Operational Network Research Articles

Operational Phased Array Radar Network for Natural Hazard Monitoring and Warnings in Urban Environments over the Greater Bay Area, China

Abstract The continuous development of modern Doppler weather radars has substantially augmented our capacity to monitor severe convective storms and gain insights into their dynamic and microphysical structures. Although conventional parabolic antenna-based S-band operational radars are valuable, they exhibit limitations such as low scanning speed and reduced information acquisition in the low atmosphere region, particularly at far ranges from the radar, leading to suboptimal observations of fast-evolving storms. To address these limitations, a dense X-band polarimetric phased array radar (PAR) network, consisting of more than 50 radars, has been strategically constructed and deployed in the Greater Bay Area in South China, which is currently the largest PAR network worldwide. The PARs exhibit satisfactory performance in hydrometeor classification, hail identification, and quantitative precipitation estimation, demonstrating reliable polarimetric data quality. The paper also presents compelling evidence demonstrating the effectiveness of the PAR network in detecting the tornado vortex and capturing fine horizontal and vertical structures of convective storms when compared to nearby S-band operational radars. In addition, the assimilation of supplemental PAR data using the ensemble Kalman filter has yielded discernible vortex circulation fields for tornadic storms, enabling effective prediction of tornadogenesis, which is unattainable solely by assimilating S-band operational radar data. As the PAR network is put into operational use, significant advancements are anticipated in understanding and monitoring severe weather systems in South China.

Explainable Optimisation through Online and Offline Hyper-heuristics

Abstract - Research in the explainability of optimisation techniques has largely focused on metaheuristics and their movement of solutions around the search landscape. Hyper-heuristics create a different challenge for explainability as they make use of many more operators, or low-level heuristics and learning algorithms which modify their probability of selection online. This paper describes a set of methods for explaining hyper-heuristics decisions in both online and offline scenarios using selection hyper-heuristics as an example. These methods help to explain various aspects of the function of hyper-heuristics both at a particular juncture in the optimisation process and through time. Visualisations of each method acting on sequences provide an understanding of which operators are being utilised and when, and in which combinations to produce a greater understanding of the algorithm-problem nexus in hyper-heuristic search. These methods are demonstrated on a range of problems including those in operational research and water distribution network optimisation. They demonstrate the insight that can be generated from optimisation using selection hyper-heuristics, including building an understanding of heuristic usage, useful combinations of heuristics and heuristic parameterisations. Furthermore the dynamics of heuristic utility are explored throughout an optimisation run and we show that it is possible to cluster problem instances according to heuristic selection alone, providing insight into the perception of problems from a hyper-heuristic perspective.

Self-Organized Operational Neural Networks for Early Glaucoma Diagnosis in Digital Fundus Images

Glaucoma leads to permanent vision disability by damaging the optical nerve that transmits visual images to the brain. The fact that glaucoma does not show any symptoms as it progresses and cannot be stopped at the later stages, makes it critical to be diagnosed in its early stages. Although various deep learning models have been applied for detecting glaucoma from digital fundus images, due to the scarcity of labeled data, their generalization performance was limited along with high computational complexity and special hardware requirements. In this study, compact Self-Organized Operational Neural Networks (Self-ONNs) are proposed for early detection of glaucoma in fundus images and their performance is compared against the conventional (deep) Convolutional Neural Networks (CNNs) over three benchmark datasets: ACRIMA, RIM-ONE, and ESOGU. The experimental results demonstrate that Self-ONNs not only achieve superior detection performance but can also significantly reduce the computational complexity making it a potentially suitable network model for biomedical datasets especially when the data is scarce.

Single and entangled photon pair generation using atomic vapors for quantum communication applications

Significant progress has been achieved in leveraging atomic systems for the effective operation of quantum networks, which are essential for secure and long-distance quantum communication protocols. The key elements of such networks are quantum nodes that can store or generate both single and entangled photon pairs. The primary mechanisms leading to the production of single and entangled photon pairs revolve around established techniques such as parametric down-conversion, four-wave mixing, and stimulated Raman scattering. In contrast to solid-state platforms, atomic platforms offer a more controlled approach to the generation of single and entangled photon pairs, owing to the progress made in atom manipulation techniques such as trapping, cooling, and precise excitation schemes facilitated by the use of lasers. This review article delves into the techniques implemented for generating single and entangled photon pairs in atomic platforms, starting with a detailed discussion of the fundamental concepts associated with single and entangled photons and their characterization techniques. The aim is to evaluate the strengths and limitations of these methodologies and offer insights into potential applications. Additionally, the article will review the extent to which these atomic-based systems have been integrated into operational quantum communication networks.

Financial and Market Implications of the CSX-Conrail Acquisition

Conrail, Consolidated Rail Corporation, founded in 1976, was established due to the bankruptcy of multiple railway companies in the Northeast and Midwest regions of the United States. The federal government intervened to establish the company with the aim of restoring the services and profitability of bankrupt railways. As it developed, Conrail's railway network covered major cities and industrial centers in the Northeast and Midwest of the United States, possessing a strong market position and strategic value. Therefore, Conrail had become a target for mergers and acquisitions by large railway companies such as CSX and Norfolk Southern. Through mergers and acquisitions, the acquiring party could expand market coverage, enhance operational network connectivity and competitiveness. This is a complex and multi-layered issue, the CSX acquisition of Conrail involved financial and market implications, legal obstacles, competitive pressures, and strategic objectives. By analyzing these themes in depth, we could understand the key issues and response strategies that enterprises face in the M&A process, providing valuable experience for future M&A transactions. The study applied methods such as comparative analysis and case model analysis to analyze the main challenges, response strategies, and effects comprehensively and systematically in the M&A process, providing reference for similar cases. Overall, CSX's acquisition of Conrail demonstrates how companies can achieve acquisition goals and long-term development through flexible strategies, effective communication, and professional management in the face of complex legal, market, and operational challenges.

Bearing fault detection in adjustable speed drives via self-organized operational neural networks

AbstractAdjustable speed drives (ASDs) are widely used in industry for controlling electric motors in applications such as rolling mills, compressors, fans, and pumps. Condition monitoring of ASD-fed induction machines is very critical for preventing failures. Motor current signature analysis offers a non-invasive approach to assess motor condition. Application of conventional convolutional neural networks provides good results in detecting and classifying fault types for utility line-fed motors, but the accuracy drops considerably in the case of ASD-fed motors. This work introduces the use of self-organized operational neural networks to enhance the accuracy of detecting and classifying bearing faults in ASD-fed induction machines. Our approach leverages the nonlinear neurons and self-organizing capabilities of self-organized operational neural networks to better handle the non-stationary nature of ASD operations, providing more reliable fault detection and classification with minimal preprocessing and low complexity, using raw motor current data.

Remote sensing of turbid coastal and estuarine waters with VIIRS I (375 m) and M (750 m) bands

ABSTRACT The Visible Infrared Imaging Radiometer Suite (VIIRS) is a visible, near and shortwave, to thermal infrared multispectral scanning instrument operational on three polar orbiting satellites, Suomi-NPP, JPSS-1, and JPSS-2. In the present paper, the processing of VIIRS using ACOLITE is introduced, using the Dark Spectrum Fitting (DSF) algorithm for processing of the visible to shortwave infrared bands. ACOLITE now includes support for processing both the imaging (I) and moderate (M) resolution bands at 375 m and 750 m spatial resolution, respectively. In most conditions encountered in the present study, the SWIR bands (either I or M) are automatically selected by the DSF for performing the aerosol correction. The processing is evaluated for turbid water remote sensing via autonomous hyperspectral radiometry from four sites across coastal and estuarine waters: two sites in Belgium and one each in France and Argentina. Through analysis of hundreds of matchups between the satellite and in situ measurements, a generally good performance is found for both I and M bands, especially for bands with the largest water signal, i.e. bands between 490 and 670 nm, where on average relative differences of 10–15% were found. Reflectance biases are generally less than 0.01, with a negative sign in the green and red bands and a positive sign in the blue and NIR bands. Similar matchup results are found for the I and M red and NIR bands, with a slightly higher scatter for the NIR bands. An additional comparison with OCSSW/l2gen processing of the M band data is performed for various configurations. Overall, DSF performance is better in the visible bands, whereas l2gen outputs are more closely aligned with the in situ measurements in the NIR. On average, negative biases are found for all l2gen configurations, up to −0.02 in the blue bands. Using either the SWIR1 + 2 or SWIR1 + 3 bands for the aerosol correction gives the best performance for l2gen processing. For the three VIIRS instruments separately, the average spectral differences with in situ measurements are comparable, with the most important deviation occurring at the Suomi-NPP shortest blue bands, where DSF processing gives a larger positive bias, up to nearly 0.02. For these bands, results from l2gen correspond more closely across the three instruments – although with significant negative biases for all three sensors up to −0.02 – presumably due to the use of system vicarious calibration gains in that processor. An operational network of autonomous hyperspectral instruments provides validation data for any overpassing optical imaging satellite in its commissioning or operational phase and eliminates the need for spectral interpolation or band shifting. In the case of VIIRS specifically, the hyperspectral instruments provide adequate data for the validation of the 20, 40 and 80 nm wide bands. With three operational wide-swath instruments, which provide largely interoperable data, a high frequency of observations is available, especially for study areas at higher latitudes. The novel exploitation of the I bands is now possible, thanks to the free and open source availability of ACOLITE. The advantage of the higher resolution I band data, combined with multiple VIIRS overpasses per day, is demonstrated for mapping turbidity in nearshore regions with high spatial variabilty and for detecting under-resolved floating algae.

Operational Hybrid Neural Network Model for NO$_{x}$ Forecast and Control in Real-World 2-GW Coal-Fired Power Plant

Operational Hybrid Neural Network Model for NO$_{x}$ Forecast and Control in Real-World 2-GW Coal-Fired Power Plant

Analysing group‐ and metatechnology efficiencies and technology gap of airlines for a sustainable future

AbstractAirline performance evaluation, which involves incorporating sustainable concepts, provides stakeholders and managers with an objective basis for decision‐making. This evaluation considers not only conventional metrics but also environmental impacts, contributing to the advancement of sustainable development goals. This study incorporates a two‐stage network of airline operations, which implies that airlines utilise limited resources to meet their service capacity in the first stage and generate passenger and cargo services alongside carbon emissions in the second stage. The study proposes a union dynamic network data envelopment analysis model, which integrates group technology and meta‐technology into a unified model. This model not only solves the unreasonable technology gap ratio problem but also accounts for the technological heterogeneity (whether or not to participate in a strategic alliance) and the dynamic efficiency of different airlines in the network structure during different operation periods. A sample of 24 global airlines from 2017 to 2019 is used in the study. The empirical results show that the dynamic total meta‐efficiency of the alliance group is significantly greater than that of the nonalliance group over the 3 years. This suggests that joining a strategic alliance has a positive effect on airlines' operational efficiency while reinforcing their commitment to sustainability initiatives.

Distributed Nonconvex Optimization for Control of Water Networks with Time-coupling Constraints

AbstractIn this paper, we present a new control model for optimizing pressure and water quality operations in water distribution networks. Our formulation imposes a set of time-coupling constraints to manage temporal pressure variations, which are exacerbated by the transition between pressure and water quality controls. The resulting optimization problem is a nonconvex, nonlinear program with nonseparable structure across time steps. This problem proves challenging for state-of-the-art nonlinear solvers, often precluding their direct use for near real-time control in large-scale networks. To overcome this computational burden, we investigate a distributed optimization approach based on the alternating direction method of multipliers (ADMM). In particular, we implement and evaluate two algorithms: a standard ADMM scheme and a two-level variant that provides theoretical convergence guarantees for our nonconvex problem. We use a benchmarking water network and a large-scale operational network in the UK for our numerical experiments. The results demonstrate good convergence behavior across all problem instances for the two-level algorithm, whereas the standard ADMM approach struggles to converge in some instances. With an appropriately tuned penalty parameter, however, both distributed algorithms yield good quality solutions and computational times compatible with near real-time (e.g. hourly) control requirements for large-scale water networks.

Does afforestation increase soil water buffering? A demonstrator study on soil moisture variability in the Alpine Geroldsbach catchment, Austria

This study employed an operational monitoring network to measure soil moisture and runoff behaviour continuously in the Alpine catchment Geroldsbach-Götzens, Austria. We hypothesize that afforestation can have a positive impact on soil water buffering. To analyse the impact of soil properties and vegetation cover changes on soil water dynamics, four experimental plots were established on grassland and monitoring stations were installed in the forest. The rainfall test site is equipped with an automatic weather station to obtain meteorological observations, and weirs to measure surface runoff of natural occurring precipitation events and artificial rainfall simulations. In the plots, 200 soil moisture sensors were installed at five different depths, aimed to track and visualize infiltration and subsurface flow processes. Another twenty sensors monitored soil moisture at different afforestation stages in the forested part of the catchment. The measurements show that soils covered with young and old-growth forest have a higher and more stable soil moisture content than grassland and soils with a lack of vegetation throughout the seasons. We observed large spatial differences at plot scale, where the spatial variability of soil moisture increases with depth and is highest during convective precipitation. The initial conditions and rainfall characteristics play an important role in infiltration processes and soil water storage. Our rainfall test site demonstrated the challenges of innovative monitoring techniques and that it offers opportunities for more experiments to gather evidence-based data as input for flood models. Overall findings confirm the sponge effect of forest soils and indicate that afforestation as Nature-Based Solution reduces the temporal soil moisture variability, buffering soil water during precipitation events, which can be beneficial for runoff reduction in Alpine catchments.

Results from a retrospective case finding and re-engagement exercise for people previously diagnosed with hepatitis C virus to increase uptake of directly acting antiviral treatment

BackgroundDirect acting antivirals (DAAs) for the Hepatitis C virus (HCV) have shifted the World Health Organisation global strategic focus to the elimination of HCV by 2030. In England, the UK Health Security Agency (UKHSA) led a national ‘patient re-engagement exercise’, using routine surveillance data, which was delivered through the HCV Operational Delivery Networks (ODNs) with support from National Health Service England (NHSE), to help find and support people with a positive HCV PCR test result to access treatment. We report a quantitative evaluation of outcomes of this exercise.MethodsIndividuals with a recorded positive HCV antibody or PCR result between 1996 and 2017 were identified using UKHSA’s records of HCV laboratory diagnosis. Linkage with established health-care datasets helped to enhance patient identification and minimise attempts to contact deceased or previously treated individuals. From September to November 2018 each ODN was provided with a local list of diagnosed individuals. ODNs were asked to perform further data quality checks through local systems and then write to each individual’s GP to inform them that the individual would be contacted by the ODN to offer confirmatory HCV PCR testing, assessment and treatment unless the GP advised otherwise. Outcomes of interest were receipt of treatment, a negative PCR result, and death. Data were collected in 2022.ResultsOf 176,555 individuals with a positive HCV laboratory report, 55,329 individuals were included in the exercise following linkage to healthcare datasets and data reconciliation. Participants in the study had a median age of 51 years (IQR: 43, 59), 36,779 (66.5%) were males, 47,668 (86.2%) were diagnosed before 2016 and 11,148 (20.2%) lived in London. Of the study population, 7,442 (13.4%) had evidence of treatment after the re-engagement exercise commenced, 6,435 (11.6%) were reported as PCR negative (96% had no previous treatment records), 4,195 (7.6%) had prescription data indicating treatment before the exercise commenced or were reported to have been treated previously by their ODN, and 2,990 (5.4%) had died. The status of 32,802 (59.3%) people remains unknown.ConclusionsA substantial number of those included had treatment recorded after the exercise commenced, however, many more remain unengaged. Evaluation of the exercise highlighted areas that could be streamlined to improve future exercises.

Energy Performance of LR-FHSS: Analysis and Evaluation.

Long-range frequency hopping spread spectrum (LR-FHSS) is a pivotal advancement in the LoRaWAN protocol that is designed to enhance the network's capacity and robustness, particularly in densely populated environments. Although energy consumption is paramount in LoRaWAN-based end devices, this is the first study in the literature, to our knowledge, that models the impact of this novel mechanism on energy consumption. In this article, we provide a comprehensive energy consumption analytical model of LR-FHSS, focusing on three critical metrics: average current consumption, battery lifetime, and energy efficiency of data transmission. The model is based on measurements performed on real hardware in a fully operational LR-FHSS network. While in our evaluation, LR-FHSS can show worse consumption figures than LoRa, we find that with optimal configuration, the battery lifetime of LR-FHSS end devices can reach 2.5 years for a 50 min notification period. For the most energy-efficient payload size, this lifespan can be extended to a theoretical maximum of up to 16 years with a one-day notification interval using a cell-coin battery.

Classification accuracy and compatibility across devices of a new Rapid-E+ flow cytometer

Abstract. The study evaluated a new model of a Plair SA airflow cytometer, Rapid-E+, and assessed its suitability for airborne pollen monitoring within operational networks. Key features of the new model are compared with the previous one, Rapid-E. A machine learning algorithm is constructed and evaluated for (i) classification of reference pollen types in laboratory conditions and (ii) monitoring in real-life field campaigns. The second goal of the study was to evaluate the device usability in forthcoming monitoring networks, which would require similarity and reproducibility of the measurement signal across devices. We employed three devices and analysed (dis-)similarities of their measurements in laboratory conditions. The lab evaluation showed similar recognition performance to that of Rapid-E, but field measurements in conditions when several pollen types were present in the air simultaneously showed notably lower agreement of Rapid-E+ with manual Hirst-type observations than those of the older model. An exception was the total-pollen measurements. Comparison across the Rapid-E+ devices revealed noticeable differences in fluorescence measurements between the three devices tested. As a result, application of the recognition algorithm trained on the data from one device to another led to large errors. The study confirmed the potential of the fluorescence measurements for discrimination between different pollen classes, but each instrument needed to be trained individually to achieve acceptable skills. The large uncertainty of fluorescence measurements and their variability between different devices need to be addressed to improve the device usability.

An experimental study on beamforming architecture and full-duplex wireless across two operational outdoor massive MIMO networks

Full-duplex (FD) wireless communication refers to a communication system in which both ends of a wireless link transmit and receive data simultaneously in the same frequency band. One of the major challenges of FD communication is self-interference (SI), which refers to the interference caused by transmitting elements of a radio to its own receiving elements. Fully digital beamforming is a technique used to conduct beamforming and has been recently repurposed to also reduce SI. However, the cost of fully digital systems dramatically increases with the number of antennas, as each antenna requires an independent Tx-Rx RF chain. Hybrid beamforming systems use a much smaller number of RF chains to feed the same number of antennas, and hence can significantly reduce the deployment cost. In this paper, we aim to quantify the performance gap between these two radio architectures in terms of SI cancellation and system capacity in FD multi-user Multiple Input Multiple Output (MIMO) setups. We first obtained over-the-air channel measurement data on two outdoor massive MIMO deployments over the course of three months. We next study SoftNull and M-HBFD as two state-of-the-art transmit (Tx) beamforming based FD systems, and introduce two new joint transmit-receive (Tx-Rx) beamforming based FD systems named TR-FD2 and TR-HBFD for fully digital and hybrid radio architectures, respectively. We show that the hybrid beamforming systems can achieve 80%–99% of the fully digital systems capacity, depending on the number of users. Our results show that it is possible to get many benefits associated with fully digital massive MIMO systems with a hybrid beamforming architecture at a fraction of the cost.

Four-port dual-band textile MIMO antenna for biomedical health monitoring systems: on-body mutual coupling reduction characterization

The paper outlines a methodology to diminish mutual coupling in 4-port dual-band MIMO textile antenna for biomedical applications. This antenna leverages MIMO technology and Wireless Body Area Network (WBAN) for operation in two distinct frequency bands at (3.5 & 2.45 GHz). The antenna is made up of four octagonal patch antennas, each having a bar and a split-ring (SR) slot with 47.2 × 31 mm2 dimensions for each patch. A hybrid mutual coupling (MC) approach was investigated with closely spaced patches (up to 0.05λ). Various bending setups have been selected along with flat case to examine the antennas’ resilience which demonstrate such agreement between measured and simulated findings. Furthermore, the MC is only −20 dB, the envelope correlation coefficient (ECC) is 0.001, and maximum peak measured gain of 5.2 dBi is achieved with lowest peak specific absorption rate (SAR) value. Even when bent at a 60° angle along with y-axis and x-axis, the antenna retains a decent gain of 1.861 dBi in the low frequency region and 5.479 dBi at high frequency band. Surprisingly, the antenna outperforms the attenuation produced by the lossy effects of the human body, indicating a favorable alignment between the modelled and observed findings.

DTL-5G: Deep transfer learning-based DDoS attack detection in 5G and beyond networks

Network slicing is considered as a key enabler for 5G and beyond mobile networks for supporting a variety of new services, including enhanced mobile broadband, ultra-reliable and low-latency communication, and massive connectivity, on the same physical infrastructure. However, this technology increases the susceptibility of networks to cyber threats, particularly Distributed Denial-of-Service (DDoS) attacks. These attacks have the potential to cause service quality degradation by overloading network function(s) that are central to network slices to operate seamlessly. This calls for an Intrusion Detection System (IDS) as a shield against a wide array of DDoS attacks. In this regard, one promising solution would be the use of Deep Learning (DL) models for detecting possible DDoS attacks, an approach that has already made its way into the field given its manifest effectiveness. However, one particular challenge with DL models is that they require large volumes of labeled data for efficient training, which are not readily available in operational networks. A possible workaround is to resort to Transfer Learning (TL) approaches that can utilize the knowledge learned from prior training to a target domain with limited labeled data. This paper investigates how Deep Transfer Learning (DTL) based approaches can improve the detection of DDoS attacks in 5G networks by leveraging DL models, such as Bidirectional Long Short-Term Memory (BiLSTM), Convolutional Neural Network (CNN), Residual Network (ResNet), and Inception as base models. A comprehensive dataset generated in our 5G network slicing testbed serves as the source dataset for DTL, which includes both benign and different types of DDoS attack traffic. After learning features, patterns, and representations from the source dataset using initial training, we fine-tune base models using a variety of TL processes on a target DDoS attack dataset. The 5G-NIDD dataset, which has a sparse amount of annotated traffic pertaining to several DDoS attack generated in a real 5G network, is chosen as the target dataset. The results show that the proposed DTL models have performance improvements in detecting different types of DDoS attacks in 5G-NIDD dataset compared to the case when no TL is applied. According to the results, the BiLSTM and Inception models being identified as the top-performing models. BiLSTM indicates an improvement of 13.90%, 21.48%, and 12.22% in terms of accuracy, recall, and F1-score, respectively, whereas, Inception demonstrates an enhancement of 10.09% in terms of precision, compared to the models that do not adopt TL.

A Localized Quantitative Precipitation Estimation for S-band Polarimetric Radar in Taiwan

Abstract A polarimetric radar quantitative precipitation estimation to estimate rain rate (R) from specific attenuation (A) has been applied in Taiwan's operational Quantitative Precipitation Estimation and Segregation Using Multiple Sensors (QPESUMS) system since 2016. A 3-yrs' (2016-2018) drop size distribution dataset from an operational Parsivel network was used to derive a localized coefficient as well as the α(K) function in the R(A) scheme for S-band radar, where α is a key parameter in the estimation of A and K is the linear fitted slope of differential reflectivity (ZDR) versus reflectivity (Z). The local DSD data was also used to derive localized R(Z) and R(KDP) relationships, and the relationships were evaluated using radar observations in heavy rain cases. A synthetic QPE combining the localized R(A), R(Z), and R(KDP) relationships is compared to its operational counterpart and showed about 8 % reduction in normalized mean error for the Mei-Yu cases. Typhoon cases exhibited similar improvements by the localized QPE relationships, but showed higher uncertainties than in the Mei-Yu cases. The higher uncertainties in the typhoon QPE verification was likely due to the stronger winds in typhoons than in the Mei-Yu events that caused greater mismatches between the radar observations at an altitude and the gauges at the ground. Overall, the results demonstrated advantages of localized radar rainfall relationships derived from the disdrometer data to improve the accuracy of the operational rainfall estimation products.

Green transformation and performance synergy efficiency of china’s thermal power enterprises on the basis of the environmental tax burden

Global climate change and the collection of environmental protection taxes are accelerating the green transformation of thermal power enterprises. This study selected Chinese thermal power listed companies as samples and used a dynamic three-stage (operational, green transformation, and market performance) network DEA model to evaluate their transformation efficiency and corporate performance. This paper incorporates targeted indicators such as ESG (environment, society, governance) and stock prices into the model and conducts a comparative study on the basis of macro policies and the geographical location of the enterprise. A comparative analysis was conducted on the efficiency of enterprises before and after the adjustment of the environmental tax burden, using the environmental tax burden as an exogenous variable. Thus, the following conclusions can be drawn: there is a certain positive correlation between the collaborative efficiency of the two links of thermal power enterprises and the economic development of their respective regions. Moreover, the green transformation efficiency of most thermal power enterprises is superior to the market performance efficiency. The environmental tax burden mainly improves the overall efficiency of thermal power enterprises by improving their operational efficiency and efficiency in the green transformation stage without affecting market performance. To further improve efficiency, thermal power enterprises should actively communicate with stakeholders to strive for more financial relief.

Mapping Pressure Surge Source in Urban Water Networks: Integrating Low‐ and High‐Frequency Pressure Data With an Illustrative Real Case Study

AbstractThis paper introduces a novel methodology that integrates low‐ and high‐frequency pressure monitoring in the parts of the urban water distribution network characterized by the largest number of repairing operations. This integrated approach aims to pinpoint the source and characterize potentially dangerous pressure variations. The actual behavior of a real operational water distribution network is considered as an example to verify the effectiveness of the proposed methodology. The analysis of the acquired pressure signals allows for mapping frequent pressure surges and identifying their source in the functioning conditions of a big user. Notwithstanding these pressure variations are not overly large, according to the literature, their near‐continuous nature poses a risk of inducing pipe breaks, and then leakage, due to fatigue. The proposed method integrating routine low‐frequency measurements with targeted high‐frequency surveys provides a cost‐effective, proactive management strategy.