Abnormal Conditions Research Articles

Evaluation of osteomeatal complex by cone-beam computed tomography in patients with maxillary sinus pathology and nasal septum deviation

BackgroundThis study aimed to determine if there is a relationship between the presence of maxillary sinus pathology, nasal septum deviation and various lengths of the osteomeatal complex.MethodsA total of 223 CBCT images were included in the study. The lengths of the osteomeatal complex (maxillary sinus ostium width, infundibulum length, maxillary sinus ostium height) were analyzed. The presence of maxillary sinus pathology, nasal septum deviation, age, sex, right-left, septum deviation level, and the relationship between pathology level and all variables were evaluated.ResultsThe average maxillary sinus ostium width, ostium height and infundibulum length were 3.06 ± 0.70 mm, 30.10 ± 5.43 mm and 8.82 ± 1.86 mm, respectively. Ostium width was significantly higher in the healthy group than in the groups evaluated in the presence of deviation and pathology. A significant difference was found in infundibulum length only between the healthy condition and the condition evaluated in the presence of deviation. No significant difference was observed between the groups in terms of ostium height. In all groups, ostium height and infundibulum length were significantly higher in men than in women. The age group with the highest average ostium height was found in the 35–44 age group (p < 0.001).ConclusionIdentifying normal and abnormal conditions in the osteomeatal complex area is important for diagnosing the cause of a patient's complaint, guiding the surgical procedures to be performed, and preventing possible complications that may arise during surgical procedures.

How fast does the brain recover after an epileptic seizure?

The postictal state, an abnormal cerebral condition following a seizure until the return to the interictal baseline, is frequently overlooked, despite often exceeding ictal duration and significantly impacting patients' lives. This study analyzes stereo-EEG (SEEG) signal dynamics using permutation entropy to quantify recovery time (postictal alteration time - PAT) in focal epilepsy and its clinical correlations. The average PAT was 4.5 min, extending up to an hour and was highest in temporal epilepsy and hippocampal sclerosis. Correlating with age at seizure onset and at SEEG, PAT provides a solution for operationally defining the postictal state and guiding interventions.

Surface Wear Monitoring System of Industrial Transformer Tap-Changer Contacts by Using Voice Signal

Surface wear of the tap-changer contacts of industrial transformers (due to frequent switching times) easily leads to operation failure of industrial transformers, which affects the safety and stability of the transmission network. In this paper, an intelligent voice signal monitoring system was proposed for the abnormal condition (surface wear) of tap-changer contacts. This monitoring system was composed of a voice signal acquisition system, voice analysis system and voice processing system. First, the voice signal of the tap-changer contacts was collected, and the collected voice signal was analyzed in the time domain and the frequency domain. Secondly, the characteristic curve of the voice signal was proposed, and the voice curve was compared with that of the normal operation state. In this case, the running state and surface wear abnormal situation of the tap changer could be monitored and determined, and the cause of the abnormal state could also be further analyzed. This method solved the surface wear problem of the tap changer in industrial transformers, which could be not monitored effectively in real time. This method improved the operational reliability of industrial transformers and had high economic and social benefits.

Navigating Abnormal Environmental Conditions: Traffic Signal and Obstacle Detection for Autonomous Vehicles

Autonomous cars are the future smart cars anticipated to be efficient and crash avoiding ideal urban car of the future. The purpose of the project is to build up a car which can move with and without any driver. It just need a command from the user and then it will start moving towards the destination. The project aims to build a autonomous car prototype using Raspberry Pi processor. Sensor interfacing is used to provide necessary data from the real world to the car. The car is capable of reaching the given destination safely and intelligently thus avoiding the risk of human errors. Many existing algorithms like traffic light detection, obstacle detection and speed control are combined together to provide the necessary control to the car. The technology that autonomous cars and “connected vehicles” use would allow the vehicles to communicate with roadside infrastructure like traffic lights and road congestion, and then use this information to curtail fuel consumption and emissions significantly.

Forecasting Oil Prices with Non-Linear Dynamic Regression Modeling

The recent energy crisis has renewed interest in forecasting crude oil prices. This paper focuses on identifying the main drivers determining the evolution of crude oil prices and proposes a statistical learning forecasting algorithm based on regression analysis that can be used to generate future oil price scenarios. A combination of a generalized additive model with a linear transfer function with ARIMA noise is used to capture the existence of combinations of non-linear and linear relationships between selected input variables and the crude oil price. The results demonstrate that the physical market balance or fundamental is the most important metric in explaining the evolution of oil prices. The effect of the trading activity and volatility variables are significant under abnormal market conditions. We show that forecast accuracy under the proposed model supersedes benchmark specifications, including the futures prices and analysts’ forecasts. Four oil price scenarios are considered for expository purposes.

Time, Clocks, and Illnesses in Paul Harding’s Tinkers

ABSTRACT Paul Harding’s Tinkers was awarded the Pulitzer Prize for Fiction in 2010. This article examines how the novel employs a unique temporal rhetoric to present a post-Enlightenment worldview. As the chronological narrative sequence is disrupted by the main character’s reminiscences and hallucinations, time becomes disjunctive and fragmentary, which suggests that chaos often prevails in human consciousness. The narrative temporality thus becomes ontologically unstable and unreliable, representing a condition of abnormality and aberration. Moreover, this temporal condition is connected with the novel’s repeated descriptions of clock malfunctions and human illnesses, thereby further emphasizing the idea that disorders are an inevitable human condition. As Harding views the precise mechanisms of clocks as an embodiment of Enlightenment rationalism, the emphasis on these disorders signifies a subtle critique of Enlightenment ideals and a receptive attitude toward the inherent chaotic condition of life.

Visual smoke recognition based on an inverse-radiating attention pyramid network

In thermal power generation systems, the exhaust gases produced during the process of power generation should be fully burnt before being discharged into the air through a high-altitude chimney, to help maintain safe production and to avoid pollution. A thermal power generation system will emit white smoke when operating normally; otherwise, under abnormal conditions: (1) large amounts of dark smoke may be released into the air due to incomplete combustion of the exhaust gases, leading to hazards to public health and the environment; (2) colorless smoke can be emitted if toxic exhaust gases are directly released into the air, also causing dangerous pollution. To address these issues, we have developed an inverse-radiating attention pyramid network (IRAP-Net) for visual smoke recognition, directed towards monitoring thermal power generation systems. The proposed IRAP-Net exploits various visual smoke features. IRAP-Net is based on three pyramid blocks, composed of 3, 4 and 5 convolution modules, respectively. An attention mechanism is introduced into each pyramid block selective of features. Lastly, an inverse-radiating connection converges all the outputs of the pyramid blocks in a feedforward manner, thereby merging low-level and high-level features. Experiments on a large smoke image database show that the proposed IRAP-Net achieves a recognition rate exceeding 95.5%, superior to other state-of-the-art (SOTA) models.

Understanding the effects of flash drought on vegetation photosynthesis and potential drivers over China

Flash droughts characterized by rapid onset and intensification are expected to be a new normal under climate change and potentially affect vegetation photosynthesis and terrestrial carbon sink. However, the effects of flash drought on vegetation photosynthesis and their potential dominant driving factors remain uncertain. Here, we quantify the susceptibility and response magnitude of vegetation photosynthesis to flash drought across different ecosystems (i.e., forest, shrubland, grassland, and cropland) in China based on reanalysis and satellite observations. By employing the extreme gradient boosting model, we also identify the dominant factors that influence these flash drought-photosynthesis relationships. We show that over 51.46 % of ecosystems across China are susceptible to flash drought, and grasslands are substantially suppressed, as reflected in both sensitivity and response magnitude (with median gross primary productivity anomalies of −0.13). We further demonstrate that background climate differences (e.g., mean annual temperature and aridity) predominantly regulate the response variation in forest and shrubland, with hotter/colder or drier ecosystems being more severely suppressed by flash drought. However, in grasslands and croplands, the differential vegetation responses are attributed to the intensity of abnormal hydro-meteorological conditions during flash drought (e.g., vapor pressure deficit (VPD) and temperature anomalies). The effects of flash droughts intensify with increasing VPD and nonmonotonically relate to temperature, with colder or hotter temperatures leading to more severe vegetation loss. Our results identify the vulnerable ecological regions under flash drought and enable a better understanding of vegetation photosynthesis response to climate extremes, which may be useful for developing effective management strategies.

An Improved LKF Based SOC Estimation and a Power Management Strategy to Enhance the Cycle Life of BES in a Microgrid

Nowadays, utility grid experiences significant challenges due to increased adoption of renewable energy resources (RES). The intermittency of RES is ridden by using battery energy storage (BES). The system control often aims at maximizing self-consumption, neglecting its effect on effective life of BES. A power management strategy (PMS) is proposed in this work that helps to increase the extraction of maximum energy without compromising on operational robustness of BES for grid applications. Maximum depth of discharge (DOD) is limited to a certain mean state of charge (SOC) to improve the cycle life of BES. A lifecycle model is derived for BES to compute a compensating term for estimating the DOD limit after each cycle. An enhancement to existing Linear Kalman filtering (LKF) technique is also presented in this work for SOC estimation of BES. A significant reduction in root mean square error (RMSE) is achieved using improved LKF-based SOC estimation technique. A second-order generalized integrator with a pre-filter based frequency locked loop (SOGI-WPF-FLL) controls microgrid under abnormal utility grid and load conditions. Resynchronization of microgrid with utility grid is demonstrated using SOGI-WPF-FLL filter without causing any maloperations. System is simulated under various operating conditions in MATLAB/Simulink environment and validated on a real-time OP5700-based test-bench.

Optimization of Abnormal Hydraulic Fracturing Conditions of Unconventional Natural Gas Reservoirs Based on a Surrogate Model

Abnormal conditions greatly reduce the efficiency of hydraulic fracturing of unconventional gas reservoirs. Optimizing the fracturing scheme is crucial to minimize the likelihood of abnormal operational conditions, such as pressure channeling, casing deformation, and proppant plugging. This paper proposes a novel machine learning-based method for optimizing abnormal conditions during hydraulic fracturing of unconventional natural gas reservoirs. Firstly, the main controlling factors of abnormal conditions are selected through a hybrid controlling analysis, upon which a surrogate model is established for predicting the occurrence probability of abnormal conditions, rather than whether abnormal conditions happen or not. Subsequently, a machine learning-based optimization algorithm is developed to minimize the occurrence probability of abnormal conditions, acknowledging their inevitability during the fracturing process. The optimal results demonstrate the proposed method outperforms traditional methods, on average. The proposed methodology is more in line with the needs of practical operation in an environment full of uncertainty.

Contact Lens that Enabled Early Diagnosis for Diseases Induced by Oxidative Stress and Potassium Ion (K+) in Ocular Tissues

The present study used sensor technology to design a lens that could replace a docto.r It consists of an oxidative stress sensor, a (K+) ion sensor and a pressure sensor placed on a Lotrafilcon A silicone hydrogel lens for early diagnosis, as well as a recording and display device that the user can use on their own, recording 24 hours a day and alerting when needed. Additionally, power will be provided to the sensors for data transmission via an external wireless power transfer device. The oxidative stress sensor detects oxidative stress in the structures of the eye and indicates that the patient may have an abnormal condition like diabetic retinopathy, glaucoma and cataracts. Likewise, the (K +) ion sensor detects the (K +) ion concentration in the ocular cells and detects abnormal conditions where the concentration is elevated. The pressure sensor allows the intraocular pressure of patients diagnosed with glaucoma to be kept under control. This way, diseases can be diagnosed early, and continuous monitoring and control of the disease will be ensured. This will prevent the user from having to make frequent visits to the hospital, while also reducing the workload.

Bio-Inspired Electrodes with Rational Spatiotemporal Management for Lithium-Ion Batteries.

Lithium-ion batteries (LIBs) are currently the predominantenergy storage power source. However, the urgent issues of enhancing electrochemical performance, prolonging lifetime, preventing thermal runaway-caused fires, and intelligent application are obstacles to their applications. Herein, bio-inspired electrodes owning spatiotemporal management of self-healing, fast ion transport, fire-extinguishing, thermoresponsive switching, recycling, and flexibility are overviewed comprehensively, showing great promising potentials in practical application due to the significantly enhanced durability and thermal safety of LIBs. Taking advantage of the self-healing core-shell structures, binders, capsules, or liquid metal alloys, these electrodes can maintain the mechanical integrity during the lithiation-delithiation cycling. After the incorporation of fire-extinguishing binders, current collectors, or capsules, flame retardants can be released spatiotemporally during thermal runaway to ensure safety. Thermoresponsive switching electrodes are also constructed though adding thermally responsive components, which can rapidly switch LIB off under abnormal conditions and resume their functions quickly when normal operating conditions return. Finally, the challenges of bio-inspired electrode designs are presented to optimize the spatiotemporal management of LIBs. It is anticipated that the proposed electrodes with spatiotemporal management will not only promote industrial application, but also strengthen the fundamental research of bionics in energy storage.

A Study of Adaptive Threshold Based on the Reconstruction Model for Marine Systems and Their Equipment Failure Warning

To achieve the failure warning of marine systems and their equipment (MSAE), the threshold is one of the most prominent issues that should be solved first. In this study, a fusion model based on sparse Bayes and probabilistic statistical methods is applied to determine a new and more accurate adaptive alarm threshold. A multistep relevance vector machine (RVM) model is established to realize the parameter reconstruction in which the internal uncertainties caused by the degradation process and the external uncertainty caused by the loading, environment, and disturbances were considered. Then, a varying moving window (VMW) method is employed to determine the window size and achieve continuous data reconstruction. Further, the model based on Johnson distribution systems is utilized to complete the transformation of the residual parameters and calculate the adaptive threshold. Finally, the proposed adaptive decision threshold is successfully involved in the actual examples of the peak pressure and exhaust temperature of marine diesel engines. The results show that the proposed method can realize the continuous health condition monitoring of MSAE, successfully detect abnormal conditions in advance, achieve an early warning of failure, and reserve sufficient time for decision-making to prevent the occurrence of catastrophic disasters.

Adaptive prediction for effluent quality of wastewater treatment plant: Improvement with a dual-stage attention-based LSTM network

The accurate effluent prediction plays a crucial role in providing early warning for abnormal effluent and achieving the adjustment of feedforward control parameters during wastewater treatment. This study applied a dual-staged attention mechanism based on long short-term memory network (DA-LSTM) to improve the accuracy of effluent quality prediction. The results showed that input attention (IA) and temporal attention (TA) significantly enhanced the prediction performance of LSTM. Specially, IA could adaptively adjust feature weights to enhance the robustness against input noise, with R2 increased by 13.18%. To promote its long-term memory ability, TA was used to increase the memory span from 96 h to 168 h. Compared to a single LSTM model, the DA-LSTM model showed an improvement in prediction accuracy by 5.10%, 2.11%, 14.47% for COD, TP, and TN. Additionally, DA-LSTM demonstrated excellent generalization performance in new scenarios, with the R2 values for COD, TP, and TN increasing by 22.67%, 20.06%, and 17.14% respectively, while the MAPE values decreased by 56.46%, 63.08%, and 42.79%. In conclusion, the DA-LSTM model demonstrated excellent prediction performance and generalization ability due to its advantages of feature-adaptive weighting and long-term memory focusing. This has forward-looking significance for achieving efficient early warning of abnormal operating conditions and timely management of control parameters.

Platelet Biorheology and Mechanobiology in Thrombosis and Hemostasis: Perspectives from Multiscale Computation.

Thrombosis is the pathological clot formation under abnormal hemodynamic conditions, which can result in vascular obstruction, causing ischemic strokes and myocardial infarction. Thrombus growth under moderate to low shear (<1000 s-1) relies on platelet activation and coagulation. Thrombosis at elevated high shear rates (>10,000 s-1) is predominantly driven by unactivated platelet binding and aggregating mediated by von Willebrand factor (VWF), while platelet activation and coagulation are secondary in supporting and reinforcing the thrombus. Given the molecular and cellular level information it can access, multiscale computational modeling informed by biology can provide new pathophysiological mechanisms that are otherwise not accessible experimentally, holding promise for novel first-principle-based therapeutics. In this review, we summarize the key aspects of platelet biorheology and mechanobiology, focusing on the molecular and cellular scale events and how they build up to thrombosis through platelet adhesion and aggregation in the presence or absence of platelet activation. In particular, we highlight recent advancements in multiscale modeling of platelet biorheology and mechanobiology and how they can lead to the better prediction and quantification of thrombus formation, exemplifying the exciting paradigm of digital medicine.

Forward problem of electrocardiography based on cardiac source vector orientations

To localize the unusual cardiac activities non-invasively, one has to build a prior forward model that relates the heart, torso, and detectors. This model has to be constructed to mathematically relate the geometrical and functional activities of the heart. Several methods are available to model the prior sources in the forward problem, which results in the lead field matrix generation. In the conventional technique, the lead field assumed the fixed prior sources, and the source vector orientations were presumed to be parallel to the detector plane with the unit strength in all directions. However, the anomalies cannot always be expected to occur in the same location and orientation, leading to misinterpretation and misdiagnosis. To overcome this, the work proposes a new forward model constructed using the VCG signals of the same subject. Furthermore, three transformation methods were used to extract VCG in constructing the time-varying lead fields to steer to the orientation of the source rather than just reconstructing its activities in the inverse problem. In addition, the unit VCG loop of the acute ischemia patient was extracted to observe the changes compared to the normal subject. The abnormality condition was achieved by delaying the depolarization time by 15ms. The results involving the unit vectors of VCG demonstrated the anisotropic nature of cardiac source orientations, providing information about the heart’s electrical activity.

A PHREEQC-Based Tool for Planning and Control of In Situ Chemical Oxidation Treatment

This article describes a tool that can be used to improve the effectiveness of the ISCO (in situ chemical oxidation) method. It is an Excel-based application that uses Visual Basic, PHREEQC, and Python. The main functions are feedback control solutions. There are several ideas that can optimise ISCO treatment when using the geochemical model: (i) looping real-time data into the geochemical model and using them to estimate the actual rate, (ii) using spatial distribution maps for delineating zones that are susceptible or resistant to oxidation, (iii) visualising the permanganate consumption that could indicate the right time for further action, and (iv) using alarm reports of the abnormal physico-chemical conditions that jeopardise successful injection.

Thermal structural damage and thermal decomposition characterization of GAP propellants with nitrate ester plasticizers

AbstractGAP and nitrate ester compounds are introduced into the solid propellant formulation as energetic binders and energetic plasticizing agents, respectively, to further enhance the energy level of solid propellants. However, under abnormal thermal conditions, various components within GAP propellants, especially nitrate ester plasticizers, can collectively result in the generation of a large number of voids within the propellant due to factors such as thermal stress and slow component decomposition. This phenomenon can impact the safety of solid rocket engines, necessitating research into their thermal decomposition processes and thermal damage structures. In this study, the thermal decomposition characteristics and gas products of GAP propellants with different nitrate ester plasticizer formulations were investigated using DSC‐TG and FT‐IR. The damage structure of GAP propellants heated under unignited conditions was studied through Micro‐CT, examining the influence of heating conditions and nitrate ester plasticizers on the thermal damage structure of GAP propellants. During heating, the thermal damage structure of GAP propellants was found to include voids generated within the GAP binder and cracks at the interface between the GAP binder and particles, with nitroglycerin as a plasticizer exacerbating the thermal damage of GAP propellants (about 2.2–2.9 times).

Grid synchronization method based on three novel types of third-order generalized integrators

Phase-locked loop (PLL) is widely used to estimate synchronous information such as amplitude and phase angle of grid voltage. It plays a crucial role in distributed renewable energy grid-connected power generation. However, the presence of grid harmonics and DC offset voltage (DCOV) can affect the accurate extraction of the fundamental voltage component, leading to phase-locked deviation. This paper introduces three novel or improved third-order generalized integrators (TOGI) with DCOV rejecting function to address this issue. A unified dual third-order generalized integrator (DTOGI) structure is designed based on the three TOGI structures. This DTOGI structure is further combined with a moving average filter (MAF) to propose three innovative three-phase PLLs. Unlike other PLL filtering methods that inadequately consider DCOV, the proposed PLL methods not only reject DCOV but also eliminate the fundamental voltage negative sequence component (FVNSC) and each order harmonic component. They demonstrate strong robustness and excellent filtering performance even under abnormal grid conditions. Among the three methods, the DII-TOGI based PLL exhibits superior dynamic performance compared to the other two methods, it can improve dynamic characteristics by about 10 % compared to the other two methods. The effectiveness of the proposed methods is verified through extensive Matlab/Simulink simulations and experiments.

Exploring the effects of overvoltage unbalances on three phase induction motors: Insights from motor current spectral analysis and discrete wavelet transform energy assessment

This paper explores the diagnosis of faults related to 10 % and 20 % overvoltages in phase A of a three-phase induction motor. The study is based on numerical simulation results using MATLAB software. The primary objective of this research is to investigate the impact of such voltage unbalance on induction motors, principally on the three-phase stator currents and the electromagnetic torque. The results demonstrated that this voltage aberration causes significant harm to the motor. This is evidenced by the resulting highly unbalanced stator currents and oscillatory torque leading to subsequent mechanical disturbances.The study's second objective is to diagnose anomalies using Motor Current Spectral Analysis (MCSA), encompassing a comprehensive analysis of all three phases. The analysis proved to be effective in detecting such faulty circumstances. The direct current (Id) and the quadrature current (Iq) were also analyzed to reinforce the study. Providing an alternative perspective to explore the consequences of the imbalance. To enhance these findings and validate the previous analysis, the energy analysis based on Discrete Wavelet Transform (DWT) was applied to the three phases, Id and Iq currents.The assessment focused on energy analysis, enabled the detection of abnormal conditions, and demonstrated a noteworthy coherence with spectral analysis. The observed congruence provides evidence for the dependability and effectiveness of these methods in identifying faults, whether they are used in combination or separately.