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This paper proposed a Constructive Rapid Application Development Model based Software Cost Estimation Technique (CORADMO based SCET) for accurate software cost estimation. The data requirements, cost drivers, constraints and priorities are given as an input to the Fuzzy Inference System (FIS). The processed output such as effort, time and cost for nominal plan, shortest schedule plan, and least cost plan are computed in the Fuzzy Inference System (FIS). Further to reduce the effort time and cost means, the output optimized by Dragon fly Whale Optimization (DWO)which provides the best estimated effort, time and cost as an output for software development. The proposed CORADMO based SCET model is evaluated by NASA 93 dataset using MATLAB. The performance of the CORADMO based SCET approach is assessed in terms of Mean Magnitude of Relative Error, Pred (25%), and Magnitude of Relative Error attains the values of 98.77%, 92.55%, and 93.45% respectively. Finally, the CORADMO based SCET model justifies the suitability of Dragon fly Whale Optimization with the proposed fuzzy logic.

Globally, cardiovascular disease kills more than 500000 people every year, thus becoming the primary reason for death. Nevertheless, cardiovascular health monitoring is essential for accurate analysis and therapy of heart disease. In this work, a novel deep learning-based StrIppeD NAS-Network (SID-NASNet) for arrhythmia categorization into octa-classes with electrocardiogram (ECG) signals is presented. First, the ECG signals are recorded in real time using 12-lead electrodes. Then, the Discrete Wavelet Transform (DWT) is used to denoise the signals to reduce repetition and increase resilience. The noise-free ECG signals are fed into a K-means clustering algorithm to group ECG signal segments into a set number of clusters to identify patterns that may indicate heart abnormalities. Subsequently, the deep learning-based NASNet with Stripped convolutional layers is used to detect ECG irregularities of arrhythmia. Each sample point is examined for its local fractal dimension before extracting the heartbeat waveforms within a predetermined window length. A bio-inspired Dingo Optimization (DO) algorithm is used in the SID-NASNet to normalize the parameters to improve the efficiency of the network with low network complexity. The efficiency of the proposed SID-NASNet is assessed with specificity, accuracy, precision, F1 score and recall based on the MIT-BIH arrhythmia dataset. From the test results, the proposed SID-NASNet achieves an accuracy of 98.22% for effective categorization of ECG signals. The proposed SID-NASNet improves the overall accuracy of 1.24%, 3.76%, 1.87%, and 0.22% better than ECG-NET, Deep Learning (DL)-based GAN, 1D-CNN, and GAN-Long-Short Term Memory (LSTM), respectively.

As digital products communicate over public networks, digital image security becomes essential. Despite advancements in encryption technology, protecting data from secure images remains a complex computational issue that necessitates using an encrypted environment to protect data transmitted from devices over networks. Encryption is critical for protecting sensitive information, especially images, from unauthorized access or failure. Image encryption is distinct from text encryption. Images contain extensive data with high redundancy and a high correlation between nearby pixels, making them difficult to process with traditional technology. In recent decades, chaos maps have become popular in the crypto community. This work proposes a new encryption technology based on chaotic map substitution boxes (S-box) and cellular automata (CA) to address the frequent challenges in chaotic encryption methods. To address the inadequate randomness provided by the 1D chaotic map, this work presented a 4D memristive hyperchaos with a more excellent chaotic range, increased uncertainty, and ergodicity as an alternative to the software-based approach, which is vulnerable and offers low throughput. The suggested encryption technique was implemented on an Intel Cyclone IV EP4CE115F29C7 FPGA. It is less prone to tampering and has a better throughput. This suggested encryption scheme employs 1384 LEs. In comparison to traditional algorithms, the suggested Number of Pixels Change Rate (NPCR) (99.9706%), unified averaged changed intensity (UACI) (33.3956%), and information entropy (IE) (7.984) accomplish more effective. Statistical studies such as IE, histogram, correlation, peak signal-to-noise ratio (PSNR), differential analysis such as NPCR and UACI, and noise attack analyses are employed to validate the suggested encryption design. Regarding security, the suggested hybrid method outperforms conventional algorithms while protecting image quality.

Introduction Opioids and imaging are considered low-value care for most people with low back pain. Yet around one in three people presenting to the emergency department (ED) will receive imaging, and two in three will receive an opioid. NUDG-ED aims to determine the effectiveness of two different behavioural ‘nudge’ interventions on low-value care for ED patients with low back pain.Methods and analysis NUDG-ED is a 2×2 factorial, open-label, before-after, cluster randomised controlled trial. The trial includes 8 ED sites in Sydney, Australia. Participants will be ED clinicians who manage back pain, and patients who are 18 years or over presenting to ED with musculoskeletal back pain. EDs will be randomly assigned to receive (i) patient nudges, (ii) clinician nudges, (iii) both interventions or (iv) no nudge control. The primary outcome will be the proportion of encounters in ED for musculoskeletal back pain where a person received a non-indicated lumbar imaging test, an opioid at discharge or both. We will require 2416 encounters over a 9-month study period (3-month before period and 6-month after period) to detect an absolute difference of 10% in use of low-value care due to either nudge, with 80% power, alpha set at 0.05 and assuming an intra-class correlation coefficient of 0.10, and an intraperiod correlation of 0.09. Patient-reported outcome measures will be collected in a subsample of patients (n≥456) 1 week after their initial ED visit. To estimate effects, we will use a multilevel regression model, with a random effect for cluster and patient, a fixed effect indicating the group assignment of each cluster and a fixed effect of time.Ethics and dissemination This study has ethical approval from Southwestern Sydney Local Health District Human Research Ethics Committee (2023/ETH00472). We will disseminate the results of this trial via media, presenting at conferences and scientific publications.Trial registration number ACTRN12623001000695.

Background Rapid detection of intracranial arterial occlusion in patients with ischemic stroke is important to facilitate timely reperfusion therapy. We compared the diagnostic accuracy of neurologists and radiologists against RapidAI (iSchema View, Menlo Park, CA) software for occlusion detection. Methods Adult patients who presented to a single comprehensive stroke center over a 5‐month interval with clinical suspicion of ischemic stroke and who underwent multimodality imaging with RapidAI interpretation were included. There were 8 assessors: 1 radiologist, 5 neurologists, and 2 radiology trainees. The reference standard was large‐vessel occlusion (LVO) or medium‐vessel occlusion (MVO) diagnosed by a panel of 4 interventional neuroradiologists. Positive likelihood ratio (LR) and negative LR were used to indicate how well readers correctly classified the presence of intracranial occlusions compared with the reference standard. The positive LR and negative LR for each reader were plotted on an LR graph using RapidAI LRs as comparator. Results The assessors read scans from 500 patients (49.6% men). The positive LR of RapidAI for detection of LVO was 8.49 (95% CI, 5.75–12.54), and the negative LR was 0.41 (95% CI, 0.28–0.58). The positive LR for LVO or MVO for RapidAI was 5.0 (95% CI, 3.28–7.63), and the negative LR was 0.66 (95% CI, 0.56−0.79). Sensitivity for LVO (0.65–0.96) and for LVO or MVO (0.62–0.94) was higher for all readers compared with RapidAI (0.62 and 0.39, respectively). Six of 8 readers had superior specificity to RapidAI for LVO (0.75–0.98 versus 0.93) and LVO or MVO (0.55–0.95 versus 0.92). Conclusions Experienced readers of acute stroke imaging can identify LVOs and MVOs with higher accuracy than RapidAI software in a real‐world setting. The negative LR of RapidAI software was not sufficient to rule out LVO or MVO.

Activity trackers and wearables allow accurate determination of physical activity, basic vital parameters, and tracking of complex medical conditions. This review attempts to provide a roadmap for the development of these applications, outlining the basic tools available, how they can be combined, and what currently exists in the marketplace for spine patients.Various types of sensors currently exist to measure distinct aspects of user movement. These include the accelerometer, gyroscope, magnetometer, barometer, global positioning system (GPS), Bluetooth and Wi-Fi, and microphone. Integration of data from these sensors allows detailed tracking of location and vectors of motion, resulting in accurate mobility assessments. These assessments can have great value for a variety of healthcare specialties, but perhaps none more so than spine surgery. Patient-reported outcomes (PROMs) are subject to bias and are difficult to track frequently - a problem that is ripe for disruption with the continued development of mobility technology.Currently, multiple mobile applications exist as an extension of clinical care. These include Manage My Surgery (MMS), SOVINITY-e-Healthcare Services, eHealth System, Beiwe Smartphone Application, QS Access, 6WT, and the TUG app. These applications utilize sensor data to assess patient activity at baseline and postoperatively. The results are evaluated in conjunction with PROMs. However, these applications have not yet exploited the full potential of available sensors.There is a need to develop smartphone applications that can accurately track the functional status and activity of spine patients, allowing a more quantitative assessment of outcomes, in contrast to legacy PROMs.

Background: Studies on the bacterial diseases from the Andaman and Nicobar Islands are scanty and have only two reports recorded during 1996 and 1999. A case of emaciation and abdominal dropsy caused by different strains of Aeromonas hydrophila was recorded by earlier workers in the Andaman, Islands. In the present work, a similar disease syndrome was recorded in Barbonymus gonionotus and Cyprinus carpio var. Koi. The present study served to investigate the disease's cause and the pathogen associated with it. Methods: This study details the isolation, biochemical and molecular characterization of three bacterial pathogens associated with emaciated, tail rot syndrome (ETS) and big belly syndrome (BBS) in B. gonionotus and C. carpio var. Koi respectively. Histology and re-infectivity study was also done for the infected host tissues and with Catla catla fingerlings. Result: This study constitutes the first report of three bacterial pathogens from the Andaman Islands, India viz., Aeromonas veronii, Aeromonas sp. and Pseudomonas alcaligenes causing emaciation and abdominal dropsy, with biochemical, molecular, histopathological and experimental challenge study evidence. [ABSTRACT FROM AUTHOR]

A wireless sensor network (WSN) is made up of several independent sensor nodes that are able to interpret, analyze, and work with data. It is generally recognized that security and limited energy are the two challenging tasks with WSNs. To address these challenges, a novel Aquila-optimized fuzzy deep belief network (AO-FDBN) model has been proposed in this paper. The suggested AO-FDBN framework consists of three stages. Initially, the Aggregator has been selected by using the Aquila optimization algorithm. Secondly, the data from the aggregator are encrypted by using the blowfish algorithm. Finally, the optimal route has been selected by using the fuzzy-deep belief network (DBN). Packet delivery ratio (PDR), transport delay, energy usage, and network lifetime are evaluated between the suggested framework and current methods. Experimental results specify that the suggested AO-FDBN approach achieves higher performance of 22.617%, 14.22%, and 15.64% than TEEFCA, HESC, and SEPC methods. This system is more effective and secure for real-time applications. [ABSTRACT FROM AUTHOR]

According to Food and Agriculture Organization of the United Nations (FAO), aquaculture has grown faster and its expansion aimed at meeting the increase of world fish demand, and preserving natural fish stocks. Currently, to produce fish in quantity and quality requires reduction of the environmental impact from aquaculture, through the improvement of culture systems. Disease is the major factor affecting the development and expansion in aquaculture. Losses due to disease in shrimp farming are high. Various approaches to minimize the impact of disease on production are possible. Another approach to keep the pathogen pressure low is polyculture of shrimp and finfish. This practice makes shrimp farming more sustainable by reducing the environmental impact and the incidence of shrimp disease. Antimicrobial peptides in the fish skin kill shrimp pathogens, keeping pathogen pressure of bacteria and viruses low. In polyculture, shrimps can eat tilapia faeces and unused fish feed, while tilapia filter phytoplankton, reducing the risk of low dissolved oxygen levels at night. In addition, shrimp bioturbation at the pond bottom returns nutrients to the water column, enhancing phytoplankton production and consequently the natural feed available for the tilapia. Biofloc technology (BFT) is one of the most applicable and promising systems for sustainable aquaculture development. This technology is essentially based on the recycling of nutrients via microorganisms, primarily (i) heterotrophic bacteria, which convert nitrogen compounds into microbial biomass, in addition to serves as a source of food for aquatic organisms, and (ii) chemoautotrophic bacteria, which convert ammonia to nitrite and nitrate. [ABSTRACT FROM AUTHOR]

Fruits are structures that contain seeds that are created from the ovaries of only blooming plants. A fruit is a soft, component of a blooming plant that bears seeds. It is created from angiosperm ovaries and is unique to this plant group. In this paper proposed a novel Deep Learning-based Fruits Items Classification (SOOTY FRUIT) method to classify the variety of Fruits items from the dataset with their calorie values (CV). Initially, the images are processed using the Gaussian adaptive bilateral (GAB) Filter approach to improve image quality and eliminate noise. Consequently, the segmented images are pre-processed utilizing Attention V-net algorithm. The extracted features are then normalized utilized the Sooty Tern Optimization Algorithm (STOA). Fruits items are classified using Ghostnet based on these relevant features. As compared to existing methods, the proposed SOOTY FRUIT shows better results in terms of Accuracy. The accuracy of the proposed technique can be as high as 99.95%, while that of traditional models like the attention-based densely connected convolutional networks with convolution autoencoder (CAE-AND), Faster-Region-based Convolutional Neural Network (F-RCNN), and Attention Fusion Network (AFN) is 84.9%, 87.58%, and 93.91%, respectively. [ABSTRACT FROM AUTHOR]

Background: An experimental study was conducted during June-November 2020 at Tungjoy village of Manipur, India, to assess the assemblage pattern, abundance and seasonal dynamics of phytoplankton in simultaneously integrated paddycum-fish culture (IPFC). Methods: The experimental IPFC fields were prepared by enhancing width and height of dykes and constructing central refuge pond. Rice saplings were manually transplanted into the prepared fields in June 2020 while the common carp fingerlings were introduced 30 days after rice-transplantation. Both rice and fish were harvested in November 2020. Assemblage pattern, abundance and dynamics of phytoplankton, soil nutrients and water quality were analysed for three seasons (monsoon, postmonsoon and winter) in paddy fields without fish (PF) compared with IPFC fields. Relevant fish growth parameters were analysed on monthly basis. Result: The study recorded 37 genera of phytoplankton belonging to 25 families and 18 orders falling under class Bacillariophyceae, Chlorophyceae and Cyanophyceae. Bacillariophyceae was the most diverse class. Phytoplankton density ranges from 14.00±1.57 x 103 to 33.25±3.60 x 103 μL -1 and 9.05±2.27 x 103 to 33.06±3.45 x 103 μL -1 in PF and IPFC, respectively, wherein Chlorophyceae dominated during monsoon and post-monsoon. Phytoplankton in IPFC was lower than PF, indicating effective utilisation by the reared fish. Fish production from IPFC fields was 326 kg ha-1 with 83% survival rate while rice production was enhanced by 7.4% as compared to rice monoculture. [ABSTRACT FROM AUTHOR]