Observation Window Research Articles

Utilizing the Achievements of Data Acquiring Solution and Cloud Service Platform for China-Africa Cooperation on Satellite Remote Sensing Application

Abstract. This study examines the collaborative approach in China-Africa satellite remote sensing initiatives based on solutions of satellite data acquiring, sharing and application experiences. It specifically addresses the data coverage requirements and current coverage scenarios of China's natural resources land satellites in Africa. By establishing the China-Africa Cooperation Centers on Satellite Remote Sensing Application, adopting a multi-level distributed technology center model, and implementing cross-continental remote sensing cloud services through an internet-based cloud platform, this initiative aims to enhance the direct utilization of satellite data in Africa. Furthermore, strategic satellite observation window designs tailored to seasonal changes have increased the frequency of valid coverage over the entire African continent to at least once a year. Such advancements pave the way for real-time monitoring applications, particularly in crucial areas such as sustainable development projects and disaster response efforts. Therefore, the study also provides some useful case studies that are anchored on the Sustainable Development Goals.

The effect of oral anticoagulants on the incidence of dementia in patients with atrial fibrillation: a systematic review and meta-analysis

Abstract Background Atrial fibrillation (AF) is a common cardiac arrhythmia, with dementia being an increasingly recognised complication of this. Treatment with oral anticoagulant (OAC) therapy may be protective in developing this complication. Purpose This meta-analysis aims to investigate the validity of this hypothesis, whilst additionally exploring the effect of specific OAC medication subtypes on the incidence of dementia. Methods A comprehensive search of MEDLINE and Embase for relevant studies reporting the efficacy of OAC therapy for the incidence of dementia in patients with AF was conducted from its inception until March 2023. This included observational studies that reported odds ratio, risk ratio and hazard ratio for adult AF patients. Studies including patients with prior OAC use or prior history of dementia were excluded. Statistical analysis was performed using a random-effects model to calculate the pooled hazard ratio and their corresponding 95% confidence interval for all outcomes. Subgroup analyses were also performed according to the study design, presence of observational window, and prior history of stroke in patients. Results A total of 13 studies were included in the study. Although the heterogeneity was high in all comparisons, we demonstrated that OAC usage lowered the risk of developing dementia in AF patients when compared to the non-OAC cohort. Looking more specifically at medication subtypes, direct oral anticoagulants (DOACs) and vitamin K agonists (VKA) were both effective in reducing the likelihood of AF patients developing dementia when compared to non-OAC treatments. Conclusion Our meta-analysis confirms that administration of OAC and its subtypes (VKA and DOAC) are effective pharmacotherapies in managing dementia in patients with AF. These findings have important clinical implications for the management of AF patients and highlight the need for further investigation of the protective effects of OAC therapy on cognitive decline. Future research should explore the long-term effects of OAC therapy on cognitive function in AF patients.

Machine learning methods for automated interstellar object classification with LSST

The Legacy Survey of Space and Time (LSST), to be conducted with the Vera C. Rubin Observatory, is poised to revolutionize our understanding of the Solar System by providing an unprecedented wealth of data on various objects, including the elusive interstellar objects (ISOs). Detecting and classifying ISOs is crucial for studying the composition and diversity of materials from other planetary systems. However, the rarity and brief observation windows of ISOs, coupled with the vast quantities of data to be generated by LSST, create significant challenges for their identification and classification. This study aims to address these challenges by exploring the application of machine learning algorithms to the automated classification of ISO tracklets in simulated LSST data. We employed various machine learning algorithms, including random forests (RFs), stochastic gradient descent (SGD), gradient boosting machines (GBMs), and neural networks (NNs), to classify ISO tracklets in simulated LSST data. Our results demonstrate that GBM and RF algorithms outperform SGD and NN algorithms in accurately distinguishing ISOs from other Solar System objects. RF analysis shows that many derived Digest2 values are more important than direct observables (right ascension, declination, and magnitude) in classifying ISOs from the LSST tracklets. The GBM model achieves the highest precision, recall, and F1 score, with values of 0.9987, 0.9986, and 0.9987, respectively. These findings lay the foundation for the development of an efficient and robust automated system for ISO discovery using LSST data, paving the way for a deeper understanding of the materials and processes that shape planetary systems beyond our own. The integration of our proposed machine learning approach into the LSST data processing pipeline will optimize the survey's potential for identifying these rare and valuable objects, enabling timely follow-up observations and further characterization.

The Immediate Partial Removal of Cumulus-Oocyte Complexes: A Refined Approach for Rapid Observation of In Vitro Fertilization.

Despite the rapid advancements in clinical and laboratory technologies for in vitro fertilization (IVF), a significant proportion (10%-15%) of patients continue to experience fertilization disorders, leading to low fertilization rates and the production of nonviable embryos. Short-term fertilization involves the early removal of granulosa cells to observe the extrusion of the second polar body, enabling the assessment of fertilization and early remedial measures to address low fertilization rates and complete fertilization failure. However, the observation of short-term fertilization in IVF is impeded by challenges such as excessively large unprocessed clusters of cumulus-oocyte complexes and adhesion between eggs, necessitating complex external procedures for granulosa cell removal, and prolonged observation duration. To tackle this issue, this study proposes a method of immediate partial removal of granulosa cells post egg retrieval. This approach streamlines subsequent stages of short-term fertilization and traditional fertilization monitoring, reduces the time needed for oocyte extrusion, minimizes the likelihood of external environmental impacts on the oocytes, and decreases the risk of missing the critical fertilization observation window. Consequently, the study yields novel clinical evidence aimed at enhancing the operational efficiency of embryonic laboratories in IVF.

Digital phenotyping in bipolar disorder: Using longitudinal Fitbit data and personalized machine learning to predict mood symptomatology.

Effective treatment of bipolar disorder (BD) requires prompt response to mood episodes. Preliminary studies suggest that predictions based on passive sensor data from personal digital devices can accurately detect mood episodes (e.g., between routine care appointments), but studies to date do not use methods designed for broad application. This study evaluated whether a novel, personalized machine learning approach, trained entirely on passive Fitbit data, with limited data filtering could accurately detect mood symptomatology in BD patients. We analyzed data from 54 adults with BD, who wore Fitbits and completed bi-weekly self-report measures for 9 months. We applied machine learning (ML) models to Fitbit data aggregated over two-week observation windows to detect occurrences of depressive and (hypo)manic symptomatology, which were defined as two-week windows with scores above established clinical cutoffs for the Patient Health Questionnaire-8 (PHQ-8) and Altman Self-Rating Mania Scale (ASRM) respectively. As hypothesized, among several ML algorithms, Binary Mixed Model (BiMM) forest achieved the highest area under the receiver operating curve (ROC-AUC) in the validation process. In the testing set, the ROC-AUC was 86.0% for depression and 85.2% for (hypo)mania. Using optimized thresholds calculated with Youden's J statistic, predictive accuracy was 80.1% for depression (sensitivity of 71.2% and specificity of 85.6%) and 89.1% for (hypo)mania (sensitivity of 80.0% and specificity of 90.1%). We achieved sound performance in detecting mood symptomatology in BD patients using methods designed for broad application. Findings expand upon evidence that Fitbit data can produce accurate mood symptomatology predictions. Additionally, to the best of our knowledge, this represents the first application of BiMM forest for mood symptomatology prediction. Overall, results move the field a step toward personalized algorithms suitable for the full population of patients, rather than only those with high compliance, access to specialized devices, or willingness to share invasive data.

A simulation study on the sub-threshold joint gravitational wave-electromagnetic wave observation on binary neutron star mergers

Abstract The coalescence of binary neutron stars (BNS) is a prolific source of gravitational waves (GWs) and electromagnetic (EM) radiation, offering a dual observational window into the Universe. Lowering the signal-to-noise ratio (S/N) threshold is a simple and cost-effective way to enhance the detection probability of GWs from BNS mergers. In this study, we introduce a metric of the purity of joint GW and EM detections Pjoint, which is in analogue to Pastro in GW only observations. By simulating BNS merger GWs jointly detected by the HLV network and EM counterparts (kilonovae and short Gamma-ray bursts, sGRBs) with an assumed merger rate density of BNS, we generate catalogs of GW events and EM counterparts. Through this simulation, we analyze joint detection pairs, both correct and misidentified. We find the following: 1. For kilonovae, requiring Pjoint > 95% instead of $P_{\rm astro}>95~{{\%}}$ reduces the S/N from 9.2 to 8.5-8.8, allowing 5-13 additional joint detections per year and increasing the GW detection volume by 9-17%; 2. For sGRBs, requiring Pjoint > 95% instead of Pastro reduces the S/N from 9.2 to 8.1-8.5; 3. Increasing kilonova or sGRB detection capability does not improve Pjoint due to a higher rate of misidentifications. We also show that sub-threshold GW and kilonova detections can reduce the uncertainty in measuring the Hubble constant to 89-92% of its original value, and sub-threshold GW and sGRB observations can enhance the precision of constraining the speed of GWs to 88% of previously established values.

Obsessive-compulsive symptoms relating to psychosocial functioning for people with schizophrenia, schizoaffective disorder, or bipolar disorder.

To assess the psychosocial functioning concerning obsessive-compulsive symptoms (OCS) and/or obsessive-compulsive disorder (OCD) comorbidity in people with schizophrenia, schizoaffective disorder, or bipolar disorder diagnosed in a large case register database in Southeast London. Data were retrieved from the South London and Maudsley NHS Foundation Trust Biomedical Research Centre (SLaM BRC) register using Clinical Record Interactive Search (CRIS) system, a platform allowing research on full but de-identified electronic health records for secondary and tertiary mental healthcare services. Information of schizophrenia, schizoaffective disorder, bipolar disorder diagnosis and OCS/OCD status was ascertained from structural or free-text fields through natural language processing (NLP) algorithms based on artificial intelligence techniques during the observation window of January 2007 to December 2016. Associations between comorbid OCS/OCD and recorded Health of the Nation Outcome Scales (HoNOS) for problems with activities of daily living (ADLs), living conditions, occupational and recreational activities, and relationships were estimated by logistic regression with socio-demographic confounders controlled. Of 15,412 subjects diagnosed with schizophrenia, schizoaffective disorder, or bipolar disorder, 2,358 (15.3%) experienced OCS without OCD, and 2,586 (16.8%) had OCD recorded. The presence of OCS/OCD was associated with more problems with relationships (adj.OR = 1.34, 95% CI: 1.25-1.44), ADLs (adj.OR = 1.31, 95%CI: 1.22-1.41), and living conditions (adj.OR = 1.31, 95% CI: 1.22-1.41). Sensitivity analysis revealed similar outcomes. Comorbid OCS/OCD was associated with poorer psychosocial functioning in people with schizophrenia, schizoaffective disorder, or bipolar disorder. This finding highlights the importance of identification and treatment of comorbid OCS among this vulnerable patient group.

The Negative Pressure Wound Therapy for Flap Salvage

Negative Pressure Wound Therapy (NPWT) effectively manages complex wounds by promoting fluid drainage, stabilizing the wound environment, and reducing bacterial load. It aids in granulation tissue formation and modulation of inflammatory reactions. NPWT is increasingly used in reconstructive surgery, particularly in flap procedures, enhancing wound healing and cosmetic outcomes while reducing flap complications. Despite concerns about flap compromise, clinical trials demonstrate low flap loss rates with NPWT. Strategies like observation windows and implantable Doppler devices help overcome monitoring challenges. However, conclusive evidence regarding NPWT's safety and impact on flap vascularity is limited due to scarce studies. The study involved 40 healthy adult patients, mostly male, with soft tissue defects primarily caused by road traffic accidents. They were split into two groups: NPWT (25 patients) and conventional dressing (15 patients). The NPWT group was further categorized into high pressure (8 patients) and low pressure (17 patients) subgroups. Results showed lower flap edema in NPWT groups compared to controls, but high pressure NPWT led to higher flap ischemia and infection rates. Overall, low pressure NPWT yielded the best clinical and aesthetic outcomes with fewer complications. The study suggests a delayed NPWT approach and low-pressure settings for optimal vascular perfusion in Fasciocutaneous flaps, emphasizing the need for further trials to establish its efficacy and determine ideal pressure settings for different flap types.

Optimization of low-thrust periodic reconfiguration in Tianqin orbit

Tianqin is a project scheduled for detecting gravitational waves in space. The project requires high levels of constellation stability. This paper optimizes the Tianqin trajectory over 5 years using low-thrust maneuvers and a ‘3 months on +3 months off’ observation window scheme to improve constellation stability and reduce spacecraft fuel consumption. The result is a series of stable orbit configurations. The problem of fuel optimization is solved efficiently by combining a normalization strategy, a homotopic approach, and switching detection. Additionally, the optimization strategy quantitatively evaluates the fuel consumption required to maintain the stability of the Tianqin orbits. This evaluation provides a valuable reference for the performance design of the low-thrust propulsion system in the spacecraft.

Multiaxial creep deformation investigation of miniature cruciform specimen for type 304 stainless steel at 923 K using non-contact displacement-measuring method

Abstract Multiaxial creep investigations at high temperatures are required to guarantee the integrity of structural materials such as boiler pipes for power generation and turbine blades for aviation and industrial applications. The multiaxial creep testing method using a cruciform specimen has several advantages, and the authors successfully downsized the specimen. The authors also developed a technique for the in situ observation of the specimen in an electric furnace and the non-contact displacement measurement in the development of a biaxial tensile creep testing machine for the miniature cruciform specimen. By observing the specimen at a target mark during the creep testing through an observation window installed at the bottom of the furnace, it was possible to visualize the specimen surface variations and obtain the axial strain from the locus of the target marks. The Mises-type equivalent strain was calculated from the axial strains obtained in the equi-biaxial tensile multiaxial creep testing and was compared with the strains obtained in the uniaxial creep testing. The Mises-type equivalent strain can be expressed using a unified method up to approximately 5% of the initial strain in the test, even in the multiaxial stress state.

Predictive mining of multi-temporal relations

In this paper, we propose a methodology for deriving a new kind of approximate temporal functional dependencies, called Approximate Predictive Functional Dependencies (APFDs), based on a three-window framework and on a multi-temporal relational model. Different features are proposed for the Observation Window (OW), where we observe predictive data, for the Waiting Window (WW), and for the Prediction Window (PW), where the predicted event occurs. We then consider the concept of approximation for such APFDs, introduce new error measures, and discuss different strategies for deriving APFDs. We discuss the quality, i.e., the informative content, of the derived AFDs by considering their entropy and information gain. Moreover, we outline the results in deriving APFDs focusing on the Acute Kidney Injury (AKI). We use real clinical data contained in the MIMIC III dataset related to patients from Intensive Care Units to show the applicability of our approach to real-world data.

Tilted Disk Precession and Negative Superhumps in HS 2325+8205: A Multiwindow Analysis

Tilted disk precession exists in different objects. Negative superhumps (NSHs) in cataclysmic variable stars are believed to arise from the interaction between the reverse precession of a tilted disk and the streams from the secondary star. Utilizing Transiting Exoplanet Survey Satellite photometry, we present a comprehensive investigation into the tilted disk precession and NSHs in the dwarf nova (DN) HS 2325+8205, employing eclipse minima, eclipse depths, NSH frequencies, and NSH amplitudes and the correlation between them as the windows. We identified NSHs with a period of 0.185671(17) day in HS 2325+8205. The NSH frequency exhibits variability with a period of 3.943(9) days, akin to the tilted disk precession period validated in nova-like stars (SDSS J0812) and intermediate polars (IPs; TV Col). The O − C of the eclipse minima were similarly found to vary cyclically in a period of 4.135(5) days, characterized by a faster rise than fall. Furthermore, the NSH amplitude exhibits complex and diverse variations, which may be linked to changes in the disk radius, the mass transfer rate, and the apparent area of the hot spot. For the first time in DNe, we observe biperiodic variations in eclipse depth (P 1 = 4.131(4) days and P 2 = 2.065(2) days ≈ P prec/2) resembling those seen in IPs, suggesting that variations with P 2 are not attributable to an accretion curtain, as previously suspected. Moreover, NSH amplitude and eclipse depth decrease with increasing NSH frequency, while NSH amplitude correlates positively with eclipse depth. These complex variations observed across multiple observational windows provide substantial evidence for the understanding of tilted disk precession and NSHs.

ODIN: Improved Narrowband Lyα Emitter Selection Techniques for z = 2.4, 3.1, and 4.5

Lyman-alpha-emitting galaxies (LAEs) are typically young, low-mass, star-forming galaxies with little extinction from interstellar dust. Their low dust attenuation allows their Lyα emission to shine brightly in spectroscopic and photometric observations, providing an observational window into the high-redshift Universe. Narrowband surveys reveal large, uniform samples of LAEs at specific redshifts that probe large-scale structure and the temporal evolution of galaxy properties. The One-hundred-deg2 DECam Imaging in Narrowbands (ODIN) utilizes three custom-made narrowband filters on the Dark Energy Camera (DECam) to discover LAEs at three equally spaced periods in cosmological history. In this paper, we introduce the hybrid-weighted double-broadband continuum estimation technique, which yields improved estimation of Lyα equivalent widths. Using this method, we discover 6032, 5691, and 4066 LAE candidates at z = 2.4, 3.1, and 4.5 in the extended COSMOS field (∼9 deg2). We find that [O ii] emitters are a minimal contaminant in our LAE samples, but that interloping Green Pea–like [O iii] emitters are important for our redshift 4.5 sample. We introduce an innovative method for identifying [O ii] and [O iii] emitters via a combination of narrowband excess and galaxy colors, enabling their study as separate classes of objects. We present scaled median stacked spectral energy distributions for each galaxy sample, revealing the overall success of our selection methods. We also calculate rest-frame Lyα equivalent widths for our LAE samples and find that the EW distributions are best fit by exponential functions with scale lengths of w 0 = 53 ± 1, 65 ± 1, and 59 ± 1 Å, respectively.

Recommendations on bioacoustical metrics relevant for regulating exposure to anthropogenic underwater sounda).

Metrics to be used in noise impact assessment must integrate the physical acoustic characteristics of the sound field with relevant biology of animals. Several metrics have been established to determine and regulate underwater noise exposure to aquatic fauna. However, recent advances in understanding cause-effect relationships indicate that additional metrics are needed to fully describe and quantify the impact of sound fields on aquatic fauna. Existing regulations have primarily focused on marine mammals and are based on the dichotomy of sound types as being either impulsive or non-impulsive. This classification of sound types, however, is overly simplistic and insufficient for adequate impact assessments of sound on animals. It is recommended that the definition of impulsiveness be refined by incorporating kurtosis as an additional parameter and applying an appropriate conversion factor. Auditory frequency weighting functions, which scale the importance of particular sound frequencies to account for an animal's sensitivity to those frequencies, should be applied. Minimum phase filters are recommended for calculating weighted sound pressure. Temporal observation windows should be reported as signal duration influences its detectability by animals. Acknowledging that auditory integration time differs across species and is frequency dependent, standardized temporal integration windows are proposed for various signal types.

Why Are Children of Immigrants Less Geographically Mobile? Examining the Role of Economic Disadvantage and Family Networks

Previous work has found that adult children of international migrants in Western Europe have lower internal migration rates than individuals of native origin. This gap is important for differences in well-being, educational opportunities, and labor market outcomes. So far, however, little is known about the reasons for the greater geographical stability of migrant children. Theories suggest that structural differences such as economic resources as well as preferences for living near family may explain their lower internal migration rates. The current study tests these explanations by examining unique longitudinal register data from the Netherlands in which we follow the internal migration trajectories of people aged 18–50 in an observation window of 16 years between 2006 and 2022. We compare individuals of native origin with children of immigrants from Turkey and Morocco, two of the largest migrant populations in the country. Event history models confirmed that once socio-demographic characteristics were controlled for, children of migrants were less likely to migrate internally than individuals of native origin. Mediation analysis showed that economic resources did not explain the negative association; instead, the lower migration rates observed among children of migrants were mediated by geographical proximity to kin. Because migrant family networks are more geographically concentrated, children of migrants are more often discouraged from moving away. These findings highlight the pivotal role of family networks in explaining migrant-native differences in migration decision-making.

Association of Daily Doses of Buprenorphine With Urgent Health Care Utilization

Higher buprenorphine doses may benefit the increasing number of individuals using fentanyl and other synthetic opioids, but there is little empirical evidence on the efficacy of such higher doses. To examine the association between higher buprenorphine doses (above 16 mg and 24 mg) and subsequent emergency department (ED) or inpatient service use among patients diagnosed with opioid use disorder. This cross-sectional study was a retrospective analysis of health data from Optum's deidentified Clinformatics Data Mart Database from 2016 to 2021 for commercially insured individuals aged 18 years or older diagnosed with opioid use disorder (OUD). Eligible participants initiated buprenorphine after at least 90 days of enrollment and were dispensed at least a 14-day supply of buprenorphine. Data were analyzed from September 2023 through February 2024. Maximum buprenorphine dose received by a patient for 14 or more days: more than 24 mg, more than 16 mg to 24 mg, more than 8 mg to 16 mg, or 1 mg to 8 mg. Days from initiation of the maximum buprenorphine dose to an ED or inpatient visit for a behavioral health diagnosis, controlling for patient demographics, comorbid conditions, time to reaching maximum dose, buprenorphine discontinuation, and pre-buprenorphine health care utilization. A total of 35 451 individuals with an OUD diagnosis who began buprenorphine treatment were identified (mean [SD] age, 46.2 [15.1] years; 20 983 male [59.2%]; 3326 Black [9.4%], 2411 Hispanic [6.8%], 26 712 White [75.3%]). The most common dose was more than 8 mg to 16 mg daily (14 802 patients [42.9%]), with 9669 patients (27.3%) in the 1 mg to 8 mg tier, 10 329 patients (29.1%) in the 8 mg to 16 mg tier, and 651 patients (1.8%) in the tier receiving more than 24 mg. Among all patients receiving buprenorphine, 12.5% experienced an ED or inpatient visit. Survival analysis shows patients receiving doses more than 24 mg and between 16 mg to 24 mg had longer times to ED or inpatient use than patients receiving from 8 mg to 16 mg (time ratio [TR], 1.11; 95% CI, 1.02 to 1.20) and more than 24 mg (TR, 1.37; 95% CI, 1.04 to 1.81). Findings for doses above 16 mg daily were consistent for observation windows as short as 365 days (more than 24 mg: TR, 1.48; 95% CI, 1.01-2.18; more than 16 mg to 24 mg: TR, 1.19; 95% CI, 1.06-1.32). These findings contribute to the sparse empirical research regarding potential benefits of higher-dose buprenorphine treatment of individuals with OUD. Clinicians should be aware of the potential effects of higher buprenorphine doses on health care utilization while policymakers work to ensure equitable access to individuals who could potentially benefit from higher doses.

Revision Shoulder Arthroplasty: Predictors of Subsequent Revision Surgery and Economic Burden amongst Medicare Beneficiaries

Revision shoulder arthroplasty continues to add an increasing burden on patients and the healthcare system. This study aimed to delineate long-term shoulder arthroplasty revision incidence, quantify associated Medicare spending, and identify relevant predictors of both revision and spending. The complete 2016-2022(Q3) Medicare fee-for-service inpatient and outpatient claims data was analyzed. Patients receiving a primary total shoulder arthroplasty for osteoarthritis, rotator cuff pathology, or inflammatory arthropathy were included and subsequent ipsilateral revision surgeries were identified. The time to revision was modeled using the Prentice, Williams, and Peterson Gap Time Model. Medicare spending within 90 days post-discharge was modeled using a generalized linear model. The analysis was subdivided by index procedure type: anatomic total shoulder arthroplasty (TSA) and reverse shoulder arthroplasty (RSA). A total of 82,949 primary TSAs and 172,524 RSAs were identified. Compared to index TSA cases, index RSA cases had a lower first revision rate in an observation window of nearly 7 years (1.9% vs. 3.5%, p<0.001), but a higher rate of second (11.4% vs. 4.9%, p<0.001) as well as third revision (13.8% vs. 13.8%, p=0.449). TSA spending was significantly lower than RSA spending for the index procedure ($21,531 vs. $23,267, p<0.001), first ($23,096 vs. $26,414, p<0.001), and second ($25,060 vs. $29,983, p<0.001) revision. There was no statistically significant difference in third revision between TSA and RSA groups ($31,313 vs. $30,829, p=0.860). Age, sex, race, and rheumatoid arthritis were among the top predictors of revisions. Top predictors of Medicare spending included having a non-osteoarthritis surgical indication, a hospital stay of three or more days, a discharge to a setting other than home, malnutrition, dementia, stroke, major kidney diseases, and being operated on in a teaching hospital. Compared with TSA, RSA was associated with a lower first revision rate, but a higher subsequent revision rate. An index RSA procedure was also associated with higher initial Medicare spending as well as subsequent revision surgery spending compared with an index TSA procedure. Demographics and comorbid medical conditions were among the top predictors of revisions, while procedure-related factors predicted Medicare spending.

Transient Electromagnetic Field Response Analysis Considering Dam Boundary Conditions

Abstract The transient electromagnetic (TEM) method has outstanding advantages in detecting depth and response information and can be used to detect anomalies hidden deep inside dams. However, the current data analysis method based on geological exploration is inapplicable to the interpretation of dam detection data, the main reason being the lack of consideration of the influence of dam boundary conditions on the diffusion of the TEM field. In this work, by establishing a numerical model of a dam for TEM detection, the effects of the dam slope ratio, upstream water body, overburden, and other dam boundary conditions on the TEM field were studied. The results showed that the slope ratio made the propagating TEM field to exhibit a constraining behavior, and a negative deviation could be seen in the attenuation curve, indicating a false high-resistance anomaly. The upstream water body distorted the diffusion of the electromagnetic field, and the low-resistance covering layer caused a low-retardation retention phenomenon in the magnetic field, leading to a positive deviation in the attenuation curve, indicating a false low-resistance anomaly. The influence of the boundary conditions was present throughout the entire observation window in which the detection was performed, and the sensitivity of the magnetic field to these boundary conditions was significantly higher than that of the change rate of the magnetic field. With the increase in the observation time, the main influencing factors were the slope ratio, overburden layer, and upstream water body.

Detecting Process Duration Drift Using Gamma Mixture Models in a Left-Truncated and Right-Censored Environment

Within the realm of business context, process duration signifies time spent by customers between successive activities. This temporal perspective offers important insight to customer behavior, highlighting potential bottlenecks, and influencing business management decisions. The distribution of these process duration often changes over time due to factors such as seasonality, emerging legislation, changes to supply chains, and customer demand. Referred to as concept drift, these variations pose challenges for robust process modeling, understanding, and refinement. Subsequently, gamma mixture models are widely employed to model durations. These source data can, however, become left-truncated and right-censored within any specific observation window thereby necessitating a (well-known) modification to the likelihood function. The approach reported in this article leveraged this adapted likelihood across a series of observation windows, applying the likelihood ratio test to identify duration changes/concept drift. Due to its flexibility in modelling any duration distribution, the gamma mixture model was used with Nelder–Mead optimized likelihood for the left-truncated and right-censored data. The number of gamma components was determined by the Bayesian information criterion. The proposed framework underwent validation through simulated exponential samples, leading to recommendations for its practical application. Subsequently, we applied the methodology to three real-life event logs exhibiting diverse characteristics. Experimental results showcase the effectiveness of our approach in terms of data fitting, as compared to Kaplan–Meier curves, and in detecting instances of drift. This comprehensive validation underscores the practical utility and reliability of our framework for dynamic business scenarios.

Abnormal pattern recognition for online inspection in manufacturing process based on multi-scale time series classification

The collection of large volumes of temporal data during the production process is streamlined in a cyber manufacturing environment. The ineluctable abnormal patterns in these time series often serve as indicators of potential manufacturing faults. Consequently, the presence of effective analytical methods becomes essential for monitoring and recognizing these abnormal manufacturing patterns. However, the extensive process data may contain various minor abnormal patterns, typically reflecting changes in production status influenced by multiple anomalous causes. This study introduces an approach for recognizing abnormal manufacturing patterns through multi-scale time series classification (TSC). Long-term process signals undergo slicing using dynamically sized observation windows and subsequent classification at multiple scales employing our proposed TSC model, the distance mode profile-multi-branch dilated convolution network (DMP-MDNet). DMP-MDNet comprises two key modules aimed at bypassing complicated feature engineering and enhancing generalization capability. The first module, DMP, uses similarity measurement to encode scale- and magnitude-invariant temporal properties. Subsequently, the MDNet, equipped with multi-receptive field sizes, effectively leverages multi-granularity data for accurate classification. The effectiveness of our method is demonstrated through the analysis of a real-world body-in-white production dataset and various widely used public TSC datasets, showing promising applicability in actual manufacturing processes.