CLINICAL OBSERVATION ON AURICULAR ACUPUNCTURE COMBINED WITH LOCAL ANAESTHESIA IN CERVICAL LAMINOPLASTY FOR CERVICAL SPONDYLISIS

Auricular acupuncture combined with local anaesthesia in cervical larninoplasty was studied. The aim of the study was to observe the analgesic action of this anaesthesia and the effects on respiratory and circulatory function. 70 patients were included in the study. There were 55 male and 15 female patients, aged between 39 and 67 years old. The unilateral otopoints including Shenmen, cervical vertebrae, sympathetic, subcortex, external lung and kidney points were used. The sterilized filiform needle of 1 - 1. 5 cm were inserted into each otopoint and connected to 57 - 6 electrcrpulse stimulator being stimulated with continuous wave. Local infiltration anaesthesia was also used with 1 - 2 g/L Lignocaine. The results showed that all the patients were conscious, quiet and co-operative with doctors.The respiration, blood pressure and heart rate were all stable. Analgesie action was rather definite. All the patients recovered quickly after operation. We consider that this anaesthesia is a very simple and effective method for cervical laminoplasty.

Day Case Haemorrhoidectomy under Local Anaesthesia and Conscious Sedation

Background: Excisional haemorrhoidectomy engenders considerable pain and has popularly been managed as an in-patient procedure. There has been anxiety over a major complication occurring in the community instead of in the hospital which is still pervasive in the developing world despite evidence to the contrary. Aim: It compared the post operative complications, time to bowel action, and post-operative pain scores in patients who had open haemorrhoidectomy either under spinal anaesthesia as in-patient or under local anaesthesia as day case procedure. Materials and Methods: The study involved two populations of patients who underwent open haemorrhoidectomy either under spinal anesthesia or under local anaesthesia with conscious sedation at the Korle Bu Teaching Hospital between 2008 and 2013. Results: It involved 275 patients made up of 145 and 130 in the spinal and local aneasthesia groups respectively. Their mean age was 43.1, SD ± 13.2 and median 41 years. Complications occurred in 44 patients (16%), 24 and 20 in the spinal and local aneasthesia groups respectively, with bleeding being the most frequent [11/44, (25%)] and significant. More wound bleeding occurred in the spinal than the local anaesthesia group, 7 vs. 2 patients. Except one day only (p = 0.0001) the mean pain scores on days 2, 3, 5 and 7 were statistically significantly lower in the spinal group than in the local group. The median time to bowel motion was 4 days in both groups. Conclusion: The post operative outcomes in the two populations were similar except the more frequent bleeding noted in the spinal anaesthesia group. Day case haemorrhoidectomy is safe in centres where day case surgery is routinely performed.

Thyroidectomy under Local Anaesthesia: A Viable Option in a Resource Limited Community

Background: In most centers worldwide, thyroidectomy is performed under general anaesthesia as a result of advances in anaesthetic technique, consideration for patients’ safety and surgeons’ convenience. However, in some developing countries such as Nigeria, facilities and expertise for general anaesthesia are not equitably distributed. As such, they are not available in some health centers especially in the rural communities. Hence, the need to explore other suitable alternatives such as operating under local anaesthesia. Aim: This study aims to highlight the feasibility and safety of thyroidectomy under local anaesthesia at a surgical outreach in a rural community in Nigeria. Patients and Methods: The study site was conducted at Bethany Medical Centre, Gboko, Benue State, Nigeria. It was a one-week surgical outreach. Neck infiltration with local anaesthesia was carried out using 2% xylocaine with adrenaline 1:200,000 and a standard open technique was used to carry out all thyroidectomies. Results: Out of seventy (70) patients that presented during the study period, 31 (44.3%) met the inclusion criteria and were operated within the seven (7) days period. There were 3 (10.7%) males and 28 (89.3%) females. There ages ranged between 22 to 65 years, average was 43 years. The average duration of surgery was 90 minutes, and 3 days’ hospital stay. Those followed up two weeks post-operation recuperated well with no notable complications. Conclusion: Thyroidectomy under local anaesthesia is safe and feasible in our rural communities and in selected cases can be a suitable alternative to general anaesthesia.

Adult Cleft Lip Repair under Local Anaesthesia: The Ghana Experience

Background: Unlike developed countries where adult primary cleft lip and palate cases are barely nonexistent, developing countries still have a backlog of adults with unrepaired cleft lip and palate. Method: A retrospective review of adult/adolescent cleft lip repair under local anesthesia was performed between 2012 and 2015. Results: Fifty six (56) adolescent and adults were seen comprising 35 females and 21 males. Forty two patients presented with unrepaired unilateral cleft lip of which only 6 were complete;4 were unrepaired bilateral cleft lip and 10 were revisions. The lowest age was 13 years (two patients) and the highest age was 66 years (one patient). The mean weight was 54 kg. The mean anaesthetic time including waiting time was 12.94 minutes and mean operation time was 56.52 minutes. Majority of the patients were discharged same day except for five who needed to stay overnight because of distance from their home. There were no reported early postoperative complications and wound healing was uneventful for all the patients. Conclusion: Cleft lip repair in adults under local anesthesia is safe, effective and less expensive. A modification in technique with minimal dissection and efficiency is essential in such cases.

Analyzing the Impact of Scene Transitions on Indoor Camera Localization through Scene Change Detection in Real-Time

Real-time indoor camera localization is a significant problem in indoor robot navigation and surveillance systems.The scene can change during the image sequence and plays a vital role in the localization performance of robotic applications in terms of accuracy and speed.This research proposed a real-time indoor camera localization system based on a recurrent neural network that detects scene change during the image sequence.An annotated image dataset trains the proposed system and predicts the camera pose in real-time.The system mainly improved the localization performance of indoor cameras by more accurately predicting the camera pose.It also recognizes the scene changes during the sequence and evaluates the effects of these changes.This system achieved high accuracy and real-time performance.The scene change detection process was performed using visual rhythm and the proposed recurrent deep architecture,which performed camera pose prediction and scene change impact evaluation.Overall,this study proposed a novel real-time localization system for indoor cameras that detects scene changes and shows how they affect localization performance.

Survey of Indoor Localization Based on Deep Learning

This study comprehensively examines the current state of deep learning (DL) usage in indoor positioning.It emphasizes the significance and efficiency of convolutional neural networks (CNNs) and recurrent neuralnetworks (RNNs). Unlike prior studies focused on single sensor modalities like Wi-Fi or Bluetooth, this researchexplores the integration of multiple sensor modalities (e.g.,Wi-Fi, Bluetooth, Ultra-Wideband, ZigBee) to expandindoor localization methods, particularly in obstructed environments. It addresses the challenge of precise objectlocalization, introducing a novel hybrid DL approach using received signal information (RSI), Received SignalStrength (RSS), and Channel State Information (CSI) data to enhance accuracy and stability. Moreover, thestudy introduces a device-free indoor localization algorithm, offering a significant advancement with potentialobject or individual tracking applications. It recognizes the increasing importance of indoor positioning forlocation-based services. It anticipates future developments while acknowledging challenges such as multipathinterference, noise, data standardization, and scarcity of labeled data. This research contributes significantly toindoor localization technology, offering adaptability, device independence, and multifaceted DL-based solutionsfor real-world challenges and future advancements. Thus, the proposed work addresses challenges in objectlocalization precision and introduces a novel hybrid deep learning approach, contributing to advancing locationcentricservices.While deep learning-based indoor localization techniques have improved accuracy, challenges likedata noise, standardization, and availability of training data persist. However, ongoing developments are expectedto enhance indoor positioning systems to meet real-world demands.

Improved PSO-Extreme Learning Machine Algorithm for Indoor Localization

Wi Fi and fingerprinting localization method have been a hot topic in indoor positioning because of their universality and location-related features.The basic assumption of fingerprinting localization is that the received signal strength indication(RSSI)distance is accord with the location distance.Therefore,how to efficiently match the current RSSI of the user with the RSSI in the fingerprint database is the key to achieve high-accuracy localization.In this paper,a particle swarm optimization-extreme learning machine(PSO-ELM)algorithm is proposed on the basis of the original fingerprinting localization.Firstly,we collect the RSSI of the experimental area to construct the fingerprint database,and the ELM algorithm is applied to the online stages to determine the corresponding relation between the location of the terminal and the RSSI it receives.Secondly,PSO algorithm is used to improve the bias and weight of ELM neural network,and the global optimal results are obtained.Finally,extensive simulation results are presented.It is shown that the proposed algorithm can effectively reduce mean error of localization and improve positioning accuracy when compared with K-Nearest Neighbor(KNN),Kmeans and Back-propagation(BP)algorithms.

Surgical Management of Fracture Neck of Femur in a Medically Unfit ASA3/4 Patient Selection Using Direct Infiltration Local Anaesthesia

Patients who suffer a Fractured Neck of Femur (NOF) have a high mortality and morbidity rate with up to 20% needing long term care post fracture and a further 30% not returning to their pre fracture functioning. Hip fracture accounts for 87% of total fragility fractures. We describe an anaesthetic technique of fixation of fracture of the femoral neck under direct infiltration local anaesthesia;that can be performed on the sick elderly patient. Twenty-eight NOF fractures were included in this series (24 DHS, 4 Hemiarthroplasty);twenty-three procedures were completed (82.14%);no patient required conversion to another form of anaesthesia either general or spinal;five patients required some degree of light sedation due to agitation (17.8%). This method presents itself as an option in managing patient with high comorbidities which can also be implemented in impoverished areas with limited access to operating surgical facilities.

Deep brain implantable microelectrode arrays for detection and functional localization of the subthalamic nucleus in rats with Parkinson’s disease

The subthalamic nucleus(STN)is considered the best target for deep brain stimulation treatments of Parkinson’s disease(PD).It is difficult to localize the STN due to its small size and deep location.Multichannel microelectrode arrays(MEAs)can rapidly and precisely locate the STN,which is important for precise stimulation.In this paper,16-channel MEAs modified with multiwalled carbon nanotube/poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate)(MWCNT/PEDOT:PSS)nanocomposites were designed and fabricated,and the accurate and rapid identification of the STN in PD rats was performed using detection sites distributed at different brain depths.These results showed that nuclei in 6-hydroxydopamine hydrobromide(6-OHDA)-lesioned brains discharged more intensely than those in unlesioned brains.In addition,the MEA simultaneously acquired neural signals from both the STN and the upper or lower boundary nuclei of the STN.Moreover,higher values of spike firing rate,spike amplitude,local field potential(LFP)power,and beta oscillations were detected in the STN of the 6-OHDA-lesioned brain,and may therefore be biomarkers of STN localization.Compared with the STNs of unlesioned brains,the power spectral density of spikes and LFPs synchronously decreased in the delta band and increased in the beta band of 6-OHDA-lesioned brains.This may be a cause of sleep and motor disorders associated with PD.Overall,this work describes a new cellular-level localization and detection method and provides a tool for future studies of deep brain nuclei.

Morbidity and Mortality during Anaesthesia in Patients with versus without Diabetes: Single-Centre Cohort Study

Background and objective: Classically, diabetic subjects are at high risk of anaesthesia compared with general population. However, some recent publications have shown contrasting and sometimes contrary results. The aim of our study was to evaluate morbidity and mortality during and after anaesthesia in patients with versus without diabetes operated on at Monkole Hospital over the last ten years. Methods: Retrospective cohort study including all patients who underwent all-comers surgery excluding cardiac surgery between 2011 and 2021. Each diabetic patient was matched to 2 non-diabetic controls on age and sex. The evaluation criterion was the frequency of occurrence of at least one perioperative complication and/or death up to day 30. A multivariate analysis using a Cox model was used to determine the factors associated with the occurrence of this morbidity and mortality. The model was adjusted for comorbidities, preoperative hyperglycaemia, ASA score, type of anaesthesia and severity of surgery. Results: A total of 351 diabetic patients (mean age 53.3 ± 14.18 years) and 701 non-diabetic patients (mean age 53.52 ± 14.7 years) were included and analysed. Preoperatively, hyperglycaemia (blood glucose > 180 mg/dl) was observed in 24.3% of diabetic patients compared with 1.6% of non-diabetic patients. The incidence of overall perioperative complications was 25.6% in diabetic patients compared with 28.6% in non-diabetic patients (p = 0.27). The risk factors associated with this morbidity were general anaesthesia with oro-tracheal intubation vs loco-regional anaesthesia (OR = 3.06 [95%CI: 1.91 - 4.94];p Conclusion: This study shows that there is not significant increase in perioperative morbidity and mortality in diabetic patients compared with non-diabetic ones of similar severity. These results suggest that diabetes itself (excluding associated comorbidities) has only a minor impact on perioperative morbidity and mortality.

Assessment of earthquake location uncertainties for the design of local seismic networks

The ability to estimate earthquake source locations,along with the appraisal of relevant uncertainties,is paramount in monitoring both natural and human-induced micro-seismicity.For this purpose,a monitoring network must be designed to minimize the location errors introduced by geometrically unbalanced networks.In this study,we first review different sources of errors relevant to the localization of seismic events,how they propagate through localization algorithms,and their impact on outcomes.We then propose a quantitative method,based on a Monte Carlo approach,to estimate the uncertainty in earthquake locations that is suited to the design,optimization,and assessment of the performance of a local seismic monitoring network.To illustrate the performance of the proposed approach,we analyzed the distribution of the localization uncertainties and their related dispersion for a highly dense grid of theoretical hypocenters in both the horizontal and vertical directions using an actual monitoring network layout.The results expand,quantitatively,the qualitative indications derived from purely geometrical parameters(azimuthal gap(AG))and classical detectability maps.The proposed method enables the systematic design,optimization,and evaluation of local seismic monitoring networks,enhancing monitoring accuracy in areas proximal to hydrocarbon production,geothermal fields,underground natural gas storage,and other subsurface activities.This approach aids in the accurate estimation of earthquake source locations and their associated uncertainties,which are crucial for assessing and mitigating seismic risks,thereby enabling the implementation of proactive measures to minimize potential hazards.From an operational perspective,reliably estimating location accuracy is crucial for evaluating the position of seismogenic sources and assessing possible links between well activities and the onset of seismicity.

SNR and RSSI Based an Optimized Machine Learning Based Indoor Localization Approach:Multistory Round Building Scenario over LoRa Network

In situations when the precise position of a machine is unknown,localization becomes crucial.This research focuses on improving the position prediction accuracy over long-range(LoRa)network using an optimized machine learning-based technique.In order to increase the prediction accuracy of the reference point position on the data collected using the fingerprinting method over LoRa technology,this study proposed an optimized machine learning(ML)based algorithm.Received signal strength indicator(RSSI)data from the sensors at different positions was first gathered via an experiment through the LoRa network in a multistory round layout building.The noise factor is also taken into account,and the signal-to-noise ratio(SNR)value is recorded for every RSSI measurement.This study concludes the examination of reference point accuracy with the modified KNN method(MKNN).MKNN was created to more precisely anticipate the position of the reference point.The findings showed that MKNN outperformed other algorithms in terms of accuracy and complexity.

Dynamical localization in a non-Hermitian Floquet synthetic system

We investigate the non-Hermitian effects on quantum diffusion in a kicked rotor model where the complex kicking potential is quasi-periodically modulated in the time domain.The synthetic space with arbitrary dimension can be created by incorporating incommensurate frequencies in the quasi-periodical modulation.In the Hermitian case,strong kicking induces the chaotic diffusion in the four-dimension momentum space characterized by linear growth of mean energy.We find that the quantum coherence in deep non-Hermitian regime can effectively suppress the chaotic diffusion and hence result in the emergence of dynamical localization.Moreover,the extent of dynamical localization is dramatically enhanced by increasing the non-Hermitian parameter.Interestingly,the quasi-energies become complex when the non-Hermitian parameter exceeds a certain threshold value.The quantum state will finally evolve to a quasi-eigenstate for which the imaginary part of its quasi-energy is large most.The exponential localization length decreases with the increase of the non-Hermitian parameter,unveiling the underlying mechanism of the enhancement of the dynamical localization by nonHermiticity.

Scenario Modeling-Aided AP Placement Optimization Method for Indoor Localization and Network Access

Owing to the ubiquity of wireless networks and the popularity of WiFi infrastructures,received signal strength(RSS)-based indoor localization systems have received much attention.The placement of access points(APs)significantly influences localization accuracy and network access.However,the indoor scenario and network access are not fully considered in previous AP placement optimization methods.This study proposes a practical scenario modelingaided AP placement optimization method for improving localization accuracy and network access.In order to reduce the gap between simulation-based and field measurement-based AP placement optimization methods,we introduce an indoor scenario modeling and Gaussian process-based RSS prediction method.After that,the localization and network access metrics are implemented in the multiple objective particle swarm optimization(MOPSO)solution,Pareto front criterion and virtual repulsion force are applied to determine the optimal AP placement.Finally,field experiments demonstrate the effectiveness of the proposed indoor scenario modeling method and RSS prediction model.A thorough comparison confirms the localization and network access improvement attributed to the proposed anchor placement method.

A UWB/IMU-Assisted Fingerprinting Localization Framework with Low Human Efforts

With the rapid development of smart phone,the location-based services(LBS)have received great attention in the past decades.Owing to the widespread use of WiFi and Bluetooth devices,Received Signal Strength Indication(RSSI)fingerprintbased localization method has obtained much development in both academia and industries.In this work,we introduce an efficient way to reduce the labor-intensive site survey process,which uses an UWB/IMU-assisted fingerprint construction(UAFC)and localization framework based on the principle of Automatic radio map generation scheme(ARMGS)is proposed to replace the traditional manual measurement.To be specific,UWB devices are employed to estimate the coordinates when the collector is moved in a reference point(RP).An anchor self-localization method is investigated to further reduce manual measurement work in a wide and complex environment,which is also a grueling,time-consuming process that is lead to artificial errors.Moreover,the measurements of IMU are incorporated into the UWB localization algorithm and improve the label accuracy in fingerprint.In addition,the weighted k-nearest neighbor(WKNN)algorithm is applied to online localization phase.Finally,filed experiments are carried out and the results confirm the effectiveness of the proposed approach.

Source localization in signed networks with effective distance

While progress has been made in information source localization,it has overlooked the prevalent friend and adversarial relationships in social networks.This paper addresses this gap by focusing on source localization in signed network models.Leveraging the topological characteristics of signed networks and transforming the propagation probability into effective distance,we propose an optimization method for observer selection.Additionally,by using the reverse propagation algorithm we present a method for information source localization in signed networks.Extensive experimental results demonstrate that a higher proportion of positive edges within signed networks contributes to more favorable source localization,and the higher the ratio of propagation rates between positive and negative edges,the more accurate the source localization becomes.Interestingly,this aligns with our observation that,in reality,the number of friends tends to be greater than the number of adversaries,and the likelihood of information propagation among friends is often higher than among adversaries.In addition,the source located at the periphery of the network is not easy to identify.Furthermore,our proposed observer selection method based on effective distance achieves higher operational efficiency and exhibits higher accuracy in information source localization,compared with three strategies for observer selection based on the classical full-order neighbor coverage.

Two-person device-free localization system based on ZigBee and transformer

Most studies on device-free localization currently focus on single-person scenarios.This paper proposes a novel method for device-free localization that utilizes ZigBee received signal strength indication(RSSI)and a Transformer network structure.The method aims to address the limited research and low accuracy of two-person device-free localization.This paper first describes the construction of the sensor network used for collecting ZigBee RSSI.It then examines the format and features of ZigBee data packages.The algorithm design of this paper is then introduced.The box plot method is used to identify abnormal data points,and a neural network is used to establish the mapping model between ZigBee RSSI matrix and localization coordinates.This neural network includes a Transformer encoder layer as the encoder and a fully connected network as the decoder.The proposed method's classification accuracy was experimentally tested in an online test stage,resulting in an accuracy rate of 98.79%.In conclusion,the proposed two-person localization system is novel and has demonstrated high accuracy.

Iterative Subregion Correction Preconditioners with Adaptive Tolerance for Problems with Geometrically Localized Stiffness

We present a class of preconditioners for the linear systems resulting from a finite element or discontinuous Galerkin discretizations of advection-dominated problems.These preconditioners are designed to treat the case of geometrically localized stiffness,where the convergence rates of iterative methods are degraded in a localized subregion of the mesh.Slower convergence may be caused by a number of factors,including the mesh size,anisotropy,highly variable coefficients,and more challenging physics.The approach taken in this work is to correct well-known preconditioners such as the block Jacobi and the block incomplete LU(ILU)with an adaptive inner subregion iteration.The goal of these preconditioners is to reduce the number of costly global iterations by accelerating the convergence in the stiff region by iterating on the less expensive reduced problem.The tolerance for the inner iteration is adaptively chosen to minimize subregion-local work while guaranteeing global convergence rates.We present analysis showing that the convergence of these preconditioners,even when combined with an adaptively selected tolerance,is independent of discretization parameters(e.g.,the mesh size and diffusion coefficient)in the subregion.We demonstrate significant performance improvements over black-box preconditioners when applied to several model convection-diffusion problems.Finally,we present performance results of several variations of iterative subregion correction preconditioners applied to the Reynolds number 2.25×10^(6)fluid flow over the NACA 0012 airfoil,as well as massively separated flow at 30°angle of attack.

Multiple Targets Localization Algorithm Based on Covariance Matrix Sparse Representation and Bayesian Learning

The multi-source passive localization problem is a problem of great interest in signal pro-cessing with many applications.In this paper,a sparse representation model based on covariance matrix is constructed for the long-range localization scenario,and a sparse Bayesian learning algo-rithm based on Laplace prior of signal covariance is developed for the base mismatch problem caused by target deviation from the initial point grid.An adaptive grid sparse Bayesian learning targets localization(AGSBL)algorithm is proposed.The AGSBL algorithm implements a covari-ance-based sparse signal reconstruction and grid adaptive localization dictionary learning.Simula-tion results show that the AGSBL algorithm outperforms the traditional compressed-aware localiza-tion algorithm for different signal-to-noise ratios and different number of targets in long-range scenes.

LFM Radar Source Passive Localization Algorithm Based on Range Migration

Traditional single-satellite passive localization algorithms are influenced by frequency and angle measurement accuracies,resulting in error estimation of emitter position on the order of kilometers.Subsequently,a single-satellite localization algorithm based on passive synthetic aper-ture(PSA)was introduced,enabling high-precision positioning.However,its estimation of azimuth and range distance is considerably affected by the residual frequency offset(RFO)of uncoopera-tive system transceivers.Furthermore,it requires data containing a satellite flying over the radia-tion source for RFO search.After estimating the RFO,an accurate estimation of azimuth and range distance can be carried out,which is difficult to achieve in practical situations.An LFM radar source passive localization algorithm based on range migration is proposed to address the dif-ficulty in estimating frequency offset.The algorithm first provides a rough estimate of the pulse repetition time(PRT).It processes intercepted signals through range compression,range interpola-tion,and polynomial fitting to obtain range migration observations.Subsequently,it uses the changing information of range migration and an accurate PRT to formulate a system of nonlinear equations,obtaining the emitter position and a more accurate PRT through a two-step localization algorithm.Frequency offset only induces a fixed offset in range migration,which does not affect the changing information.This algorithm can also achieve high-precision localization in squint scenar-ios.Finally,the effectiveness of this algorithm is verified through simulations.