multiple number of antennas are active to transmit data symbols in any time interval for underwater acoustic communication (UWAC). In SMTs, the receiver requires perfect channel state information (P-CSI) for accurate data detection. However, it is impractical that the perfect channel knowledge is available at the receiver. Therefore, channel estimation is of critical importance to obtain the CSI. This paper proposes the pilot-based recursive least-square (RLS) adaptive channel estimation method over the underwater time-varying MIMO channel. Furthermore, maximum likelihood (ML) decoder is used to detect the transmitted data and antennas indices from the received signal and the estimated UWA-MIMO channel. The numerical computation of mean square error (MSE) and bit error rate (BER) performance are computed for different SMTs like SM, GSM and FSGM using Monte Carlo iterations. Simulation results demonstrate that the RLS channel estimation method achieves the nearly same BER performance as P-CSI.

Output from imaging sensors based on CMOS and CCD devices is prone to noise due to inherent electronic fluctuations and low photon count. The resulting noise in the acquired image could be effectively modelled as signal-dependent Poisson noise or as a mixture of Poisson and Gaussian noise. To that end, we propose a generalized framework based on detection theory and hypothesis testing coupled with the variance stability transformation (VST) for Poisson or Poisson–Gaussian denoising. VST transforms signal-dependent Poisson noise to a signal independent Gaussian noise with stable variance. Subsequently, multiscale transforms are employed on the noisy image to segregate signal and noise into separate coefficients. That facilitates the application of local binary hypothesis testing on multiple scales using empirical distribution function (EDF) for the purpose of detection and removal of noise. We demonstrate the effectiveness of the proposed framework with different multiscale transforms and on a wide variety of input datasets.

The purpose of this study is to contribute to the literature of service qualityimportance in restaurant industry. The study has been based upon theServqual technique and Dineserv tool of improving the quality by the serviceproviding organizations. The study is undertaken from the perspective ofPakistani Restaurant Industry and the customers' perceptions vis-à-visrestaurant dining. Two variables of Servqual, i.e. Tangibles andResponsiveness, have been examined to demonstrate the significance ofservice quality on customer satisfaction. The results endorse the importanceof enhanced complementary service standards in restaurant industry. Finally,the findings provide an insight for the Pakistani restaurant service providingestablishments and suggestion have been made for the caretakers of theindustry on ways to improve service quality.

Rumex acetosa (R. acetosa) has been used in folk remedies for gastrointestinal disorders and cutaneous diseases. Rumex species, in particular, contain abundant anthraquinones. Anthraquinone glycosides and aglycones show different bioactive effects. However, information on the pharmacokinetics of anthraquinone glycosides is limited, and methods to quantify anthraquinone glycosides in plasma are rarely available. A simple and sensitive liquid chromatography-tandem mass spectrometric bioanalytical method for the simultaneous determination of both anthraquinone glycosides and their aglycones, including emodin, emodin-8-O-β-d-glucoside, chrysophanol, chrysophanol-8-O-β-d-glucoside, physcion, and physcion-8-O-β-d-glucoside , in a low volume of rat plasma (20 µL) was established. A simple and rapid sample preparation was employed using methanol as a precipitating agent with appropriate sensitivity. Chromatographic separation was performed on HPLC by using a biphenyl column with a gradient elution using 2 mM ammonium formate (pH 6) in water and 2 mM ammonium formate (pH 6) in methanol within a run time of 13 min. The anthraquinones were detected on triple-quadrupole mass spectrometer in negative ionization mode using multiple-reaction monitoring. The method was validated in terms of selectivity, linearity, accuracy, precision, recovery, and stability. The values of the lower limit of quantitation of anthraquinones were 1–20 ng/mL. The intra-batch and inter-batch accuracies were 96.7–111.9% and the precision was within the acceptable limits. The method was applied to a pharmacokinetic study after oral administration of R. acetosa 70% ethanol extract to rats at a dose of 2 g/kg.

In this paper, a novel time-frequency signature using resonance-based sparse signal decomposition (RSSD), phase space reconstruction (PSR), time-frequency distribution (TFD) and manifold learning is proposed for feature extraction of ship-radiated noise, which is called resonance-based time-frequency manifold (RTFM). This is suitable for analyzing signals with oscillatory, non-stationary and non-linear characteristics in a situation of serious noise pollution. Unlike the traditional methods which are sensitive to noise and just consider one side of oscillatory, non-stationary and non-linear characteristics, the proposed RTFM can provide the intact feature signature of all these characteristics in the form of a time-frequency signature by the following steps: first, RSSD is employed on the raw signal to extract the high-oscillatory component and abandon the low-oscillatory component. Second, PSR is performed on the high-oscillatory component to map the one-dimensional signal to the high-dimensional phase space. Third, TFD is employed to reveal non-stationary information in the phase space. Finally, manifold learning is applied to the TFDs to fetch the intrinsic non-linear manifold. A proportional addition of the top two RTFMs is adopted to produce the improved RTFM signature. All of the case studies are validated on real audio recordings of ship-radiated noise. Case studies of ship-radiated noise on different datasets and various degrees of noise pollution manifest the effectiveness and robustness of the proposed method.

Since local feature detection has been one of the most active research areas in computer vision during the last decade and has found wide range of applications (such as matching and registration of remotely sensed image data), a large number of detectors have been proposed. The interest in feature-based applications continues to grow and has thus rendered the task of characterizing the performance of various feature detection methods an important issue in vision research. Inspired by the good practices of electronic system design, a generic framework based on the repeatability measure is presented in this paper that allows assessment of the upper and lower bounds of detector performance and finds statistically significant performance differences between detectors as a function of image transformation amount by introducing a new variant of McNemar’s test in an effort to design more reliable and effective vision systems. The proposed framework is then employed to establish operating and guarantee regions for several state-of-the art detectors and to identify their statistical performance differences for three specific image transformations: JPEG compression, uniform light changes and blurring. The results are obtained using a newly acquired, large image database (20,482 images) with 539 different scenes. These results provide new insights into the behavior of detectors and are also useful from the vision systems design perspective. Finally, results for some local feature detectors are presented for a set of remote sensing images to showcase the potential and utility of this framework for remote sensing applications in general.

Spikes frequently occur in power quality (PQ) disturbance signals due to various causes such as switching of the inductive loads and the energization of the capacitor bank. Such signals are difficult to analyze using existing time-frequency (TF) methods as these signals have two orthogonal directions in a TF plane. To address this issue, this paper proposes an adaptive TF distribution (TFD) for the analysis of PQ signals. In the proposed adaptive method, the smoothing kernel’s direction is locally adapted based on the direction of energy in the joint TF domain, and hence an improved TF resolution can be obtained. Furthermore, the performance of the proposed adaptive technique in analyzing electrical PQ is thoroughly studied for both synthetic and real world electrical power signals with the help of extensive simulations. The simulation results (specially for empirical data) indicate that the adaptive TFD method achieves high energy concentration in the TF domain for signals composed of tones and spikes. Moreover, the local adaptation of the smoothing kernel in the adaptive TFD enables the extraction of TF signature of spikes from TF images, which further helps in measuring the energy of spikes in a given signal. This new measure can be used to both detect the spikes as well as to quantify the extent of distortion caused by the spikes in a given signal.