Stages Of Degradation Research Articles

An Adaptive Method for Predicting Bearing Remaining Useful Life Across Various Degradation Stages

Abstract Bearing degradation is a multi-stage, multi-trend and highly complex process, significant information discrepancies and extreme imbalances exist in degradation data across different stages. These complexities hinder the accuracy of predictive model in predicting the remaining useful life throughout all stages of the bearing's degradation. In this paper, a novel prediction model based on Adaptive Convolutional Neural Network (ACNN)-Multiple Kernel Convolutional Long Short-Term Memory (MKConvLSTM) is proposed, which utilizes adaptive feature extraction and multi-scale dynamic selection to solve the problem of multi-stage, multi trend and highly complex information in bearing degradation. First, the ACNN is used to perform convolutional feature extraction and adaptive mapping on input samples, effectively distinguishing the degradation stages Then, the MKConvLSTM generates features at different time scales and dynamically selects these features to capture temporal information during the degradation process, enriching the model's capability to represent complex information and improving its predictive performance. To validate the effectiveness of the proposed model, experiments were conducted on the PHM2012 datasets and XJTU datasets. The MAE and RMSE of ACNN-MKConvLSTM reaches 0.078 and 0.099 on the first dataset, 0.086 and 0.107 on the second dataset, respectively. approximately 23% and 17% improvement Compared to the baseline model, respectively. Experimental results demonstrate that the model exhibits high accuracy and robustness in bearing remaining useful life prediction, effectively addressing the impact of feature variations across different degradation stages on prediction performance.

The Potential to Reconstruct 20th Century Soil Organic Carbon Erosion in Rangelands From Small Reservoir Sediments

ABSTRACTSoil erosion and soil organic carbon (SOC) loss are not always linked linearly because SOC‐rich topsoil is eroded at the initial stages of degradation, while horizons with lower SOC content are eroded later, but often at higher rates. Small, silted‐up farm reservoirs potentially document this change during the period of sediment accumulation. This study tests the specific potential of small farm reservoir sediments from the South African Karoo to reconstruct 20th century SOC and total nitrogen (TN) change in rangeland soils. Five reservoir sediment profiles were sampled and texture, total organic carbon (TOC), TN and 137Cs of the samples were analyzed and compared. The results show that there clearly distinguishable flood couplets have been preserved in the sediment, illustrating their suitability for the chronological reconstruction of soil erosion and SOC. With one exception, the older sediments contain more TOC and TN than the younger ones. The TOC changed mostly in earlier than later stages of deposition, which is indicative of soil degradation early after the construction of the dams in the 1920s and 1930s. These distinct changes illustrate that the small reservoir sediments have the potential to reconstruct the impact of land‐use and associated soil erosion on SOC change in rangelands. Their analysis can therefore contribute to a better understanding of the land‐use associated changes of the global carbon cycle during the 20th century.

Meadow degradation reduces microbial β diversity and network complexity while enhancing network stability

Continuing meadow degradation under the dual impacts of climate change and human activities has altered microbially-mediated biogeochemical cycles. However, microbial diversity and network stability in response to meadow degradation and their relationships to environmental variables remain insufficiently understood. This study focused on alpine meadows with varying degrees of degradation in the hinterland of the Qinghai-Tibetan Plateau. It analyzed the soil microbial diversity (α and β diversity) and ecological network topology characteristics under the degradation of alpine meadows. Furthermore, the extent to which these factors elucidate the environmental changes observed during degradation was explored. The results demonstrated that: (1) the α diversity of soil microbial under degradation of alpine meadows exhibited an increasing trend, although not significantly. The β diversity (community dissimilarity) exhibited a significant decreasing trend. These indicated that meadow degradation resulted in microbial homogenization intra- and inter-community. In the light degradation stage, the β diversity of bacteria and fungi significantly increased with geographic and environmental distance rise. Only bacterial β diversity significantly increased with geographical distance in the moderate degradation stage and environmental distance in the heavy degradation stage, respectively. This indicated that meadow degradation possibly altered microbial assembly, especially for fungi. (2) The degree of soil bacterial nodes exhibited a significant decreasing trend with degradation and fungal eigencentrality demonstrated a significant decreasing trend. After removing the identical nodes, the natural connectivity of the fungal network exhibited a significant decreasing trend with degradation. This suggested that the stability of the soil microbial network increased while the network complexity decreased under meadow degradation. These changes possibly marked the irreversible course of alpine meadow degradation. (3) The Mantel test indicated that bacterial diversity and network topological features were more strongly associated with environmental factors than those of fungi. Akaike information criterion (AIC) values and the upset matrix plots of variance partitioning based on Redundancy analysis (RDA) indicated that higher rates of soil factors, such as soil pH, organic carbon (SOC) content, and total potassium (TK), elucidated changes in microbial community diversity and network topological features. This study highlights the importance of soil microorganisms for ecological stability and the role of microorganisms should be emphasized in future efforts to restore alpine grasslands.

Communication—Prediction of Area Specific Resistance of Solid Oxide Cell Stacks from Electrochemical Impedance Spectra

A support vector regression model was trained to predict the area specific resistance of solid oxide cell stacks from electrochemical impedance spectroscopy measurements. In particular, the minimum necessary frequency range required for accurate predictions was investigated. Therefore, 711 pairs of DC polarization and electrochemical impedance spectroscopy measurements, conducted at the same stage of stack degradation and under similar measurement conditions, were evaluated. Prediction accuracies of less than 8% mean absolute percentage error and coefficients of determination greater than 93% were achieved. The 101–102 Hz range is sufficient for accurate predictions, allowing an efficient and non-destructive etimation of DC polarization measurements.

Dynamics of microbial-induced oil degradation at the microscale.

Microbial-induced oil degradation (MIOD) has a wide range of applications, such as microbial enhanced oil recovery and bioremediation of oil pollution. However, our understanding of MIOD is still far from complete. Particularly, how is the dynamics of degradation process at the microscale level with a single-cell resolution remains to be disclosed. In this work, using hexadecane droplets in water as a model system, we have studied the dynamics of hexadecane degradation by different strains, including Pseudomonas aeruginosa PAO1, IMP68, O-2-2, and Dietzia sp. DQ12-45-1b, at the microscale. Based on visualization of MIOD, the dynamics of MIOD can be characterized by a three-stage process, including adhesion, adaptation, and degradation stages. Although different strains showed similar three-stage dynamics of MIOD, the effective degradation rate varied and followed an order of PAO1 > O-2-2 > IMP68 > DQ12-45-1b under aerobic conditions. Different oxygen conditions were also tested, and the dynamics of MIOD was slowed down under anaerobic conditions in comparison to under aerobic conditions. Further investigations at the degradation stage revealed that biofilms formed at the oil-water interface enhanced oil degradation, but a prerequisite for such enhanced degradation is proper stimulation of biofilm cells in the course of biofilm formation. The findings in this work provided a detailed picture on the dynamics of MIOD at the microscale and would be beneficial for better applications of MIOD.IMPORTANCEMicrobial-induced oil degradation is environmental friendly and economic and has become a promising technique in the fields of enhanced oil recovery and remediation of crude oil-polluted environments. For better applications of microbial-induced oil degradation, understanding the degradation dynamics particularly at the microscale is crucial. In this study, we investigated the degradation dynamics of hexadecane oil droplets incubated with different strains, including Pseudomonas aeruginosa PAO1, O-2-2, IMP68, and Dietzia sp. DQ12-45-1b at the microscale by employing microdroplet-based methods and bacterial tracking techniques. The findings in this study provided a detailed picture on the dynamics of microbial-induced oil degradation at the microscale, which will deepen our understandings on the biodegradation mechanisms of alkanes and shed insights for developing more effective biodegradation techniques.

Bio-composites from barley, wheat, and cassava flours reinforced with oil palm residues: Characterization and tensile mechanical performance

This study explores the production of bio-composites from barley, wheat, and cassava flours, reinforced with varying ratios of oil palm residues. The research emphasizes principles of circular economy and sustainability. Both flours and reinforcements underwent characterization to elucidate how their physicochemical properties affect the mechanical behavior of the bio-composites. Barley flour exhibited significantly higher levels of protein (11.11 %), crude fiber (5.64 %), and ash (2.80 %) compared to wheat and cassava flours. Conversely, cassava flour stood out for its high carbohydrate concentration (approximately 97.8 % on a dry weight basis). Characterization highlighted enhanced adhesion between reinforcements in bio-composites with cassava flour, alongside morphological distinctions on fractured surfaces. Fourier-transform infrared (FTIR) analysis unveiled several functional groups in the bio-composites, while thermogravimetric analysis delineated five stages of thermal degradation. In terms of mechanical properties, bio-composites with barley flour showed higher elastic modulus values (970.5 MPa), while those with 12 % OPKS exhibited tensile strengths of 1.7–2.1 MPa. The interaction among components emerged as a fundamental factor influencing the mechanical properties of bio-composites, underscoring the necessity of considering reinforcement composition and distribution during fabrication.

Potentially Pathogenic Vibrio spp. in Algal Wrack Accumulations on Baltic Sea Sandy Beaches

The Vibrio bacteria known to cause infections to humans and wildlife have been largely overlooked in coastal environments affected by beach wrack accumulations from seaweed or seagrasses. This study presents findings on the presence and distribution of potentially pathogenic Vibrio species on coastal beaches that are used for recreation and are affected by red-algae-dominated wrack. Using species-specific primers and 16S rRNA gene amplicon sequencing, we identified V. vulnificus, V. cholerae (non-toxigenic), and V. alginolyticus, along with 14 operational taxonomic units (OTUs) belonging to the Vibrio genus in such an environment. V. vulnificus and V. cholerae were most frequently found in water at wrack accumulation sites and within the wrack itself compared to sites without wrack. Several OTUs were exclusive to wrack accumulation sites. For the abundance and presence of V. vulnificus and the presence of V. cholerae, the most important factors in the water were the proportion of V. fucoides in the wrack, chl-a, and CDOM. Specific Vibrio OTUs correlated with salinity, water temperature, cryptophyte, and blue-green algae concentrations. To better understand the role of wrack accumulations in Vibrio abundance and community composition, future research should include different degradation stages of wrack, evaluate the link with nutrient release, and investigate microbial food-web interactions within such ecosystems, focusing on potentially pathogenic Vibrio species that could be harmful both for humans and wildlife.

Efficient conversion from food waste to composite carbon source through rapid fermentation and ceramic membrane filtration

Anaerobic fermentation of food waste (FW) produces a broth rich in small-molecule organic substances, which has the potential as a composite carbon source for denitrification in wastewater treatment. In this study, the idea was tested by optimizing the fermentation process at different hydraulic residence time (HRT), refining fermentation broth through ceramic membrane filtration, and comparing the performance of fermentation filtrate and other commercial carbon sources. A short HRT of 3 days was a suitable fermentation condition with 88% polysaccharide degradation. Acetic acid contributed 40% of soluble chemical oxygen demand in the fermentation broth, followed by ethanol, propanol, lactic acid, and propionic acid, and the five products accounted for 80%. Ceramic membrane filtration can recover more than 70% of dissolved organic matter and more than 60% of small molecular organic matter and simultaneously remove 99% of SS, 41% of total nitrogen, and 62% of total phosphorus. At the rapid degradation stage, the denitrification rates reached 6.68–10.39 mg NOx−-N/(g VSS·h), which was on par with commercial carbon sources. The short fermentation and the rapid membrane separation were integrated to create an efficient treatment system, which provided a feasible pathway to utilize FW combining wastewater treatment.

Bearing remaining life prediction method based on ARAD -ELN and multi-stage wiener process

Abstract Stochastic process-based models are extensively utilized in health assessments and Remaining Useful Life (RUL) predictions of bearings. Nevertheless, bearings in actual operation undergo multiple degradation stages, each characterized by its unique trend of degradation. The application of a singular stochastic process for RUL prediction falls short of achieving optimal performance. Consequently, this paper introduces a multi-stage Wiener process-based approach for the prediction of bearings' RUL. Initially, to tackle the challenge of imbalanced sample sizes across different degradation stages of bearings, an ensemble learning-based neural network (ELN) enhanced by ARIMA Residual Anomaly Detection (ARAD) for the identification of bearing degradation stages is proposed. Subsequently, taking into account the temporal, unit-to-unit, and nonlinear variabilities of the degradation process at each stage, a Wiener process-based multi-stage degradation model for bearings is developed. A method for parameter estimation and updating, utilizing Kalman filtering and Maximum Likelihood Estimation (K-M) is introduced. Finally, the proposed model undergoes validation using both simulated data and the XJTU-SY bearing dataset. Experimental results of three RUL predictions show that the proposed method leads the benchmark model with RMSE values of 3.61, 2.92 and 7.24 respectively, affirming that the proposed model can precisely classify equipment degradation stages and predict RUL with high accuracy and stability.

Thermal Properties of Seed Cake Biomasses and Their Valorisation by Torrefaction

Seed cakes, by-products from the cold press extraction of vegetable oils, are valuable animal feed supplements due to their high content of proteins, carbohydrates, and minerals. However, the presence of anti-nutrients, as well as the rancidification and development of aflatoxins, can impede their intended use, requiring alternative treatment and valorisation methods. Thermal treatment as a procedure for the conversion of seed cakes from walnuts, hemp, pumpkin, flax, and sunflower into valuable products or energy has been investigated in this paper. Thermogravimetry shows the particular behaviour of seed cakes, with several degradation stages at around 230–280 and 340–390 °C, before and after the typical degradation of cellulose. These are related to the volatilisation of fatty acids, which are either free or bonded as triglycerides, and with the thermal degradation of proteins. Torrefaction at 250 °C produced ~75–82 wt% solids, with high calorific values of 24–26 kJ/g and an energy yield above 90%. The liquid products have a complex composition, with most parts of the compounds partitioning between the aqueous phase (strongly dominant) and the oily one (present in traces). The structural components of seed cakes (hemicelluloses, cellulose, and lignin) produce acetic acid, hydroxy ketones, furans, and phenols. In addition to these, most compounds are nitrogen-containing aromatic compounds from the degradation of protein components, which are highly present in seed cakes.

Asymmetric-Based Residual Shrinkage Encoder Bearing Health Index Construction and Remaining Life Prediction.

Predicting the remaining useful life (RUL) of bearings is crucial for maintaining the reliability and availability of mechanical systems. Constructing health indicators (HIs) is a fundamental step in the methodology for predicting the RUL of rolling bearings. Traditional HI construction often involves determining the degradation stage of the bearing by extracting time-frequency domain features from raw data using a priori knowledge and setting artificial thresholds; this approach does not fully utilize the vibration information in the bearing data. In order to address the above problems, this paper proposes an Asymmetric Residual Shrinkage Convolutional Autoencoder (ARSCAE) model. The asymmetric structure of the ARSCAE model is characterized by the soft thresholding of signal features in the encoder part to achieve noise reduction. The decoder part consists of convolutional and pooling layers for data reconstruction. This model can directly construct HIs from the original vibration signals collected, and comparisons with other models show that it constructs better HIs from the original vibration signals. Finally, experiments on the FEMTO dataset show that the results indicate that the HIS constructed by the ARSCAE model has better lifetime prediction capability compared to other methods.

Influence of the painting medium on the alteration process of smalt in oil paintings studied using combined K and Co K-edge XANES

This study investigates the alteration processes affecting the smalt pigment in historical artworks through a combination of analytical techniques including X-ray Absorption Near Edge Structure (XANES) spectroscopy at the potassium (K) and cobalt (Co) K-edges, coupled with scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDX) and micro X-ray fluorescence elemental mapping (µ-XRF). Laboratory mock-up samples prepared with different oil binding media were artificially aged, revealing varying rates of smalt degradation. µ-XANES analyses at the K K-edge provided grain-scale insights into smalt alteration, highlighting the influence of binding media composition on degradation kinetics. Crossed clustering data analysis over K µ-XANES acquired on both model samples and historical cross-sections corroborated findings and identified distinct spectral signatures associated with different stages of smalt degradation. Furthermore, identification of potential transformation products such as potassium alum in historical samples emphasized the dynamic nature of pigment alteration processes.

Characteristics and Drivers of Soil Carbon, Nitrogen, and Phosphorus Ecological Stoichiometry at the Heavy Degradation Stage of the Alpine Meadow

An in-depth understanding of the soil nutrient status and balance relationship can help the effective recovery and management of alpine degraded meadows. In order to study the balance relationship among soil carbon, nitrogen, and phosphorus nutrients during the heavy degradation stage of meadows, field sampling and investigation, indoor analysis, and mathematical statistics were used to explore the characteristics and driving factors of changes in soil carbon, nitrogen, and phosphorus content, storage, and ecological stoichiometry during the heavy degradation stage of alpine meadows in the Sanjiangyuan region. The results showed that in the heavy degradation stage, miscellaneous grass plants occupied absolute dominance, soil C∶N∶P was approximately 32.83∶3.87∶0.67, and there was certain nitrogen limitation. The coefficients of variation of soil carbon, nitrogen, and phosphorus content were in the following order： organic carbon （1.09） > total nitrogen （0.63） > total phosphorus （0.29）. The organic carbon content and the carbon and nitrogen ratio showed a significant linear decreasing trend with the increase in the grassland degradation index （GDI）, while the total phosphorus content and organic carbon storage showed a significant non-linear change, in which the total phosphorus content showed a significant gentle U-shaped distribution, and the organic carbon storage decreased more gently at the beginning of the heavy degradation stage and then decreased sharply when the GDI was 57.9. The results of Mantel correlation analysis showed that the soil carbon to nitrogen ratio, carbon to phosphorus ratio, and nitrogen to phosphorus ratio showed significant correlation with organic carbon content and storage and total nitrogen storage. The results of structural equation modeling indicated that soil water content had direct effects as well as indirect through vegetation factors, soil carbon, nitrogen, and phosphorus ecological stoichiometry ratios, and soil water content and vegetation factors （height, cover, and biomass） were key environmental factors affecting soil ecological stoichiometry. The research results can provide scientific basis and practical guidance for the restoration of heavily degraded grassland in alpine meadows.

A self-supervised assisted label-efficient method for online remaining useful life prediction

Remaining Useful Life (RUL) prediction is crucial for safe and efficient production in industrial equipment. Conventional methods struggle in online environments, facing challenges like data distribution differences across working conditions, model cold start problem under new equipment, and lack of labels in online scenarios. To address these challenges, we propose a novel framework for online RUL prediction that leverages knowledge from self-supervised tasks via metric learning. First, a label-efficient self-supervised learning method incorporating soft triplet loss is proposed to transfer degradation knowledge. Second, we develop a two-stage method to distinguish different degradation stages of the equipment for better alignment of data distribution. Third, we introduce a pseudo-label dynamic self-updating strategy and a sample entropy-based data replay strategy, fine-tuning the offline model with newly acquired data. Our method outperforms state-of-the-art unsupervised approaches, reducing RMSE and MAPE by 16.02% and 8.40%, respectively, and achieves performance comparable to supervised methods.

Fe2O3, Al2O3, or sludge ash-catalyzed pyrolysis of typical 3D printing waste toward tackling plastic pollution

Catalytic pyrolysis offers a potential solution to tackling plastic pollution by transforming plastic waste into valuable chemicals. This study explored the catalytic pyrolysis of 3D printing waste (3DPW), specifically focusing on photosensitive resin waste (PRW) and polycaprolactone waste (PCLW), with Al2O3, Fe2O3, or sludge ash (SA) containing Fe/Al. The study revealed a synergistic effect between the catalyst and 3DPW, influencing the pyrolysis properties and kinetic models. The addition of Fe2O3 significantly accelerated the main degradation stages, promoting the releases of 2-Ethylacrolein (64.78 % from PRW) and 2-Oxepanone (16.45 % from PCLW), as well as decreasing the acidic products. The catalytic pyrolysis changed the valence state of Fe, with some Fe(III) shifting to Fe(II), accompanied by the release of CO2. The addition of Al2O3 or SA generated new gaseous products (e.g., 2.93 % 1,3-Pentadiene, 2-methyl-, (E)-) through volatile reforming. The joint optimization of the multi-response artificial neural network revealed PCLW/Fe2O3 between 325–399 °C (D = 0.669) as the optimal operation for achieving both minimal remaining mass and maximum decomposition rate. These findings offer actionable insights into the catalytic pyrolysis of 3DPW, promoting its efficient treatment and clean reutilization.

Studies on a Mobile Acoustic Emission Sensor Verification Device

Despite the fact that there are two ASTM standards, ASTM 1106 for primary calibration and ASTM E 1781 for secondary calibration of acoustic emission sensors, there is no company or institution currently offering calibration services based on these standards due to lack of availability or practical aspects. Another option is CEN ISO/TR 13115, which needs a large volumetric propagation medium leading to similar practical limitations as for the ASTM standards. Many acoustic emission users try to fill the gap of calibration services by using simplified verification standards. However, there is currently no verification standard suitable for using in the field. In that sense, one ambition of the project CalibrAEte is to establish a new standard for primary and secondary calibration that can be implemented in laboratories without a huge invest. Another objective of the project is to establish a verification procedure using a device to verify acoustic emission sensors by a wave-based approach in the field. The proposed approach uses a durable propagation medium that carries the elastic waves from a repeatable source to the sensor under test. This research focuses on such a mobile verification device in the sense that an optimal configuration is proposed. This contribution will present the major aspects and details of an acoustic emission sensor verification setup that can be used not just in the laboratory environment but also in harsh field conditions. It will be shown the performance of the reference sensor developed for this purpose along with the setup itself, which was partially developed by aid of numerical methods. Also, experimental results in different temperature conditions, will be presented to assess the robustness of the setup. It also be presented a sensitivity analysis of the setup using different sensors in different stages of degradation.

Robust feature design for early detection of ball screw preload loss

The ball screw is a critical device for precision linear motion control that has widespread applications in industrial robots, computer numerical control (CNC) machines, and high-precision leveling systems, among others. Because high-precision positioning is ensured by the addition of a preload to the ball screw system, it is crucial to detect and monitor the loss of preload at the earliest possible stage of degradation. The degradation process of the ball screw can be characterized in two stages: the initial reduction of preload without backlash, followed by a loss of preload with an emergence of backlash. To explore the change of the ball screw dynamics caused by degradation, a novel fixed cycle feature test (FCFT) is implemented in combination with multi-level mass experiments and a run-to-failure (RTF) test. The relationships of the ball screw dynamics with preload, worktable mass, and axis position are investigated, with a focus on the axial natural frequency as an indicator of preload loss. Experimental results validate the axial natural frequency’s role as a reliable early detector of preload loss for interventive use in a prognostic system.

Thermal Behaviour, Chemical Composition and Morphological analysis ofCotton Stalk for Efficient Biochar Production

The escalating concern over air pollution from crop stubble burning necessitates responsible biomass disposal or utilisation. In this study, characterisation of cotton stalk was carried out to assess its feasibility for biochar production. The proximate analysis revealed that cotton stalk had moisture content of 8.65%, bulk density of 215.8 kg m-3, volatile matter of 71.65%, and fixed carbon content of 16.03%. The ultimate analysis showed that cotton stalks were predominantly composed of carbon (64.88%), hydrogen (6.58%) , and oxygen (2.55%), with a low (0.66%) nitrogen content . Thermo-gravimetric analysis (TGA) highlighted the pyrolysis process, identifying a peak temperature of approximately 500°C and distinct stages of thermal degradation: moisture loss up to 150°C, hemicellulose decomposition between 200°C and 300°C, cellulose decomposition from 300°C to 400°C, and lignin decomposition from 400°C to 500°C. X-ray diffraction (XRD) analysis confirmed the presence of crystalline cellulose with a diffraction peak at 2θ = 22.5°, indicating significant structural stability during pyrolysis for biochar production. Fourier-transform infrared (FTIR) spectroscopy revealed characteristic peaks associated with cellulose and lignin, underscoring the retention of essential structural components in biochar during biomass conversion. These analyses demonstrated the potential of cotton stalks as a viable feedstock for biochar production, providing a sustainable method for agricultural waste management and soil health improvement.

Shift in potential pathogenic bacteria during permafrost degradation on the Qinghai-Tibet Plateau

Permafrost acts as a potential pathogen reservoir. With accelerating climate change and intensifying permafrost degradation, the release of these pathogens poses significant threats to ecosystems and public health. However, the changes in pathogenic communities during permafrost degradation remain unclear. This study utilized quantitative PCR and Illumina high-throughput sequencing to analyze the composition and quantities of potential pathogenic bacteria in four types of permafrost soil on the northeast edge of the Qinghai-Tibet Plateau (QTP): sub-stable permafrost (SSP), transition permafrost (TP), unstable permafrost (UP), and extremely unstable permafrost (EUP). The results showed that during permafrost degradation, the quantity of potential pathogenic bacteria decreased from 7.8 × 106 to 3.1 × 106 copies/g. Both the Richness and Shannon indices initially declined from SSP, to TP, UP, and then began to rise when permafrost degraded to EUP. A total of 216 potential pathogenic bacterial species were identified, including 166 animal pathogens, 28 zoonotic pathogens, and 22 plant pathogens. The pathogenic community intergroup differences (ANOSIM), unique taxa, and dominant pathogen analysis indicated the significant changes in pathogenic communities during permafrost degradation. The potential pathogenic community was significantly influenced by non-pathogenic bacterial communities (Procrustes analysis), with soil moisture being the primary environmental factor, followed by TDS, soil organic carbon, and total nitrogen. SourceTracker2 analysis indicated that the majority of potential pathogenic bacteria in the soil originated from external sources, only a small portion coming from the permafrost itself. These findings suggest that a large number of pathogens were released into the environment while also preserving amount from external sources. It elucidates that each stage of permafrost degradation presents unique biosecurity risks. This study highlights the release and redistribution of pathogenic bacteria associated with the potential public health risks. It provides the crucial insights into the ecological dynamics of permafrost degradation, emphasizing the need for ongoing monitoring and proactive management strategies.

A procedure for assessing of machine health index data prediction quality

The paper discusses the challenge of evaluating the prognosis quality of machine health index (HI) data. Many existing solutions in machine health forecasting involve visual assessing of the quality of predictions to roughly gauge the similarity between predicted and actual samples, lacking precise measures or decisions. In this paper, we introduce a universal procedure with multiple variants and criteria. The overarching concept involves comparing predicted data with true HI time series, but each procedure variant has a specific pattern determined through statistical analysis. Additionally, a statistically established threshold is employed to classify the result as either a reliable or non-reliable prognosis. The criteria include both simple measures (MSE, MAPE) and more advanced ones (Space quantiles-inclusion factor, Kupiec’s POF, and TUFF statistics). Depending on the criterion chosen, the pattern and decision-making process vary. To illustrate effectiveness, we apply the proposed procedure to HI data sourced from the literature, covering both warning (linear degradation) and critical (exponential degradation) stages. While the method yields a binary output, there is potential for extension to a multi-class classification. Furthermore, experienced users can use the quality measure expressed in percentage for more in-depth analysis.