Abnormal Events Research Articles

RNA sequencing facilitates the identification of genetic causes of Duchenne muscular dystrophy and proposes a stepwise DMD diagnostic procedure.

A certain percentage of Duchenne muscular dystrophy (DMD) patients remain genetically undiagnosed after routine genetic testing. Accurate genetic diagnosis is crucial for determining eligibility for mutation-specific therapies and providing relatives with reliable genetic and reproductive counselling. In this study, we utilized RNA sequencing to achieve precise genetic diagnoses in three DMD patients. We identified a deep intronic variant, NC_000023.11:g. 32644691A>C (NM_004006.3:c.1149+273T>G), responsible for creating a novel exon in one patient. An abnormal splicing event was also observed in the second patient. Additionally, RNA sequencing of the pathological muscle samples revealed differentially expressed genes. Based on these findings, we proposed a comprehensive, stepwise diagnostic procedure for DMD. Our study suggests that RNA sequencing can be instrumental in diagnosing disease-causing intronic variants, and the proposed procedure aims to enhance the clarity and accuracy of genetic diagnoses in DMD.

Abstract 4135239: Pausing Under Pressure: Ocular Hypertension as an Atypical Cause of Heart Block

A 67-year-old male with a history of hypertension presented to the emergency department with abnormal cardiac monitor results. Prior to this, he was diagnosed with primary open-angle glaucoma by ophthalmology and underwent left-sided aqueous tube shunt placement, which was complicated by hypotony requiring revision and holding of all topical glaucoma medications. Concurrently, he was noted to have an irregular heart rhythm during a routine physical. In office ECG showed normal sinus rhythm, but subsequent 14-day continuous ECG monitor showed sinus bradycardia with nadir heart rate of 21 beats-per-minute and 114 ventricular pauses (longest 4.8 seconds) primarily due to second-degree atrioventricular (AV) block, Mobitz type 1, but also periods of high-grade AV block. He was sent to the emergency department for expedited pacemaker evaluation. On further review, heart block and ventricular pauses occurred exclusively over a 12-hour period that temporally coincided with a hospitalization for acute ocular hypertension following shunt revision. His intraocular pressure peaked at 56 mmHg (normal range 10-21 mmHg), then normalized to 21 mmHg after acetazolamide and anterior chamber paracentesis. Bradycardia resolved following ophthalmic intervention. Bradyarrhythmias were attributed to excess vagal tone from acute ocular hypertension via an atypical but recognized cause of the oculocardiac reflex, and pacemaker placement was avoided. The oculocardiac reflex is a reflex bradycardia involving the trigeminal and vagus nerves. Cardiac effects are consistent with other vagally-mediated bradyarrhythmias, including sinus bradycardia, AV block, and even asystole. This reflex occurs commonly during traction on extraocular muscles during strabismus surgery, but can be seen in other orbital and ocular stimuli. To our knowledge, this is the first documented case of ocular hypertension leading to atrioventricular block through this mechanism. This case illustrates the importance of (1) clinical context of abnormal events during extended cardiac monitoring, (2) understanding the oculocardiac reflex as a cause of bradyarrhythmias, and (3) ruling out reversible medical causes of dysrhythmias prior to permanent device implantation.

Increased c-MYC Expression Associated with Active IGH Locus Rearrangement: An Emerging Role for c-MYC in Chronic Lymphocytic Leukemia

This review examines the pivotal role of c-MYC in Chronic Lymphocytic Leukemia (CLL), focusing on how its overexpression leads to increased genetic instability, thereby accelerating disease progression. MYC, a major oncogene, encodes a transcription factor that regulates essential cellular processes, including cell cycle control, proliferation, and apoptosis. In CLL cases enriched with unmutated immunoglobulin heavy chain variable (IGHV) genes, MYC is significantly overexpressed and associated with active rearrangements in the IGH immunoglobulin heavy chain locus. This overexpression results in substantial DNA damage, including double-strand breaks, chromosomal translocations, and an increase in abnormal repair events. Consequently, c-MYC plays a dual role in CLL: it promotes aggressive cell proliferation while concurrently driving genomic instability through its involvement in genetic recombination. This dynamic contributes not only to CLL progression but also to the overall aggressiveness of the disease. Additionally, the review suggests that c-MYC’s influence on genetic rearrangements makes it an attractive target for therapeutic strategies aimed at mitigating CLL malignancy. These findings underscore c-MYC’s critical importance in advancing CLL progression, highlighting the need for further research to explore its potential as a target in future treatment approaches.

Causes of Multi-Mechanism Abnormal Formation Pressure in Offshore Oil and Gas Wells

This study thoroughly investigates the complex origins of abnormal formation pressure in offshore oil and gas wells, taking the Rio del Rey Basin in Cameroon as a case study. Renowned for its abundant oil and gas resources, the area faces unique challenges in predicting overpressure due to its high-temperature and high-pressure reservoir characteristics. By quantitatively analyzing the main mechanisms such as undercompaction, high-temperature fluid expansion, and mud diapirism, the study addresses the complexities of overpressure prediction. This paper introduces an innovative analytical framework that combines hierarchical clustering algorithms with the LightGBM model. Further refined by the application of Bayesian optimization, the model intelligently adjusts hyperparameters to enhance predictive accuracy. Utilizing well logging data and applying machine learning techniques, the paper classifies and identifies different mechanisms causing abnormal pressures, achieving a model prediction accuracy of 0.942. The research findings highlight the predominant role of the undercompaction mechanism, accounting for approximately 70% of the abnormal high-pressure events in the study area. Fluid expansion and shale diapirism contribute smaller but significant proportions of 10% and 20%, respectively. These quantitative insights into the pressure mechanisms are vital for optimizing drilling operations and reducing risks in oil and gas exploration. The study’s hybrid approach, integrating geophysical analysis with advanced computational techniques, sets a precedent for future research. It provides new avenues for applying machine learning to understand complex geological phenomena in similar geological environments and makes a significant contribution to the strategic planning of hydrocarbon exploration and production activities.

Hydrogen-fueled PFI SI engine investigation for near-zero NOx emissions in de-throttled and supercharged ultra-lean burn conditions

This study investigates the efficiency and combustion characteristics of a hydrogen port fuel injection (PFI) spark ignition (SI) engine under various load levels, excess air ratios and intake pressures. Experiments were conducted on a single-cylinder research engine to evaluate the effects of throttle opening and supercharging on pumping work and combustion parameters. The key findings indicate that abnormal combustion events are more closely related to the relative air-fuel ratio (λ) than to load, further limiting load increases during de-throttled operation. Additionally, combustion variability compromised mixture enleanment in both naturally aspirated and supercharged conditions. The gross indicated efficiency was approximately 40.7% across the range of 2.2 < λ < 3.5 with minimal variation. Efficiency gains were linked to reduced heat transfer losses in lean conditions, as validated by computational heat transfer, thermodynamic and fluid dynamic models on Gamma Technologies GT-ISE software. Moreover, nitrogen oxides (NOx) emissions were nearly zero for all test conditions when λ was above 2.2. These findings provide crucial insights into optimizing hydrogen engine performance under various intake pressures, highlighting the potential for achieving high efficiency and low emissions.

Diagnostic et prise en charge de l'infection congénitale à cytomégalovirus

AbstractCongenital cytomegalovirus (CMV) infection affects almost 1 % of newborns, with a risk of sequelae of 17-20 %. Primary maternal infection during the first trimester of pregnancy is the most significant risk factor for severe disease. To prevent primary maternal infection, primary prevention strategies (hygiene and information measures) have been shown to be effective but are still insufficient. Although not yet recommended by the health authorities, screening enables cases of primary maternal infection to be detected as early as possible, so that transmission can be prevented by administering valaciclovir. This strategy has proved effective, reducing maternal-fetal transmission of CMV by 70 %. Screening is based on CMV serology (IgG and IgM), supplemented by IgG avidity testing if IgM are present. It is essential that screening and initiation of treatment should be as early as possible to optimize the efficacy of this strategy. PCR on amniotic fluid, proposed in the case of a first trimester maternal primary infection, can be used to diagnose fetal infection. It is also indicated in the event of abnormalities detected on ultrasound. CMV infection in newborns can be confirmed by PCR on saliva or urine, and treatment with valaganciclovir can be initiated in cases of symptomatic congenital infection.

Modelling and monitoring multi-relational networks with ordinal information

Network relationships can be widely seen among entities in various fields such as social networks, supply networks and Internet of Things (IoT). Sometimes abnormal events such as cyber-attacks occur to cause an abrupt increase or decrease in the traffic of networks. Many anomaly detection methods have been developed to identify such abnormal events in networks. In recent years, statistical process control (SPC) has attracted more and more attention in network anomaly detection. However, many of the existing statistical models regard the interaction between two nodes in unweighted directed networks as a binary variable, i.e. presence and absence of contacts, which fails to reflect the intensity level of interactions. This article proposes a new model to describe the dyadic interactions with several ordinal levels and introduces special quantities to incorporate the ordinal information into the model. The model can be expressed in a matrix form to enable easy parameter estimation and derivation of a quadratic monitoring statistic. Numerous simulation studies show that the proposed methods detect anomalies in multi-relational networks more quickly than existing monitoring methods. A case study exhibits the implementation and superiority of the proposed method.

Construction of a Digital Twin System and Dynamic Scheduling Simulation Analysis of a Flexible Assembly Workshops with Island Layout

Assembly Workshops with Island Layout (AWIL) possess flexible production capabilities that realize product diversification. To cope with the complex scheduling challenges in flexible workshops, improve resource utilization, reduce waste, and enhance production efficiency, this paper proposes a production scheduling method for flexible assembly workshops with an island layout based on digital twin technology. A digital twin model of the workshop is established according to production demands to simulate scheduling operations and deal with complex scheduling issues. A workshop monitoring system is developed to quickly identify abnormal events. By employing an event-driven rolling-window rescheduling technique, a dynamic scheduling service system is constructed. The rolling window decomposes scheduling problems into consecutive static scheduling intervals based on abnormal events, and a genetic algorithm is used to optimize each interval in real time. This approach provides accurate, real-time scheduling decisions to manage disturbances in workshop production, which can enhance flexibility in the production process, and allows rapid adjustments to production plans. Therefore, the digital twin system improves the sustainability of the production system, which will provide a theoretical research foundation for the real-time and unmanned production scheduling process, thereby achieving sustainable development of production.

Hazard Analysis for Massive Civil Aviation Safety Oversight Reports Using Text Classification and Topic Modeling

There are massive amounts of civil aviation safety oversight reports collected each year in the civil aviation of China. The narrative texts of these reports are typically short texts, recording the abnormal events detected during the safety oversight process. In the construction of an intelligent civil aviation safety oversight system, the automatic classification of safety oversight texts is a key and fundamental task. However, all safety oversight reports are currently analyzed and classified into categories by manual work, which is time consuming and labor intensive. In recent years, pre-trained language models have been applied to various text mining tasks and have proven to be effective. The aim of this paper is to apply text classification to the mining of these narrative texts and to show that text classification technology can be a critical element of the aviation safety oversight report analysis. In this paper, we propose a novel method for the classification of narrative texts in safety oversight reports. Through extensive experiments, we validated the effectiveness of all the proposed components. The experimental results demonstrate that our method outperforms existing methods on the self-built civil aviation safety oversight dataset. This study undertakes a thorough examination of the precision and associated outcomes of the dataset, thereby establishing a solid basis for furnishing valuable insights to enhance data quality and optimize information.

The kinetics of nucleolar precursor bodies clustering at the pronuclei interface: Positive correlations with the morphokinetic characteristics of cleaving embryos and euploidy in preimplantation genetic testing programs

Objective: This study investigated potential relationships between the kinetics of nucleolar precursor bodies (NPBs) in the pronucleus and developmental morphokinetics and euploidy in human preimplantation genetic testing for aneuploidy (PGT-A) cycles.Methods: The morphokinetic analysis of 200 blastocysts obtained from 53 PGT-A cycles was performed retrospectively in a time-lapse incubator. At the time of pronuclear breakdown (PNBD), we categorized the blastocysts into two groups based on the kinetic degree of clustering NPBs at the interface of the two pronuclei: clustered NPBs (CL) and non-clustered NPBs (NCL). We then compared morphokinetic parameters, abnormal behavioral events, and the rate of aneuploidy between the two groups.Results: Pronuclear fading and the first cleavage occurred earlier in the NCL group than in the CL group. However, the initiation of blastocyst formation and blastocyst expansion was delayed in the NCL group relative to the CL group. No differences were found in the rate of abnormal cleavage events, such as multinucleation at the 2-cell stage, direct cleavage from one to three cells, and from two to five cells between the CL and NCL groups. However, the fragmentation rate at the 8-cell stage was higher in the NCL group than in the CL group (10.3% vs. 1.9%, p&lt;0.05). Additionally, the euploid rate in the CL group was significantly higher than in the NCL group (37.9% vs. 12.4%, p&lt;0.05).Conclusion: These results demonstrate the effectiveness of combining NPB clustering at PNBD with morphokinetics as a parameter for selecting embryos with higher developmental potential in in vitro fertilization.

Effect of sacubitril and valsartan combined with conventional therapy on patients with heart failure

Purpose: To investigate the clinical effectiveness of the combination of sacubitril and valsartan with conventional therapy in the treatment of patients with heart failure. Methods: This was a retrospective study comprising 100 heart failure patients randomized into study (n = 57) and control groups (n = 43). The study group received sacubitril/valsartan along with conventional drug therapy while control group received only conventional drugs, viz, irbesartan, metoprolol slow release, and furosemide tablets. Echocardiogram showing left ventricular end-systolic diameter (LVESD), left ventricular end-diastolic diameter (LVEDD), left anterior descending artery (LAD), Nterminal pro-b-type natriuretic peptide (NT-proBNP), C-reactive protein (CRP), glomerular filtration rate (eGFR), and homocysteine (HCY) of the two groups were compared before and after treatment. Multiple regression was used to analyze the correlation between re-hospitalization and sacubitril/valsartan intervention. Results: The study group showed significantly lower LVEF, LVESD, LVEDD, LAD, NT-proBNP, and homocysteine levels after treatment compared to control group (p &lt; 0.05). Re-hospitalization for abnormal cardiovascular events between the two groups was significantly different in the adjusted Cox proportional hazards regression model. Furthermore, multiple regression analysis showed that sacubitril/valsartan treatment was the independent variable (p &lt; 0.001). Conclusion: Sacubitril/valsartan improves heart function, with reduced incidence of adverse effects without affecting renal function. Further studies are required to validate these findings by expanding sample size, strictly controlling data quality and strengthening follow-up.

Pose-oriented scene-adaptive matching for abnormal event detection

For intelligent surveillance systems, abnormal event detection automatically analyses surveillance video sequences and detects abnormal objects or unusual human actions at the frame level. Due to the lack of labelled data, most approaches are semi-supervised based on reconstruction or prediction methods. However, these methods may not generalize well to unseen scene contexts. To address this issue, we present a novel self and mutual scene-adaptive matching method for abnormal event detection. In the framework, we propose synergistic pose estimation and object detection, which effectively integrates human pose and object detection information to improve pose estimation accuracy. Then, the poses are resized to reduce the spatial distance between the source and target domains. The improved pose sequences are further fed into a spatio-temporal graph convolutional network to extract the geometric features. Finally, the features are embedded in a clustering layer to classify action types and compute normality scores. The training data is taken from the training part of common video anomaly detection datasets: UCSD PED1 & PED2, CHUK Avenue, and ShanghaiTech Campus. The proposed framework is evaluated on video sequences with unseen scene contexts in the UCSD PED2 and ShanghaiTech Campus datasets. The detection accuracy and efficiency are also evaluated in detail, and the proposed method for abnormal event detection achieves the highest AUC performance, 84.6%, on the ShanghaiTech Campus dataset and relatively high AUC performance, 96.9% and 74.8%, on UCSD PED2 & PED1 datasets. Compared with other state-of-the-art works, the performance analysis and results confirm the robustness and effectiveness of our proposed framework for cross-scene abnormal event detection.

Anomaly Detection over Streaming Graphs with Finger-Based Higher-Order Graph Sketch

A streaming graph is a constantly growing sequence of edges, which forms a dynamic graph that changes with every edge in the stream. An anomalous behavior in a streaming graph can be modeled as an edge or a subgraph that is unusual compared to the rest of the graph. Identifying anomalous behaviors in real time is essential to the early warning of abnormal or notable events. Due to the complexity of the problem, little work has been reported so far to solve the problem. In this paper, we propose Finger-based Higher-order Graph Sketch (FHGS for short), which is an approximate data structure for streaming graphs with linear memory usage, high update speed, and high accuracy and supports both edge and subgraph anomaly detection. FHGS first maps each edge into a matrix based on hash functions, and then counts its frequency in a time window with unique fingerprints for detecting anomalies. Extensive experiments confirm that our approach generate high-quality results compared to baseline methods.

Abnormal detection in nuclear security videos based on label-specific autoencoders and reconstruction errors comparison

Abnormal detection in surveillance cameras never fails to be a crucial and challenging task across many fields, aimed at protecting the property safety of citizens and society from abnormal events. Currently, the most common solution for abnormal detection proves to be the combination of autoencoder models and unsupervised training scheme. Due to the scarcity of abnormal samples, all training samples are typically treated as normal samples when constructing the autoencoder calculation models, which generate reconstructed video frames with the same dimension as input data. In this approach, the identification of testing samples is determined by calculating the reconstruction errors between raw and reconstructed video frames. However, such calculation frameworks turn out to be unpractical for nuclear security, as the threshold value of reconstruction errors is overly sensitive, often leading to extreme and unacceptable results. In this manuscript, a novel framework for abnormal detection in nuclear security is proposed. Unlike common unsupervised training scheme, this approach constructs two label-specific autoencoders, while one is trained exclusively with normal samples and the other one is only fed with abnormal samples. Therefore, there is no need to predefine the threshold value for reconstruction errors, as it is replaced by comparing the two label-specific reconstruction errors. Besides, two training schemes for the label-specific autoencoders are proposed and analyzed. Based on a self-collected dataset for nuclear security, the results show that the proposed framework is both practical and appliable, with both training schemes achieving an acceptable accuracy of 0.8182.

The Attribution of February Extremes over North America: A Forecast-Based Storyline Study

Abstract The importance of extreme event attribution rises as climate change causes severe damage to populations resulting from unprecedented events. In February 2019, a planetary wave shifted along the U.S.–Canadian border, simultaneously leading to troughing with anomalous cold events and ridging over Alaska and northern Canada with abnormal warm events. Also, a dry-stabilized anticyclonic circulation over low latitudes induced warm extreme events over Mexico and Florida. Most attribution studies compare the climate model simulations under natural or actual forcing conditions and assess probabilistically from a climatological point of view. However, in this study, we use multiple ensembles from an operational forecast model, promising statistical as well as dynamically constrained attribution assessment, often referred to as the storyline approach to extreme event attribution. In the globally averaged results, increasing CO2 concentrations lead to distinct warming signals at the surface, resulting mainly from diabatic heating. Our study finds that CO2-induced warming eventually affects the possibility of extreme events in North America, quantifying the impact of anthropogenic forcing over less than a week’s forecast simulation. Our study assesses the validity of the storyline approach conditional on the forecast lead times, which is hindered by rising noise in CO2 signals and the declining performance of the forecast model. The forecast-based storyline approach is valid for at least half of the land area within a 6-day lead time before the target extreme occurrence. Our attribution results highlight the importance of achieving net-zero emissions ahead of schedule to reduce the occurrence of severe heatwaves.

Identification and analysis of short indels inducing exon extension/shrinkage events.

The search for genetic variants that act as causative factors in human diseases by disrupting the normal splicing process has primarily focused on single nucleotide variants (SNVs). It is worth noting that insertions or deletions (indels) have also been sporadically reported as causative disease variants through their potential impact on the splicing process. In this study, to perform identification of indels inducing exon extension/shrinkage events, we used individual-specific genomes and RNA sequencing (RNA-seq) data pertaining to the corresponding individuals and identified 12 exon extension/shrinkage events that were potentially induced by indels that disrupted authentic splice sites or created novel splice sites in 235 normal individuals. By evaluating the impact of these abnormal splicing events on the resulting transcripts, we found that five events led to the generation of premature termination codons (PTCs), including those occurring within genes associated with genetic disorders. Our analysis revealed that the potential functions of indels have been underexamined, and it is worth considering the possibility that indels may affect splice site usage, using RNA-seq data to discover novel potentially disease-associated mutations.

Vision-based digital shadowing to reveal hidden structural dynamics of a real supertall building

Vision-based digital shadowing is a highly efficient way to monitor the health of buildings in use. However, previous studies on digital shadowing have been limited to laboratory experiments. This paper proposes a novel computer-vision-based digital shadow workflow and presents its successful application in a real engineering case. In this case, a 345.8-m supertall building experienced unexpected shaking under normal meteorological conditions. This study established a digital shadow of the building using three-dimensional displacement measurements based on super-resolution monocular vision, revealing the hidden structural dynamics and inherent mechanical reasons for the abnormal shaking. The proposed digital shadowing workflow is a feasible roadmap for developing vision-based digital shadows of real-world structures using low-cost cameras. The abnormal vibration event in the supertall building considered in this study is the first of its type worldwide. The results of this study offer practical strategies and invaluable insights into the prevention and mitigation of this type of global risk, thereby contributing to the lifespan extension of buildings in use worldwide. Furthermore, with the increasing number of general sensing devices, such as surveillance cameras in cities, the proposed method may unleash the immense potential of general sensing devices in achieving the leap from structural health monitoring to city health monitoring.

Dysfunction of ATP7B Splicing Variant Caused by Enhanced Interaction With COMMD1 in Wilson Disease

Background & AimsThe association between Wilson disease and various ATP7B mutations is well-established; however, the molecular mechanism underlying the functional consequence of these mutations, particularly the splicing mutations, remains unclear. This study focused on the ATP7B c.1543+1G>C variant, to reveal a universal pathogenic mechanism of the ATP7B mutants with altered N-terminus. MethodsThe splicing assay and RNA pull-down were performed to explore the mechanism of the aberrant splicing. The ATP7B knockout HuH-7 cell line and Atp7b-/- mice were created, and the functional consequence of the mutant ATP7B were evaluated in-vitro and in-vivo. ResultsThe c.1543+1G>C mutation resulted in the skipping of ATP7B exon 3, and the mutant ATP7B showed a loss of trans-Golgi network (TGN) localization and was degraded via the ubiquitin-proteasome pathway, facilitated by enhanced interactions with COMMD1. Elevated intercellular copper concentration and reduced survival rate were observed in HuH-7 cells expressing mutant ATP7B. Restoration of wild-type ATP7B in Atp7b-/- mice resulted in a substantial improvement in phenotype, while mice treated with mutant ATP7B did not demonstrate equivalent benefits. ConclusionsOur research investigated the pathogenicity and mechanism of ATP7B c.1543+1G>C variant, with particular focus on its enhanced interaction with COMMD1 as a potential universal mechanism contributing to the dysfunction of various ATP7B variants. These findings provide a foundation for the development of innovative therapeutic strategies that target abnormal splicing events in a range of hereditary diseases, including Wilson disease.

Exploring Techniques for Abnormal Event Detection in Video Surveillance

This study explores and evaluates the effectiveness of various abnormal event detection techniques in video surveillance, addressing challenges such as intrusions, accidents, and suspicious activities. Through a systematic review of related papers, the study reveals the prevalence of traditional methods like background subtraction and motion detection despite their limitations in complex scenarios. It highlights the increasing use of deep learning techniques, particularly CNNs and RNNs, which show promise but require substantial labeled data. The findings underscore the importance of selecting proper detection techniques based on specific surveillance scenarios and emphasize the need for extensive labeled datasets for deep learning methods. The originality of this study lies in its comprehensive review and comparison of various abnormal event detection techniques, providing valuable insights and practical implications for advancing video surveillance systems.

Enhancing fault detection in multivariate industrial processes: Kolmogorov–Smirnov non-parametric statistical approach

The accurate detection of abnormal events in modern process plants is extremely important. The detection of faults of small magnitude and in a noisy process environment is still a challenge that many industries face. In this paper, a Kolmogorov–Smirnov (KS) based non-parametric statistical fault indicator is proposed to identify a variety of sensor faults in process plants. The data collected from most modern process plants are randomly varying and non-Gaussian, due to which the multivariate scheme based on Independent Component Analysis (ICA) is considered. The KS-based indicator is amalgamated with the ICA multivariate model, which yields a novel ICA-KS-based fault detection (FD) scheme. The KS statistical indicator compares any two distributions and checks if they are similar or dissimilar. The potential of the KS indicator is extended in the FD domain, where the residuals of training and testing data are compared in a sliding window. The ability of the proposed ICA-KS-based FD strategy is validated on a simulated Distillation column (DC) process and the benchmark Tennessee Eastman (TE) process to identify a variety of sensor faults. The simulation results demonstrate the effectiveness of the detection performance of the ICA-KS scheme, which outperforms conventional FD-based methods with a high detection rate.