Abnormal Situations Research Articles

An innovative approach to organizing the monitoring of gas wells is considered, which involves the creation of an automated system with an emergency shutdown function for damaged loops. The main objective of the study is to improve the safety and reliability of gas production and underground gas storage facilities by promptly detecting malfunctions in well equipment and responding to potential emergencies in a timely manner. One option for implementing this method is to use a pneumometric approach. Pressure tubes act as sensitive elements, registering the dynamic pressure that arises as a result of gas flow around the tube. The measuring system determines the flow rate based on the difference between the total and static pressure, which allows the gas flow rate to be obtained. The results of modelling the dynamics of wells under various accident scenarios (when the trail breaks) have been analyzed, confirming the effectiveness of the proposed method. The system allows not only to respond quickly to the occurrence of defects, but also to prevent the development of emergency situations, which has a positive effect on the level of industrial safety and the environmental situation in the production area. The application of this approach may be relevant both for new buildings and for the modernization of existing facilities. The proposed monitoring method, thanks to a computer node that collects, processes and analyses data received from peripheral sensors in real time with the ability to automatically disconnect damaged loops , is an effective means of increasing the technological reliability of gas storage and gas production systems, significantly reducing the risks associated with gas leaks and emergencies. The system is designed with a modular structure, which makes it easy to adapt to different numbers of wells, change the topology or integrate with existing technological equipment without the need for complete reconstruction. The system is based on a combination of modern parameter control devices (pressure, temperature, gas flow) and signal logic processing modules that generate commands to shut down individual infrastructure elements in the event of abnormal situations.

Older persons often stay in conflict zones, abandoned by younger generations and neglected by the government, putting them at risk of becoming victims of violence. This meta-ethnographic study aims to review and synthesise qualitative research on violence in contexts of war and armed conflicts as experienced by older persons and explore how violence in war and armed conflicts affects the health and well-being. Databases (CINAHL, PsychINFO, Web of Science, and Scopus) were searched for studies with a qualitative approach and participants aged ≥ 55 years. Twenty qualitative studies were included, describing experiences of persons from seven countries. Guarding the past and ensuring a future was established as an overarching metaphor in a lines-of-argument synthesis, accompanied by five themes: To endure a violent situation; Home - the heart of existence; To witness a fragile family line; Alienated and abandoned by society- adding insult to injury and Maintaining normality in an abnormal situation. Through interpretation, an understanding emerges of how separation from loved ones, the breakdown of healthcare services, and remaining in conflict areas can significantly increase vulnerability, while simultaneously demonstrating the resilience of older persons and their willingness to serve as resources within their communities.

With the rapid development of big data and artificial intelligence technology, the scale and complexity of enterprise behavior data are increasing, and traditional anomaly detection methods have been difficult to meet the needs of practical applications. In this study, two advanced neural network architectures, Graph Convolutional Network (GCN) and Bidirectional Recurrent Neural Network (BiRNN), are introduced. From the perspective of big data analysis, a large number of business behaviors of enterprises are analyzed and abnormal business behaviors are found. The GCNs and GCN-BiRNNs composite model, which includes GCN and BiRNN, is constructed. The effectiveness of the algorithm in the task of abnormal enterprise behavior identification is verified by experiments. The experimental results show that the model can accurately identify the abnormal situation in the enterprise behavior, and has high recognition accuracy and robustness. This research not only provides a new technical means for enterprise risk management, but also provides a new idea for the application of deep learning in the field of anomaly detection.

Abstract Profiles of aerosol and trace gases are crucial for assessing air pollution changes, identifying the high‐altitude transport of pollutants, and providing a foundation for tracing pollution sources. This study introduces the USTC Parameterized Retrieval Method (USTC‐PRM), an algorithm for retrieving profiles of aerosol extinction and trace gas concentration from MAX‐DOAS measurements. Using the Radiative Transfer Model (RTM), we evaluate the impact of various observation geometries and profile shapes on the air mass factor (AMF) and establish the look‐up table (LUT). USTC‐PRM overcomes the underestimation of high‐altitude aerosols by optimal estimation method (OEM) since it does not rely on prior profiles. The correlation between the retrieved AOD and AERONET AOD is 0.896, compared to 0.826 for the contrasted OEM. For trace gas retrieval, we propose a real‐time LUT establishment method based on retrieved aerosol profiles, significantly reducing memory requirements by over 90% (7.8 GB) and improving the correlation with in situ measurements from 0.867 to 0.911. Additionally, we introduce the concept of look‐up error table (LET) to quantify the AMF bias by retrieving it from LUT. We establish a quality evaluation system based on fitting results, LUT errors, and parameter statistics. Using synthetic data and long‐term MAX‐DOAS measurements, USTC‐PRM demonstrates high performance in retrieving profiles under various aerosol scenarios across high, medium, and low extinction levels, while also identifying abnormal situations, such as foggy and cloudy conditions. USTC‐PRM provides a robust, accurate and efficient approach for MAX‐DOAS profile retrieval, which can be utilized for studying regional transport and tracing atmospheric pollutants.

Liver function is critical for high-producing dairy cows to achieve high milk production and good fertility, as well as to avoid periparturient health problems. Key processes include gluconeogenesis, fatty acid metabolism, protein synthesis, amino acid metabolism and urea formation, bile acid synthesis, detoxification, endocrine functions, and immune functions. Various tests have been used to assess liver function. Fatty liver develops when fatty acid uptake exceeds the liver's capacity to oxidize fatty acids and export triacylglycerols and may negatively affect hepatic function. Metabolomics, transcriptomics, and proteomics are opening new insights into hepatic adaptations in normal and abnormal situations, such as the roles of acylcarnitines, lysophospholipids, and sphingolipids. Nutritional strategies such as controlled energy dry cow diets and supplemental rumen-protected methionine and choline help maintain liver function during the periparturient period. Nutritional manipulations that impact liver function help to promote health and productivity of high-producing dairy cows.

Wireless Capsule Endoscopy (WCE) is one of the most advanced medical instruments, which can be used for non-invasive imaging detection of the digestive tract by patients taking microcapsules orally. This advanced technology enables medical professionals to evaluate the abnormal situations in the gastrointestinal tract efficiently, analyse the potential problems strictly, discuss the diagnosis and evaluation comprehensively, and make well-founded treatment decisions. A scoping review was undertaken, gathering the most relevant sources, utilising a detailed literature search of medical and academic databases including EMBASE, PubMed, Cochrane, IEEE, Google Scholar, and the Google search engine. Of the 39 articles reviewed, 12 focused on the mechanical structure of WCE, 17 on intestinal lesion detection, and 10 on intestinal 3D reconstruction techniques. We conducted a thorough analysis of the active mechanical structures specifically designed to meet physiological demands and adapt to the dynamic gastrointestinal environment. Furthermore, we performed a comprehensive comparison and evaluation of various detection algorithms, discussing the characteristics of relevant datasets that significantly impact the diagnostic performance of WCE technologies. Lastly, we reviewed the current state and progress of 3D reconstruction techniques. WCE can greatly improve the defects of current gastrointestinal examination technology, reduce patient pain, and enrich medical means. However, a large number of software and hardware problems need to be solved before being applied to clinical practice.

Dopamine, a critical neurotransmitter in the central nervous system, plays a vital part in regulating motor function, cognition, and emotional responses. Abnormal dopamine situations are nearly associated with colorful neurological diseases, including Parkinson’s complaint, schizophrenia, and depression. Accurate and real- time monitoring of dopamine can considerably enhance early opinion, complaint progression shadowing, and treatment efficacity evaluation. In recent times, biosensor- grounded approaches have surfaced as important tools for dopamine discovery due to their high perceptivity, particularity, rapid-fire response, and implicit for miniaturization and integration into movable individual bias. This review highlights recent advancements in electrochemical, optic, and nanomaterial- enhanced biosensors for dopamine monitoring. It also discusses current challenges similar as selectivity in complex natural matrices, detector stability, and clinical restatement, while proposing unborn directions for the development of coming- generation biosensors aimed at perfecting the early opinion and operation of neurological diseases.

Abstract Fault detection and diagnosis (FDD) plays a vital role in abnormal situation management of chemical industrial processes. Current FDD technologies mostly rely on data-driven solutions by making full use of abundant process data collected by the state-of-the-art distributed process instruments and sensors. Deep learning algorithms were widely used among all the data-driven algorithms. Industrial process time series data could be processed with ease by deep learning algorithms, particularly transformer-based models because of their multi-head attention mechanism. Different lengths of snippets of sequence (or subsequence) would have a multitude of perspectives viewed by the deep learning algorithms, subsequently impacting their FDD performance. This study, therefore, aims to investigate the effects of varying subsequence lengths on the FDD performance of common deep learning algorithms, consisting of a multilayer perceptron, convolutional neural network, long short-term memory, transformer, industrial process optimisation—vision transformer (IPO-ViT) using two benchmark case studies, namely continuous stirred tank reactor (CSTR) and Tennessee Eastman Process (TEP). Additionally, faulty data are rare to occur, and the fault labelling process is generally tedious and expensive to perform. The effects of labelled training data sizes were also studied on the FDD performance. The findings clearly indicate that the IPO-ViT, a variant of transformer-based models, exhibited the best FDD performance under 10% and 50% subsequence length of data on CSTR and TEP case studies, respectively, for optimal feature extraction, even with 10% of fully labelled input data.

To develop a detection system for abnormal situations, such as accidents, for a person living alone, we investigated a method to update a stochastic model of daily indoor activity sounds (daily sounds). This method adapts to changes in the living environment, updating clusters of daily sounds by the k‐means clustering method. Additional daily sounds were periodically incorporated, the clusters were updated, and the parameters of the stochastic model were recalculated. The results indicate the feasibility of continuously updating the stochastic model for long‐term daily sounds. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

To measure the state of ethanol differentiation process, a fiber option sensing based process state monitoring system is designed. It includes a laser, demo module, PC processing module, fiber option sensing network, and state feedback control unit. A data mining algorithm for multi-parameter demo is proposed to accurately achieve temperature and strain field classification and combine accuracy to the different weights of temperature and strain on different positions, improving the correlation between wavelengths offset and state parameters. The experiment compared four common abnormal situations, The experiment adopts the method of collecting the temperature field inside the tank and the stress field outside the tank, with strain testing of 100–5000 με and temperature testing range of 0–120 °C. The results showed that the average temperature sensitivity after calibration was 0.0102 nm / °C, and the linearity was 0.9959. The average strain sensitivity is 0.499 pm/με, and the linearity is 0.9982. Feedback control has the ability to adjust state fluctuations online, and the feedback time varies for different types of anomalies. The temperature and strain wavelength deviations after correction for all four cases are less than ±1 °C and ±50 με.

Similarly to culturally determined habits such as greetings, culture reaches the deepest spheres of human activity – those related to values, behavior patterns and sanctions connected to the breach of those patterns. The Anglo-Saxon institution of cultural defense is an example of the legal system’s response to a situation of cultural conflict – a conflict an individual might experience when an internalized cultural norm is contradictory to a binding legal norm on the territory, where they live and stay. The basis of cultural defense – mitigating or excluding one’s criminal responsibility in a situation where they violate a legal norm while staying to their cultural norm – is an assumption that no one, not even the state apparatus, has a monopoly over values and behaviors related to them. One of the spheres where cultural defense could be applied is guilt. The author states a thesis that there is a possibility of a situation in which a cultural norm and its influence on an individual is so strong that it de facto disrupts their decisive process, putting them in an abnormal motivational situation. Following this thesis, the author poses a question whether there is a possibility of limiting one’s guilt because of their behavior being influenced by a cultural imperative. Furthermore, the author proposes a legal basis for the assumption – Article 1 § 3 of the Polish Criminal Code. The proposed interpretation enables an application of “cultural defense” in the sphere of guilt, without additional legislative activities.

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 221094, “Early Stuck-Pipe Detection Based on Time-Series Analysis,” by Xiaoyan Shi, Yong Ji, and Meng Cui, SPE, CNPC, et al. The paper has not been peer reviewed. _ Recognizing stuck-pipe signs early and adopting appropriate action can greatly reduce nonproductive time during drilling and improve drilling safety. This paper proposes a time-series-analysis approach and describes a reliable, easy-to-use tool to automatically detect stuck pipe accurately and early. Based on an in-depth theoretical analysis and historical stuck-pipe-data analysis, primarily early stuck-pipe indicators seen in drilling operations are identified. Method In general, stuck-pipe incidents can be classified as differential and mechanical. The mechanisms and symptoms of differential sticking and the primary types of mechanical sticking are detailed in the complete paper. Stuck-Pipe Indicators. Although the causes of stuck pipe vary, the signs of different stuck-pipe types reflected in surface measured data are very similar. When stuck-pipe incidents happen or are imminent, the value of the hookload increases as the drillstring is pulled up; the surface measured torque value experiences obvious fluctuation or a sudden increase; the rotation speed of the rotary table drops suddenly; and the standpipe-pressure value increases, except for differential sticking and key seating sticking. Stuck-Pipe-Detection Model. The workflow of the stuck-pipe-detection model is as follows: Raw time series data are fed to data-cleaning algorithms for quality checking and improvement. Cleaned data are consumed by the model to classify drilling states and analyze time-series characteristics. The time-series analysis technique and machine-learning algorithms are used together to detect abnormal trends and patterns of multivariate time-series data. Combined with stuck-pipe indicators seen under different drilling states, stuck-pipe-incident signs can be recognized automatically. Stuck-Pipe-Detection Tool. Based on the stuck-pipe-detection model, a software tool was developed. The tool includes five different modules: data acquisition, data quality assurance, data visualization, stuck-pipe detection, and result display. The data flow and interaction relationships between different modules are shown in Fig. 1. The data-acquisition module acquires data both from historical data files and from real-time data servers and feeds the data to the data quality assurance module one data point by one data point. The data quality assurance module cleans the data, including rejecting duplicate data points, filling vacancies, removing noise, and passing data to the data-visualization and stuck-pipe-detection modules. The data-visualization module provides graphic displays of the input data and provides experts with a tool to monitor the drilling process and identify abnormal situations by analyzing real-time data. The stuck-pipe-detection module is responsible for detecting stuck-pipe signs in different drilling states based on multiple-input-channel data, and the detection results are fed to the result-display module. This module supports the interaction with end users of the software. The user inputs of the detection result will be passed back to the stuck-pipe-detection module to improve the detection algorithm.

In the context of increasing security demands for smart buildings, artificial intelligence particularly deep learning offers effective solutions for automated access management. This study proposes the design of a machine learning model based on a convolutional neural network (CNN) to develop an intelligent visual recognition access control system. The approach relies on analyzing images captured at access points to automatically and reliably identify authorized individuals. After presenting the fundamental theoretical concepts of machine learning and deep learning, the study details the implementation of a CNN model using the TensorFlow library. This includes dataset preparation, supervised training, and performance evaluation using relevant metrics. Experimental results demonstrate high accuracy in classifying normal and abnormal situations, confirming the approach’s potential for embedded security applications. Finally, this work highlights avenues for improvement, including multimodal data integration, real-time processing optimization, and enhanced personal data protection mechanisms.

Gender inequality, accompanied by the progress of human thoughts, has been extensively discussed in multiple fields of study, but there is still limited research on the exact impact of public education. Is public education one of the decisive factors in the extent of gender inequality? To explore the answer and the underlying mechanism, this paper examines the relationship between public education and gender inequality by selecting three different indicators (total general government expenditure on education, average years of schooling and gross enrollment ratio in primary education) to measure the national educational level and the under-five child mortality sex ratio to measure gender inequality, collecting data from 1990 to 2021 in eight countries that represent different income and religious levels, and constructing three regression models in separate. Overall, in line with the regression results and horizontal and vertical comparisons, as the three indicators of educational level rise, there is the commonality that the mortality sex ratio decreases; however, results vary across groups of different income and religious levels. Finally, some probable explanations of those abnormal situations and possible policy implications are presented.

Taking the continuous rolling process of seamless steel pipes as research object, a monitoring model for the continuous rolling process of seamless steel pipes based on the kernel entropy component analysis method is established, and abnormal production situations and quality problems are diagnosed based on the kernel space fault contribution rate algorithm. Through process monitoring and fault diagnosis experiments on simulation data and seamless steel pipe onsite production data, the experimental results demonstrated the effectiveness of the proposed method in the field. Compared with common process monitoring methods, the actual production data results show that the kernel entropy component analysis–dissimilarity method has better monitoring performance, with an FDR of 97.0% and a FAR of 1.0%. Finally, a process monitoring and fault diagnosis system for the production site is developed for producing abnormal alarms.

Unani Medicine is Comprehensive System of Medicine as its is based on the Principles of Father of Medicine Hippocrates/ Buqrat (460-377 BC) who proposed pivotal theories regarding Tib (Medicine), especially Unani Medicine is Concerned, Nazariya Mawalid-e- Salasa (Tri- Matter Theory), Nazariya Akhlat (Humoural Theory), Nazariya Tabiya’t (Physic) along with these theories Unani Medicine also follows some basic factors which are called as Umoor e Tabiya (Natural factors/Physiological principles).One of the unique feature of Unani Medicine principles is that it follows Holistic approach in management and prevention of Halat Sehat (Health State) and Halat Maraz (Diseased condition).Treatment Module comprises of four different and solid types which is always kept in mind while looking solutions for any abnormal situation and these are Elaj bil tadbir (treatment by regimens ) , Elaj bil Ghiza (dietotherapy), Elaj bil Dawa (treatment by Unani medicines ) and as a last but not least option Elaj Bil Yad ( treatment by Surgery).Elaj bil Tadbir is gaining very much attention in preventing and treating different diseases like Joint disorders ,skin diseases and various life style disorders like High blood pressure. Out of different regimens discussed in Unani Medicine one of them is Hijamah.

Laid at the depth of the seabed, the offshore cables have large energy output and long transmission distance. Buried at the depth of the seabed, the outside of the composite cable contains materials required by the network and can be used for communication with the power transmission network. Therefore, the composite cable laid in the deep sea has high practicability, and at the same time, it puts forward higher requirements for cable manufacture. Submarine cables are more prone to equipment failure because of the higher cost of the products used, the harsh laying environment, and the physical damage caused by natural disasters. In this study, we propose a submarine cable fault diagnosis model. We use the lightweight deep neural network for fault diagnosis. Besides, the modular design of network nodes is adopted for cable fault diagnosis. The improved DE-PSO algorithm is applied to process the electric abnormal signals of the neural network detection receiving signals, which are decomposed into different information layers. A plurality of Fourier transform external characteristics are processed to obtain the time-frequency external characteristics. The asynchronous operations accomplish the task of transmitting and receiving data across the network. Through real-time monitoring of operational data and comparative analysis between detected values and predicted values, this methodology effectively improves detection accuracy and enhances system precision. Experiments have verified that compared with the traditional unified service strategy, the distributed monitoring strategy model of the Internet of Things (IoT) has lower resource utilization, and also has higher calculation accuracy and response time to realize the overall execution efficiency of the submarine cable IoT monitoring equipment. The model can improve the conversion of serial data and facilitate the transmission and reception of network data. High-precision data processing and analysis capabilities can effectively analyze the cable’s abnormal situation and respond faster to the priority of the cable flaw detection business, thus reducing the risk of submarine cable failure and economic losses.

ABSTRACT Accurately estimating battery capacity is highly important for extending battery life, optimising energy management, and ensuring the continuous operation of equipment. However, the real-world battery charging process is not as fully charged as in the laboratory under ideal conditions. Numerous unexpected situations pose a great challenge to the accurate estimation of battery capacity. By analysing real-world charging data from 20 on-road electric vehicles (EVs) spanning more than 2 years, six major categories encompassing 20 specific abnormal charging scenarios are identified. Some powerful measures, such as segmented estimation of interrupted charging, have been taken to address these abnormal charging situations. Then, an algorithm is designed to calculate the battery pack capacity for on-road EVs. This algorithm incorporates segmented estimation and reference capacity correction to reduce the volatility of capacity estimation. The test and comparison results show that our algorithm significantly reduces outliers in the estimation results, effectively overcoming the impact of abnormal charging on capacity estimation.

All-solid-state battery safety in abnormal thermal situations: Crack propagation and lithium dendrite growth

Eyes are one of the vital organs for human beings, which serve as a primary gateway to perceive the surroundings. An abnormal situation, namely tessellated eye, is commonly caused by myopia, which has a characteristic mosaic-like pattern that can cause early vision loss, particularly in infants and youngsters. This work contributes with the usage of a variety of deep learning models to diagnose tessellated and normal fundus images automatically which will improve early detection that can lead to the prevention of vision loss. This study uses a standard dataset of 732 annotated fundus images obtained from Mendeley, Kaggle and a local ophthalmology centre. It also uses a variety of Convolutional Neural Network (CNN) architectures, including VGG16, VGG19, ResNet50 and sequential models, that are experimented for determining the best model and examined. Initially, the fundus images are pre-processed and enhanced to improve model resilience. Out of all the architectures, ResNet50 outperformed as the best model, with an accuracy of 79.45%, while VGG16 with data augmentation reported the best accuracy of 90.8%. Grad-CAM (Gradient-weighted Class Activation Mapping), an Explainable Artificial Intelligence (XAI) mechanism, is used to create heatmaps for interpretability, emphasising spots and pathologies that contribute to the model’s experimentation and judgements. The outcomes of this research highlight potential models namely ResNet50 and augmented VGG16 for reliably diagnosing the fundus images as tessellated or normal. The study also seeks to serve as a platform for future investigation of classifying various automated retinal diseases.