Alignment Technique Research Articles

DOES A NOVEL INTRAOPERATIVE NAVIGATION PLATFORM ALLOW TO RETAIN NATIVE KNEE FUNCTION FOLLOWING TOTAL KNEE ARTHROPLASTY?

IntroductionThis study aimed to evaluate the effectiveness of a novel intraoperative navigation platform for total knee arthroplasty (TKA) in restoring native knee joint kinematics and strains in the medial collateral ligament (MCL) and lateral collateral ligament (LCL) during squatting motions.MethodSix cadaver lower limbs underwent computed tomography scans to design patient-specific guides. Using these scans, bony landmarks and virtual single-line collateral ligaments were identified to provide intraoperative real-time feedback, aided in bone resection, implant alignment, tibiofemoral kinematics, and collateral ligament elongations, using the navigation platform. The specimens were subjected to squatting (35°-100°) motions on a physiological ex vivo knee simulator, maintaining a constant 110N vertical ankle load regulated by active quadriceps and bilateral hamstring actuators. Subsequently, each knee underwent a medially-stabilized TKA using the mechanical alignment technique, followed by a retest under the same conditions used preoperatively. Using a dedicated wand, MCL and LCL insertions—anterior, middle, and posterior bundles—were identified in relation to bone-pin markers. The knee kinematics and collateral ligament strains were analyzed from 3D marker trajectories captured by a six-camera optical system.ResultBoth native and TKA conditions demonstrated similar patterns in tibial valgus orientation (Root Mean Square Error (RMSE=1.7°), patellar flexion (RMSE=1.2°), abduction (RMSE=0.5°), and rotation (RMSE=0.4°) during squatting (p>0.13). However, a significant difference was found in tibial internal rotation between 35° and 61° (p<0.045, RMSE=3.3°). MCL strains in anterior (RMSE=1.5%), middle (RMSE=0.8%), and posterior (RMSE=0.8%) bundles closely matched in both conditions, showing no statistical differences (p>0.05). Conversely, LCL strain across all bundles (RMSE<4.6%) exhibited significant differences from mid to deep flexion (p<0.048).ConclusionThe novel intraoperative navigation platform not only aims to achieve planned knee alignment but also assists in restoring native knee kinematics and collateral ligament behavior through real-time feedback.AcknowledgmentThis study was funded by Medacta International (Castel San Pietro, Switzerland).

Effect of tibiofemoral alignment on simulated knee contact forces during gait in mechanically and kinematically aligned total knee arthroplasty patients.

The goal of the study was to apply a musculoskeletal knee model that considers individual tibiofemoral alignment (TFA) and to investigate its effect on knee contact force (KCF) during gait in mechanically (MA) and kinematically aligned (KA) total knee arthroplasty (TKA) patients. Total, medial, and lateral KCF was estimated from pre- and postoperative gait data of TKA patients (MA: n = 26, KA: n = 22). Preoperative KCF was compared between the generic and the adapted model using t-tests and statistical parametric mapping (SPM). The TFA-adapted model was then used to analyze pre- to postoperative differences in MA and KA patients. The factor of TFA increased estimates of KCF during the stance phase and led to higher peak contact forces (3-5%, p < 0.05). SPM analyses of pre- to postoperative KCF revealed no significant differences across the gait cycle, however, postoperative peak KCF was significantly increased in both groups (10-18%, p < 0.05). No group differences were observed when comparing KCF between MA and KA patients. Integrating TFA into the model led to higher estimations of KCF. Applying the adapted model, pre- to postoperative differences in KCF were the same for both TKA groups suggesting that both alignment techniques had comparable effects on knee loading post-TKA.

Spatial Aligned Mean: A Method to Improve Consensus Forecasts of Precipitation from Convection-Allowing Model Ensembles

Abstract Ensembles of convection-allowing model (CAM) forecasts are increasingly being used in operational numerical weather forecasting. Several approaches have been devised to find consensus among ensemble forecast fields, including the arithmetic ensemble mean and, more recently, the patchwise localized probability-matched (LPM) mean. However, differences in spatial distribution and intensity of precipitation features among ensemble members make it difficult to construct an ensemble mean product that characterizes the consensus while preserving precipitation structures forecasted by the individual ensemble members. To overcome this problem, this study aims to develop and test a method for improving ensemble consensus precipitation forecasts by directly considering the spatial offsets among ensemble members. This study uses a multiscale spatial alignment technique to align the precipitation features of each ensemble member to a common location, and the spatial aligned mean (SAM) is obtained by averaging the realigned members. It is shown that implementing SAM and subsequently applying the LPM technique to the average of all aligned members (SAM–LPM) can significantly improve the warm season precipitation forecast scores using common metrics such as equitable threat score (ETS). Also, improvement in the structure of features of heavy rainfall is shown from summer 2023 flash-flooding cases. Thus, SAM and SAM–LPM can be excellent candidate methods for calculating an ensemble consensus and providing ensemble consensus guidance to forecasters. Significance Statement High-impact rainfall events, such as flash floods, result in many billion-dollar loss events in the United States each year. This study seeks to improve the prediction of such events when using guidance from convection-allowing model (CAM) ensemble forecasts, such as the U.S. operational High-Resolution Ensemble Forecast (HREF) and the nascent Rapid Refresh Forecast System (RRFS). The proposed method, the spatial aligned mean (SAM), directly addresses the common issue of disparity in the predicted location of convective systems among ensemble members that confounds traditional ensemble consensus methods. In this study, it is found that SAM improves ensemble consensus guidance for high-impact rainfall events in both the HREF and the Center for Analysis and Prediction of Storms (CAPS) Finite-Volume Cubed-Sphere (FV3)-limited area model (LAM) CAM ensemble forecast system, a proxy for the future RRFS.

Risk Factors for Postoperative Shoulder Imbalance in Patients with Lenke Type 1 and 2 Scoliosis Treated using the Vertebral Coplanar Alignment Technique.

This was a multicenter retrospective cohort study. We investigated the incidence of postoperative shoulder imbalance (PSI) and its risk factors in patients with Lenke types 1 and 2 scoliosis corrected using vertebral coplanar alignment (VCA). PSI in scoliosis affects patient quality of life. While other correction methods have reported a high correction rate for the main thoracic curve (MTC) in relation to PSI, this correlation has not been confirmed for the VCA technique. We studied 176 patients with Lenke types 1 and 2 scoliosis who underwent posterior corrective fusion surgery using the VCA technique at 11 institutions. At two years postoperatively, patients were divided into two groups based on radiographic shoulder height (RSH): PSI- (RSH<2cm) and PSI+ (RSH≧2cm) groups. We analyzed the risk factors for PSI. The overall incidence of PSI two years postoperatively was 11.4% (20/176), with 9.2% (11/119) and 15.8% (9/57) in patients with Lenke types 1 and 2, respectively. Contrary to a previous study, a high MTC correction rate did not emerge as a risk factor for PSI. Instead, preoperative left shoulder elevation and low postoperative thoracic kyphosis and greater T1 tilt and high apical vertebral body-to-rib ratio were associated with PSI in patients with Lenke type 1. Preoperative left shoulder elevation and a low postoperative proximal thoracic curve (PTC) correction rate were identified as risk factors for PSI in patients with Lenke type 2. Our results suggest that proper correction of the PTC, rather than compromise MTC correction, may help prevent PSI in the VCA technique. This method is particularly advantageous for addressing Lenke type 1 scoliosis and yields favorable outcomes in shoulder balance. Patients with preoperative left shoulder elevation, especially Lenke type 2, are at high risk of developing PSI. 4.

Carbon dots induced homeotropic alignment in a negative dielectric nematic liquid crystal material

Abstract Recently, doping guest materials such as quantum dots (QDs) into liquid crystals (LCs) has been of great interest since their addition substantially enhances the properties of LC and opens new avenues for scientific advancement. Here, we report the induction of homeotropic alignment in cells without alignment layers of the negative dielectric nematic liquid crystal, N-(4-Methoxybenzylidene)-4-butylaniline (MBBA) by doping with carbon dots (CDs ~2.8±0.72 nm). The CDs-MBBA composites (CDs concentration: 0.002, 0.01, 0.03, 0.1 and 0.3 wt%) were investigated using optical polarising microscopy, electro-optical and dielectric techniques. Polarizing optical micrographs and voltage dependent optical transmission revealed the induced homeotropic alignment for all the composites under investigation. Interestingly, the least concentrated sample, 0.002 wt% exhibited partial homeotropic alignment. However, due to light leakage, the optical transmission value below threshold voltage was relatively higher than the rest of the composites. MBBA is a negative dielectric material, hence the application of a voltage across the cell was able to switch the alignment from a dark to a bright state for all composites. However, above a certain voltage (&gt; threshold voltage), the bright state produced some instabilities. The value of dielectric permittivity was observed to decrease with increasing concentration, confirming the effect of CDs in producing homeotropic alignment in MBBA. Measurements as a function of temperature were conducted to examine the thermal stability of the induced alignment. The alignment was found to be stable throughout the nematic phase of MBBA. The induction of such alignment without conventional alignment (i.e., rubbing of polyimides) technique can be helpful in addressing the evolving display demands by making liquid crystal displays (LCDs) and other display devices cost effective.

Efficient Parameter Optimisation for Quantum Kernel Alignment: A Sub-sampling Approach in Variational Training

Quantum machine learning with quantum kernels for classification problems is a growing area of research. Recently, quantum kernel alignment techniques that parameterise the kernel have been developed, allowing the kernel to be trained and therefore aligned with a specific dataset. While quantum kernel alignment is a promising technique, it has been hampered by considerable training costs because the full kernel matrix must be constructed at every training iteration. Addressing this challenge, we introduce a novel method that seeks to balance efficiency and performance. We present a sub-sampling training approach that uses a subset of the kernel matrix at each training step, thereby reducing the overall computational cost of the training. In this work, we apply the sub-sampling method to synthetic datasets and a real-world breast cancer dataset and demonstrate considerable reductions in the number of circuits required to train the quantum kernel while maintaining classification accuracy.

Electric Field-Induced Ordered-Structural Aerogels Enable Superinsulation and Multifunctionality.

1D flexible fibers assembled 3D porous networked ceramic fiber aerogels (CFAs) are developed to overcome the brittleness of traditional ceramic particle aerogels. However, existing CFAs with disordered and quasi-ordered structures fail to balance the relationship between flexibility, robustness, and thermal insulation. Creating novel architectural CFAs with an excellent combination of performances has proven extremely challenging. In this paper, a novel strategy is adopted to fabricate porous mullite fibrous aerogels (MFAs) with ordered structures by combining fiber sedimentation and electric field-induced fiber alignment techniques. For the first time, electric field-induced alignment of ceramic fibers is utilized to prepare bulk aerogels on a large scale. The resulting MFAs exhibit ultra-low high-temperature thermal conductivity of 0.0830 W m-1 K-1 at 1000°C, anisotropic mechanical and sound absorption performances, and multifunctionality in terms of the combination of thermal insulation, sound absorption, and hydrophobicity. The successful synthesis of such fascinating materials may provide new insights into the design and development of multifunctional CFAs for various applications.

A multi-source domain feature adaptation network for potato disease recognition in field environment.

Accurate identification of potato diseases is crucial for reducing yield losses. To address the issue of low recognition accuracy caused by the mismatch between target domain and source domain due to insufficient samples, the effectiveness of Multi-Source Unsupervised Domain Adaptation (MUDA) method in disease identification is explored. A Multi-Source Domain Feature Adaptation Network (MDFAN) is proposed, employing a two-stage alignment strategy. This method first aligns the distribution of each source-target domain pair within multiple specific feature spaces. In this process, multi-representation extraction and subdomain alignment techniques are utilized to further improve alignment performance. Secondly, classifier outputs are aligned by leveraging decision boundaries within specific domains. Taking into account variations in lighting during image acquisition, a dataset comprising field potato disease images with five distinct disease types is created, followed by comprehensive transfer experiments. In the corresponding transfer tasks, MDFAN achieves an average classification accuracy of 92.11% with two source domains and 93.02% with three source domains, outperforming all other methods. These results not only demonstrate the effectiveness of MUDA but also highlight the robustness of MDFAN to changes in lighting conditions.

Kinematic versus mechanical alignment: A systematic review of systematic reviews and meta-analyses of randomised controlled trials.

The purpose of this study was to review the currently available systematic reviews and meta-analyses comparing kinematic alignment (KA) and mechanical alignment (MA). A literature search was performed to obtain all systematic review and meta-analyses comparing KA to MA that included one or more randomised controlled trials. A total of 18 studies were obtained, three of which were systematic reviews without meta-analysis. Studies were evaluated based on their meta-analysis methodology, appropriate inclusion criteria, the use of correct definitions of each alignment technique, and risk of bias. These 18 studies included between 3 and 14 RCTs in each study. From the perspective of study design, the majority of papers had low risk of bias. In contrast, most of these reviews had technical issues pertaining to study inclusion in their meta-analyses that would potentially compromise their conclusions. These included mixing time points in the analysis, duplicate inclusion of patients in a meta-analysis, inclusion of studies with incorrect definitions of KA, inclusion of studies performed with restricted kinematic alignment with the KA group, and inappropriate combination of studies with bilateral total knee arthroplasty (TKA) with studies with unilateral TKA. The current literature is inadequate to determine if there is any advantage to KA compared to MA in TKA. Claims made in systematic reviews and meta-analyses on the subject must be carefully scrutinised based not only on risk of bias but also on the included study populations, the surgical methodology of each underlying study, and the authors' understanding of the definitions of each alignment technique. Level 1 based on this study being a systematic review with the inclusion of only systematic reviews and meta-analyses of randomised controlled trials.

Fractional Talbot Lithography for Predesigned Large-Area Liquid-Crystal Alignment

To address the increasing demands for cost-effective, large-area, and precisely patterned alignment of liquid crystals, a fractional Talbot lithography alignment technique was proposed. A light intensity distribution with a double spatial frequency of a photomask could be achieved based on the fractional Talbot effect, which not only enhanced the resolution of lithography but also slashed system costs with remarkable efficiency. To verify the feasibility of the alignment method, we prepared a one-dimensional polymer grating as an alignment layer. A uniform alignment over a large area was achieved thanks to the perfect periodicity and groove depth of several hundred nanometers. The anchoring energy of the alignment layer was 1.82 × 10−4 J/m2, measured using the twist balance method, which surpassed that of conventional rubbing alignment. Furthermore, to demonstrate its ability for non-uniform alignment, we prepared polymer concentric rings as an alignment layer, resulting in a liquid-crystal q-plate with q = 1 and α0 = π/2. This device, with a wide tuning range (phase retardation of ~6π @ 633 nm for 0 to 5 V), was used to generate special optical fields. The results demonstrate that this approach allows for the uniform large-area orientation of liquid-crystal molecules with superior anchoring energy and customizable patterned alignment, which has extensive application value in liquid-crystal displays, generating special optical fields and intricate liquid-crystal topological defects over a large area.

Effect of tibia-first, restricted functional alignment technique on gap width changes, and component positioning in robotic arm-assisted total knee arthroplasty.

This study aims to quantitatively assess the predictability of post-resection gap dimensions and the attainment of balanced gaps using robotic arm-assisted total knee arthroplasty (TKA). This retrospective cohort study included 100 consecutive patients who underwent robotic arm-assisted TKA for knee osteoarthritis using a restricted functional alignment (FA) technique. Tibial cuts were performed based on preoperative tibial anatomy within predefined boundaries, followed by femoral component adjustments according to tensioned soft tissues to optimise gap balance. The primary outcome was the proportion of balanced gaps, defined as differential laxities of ≤2 mm, across extension, flexion, lateral, and medial gap measurements. Ligament balancing in lateral and medial compartments was assessed using a robotic system at 10° and 90° flexion to evaluate if restricted FA facilitated a balanced knee. Secondary outcomes included implant alignment, resection depth, and patient-reported outcome measures (PROMs). Significant increases in both lateral and medial gaps at 10° and 90° flexion were observed following tibial and femoral bone resections (p < 0.001). At extension, average gap changes were 0.9 mm (lateral) and 1.6 mm (medial) after tibial cuts, and 0.5 mm (lateral) and 1.2 mm (medial) after femoral cuts. At 90° flexion, changes were 0.3 mm (lateral) and 1.7 mm (medial) following tibial cuts, and 1.0 mm (lateral) and 1.4 mm (medial) after femoral cuts. Despite these variations, the tibia-first, gap-balancing technique achieved overall balance in 98% of gap measurements. The tibial component was placed at an average of 2.1° varus, while the femoral component was positioned at 0.3° varus and 1.3° external rotation relative to the surgical transepicondylar axis. Significant improvements in PROMs were noted between preoperative and one-year postoperative evaluations (all p < 0.05). The tibia-first, restricted FA technique achieved a well-balanced knee in 98% of cases, despite inconsistent gap increments observed between initial assessments and post-resection. Therapeutic Level IV.

Contrastive Enhanced Learning for Multi-Label Text Classification

Multi-label text classification (MLTC) aims to assign appropriate labels to each document from a given set. Prior research has acknowledged the significance of label information, but its utilization remains insufficient. Existing approaches often focus on either label correlation or label textual semantics, without fully leveraging the information contained within labels. In this paper, we propose a multi-perspective contrastive model (MPCM) with an attention mechanism to integrate labels and documents, utilizing contrastive methods to enhance label information from both textual semantic and correlation perspectives. Additionally, we introduce techniques for contrastive global representation learning and positive label representation alignment to improve the model’s perception of accurate labels. The experimental results demonstrate that our algorithm achieves superior performance compared to existing methods when evaluated on the AAPD and RCV1-V2 datasets.

Enhancing the imidase activity of BpIH toward 3-isobutyl glutarimide via semi-rational design

(R)-3-Isobutylglutarate monoamide (R-IBM) is a key intermediate in the synthesis of the analgesic drug pregabalin. Recently, the imidase BpIH derived from Burkholderia phytofirmans was identified as a promising catalyst for the industrial production of R-IBM. Notably, this catalyst has the distinct advantage of achieving a 100% theoretical yield from 3-isobutyl glutarimide (IBI). In this study, homology modeling and structure alignment techniques were used to determine the substrate binding pocket of BpIH. Semi-rational design was used to analyze the amino acid residue conservation in the binding pocket region of BpIH. Interestingly, mutations of several low-conserved amino acid located 6–9 Å from the substrate significantly enhanced the catalytic activity of BpIH. Among them, the triple mutant Y37FH133NS226I (YHS-I) showed approximately a fivefold increase in enzyme activity and a significantly improved catalytic efficiency (kcat/Km). Under the same reaction time and conditions, YHS-I successfully converted IBI into R-IBM with a conversion rate of 88.87%, with an enantiomeric excess (ee) of the product exceeding 99.9%. In comparison, wild-type BpIH had a conversion rate of only 38.15%. Molecular dynamics and docking results indicated that YHS-I had higher rigidity around the mutation sites. The synergistic substitutions of Y37F, H133N, and S226I altered the interaction network within the mutation site, enhancing the protein’s affinity for the substrate and improving catalytic efficiency.Key points• 100% theoretical yield of R-IBM by BpIH compared with 50% by resolution• Semi-rational design of BpIH based on conservativity with homologous enzymes• Mutant with enzyme activity of sixfold and product ee value of 99.9%

Revision Surgery due to Patient Dissatisfaction with Remaining Valgus leg Alignment after Kinematically Aligned Total Knee Arthroplasty: A Case Report

BACKGROUND: The kinematic alignment (KA) technique aims to achieve true resurfacing of the knee joint and recreate prearthritic anatomy while preserving soft tissues. However, it remains uncertain whether all prearthritic anatomies are biomechanically equivalent and if KA is suitable for cases with extreme anatomical variations. CASE PRESENTATION: The case of a 75-year-old female patient with a moderate valgus deformity who underwent kinematically aligned total knee arthroplasty (TKA) was reported. The patient required early revision surgery due to persistent pain in the medial collateral ligament area and dissatisfaction with the postoperative leg alignment. CONCLUSION: There is a paucity of studies addressing the outcomes of kinematically aligned TKA in patients with valgus knees. Concerns persist regarding the suitability of unrestricted KA for all anatomical variations, particularly extreme ones. This case underscores the importance of considering restricted KA as a potentially safer alternative. In addition, patient perception of limb alignment and appearance significantly impacts the success of total knee arthroplasty, particularly in cases with valgus deformities.

Boundaries in Kinematic Alignment: Why, When, and How.

The use of alternative alignments in total knee arthroplasty (TKA) has recently been increasing in popularity: many of these alignments have been included in the broad spectrum of "kinematic alignment." This alternative approach was recommended to increase patients' satisfaction since many studies based on patient-reported outcome measures (PROMs) showed that every fifth patient is not satisfied with the surgical outcome. In fact, the original kinematic alignment technique was designed as a "pure resurfacing" technique, maintaining the preoperative axes (flexion-extension and axial rotation) of the knee. In adjunct, many new classifications of the preoperative limb deformity have been proposed to include a large range of knee anatomies, few of them very atypical. Following those classifications, many surgeons aimed for a reproduction of unusual anatomies putting in jeopardy the survivorship of the implant according to the classical "dogma" of a poor knee kinematics and TKA biomechanics if the final hip-knee-ankle (HKA) axis was not kept within 5 degrees from neutral. This article reviews the literature supporting the choice of setting alignment boundaries in TKA when surgeons are interested in reproducing the constitutional knee anatomy of the patient within a safe range.

GRA: Graph Representation Alignment for Semi-Supervised Action Recognition.

Graph convolutional networks (GCNs) have emerged as a powerful tool for action recognition, leveraging skeletal graphs to encapsulate human motion. Despite their efficacy, a significant challenge remains the dependency on huge labeled datasets. Acquiring such datasets is often prohibitive, and the frequent occurrence of incomplete skeleton data, typified by absent joints and frames, complicates the testing phase. To tackle these issues, we present graph representation alignment (GRA), a novel approach with two main contributions: 1) a self-training (ST) paradigm that substantially reduces the need for labeled data by generating high-quality pseudo-labels, ensuring model stability even with minimal labeled inputs and 2) a representation alignment (RA) technique that utilizes consistency regularization to effectively reduce the impact of missing data components. Our extensive evaluations on the NTU RGB+D and Northwestern-UCLA (N-UCLA) benchmarks demonstrate that GRA not only improves GCN performance in data-constrained environments but also retains impressive performance in the face of data incompleteness.

A Novel Device for Micro-Droplets Generation Based on the Stepwise Membrane Emulsification Principle.

This paper presents a novel design of the device to generate microspheres or micro-droplets based on the membrane emulsification principle. Specifically, the novelty of the device lies in a proposed two-layer or stepwise (by generalization) membrane structure. An important benefit of the stepwise membrane is that it can be fabricated with the low-cost material (SU-8) and using the conventional lithography technology along with a conventional image-based alignment technique. The experiment to examine the effectiveness of the proposed membrane was conducted, and the result shows that microspheres with the size of 2.3 μm and with the size uniformity of 0.8 μm can be achieved, which meets the requirements for most applications in industries. It is noted that the traditional membrane emulsification method can only produce microspheres of around 20 μm. The main contribution of this paper is thus the new design principle of membranes (i.e., stepwise structure), which can be made by the cost-effective fabrication technique, for high performance of droplets production.

Investigating the effect of SiO2 thin films with sculptured topographies on the alignment properties of liquid crystals

In the present work, sculptured thin films (STF) based on silicon dioxide (SiO2) induced on the indium tin oxide (ITO) coated substrates via glancing angle deposition (GLAD) technique using the physical vapor deposition (PVD) process. Our main motive to perform this work is to address the issues we have faced with the traditional alignment method and give a try to overcome those problems. Keeping these challenges in our mind, we are giving here an alternative to traditional method and explored this different alignment technique with GLAD process. We have fabricated a liquid crystal device (LCD) in a way of electrically-controlled birefringence (ECB) and placed the engineered substrates in anti-parallel alignment. Morphological, optical and electro-optic behaviour of resulting device has been studied in the ∼5 μm thick device. Effect of applied voltage on the morphology and electro-optic behaviour of liquid crystal has been established. Interestingly, LC material demonstrates electrically switchable birefringence colours viz. brown, green, pink, purple and mauve colour, under applied electric field in room temperature at 10X magnification under polarizing optical microscope. A very nice extinction and contrast has been obtained with fabricated ECB device, which elucidate the device quality with impeccable alignment without any disclination defects. Besides, the electro-optic characteristics it also shows a significant impact on the liquid crystal properties. The obtained results demonstrate nice optical retardation, a lower Frederick's threshold voltage i.e. 1.5 V and 19.03 dB higher contrast respectively. An impressive switching response time τon of 5.4 ms and τoff = 600 µs at 20 V has attained with respective device as well. Such change in parameters may correspond to the higher electrical torque experienced by the LC molecules on the application of applied field. Anchoring energy and pretilt angle of respective system has been calculated and correlated with observed behaviour. Thus, engineered device with this superior alignment method show remarkable electro-optic results which can open a new way for the development of fast LCD for advanced optical device applications.

Entropy-guided robust feature domain adaptation for electroencephalogram-based cross-dataset drowsiness recognition

To accurately identify driver drowsiness using Electroencephalogram (EEG), a significant amount of time is typically required to gather data from either the same subject or multiple subjects under the same experimental condition. The prolonged period required for data collection impedes the practical application of EEG. In this paper, we consider the challenging task of cross-dataset driver drowsiness recognition, where the EEG data collected by different devices are expected to be conveniently recognized by a classifier trained on an existing dataset. In order to solve the problem of distribution drift, we propose an Entropy-Guided Robust Feature (EGRF) adaptation framework. This framework utilizes the Entropy Minimization (EM) technique for domain alignment and an ensemble-like structure, consisting of multiple branch classifiers and a dropout layer, to enhance the robustness. The proposed method has been tested across two public datasets and achieves 2-class recognition accuracies of 77.4% and 85.9%. The innovative feature-robust block, combined with the EM loss, leads to substantial performance improvement, proving the effectiveness of our framework for cross-dataset driver drowsiness recognition. Our research demonstrates a promising direction for development of a calibration-free driver drowsiness monitoring system in the future.

A compliance assessment system for Incident Management process

The Incident Management (IM) process is one of the core activities for increasing the overall security level of organizations and better responding to cyber attacks. Different security frameworks (such as ITIL and ISO 27035) provide guidelines for designing and properly implementing an effective IM process. Currently, assessing the compliance of the actual process implemented by an organization with such frameworks is a complex task. The assessment is mainly manually performed and requires much effort in the analysis and evaluation. In this paper, we first propose a taxonomy of compliance deviations to classify and prioritize the impacts of non-compliant causes. We combine trace alignment techniques with a new proposed cost model for the analysis of process deviations rather than process traces to prioritize interventions. We put these contributions into use in a system that automatically assesses the IM process compliance with a reference process model (e.g., the one described in the chosen security framework). It supports the auditor with increased awareness of process issues to make more focused decisions and improve the process’s effectiveness. We propose a benchmark validation for the model, and we show the system’s capability through a usage scenario based on a publicly available dataset of a real IM log. The source code of all components, including the code used for benchmarking, is publicly available as open source on GitHub.