Accounting-based variables as an early warning indicator of financial distress in crisis and non-crisis periods

Financial integration in the Association of Southeast Asian Nations (ASEAN) region is a key focus of the ASEAN Economic Community. Whereas many studies focus on modelling corporate default, this paper identifies early warning indicators of financial distress before a default, using multiple discriminant analysis (MDA) models with a sample of listed and delisted companies in the ASEAN region. The analysis examines 720 companies in 10 different industries across six ASEAN countries from 1997 to 2016. The study constructs individual models for each country as well as an overall model for the entire region, using both in-sample and out-of-sample approaches. This overall model could be useful for an integrated banking system. To ensure robustness, the study also separately examines the predictive performance of the MDA models across different economic crises: the Asian financial crisis (AFC) from 1997 to 2000, the global financial crisis (GFC) from 2007 to 2009 and their pre- and post-crisis periods. We find that profitability ratios are the best indicators of financial distress in the ASEAN region, followed by liquidity and leverage ratios. In addition, our findings reveal common indicators that can be used to predict financial distress across ASEAN countries. The single model performs reasonably well in predicting financial distress 1 year ahead. In addition, the model is extended to incorporate a market-based indicator into the MDA models, the distance to default. However, the inclusion of this indicator does not significantly improve the accuracy of the models in predicting financial distress at listed firms in the ASEAN region

Federated Deep Reinforcement Learning-based Bitrate Adaptation for Dynamic Adaptive Streaming over HTTP

In video streaming over HTTP, the bitrate adaptation selects the quality of video chunks depending on the current network condition. Some previous works have applied deep reinforcement learning (DRL) algorithms to determine the chunk's bitrate from the observed states to maximize the quality-of-experience (QoE). However, to build an intelligent model that can predict in various environments, such as 3G, 4G, Wifi, \textit{etc.}, the states observed from these environments must be sent to a server for training centrally. In this work, we integrate federated learning (FL) to DRL-based rate adaptation to train a model appropriate for different environments. The clients in the proposed framework train their model locally and only update the weights to the server. The simulations show that our federated DRL-based rate adaptations, called FDRLABR with different DRL algorithms, such as deep Q-learning, advantage actor-critic, and proximal policy optimization, yield better performance than the traditional bitrate adaptation methods in various environments.Comment: 13 pages, 1 colum

Fiber-optic chemical sensors for competitive binding fluoroimmunoassay

This paper describes the development of a fiber-optic chemical sensor based on the principle of competitive-binding fluorescence immunoassay. Rabbit immunoglobln G (IgG) is covalently Immobilized on the distal sensing tip of a quartz optical fiber. The sensor is exposed to fluorescein Isothiocyanate (FITC) labeled and unlabeled anti-rabbit IgG. The 488-nm line of an argon-ion laser provides excitation of sensor-bound analyte. This results in fluorescence emission at the optical fiber‧s sensing tip. Sensor response is inversely proportional to the amount of unlabeled anti-IgG In the sample. Limits of detection (LOD) vary with Incubation time, sample size, and measurement conditions. For 10-μL samples, typical LOD are 25 fmol of unlabeled antibody In a 20-min Incubation period. These results Indicate that each fiber-optic fluoroimmunosensor can be constructed to perform a single sensitive, rapid, low-volume immunoassay, in in situ or benchtop applications. © 1987, American Chemical Society. All rights reserved

Methane emission factors from vietnamese rice production: Pooling data of 36 field sites for meta-analysis

Rice production is a significant source of greenhouse gas (GHG) emissions in the national budget of many Asian countries, but the extent of emissions varies strongly across agro-environmental zones. It is important to understand these differences in order to improve the national GHG inventory and effectively target mitigation options. This study presents a meta-analysis of CH4 database emission factors (EFs) from 36 field sites across the rice growing areas of Vietnam and covering 73 cropping seasons. The EFs were developed from field measurements using the closed chamber technique. The analysis for calculating baseline EFs in North, Central and South Vietnam in line with the Intergovernmental Panel on Climate Change (IPCC) Tier 2 methodology was specified for the three cropping seasons being early-(E), mid-(M) and late-year (L) seasons. Calculated average CH$_{4}$ EFs are given in kg ha$^{-1}$ d$^{-1}$ and reflect the distinct seasons in North (E: 2.21; L: 3.89), Central (E: 2.84; M+L: 3.13) and South Vietnam (E: 1.72; M: 2.80; L: 3.58). Derived from the available data of the edapho-hydrological zones of the Mekong River Delta, season-based EFs are more useful than zone-based EFs. In totality, these average EFs indicate an enormous variability of GHG emissions in Vietnamese rice production and represent much higher values than the IPCC default. Seasonal EFs from Vietnam exceeded IPCC defaults given for Southeast Asia corresponding to 160% (E), 240% (M) and 290% (L) of the medium value, respectively

Accurate in vivo tumor detection using plasmonic-enhanced shifted-excitation Raman difference spectroscopy (SERDS)

For the majority of cancer patients, surgery is the primary method of treatment. In these cases, accurately removing the entire tumor without harming surrounding tissue is critical; however, due to the lack of intraoperative imaging techniques, surgeons rely on visual and physical inspection to identify tumors. Surface-enhanced Raman scattering (SERS) is emerging as a non-invasive optical alternative for intraoperative tumor identification, with high accuracy and stability. However, Raman detection requires dark rooms to work, which is not consistent with surgical settings. Methods: Herein, we used SERS nanoprobes combined with shifted-excitation Raman difference spectroscopy (SERDS) detection, to accurately detect tumors in xenograft murine model. Results: We demonstrate for the first time the use of SERDS for in vivo tumor detection in a murine model under ambient light conditions. We compare traditional Raman detection with SERDS, showing that our method can improve sensitivity and accuracy for this task. Conclusion: Our results show that this method can be used to improve the accuracy and robustness of in vivo Raman/SERS biomedical application, aiding the process of clinical translation of these technologies. © The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/). See http://ivyspring.com/terms for full terms and conditions

Beamsplitting using self-imaging

The production of a variable array of optical point sources from a single point source can be achieved through the self-imaging properties inherent in a rectangular waveguide. Two prototype devices, based upon this concept, were designed and constructed. The resulting output patterns are discussed along with future design considerations and applications

Smart Gold Nanostructures for Light Mediated Cancer Theranostics: Combining Optical Diagnostics with Photothermal Therapy

This is the final version. Available from Wiley via the DOI in this record. Nanotheranostics, which combines optical multiplexed disease detection with therapeutic monitoring in a single modality, has the potential to propel the field of nanomedicine toward genuine personalized medicine. Currently employed mainstream modalities using gold nanoparticles (AuNPs) in diagnosis and treatment are limited by a lack of specificity and potential issues associated with systemic toxicity. Light-mediated nanotheranostics offers a relatively non-invasive alternative for cancer diagnosis and treatment by using AuNPs of specific shapes and sizes that absorb near infrared (NIR) light, inducing plasmon resonance for enhanced tumor detection and generating localized heat for tumor ablation. Over the last decade, significant progress has been made in the field of nanotheranostics, however the main biological and translational barriers to nanotheranostics leading to a new paradigm in anti-cancer nanomedicine stem from the molecular complexities of cancer and an incomplete mechanistic understanding of utilization of Au-NPs in living systems. This work provides a comprehensive overview on the biological, physical and translational barriers facing the development of nanotheranostics. It will also summarise the recent advances in engineering specific AuNPs, their unique characteristics and, importantly, tunability to achieve the desired optical/photothermal properties.Engineering and Physical Sciences Research Council (EPSRC

Measurement of the main and critical parameters for optimal laser treatment of heart disease

Abstract: Laser light is frequently used in the diagnosis and treatment of patients. As in traditional treatments such as medication, bypass surgery, and minimally invasive ways, laser treatment can also fail and present serious side effects. The true reason for laser treatment failure or the side effects thereof, remains unknown. From the literature review conducted, and experimental results generated we conclude that an optimal laser treatment for coronary artery disease (named heart disease) can be obtained if certain critical parameters are correctly measured and understood. These parameters include the laser power, the laser beam profile, the fluence rate, the treatment time, as well as the absorption and scattering coefficients of the target treatment tissue. Therefore, this paper proposes different, accurate methods for the measurement of these critical parameters to determine the optimal laser treatment of heart disease with a minimal risk of side effects. The results from the measurement of absorption and scattering properties can be used in a computer simulation package to predict the fluence rate. The computing technique is a program based on the random number (Monte Carlo) process and probability statistics to track the propagation of photons through a biological tissue

Plasmon oscillations in ellipsoid nanoparticles: beyond dipole approximation

The plasmon oscillations of a metallic triaxial ellipsoid nanoparticle have been studied within the framework of the quasistatic approximation. A general method has been proposed for finding the analytical expressions describing the potential and frequencies of the plasmon oscillations of an arbitrary multipolarity order. The analytical expressions have been derived for an electric potential and plasmon oscillation frequencies of the first 24 modes. Other higher orders plasmon modes are investigated numerically.Comment: 33 pages, 12 figure

Carbon dioxide reforming of methane over modified iron-cobalt alumina catalyst : Role of promoter

Cobalt-based catalysts are widely employed in methane dry reforming but tend to deactivate quickly due to coke deposits and metal sintering. To enhance the performance, iron, a cost-effective promoter, is added, improving cobalt's metal dispersibility, reducibility, and basicity on the support. This addition accelerates carbon gasification, effectively inhibiting coke deposition. Methods: A series of iron-doped cobalt alumina MFe-5Co/Al2O3 (M= 0, 0.4, 0.8, 1, 2 wt.%) were prepared via simple incipient-wetness impregnation. The catalysts were thoroughly characterized via modern techniques including BET, XRD, H2-TPR, CO2-TPD. Significant findings: The addition of iron had a minimal impact on the properties of γ-Al2O3, but it significantly affected the dispersibility of cobalt. At an optimal dosage of 0.8 wt.%, there was a notable decrease of 29.44% in Co3O4 particle size. However, excessive iron loading induced agglomeration of Co3O4, which was reversible. The presence of iron also resulted in a decrease in the reduction temperature of Co3O4. The material's basicity was primarily influenced by the loading of iron, reaching its highest value of 705.7 μmol CO2 g−1 in the 2Fe-5Co/Al2O3. The correlation between catalytic activity and the physicochemical properties of the material was established. The 0.8Fe-5Co/Al2O3 sample exhibited excellent performance due to the favorable dispersibility of cobalt, its reducibility, and its affordable basicity