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In the present study, we evaluated the biological response of Achirus lineatus to water accommodated fraction (WAF) of light crude oil (American Petroleum Institute gravity 35°) during a sub-chronic bioassay (14 and 28 days) at two different concentrations: 5% v/v (1.20 µg∙L− 1 expressed as total polycyclic aromatic hydrocarbons [∑25 PAH]) and 10% v/v (6.61 µg∙L− 1 [∑25 PAH]). The responses were evaluated through the biomarker response index (BRI), the integrated biomarker response (IBRv2) and the bioconcentration factor (BCF). The results showed an increase in biological response in relation to WAF concentration and exposure time, which resulted in a slight and moderate disturbance in the basal condition and bioconcentration level of metals (Pb > Ni > V > Cd) in fish tissue. Results in the present study denote that flatfish such as A. lineatus may be negatively influenced by spilled light crude oil. [ABSTRACT FROM AUTHOR]

Safety, efficiency, and reliability are essential requirements for aero-engines. Timely and accurate diagnosis of engine faults enables effective planning of maintenance operations and reduces downtime. Although traditional physics-based methods perform well under controlled test bench scenarios, their effectiveness in handling very noisy data and missing values is limited, constraining their utility in real-world settings. To address these gaps, we propose a fusion autoencoder that combines physics-informed and pattern-informed techniques, augmented with a Beta-Variational Autoencoder learning backbone to enhance the robustness of the model. Additionally, a novel health index called the piecewise anomaly index is proposed that can detect and classify faults simultaneously. To evaluate the efficacy of the novel framework, we modified the New Commercial Modular Aero-Propulsion System Simulation (N-CMAPSS) dataset to simulate real-world scenarios and conducted experiments. The results show that the proposed method can detect faults earlier than common techniques, while also achieving accurate fault classification and degree determination with the new index. [ABSTRACT FROM AUTHOR]

Contact probe is broadly used for the continuous monitoring of microelectronic components in manufacturing industries. False rejection of fine product due to defective contact probe significantly reduces the yield in production. Traditionally, defect detection for contact probes heavily depends on a valid range manually defined by engineers over the measured value of certain parameters. However, the subjective range defined according to engineer experience is prone to trigger a high rate of false alarms due to the inherent noise in the measured parameters. To address this issue, we construct a health index (HI) with the contact resistance‐directly‐related features to help monitor and assess the condition of contact probe. Based on the established HI, we develop Long Short‐Term Memory (LSTM) encoder‐decoder machine learning model to assess the condition of contact probe by forecasting the HI value in the future. Encoders from LSTM and convolutional neural network (CNN) are selected as the encoder‐decoder architecture for the sequence‐to‐sequence prediction due to their advantage in extracting the correlation of features at different scales. An explainable Artificial Intelligence (XAI) technique named Local interpretable model‐agnostic explanations (LIME) is used to quantify the contribution of each feature to the model prediction. The encoder from CNN is found to outperform the LSTM encoder in extracting the inter‐feature correlation. Finally, the predicted HI is used to signal the alarm for the maintenance action of contact probe when its value is below a predefined threshold. Comparison between the action alarm triggered by the developed HI and the actual maintenance records suggests that the proposed approach achieves at least 75% accuracy for the triggered alarm in the next 15 mins. [ABSTRACT FROM AUTHOR]

Fan blades are ones of the core components of the gas path of a high bypass ratio turbofan engine, and fan outlet guide vane detachment is a severe failure mode. This failure could potentially damage the aircraft or other engine components, leading to catastrophic accidents. Therefore, early warning of fan outlet guide vane detachment has become an important task. However, due to the subtle early features of this type of failure, existing methods struggle to effectively warn against it. Therefore, to address this issue, a failure warning method based on deep feature extraction and support vector data description (SVDD) is proposed using monitoring data, aiming to achieve early warning of fan outlet guide vane detachment. First, a modeling method based on engine gas path performance identification is used to establish an observation model of specific engine performance parameters for deep feature extraction. The difference between the real state quantity and the model observation quantity is used as the feature of whether the aero engine has a failure. Second, the SVDD algorithm is used to establish a decision boundary, realizing the automatic division of failure data. The threshold generated by the decision boundary can provide an alarm within a certain time before the failure occurs. Finally, after multiple calculations, the results show that in the interval from the early stage of the failure to the occurrence of the failure, the performance parameters characterizing its health status have a large deviation from the observation quantity, indicating the effectiveness of the selected features. The failure simulation data generated using data augmentation methods are compared and verified with real data. Compared with the actual time of the failure, the warning model realizes the warning of the failure on average 3.14 h in advance. [ABSTRACT FROM AUTHOR]

Scholars and engineers attach great importance to fault detection in mechanical systems due to the unpredictable faults that arise from long-term operations under complex and extreme conditions. The fact that each type of fault embodies unique characteristics makes it challenging to obtain sufficient fault samples, and conventional machine learning methods fail to provide satisfactory fault diagnosis results. To address this issue, a simulation-driven fault detection method has been proposed in this paper. Firstly, the DT model of the gear transmission system was established. An improved multi-objective sparrow search algorithm (MOSSA) was employed to update the model and obtain an adequate number of simulation fault samples as well. Secondly, a two-stage adversarial domain adaptation model with full-scale feature fusion (ADAM-FF) was utilized to align and integrate the features of simulated and generated fault samples. This enables model training and classification of combined samples, facilitating the detection of unknown faults in actual measurements. Lastly, a simulation-driven equipment health index assessment model which accurately and non-destructively evaluates the degradation status of the equipment was introduced. This model effectively quantifies the extent of equipment degradation, thereby facilitating the transfer from the simulation realm to practical engineering applications. To validate the effectiveness of the proposed fault detection method, an experimental study was conducted on the extruder gear reducer of a petrochemical enterprise. The proposed fault detection method has the potential for widespread application across a range of large-scale mechanical equipment. As such, the utilization of this method will enable proactive maintenance planning, ensure safe and stable equipment operations, and minimize energy loss. [ABSTRACT FROM AUTHOR]

Avoiding downtime is one of the major concerns of manufacturing industries. In the era of connected industry, acquiring data has become cheaper than ever; however, turning that data into actionable insights for operators is not always straightforward. In this work, we present a manufacturing scenario involving a circular blade rubber cutting machine, where the goal is to minimize downtime. Historical cutting data are available, and the aim is to provide the machine operators with an intuitive tool that helps them reduce this downtime. This work demonstrates how, in an Industry 4.0 environment, data can be leveraged to minimize downtime. To achieve this, different survival model approaches are compared, a Health Index (HI) is developed, and the model deployment is analysed, highlighting the importance of understanding the model as a dynamic system in which the operator plays a key role. [ABSTRACT FROM AUTHOR]

In order to extend the service life of a highway power supply system and the level of new energy consumption, a power dispatching strategy considering the health status of multi-energy conversion equipment is proposed in this paper. Firstly, the energy and load forms of the highway power supply system are introduced, and the structure of the multi-energy conversion equipment, the topological structures of the DC–DC and DC–AC modules, and the operating characteristics are analyzed. Secondly, the module temperatures and output voltages are used as main parameters to establish the health indexes of DC–DC and DC–AC modules, and then the health index of the multi-energy conversion equipment is further calculated. Thirdly, the new energy consumption index is defined, and a multi-objective optimization model for power dispatching of highway power supply systems is established with the goal of improving the health index of multi-energy conversion equipment and the new energy consumption index. The case study shows that the power dispatching strategy in this paper can better control the temperature of each module, improve the health status of multi-energy conversion equipment, and have a high level of new energy consumption. [ABSTRACT FROM AUTHOR]

The assessment of rolling bearing reliability is vital for ensuring mechanical operational safety and minimizing maintenance costs. Due to the difficulty in obtaining data on the performance degradation and failure time of rolling bearings, traditional methods for reliability assessment are challenged. This paper introduces a novel hybrid method for the reliability assessment of rolling bearings, combining the convolutional neural network (CNN)‐convolutional block attention module (CBAM)‐ bidirectional long short‐term memory (BiLSTM) network with the Wiener process. The approach comprises three distinct stages: Initially, it involves acquiring two‐dimensional time‐frequency representations of bearings at various operational phases using Continuous Wavelet Transform. Subsequently, the CNN‐CBAM‐BiLSTM network is employed to establish health index (HI) for the bearings and to facilitate the extraction of deep features, serving as input for the Wiener process. The final stage applies the Wiener process to evaluate the bearings' reliability, characterizing the HI and quantifying uncertainties. The experiment is performed on bearing degradation data and the results indicate the effectiveness and superiority of the proposed hybrid method. [ABSTRACT FROM AUTHOR]

Keandalan operasi transformator sangat tergantung pada kondisi isolasi yang baik. Penelitian ini bertujuan untuk menganalisis tahanan isolasi dan mengestimasi umur transformator dengan menggunakan metode Health Index di PT PLN (Persero) UPT Cawang. Metode Health Index ini mengintegrasikan berbagai parameter kondisi transformator, termasuk hasil pengukuran tahanan isolasi, pengujian minyak isolasi, dan parameter operasional lainnya, untuk memberikan gambaran komprehensif tentang kondisi kesehatan transformator. Data diperoleh dari pengujian lapangan dan laboratorium yang dilakukan secara periodik. Hasil analisis menunjukkan bahwa beberapa transformator memiliki nilai tahanan isolasi yang mendekati batas minimal yang direkomendasikan, mengindikasikan adanya penurunan kualitas isolasi. Estimasi umur transformator dilakukan berdasarkan nilai Health Index yang dihitung, dengan mempertimbangkan degradasi material isolasi dan kondisi operasional transformator. Transformator dengan nilai Health Index yang rendah diidentifikasi memiliki risiko tinggi terhadap kegagalan operasional dan memerlukan tindakan pemeliharaan atau penggantian segera. Studi ini memberikan wawasan penting bagi manajemen pemeliharaan di PT PLN (Persero) UPT Cawang untuk meningkatkan keandalan dan efisiensi operasi transformator. Implementasi metode Health Index terbukti efektif dalam memprediksi umur sisa transformator dan merencanakan strategi pemeliharaan preventif. Dengan demikian, dapat diharapkan peningkatan kontinuitas layanan penyaluran energi listrik dan pengurangan risiko gangguan operasional. Kata kunci: Estimasi Umur, Health Index, Pemeliharaan Preventif, Tahanan Isolasi, Transformator.

Transformers are among the most important components in the energy grid due to their missions and high costs. The challenging operating conditions deteriorate their components and shorten the life of the transformers. The health index approach is a critical and effective method for monitoring transformers in the operating environment, early diagnosis of possible malfunctions, and evaluation of their general condition. DGA, OQA, and PIF parameters, which represent the condition of the insulation materials, which mainly determine the life of transformers, constitute the basic inputs of the health index approach. In this study, a new method was proposed to determine the DGAF score based on the dissolved gases in mineral oil. With this method, in addition to the delta and rate values of the gases, the past DGA results of the transformer were also considered in determining the DGAF. In this way, faults experienced during operation are included in the health index calculation. The proposed method was tested using 36 DGA results obtained over a period of approximately 10 years from a transformer operating in the grid. The obtained results are presented in comparison with the traditional DGAF scoring method. [ABSTRACT FROM AUTHOR]

Copyright of Techno.com is the property of Universitas Dian Nuswantoro, Fakultas Ilmu Komputer and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background and Objective: The presence of toxic mercury in fish has caused global concern, as one of the main ways of humans are exposed to it is through fish consumption. Therefore, the main goal on this research was to determine the concentration of mercury in fish and evaluate the health risk to consumers. Materials and Methods: Samples of pike, common perch, European perch, common carp and goldfish were collected from Anzali wetland. Mercury concentration was measured using a graphite furnace atomic absorption spectrometer. Differences in mercury concentration among the species and the comparison of mercury concentration with the standards were analyzed using one-way analysis of variance and one sample t-test, respectively. Results: The average concentrations of mercury for pike, common perch, European perch, common carp and goldfish were 59.59, 67.55, 30.45, 9.84, and 10.44 µg/kg ww, respectively. The results showed significant differences in mercury concentrations among different species. The concentrations of mercury in all samples were below the permissible limits of international standards (300 and 500 µg/kg dw), and the HQ index values were less than 1. Therefore, the results indicated no potential risk to consumer health. Additionally, the consumption of European perch, common carp and goldfish is considered safe for sensitive people (children and pregnant women). Conclusion: In general, there was no potential concern regarding mercury exposure from consuming the studied fish species. [ABSTRACT FROM AUTHOR]

A transmission tower is one of the components in power infrastructure supporting overhead power lines (OHL). Electrical components, structural, and environmental factors are classified as the primary concern as they can cause catastrophic failure in transmission lines. Transmission towers are in various environments, such as coastal and industrial areas, with different atmospheric corrosion levels due to various corrosive pollutants. For maintenance planning, it is essential to consider the effects of corrosion on towers by physical evaluation influenced by atmospheric corrosion. The physical evaluation of each element uses a scoring or rating method ranging from one to five. The Analytic Hierarchy Process (AHP) and Health Index (HI) are used to evaluate the overall condition of the towers. The study discovered that soil corrosivity in coastal areas is high, and atmospheric corrosion is due to chloride content. Although the pollutants in those areas are high and corrosive, the physical evaluation found that most industrial, coastal, and road towers are in good condition at a rating of 4 and 5. The HI result is the dominance of 71% to 85%, which indicates that the towers are in good health. [ABSTRACT FROM AUTHOR]

One of the main tasks of condition-monitored maintenance and repair of electrical equipment is to assess its technical condition. The current regulations provide a procedure for assessing the technical condition of electrical equipment by calculating the health index (HI). To calculate the HI of a 110 kV tank-type SF6 circuit breaker, it is necessary to evaluate 46 parameters. The value of the HI should be updated and predicted for the next 5 years at least once a year. However, to update about half of the 46 parameters, it is required to disable the circuit breaker. This paper proposes an algorithm for automating the evaluation of the HI of a circuit breaker and determining the HI from a limited set of data obtained from the automated systems installed at the facility. A complete set of retrospective data is used for machine learning model, which allows automatic calculation of the HI of the circuit breaker during operation without the participation of maintenance personnel. Retrospective data on 20% of like equipment are used for training the model. After that, the HI of the remaining equipment can be automatically determined from a limited data set, without human intervention. This will allow us to reduce the resources for calculating the HIs of circuit breakers and to determine the HI of a circuit breaker during operation, without taking it out of operation [ABSTRACT FROM AUTHOR]

Conversion of dumpsites to farm lands in several communities is a usual practice in Nigeria. Wastes accumulate heavy metals in a variety of forms. This study assessed the concentration, degrees of contamination, and attendant health risk of heavy metals (HMs), using two major indigenous vegetables (Amaranthus viridis and Talinum triangulare) grown on five major dumpsites in Lagos state. After wet digestion, the mean concentrations of the HMs in the vegetable samples were evaluated using atomic absorption spectrophotometer (AAS). Daily intake of metals (DIM), target hazard quotient (THQ), and hazard index (HI) biomathematics were employed in the assessment of non-carcinogenic health risk. Incremental lifetime cancer risk (ILCR) assessment was used to assess carcinogenicity. The obtained result shows that the concentrations of HMs fell within the following ranges: (0.37 to 0.59), (0.07 to 1.36), (0.30 to 1.92), (0.00 to 0.03), and (0.00 to 0.04) mg/kg; for zinc (Zn), lead (Pb), Iron (Fe), cadmium (Cd), and chromium (Cr), respectively, with low to moderate variability. At Ikorodu dumping site, the Pb concentration was above the World Health Organization (WHO) permissible range and has the highest contamination factor. DIM for Pb was also above threshold values (> 1) in both adults and children, while the THQ values for Fe, Pb, and Cd were above 1 (> 1) in both adults and children. HI values for the vegetables exceeded WHO normal range (> 1), except Abule-Egba dumps' samples (70% HI greater than 1 in adults and 90% HI greater than 1 in children). Additionally, the ILCR values of above 50% of the samples were above the WHO (10−6) limits, with the highest value in children (Cd, 1.064 × 10−3) indicating high risk of carcinogenicity over a life time of exposure. Thus, the results revealed great health risk from consumption of vegetables from the four major dumping sites, with children being at greater risk. [ABSTRACT FROM AUTHOR]

• Patient reported outcome measure for all limb girdle muscular dystrophies (LGMDs). • This outcome measure is specific to LGMD and measures 15 areas of disease burden. • This measure is designed for use in clinical trials to capture patient experience. Limb girdle muscular dystrophy (LGMD) is a debilitating disease and the fourth most common muscular dystrophy. This study describes the development of the LGMD-Health Index (LGMD-HI). Participants were aged >18 years and recruited from three LGMD registries and GRASP-LGMD consortium. The initial instrument, comprised of 16 thematic subscales and 161 items, underwent expert review resulting in item removal as well as confirmatory factor analysis followed by inter-rater reliability and internal consistency of the subscales. Following expert review, one subscale and 59 items were eliminated. Inter-rater reliability was assessed and five items were removed due to Cohen's kappa <0.5. The final subscales had high internal consistencies with an average Cronbach alpha of 0.92. Test re-test reliability of the final instrument was high (intraclass correlation coefficient=0.97). Known groups validity testing showed a statistically significant difference in LGMD-HI scores amongst subjects based on ambulation status (28.7 vs 50.0, p < 0.0001), but not sex, employment status, or genetic subtype. The final instrument is comprised of 15 subscales and 97 items. The LGMD-HI is a disease-specific, patient-reported outcome measure designed in compliance with published FDA guidelines. This instrument is capable of measuring burden of disease with no significant variation based on LGMD subtype. [ABSTRACT FROM AUTHOR]

The association of hydration knowledge and health habits with hydration status and fluid intake is rarely examined. We sought to determine whether knowledge or physical health behaviors predict physiological hydration status and fluid intake. Ninety-six participants (59 female; 27 ± 10 year) completed the previously validated hydration survey. Participants then recorded total fluids consumed (TFC), collected urine, and tracked void frequency for 24 h. Hydration status was assessed via 24 h urine specific gravity (USG) and osmolality (Uosm). Health behaviors included self-reported physical activity, BMI, smoking, alcoholic drinking, and sleep status. TFC was significantly correlated with 24 h USG (r = −0.390; p < 0.001), Uosm (r = −0.486; p < 0.001), total urine volume (r = 0.675; p < 0.001), and void frequency (r = 0.518; p < 0.001). Hydration knowledge was not correlated with 24 h USG (r = 0.085; p = 0.420), Uosm (r = 0.087; p = 0.419), urine total volume (r = 0.019; p = 0.857), void frequency (r = 0.030; p = 0.771), or TFC (r = 0.027; p = 0.813). Hydration knowledge did not predict 24 h USG (LR+ = 1.10; LR− = 0.90), Uosm (LR+ = 0.81; LR− = 1.35), or TFC (LR+ = 1.00; LR− = 1.00). Health habits did not predict 24 h USG, Uosm, or TFC. In conclusion, self-reported 24 h diet and fluid log recording is comparable to hydration status verification via 24 h urine collection. Hydration knowledge and health habits are not related to, or predictive of, hydration status. [ABSTRACT FROM AUTHOR]

[Objective] DC circuit breaker is a key equipment in metro traction power supply system. To improve the reliability of the traction and power supply system through reasonable maintenance strategy, it is necessary to evaluate the health condition of DC circuit breaker accurately. [Method] Health indexes are used to characterize the overall health condition of the equipment and establish a comprehensive evaluation model of the equipment health condition. On the basis that the changes of the equipment characteristic parameters reflect the equipment health condition, evaluation indexes are reasonably selected and a multi-index evaluation system of equipment operating condition is established. With the method of hierarchy and entropy weight, the calculation method of the weight of each sub-index is improved, making the equipment health condition evaluation model more reasonable. The equipment health index is calculated based on the evaluation model, and the service life model based on historical health data is established to fit the trend of the equipment health index, evaluate the future health condition and predict the remaining service life. The proposed evaluation model is validated by the actual data of a traction substation. [Result & Conclusion] The example analysis results show that the proposed evaluation model of DC circuit breaker is effective and rational to some extent in that it can scientifically judge and predict the healthy condition and remaining service life of the DC circuit breaker.

Monitoring and protecting freshwater habitats are paramount for a sustainable water management perspective. This study investigated potentially toxic elements (PTEs) in the potamic water of the Anday Stream Basin (Türkiye), Black Sea Region, for a hydrological year (from May 2020 to April 2021). Among PTEs, the highest average values were recorded for sodium (Na) at 41.3 mg/L and the lowest for mercury (Hg) at 0.009 μg/L and noted under quality guidelines. The stream was found to be at the level of "Low Heavy Metal Pollution" and "Low Contamination" based on the ecotoxicological risk indices. The highest calculated hazard quotient (HQ) value of 1.21E-02 for Cd was noted in the children via the dermal pathway and the lowest of 6.91E-06 for Fe in adults via the ingestion pathway. Results revealed a higher hazard index (HI) value of 1.50E-02 for Cd to children and the lowest of 1.98E-05 for Fe to adults. As a result of applying agricultural risk indices, the stream showed sodium adsorption ratio values less than 6 and was found to be "Excellent" for agriculture. However, the sodium percentage values were less than 20 and found "Permissible" and the magnesium hazard > 50 and noted as "Unsuitable" for agriculture. Statistical analysis revealed that natural factors mainly attributed to PTE contamination of the Anday Stream Basin. [ABSTRACT FROM AUTHOR]

Club convergence research has become a prominent focus in economic growth and development research over the past thirty years. When evaluating the degree of national well-being in studies on the convergence of living standards across various locations, gross domestic product (GDP) per capita is frequently used. Utilizing the convergence and clustering technique put out by Phillips and Sul (2007), the analysis yields a better health index. Five health indicators are included in the index. A final health index is obtained by combining the indicators using principal component analysis (PCA). Overall, the results of the study reject the notion that every district in Pakistan reaches a single state of equilibrium for the health index. Four convergence clubs were discovered after testing club convergence. The results show that health outcomes varied throughout districts, emphasizing the necessity for policies designed to lessen these spatial inequities. [ABSTRACT FROM AUTHOR]

Background and Objective: One of the significant challenges in mining areas is the pollution of the environment by heavy metals. Therefore, it is crucial to assess the pollution risk associated with mining wastes and take action to mitigate their environmental impact. The current study assessed the risk potential of recently deposited tailings in the Songun copper mining area. Materials and Methods: Based on the conditions of tailings, 26 samples were randomly selected from the recently deposited mine wastes. Twenty-two thin and thin polished sections were prepared for lithology and mineralogy studies. Inductively-Coupled Plasma Mass Spectrometry (ICP-MS) was employed to analyze all 26 samples, while X-ray diffraction method (XRD) was used to analyze a subset of 10 samples. Results: Sulfide minerals, as the main source of environmental pollution, remain intact and unaffected in the tailings. However, the majority of potentially toxic elements (PTEs) exhibit higher concentrations in the waste composition than the standard levels, resulting in a total ecological risk index of 49.93. Geochemical indicators highlight significant pollution levels for elements such as lead (Pb), arsenic (As), and copper (Cu). The values of the non-carcinogenic risk index for children (except As and Fe) and adults are lower than 1, indicating a non-significant non-carcinogenic health risk. However, the carcinogenicity index also indicates a significant carcinogenic risk in the case of long exposure to wastes, particularly for children. Conclusion: Therefore, wastes pose a significant environmental risk potential, and due to this risk, proper management of their storage is necessary to prevent the release of PTEs into the environment. [ABSTRACT FROM AUTHOR]

To address the challenge of distinguishing the health status of bearings, in this paper, a health index (HI) is developed through utilization of the multiple target time-varying black widow optimization–bidirectional gating recurrent unit (MTBWO-BiGRU) model and the Bray–Curtis distance. This index offers a visual representation of the health status of bearings, enabling more intuitive monitoring and prediction. The first step involves utilizing L1 regularization to extract effective features as degradation elements from the current bearing vibration data. Additionally, the characteristics of the initial time window of the vibration data serve as the health features. Next, the HI of the bearing is constructed by computing the Bray–Curtis distance between the bearing's degradation characteristics and health features. The cloud monitoring platform constantly tracks the health of the bearing and employs the MTBWO-BiGRU model to anticipate the forthcoming state of health. The platform generates an immediate alert when the HI of the bearing overtakes the alteration rate threshold and foresees the condition of the bearing. We compare the MTBWO-BiGRU model with the bidirectional long short-term memory (BiLSTM) and BiGRU models. The results indicate an accuracy level of 92.57%, which is evidently higher than that obtained when using the other two models. Moreover, the MTBWO-BiGRU model is lighter, demonstrating the practicality of the proposed approach. [ABSTRACT FROM AUTHOR]

Around the world, undernutrition causes more than half of all deaths in children under 5 years of age. It also increases children's vulnerability to common diseases, impairs recovery, and stunts their physical and mental development. This paper investigates the state of child health and the assessment of health inequalities in children under 5 years, utilising Multiple Indicators Cluster Surveys across 36 districts in Punjab, Pakistan. We suggest a new household-based health index to identify health disparities and to help policymakers implement more successful domestic policies to offset inequalities. Additionally, this study estimates the health disparities for robustness checks applying the Palma ratio and Gini coefficient. The study's findings revealed that every second child in Punjab is malnourished and that 51% of children in Punjab had not received all of their recommended vaccinations. The results also show that health disparities in low-income districts are worse than in high-income districts. The findings further demonstrate that important contributors to health disparities include parental illiteracy, poverty, and political backwardness. This study recommends a multifaceted policy intervention to address child immunization, undernutrition, and infectious disease legislation, as well as income inequality, to reduce health disparities. [ABSTRACT FROM AUTHOR]

Copyright of Urban Mass Transit is the property of Urban Mass Transit Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Health index is an essential tool to evaluate the condition of power transformers. Generally, there are two methodologies to evaluate the health index of power transformers: conventional methods and machine learning-based approaches. The main challenge in employing the health index to assess transformer’s condition is the lack of supporting data, which hinders its ability to accurately reflect the actual health condition of the transformers. While several studies on estimating the transformers health index using machine learning techniques can be found in the literatures, not much attention was given to the cumulative impact of multiple data unavailability on the calculation’s accuracy. This paper presents a comprehensive analysis of health index predictions using two machine learning methods: specifically, regression and classification against conventional methods of weighting and scoring. Furthermore, the paper presents some strategies to overcome the unavailability of multiple data. Six methods are presented and evaluated across 15 missing data scenarios to assess their effectiveness compared with scenarios featuring complete data. Results reveal that Gradient Boosting Regression has the most accurate accuracy in predicting the health index with a 95%, assuming complete data, and 97.87% considering data unavailability. The paper also presents an economic analysis to highlight the profitability associated with predicting missing data compared with the certainty level and evaluation using asset owner interpretation to assess the feasibility of the proposed method. The main contribution of the paper is the presentation of a comprehensive view for asset owners to make informed decisions in case of data unavailability.

The disconnector fault in gas insulated switchgear (GIS) is commonly caused by the combined effects of mechanical, thermal, and electrical factors. Throughout the progression from defect development to a severe fault, various signals, including abnormal vibration, temperature changes, and partial discharge, are generated. The simultaneous measurement and analysis of multi-parameter information pertaining to the GIS disconnector are crucial for accurately determining its operating condition. This study focuses on simulating typical defects in a 220 kV GIS disconnector to investigate the evolution patterns of vibration signals, temperature variations, and partial discharge signals under different contact states. The aim is to establish the correlation between these signals and the condition of the GIS disconnector. Additionally, the study proposes a comprehensive evaluation method for GIS disconnector by incorporating the health index theory and an improved analytic hierarchy process. The proposed method is subsequently applied to practical GIS, demonstrating consistency between the identified fault and the analysis results obtained through the multi-parameter comprehensive evaluation approach. Overall, this research introduces a highly feasible new method for the detection of GIS disconnector conditions.

Avoiding downtime is one of the major concerns of manufacturing industries. In the era of connected industry, acquiring data has become cheaper than ever; however, turning that data into actionable insights for operators is not always straightforward. In this work, we present a manufacturing scenario involving a circular blade rubber cutting machine, where the goal is to minimize downtime. Historical cutting data are available, and the aim is to provide the machine operators with an intuitive tool that helps them reduce this downtime. This work demonstrates how, in an Industry 4.0 environment, data can be leveraged to minimize downtime. To achieve this, different survival model approaches are compared, a Health Index (HI) is developed, and the model deployment is analysed, highlighting the importance of understanding the model as a dynamic system in which the operator plays a key role.

In order to extend the service life of a highway power supply system and the level of new energy consumption, a power dispatching strategy considering the health status of multi-energy conversion equipment is proposed in this paper. Firstly, the energy and load forms of the highway power supply system are introduced, and the structure of the multi-energy conversion equipment, the topological structures of the DC–DC and DC–AC modules, and the operating characteristics are analyzed. Secondly, the module temperatures and output voltages are used as main parameters to establish the health indexes of DC–DC and DC–AC modules, and then the health index of the multi-energy conversion equipment is further calculated. Thirdly, the new energy consumption index is defined, and a multi-objective optimization model for power dispatching of highway power supply systems is established with the goal of improving the health index of multi-energy conversion equipment and the new energy consumption index. The case study shows that the power dispatching strategy in this paper can better control the temperature of each module, improve the health status of multi-energy conversion equipment, and have a high level of new energy consumption.

The aging process of transmission lines has a direct impact on the reliability and safety of the power grid. Therefore, an accurate age estimation method is imperative for effective maintenance planning and infrastructure investment. This paper introduces a systematic methodology for estimating the age of overhead transmission lines, utilizing the percentage statistical health index (%SHI) and the failure probability curve-fitting (FPCF) method. The %SHI, employing a scoring and weighting approach derived from test results and inspections, is used to assess the actual condition of transmission line equipment. Additionally, the FPCF approach is applied to illustrate the connection between the SHI and the likelihood of failure, facilitating the assessment of transmission line age by fitting failure probability curves to the SHI data. This age is directly associated with the probability of experiencing a failure. The evaluation was conducted on 924 towers situated along four transmission lines connecting the 115 kV substations S1–S2, S3–S4, S5–S6, and S7–S8. These transmission lines are in four regions with diverse terrain and environments such as mountains, rice fields, and more. In the SHI calculation, practical testing results and historical failure data were applied. The results clearly indicate that there were notable disparities in the age estimations for transmission lines in diverse geographical regions of Thailand when compared to their actual ages. These discrepancies can be attributed to various factors, including the local environment, such as rainfall, flooding, and salt-laden air as well as specific geographical features like mountainous and coastal terrain. To mitigate the deterioration of transmission lines in all regions, it is essential to implement a proactive maintenance strategy. This strategy should involve more frequent inspections, the use of advanced monitoring technologies, and the establishment of robust maintenance procedures, with which it would become possible to enhance the accuracy of equipment condition assessments, ultimately resulting in an overall improvement in the reliability of transmission lines. [ABSTRACT FROM AUTHOR]

Techniques for prognostic and health management are becoming common in the electronic domain to reduce the cost of failures. Typically, the proposed techniques rely either on physics-based or data-driven models. Only a few studies explored hybrid models to combine the advantages of both models. For this reason, this work investigates the potential of hybrid modeling by presenting a new framework for the diagnostics and prognostics of an electronic system. The methodology is validated on simulation data describing the behavior of a QFN package subject to die delamination. The main results of this work are twofold. First, it is shown that the hybrid model can achieve better performance than the performance obtained by either the physics-based or the data-driven models alone. Second, a baseline is set for the best performance achievable by the hybrid model, allowing us to estimate the remaining useful life of the package. [ABSTRACT FROM AUTHOR]

The assessment of the health status and prediction of the lifespan of cable equipment are critical for ensuring the stability and efficiency of the power grid. This paper develops a temperature-current-capacity-life calculation model for cables, considering the fast and slow charging demands of electric vehicles (EVs). Analyses under scenarios of rapid and slow charging demands are conducted, introducing a cable health index and establishing a health status assessment framework based on this index. The framework accounts for various factors leading to cable faults, offering a comprehensive evaluation of the health status of cables with different fault rates. Building upon this, a prediction method using the Fire Hawk Optimization (FHO) Algorithm and Convolutional Neural Network (CNN) is proposed. This method enhances performance by optimizing the hyperparameters of Bidirectional Gated Recurrent Unit (BiGRU) through FHO, effectively searching and determining the optimal hyperparameter configuration. The impact of different scenarios and varying EV penetration rates on cable temperature is analyzed through case studies, facilitating the assessment and prediction of health status. [ABSTRACT FROM AUTHOR]

Avoiding downtime is one of the major concerns of manufacturing industries. In the era of the connected industry, acquiring data has become cheaper than ever but, at the same time, how to turn that data into operator actionable insight is not always obvious. This work proposes an approach to develop a Health Index that is based on the cross-matched data created in an industrial setup. For the creation of this Health Index a usage based model that considers the type of material being cut is proposed and compared with a simpler Weibull distribution based survival approach. In addition, different strategies that consider key economic actors, such as unprogrammed blade replacement and programmed replacement, are consider to re-scale the Health Index. According to the results, considering the process variability improves the accuracy of the modelling of the data. However, a good definition of the cost is really the most important and decisive factor for the development of the Health Index. [ABSTRACT FROM AUTHOR]

Faults in wind turbine rotating components contribute significantly to malfunctions and downtime. A prevalent strategy to reduce the Cost of Energy (CoE) in wind energy production focuses on minimizing maintenance expenses associated with these turbine components. An accurate Remaining Useful Life (RUL) diagnosis of these components is crucial for maintenance planning, ensuring uninterrupted energy quality and cost-efficiency. This paper introduces a refined method for RUL prediction of wind turbine rotating components using a Health Index (HI) derived from vibration signals. Performing HI construction by extracting all features from the vibration signal and selecting the best features to build HIs using on Principal Component Analysis (PCA) and some abnormal areas that deviate from the bearing damage trend can be eliminated. After constructing a HI use the similarity model and degradation models to predict RUL. Research results show that this degradation method can provide a reliable means to predict the RUL of wind turbine rotating components based on vibration signals. More importantly, predicting RUL in this way can significantly reduce operating and maintenance costs by providing wind turbine rotating operators with sufficient advance notice to plan repairs or replacements before any component failure occurs. [ABSTRACT FROM AUTHOR]

Starting from the current need for the safety of energy systems, in which power transformers play a key role, the study of the health of power transformers in service is a difficult and complex task, since the assessment consists of identifying indicators that can provide accurate data on the extent of degradation of transformer components and subcomponents, in order to establish a model for predicting the remaining life of transformers. Therefore, this paper proposes a model for assessing the remaining service life by diagnosing the condition of the transformer based on the health index (HI) obtained from a multi-parameter analysis. To determine the condition of power transformers, a number of methods are presented based on the combination of the combined Duval pentagon (PDC) method and ethylene concentration (C2H4) to determine the fault condition, the combination of the degree of polymerisation (DP) and moisture to determine the condition of the cellulose insulation and the use of the oil quality index (OQIN) to determine the condition of the oil. For each of the classification methods presented, applications based on machine learning (ML), in particular support vector machine (SVM), have been implemented for automatic classification using the Matlab development environment. The global algorithmic approach presented in this paper subscribes to the idea of event-based maintenance. Two case studies are also presented to validate SVM-based classification methods and algorithms. [ABSTRACT FROM AUTHOR]

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Prognostic studies of industrial systems essentially focus on health deterioration analysis that has recently been oriented toward data analytics and learning systems. In general, real degradation phenomena suffer from complex drifted data in which degradation patterns are hidden and change over time. Accordingly, such a process requires a well-structured processing and extraction mechanism to reveal such patterns, which facilitates the transition to other model reconstruction and investigation tasks. In this context, to provide additional simplicity of data processing in the field, a complete software package is designed and grouped into a single function that is fully automated and does not require human intervention. The package named ProgMachina (i.e., prognostic machine) provides a featured list of processed features from a life cycle that passed through denoising, filtering, outlier removal, and scaling process to ensure data significance in terms of degradation. The package allows for the use of a time window with a specific overlap to ensure that the scanning process of all possible degradation patterns is properly done. Additionally, an exponential function is used to identify a corresponding health index of degraded signals. In addition, a set of well-known metrics is used to assess the degradation of extracted features. Data visualization and many previous experiments on machines show the effectiveness of such a methodology in terms of obtained prediction accuracy and degradation assessment. The package is designed with Matlab software and made available online to be exploited in similar fields. [ABSTRACT FROM AUTHOR]

To solve the problem that the operation state of transformer is difficult to quantify, a method of quantitative evaluation and prediction of transformer operating state is proposed, which combines the information entropy of matter element and Support Vector Machine. In the evaluation, various hydrogen gases in the transformer operation are taken as the evaluation indexes and the three-dimensional cross compound element is constructed. The analytic hierarchy process (AHP) is used to determine the theoretical weight of the evaluation index, and the entropy method is used to determine the objective weight of the evaluation index, and the final weight is the joint weight of the theoretical weight and the objective weight. Transformer Health index is calculated by using complex element correlation entropy. In prediction, the grid search, genetic algorithm (GA) and particle swarm optimization (PSO) are used to optimize the parameters of Support Vector Machine. and the prediction model of Health index is established by SVM. Experiment results show that the Support Vector Machine based on Gauss kernel function and genetic algorithm has a prominent effect on the prediction of health index. [ABSTRACT FROM AUTHOR]

Power transformers, integral to electrical systems, necessitate substantial investment. Their operational longevity hinges critically on the quality of their insulation system. This study focuses on evaluating the condition of both oil and solid insulation within transformers, which is vital for preventing operational failures. The condition of oil insulation is influenced by a blend of chemical, electrical, and physical properties, while the aging of solid insulation is an irreversible process that ultimately dictates the end of a transformer's service life. Recent advancements, notably the guidelines from CIGRE TB 761, have provided a comprehensive framework for assessing transformer insulation. These guidelines employ a multi-parameter approach to derive a consolidated insulation index. This study applies the CIGRE TB 761 methodology to a population of 357 power transformers, facilitating a detailed comparative assessment of the oil and solid insulation. This paper discusses the methodology used and presents the results, highlighting the evaluation of both oil and solid insulation aspects. Furthermore, it investigates the relationships between various testing parameters and the overall insulation index in relation to the transformer's age. For oil insulation, key parameters include interfacial tension, color, breakdown voltage, acidity, and moisture content. Solid insulation assessment predominantly revolves around parameters such as methane, ethylene, ethane, carbon monoxide, carbon dioxide, CO2/CO ratio, and DP value. This study also correlates the insulation condition in power transformers to the operational age, and thereby contributing to more effective monitoring and maintenance strategies. [ABSTRACT FROM AUTHOR]

Flaxseed cake contains high levels of phenolic compounds, which have numerous biological activities, as well as a considerable amount of omega-3 fatty acids, such as α-linolenic acid, which remains after oil extraction. In this study, we examined the effects of flaxseed cake meal (FSCM) on the antioxidative status, lipid metabolism, egg fatty acid profile, and egg health index of white-egg laying hens. A total of 63 Hisex White laying hens were divided into three experimental treatment groups and fed diets containing 0, 5, or 10% FSCM from 48 to 58 weeks of age. Feeding with 5 and 10% FSCM did not significantly (p>0.05) influence total lipid, triglyceride, total cholesterol, very low-density lipoprotein-cholesterol, or low-density lipoprotein-cholesterol concentrations, or the high-/low-density lipoprotein ratio in the serum and egg yolk; however, 10% FSCM significantly (P0.05) antioxidant markers in the eggs and blood plasma. Notably, dietary inclusion of FSCM significantly increased (P

Emergency operations for perforated gastroduodenal ulcers are associated with a high incidence of postoperative complications. A number of studies have examined the impact of perioperative risk factors and comorbidities on postoperative morbidity after abdominal surgery, but only a few have looked at their role in perforated peptic ulcer disease.Aim. To determine possible associations between postoperative complications, comorbidity and perioperative risk factors for perforated gastroduodenal ulcer.Materials and methods. This study includes the anamnesis of 142 patients who underwent surgery for perforated gastroduodenal ulcer (PGD). In 74 cases (52.1 %), minimally invasive suturing operations were performed, in 68 cases, laparotomy suturing (47.9 %), in three cases, gastric resection was performed (2.3 %). Comparative studies between groups have not been conducted. Clinical data regarding patient characteristics, surgical techniques, and complications were collected retrospectively.Results. Postoperative complications associated with operations for perforated gastroduodenal ulcers amounted to 26.8 %, or 38 cases. A univariate analysis showed that prolonged open surgical time in female patients≥60 years of age, as well as a high American Society of Anesthesiologists (ASA) score, and the presence of preoperative shock were significant perioperative risk factors for postoperative complications. Significant comorbid risk factors included hypertension, diabetes mellitus, and lung disease. Multivariate analysis showed that long operation times, open surgery, high ASA scores, and preoperative shock were independent risk factors for postoperative complications in perforated gastroduodenal ulcer.Conclusions. High ASA, preoperative shock, open surgery, and long operative times of more than 148 minutes are high risk factors for morbidity. However, there is no association between postoperative complications and comorbidity in patients with perforated ulcers.

Transformers are among the most important components in the energy grid due to their missions and high costs. The challenging operating conditions deteriorate their components and shorten the life of the transformers. The health index approach is a critical and effective method for monitoring transformers in the operating environment, early diagnosis of possible malfunctions, and evaluation of their general condition. DGA, OQA, and PIF parameters, which represent the condition of the insulation materials, which mainly determine the life of transformers, constitute the basic inputs of the health index approach. In this study, a new method was proposed to determine the DGAF score based on the dissolved gases in mineral oil. With this method, in addition to the delta and rate values of the gases, the past DGA results of the transformer were also considered in determining the DGAF. In this way, faults experienced during operation are included in the health index calculation. The proposed method was tested using 36 DGA results obtained over a period of approximately 10 years from a transformer operating in the grid. The obtained results are presented in comparison with the traditional DGAF scoring method.