Small Interval Research Articles

Precision tests of bulk entanglement entropy

We consider linear superpositions of single particle excitations in a scalar field theory on AdS3 and evaluate their contribution to the bulk entanglement entropy across the Ryu-Takayanagi surface. We compare the entanglement entropy of these excitations obtained using the Faulkner-Lewkowycz-Maldacena formula to the entanglement entropy of linear superposition of global descendants of a conformal primary in a large c CFT obtained using the replica trick. We show that the closed form expressions for the entanglement entropy in the small interval expansion both in gravity and the CFT precisely agree. The agreement serves as a non-trivial check of the FLM formula for the quantum corrections to holographic entanglement entropy as well as the methods developed in the CFT to evaluate entanglement entropy of descendants. Our checks includes an example in which the state is time dependent and spatially in-homogenous as well another example involving a coherent state with a Bañados geometry as its holographic dual.

Four-Wave Mixing Bragg Scattering for small frequency shift from Silicon Coupled Microrings

Abstract Frequency conversion is pivotal in nonlinear optics and quantum optics for manipulating and translating light signals across different wavelength regimes. Achieving frequency conversion between two light beams with small frequency interval is a central challenge. In this work, we design a pair of coupled silicon microrings wherein coupled-induced mode-splitting exists to achieve a small frequency shift by the process of four-wavemixing Bragg scattering. As an example, the signal can be up or down converted to the idler which is 15.5 GHz spaced when two pumps align with another pair of splitted resonances. The results unveil the potential of coupled microring resonators for small interval frequency conversion in the fields of high-fidelity all-optical signal processing quantum frequency interface.

Fractional mimetic dark matter: A fractional action-like variational approach

In this paper, we propose a nonlocal extension of the mimetic dark matter model based on the FALVA implementation of fractional calculus. Our primary objective to explore how certain properties of dark matter can be modeled within the FALVA framework rather than formulate a phenomenological model. We begin by constructing the action functional of the cosmological extension of the mimetic dark model and deriving the nonlinear equations of motion in the general case. Next, we focus on a fractional-power homogeneous mimetic dark field with an exponential expansion factor. We derive the equation of motion for the lapse field and obtain its general solution. Analytical solutions are then obtained for small time intervals and arbitrary values of the fractionality parameter. These solutions enable us to establish the physical line element of spacetime, incorporating the mimetic dark matter field. From this line element, we derive the Ricci tensor, Ricci scalar, and geodesic equations.

Effect of Constraint-Induced Movement Therapy and Mirror Therapy on The Patients with Subacute Stroke in Bangladesh

Background: Subacute stroke is a health-related problem that spans a period from two days to two weeks. A marked disability results from stroke causes remarkable burden on family and society. Objective: To find out the effectiveness of constraint-induced movement therapy (CIMT) and combined mirror therapy on patients with subacute stroke. Comparison of effectiveness in between combined therapy and CIMT. Materials and Methods: Eighteen patients with subacute stroke were enrolled and randomly divided into three groups: CIMT combined with mirror therapy group, CIMT only group, and control group. Two weeks of CIMT for 6 hours a day with or without mirror therapy for 30 minutes a day were performed under supervision. All groups received conventional occupational therapy for 40 minutes a day for the same period. The CIMT-only group and control group also received additional self-exercise to substitute for mirror therapy. Results: We performed the Fugl-Meyer assessment, which was 32.67±21.70 pre-treatment and 37.00±21.06 post-treatment with a p-value of 0.011, MRC muscle grading pre-treatment 3.01±2.78 and 3.21±1.35 post-treatment with a p-value of 0.107, and Modified Barthel Index pre-treatment 29.56±27.43 and 28.33±26.71 post-treatment with a p-value of 0.104. After two weeks of treatment, we had found that CIMT along with mirror therapy, had more impact on subacute stroke patients. Conclusion: Disability limitation following stroke should focused more. Among various treatment facilities CIMT shown promising result along with exercise and mirror therapy. Care-giver’s education, tele-rehabilitation facility, supervision of exercise will reduce hazard of attending hospital in small interval. Early initiation of rehabilitation program yield better results. KYAMC Journal Volume: 15, No: 01, April 2024: 33-39.

Munich’s selective diesel vehicle ban and its impact on nitrogen dioxide concentrations: A quasi-experimental study

BackgroundThe current limit on NO2 concentrations of 40 μg/m3, set by the European Union, has been regularly exceeded in Munich, Germany. This limit will likely be reduced towards the WHO recommended target of 10 μg/m3. Against this backdrop, the city implemented a selective diesel vehicle ban within the existing low-emission zone in February 2023, targeting Euro 4 and older diesel vehicles. Our study investigated the effect of Munich’s selective diesel vehicle ban on NO2 concentrations, focusing on the half-year period following its implementation. MethodsOur study utilized a synthetic control approach (primary analysis) and a controlled interrupted time series approach (secondary analysis). These quasi-experimental methodologies create a ‘counterfactual’ no-intervention scenario, enabling comparison between observed and counterfactual scenarios to estimate an intervention effect. We employed historical controls, using routine data from multiple monitoring stations located within and outside the low-emission zone for 2014 to 2022, and considered possible confounders. ResultsNO2 concentrations within Munich’s low-emission zone showed overall declining trends from August 2014 to July 2023. Effects of the selective diesel vehicle ban were small and wide confidence intervals indicate large uncertainty in the magnitude and direction of the effect. At Landshuter Allee, the average intervention effect was −2.67 μg/m3 (95 %-CI = [-12.72; 7.38]), at Stachus it was −2.74 μg/m3 (95 %-CI = [-9.91; 4.42]), and at Lothstrasse it was −1.03 μg/m3 (95 %-CI = [-7.75; 5.69]). The secondary analysis confirmed these findings, reinforcing uncertainty about the effect of the intervention. ConclusionOur study suggests that Munich’s selective diesel vehicle ban had a limited effect on lowering NO2 concentrations. Possible explanations include the ban’s focus on Euro 4 and older diesel vehicles, many exemptions to the selective ban, and unclear enforcement. This highlights that comprehensive approaches and ongoing, well-designed monitoring and evaluation are crucial for addressing urban air pollution and protecting public health.

A double-layered fully automated insomnia identification model employing synthetic data generation using MCSA and CTGAN with single-channel EEG signals.

Insomnia was diagnosed by analyzing sleep stages obtained during polysomnography (PSG) recording. The state-of-the-art insomnia detection models that used physiological signals in PSG were successful in classification. However, the sleep stages of unbalanced data in small-time intervals were fed for classification in previous studies. This can be avoided by analyzing the insomnia detection structure in different frequency bands with artificially generated data from the existing one at the preprocessing and post-processing stages. Hence, the paper proposes a double-layered augmentation model using Modified Conventional Signal Augmentation (MCSA) and a Conditional Tabular Generative Adversarial Network (CTGAN) to generate synthetic signals from raw EEG and synthetic data from extracted features, respectively, in creating training data. The presented work is independent of sleep stage scoring and provides double-layered data protection with the utility of augmentation methods. It is ideally suited for real-time detection using a single-channel EEG provides better mobility and comfort while recording. The work analyzes each augmentation layer's performance individually, and better accuracy was observed when merging both. It also evaluates the augmentation performance in various frequency bands, which are decomposed using discrete wavelet transform, and observed that the alpha band contributes more to detection. The classification is performed using Decision Tree (DT), Ensembled Bagged Decision Tree (EBDT), Gradient Boosting (GB), Random Forest (RF), and Stacking classifier (SC), attaining the highest classification accuracy of 94% using RF with a greater Area Under Curve (AUC) value of 0.97 compared to the existing works and is best suited for small datasets.

Relationship between fatigue and quantitative electromyography findings in patients with myasthenia gravis

BackgroundFatigue is a common complaint among patients with myasthenia gravis (MG). In this study, we investigated the alterations in muscle morphology in patients with MG experiencing fatigue using quantitative electromyography (QEMG), and explored the relationship between electrophysiological findings and the severity of both fatigue and disease. MethodsWe performed QEMG of the biceps brachii muscle using the peak ratio method and multi-motor unit potential (MUP) analysis across three groups: 18 MG patients with fatigue, 34 MG patients without fatigue, and 33 healthy subjects. Stimulated single-fiber EMG was performed on the frontalis muscle. The severity of perceived fatigue and disease was subsequently assessed using the quantitative myasthenia gravis (QMG) score, the MG-activities of daily living (MG-ADL) profile, self-reported fatigue questionnaires, and handgrip strength measurements. ResultsThe QEMG study revealed a reduced mean MUP duration and size index (SI), in addition to an increased peak ratio in patients with MG (p < 0.05), which tended to be more pronounced in those experiencing fatigue. Compared to healthy subjects, MG patients with fatigue displayed a myopathic pattern characterised by a high peak ratio, short duration, and small-amplitude MUPs, without any increase in the number of phases or small time intervals. The mean peak ratio was positively correlated with the QMG, MG-ADL, and Fatigue Impact Scale total and physical subscores (p < 0.05). Further, MG patients with fatigue exhibited reduced maximum grip strength, which was positively correlated with the mean MUP duration, amplitude, SI, and thickness, and negatively correlated with the mean peak ratio (p < 0.05). No significant differences were observed in the jitter or block measurements (p > 0.05). ConclusionsThe present study investigated electrophysiological findings that were not considered or theorised in prior studies on patients with MG experiencing fatigue. The results of this study suggest that myopathic changes may be a critical pathophysiological component underlying the fatigue associated with MG.

Adaptive PSO-SO algorithm with Sobol sequence for aerodynamic physical parameter identification of projectiles

In the domain of aerodynamic physical parameter identification, conventional optimization algorithms are often limited by falling into local optima. To overcome this limitation, a novel adaptive PSO-SO algorithm based on Sobol sequences (SAPSO-SO) algorithm is proposed in this study. The algorithm integrates particle swarm optimization algorithms and snake optimization algorithms, utilizing Sobol sequences for initialization, which enhances the global search and local development ability of the algorithm by adaptively adjusting the inertia weights and learning factors. In addition, this study introduced a local optimal discriminant mechanism and a local search function to further enhance the optimization performance of the algorithms. In this study, the small interval constant method was used to subdivide the trajectory, relying on the three-degree-of-freedom ballistic model to identify the starting ballistic parameters and aerodynamic physical parameters of each small interval. The performances of the snake optimization algorithm, particle swarm optimization algorithm, C-K method, and SAPSO-SO algorithm in the identification of ballistic physical parameters were compared using the full ballistic simulation data of a high-speed rotating projectile as measurement data. The results show that the SAPSO-SO algorithm demonstrates excellent accuracy and effectiveness, especially in noisy simulation data, where its recognition accuracy is improved by 7.79% over the C-K method, highlighting its superior anti-noise performance and global optimization capability. It is comprehensively analyzed that the SAPSO-SO algorithm has strong global optimization potential in theory and shows a high degree of accuracy and stability in practical applications, independent of the selection of initial parameters.

Gravitational waves from collapse of pressureless matter in the early universe

If an early matter phase of the Universe existed after inflation with the proper power spectrum, enhanced density perturbations can decouple from the Hubble flow, turn around and collapse. In contrast towhat happens in a radiation dominated Universe where pressure nullifies deviations from sphericity in these perturbations, in a matter dominated Universe, the lack of pressure although on the one hand facilitates the gravitational collapse, it allows small deviations from sphericity to grow substantially as the collapse takes place. The subsequent collapse is complicated: initially as non-spherical deviations grow, the collapsing cloud takes the form of a “Zel'dovich pancake”. After that, the more chaotic and nonlinear stage of violent relaxation begins where shells of the cloud cross and the matter is redistributed within a factor of a few of the free fall timescale, reaching a spherical virialized state. During the whole process, strong gravitational waves are emitted due to the anisotropy of the collapse andthe small time interval that the effect takes place. The emission of gravitational waves during the stage of the violent relaxation cannot be easily estimated with an analytical model. We perform an N-body simulation to capture the behaviour of matter during this stage in order to estimate the precise spectrum of gravitational waves produced in this scenario.

Efficient approximation of global population dynamic models through statistical inference using local data

Biological growth curves are pivotal in predicting natural growth across disciplines, typically analyzed using nonlinear least squares or maximum likelihood methods. Bhowmick et al. (2014) introduced the interval-specific rate of parameters (ISRP) for growth equations, improving the estimation of relative growth rate (RGR) and model selection accuracy. Despite its effectiveness, computing these model-specific RGR estimates involves complex calculations and lacks explicit expressions for many nonlinear models. Also, for highly nonlinear models and non-monotonic data where the parameters are non-linearly related, the computation of interval estimates is almost impossible and may suffer from significant approximation errors. So, the need for a more efficient computation method for ISRP remains a significant challenge in growth studies. In this article, we propose a computational approach to obtain interval estimates of parameters based on the maximum likelihood estimation method. The likelihood function is maximized using the data on smaller intervals. Our study underscores the importance of an efficient ISRP computation technique, providing a more stable, unbiased, and normally distributed estimator. The most important advantage is that it can be implemented using existing optimizers in software packages efficiently, therefore, giving more accessibility to the practitioners. Both simulation studies and real data analysis have been carried out to validate the proposed estimation process. Additionally, its applicability to non-monotonic growth profiles and its robustness in handling highly non-linear growth equations highlight its versatility. We also developed a web application GpEM-R which is freely available for researchers and practitioners to analyze growth data.

Objective performance indicators versus GEARS: an opportunity for more accurate assessment of surgical skill.

Surgical skill evaluation that relies on subjective scoring of surgical videos can be time-consuming and inconsistent across raters. We demonstrate differentiated opportunities for objective evaluation to improve surgeon training and performance. Subjective evaluation was performed using the Global evaluative assessment of robotic skills (GEARS) from both expert and crowd raters; whereas, objective evaluation used objective performance indicators (OPIs) derived from da Vinci surgical systems. Classifiers were trained for each evaluation method to distinguish between surgical expertise levels. This study includes one clinical task from a case series of robotic-assisted sleeve gastrectomy procedures performed by a single surgeon, and two training tasks performed by novice and expert surgeons, i.e., surgeons with no experience in robotic-assisted surgery (RAS) and those with more than 500 RAS procedures. When comparing expert and novice skill levels, OPI-based classifier showed significantly higher accuracy than GEARS-based classifier on the more complex dissection task (OPI 0.93 ± 0.08 vs. GEARS 0.67 ± 0.18; 95% CI, 0.16-0.37; p = 0.02), but no significant difference was shown on the simpler suturing task. For the single-surgeon case series, both classifiers performed well when differentiating between early and late group cases with smaller group sizes and larger intervals between groups (OPI 0.9 ± 0.08; GEARS 0.87 ± 0.12; 95% CI, 0.02-0.04; p = 0.67). When increasing the group size to include more cases, thereby having smaller intervals between groups, OPIs demonstrated significantly higher accuracy (OPI 0.97 ± 0.06; GEARS 0.76 ± 0.07; 95% CI, 0.12-0.28; p = 0.004) in differentiating between the early/late cases. Objective methods for skill evaluation in RAS outperform subjective methods when (1) differentiating expertise in a technically challenging training task, and (2) identifying more granular differences along early versus late phases of a surgeon learning curve within a clinical task. Objective methods offer an opportunity for more accessible and scalable skill evaluation in RAS.

Weakly Supervised 3D Face Reconstruction with Joint Spatial and Frequency Domain Information

3D face reconstruction is an important research direction in computer vision, and its goal is to recover a 3D face model from a single face picture. In the absence of real 3D face data, how to reconstruct a 3D face with a high degree of realism has become a hot research topic in recent years. Existing reconstruction algorithms usually rely on 3D labels generated from a large number of 2D face images as training data, however, inaccurate data will seriously affect the reconstruction quality. For this reason, the paper proposes a joint spatial-frequency domain decoupled weak supervision to achieve 3D face reconstruction, the main idea of which is to construct a multi-level loss function by using the weakly supervised information extracted from the spatial domain, and separating the frequency-domain information between the input and the rendered image in the frequency domain, and minimizing the difference between the two by difference computation. The method combines deep learning with 3D deformable models to reconstruct 3D models with high quality texture and shape from only a single face image. Quantitative experiments on the AFLW2000-3D and MICC Florence datasets show that the normalized average error in the small pose interval is as low as 2.42%, and the face reconstruction accuracy in the outdoor scene is 0.98 0.22 mm. Qualitative experiments on the MoFa-test, MICA datasets show that when faced with reconstruction with different poses, lighting, and expressions , our method outperforms other state-of-the-art reconstruction methods.

An effective and small sample-size valid confidence interval for isotonic dose-response curves by inverting a partial likelihood ratio test

A dose-response curve is essential for determining the safe dosage of a drug and is widely used in bioassay and in phase 1 clinical trials. It is generally accepted that the probability of death or the probability of dose-limiting toxicity is a non-decreasing function of the dose. This paper proposes and develops an effective point-wise confidence interval for an isotonic dose-response curve, a problem without a satisfactory solution so far. We show that one subset of the observations informs the lower limit while the other subset informs the upper limit. A partial likelihood ratio test using these subsets of observations is fully investigated. The resulting confidence interval is compared to three existing methods (Morris, Meyer, and Oron) in an extensive simulation study. The new method produces tight intervals while maintaining coverage. It is therefore preferred when maintaining an actual coverage probability is crucial as often in a regulatory environment. The new method extends to one-way ANOVA of a continuous response variable in a straightforward way. An R function is provided.

Sparse large-scale high-order fuzzy cognitive maps guided by spearman correlation coefficient

Time series prediction is one of the most important applications of Fuzzy Cognitive Maps (FCMs). In general, the state of FCMs in forecasting depends only on the state of the previous moment, but in fact it is also affected by the past state. Hence Higher-Order Fuzzy Cognitive Maps (HFCMs) are proposed based on FCMs considering historical information and have been widely used for time series forecasting. However, using HFCMs to deal with sparse and large-scale multivariate time series are still a challenge, while large-scale data makes it difficult to determine the causal relationship between nodes because of the increased number of nodes, so it is necessary to explore the relationship between nodes to guide large-scale HFCMs learning. Therefore, a sparse large-scale HFCMs learning algorithm guided by Spearman correlation coefficient, called SG-HFCM, is proposed in the paper. The SG-HFCM model is specified as follows: First, the solving of HFCMs model is transform into a regression model and an adaptive loss function is utilized to enhance the robustness of the model. Second, l1-norm is used to improve the sparsity of the weight matrix. Third, in order to more accurately characterize the correlation relationship between variables, the Spearman correlation coefficients is added as a regular term to guide the learning of weight matrices. When calculating the Spearman correlation coefficient, through splitting domain interval method, we can better understand the characteristics of the data, and get better correlation in different small intervals, and more accurately characterize the relationship between the variables in order to guide the weight matrix. In addition, the Alternating Direction Multiplication Method and quadratic programming method are used to solve the algorithms to get better solutions for the SG-HFCM, where the quadratic programming can well ensure that the range of the weights and obtaining the optimal solution. Finally, by comparing with five algorithms, the SG-HFCM model showed an average improvement of 11.93% in prediction accuracy for GRNs, indicating that our proposed model has good predictive performance.

Numerical study using the finite volume method on the thermal and energy performance of a regulated underfloor heating system incorporating PCM in a ventilated room

A physical model is performed to assess the thermal and energetic advantages of using PCM in controlling and regulating the indoor temperature of a ventilated room with an underfloor heating system incorporating a PCM. The heating source is regulated to keep the cavity air temperature in a small interval assuring a certain level of comfort without wasting too much energy. By contrasting our numerical results with numerical data from the literature, the suggested model was validated. Physical processes are solved numerically using computational fluid dynamics by a detailed 2D transient simulation integrating real climatic data of Agadir, Tangier and Casablanca. The simulations carried out justified the suitability of using the PCM with Agadir and Casablanca climate types by a saving of heating power reaching 18.80 % and 17.33 %, respectively.

Thermodynamic stabilities of NdFe3Al2, Nd5Fe17 and μ compounds and evolutions of microstructures and magnetic properties of Nd-Fe-Al alloys with nanocrystallites

In the Nd-Fe-Al ternary system, thermodynamic stabilities of three compounds, NdFe3Al2, Nd5Fe17 and μ, were determined by the equilibrated alloys, indicating that both NdFe3Al2 and μ ternary compounds are metastable phases, while Nd5Fe17 compound is a stable phase. Meantime, the microstructures of five representative as-cast alloy buttons are illustrated, which are consisted of crystalline phases and the complex of nanocrystallites + amorphous matrix (NCAM). The crystalline phases including Nd, Nd3Al, Nd2Fe17-xAlx and Nd6Fe13-xAl1+x as well as the nanocrystallites such as dhcp-Nd, fcc-Nd and Nd6Fe13-xAl1+x embedded in the amorphous matrix were observed. Particularly, it is concluded that the fcc-Nd nanocrystallites may be transformed from the fcc dense packing clusters of the amorphous matrix and/or dhcp-Nd nanocrystallites. The hard magnetic properties of these as-cast alloys can be attributed to NCAM and present a non-strict proportionate effect. The alloy composed of Nd + NCAM, with curie temperature (Tc) 515 K, displays the highest intrinsic coercivity Hci 305 kA/m and remanent magnetization Br 10.8 emu/g. Its good glass-forming ability is presented by the high ratio (Tx/Tm) 0.88 and the small interval (Tm-Tx) 105 K of the crystallization temperature (Tx) with the melting temperature (Tm). Since the present investigation focuses on phase stabilities, microstructural characterizations, magnetic properties and glass-forming ability of the Nd-Fe-Al alloys, the obtained results can provide a better understanding for advancing the design of the Nd-Fe-Al based hard magnets and amorphous materials.

Temperature Field Measurements in Swirl Spray Flames Using Two-Line Planar Laser Induced Fluorescence Thermometry

Abstract This paper investigates the temperature fields in a centrally staged swirl spray combustor using two-line OH planar laser induced fluorescence (PLIF) thermometry at elevated inlet pressures and temperatures up to 0.62 MPa and 650 K. The pilot and main stages of the combustor were supplied with RP-3 kerosene. OH radicals were excited using the Q1(5) and Q1(14) transitions within the A2Σ←X2Π (1,0) band. Two laser excitation systems were operated simultaneously, where the two beams were spatially combined and separated by a small interval in time. The PLIF signals excited at the two wavelengths were captured by two identical sets of imaging system. The calibration coefficient needed for quantitative conversion from fluorescence ratio to temperature was determined based on results from independent coherent anti-Stokes Raman scattering (CARS) measurements. A joint threshold mask was developed to remove the noise and weak signals in the raw PLIF images. The high temperature zones in the temperature field were then obtained, and the pilot and main stage flames were identified. In addition, the radial position of the pilot flame showed marked variations at a nominally fixed condition. By extracting the radial profiles, a consistency between the peaks of PLIF intensity and temperature was found, suggesting that PLIF images could be a qualitative substitute for the high temperature zones in the temperature fields of these swirl spray flames. This study demonstrates the feasibility of temperature field measurements using two-line OH PLIF in aero-engine model combustors.

FSW of AA2024: effects of tool wear on energy consumption

ABSTRACT This study shows that the progressive adhesion of weld material to the tool in friction stir welding AA 2024-T3, up to tool saturation, brings about a decrease in power consumption until a plateau is reached. The cause of this behavior is the hindering of the stirring action of the tool due to the material accumulated on it. The built-up material changes the nature of the tool/material interaction and then the friction condition at their interface. The direct consequence is a decrease in the shearing strain and therefore in the heat generated by friction. Bits of this adhering material break off from the tool at intervals. Macro and micro detachments are identified. Micro-detachments happen continuously at small periodic intervals and produce vibrations. The amplitude of these vibrations increases in all their characteristic spectral components up to tool saturation. Macro-detachments generate oscillations in power consumption and leave galling on the weld bead.

Management of Severe Bilateral Symptomatic Internal Carotid Artery Stenosis: Case Report and Literature Review.

Multiple strategies for tandem severe carotid artery stenosis are reported: bilateral carotid artery endarterectomy (CEA), bilateral carotid artery stenting (CAS), and hybrid procedures (CEA and CAS). The management is controversial, considering the reported high risk of periprocedural stroke, hemodynamic distress, and cerebral hyperperfusion syndrome. We present the case of a 64-year-old patient with severe symptomatic bilateral internal carotid artery stenosis (95% stenosis on the left internal carotid artery with recent ipsilateral watershed anterior cerebral artery-medial cerebral artery (ACA-MCA) and medial cerebral artery-posterior cerebral artery (MCA-PCA) ischemic strokes and 90% stenosis on the right internal carotid artery with chronic ipsilateral frontal ischemic stroke) managed successfully with staged CEA within a 3-day interval. The patient had a history of coronary angioplasty and stenting. Strategies for brain protection included shunt placement after the evaluation of carotid stump pressure, internal carotid backflow, and near-infrared spectroscopy. A collagen and silver-coated polyester patch was used to complete the endarterectomy using a 6.0 polypropylene continuous suture in both instances. Management also included neurological consults after extubation, dual antiplatelet therapy, head CT between the two surgeries, myocardial ischemia monitoring, and general anesthesia. Staged CEA with a small time interval between surgeries can be an option to treat tandem symptomatic carotid artery stenosis in highly selected patients. The decision should be tailored according to the patient's characteristics and should also be made by a cardiology specialist, a neurology specialist, and an anesthesia and intensive care physician.

Reliability estimation for individual predictions in machine learning systems: A model reliability-based approach

The conventional aggregated performance measure (i.e., mean squared error) with respect to the whole dataset would not provide desired safety and quality assurance for each individual prediction made by a machine learning model in risk-sensitive regression problems. In this paper, we propose an informative indicator ℛx to quantify model reliability for individual prediction (MRIP) for the purpose of safeguarding the usage of machine learning (ML) models in mission-critical applications. Specifically, we define the reliability of a ML model with respect to its prediction on each individual input x as the probability of the observed difference between the prediction of ML model and the actual observation falling within a small interval when the input x varies within a small range subject to a preset distance constraint, namely ℛx=Py∗−ŷ∗≤εx∗∈Bx, where y∗ denotes the observed target value for the input x∗,ŷ∗ denotes the model prediction for the input x∗, and x∗ is an input in the neighborhood of x subject to the constraint Bx=x∗x∗−x≤δ. The developed MRIP indicator ℛx provides a direct, objective, quantitative, and general-purpose measure of “reliability” or the probability of success of the ML model for each individual prediction by fully exploiting the local information associated with the input x and ML model. Next, to mitigate the intensive computational effort involved in MRIP estimation, we develop a two-stage ML-based framework to directly learn the relationship between x and its MRIP ℛx, thus enabling to provide the reliability estimate ℛx for any unseen input instantly. Thirdly, we propose an information gain-based approach to help determine a threshold value pertaing to ℛx in support of decision makings on when to accept or abstain from counting on the ML model prediction. Comprehensive computational experiments and quantitative comparisons with existing methods on a broad range of real-world datasets reveal that the developed ML-based framework for MRIP estimation shows a robust performance in improving the reliability estimate of individual prediction, and the MRIP indicator ℛx thus provides an essential layer of safety net when adopting ML models in risk-sensitive environments.