Sovereign Wealth Funds: A Case Study of Korea Investment Corporation

Based on \u201cin-depth study\u201d, the aim of the paper is to investigate Korea Investment Corporation (KIC), a non-commodity Sovereign Wealth Fund, in order to analyze its investment strategies over the time (2005-2012) and evaluate any form of \u201cpolitical bias\u201d. Our results suggest that KIC pursues financial objectives, aiming to maximize the portfolio risk/return relationship, as it manages foreign excess reserves of those managed by Bank of Korea. We also argue that a form of \u2018internal political bias\u2019 affects investment process, as most of the financial resources are managed in-house. Overall, we support the hypothesis that in Korea Investment Corporation both financial and political objectives coexis

Young drivers’ pedestrian anti-collision braking operation data modelling for ADAS development

Smart cities and smart mobility come from intelligent systems designed by humans. Artificial Intelligence (AI) is contributing significantly to the development of these systems, and the automotive industry is the most prominent example of "smart" technology entering the market: there are Advanced Driver Assistance System (ADAS), Radar/LIDAR detection units and camera-based Computer Vision systems that can assess driving conditions. Actually, these technologies have become consumer goods and services in mass-produced vehicles to provide human drivers with tools for a more comfortable and safer driving. Nevertheless, they need to be further improved for progress in the transition to fully automated driving or simply to increase vehicle automation levels. To this end, it becomes imperative to accurately predict driver’s decisions, model human driving behaviors, and introduce more accurate risk assessment metrics. This paper presents a system that can learn to predict the future braking behavior of a driver in a typically urban vehicle-pedestrian conflict, i.e., when a pedestrian enters a zebra crossing from the curb and a vehicle is approaching. The algorithm proposes a sequential prediction of relevant operational indicators that continuously describe the encounter process. A car driving simulator was used to collect reliable data on braking behaviours of a cohort of 68 licensed university students, who faced the same urban scenario. The vehicle speed, steering wheel angle, and pedal activity were recorded as the participants approached the crosswalk, along with the azimuth angle of the pedestrian and the relative longitudinal distance between the vehicle and the pedestrian: the proposed system employs the vehicle information as human driving decisions and the pedestrian information as explanatory variables of the environmental state. In fact, the pedestrian’s polar coordinates are usually calculated by an on-board millimeter-wave radar which is typically used to perceive the environment around a vehicle. All mentioned information is represented in the form of time series data and is used to train a recurrent neural network in a supervised machine learning process. The main purpose of this research is to define a system of behavioral profiles in non-collision conditions that could be used for enhancing the existing intelligent driving systems, e.g., to reduce the number of warnings when the driver is not on a collision course with a pedestrian. Preliminary experiments reveal the feasibility of the proposed system

Relation of the work ability index to fitness for work in healthcare and public employees in a region of Northeastern Italy.

Purpose: Work ability indicates an individual's capacity to match job demands according to his/her physical and mental conditions and work circumstances. Occupational physicians should take into consideration the global health status of a worker in order to correctly assess if he/she is fit for the job. The aim of this study was to verify the association between fitness for work evaluation and Work Ability Index scores, as well as individual factors (age, gender, and anthropometric characteristics) and work-related variables (job type, years of working duration). Methods: A cross-sectional study was conducted within the occupational health surveillance of health and public employers in the Friuli-Venezia Giulia region (2018-2022). The participants voluntarily agreed to answer the standard Work Ability Index questionnaire. Data were investigated by univariable as well as multivariable regression analysis. Results: The Work Ability Index of the workers included in the study (N = 6893) resulted negatively associated with age, female sex, and body mass index. It was averagely lower in nurses and assistive personnel, and the highest in medical doctors and public employers. The fitness for work assessments was also statistically related to WAI scores. The results obtained from the univariable and the multivariable analysis were consistent. Conclusions: The Work Ability Index is an efficient tool to measure an individual's capability to sustain job demands, and can be taken into account to produce a correct fitness for work evaluation and consequently preserve workers' health status

How Healthcare Systems Negatively Impact Environmental Health? The Need for Institutional Commitment to Reduce the Ecological Footprint of Medical Services

The global healthcare industry plays a crucial role in preserving human health and well-being. However, there is a growing concern that the operation of healthcare systems may have unintended negative consequences on environment and health. Actually, healthcare systems worldwide are aimed at improving human health and prolonging life expectancy, but the pursuit of better health outcomes has environmental ramifications that are often underperceived [1,2,3,4,5,6,7]. In Western countries, the health sector represents between 8 and 10% of a country’s gross domestic product and employs 8% of total workers. This large-scale activity inevitably results in having a huge impact on the environment since it requires the use of various means of transportation, and the consumption of electricity and chemicals. Therefore, it is not a surprise that healthcare systems account for an average of 8.5% of total greenhouse gas emissions in the United States and about 6% in other Western countries [1]. Specifically, in a 2013 study, the US healthcare sector was found to be responsible for 12% of the overall national acid rain emissions, 10% of greenhouse gas emissions recorded that year and 10% of smog formation, being responsible also for 9% of air pollutants (including carcinogenic toxics) and 1% of stratospheric ozone depletion

Addressing the challenges of detecting time-overlapping compact binary coalescences

Standard detection and analysis techniques for transient gravitational waves make the assumption that detector data contains, at most, one signal at any time. As detectors improve in sensitivity, this assumption will no longer be valid. In this paper we examine how current search techniques for transient gravitational waves will behave under the presence of more than one signal. We perform searches on simulated data sets containing time-overlapping compact binary coalescences. This includes a modeled matched filter search (pycbc) and an unmodeled coherent search, coherent WaveBurst (cwb). Both of these searches are used by the LIGO-Virgo-KAGRA collaboration [1]. We find that both searches are capable of identifying both signals correctly when the signals are dissimilar in merger time, | Δ t c | ≥ 1     s , with pycbc missing only 1% of the overlapping binary black hole mergers it was provided. Both pipelines can find signal pairings within the region | Δ t c | < 1     s . However, clustering routines in the pipelines will cause only one of the two signals to be recovered, as such, the efficiency is reduced. Within this region, we find that cwb can identify both signals. We also find that matched filter searches can be modified to provide estimates of the correct parameters for each signal

Systematic calculation of molecular vibrational spectra through a complete Morse expansion

We propose an accurate and efficient method to compute vibrational spectra of molecules, based on exact diagonalization of an algebraically calculated matrix based on powers of Morse coordinate. The present work focuses on the 1D potential of diatomic molecules: as typical examples, we apply this method to the standard Lennard-Jones oscillator, and to the ab initio potential of the H2 molecule. Global cm-1 accuracy is exhibited through the H2 spectrum, obtained through the diagonalization of a 30 x 30 matrix. This theory is at the root of a new method to obtain globally accurate vibrational spectral data in the context of the multi-dimensional potential of polyatomic molecules, at an affordable computational cost.Comment: 30 pages including 6 figure

Environmental Issues and Neurological Manifestations Associated with COVID-19 Pandemic: New Aspects of the Disease?

Coronavirus (SARS-CoV-2) emerged in China in December 2019 and rapidly caused a global health pandemic. Current evidence seems to suggest a possible link with ecosystem disequilibrium and even air pollution. The primary manifestations affect respiratory and circulatory systems, but neurological features are also being reported through case reports and case series. We summarize neurological symptoms and complications associated with COVID-19. We have searched for original articles published in PubMed/Medline, PubMed Central and Google Scholar using the following keywords: "COVID-19", "Coronavirus", "pandemic", "SARS-COV-2", "neurology", "neurological", "complications" and "manifestations". We found around 1000 publications addressing the issue of neurological conditions associated with COVID-19 infection. Amongst those, headache and dizziness are the most common reported symptoms followed by encephalopathy and delirium, while the most frequent complications are cerebrovascular accidents, Guillain-Barré syndrome, acute transverse myelitis, and acute encephalitis. Specific symptoms affecting the peripheral nervous system such as hyposmia and dysgeusia are the most common manifestations recorded in the selected studies. Interestingly, it was noted that these kinds of neurological symptoms might precede the typical features, such as fever and cough, in COVID patients. Neurological symptoms and complications associated with COVID-19 should be considered as a part of the clinical features of this novel global pandemic

The Long March of Chinese Co-operatives: Towards Market Economy, Participation, and Sustainable Development

This is an Author Final Copy of a paper accepted for publication in Asia Pacific Business Review published by and copyright Taylor & Francis

Trends of Phase I Clinical Trials in the Latest Ten Years across Five European Countries

Phase 1 clinical trials represent a critical phase of drug development because new candidate therapeutic agents are tested for the first time on humans. Therefore, international guidelines and local laws have been released to mitigate and control possible risks for human health in agreement with the declaration of Helsinki and the international Good Clinical Practice principles. Despite numerous scientific works characterizing the registered clinical trials on ClinicalTrials.gov, the main features and trends of registered phase 1 clinical trials in Europe have not been investigated. This study is aimed at assessing the features and the temporal trend of distribution of phase 1 clinical studies, carried out in the five largest European countries over a ten-year period (2012-2021), and to evaluate the impact of the Italian regulatory framework on the activation of such studies

Search for gravitational-wave bursts in the third Advanced LIGO-Virgo run with coherent WaveBurst enhanced by Machine Learning

This paper presents a search for generic short-duration gravitational-wave (GW) transients (or GW bursts) in the data from the third observing run of Advanced LIGO and Advanced Virgo. We use coherent WaveBurst (cWB) pipeline enhanced with a decision-tree classification algorithm for more efficient separation of GW signals from noise transients. The machine-learning (ML) algorithm is trained on a representative set of noise events and a set of simulated stochastic signals that are not correlated with any known signal model. This training procedure preserves the model-independent nature of the search. We demonstrate that the ML-enhanced cWB pipeline can detect GW signals at a larger distance than previous model-independent searches. The sensitivity improvements are achieved across the broad spectrum of simulated signals, with the goal of testing the robustness of this model-agnostic search. At a false-alarm rate of one event per century, the detectable signal amplitudes are reduced up to almost an order of magnitude, most notably for the single-cycle signal morphologies. This ML-enhanced pipeline also improves the detection efficiency of compact binary mergers in a wide range of masses, from stellar mass to intermediate-mass black holes, both with circular and elliptical orbits. After excluding previously detected compact binaries, no new gravitational-wave signals are observed for the two-fold Hanford-Livingston and the three-fold Hanford-Livingston-Virgo detector networks. With the improved sensitivity of the all-sky search, we obtain the most stringent constraints on the isotropic emission of gravitational-wave energy from short-duration burst sources.Comment: 15 pages, 7 figure