Month: January 2025

Draft Rules to Operationalise Digital Personal Data Protection Act Out  The Wire

Abstract

This study examines the impact of Directors’ and Officers’ liability insurance (D&O insurance) on shareholder value within the framework of strategic alliances (SAs). Utilizing a dataset from companies listed on the Taiwan stock market from 2009 to 2019, our analysis identifies a significant positive correlation between D&O insurance and abnormal returns around the time of strategic alliance announcements. This beneficial effect is especially marked in international, horizontal, equity-related, and marketing alliances. Our overall findings support the monitoring mechanism hypothesis, which suggests that D&O insurance is indicative of robust governance practices. By providing coverage, D&O insurance enhances managerial efficiency and reduces the potential for agency conflicts due to information asymmetry-a frequent concern in strategic alliances-thereby enhancing shareholder value. This study contributes to the literature by highlighting how D&O insurance can act as a pivotal governance mechanism that reassures investors of a firm’s commitment to effective management and strategic alignment, particularly in complex alliance settings.

Abstract

The South Caucasus region possesses the ramified network of ground and underground water systems. All water bodies in this region, particularly rivers and groundwater resources, are mostly transboundary. This is not only a challenge for the regional countries, but also new opportunities for cooperation. The political conflicts that occur in the region from time to time and also persistent deficit of finance are the major obstacles for strengthening the transboundary cooperation. However, the ecological situation still remains acceptable for creating the impulse for development of the transboundary water cooperation.

Abstract

Background

The aim of this study was to evaluate the correlation of the volume and minimum axial area (MAA) measurements between different upper and lower boundaries used for oropharyngeal airway assessment.

Methods

Cone Beam Computed Tomography (CBCT) scans of 49 subjects taken for pre-orthognathic surgical planning were obtained retrospectively from the archives (n = 49; 32 females, 17 males; mean age = 20.9 ± 5.22). Volume and MAA of the oropharyngeal airway were measured in 32 different airway segmentations created with four different upper and eight different lower boundaries using the Dolphin3D (Dolphin Imaging & Management Solutions, Chatsworth, California, ABD) software. All measurements were performed by the same examiner and were repeated 2 weeks apart. The correlation between the measurements was evaluated with the Pearson correlation test. Intra-observer reliability was calculated with the intra-class correlation coefficient.

Results

Volume and MAA showed excellent intra-observer reliability (0.997 and 0.999 intraclass correlation coefficients, respectively) and a high level of positive correlation (r = 0.896–0.999, and r = 0.859-1.00, respectively) for all the measurements.

Conclusions

All measurements between different lower and upper boundaries showed a high correlation. It was found that the lower and upper limits assessed in this study can be used safely in future upper airway studies according to the study design.

Abstract

Background

Left ventricular (LV) myocardial contraction patterns can be assessed using LV mechanical dispersion (LVMD), a parameter closely associated with electrical activation patterns. Despite its potential clinical significance, limited research has been conducted on LVMD following myocardial infarction (MI). This study aims to evaluate the predictive value of cardiac magnetic resonance (CMR)-derived LVMD for adverse clinical outcomes and to explore its correlation with myocardial scar heterogeneity.

Methods

We enrolled 181 post-MI patients (median age: 55.7 years; 76.8% male) who underwent CMR examinations. LVMD was calculated using the CMR-feature tracking (CMR-FT) technique, defined as the standard deviation (SD) of the time from the R-wave peak to the negative strain peak across 16 myocardial segments. Entropy was quantified using an algorithm implemented with a generic Python package. The primary composite endpoints included sudden cardiac death (SCD), sustained ventricular arrhythmias (VA), and new-onset heart failure (HF).

Results

Over a median follow-up of 31 months, LVMD and border zone (BZ) entropy demonstrated relatively high accuracy for predicting the primary composite endpoints, with area under the curve (AUC) values of 0.825 and 0.771, respectively. Patients with LVMD above the cut-off value (86.955 ms) were significantly more likely to experience the primary composite endpoints compared to those with lower LVMD values (p < 0.001). Multivariable analysis identified LVMD as an independent predictor of the primary composite endpoints after adjusting for entropy parameters, strain, and left ventricular ejection fraction (LVEF) (hazard ratio [HR]: 1.014; 95% confidence interval [CI]: 1.003–1.024; p = 0.010). A combined prediction model incorporating LVMD, BZ entropy, and LVEF achieved the highest predictive accuracy, with an AUC of 0.871 for the primary composite endpoints. Spearman rank correlation analysis revealed significant linear correlations between LVMD and entropy parameters (p < 0.001 for all).

Conclusions

Myocardial heterogeneity, as assessed by LVMD and BZ entropy, represents reliable and reproducible parameters reflecting cardiac remodeling following MI. LVMD has independent prognostic value, and the combination of LVMD and BZ entropy with the guideline-recommended LVEF as a unified model enhances the accuracy of forecasting the risk of primary combined endpoints in patients after MI.

Abstract

Purpose

This study aimed to compare different treatment modalities to correct ill-fitted maxillary complete denture either by the conventional relining method or by scanning the relining impression and digitally construct a new denture regarding patient satisfaction, denture retention, and adaptation.

Materials and methods

Twelve edentulous patients suffering from loose maxillary complete dentures were selected, dentures’ borders and fitting surfaces were prepared, and relining impressions were taken, the impressions were scanned and the STL files were used for CAD/CAM milling ( computer aided designing/ computer aided manufacturing) of new maxillary dentures (Group A), then the relining impression went through the conventional laboratory steps to fabricate (Group B) maxillary dentures. Both groups were evaluated regarding patient satisfaction by a specially designed questionnaire, retention values were measured by a digital force gauge at denture insertion appointment and two weeks later, geomagic software was used to evaluate dentures adaptation to oral tissues.

Results

Both groups (A and B) were completely satisfied with their dentures except regarding esthetics, all group A and 50% of group B were satisfied. Both groups showed a statistically significant increase in retention values at the two-week follow-up period compared to those at denture insertion time, with higher values were for group B. Finally, the relined dentures showed better oral tissue adaptation than digitally constructed dentures.

Conclusion

Relined maxillary dentures showed better retention, esthetics, and denture adaptation with lower cost than digitally constructed maxillary dentures which showed acceptable retention and adaptation, with better time and data saving.

Trial registration

Clinical trials number: NCT06366321. With registration date on ClinicalTrials.gov public website: 13/ 4/ 2024.

Abstract

Adhesive joints are widely used in industries such as aerospace and automotive due to their lightweight and high mechanical performance. However, weak adhesion remains a significant issue affecting the structural integrity of these joints. Current detection methods of weak adhesion rely on destructive testing, which limits the widespread use of adhesive primary structures. This study proposes a novel nondestructive testing (NDT) technique to detect, evaluate the intensity, and localize weak adhesion in single lap joints (SLJs) using lamb waves (LWs) and machine learning (ML). The aim is to develop a ML-based pipeline capable of identifying weak adhesion with high accuracy and sensitivity, based on data from simulated and experimental SLJ samples. The proposed technique integrates LW data with convolutional neural networks (CNNs) in a ML pipeline for weak adhesion detection in SLJs. The use of a large simulated dataset combined with transfer learning allows for effective adaptation to experimental conditions, improving both the detection and localization of damage. This approach offers a significant advancement over traditional destructive testing techniques. The pipeline begins with the generation of simulated LW time-series data for SLJs with varying adhesion levels, damage locations, and sizes. After preprocessing, the data are input into a CNN, which is initially trained on synthetic data. Transfer learning is employed to fine-tune the model using a small experimental dataset. The final trained model is then applied to detect weak adhesion, estimate its intensity, and localize the damage. The proposed pipeline demonstrated high performance in both simulated and experimental datasets: regarding detection, the algorithm achieved over 95.3% accuracy in identifying damage from simulated data and near 100% detection of damaged cases in experimental data; for intensity estimation, the algorithm showed an average loss of approximately 45 MPa for weak adhesion intensity in experimental validation, with an average error of about 140 MPa and a best-case error of just near 3.6 MPa; in terms of localization, the average localization error was approximately 8 mm in the synthetic validation dataset; with respect to flexibility, the methodology is adaptable to different damage characteristics, such as existence, intensity, and localization, without requiring substantial modifications. Summing up, this study presents a novel NDT approach using ML and LW data that significantly improves the detection, evaluation, and localization of weak adhesion in adhesive joints. Its high accuracy and adaptability have the potential to enhance structural health monitoring, ensuring the safety and durability of bonded structures in critical industries.

Abstract

Iris biometrics allow contactless authentication, which makes it widely deployed human recognition mechanisms since the couple of years. Susceptibility of iris identification systems remains a challenging task due to diversity in spoof or presentation attacks (PAs) that fails to assure consistency while adopting them in real life scenarios. Hence, iris PAs are the growing concerns that gained significant attention in recent past decade. To alleviate these attacks or recognize presentation attack instruments (PAIs), iris presentation attacks detection (IPAD) algorithms are designed to distinguish a real and fabricated iris trait. Aiming at the efficient iris spoof detection mechanism, in this research work we expound a novel ensemble learning-enabled model (IensNet) that learns three pre-trained and fined-tuned deep models (i.e. DenseNet161, ResNet and VGGNet) for better accuracy and generalized performance. The novel IensNet approach offers several merits (i.e. consolidated strengths of multiple models, improved generalization ability, etc.) as compared to a simple transfer learning strategy where the knowledge is drawn from single pre-trained model. Finally, our approach learns a novel fully-connected dual layer classifier via outcome of three fine-tuned models to yield a final classification result as bonafide or spoof iris trait. Our approach is evaluated on Notre Dame LivDet iris 2017 and Notre Dame contact lenses 2015 anti-spoofing datasets. The experimental analysis of IensNet offers outstanding performance with a lower ACER of 0.2% and 1.4% for Iris-LivDet-2017 and Notre Dame contact lenses 2015 dataset respectively. Besides, IensNet exhibit promising results in cross-dataset environment with an ACA of 91.46%.

Abstract

Recent years have seen unprecedented shifts in global natural gas trade, precipitated in large part by Russia’s war on Ukraine. How this regional conflict impacts the future of natural gas markets is subject to three interconnected factors: (i) Russia’s strategy to regain markets for its gas exports; (ii) Europe’s push towards increased liquified natural gas (LNG) and the pace of its low carbon transition; and (iii) China’s gas demand and how it balances its climate and energy security objectives. A scenario modelling approach is applied to explore the potential implications of this geopolitical crisis. We find that Russia struggles to regain pre-crisis gas export levels, with the degrees of its success contingent on China’s strategy. Compared to 2020, Russia’s gas exports are down by 31–47% in 2040 where new markets are limited and by 13–38% under a pivot to Asia strategy. We demonstrate how integrating energy geopolitics and modelling enhances our understanding of energy futures.