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Brain diseases is a wide range of disorders and diseases that affect the brain. They can change a person’s behavior, personality, and capacity for thought and function. CT images are more essential than conventional clinical tests for detecting brain hemorrhage accurately. MRI images of the brain can reveal even small abnormalities in the cranial region, helping providers diagnose a wide variety of conditions, ranging from brain stroke, cancers, aneurysms, and Alzheimer’s. This paper proposes a novel Fused dual neural (FDN) network for detecting brain cancer, stroke, aneurysms, and Alzheimer using Brain Medical Images (BMI) the combination of MRI and CT. In BMI, the adaptive bilateral filter reduces noise artifacts. Google Net is used to extract features from pre-processed MRI images, and Mobile Net is used to extract features from pre-processed CT images. The integration of extracted features from Google Net and Mobile Net is fused by the Wrapper method. Finally, the Deep Belief Network is employed for classifying brain stroke, cancer, Aneurysm, and Alzheimer’s diseases using BMI images. The quantitative analysis of the suggested method is determined using the parameters like specificity, recall, precision, F1 score, and accuracy. The proposed FDN achieves a high classification accuracy rate of 98.19%, 97.68%, 94.31%, and 93.82% for detecting stroke, cancer, Aneurysm, and Alzheimer respectively. The proposed FDN model improves the overall accuracy by 5.35%, 3.14%, 9.48%, 5.33%, and 0.55% better than Faster R-CNN, CNN, Inception-V3, DCNN, and Fine-tuning Network respectively.

The Solid Oxide Fuel Cell (SOFC) offers major benefits, namely electrical efficiency and fuel versatility, for hybrid-electric aircraft propulsion. The combined development of novel SOFC concepts and corresponding manufacturing processes are regarded as key objectives for the SOFC activities at Airbus Central Research and Technology. Airbus is designing, manufacturing and testing SOFC concepts with the aim to achieve highest gravimetric power densities. Novel manufacturing technologies for metallic and ceramic materials have the potential to enable lighter functional layers for SOFCs with increased intrinsic mechanical stability and surface area. Different cell concepts with an optimized current collection are currently under development at Airbus. The micro-tubular and monolithic SOFC are seen as the most promising concepts, whereas around 2 kW/kg on a cell level has recently been achieved in a performance test at Airbus.

Approximate multipliers are a trending digital design that was specially developed for the implementation of low power and high-speed circuits. The main purpose of this design is to trade the necessity of accurate multipliers. In this work, a novel imprecise compressor was designed to develop the Hazy Multipliers for low-error resilient applications. These imprecise compressors are synthesized using a 40 nm CMOS technology. When compared with the previous approximate multiplier design the proposed Hazy Multipliers are can reduce the error up to 96%, 98.9%, 99.5% respectively than the existing methods. Finally, the proposed design is investigated on the image smoothening application to show the performance metrics of Hazy Multipliers.

A group of small sensors can participate in the wireless network infrastructure and make appropriate transmission and communication sensor networks. There are numerous uses for drones, including military, medical, agricultural, and atmospheric monitoring. The power sources available to nodes in WSNs are restricted. Furthermore, because of this, a diverse method of energy availability is required, primarily for communication over a vast distance, for which Multi-Hop (MH) systems are used. Obtaining the optimum routing path between nodes is still a significant problem, even when multi-hop systems reduce the cost of energy needed by every node along the way. As a result, the number of transmissions must be kept to a minimum to provide effective routing and extend the system's lifetime. To solve the energy problem in WSN, Taylor based Gravitational Search Algorithm (TBGSA) is proposed, which combines the Taylor series with a Gravitational search algorithm to discover the best hops for multi-hop routing. Initially, the sensor nodes are categorised as groups or clusters and the maximum capable node can access the cluster head the next action is switching between multiple nodes via a multi-hop manner. Initially, the best (CH) Cluster Head is chosen using the Artificial Bee Colony (ABC) algorithm, and then the data is transmitted utilizing multi-hop routing. The comparison result shows out the extension of networks longevity of the proposed method with the existing EBMRS, MOGA, and DMEERP methods. The network lifetime of the proposed method increased by 13.2%, 21.9% and 29.2% better than DMEERP, MOGA, and EBMRS respectively.

In recent years, finger vein recognition has gained a lot of attention and been considered as a possible biometric feature. Various feature selection techniques were investigated for intrinsic finger vein recognition on single feature extraction, but their computational cost remains undesirable. However, the retrieved features from the finger vein pattern are massive and include a lot of redundancy. By using fusion methods on feature extraction approaches involving weighted averages, the error rate is minimized to produce an ideal weight. In this research, a novel combinational model of intelligent water droplets is proposed along with hybrid PCA LDA feature extraction for improved finger vein pattern recognition. Initially, finger vein images are pre-processed to remove noise and improve image quality. For feature extraction, Linear Discriminant Analysis (LDA) and Principle Component Analysis (PCA) are employed to identify the most relevant characteristics. The PCA and LDA algorithms combine features to accomplish feature fusion. A global best selection method using intelligent water drops (GBS-IWD) is employed to find the ideal characteristics for vein recognition. The K Nearest Neighbour Classifier was used to recognize finger veins based on the selected optimum features. Based on empirical data, the proposed method decreases the equal error rate by 0.13% in comparison to existing CNN, 3DFM, and JAFVNet techniques. The overall accuracy of the proposed GBSPSO-KNN is 3.89% and 0.85% better than FFF and GWO, whereas, the proposed GBSIWD-KNN is 4.37% and 1.35% better than FFF and GWO respectively.

To compile intra-operative techniques, established imaging parameters, available equipment and software, and clinical applications of intraoperative ultrasound imaging (IOUSI) for spinal cord injury (SCI) and myelopathy.PubMed and Google Scholar were searched for relevant articles. The articles were reviewed and selected by 2 independent researchers. After article selection, data were extracted and summarized into research domains. PRISMA systematic review process was followed.Of the 2477 articles screened, 16 articles met the inclusion criteria. In patients with SCI and myelopathy, common quantitative measurements obtained using IOUSI were noted: 1) ultrasound elastography, 2) midsagittal anteroposterior diameter, 3) transverse, 4) transverse diameter, 5) maximum spinal cord compression, and 6) compression ratioTo ensure adequate decompression and to look for residual compression, the lateral and the craniocaudal margins of the laminectomy site were inspected in both axial and sagittal planes. In instances where quantitative assessment was not possible, cord decompression and degree of residual compression were gauged by inspecting the interface between the ventral border of the spinal cord and any potentially compressive elements, and by searching for symmetric and rhythmic cerebrospinal fluid pulsations. Use of contrast-enhanced ultrasoundand molecular imaging are additional advances in objective assessments for SCI and myelopathy.This review outlines the potential of IOUSI in patients presenting with SCI and myelopathy. Moreover, by identifying potential for inter-operator variability in certain subjective measurements, we illustrate the need for further research to quantify and standardize those assessments.

Hyperbilirubinemia or jaundice occurs in 60% of healthy babies and 80% of preterm infants because of an increase in unconjugated bilirubin in red blood cells. It is subjective to determine the severity of jaundice by visual assessment of the skin color of a newborn, and clinical judgement is dependent on the doctor’s knowledge. The paper explains the development of a non-invasive bilirubin detection technique called CliNicS, to check the bilirubin level of premature babies and report premature births and deaths to the health organization via an IOT network. CliNicS provides a noninvasive, transcutaneous bilirubin monitoring system using LED having a wavelength of 410 nm to 460 nm, and it also provides the treatment automatically by using LCT (LED Controlled Therapy) method. The level of bilirubin will be detected by using the photo detector, and the bilirubin measurement will be displayed on the LCD display. The bilirubin levels will be transmitted to doctors and health organizations via the IOT network. The proposed method helps to detect neonatal jaundice earlier, which reduces the risk of hyperbilirubinemia in newborns and makes it easier to measure total serum bilirubin levels than ever before.

Due to the chaotic nature of wind speed, short-term wind power prediction is a challengeable one. A reliable and accurate wind power prediction model is necessary for the wind turbine industry. Based on their improved ability to cope with complicated nonlinear issues, an increasing number of deep learning-based models are being explored for wind power prediction as artificial intelligence technologies, especially in deep neural networks. In this research, the wind power prediction model is divided into three stages. The first stage is to collect data from wind turbines. The second stage is to apply the optimal tuning of the Deep Neural Network (DNN) to predict the wind power. Here, the hybrid algorithm termedBird Swarm Merged Seagull Optimizer(BSMSO) stipulates DNN’s weight optimization points for wind power prediction; in addition, it reduces the time required for the same. Finally, the efficacy of the proposed BSMSO-DNN prediction model is proved by matching the statistical performance measures as regards toerror metrics with other existing techniques. The simulation results reveal thatthe proposed hybrid modelreduces the prediction errors significantly.

Cervical cancer is the most common and deadly malignancy affecting women worldwide. The prediction and treatment of this malignancy are necessary in order to avoid serious complications. In recent days, deep learning has enhanced the accuracy of cervical cancer prediction in its early stages. In this study, a deep learning based EN-FELM approach is proposed to detect and classify the cervical cells. Initially, the pap smear images are pre-processed to eliminate the background distortions. The EfficientNet is a reversed bottleneck MBConv used for feature extraction. Consequently, fuzzy extreme learning machine (FELM) is used to classify the healthy, benign, low squamous intraepithelial lesions (LSIL) and high squamous intraepithelial lesions (HSIL). The proposed model acquires the best classification accuracy on Herlev and SIPaKMeD datasets range of 99.6% and 98.5% respectively. As a result, the classification using FELM produces more efficient and accurate result which is significantly high compared to the traditional classifiers. The proposed EN-FELM improves the overall accuracy of 0.2%, 0.13% and 14.6% better than Autoencoder, LSTM and KNN with CNN respectively.

Securing image data from prying hackers is crucial in safeguarding the secrecy of data. Over the years, this was done by encrypting the image using an algorithm and a key, where the visible image was converted into a meaningless object. It is a difficult problem to design an image encryption technique based on chaotic systems with predictable cryptographic features. In this paper, the Quaternion, along with the Rossler attractor, was used to generate the key combination. The ciphering was done using the Least Square Approximation Algorithm (LSA). The algorithm was tested on a grayscale image database. The algorithm was initially tested in software using MATLAB R2018b, and was implemented in the Cyclone II EP2C35F672C6 device FPGA. On average, for a cipher image, the Peak Signal to Noise ratio (PSNR) was 9.09303 dB and the entropy was 7.9990 bits. For the cipher image, the Number of Pixels Change Rate (NPCR) and Unified Average Change Intensity (UACI) were 99.6039 and 33.4980, respectively. This proved that the algorithm could effectively mitigate the statistical and differential attacks. The key space was 2 (M ×N ×7 ×8), which was sufficiently high and mitigated the brute force attacks. The obtained results confirm that the cipher images resulting from the proposed ciphering scheme possess good cryptographic properties in terms of entropy, PSNR, UACI, NPCR, and keyspace analysis. Furthermore, the strength of the key is evaluated by the NIST test suite.

Unruptured Intracranial Aneurysms (UIAs) pose a significant risk of morbidity in the general population and much more so among sickle cell disease (SCD) patients. Meanwhile, the proportion of these patients with UIAs is not established just as the course and characteristics of the aneurysms are not well known.To estimate the prevalence, incidence and characteristics of UIAs in SCD patients and compare same with the metrics and features in the general population.The Data repositories, Medline (PubMed), Embase and Web of science were systematically searched from January 1st, 1990, to July 31st, 2021. Publications that passed an inclusion test were reviewed for data on the incidence and prevalence of UIAs, aneurysm characteristics and outcomes in SCD patients extracted. Findings from the included studies were appraised, using the Methodological Index for Non-randomized studies score (MINORS). The results were descriptively analysed. Given the marked heterogeneity of retrieved data, results were reported as standardized values, including the mean weighted annual incidence rate.105 SCD patients with 186 UIAs were identified in 10 retrospective studies. Mean age ranged from 10.5 to 40.18 across studies with adult (18 years) predominance. The prevalence of UIAs in SCD was 4.1% (95%CI 3.6 and 4.6) incidence rate was 1290.3/100,000 patient-years (95% CI 1018.0-1562.6). Aneurysms tended to be small (60%), anterior (76.1%), multiple (45.7%), and managed conservatively (62%) with mostly good outcomes (95%). The average MINORS score was 9.4 ± 3.1 for non-comparative studies (n = 8) and 19.5 ± 0.7 for comparative studies (n = 2).UIAs have a definite relationship with SCD, with higher incidence figures relative to the general population. Aneurysmal characteristics although largely similar, tend to be smaller in SCD patients. The low methodological quality of reviewed studies informs the need for well-designed prospective randomized controlled studies to better understand the mechanics of this relationship.

Single-center, multisurgeon, retrospective review.To evaluate the timing of return to commonly performed activities following minimally invasive spine surgery. Identify preoperative factors associated with these outcomes.Studies have reported return to activities with open techniques, but the precise timing of when patients return to these activities after minimally invasive surgery remains uncertain.Patients who underwent either minimally invasive lumbar laminectomy (MI-L) or minimally invasive transforaminal lumbar interbody fusion (MI-TLIF) were included. Patient-reported outcome measures, return to drive, return to work, and discontinuation of opioids data were reviewed. Regression was conducted to identify factors associated with return to driving by 15 days, return to work by 30 days, and for discontinuing opioids by 15 days. A composite group analysis was also performed for patients who returned to all three activities by 30 days.In total, 123 MI-L patients and 107 MI-TLIF patients were included. Overall, 88.8% of MI-L patients and 96.4% of MI-TLIF patients returned to driving in 11 and 18.5 days, respectively. In all, 91.9% of MI-L patients and 85.7% of MI-TLIF patients returned to work in 14 and 25 days. In all, 88.7% of MI-L patients and 92.6% of MI-TLIF patients discontinued opioids in a median of seven and 11 days. Overall, 96.2% of MI-L patients and 100% of MI-TLIF patients returned to all three activities, with a median of 27 and 31 days, respectively. Male sex [odds ratio (OR)=3.57] and preoperative 12-Item Short Form Physical Component Score (OR=1.08) are associated with return to driving by 15 days. Male sex (OR=3.23) and preoperative 12-Item Short Form Physical Component Score (OR=1.07) are associated with return to work by 30 days. Preoperative Visual Analog Scale back was associated with decreased odds of discontinuing opioids by 15 days (OR=0.84).Most patients return to activity following MI-L and MI-TLIF. These findings serve as an important compass for preoperative counseling.

BACKGROUND: In recent years, there has been increasing interest in outpatient spine surgery. Minimally invasive techniques have created an opportunity for ambulatory lumbar fusion, and these techniques increasingly involve advanced technologies such as navigation and robotics. OBJECTIVE: To explore the barriers, advantages, and future predictions for such technology in the context of outpatient lumbar fusions. METHODS: This is a narrative review of studies examining the advantages, limitations, and cost-effectiveness of navigation and spinal robotics in conjunction with the outcomes and costs of outpatient lumbar fusion. RESULTS: Outpatient lumbar fusion is a growing trend with ample evidence of its safety, favorable patient outcomes, and cost savings. Navigation and spinal robotics are associated with improved instrumentation accuracy and fewer complications, and the long-term cost savings can make these technologies financially practical in the outpatient setting. Future capabilities with robotics will only increase their value. CONCLUSIONS: Advanced technologies such as navigation and robotics are strategic long-term investments in the context of outpatient lumbar fusion. CLINICAL RELEVANCE: The favorable outcomes and costs associated with navigation and robotics will be relevant to any spine surgeon interested in developing an outpatient lumbar fusion program. LEVEL OF EVIDENCE: 5.

Retrospective review of prospectively collected data.To analyze the postoperative factors that led delayed discharge in patients who would have been eligible for ambulatory lumbar fusion (ALF).Assessing postoperative inefficiencies is vital to increase the feasibility of ALF.Patients who underwent single-level minimally invasive transforaminal lumbar interbody fusion and would have met the eligibility criteria for ALF were included. Length of stay (LOS); time in postanesthesia recovery unit (PACU); alertness and neurological examination, and pain scores at three and six hours; type of analgesia; time to physical therapy (PT) visit; reasons for PT nonclearance; time to per-oral (PO) intake; time to voiding; time to readiness for discharge were assessed. Time taken to meet each discharge criterion was calculated. Multiple regression analyses were performed to study the effect of variables on postoperative parameters influencing discharge.Of 71 patients, 4% were discharged on the same day and 69% on postoperative day 1. PT clearance was the last-met discharge criterion in 93%. Sixty-six percent did not get PT evaluation on the day of surgery. Seventy-six percent required intravenous opioids and60% had adequate pain control. Twenty-six percent had orthostatic intolerance. The median postoperative LOS was 26.9 hours, time in PACU was 4.2 hours, time to PO intake was 6.5 hours, time to first void was 6.3 hours, time to first PT visit was 17.7 hours, time to PT clearance was 21.8 hours, and time to discharge readiness was 21.9 hours. Regression analysis showed that time to PT clearance, time to PO intake, time to voiding, time in PACU, and pain score at three hours had a significant effect on LOS.Unavailability of PT, surgery after 1 pm , orthostatic intolerance, inadequate pain control, prolonged PACU stay, and long feeding and voiding times were identified as modifiable factors preventing same-day discharge.4.

The present study was performed to investigate on the effect of different carbon sources in biofloc based system for rearing Genetically Improved Farmed Tilapia (GIFT) fingerlings. The study comprised of utilizing four different carbon sources viz., Jaggery, Soyahull pellets, Rice starch and Molasses. The experiments were conducted in triplicates. Aquarium tank (100L capacity) was stocked with 10 fingerlings with an average body weight of 4.02 ± 0.146 g. At the end of the experimental trial, growth performance of GIFT, reared in different biofloc based systems, did not exhibit any significant variations. Maximum survival (100%) of GIFT was observed in the jaggery and soyahull pellets based biofloc systems. Digestive enzyme activity in jaggery based biofloc reared fish showed significant variation in protease (0.3127 ± 0.014 Units mg protein− 1) and amylase (0.0349 ± 0.003 Units mg protein− 1) activities when compared to fingerlings reared in other biofloc treatments. Highest values in White Blood Cells (46.57 ± 1.65 1000/cu mm), hematocrit (14.26 ± 0.38%) and mean corpuscular volume (141.57 ± 2.4 fl) were recorded in soyahull pellet carbon source biofloc supported system. Proximate composition of the soyahull pellet carbon source based biofloc showed significant variation in protein level (36.26 ± 1.5%) followed by jaggery (32.72 ± 1.130%) and molasses (33.48 ± 0.737%). The present study concluded that jaggery followed by soyahull pellet are best carbon sources, compared to other carbon sources for rearing GIFT fingerling which resulted in better growth, hematology profile and digestive enzyme activities.

In comparison to other alternative energy sources, wind power is more affordable and environmentally friendly, making it one of the most significant energy sources in the world. It is vital to monitor the condition of each wind turbine in the farm and recognize the various states of alert since difficulties with the operation as well as maintenance of wind farms considerably contribute to the rise in their overall expenses. The Supervisory Control and Data Acquisition (SCADA) data-based continuous observation of wind turbine conditions is the most widely used existing strategy to detect the fault early by preventing the wind turbine from reaching a shutdown stage. Several parameters irrelevant to the faults are saved in the SCADA system while the wind turbine is operating. To increase the efficacy of wind turbine fault diagnostics, optimally selected SCADA data parameters are required for fault prediction. Hence, this paper introduces an optimized Convolutional Neural Network (CNN)-based wind turbine fault identification method. For more precise detection, a Self-Improved Slime Mould Algorithm (SI-SMA) is used for the optimal selection of SCADA parameters as well as weight optimization of CNN. The proposed SI-SMA method is an enhanced form of the standard Slime Mould Algorithm (SMA). Eventually, an error analysis and a stability analysis are carried out to check the overall effectiveness of the suggested approach. In particular, the root mean square error (RMSE) of the implemented algorithm is lower, and it is 0.69%, 1.58%, 0.81% and 1.71% better than the existing FF, GWO, WOA and SMA models.

An experiment of 60-days duration was conducted to evaluate the effect of plant protein source by replacing fish meal protein with Azolla meal protein on growth performance, feed utilisation, survival, enzymatic activity and cost-effective feed for Etroplus suratensis fingerling. The mean initial average weight of fish 5.48±0.015g was stocked in plastic troughs (60 L) at a stocking rate of 10 fishes per trough. Three diets were formulated by inclusion of AZM protein by replacing fish meal (FM) protein at 0 (control diet), 7.5, 15, and 22.5 level. Each diet was fed to three replicate group fish at the rate of 5% of the total fish biomass, daily for a period of 60 days. Etroplus suratensis fed with AZM 15% diet attained highest weight gain, specific growth rate, and average daily growth, protein efficiency ratio (p0.05) observed. According to the finding of current study, Azolla meal can be candidate feed ingredient to replace fish meal without compromising growth performance.

Boundary recognition plays a vital role in real-world scenarios like medical imaging and surveillance. The classical edge detector fails to preserve minute details in the image. The abdominal CT images were first analyzed using two edge detection techniques based on gauss gradient are proposed here for real-time data processing. The performance of the edge detectors is validated by performance metrics and verified for benchmark dataset images. The results reveal that the gauss gradient edge detector was efficient for the boundary extraction in benchmark and medical images. The VLSI implementation of the proposed gauss gradient edge detectors is done using the Kintex 7-FPGA board, hardware implementation also generates efficient results with reduced execution time.

Introduction: Renal colic is a common emergency department (ED) presentation. Variations in assessment and management of suspected renal colic may have significant implications on patient and hospital outcomes. We developed a clinical practice guideline to standardize the assessment and management of renal colic in the ED. We subsequently compared outcomes before and after guideline implementation. Methods: The guidelines standardized the analgesia regimen, urology consult criteria, imaging modality, patient education, and followup instructions. This is a single-center, observational cohort study of patients presenting to the ED with renal colic prospectively collected after guideline implementation (December 2018 to May 2019), compared to a control group retrospectively collected before guideline implementation (December 2017 to May 2018). A total of 528 patients (pre-guideline n=283, post-guideline n=245) were included. Statistical analysis was performed with SPSS using multivariate linear regression. Results: ED length of stay (LOS) was significantly shorter after guideline implementation (pre-guideline 295.82±178.8 minutes vs. post-guideline 253.2±118.2 minutes, p=0.017). The number of computed tomography (CT) scans patients received was significantly less after guideline implementation (pre guideline 1.35±1.34 vs. post-guideline 1.00±0.68, p=0.034). Patients discharged for conservative management had a lower re-presentation rate in the post-guideline group (12.6%) than the pre-guideline group (17.2%); however, this did not reach statistical significance (p=0.18). Conclusions: Implementation of a clinical practice guideline for ureteric stones reduces the ED LOS and the total number of CT scan in patients who present with renal colic. Standardizing assessment and management of ureteric stones can potentially improve patient and hospital outcomes without compromising the quality of care.

A 60-day indoor growth trial was carried out to investigate the effects of dietary replacement of fish meal (FM) protein with black soldier fly larvae meal (BSFLM) at graded concentrations on bio-growth parameters, whole-body chemical composition, digestive enzyme activity, muscle-growth-related gene expression and haemato-biochemical responses of goldish juveniles (Carassius auratus). Six isonitrogenous and isoenergetic diets were formulated with BSFLM to substitute FM protein at 0 (T0), 20 (T20), 40 (T40), 60 (T60), 80 (T80) and 100 percent (T100). The aquaria with 20 fishes in each replicate were fed with experimental diet twice a day. Gold fish juveniles fed T60 diet exhibited maximum growth performances and feed utilization. However, escalating the percentage of fishmeal substitution with BSFLM above 60 percentage led to significant reduction in growth and feed utilization of goldfish juveniles. The protease activity was high in T60 and T80, wherein the lipase and amylase activity were high in T100. The relative mRNA expression of GH and MyoD was significantly upregulated in fish fed T40 and T60 diets, while the myostatin was significantly downregulated fish fed T40 and T60 diets. Thus, the fishmeal protein substituted at 60 percent with BSFLM with an inclusion level of 20.1 g/kg of diet will be well suitable for the goldfish juveniles.

Wireless underground sensor networks (WUSNs) are sub-ground-surface sensor node networks designed to establish real-time tracking capacities in diverse underground ecosystems composed of soil, water, oil, and other materials. The contact medium is the key distinction between USNs and terrestrial wireless sensor networks. The communication properties of electromagnetic (EM) waves in underwater resources like mud, water, oil, and other materials, as well as major variations between propagation in air, make classification of the underground wireless channel difficult. From the transmission of data, the channels modelling is involved to remove the noise and reduce the energy. After that Multi-driven Clustering Algorithm (MCA) method is used to cluster the channel based on the EDT method (Energy, Distance, and Time). Completing the transmission, the data can be transmitted to the destination. The experimental portion of the proposed method is analyzed by varying parameters such as energy, throughput, channel response, and time duration of the data transmission. From the experimental analysis, it is observed that 55% and 45% more energy levels can be saved with the proposed MCA method than with the LEACH methods for DR and SW media methods, respectively. The throughput of the proposed method is proven with 45% and 42% improvement over the time horizon in the median under the frequency for soil of the LEACH and LEACH-CO2 methods, correspondingly.

Study Design: Literature review. Objectives: To review the evidence for surface-based navigation in minimally-invasive spine surgery (MIS), provide an outline for its workflow, and present a wide range of MIS case examples in which surface-based navigation may be advantageous. Methods: A comprehensive review of the literature and compilation of findings related to surface-based navigation in MIS was performed. Workflow and case examples utilizing surface-based navigation were described. Results: The nascent literature regarding surface-based intraoperative navigation (ION) in spine surgery is encouraging and initial studies have shown that surface-based navigation can allow for accurate pedicle screw placement and decreased operative time, fluoroscopy time, and radiation exposure when compared to traditional fluoroscopic imaging. Surface-based navigation may be particularly useful in MIS cervical and lumbar decompressions and MIS lumbar instrumentation cases. Conclusions: Overall, it is possible that surface-based ION will become a mainstay in the armamentarium of enabling technologies utilized by minimally-invasive spine surgeons, but further studies are needed assessing its accuracy, complications, and cost-effectiveness.

With this manuscript the authors sought to write a succinct review of the origins, as well as the latest advancements in endoscopic spine surgery to serve as a reference frame for physicians looking to learn this approach.At its infancy, the indications for posterolateral and transforaminal endoscopic decompression remained narrow, which prevented the procedure from gaining rapid traction during those days. However, more recently the tides have turned and an increasing number of surgeons are starting to adopt this technique given all its advantages. With the advent of higher quality camera systems and instruments, indications to use a minimally invasive option have gotten significantly broader. The most basic indication for the use of this technology is a soft disc herniation causing compromise of a neural structure that has failed to be managed successfully with non-surgical therapies. The use of endoscopic techniques provides significant advantages to patient outcomes and patient recovery. Endoscopic procedures should not be used as a blanket approach to nerve root decompression, as they certainly have limitations. Most contraindications to this procedure are relative and serve mostly as points to consider when selecting the methods to address neural compression. As these techniques become more widely accepted, we expect its reach and indications to continue to broaden and diversify. The full integration of navigation technologies will likely leapfrog this procedure into the mainstream use.

Objective Spinal intradural arachnoid cysts (SIACs) are rare pathological lesions that can arise via outpouchings of the arachnoid layer in the spinal canal that can result in neurological deficits. We performed a systematic literature review regarding the current surgical techniques used in the management of SIACs and discussed the prevailing hypotheses surrounding the etiology of SIACs. Methods A systematic search of the literature was performed in December 2020 using EMBASE and MEDLINE for reports regarding the surgical management of SIACs. Data were collected regarding the demographics of the patients, classification system used, presence or absence of syrinxes, preoperative imaging modality, surgical approach and extent of resection, and postoperative outcomes and follow-up. Results Our search yielded 19 reports for inclusion in the present study. The 19 studies included a total of 414 cases, with an overall male/female ratio of 0.93:1. The most common site for the SIACs was the thoracic spinal cord at 77.5%. The symptoms were very similar across the 19 studies. Of the 19 studies, 15 had used resection to manage the SIACs, 10 had used fenestration or marsupialization, and 4 had used cystoarachnoid or cystoperitoneal shunts. Conclusions SIACs are rare and debilitating spinal pathological lesions, with the etiology of primary SIACs still not fully elucidated. Multiple surgical approaches have been effective, with the optimal operative strategy largely dependent on the individual patient and cyst factors on a case-by-case basis.