Analysis Of Monitoring Data Research Articles

The health impact of PM2.5 and O3 in Beijing modified by infiltration factors from 2014 to 2022.

PM2.5 and O3 are significant threats to the health of residents and modern residents spend more than 80% of their time indoors, so quantifying indoor exposure of residents has a positive influence on formulating policies and improving health indicators. Analysis of monitoring data shows a consistent decrease in the annual average concentration of outdoor PM2.5 in Beijing from 90.1 to 30.4μg/m3, and the equivalent human exposure concentration also declined from 55.2 to 18.6μg/m3 modified by infiltration factors from 2014 to 2022, while during the peak season, the average value of O3-8h concentration in Beijing has consistently remained between 159.3 and 225.3μg/m3; the equivalent human exposure concentration remained between 64.7 and 92.3μg/m3 modified by infiltration factors from 2014 to 2023. The equivalent exposure concentration is calculated by weighting the concentration of indoor and outdoor pollutants, the proportion of indoor and outdoor activity time of the population, and the permeability coefficient of outdoor pollutants into the room, so as to quantify the actual concentration of pollutants exposed to the human body in a certain time. Previous studies have primarily focused on the impact of annual changes in pollutant concentrations on health estimates, often neglecting indoor concentrations and behavior patterns. This limitation should be addressed. Therefore, this study utilized equivalent human exposure concentration and AirQ + , the authoritative software released by the World Health Organization (WHO), to evaluate quantitatively the impact of PM2.5 and O3 on the health of Beijing residents by combining pollutant concentration, annual population, and mortality of various diseases and other indicators to enhance the credibility of the study. The number of deaths related to PM2.5 has decreased from 28,182 people in 2014 to 10,250 people in 2022 (age ≥ 30). The number of premature deaths in 2022 was only 36.4 percent of that in 2014 and was decreasing by 1992 per year. The number of deaths related to O3 varies between 550 and 3358 people. Lowering PM2.5 and O3 concentrations can effectively reduce natural premature death as well as cardiovascular and respiratory diseases caused by air pollution. The government should persist in enhancing the regulation of PM2.5 and accord significance to the oversight of O3. Concurrently, there is a need to reinforce the cooperative regulation addressing both PM2.5 and O3.

Integrating Learning-Driven Model Behavior and Data Representation for Enhanced Remaining Useful Life Prediction in Rotating Machinery

The increasing complexity of modern mechanical systems, especially rotating machinery, demands effective condition monitoring techniques, particularly deep learning, to predict potential failures in a timely manner and enable preventative maintenance strategies. Health monitoring data analysis, a widely used approach, faces challenges due to data randomness and interpretation difficulties, highlighting the importance of robust data quality analysis for reliable monitoring. This paper presents a two-part approach to address these challenges. The first part focuses on comprehensive data preprocessing using only feature scaling and selection via random forest (RF) algorithm, streamlining the process by minimizing human intervention while managing data complexity. The second part introduces a Recurrent Expansion Network (RexNet) composed of multiple layers built on recursive expansion theories from multi-model deep learning. Unlike traditional Rex architectures, this unified framework allows fine tuning of RexNet hyperparameters, simplifying their application. By combining data quality analysis with RexNet, this methodology explores multi-model behaviors and deeper interactions between dependent (e.g., health and condition indicators) and independent variables (e.g., Remaining Useful Life (RUL)), offering richer insights than conventional methods. Both RF and RexNet undergo hyperparameter optimization using Bayesian methods under variability reduction (i.e., standard deviation) of residuals, allowing the algorithms to reach optimal solutions and enabling fair comparisons with state-of-the-art approaches. Applied to high-speed bearings using a large wind turbine dataset, this approach achieves a coefficient of determination of 0.9504, enhancing RUL prediction. This allows for more precise maintenance scheduling from imperfect predictions, reducing downtime and operational costs while improving system reliability under varying conditions.

Effectiveness evaluation of cooling measures for express highway construction in permafrost regions based on GPR and ERT

Global warming and human activities are accelerating the degradation of permafrost on the Qinghai-Tibet Plateau (QTP), leading to significant settlement and cracking issues in the local express highway infrastructures. In response, the Gonghe-Yushu Express Highway (GYE) on the east edge of the QTP incorporated extensive cooling measures during its construction to enhance embankment stability. Despite these efforts, field investigations have disclosed that embankment diseases persist across various sections, including those with implemented cooling measures. This study focuses on a specific test and demonstration section of the GYE, employing a suite of cooling measures to assess their engineering effectiveness. Utilizing a combination of multi-time ground-penetrating radar (GPR) and electrical resistivity tomography (ERT) detection, alongside on-site disease investigations and temperature monitoring, this research comprehensively evaluates the efficacy of different cooling interventions. Findings indicate that although cooling measures generally curb permafrost degradation in areas with ice-rich and ice-saturated soils, they fall short in sections with massive ground ice. Of the six cooling measures examined in the demonstration section, ventilation duct embankments emerge as the most effective, whereas crushed-rock layer embankments rank as the least. The study further reveals that the combined use of XPS insulation boards and two-phase closed thermosyphons inadequately addresses the issue of central heat accumulation in broad-width express highways, reducing uneven settlement issues but aggravating longitudinal cracking. Comparative analysis of on-site surveys and monitoring data suggests that regular application of GPR and ERT techniques can proficiently assess the performance of cooling measures.

Modification of the existing maximum residue level for flonicamid in honey.

In accordance with Article 6 of Regulation (EC) No 396/2005, the applicant ISK Biosciences Europe N.V. submitted a request to the competent national authority in Finland to modify the existing maximum residue level (MRL) for the active substance flonicamid in honey. The data submitted in support of the request were not found appropriate to derive an MRL proposal for honey. The assessment was complemented by an analysis of the monitoring data available from the EU monitoring programmes (conducted during 2009-2023). The results from the monitoring data analysis suggest that the current MRL is still sufficient to account for the residue uptake in honey resulting from plant protection products uses of flonicamid on melliferious crops. Adequate analytical methods for enforcement are available to control the residues of flonicamid in honey according to the residue definition as the sum of flonicamid, TFNA and TFNG, expressed as flonicamid at the validated limit of quantification (LOQ) of 0.01 mg/kg for each compound. Based on the risk assessment results, EFSA concluded that the short-term and long-term intake of residues in honey at a level of 0.05 mg/kg according to the monitoring data is unlikely to present a risk to consumer health.

Based on Flac3D Research and Analysis of the Mining Pressure Development Law in the Top Coal Section of the Roadway Support of the Working Face Transport

Top coal supporting roadway is widely used in the transportation roadway of thick coal seam face, which can not only increase the coal extraction rate but also reduce the driving cost. Based on the engineering geological conditions of the transportation lane of the working face of Zhongma Cun mine, the law of mine pressure development in the roadway of the top coal section is studied and analyzed based on Flac3D and field monitoring methods. The numerical simulation analysis shows that when the working face is pushed to 55m for the first time, the displacement of the area affected by the leading abutment pressure and the surrounding rock on the mining side of the roadway reaches the peak value, and the step distance of the first time is 55m. According to the analysis of stress monitoring data of single pillar and roof anchor cable on site, the first pressure occurs when the working face advances to 51m, which is consistent with the numerical simulation conclusion.

Correlation between insulin-like growth factor and complexity of glucose time series index in patients with newly diagnosed acromegaly: a PILOT study.

Acromegaly has a high risk of abnormal glucose metabolism. The complexity of the glucose time series index (CGI) is calculated from refined composite multi-scale entropy analysis of the continuous glucose monitoring (CGM) data. CGI is a new indicator of glucose imbalance based on ambulatory glucose monitoring technology, which allows for earlier response to glucose metabolism imbalance and correlates with patient prognosis. To compare the differences in glucose metabolic profile and CGI between acromegaly with normal glucose tolerance (NGT) and healthy subjects. Eight newly diagnosed patients with acromegaly (GH group) and eight age- and gender-matched healthy subjects (Control group) were included in this study. All participants underwent oral glucose tolerance test (OGTT) and 72-h CGM. A refined composite multi-scale entropy analysis was performed on the CGM data to calculate the CGI and we compare the differences in glycemic profiles and CGI between the two groups. After OGTT, compared with the control group, patients in the GH group had higher 2 h blood glucose (BG) (mmol/L) [GH vs control, 6.7 (6.1, 7.0) vs 5.2 (3.8, 6.3), P = 0.012], 3 h BG [5.1 (3.8, 6.5) vs 4.0 (3.4, 4.2), P = 0.046], mean BG [6.3 (6.1, 6.5) vs 5.5 (5.1, 5.9), P = 0.002], 2 h insulin (mU/L) [112.9 (46.8, 175.5) vs 34.1 (17.1, 55.6), P = 0.009], and 3 h insulin [26.8 (17.1, 55.4) vs 10.4 (4.2, 17.8), P = 0.016]. CGI was lower in the GH group [2.77 (1.92, 3.15) vs 4.2 (3.3, 4.8), P = 0.008]. Spearman's correlation analysis showed insulin-like growth factor (IGF) (r = -0.897, P < 0.001) and mean glucose (r = -0.717, P = 0.003) were significantly negatively correlated with CGI. Multiple linear stepwise regression showed that IGF-1 (r = -0.652, P = 0.028) was independent factor associated with CGI in acromegaly. IGF-1 was significantly associated with CGI, and CGI may serve as a novel marker to evaluate glucose homeostasis in acromegaly with normal glucose tolerance.

Hydraulic impact of pressure transients from water conveyance tunnel on the complex hydrogeological system: A case study HPP Pirot, Serbia

Hydraulic tunnels, integral components of hydropower plant (HPP) systems, traversing diverse rock masses, pose challenges for long-term operation. The study’s main goal was to investigate how hydraulic transients originating from the headrace tunnel transfer through the concrete lining and manifest in surrounding aquifer systems. In this experimental study at the water pressure tunnel of HPP Pirot (Serbia), a novel monitoring approach was employed. The internal piezometers with highly sensitive probes are distributed strategically along the tunnel in different geological environments. This approach involved monitoring of water pressure in the surrounding rock masses and observing the impact of hydraulic transients generated by HPP operation. A comprehensive analysis of monitoring data, incorporating ordinal and partial correlations and considering geological features, provides a profound understanding of groundwater dynamics, pressure transient phenomena, and hydrogeological characterization during HPP operation. The study highlights the complex and spatially diverse hydrogeological environment, emphasizing the varying reactivity of pore pressure during HPP operation, particularly concerning Water Mass Oscillations (WMO). Reactive piezometers near the surge tank demonstrate significant pressure variations, while those located further away display synchronous pressure changes with reduced amplitudes. Schematic hydrogeological models have been created based on the hydraulic impacts of HPPs in different geological environments along the tunnel route. The water pressure response enabled aquifer characterization of (1) a thin-layered, low-fissured aquifer closely related to the reservoir, with low influence on WMO transients; (2) a low-fissured, compartment aquifer or non-aquifer with minimal to no influence on WMO transients; (3) a well-structured karst aquifer system influenced by WMO transients; (4) a fault-controlled zone of deep hypogene karst aquifer discharge highly influenced by WMO transients; and (5) an aquifer with a dominant development of the karst conduit system highly influenced by WMO transients. The study highlights the significance of monitoring water pressure within the rock mass as a fundamental element in analyzing the interaction between water pressure tunnels and the hydrogeological environment. Applying the presented instrumentation and methodology facilitates effective monitoring in research areas where pressure tunnels are constructed.

HIT4HYPOS Continuous Glucose Monitoring Data Analysis: The Effects of High-Intensity Interval Training on Hypoglycemia in People With Type 1 Diabetes and Impaired Awareness of Hypoglycemia

Aims: To assess the impact of high-intensity interval training (HIIT) on hypoglycemia frequency and duration in people with type 1 diabetes (T1D) with impaired awareness of hypoglycemia (IAH). Methods: Post hoc analysis of four weeks of continuous glucose monitoring (CGM) data from HIT4HYPOS; a parallel-group study comparing HIIT + CGM versus no exercise + CGM in 18 participants with T1D and IAH. Results: When compared with those participating individuals not exercising, HIIT did not increase total hypoglycemia frequency, THypo(L1) 1.44 [1.00-2.77]% versus 2.53 [1.46-4.23]%; P = .335, THypo(L2) 0.25 [0.09-0.37]% versus 0.45 [0.20-0.78]%; P = .146, HIIT + CGM versus CGM, respectively, rate ( EventPerWeekHypo 5.30 [3.35-8.27] #/week vs 7.45 [3.54-10.81] #/week, P = .340) or duration ( DurationHypo 33.33 [27.60-39.10] minutes vs 39.56 [31.00-48.38] minutes; P = .219, HIIT + CGM vs CGM, respectively). There was a reduction in nocturnal hypoglycemia in those who carried out HIIT, THypo(L1) 0.50 [0.13-0.97]% versus 2.45 [0.77-4.74]%; P = .076; THypo(L2) 0.00 [0.00-0.03]% versus 0.49 [0.13-0.74]%; P = .006, HIIT + CGM versus CGM, respectively. Conclusions/interpretation: Based on CGM data collected from a real-world study of four weeks of HIIT versus no exercise in individuals with T1D and IAH, we conclude that HIIT does not increase hypoglycemia, and in fact reduces exposure to nocturnal hypoglycemia.

Artificial intelligence based real-time prediction of imminent heart failure hospitalisation in patients undergoing non-invasive telemedicine.

Remote patient management may improve prognosis in heart failure. Daily review of transmitted data for early recognition of patients at risk requires substantial resources that represent a major barrier to wide implementation. An automated analysis of incoming data for detection of risk for imminent events would allow focusing on patients requiring prompt medical intervention. We analysed data of the Telemedical Interventional Management in Heart Failure II (TIM-HF2) randomized trial that were collected during quarterly in-patient visits and daily transmissions from non-invasive monitoring devices. By application of machine learning, we developed and internally validated a risk score for heart failure hospitalisation within seven days following data transmission as estimate of short-term patient risk for adverse heart failure events. Score performance was assessed by the area under the receiver-operating characteristic (ROCAUC) and compared with a conventional algorithm, a heuristic rule set originally applied in the randomized trial. The machine learning model significantly outperformed the conventional algorithm (ROCAUC 0.855 vs. 0.727, p < 0.001). On average, the machine learning risk score increased continuously in the three weeks preceding heart failure hospitalisations, indicating potential for early detection of risk. In a simulated one-year scenario, daily review of only the one third of patients with the highest machine learning risk score would have led to detection of 95% of HF hospitalisations occurring within the following seven days. A machine learning model allowed automated analysis of incoming remote monitoring data and reliable identification of patients at risk of heart failure hospitalisation requiring immediate medical intervention. This approach may significantly reduce the need for manual data review.

Smart surveillance: deploying drones for efficient and adaptive monitoring solutions

The growing need for real-time and adaptable surveillance solutions has prompted the investigation of cutting-edge technologies within security and monitoring frameworks. This paper introduces an innovative surveillance approach that utilizes drones to improve situational awareness and operational efficiency across various domains, including public safety, border security, and environmental monitoring. The proposed system integrates autonomous drones equipped with sophisticated imaging capabilities, data processing techniques, and machine learning algorithms, facilitating real-time monitoring, threat identification, and comprehensive data analysis. Notable features of this system include autonomous path planning, the ability to adapt to changing environmental conditions, and effective communication with ground control stations. By merging drone technology with AI-enhanced analytics, this approach provides a scalable and cost-efficient surveillance solution that minimizes the need for human intervention while enhancing accuracy and response times. In this paper, we detail the design, implementation, and performance assessment of our system, underscoring its potential to transform conventional surveillance practices.

How water, temperature, and seismicity control the preconditioning of massive rock slope failure (Hochvogel)

Abstract. The anticipation of massive rock slope failures is a key mitigation strategy in a changing climate and environment requiring a precise understanding of pre-failure process dynamics. Here we exploit &gt;4 years of multi-method high-resolution monitoring data from a large rock slope instability close to failure. To quantify and understand the effect of possible drivers (water from rain and snowmelt, internal rock fracturing, and earthquakes), we correlate slope displacements with environmental data, local seismic recordings, and earthquake catalogues. During the snowmelt phase, displacements are controlled by meltwater infiltration with high correlation and a time lag of 4–9 d. During the snow-free summer, rainfall induces accelerations with a time lag of 1–16 h for up to several days without a minimum activation rain sum threshold. Rock fracturing, linked to temperature and freeze–thaw cycles, is predominantly near the surface and unrelated to displacement rates. A classic Newmark analysis of recent and historic earthquakes indicates a low potential for immediate triggering of a major failure at the case site, unless it is already very close to failure. Seismic topographic amplification of the peak ground velocity (PGV) at the summit ranges from a factor of 2–11 and is spatially heterogeneous, indicating a high criticality of the slope. The presented in-depth monitoring data analysis enables a comprehensive rockfall driver evaluation and indicates where future climatic changes, e.g. in precipitation intensity and frequency, may alter the preconditioning of major rock slope failures.

In situ Synchrotron X-ray Metrology Boosted by Automated Data Analysis for Real-time Monitoring of Cathode Calcination.

Synchrotron X-ray-based in situ metrology is advantageous for monitoring the synthesis of battery materials, offering high throughput, high spatial and temporal resolution, and chemical sensitivity. However, the rapid generation of massive data poses a challenge to on-site, on-the-fly analysis needed for real-time process monitoring. Here, a weighted lagged cross-correlation (WLCC) similarity approach is presented for automated data analysis, which merges with in situ synchrotron X-ray diffraction metrology to monitor the calcination process of the archetypal nickel-based cathode, LiNiO2. The WLCC approach, incorporating variables that account for peak shifts and width changes associated with structural transformations, enables rapid extraction of phase progression within 10 seconds from tens of diffraction patterns. Details are captured, from initial precursors to intermediates and the final layered LiNiO2, providing information for agile on-site adjustments during experiments and complementing post hoc diffraction analysis by offering insights into early-stage phase nucleation and growth. Expanding this data-powered platform paves the way for real time calcination process monitoring and control, which is pivotal to quality control in battery cathode manufacturing.

ATC-CNN-Based IoT Framework for Real-Time Cardiovascular Monitoring: A Comparative Analysis with Deep Learning Models

Background: Cardiovascular diseases (CVDs), including hypertension, coronary heart disease, and ventricular fibrillation, remain the leading cause of death worldwide. The COVID-19 pandemic accelerated the adoption of telecardiology to minimize in-person interactions, enhancing access to cardiovascular care, especially in remote areas. However, traditional real-time monitoring (RTM) systems often require patient visits, creating a need for cost-effective, user-friendly alternatives that leverage IoT and AI for improved patient management. Methods: This study developed an IoT-enabled RTM system integrated with an Adaptive Thresholding Classifier-Convolutional Neural Network (ATC-CNN) framework for managing cardiovascular patients (CP). Data from a heart failure dataset in the UCI Machine Learning Archive was used to train and evaluate the model. Multiple deep learning (DL) algorithms, including MLP, CNN, LSTM, RNN, and the proposed ATC-CNN, were assessed based on accuracy, precision, recall, and F1 scores. Results: The ATC-CNN model achieved an accuracy of 0.9831, outperforming other DL models, including MLP, CNN, LSTM, and RNN. It demonstrated superior capabilities in handling complex cardiovascular data, offering reliable and timely diagnosis with high precision and recall. The integration of IoT and AI facilitated continuous monitoring and real-time data analysis, addressing key limitations of traditional RTM systems. Conclusion: The ATC-CNN framework shows great potential for revolutionizing cardiovascular care by enhancing remote monitoring capabilities and clinical decision-making. It provides an effective, real-time solution for early diagnosis and intervention, particularly in remote and underserved areas. Future research should focus on expanding datasets and refining the model for broader adaptability in diverse healthcare settings.

Advancements in Soft Robotic Implants for Long-Term Drug Delivery and Tissue Monitoring

Soft robotic implants are a major upgrade in medical devices providing novel means for long-term drug administration and tissue tracking. The eco-friendly biodegradable body-prosthetic will not only be compatible with the human body, but can also mingle completely without inducing foreign-body responses and discomfort. Since these implants provide programmed and localized drug delivery; hence the significance of these sutures could be found in administering the drugs at a specific site and specific interval so as to diminish the side effects and improved therapeutic outcomes. Modern developments of soft robotics implants have improved their design, performance, interaction with biological and other natural materials. Material developments are becoming smarter, where responsive materials in the form of: smart materials, which can react to environmental stimuli and adjust drug delivery rates immediately (and); etc. Moreover, the evolution of microfabrication and nanotechnology also created high precision and less invasive devices, rendering more efficient and well-accepted to patients. These implants are especially valuable for long-term afflictions like diabetes and cancer as they streamline the ongoing, precise delivery of medications. They also have a crucial role in pain management and postoperative care, effectively providing prolonged release of analgesics, and ultimately enhance patient comfort thus contributing to smoother recovery. For tissue monitoring, implantable flexible soft robotic devices with integrated sensors and actuators are able to provide real-time data collection and analysis for the continuous monitoring of vital physiological information, such as cardiovascular health, neural activity, skin conditions (wound healing) etc. This is important, particularly for early detection of complications and receiving timely interventions to optimize patient outcomes. In the future there are a number of intriguing applications that could be developed with these soft robotic implants like personalized medicine and integration with other state-of-the-art medical technologies. Further research and development should/hopefully will address outstanding challenges e.g. technical, regulatory etc to allow more wide-ranging clinical application with consequent transformative impacts on healthcare. Finally, soft robotic implants show great potential to improve drug delivery and tissue monitoring, with continuous development bringing the field closer to better performing medical solutions.

Review of Phonocardiogram Signal Analysis: Insights from the PhysioNet/CinC Challenge 2016 Database

The phonocardiogram (PCG) is a crucial tool for the early detection, continuous monitoring, accurate diagnosis, and efficient management of cardiovascular diseases. It has the potential to revolutionize cardiovascular care and improve patient outcomes. The PhysioNet/CinC Challenge 2016 database, a large and influential resource, encourages contributions to accurate heart sound state classification (normal versus abnormal), achieving promising benchmark performance (accuracy: 99.80%; sensitivity: 99.70%; specificity: 99.10%; and score: 99.40%). This study reviews recent advances in analytical techniques applied to this database, and 104 publications on PCG signal analysis are retrieved. These techniques encompass heart sound preprocessing, signal segmentation, feature extraction, and heart sound state classification. Specifically, this study summarizes methods such as signal filtering and denoising; heart sound segmentation using hidden Markov models and machine learning; feature extraction in the time, frequency, and time-frequency domains; and state-of-the-art heart sound state recognition techniques. Additionally, it discusses electrocardiogram (ECG) feature extraction and joint PCG and ECG heart sound state recognition. Despite significant technical progress, challenges remain in large-scale high-quality data collection, model interpretability, and generalizability. Future directions include multi-modal signal fusion, standardization and validation, automated interpretation for decision support, real-time monitoring, and longitudinal data analysis. Continued exploration and innovation in heart sound signal analysis are essential for advancing cardiac care, improving patient outcomes, and enhancing user trust and acceptance.

A Study on the Factors Controlling the Kinematics of a Reactivated and Slow-Moving Landslide in the Eastern Liguria Region (NW Italy) through the Integration of Automatic Geotechnical Sensors

This paper deals with the investigation of factors influencing the movement patterns of a reactivated slow-moving landslide situated in the eastern Liguria region (NW Italy) through the analysis of extensive ground-based hydrological and geotechnical monitoring data. Subsurface horizontal displacement and pore water pressure data were acquired simultaneously by means of automatic sensors positioned at pre-existing and localized failure zones. The joint examination of field measurements enabled us to explore the connections between rain, pore water pressure, and displacements. The results of continuous displacement monitoring showed that the landslide kinematics involved phases of extremely slow movements alternated with periods of relative inactivity. Both stages occurred prevalently at seasonal scale displaying similar durations. The slow-motion phases took place at relatively constant pore water pressure and were ascribed to mechanisms of viscous shear displacements along failure surfaces. Inactive phases entailed no significant deformations, mostly corresponding to prolonged dry periods. The two motion patterns were interrupted by episodic sharp deformations triggered by delayed (preparation periods from 4 to 11 days) rainfall-induced pore water pressure peaks, which were ascribed to sliding mechanisms taking place through rigid-plastic frictional behaviour. During these deformation events, hysteresis relationships between pore water pressure and displacement were found, revealing far more complex hydro-mechanical behaviour.

A Prediction Method for the Average Winding Temperature of a Transformer Based on the Fully Connected Neural Network

The average winding temperature of a transformer (AWTT), serving as a key indicator for assessing the running state of the transformer, is of utmost importance in determining a transformer’s electrical properties and the insulation longevity of the transformer. An accurate prediction of AWTT is essential for ensuring the safe operation of the transformer. A novel method for predicting AWTT is introduced based on the analysis of field monitoring data. Firstly, the thermal characteristics and operational mechanisms of oil-immersed transformers are examined. Secondly, a factor analysis model is developed to streamline the network structure, accounting for the strong correlations among ambient temperature, load current, and top oil temperature. Thirdly, the independent temperature factor and load factor are extracted as pivotal features, and then input into the fully connected neural network to predict AWTT. Through a case study involving a 110 kV/10 kV oil-immersed transformer, the results show that the proposed method reduces redundant correlation information compared to traditional methods and improves the prediction accuracy of AWTT, establishing a foundation for further transformer state assessments.

Characterisation of the ringed seal (Pusa hispida) acoustic repertoire during spring in the Western Canadian Arctic

ABSTRACT Understanding the acoustic repertoire of a species is crucial for comprehending its ecology and can also provide a valuable tool in the analysis of passive acoustic monitoring data. Despite being the most abundant seal species in the Arctic, the call repertoire of ringed seals (Pusa hispida) remains poorly studied. This paper aims to provide a comprehensive and quantitative description of the calls produced by ringed seals. Data collection occurred in May 2022 near Ulukhaktok in the Western Canadian Arctic. Acoustic recorders were deployed in cracks in the sea ice that were regularly used by ringed seals as haul-out areas. All calls were counted and classified into one of three categories: yelps, barks and growls. High-quality calls were further analysed with ten acoustic parameters calculated for each signal. Cluster and classification and regression tree (CART) analyses were used to assess if the visual classification of calls was supported by the acoustic parameters calculated. The cluster and CART analyses affirmed the presence of all three distinct call types in the ringed seal acoustic repertoire. Classification of unseen calls using the CART model demonstrated an accuracy of 98%. The findings presented here serve as foundational information on the acoustic repertoire of ringed seals.

Climate-driven context-dependent structure of population cycles.

Multiannual population cycles of small mammals are of interest within population biology. We propose an approach for multidimensional autoregressive (AR) time series and analyse monitoring data on grey-sided voles (Myodes rufocanus) in Japan to investigate one or possibly multiple multiannual cycles that drive population dynamics. Temperature, through modifying rodent communities, is found to be a key factor shaping population dynamics. Warmer areas are the main habitat for other rodent species resulting in low vole abundance/dominance, as opposed to higher vole dominance in colder areas-a pattern associated with the AR structure and population cycle. Vole populations in simple rodent communities exhibit an AR(2) cycle of 2-3 years. In areas with complex rodent communities, vole dynamics follows an AR(4) process and a combination of two cycles with different lengths. The AR structure varies in relatively small spatial scales, thus widening the scope of AR analyses needed. Historically, vole abundance increased in the late 1970s and decreased from the 1980s, with warm winters shown to be associated with the decline of vole abundance in the AR(4) populations. This significant association between the AR order, population dynamics, temperature and rodent community provides insights into the declining trends observed in rodent populations of the Northern Hemisphere.

The failure mechanism of the Baishi landslide in Beichuan County, Sichuan, China

The Baishi landslide was located in the western part of Beichuan County, Sichuan Province, China. The landslide experienced multiple minor collapses at the front part, accompanying with numerous tensile cracks. To comprehensively grasp the stability conditions and predict the moment of failure of the landslide, deformation monitoring of the landslide has been carried out from the moment that the landslide was reported until it failed. This study analyzed the different phases of landslide deformation and its failure mechanism through the analysis of monitoring data. The result showed that the failure manifests both the retrogressive and advancing features. The landslide was divided into several zones based on the spatial variation of the deformation characteristics. Moreover, the improved tangential angle criterion is applied to categorize the deformation phases of a landslide. Investigating the surface displacement vectors and vector angles of landslides plays a significant role for ascertaining the failure and sliding mechanism. The monitoring results revealed that the front part of the landslide played a key role in the stability of the landslide. Therefore, the monitoring data from this zone were crucial for predicting the moment of complete landslide failure.