Localization Techniques Research Articles

Location-Based Hybrid Video Streaming Protocol for VANETs

The services and applications provided by video streaming over Vehicular Ad-hoc Networks (VANETs) have recently become very attractive to technology users. In VANETs, the moving vehicles on the roads take great advantage of this radio link by exchanging information with each other related to the safety, traffic jams, accidents, and condition of the roads via video streaming. On the other hand, this transmission domain poses many challenging issues like frequent disconnections due to fast-speed vehicles, longer transmission delays, and inefficient link consumption etc. numbers of routing strategies have been proposed in the literature that aimed to address these challenges of an Intelligent Transportation System (ITS). This article is based on a Location-Based Hybrid Video Streaming Protocol (LBHVSP) that has been proposed with the intent to minimize the challenges related to video transmission as well as the attainment of high network performance. The idea presented is a motivation from the dynamic nature of transceiver and receiver transmission power, accompanied by the range-based localization techniques for inter-space computation of vehicular nodes. LBHVSP is a 2-way protocol that works as a combination of infrastructure-based and infrastructure-less wireless networks that tends to minimize the average packet delay for efficient utilization of bandwidth. It is a two-phase solution, firstly frames are transmitted to the second hop node available in the direct range that minimizes the traveling path in terms of hop count and ultimately yields minimal delay. Secondly, this technique utilizes the Road Side Units (RSUs) having wired connectivity for a faraway destination. Experimental results using different transmission scenarios reveal its efficacy by attaining maximum average packet throughput, minimum average packet drop rate, and minimum average per-packet delay in Signal Transmission Power and Packet Reception Power Threshold values. The proposed scheme has outperformed the state-of-the-art transmission scenarios in terms of static parameters. Varying transmission and reception powers subject to the inter-distance have shown remarkable outcomes. In comparison to pre-existing VANET protocols like DyTE and DSR, proposed scheme has shown an average improvement of 30% in packet drop rate, and 80% rise in throughput, approximately. Besides, our scheme has shown a remarkable decline in transmission delay as well. On comparing the LBHVSP performance against recent VANET communication protocol, it comes out that it attains the maximum throughput and delay improvement. Furthermore, to cater the packet drop, a smart re-transmission strategy should be designed particularly for multimedia streaming and video message exchange.

Machine Learning Based Localization of LoRa Mobile Wireless Nodes Using a Novel Sectorization Method

Indoor localization of wireless nodes is a relevant task for wireless sensor networks with mobile nodes using mobile robots. Despite the fact that outdoor localization is successfully performed by Global Positioning System (GPS) technology, indoor environments face several challenges due to multipath signal propagation, reflections from walls and objects, along with noise and interference. This results in the need for the development of new localization techniques. In this paper, Long-Range Wide-Area Network (LoRaWAN) technology is employed to address localization problems. A novel approach is proposed, based on the preliminary division of the room into sectors using a Received Signal Strength Indicator (RSSI) fingerprinting technique combined with machine learning (ML). Among various ML methods, the Gated Recurrent Unit (GRU) model reached the most accurate results, achieving localization accuracies of 94.54%, 91.02%, and 85.12% across three scenarios with a division into 256 sectors. Analysis of the cumulative error distribution function revealed the average localization error of 0.384 m, while the mean absolute error reached 0.246 m. These results demonstrate that the proposed sectorization method effectively mitigates the effects of noise and nonlinear signal propagation, ensuring precise localization of mobile nodes indoors.

Digital instrument simulator to optimize the development of hyperspectral systems: application for intraoperative functional brain mapping.

Intraoperative optical imaging is a localization technique for the functional areas of the human brain cortex during neurosurgical procedures. These areas can be assessed by monitoring cerebral hemodynamics and metabolism. Robust quantification of these biomarkers is complicated to perform during neurosurgery due to the critical context of the operating room. In actual devices, the inhomogeneities of the optical properties of the exposed brain cortex are poorly taken into consideration, which introduce quantification errors of biomarkers of brain functionality. Moreover, the best choice of spectral configuration is still based on an empirical approach. We propose a digital instrument simulator to optimize the development of hyperspectral systems for intraoperative brain mapping studies. This simulator can provide realistic modeling of the cerebral cortex and the identification of the optimal wavelengths to monitor cerebral hemodynamics (oxygenated and deoxygenated hemoglobin Hb) and metabolism (oxidized state of cytochromes and and cytochrome-c-oxidase oxCytb, oxCytc, and oxCCO). The digital instrument simulator is computed with white Monte Carlo simulations of a volume created from a real image of exposed cortex. We developed an optimization procedure based on a genetic algorithm to identify the best wavelength combinations in the visible and near-infrared range to quantify concentration changes in , Hb, oxCCO, and the oxidized state of cytochrome and (oxCytb and oxCytc). The digital instrument allows the modeling of intensity maps collected by a camera sensor as well as images of path length to take into account the inhomogeneities of the optical properties. The optimization procedure helps to identify the best wavelength combination of 18 wavelengths that reduces the quantification errors in , Hb, and oxCCO by 47%, 57%, and 57%, respectively, compared with the gold standard of 121 wavelengths between 780 and 900nm. The optimization procedure does not help to resolve changes in cytochrome and in a significant way but helps to better resolve oxCCO changes. We proposed a digital instrument simulator to optimize the development of hyperspectral systems for intraoperative brain mapping studies. This digital instrument simulator and this optimization framework could be used to optimize the design of hyperspectral imaging devices.

Accurate Indoor Localization with IoT Devices and Advanced Fingerprinting Methods

The Internet of things (IoT) has significantly impacted various sectors, including healthcare, environmental monitoring, transportation, and commerce, by enhancing communication networks through the integration of sensors, software, and hardware. This paper presents an accurate IoT indoor localization system based on IoT devices and fingerprinting methods. We explore indoor localization techniques using Bluetooth Low Energy (BLE) and a Radio Signal Strength Indicator (RSSI) to address the limitations of GPS in indoor environments. The study evaluates the effectiveness of iBeacon transmitters for indoor positioning, comparing the Weighted Centroid Localization (WCL) and Positive Weighted Centroid Localization (PWCL) algorithms, along with fingerprinting methods enhanced by outlier detection and mapping filters. Our methodology includes mapping a real environment onto a coordinate axis, collecting training data from 47 sampling points, and implementing four localization algorithms. The results show that the PWCL algorithm improves accuracy over the WCL algorithm, and hybrid methods further reduce localization errors. The HYBRID-MAPPED method achieves the highest accuracy, with an average error of 1.44 m.

A local radial basis function-compact finite difference method for Sobolev equation arising from fluid dynamics

In this article, a new high-order, local meshless technique is presented for numerically solving multi-dimensional Sobolev equation arising from fluid dynamics. In the proposed method, Hermite radial basis function (RBF) interpolation technique is applied to approximate the operators of the model over local stencils. This leads to compact RBF generated finite difference (RBF-FD) formula, which provides a significant improvement in the accuracy and computational efficiency. In the first stage of the proposed method, the time discretization is performed by Crank–Nicolson finite difference scheme along with temporal Richardson extrapolation technique. In the second stage, the space dimension is discretized by applying the local radial basis function-compact finite difference (RBF-CFD) method. By performing some numerical simulations and comparing the results with existing methods, the high accuracy and computational efficiency of the proposed method are clearly demonstrated. The numerical results show that the presented method has fourth-order accuracy in both space and time dimensions. Finally, it can be concluded that the proposed method is a suitable alternative to the existing numerical techniques for the Sobolev model.

A systematic assertive wide-band routing using location and potential aware technique

Delays occur when packets must be routed over several paths in a wireless sensor network with multiple origins and destinations. There are several causes, delays may occur everywhere, even in a multi-hop wireless network. Due to the broadcast nature of wireless networks, opportunistic routing was able to circumvent these delays. To avoid unnecessary delays, wide-band routing may be used to calculate the smaller path between two nodes. In this case, we address the shortcomings of the standard approach by taking into account the node's power. Path routing as well as the broadcast nature of wireless signals help mitigate the effects of shoddy wireless connections. The results show that the suggested approach outperformed the baseline in both end-to-end latency and packet delivery ratio.

Explainable AI for News Classification

Abstract: The proliferation of news content across digital platforms necessitates robust and interpretable machine learning models to classify news into predefined categories effectively. This study investigates the integration of Explainable AI (XAI) techniques within the context of traditional machine learning models, including Naive Bayes, Logistic Regression, and Support Vector Machines (SVM), to achieve interpretable and accurate news classification. Utilizing the News Category Dataset, we preprocess the data to focus on the top 15 categories while addressing class imbalance challenges. Models are trained using Term Frequency-Inverse Document Frequency (TF-IDF) vectorization, achieving an acceptable classification accuracy of 67% across all models despite the complexity introduced by the high number of classes. To elucidate the decision-making processes of these models, we employ feature importance visualizations derived from model coefficients and feature log probabilities, complemented by local interpretability techniques such as LIME (Local Interpretable Model-agnostic Explanations). These methodologies enable granular insights into word-level contributions to predictions for each news category. Comparative heatmaps across models reveal significant consistencies and divergences in feature reliance, highlighting nuanced decision-making patterns. The integration of explainability into news classification provides critical interpretive capabilities, offering transparency and mitigating the risks associated with algorithmic opacity. The findings demonstrate how XAI enhances stakeholder trust by aligning model predictions with human interpretability, particularly in ethically sensitive domains. This work emphasizes the role of XAI in fostering responsible AI deployment and paves the way for future advancements, including deep learning integration and multilingual news classification with inherent interpretability frameworks

Retraction Note: An efficient watermarking technique for tamper detectionand localization of medical images

Retraction Note: An efficient watermarking technique for tamper detectionand localization of medical images

Évaluation de l’anesthésie locale à la Xylocaïne tamponnée pour les abords pleuraux : étude DOULAPLUX

Introduction: Pleural procedures are painful interventions. While there exist recommendations aimed at preventing the pain induced by local anesthesia, they have never been evaluated with regard to the thoracic wall. The objective of this study is to evaluate the effectiveness of a specific local anesthesia technique in pleural procedures.Methods: Descriptive, monocentric, prospective study, including all initial pleural procedures performed in our unit. Pain was assessed 20 minutes after the procedure using the VAS (Visual Analog Scale) or the NRS (Numerical Rating Scale).Results: Two hundred and fifty-one patients were included. The procedures included thoracocentesis (58%), pleural or pulmonary biopsy (28%), or drainage (14%). Average pain intensity was 0.6 on a scale of 10. Over two thirds (68%) of patients had a VAS or NRS score of 0, and 91% ≤ 2. While infectious pleuritis, pneumothorax, and drainage were the most painful interventions, the pain levels remained acceptable (average VAS/NRS scores of 1.3, 1.8, 1.7 respectively).Conclusion: Buffered lidocaine local anesthesia provides excellent pain control for pleural procedures, regardless of their nature. Recommendations regarding local anesthesia apply to the thoracic wall, and their dissemination is essential to reduction of the pain induced by pleural procedures.

LRR1 involved in the abscisic acid signaling pathway to regulate the early growth and development of Arabidopsis thaliana.

Living organisms possess the remarkable capacity to swiftly adapt to fluctuations in their environment. In the context of cell signal transduction, a significant challenge lies in ensuring the effective perception of external signals and the execution of appropriate responses. To investigate this phenomenon, a recent study utilized Arabidopsis thaliana as a model plant and induced stress by administering abscisic acid (ABA), a plant hormone, to elucidate the involvement of leucine-rich repeat receptor-like kinase1 (LRR1) in ABA signaling pathways. Homozygous T-DNA insertion alleles for LRR1 and KIN7 were isolated. Quantitative real-time PCR (qRT-PCR) was performed to confirm the expression of the LRR1 gene. Subcellular localization and beta-glucuronidase (GUS) tissue labeling techniques were utilized to determine the expression pattern of the LRR1 gene in cells and tissues. Yeast two-hybrid complementation, bimolecular fluorescence complementation assay, and GST pull-down assays were conducted to validate the interaction of LRR1 proteins. Phenotypic analyses revealed that lrr1 and kin7 mutants are less sensitive to the inhibitory effects of ABA on germination and cotyledon greening that is seen in WT. Mutants LRR1 and kinase 7 (KIN7) exhibited resistance to ABA and displayed normal growth patterns under control conditions. The double mutant lrr1kin7 showed reduced responsiveness to ABA. Conversely, overexpression lines LRR1ox2 and LRR1ox10 demonstrated heightened sensitivity to ABA, resulting in severe growth reduction. qRT-PCR assay indicated that exogenous application of ABA led to significant down-regulation of ABI3, ABI4, and ABI5 transcription factors in LRR1 material compared to wild-type WT material. An investigation was conducted to determine the expression pattern and transcriptional level of LRR1 in Arabidopsis. The results revealed ubiquitous expression of LRR1 across all developmental stages and tissue tested. Subcellular localization assays confirmed the presence of LRR1 on the plasma membrane of cells. Furthermore, BiFC assay, yeast two-hybrid complementation, and GST pull-down assays demonstrated an interaction between LRR1 and PYL6 in vitro. These findings provide substantial insights into the involvement of LRR1 in the ABA signaling pathway while regulating seed germination and cotyledon greening during early development in Arabidopsis. This study significantly advances our understanding regarding the correlation between LRR1 and ABA signaling pathways with potential applications for enhancing crop stress resistance.

Broadband VHF lightning radiation sources localization by ESPRIT algorithm

Fast and fine radiation source localization algorithm is of vital importance to lightning warning and protection. Current high-accuracy localization techniques, such as the time reversal technique (TR) and the multiple signal classification (MUSIC), are based on traversal search mechanism, which takes a long time. In this paper, the estimation of signal parameters via rotational invariance technique (ESPRIT) is applied to lightning radiation source localization, and the direction of arrival (DOA) can be directly solved through the covariance matrix, so it is very efficient. For the broadband VHF signal, the incoherent signal method (ISM) is combined with ESPRIT. Two classical structures of uniform L-shaped array and uniform circular array are studied and applied to ESPRIT algorithm. As for the uniform circular array, the fourth order cumulant matrix is constructed to find the translation invariant subarrays. To unwrap the phase ambiguity caused by the calculated phase angle of ESPRIT algorithm, the total least difference of slope (TLDS) principle is proposed. The proposed ESPRIT algorithm is more than 1400 times faster than MUSIC while its localization accuracy is still pretty high, which is able to detect the radiation source under -8 dB signal to noise ratio (SNR) according to the result of numerical simulations, and can map continuous and fine lightning development channel in the experiments on the measured lightning data.

Weakly supervised chest X-ray abnormality localization with non-linear modulation and foreground control

Chest X-ray is widely used to diagnose lung diseases. Due to the demand for accelerating analysis and interpretation to reduce the workload of radiologists, there has been a growing interest in building automated systems of chest X-ray abnormality localization. However, fully supervised methods usually require well-trained radiologists to annotate bounding boxes manually, which is labor-intensive and time-consuming. As a result, weakly supervised chest X-ray abnormality localization is gaining increasing attention because it only requires image-level annotations. Existing weakly supervised object localization (WSOL) techniques, which typically utilize class activation maps, often result in incomplete coverage and fragmentation of the objects and rely on class-specific classification accuracy. In this study, we propose a novel WSOL framework for chest X-ray abnormality localization that uses VMamba as the backbone and integrates three practical components to improve localization accuracy. First, we propose a non-linear modulation module to refine Foreground Prediction Maps (FPM) by expanding the foreground activation region and enhancing its continuity. Second, we design an FPM fusion module to strengthen the foreground and suppress the background, thereby improving their separability in chest X-ray images. Third, we craft a novel foreground control loss that regulates the feature maps to refine the background and foreground activation for better foreground identification. The proposed method is evaluated on two commonly used chest X-ray datasets, the NIH chest X-ray dataset and the RSNA dataset, and demonstrates superior performance over six state-of-the-art WSOL methods. In addition, the robustness and applicability of the proposed method are evaluated using three additional datasets with varying modalities and image quality.

Assessing the impact of overhead agrivoltaic systems on GNSS signal performance for precision agriculture

Agrivoltaic systems, which integrate solar energy production with agricultural practices, present a sustainable solution to optimize land use. However, specific configurations, such as overhead solar panels, can obstruct Global Navigation Satellite System (GNSS) signals, which are vital for precision agriculture technologies. This study investigates the impact of an agrivoltaic system on GNSS signal performance compared to a conventional orchard in Kressbronn am Bodensee, Germany, using data from multiple GNSS constellations (GPS, GLONASS, Galileo, BeiDou). Key metrics such as carrier-to-noise density ratio (C/N₀), positional accuracy, time to first fix (TTFF), and dilution of precision (DOP) were analyzed. The results showed significant signal degradation under the overhead agrivoltaic system, particularly in C/N₀ and positional accuracy. While there were no significant differences in DOP metrics or TTFF, the average C/N₀ decreased from 30.62 dB-Hz in the conventional orchard to 26.92 dB-Hz in the agrivoltaic system. Moreover, the average of satellites with C/N₀ values below 24 dB-Hz was notably higher in the agrivoltaic area, indicating critical signal degradation. In addition, relying on a single constellation, like GPS, may not provide enough satellites for reliable GNSS performance.Despite the lower signal quality, the average number of satellites with C/N₀ above 24 dB-Hz remained over 22 in both areas. Combined with slight or no significant differences in other metrics compared to the conventional orchard, this suggests GNSS-based systems can support precision agriculture tasks within the agrivoltaic environment. The study highlights the importance of addressing signal interference to avoid hindering the integration of drones, robots, and other advanced technologies in agrivoltaics management, and suggests alternative solutions, such as Real-Time Kinematic (RTK) correction or Simultaneous Localization and Mapping (SLAM) techniques, to enhance performance. These findings are critical as agrivoltaic adoption grows, requiring a balance between solar energy production and the operational needs of precision agriculture.

The Royal College of Ophthalmologists' National Ophthalmology Database study of cataract surgery: report 19, a comparative study of the cost and carbon footprint of local anaesthesia techniques for cataract surgery.

Understanding the financial and environmental impact of clinical pathways is important for designing sustainable services. This study aimed to compare the cost and carbon footprint of sub-Tenon's and topical anaesthesia for cataract surgery, benchmark minimum topical anaesthesia utilisation rates, and quantify the benefits of increased topical anaesthesia usage in the United Kingdom National Health Service (NHS). The cost and carbon footprint of products and staffing for topical and sub-Tenon's anaesthesia for cataract surgery were calculated and applied to National Ophthalmology Database audit data. A mainly process-based approach was used to estimate the carbon dioxide equivalent (CO2e) of product production, usage, and waste disposal. The typical CO2e per case was 0.71 kg for topical anaesthesia and 1.19 kg for sub-Tenon's anaesthesia. Around a third of CO2e was generated by usage of unneccesary equiptment and wasteful practices. The typical cost per case was £14.60-£17.14 for topical anaesthesia, £27.74 for sub-Tenon's anaesthesia performed by an operating department practitioner and £56.15 for sub-Tenon's anaesthesia performed by a consultant anaesthetist. It is estimated that around 25,000 NHS cataract cases could annually be converted from sub-Tenon's to topical anaesthesia, which would reduce the CO2e emissions of services by 12,000 kg while saving £265,000 on product usage and between £63,500 and £773,750 on staffing. Topical anaesthesia is a cheaper and more environmentally sustainable alternative to sub-Tenon's anaesthesia for cataract surgery. Increased topical anaesthesia usage in cataract services could contribute towards the NHS aspiration of becoming "net zero" by 2040.

Local ablation disrupts immune evasion in pancreatic cancer

BackgroundPancreatic cancer (PC) is characterised by late diagnosis, tumour heterogeneity, and a peculiar immunosuppressive microenvironment, leading to poor clinical outcomes. Local ablative techniques have been proposed to treat unresectable PC patients, although their impact on activating the host immune system and overcoming resistance to immunotherapy remains elusive. MethodsWe dissected the immune-modulatory abilities triggered by local ablation in mouse and human PC models and human specimens, integrating phenotypic and molecular technologies with functional assays. ResultsLocal ablation treatment performed in mice bearing orthotopic syngeneic PC tumours triggered tumour necrosis and a short-term inflammatory process characterised by the prompt increase of HMGB1 plasma levels, coupled with an enhanced amount of circulating and tumour infiltrating myeloid cells and increased MHCII expression in splenic myeloid antigen-presenting cells. Local ablation synergised with immunotherapy to restrict tumour progression and improved the survival of PC-bearing mice by evoking a T lymphocyte-dependent anti-tumour immune response. By integrating spatial transcriptomics with histological techniques, we pinpointed how combination therapy could reshape TME towards an anti-tumour milieu characterised by the preferential entrance and colocalization of activated T lymphocytes and myeloid cells endowed with antigen presentation features instead of T regulatory lymphocytes and CD206-expressing tumour-associated macrophages. In addition, treatment-dependent TME repolarization extended to neoplastic cells, promoting a shift from squamous to a more differentiated classical phenotype. Finally, we validated the immune regulatory properties induced by local ablation in PC patients and identified an association of the short-term treatment-dependent increase of neutrophils, NLR and HMGB1 with a longer time to progression. ConclusionTherefore, local ablation might overcome the current limitations of immunotherapy in PC.

Perception of the value of simulation models by dental anesthesia teachers and implementation of simulation models for teaching.

A dental anesthesia simulation model (DASM) is a tool for the teaching and learning of local dental anesthesia techniques. We investigated how dental anesthesia teachers (DATs) perceive the value of those tools. The objective of this study was to investigate the following: the characteristics of the dental anesthesia curriculum used by DATs in Mexico; the DATs' opinion of ethical issues in the teaching of local dental anesthesia; the DATs' perception of the value of using a DASM to teach local dental anesthesia; and the characteristics of the implementation of a DASM by DATs. Dental anesthesia teachers in Mexico answered an online questionnaire with 34 items. The questionnaire had five sections that explored the objective of the study. The results were analyzed with a chi-square test (p<0.05) or with the Tau-c test (p<0.05). One hundred twenty dental anesthesia teachers participated in the study. A total of 37.5% perceived a very high value of using a DASM to teach local dental anesthesia. No significant statistical differences were found when the level of perception was analyzed using the variable of whether the participants used a simulation model for teaching. Half of the participants had access to a simulation model, but only 29.2% used a simulation model for teaching. Most of the participants did not know how to use a DASM for teaching. Dental anesthesia teachers had a positive perception of simulation models. However, they did not use them, nor were they prepared to use them.

Integrated Local Search Technique With Reptile Search Algorithm for Solving Large‐Scale Bound Constrained Global Optimization Problems

Integrated Local Search Technique With Reptile Search Algorithm for Solving Large‐Scale Bound Constrained Global Optimization Problems

UAV-Mounted RIS-Aided Multi-Target Localization System: An Efficient Sparse-Reconstruction-Based Approach

Unmanned Aerial Vehicle (UAV) technology is increasingly gaining attention in localization systems due to its flexibility and mobility. However, traditional localization techniques often fail in complex environments where line-of-sight paths are obstructed. To address this challenge, this paper presents an innovative UAV-assisted high-precision multi-target localization system. The system utilizes UAVs equipped with Reconfigurable Intelligent Surfaces to create a reflective signal path, allowing a receiver sensor to capture these signals, creating favorable conditions for multi-target localization. Exploiting the sparsity of signals, we introduce a direct positioning algorithm that leverages Atomic Norm Minimization (ANM) to estimate the target’s location. To address the high complexity of traditional ANM methods, we propose a novel Coyote-ANM-based direct localization (CADL) approach. This method combines the coyote optimization algorithm with the alternating direction method of multipliers to achieve high-accuracy positioning with reduced computational complexity. Simulation results across various signal-to-noise ratio scenarios demonstrate that the proposed algorithm significantly improves localization accuracy, achieving lower root mean square error values and faster execution times compared to traditional methods.

Hybrid TDR-MI Based Wireless Sensor Network for Underground Water Pipeline Leakage Detection and Localization Using Pressure Residuals and Classifiers

AbstractThe pipeline leakage detection and leak localization trouble is a highly demanding and dangerous issue. Underground pipelines are critical for transporting enormous fluid volumes (e.g., water) across extended distances. Not only would solving this issue save the nation a great deal of money and resources, but it will also save the environment. However, because of the harsh climatic conditions below earth, current leak detection systems are not enough for monitoring subterranean pipelines. To address these issues, this study suggests a hybrid wireless sensor network for monitoring subterranean pipelines that is based on magnetic induction and time domain reflectometry (TDR). TDR is installed in this instance below a wireless sensor network that is based on MI. TDR significantly reduces the time needed for inspection while accurately locating the leak. Based on MI technology, we provide a wireless sensor network for inexpensive, real-time leak detection in subterranean pipelines. Through the integration of data from several sensor types located within and around subterranean pipes, MISE-PIPE detects leaks. Ad-hoc WSNs are employed in pressure measurement. (WDNs) is a popular subject that has drawn attention from scholars lately. Since leak localisation has a significant influence on the human population and the economy, time and accuracy are essential components. A broad leak localisation technique is proposed using statistical classifiers operating in the residual space. Classifiers are trained using leak data from every node in the network, accounting for demand uncertainty, noise from sensor preservatives, and leak size. After localising and identifying leaks, all monitoring data is sent to the CH using the K-means clustering technique, which performs two vital tasks: optimum clustering, extending the Network Lifetime, and maintaining Quality of Service. The K-Means technique is used to optimise the clustering process. The K-means clustering technique is used to transfer all monitoring data to the CH for the purpose of pipeline leak identification and localisation. Unlike the current underground pipeline monitoring system, our proposed Hybrid TDR-MI-based wireless sensor network allows precise real-time leak identification and localisation.

Comparison of 2 Types of Local Anesthetic Techniques in the Reduction of Distal Radius Fracture: A Prospective Cohort Study.

The treatment of distal radius fractures may require manipulation of the fracture assisted by finger traction, causing pain both at the fracture site and at the fingers. The usual type of anesthesia used does not anesthetize the fingers. We conducted a prospective cohort study with two groups, hematoma block (HB) and hematoma with associated median nerve block (MHB). Characteristic variables of the patients were collected. The main variable for the analysis was pain, measured using the Visual Analogical Scale (VAS). It was measured prior to the injection (VAS1), during fracture reduction (VAS2), and 30 minutes after the injection (VAS3) in both groups. The study included a total of 140 fractures (70 anesthetized with HB), 78% were women. There were no significant differences in the variables age, sex, Elixhauser index. and need for surgery between the groups. In the HB group, the VAS means were VAS1 5.23 cm (SD 2.31), VAS2 5.80 cm (SD 2.52), and VAS3 1.89 cm (SD 1.94); while in the MHB group, VAS1 5.13 cm (SD 2.36), VAS2 3.15 cm (SD 1.70), and VAS3 1.09 cm (SD 1.38). Area of greatest pain during fracture reduction in the HB group was finger traction in 78% cases (p < .05), while in the MHB group it was the manipulation of the fracture site in 71% cases (p < .05). The study demonstrates that the use of hematoma with associated median nerve block decreases pain perception in patients with distal radius fracture that needs closed reduction, when compared to HB alone.