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Smart, compact, and battery-free sensing platforms are increasingly required for structural and health monitoring in hard-to-reach environments. Here, we demonstrate an IoT-enabled multimodal electronic capsule (e-Pill) that integrates flexible, tunable temperature and light sensors within a battery-free architecture. The developed e-Pill is among the smallest reported to date, with a diameter of 6 mm and a length of 20 mm, and can be magnetically actuated for precise navigation in structural and biological systems. Magnetic controllability enables guided motion, accurate positioning, and reliable data acquisition in confined or dynamic environments. The e-Pill detects abnormalities and defects through variations in temperature and light intensity, with real-time monitoring achieved using a customized IoT platform and smartphone interface. Designed for both ingestible and non-ingestible operation, the e-Pill is suitable for a wide range of biological and structural health monitoring applications. The ingestible e-Pill effectively tracks the release of drugs or chemical agents in complex environments. Experiments are conducted within a fish model to demonstrate the feasibility of the developed e-Pill for real-life biomedical applications.

The evolution of cloud computing results in the development of numerous huge data-centers. These data-centers use large quantities of electrical energy, which in turn results in high operating cost and high emissions of CO2. The grounds for this excessive usage of energy is the inefficient usage of data-center resources (hosts). The resources in the data-center can be in either peak or idle or sleep state. It has been experimentally proven that hosts in the idle state consumes more energy than hosts in sleep state. Through an efficient and optimal VM placement strategy, an energy efficient resource utilization can be achieved, thereby reducing the number of idle hosts in the data-center. This paper introduces two energy efficient VM placement algorithms based on bin packing heuristics considering the physical machine’s energy efficiency, Energy Efficient VM Placement (EEVMP) and Modified Energy Efficient VM Placement (MEEVMP), that can reduce the overall energy usage in the data-center. The EEVMP algorithm when compared with the default VM placement algorithm Power-Aware Best-Fit Decreasing algorithm (PABFD) of CloudSim, it reduces the energy consumption by 53%, average SLA violation by 3.5% and number of VM migrations by 64.47%. Further, we have performed MEEVMP algorithm where we achieve a reduction in energy consumption by 54.24%, average SLA violation by 4.39% and number of VM migrations by 67.713 % as compared to PABFD.

The incidence and prevalence of obesity and the metabolic syndrome have risen markedly in the past decade, representing a serious cardiovascular health hazard with significant morbidity and mortality. The etiology of the metabolic syndrome and its various pathogenic mechanisms are incompletely defined and under intense investigation. Contemporary research suggests that the adipocyte-derived hormone leptin may be an important factor linking obesity, the metabolic syndrome, and cardiovascular disorders. Although recent evidence indicates that under normal conditions leptin may be an important factor in regulating pressure and volume, during situations of chronic hyperleptinemia and leptin resistance, this hormone may function pathophysiologically for the development of hypertension and cardiac and renal diseases. Future research will determine if reduction of circulating leptin and/or blockade of its peripheral actions can confer cardiovascular and renal protection in hyperleptinemic patients with obesity and the metabolic syndrome.

In the current trend, the image retrieval (IR) system has also been shifted from traditional text-based to content-based with the advancement in technology. Many issues have been resolved by content-based IR. In a particular case of information retrieval/image retrieval (IR) systems, the number of images on the web and the usage of images are growing exponentially. Therefore, IR system needs to be scalable, flexible, modularizable and promotes reusability so that it becomes easy to deploy, develop and maintain the system. In this paper, we have presented a CBIR model using Color Histogram and Local Binary Pattern (LBP) where both are built with Microservices architecture using docker platform. The framework used in this model is logic independent therefore any CBIR system can be run using this framework. The CBIR using Color Histogram uses chi-squared distance as a similarity measure while CBIR model using LBP is implemented using Linear Support Vector Machines for image classification. In our experiments, we have achieved the average recall, precision, and F-measure using Color Histogram 22.25%, 63.12%, and 32.67%, respectively. Though, we have achieved the average recall, precision, and F-measure using LBP 77.15%, 79.90%, and 76.17%, respectively. It has been observed that LBP model is more accurate than Color Histogram for detecting different weather conditions. It has also been found that the use of Microservices architecture leads to improve the non-functional qualities of a CBIR system as compared to traditional architecture styles by a great margin.

Background This study presents the development and validation of a UV spectrophotometric method for the simultaneous estimation of teneligliptin hydrobromide hydrate (TEN) and pioglitazone hydrochloride (PIO) in pharmaceutical dosage forms. The method measures absorbance at 243 nm for TEN and 265 nm for PIO. Validation was conducted according to ICH guidelines, covering specificity, linearity, range, precision, accuracy, limit of detection, limit of quantification, and robustness. Results The method exhibited linearity in the 2–25 µg/ml concentration range for both TEN and PIO, with correlation coefficients close to 1. Precision studies showed low % RSD values, indicating excellent repeatability and minimal intra- and inter-day variability. Accuracy was confirmed through recovery studies, with results within the 98–102% range. The method demonstrated sensitivity with low LOD and LOQ values. Robustness testing showed stability and consistency under varying conditions. Analysis of tablet formulations confirmed the accurate quantification of both drugs. Conclusion The developed UV spectrophotometric method offers a simple, sensitive, and cost-effective solution for the simultaneous estimation of TEN and PIO in pharmaceutical formulations. It is suitable for routine analysis and quality assessment of tablet dosage forms.

Active queue management schemes are used to reduce the number of dropped packets at the routers. Random early detection uses dropping probability which is calculated based on the average queue size. Further it is modified according to the value of the count indicating the number of unmarked packets that have arrived after a marked packet. The impact of random variable i.e. number of packets arrived after a marked packet over the dropping pattern is investigated. The proposed model achieves smooth dropping pattern which results in improvement of quality of service parameters. A new model for dropping probability is proposed with different dropping function. The effect of new dropping probability results in the increase of the throughput and reduction of the expected end-to-end delay. An important finding is that the choice of modified dropping function significantly affects the performance measures of the networks.

Vitamin D deficiency is associated with an increased risk of many diseases (skeletal and nonskeletal). Emerging data also associate high concentrations of serum parathyroid hormone (PTH) with morbidity and increased mortality in patients both with and without known chronic kidney disease (CKD). Understanding the relationship between vitamin D and PTH and the determinants of PTH is therefore important. We performed a cross-sectional study of 203 patients with varying stages of CKD randomly recruited from the Renal Unit database at our institution. Detailed case review was performed, and samples of fasting blood were taken for biochemical analyses. We measured standard biochemistry, 25-hydroxyvitamin D (25-OHD), 1,25-OHD, and three PTH measurements [1-84 PTH, total PTH, and derived N-terminal truncated, 7-84 PTH (cPTH)]. Vitamin D deficiency was high, with 86% of patients having 25-OHD levels below 30 ng/ml. Estimated glomerular filtration rate (eGFR) was not associated with 25-OHD levels, whereas 1,25-OHD was lower in those with CKD stage 5 versus stage 4, who were not treated with vitamin D metabolites (18 vs. 65 pg/ml, respectively; P < 0.05). All three PTH measurements increased with worsening eGFR, with this finding being more pronounced in those patients who were not treated with vitamin D metabolites. The slope of the regression line of cPTH on eGFR tended to be steeper, -0.90, compared to -0.81 for total PTH and -0.80 for 1-84 PTH (P = 0.06). The ratio of total PTH to cPTH did decrease significantly through the range of CKD stages (P = 0.03). The determinants of PTH were similar for all three PTH measurements, with eGFR having a strong inverse relationship, with weaker relationships for 25-OHD and ionized calcium on multivariate analyses. We confirm that there is a complex relationship between 25-OHD, eGFR, and PTH. Total PTH, 1-84 PTH, and cPTH increase with increasing CKD stages, with a relatively greater increase in cPTH, although the clinical significance of this finding remains uncertain. The three PTH measurements had similar correlations with the biochemical and clinical variables studied, suggesting that either total PTH or 1-84 PTH can be used in clinical practice when evaluating vitamin D and PTH status.

The growing demand for efficient cloud computing solutions has highlighted the need for advanced task-scheduling techniques that minimize execution time and energy consumption. An appropriate fitness function for nature-inspired optimization may lead to improvement in performance. Our research aims to bridge this gap by proposing novel optimization-based task-scheduling techniques using nature-inspired meta-heuristic algorithms. An enhanced initialization approach for particle swarm optimization (PSO) is introduced, and an ellipsoid function as the fitness function to optimize key performance metrics, including makespan, execution time, energy consumption, and degree of imbalance, is applied. Additionally, we explore a genetic approach for further optimization. The proposed algorithms, longest job-based optimization (LJPBO), efficient task scheduling-based optimization (ETSBO), dynamic completion time-based optimization (DCTBO), and dynamic completion time-based genetic algorithm (DCTBGA), are evaluated against existing algorithms using performance measures. The results demonstrate substantial improvements of proposed algorithms LJPBO, ETSBO, DCTBO, and DCTBGA, with respect to MCTPSO with reductions in energy consumption of 50.6901%, 8.9008%, 20.2066%, and 78.0909%, respectively, for 100 VMs and 400 tasks. The experiments were conducted on a Windows machine with an Intel i7-6700 processor running at 3.4 GHz and 8 GB of memory. Further, MinMin PSO task scheduling algorithm is proposed, which achieved the lowest makespan.

The random early detection active queue management (AQM) scheme uses the average queue size to calculate the dropping probability in terms of minimum and maximum thresholds. The effect of heavy load enhances the frequency of crossing the maximum threshold value resulting in frequent dropping of the packets. An adaptive queue management with random dropping algorithm is proposed which incorporates information not just about the average queue size but also the rate of change of the same. Introducing an adaptively changing threshold level that falls in between lower and upper thresholds, our algorithm demonstrates that these additional features significantly improve the system performance in terms of throughput, average queue size, utilization and queuing delay in relation to the existing AQM algorithms.

ObjectiveTo develop and validate revised classification criteria for eosinophilic granulomatosis with polyangiitis (EGPA).MethodsPatients with vasculitis or comparator diseases were recruited into an international cohort. The study proceeded in five phases: (1) identification of candidate criteria items using consensus methodology, (2) prospective collection of candidate items present at the time of diagnosis, (3) data-driven reduction of the number of candidate items, (4) expert panel review of cases to define the reference diagnosis and (5) derivation of a points-based risk score for disease classification in a development set using least absolute shrinkage and selection operator logistic regression, with subsequent validation of performance characteristics in an independent set of cases and comparators.ResultsThe development set for EGPA consisted of 107 cases of EGPA and 450 comparators. The validation set consisted of an additional 119 cases of EGPA and 437 comparators. From 91 candidate items, regression analysis identified 11 items for EPGA, 7 of which were retained. The final criteria and their weights were as follows: maximum eosinophil count ≥1×109/L (+5), obstructive airway disease (+3), nasal polyps (+3), cytoplasmic antineutrophil cytoplasmic antibody (ANCA) or anti-proteinase 3–ANCA positivity (−3), extravascular eosinophilic predominant inflammation (+2), mononeuritis multiplex/motor neuropathy not due to radiculopathy (+1) and haematuria (−1). After excluding mimics of vasculitis, a patient with a diagnosis of small- or medium-vessel vasculitis could be classified as having EGPA if the cumulative score was ≥6 points. When these criteria were tested in the validation data set, the sensitivity was 85% (95% CI 77% to 91%) and the specificity was 99% (95% CI 98% to 100%).ConclusionThe 2022 American College of Rheumatology/European Alliance of Associations for Rheumatology Classification Criteria for Eosinophilic Granulomatosis with Polyangiitis demonstrate strong performance characteristics and are validated for use in research.