Asymmetric effects and volatility transmission from metals markets to solar energy stocks: Evidence from DCC, ADCC, and quantile regression approach

Detrimental environmental repercussions have recently given rise to an interest in green investments. Although solar energy stocks are appealing assets for ethical investors, little is known about their dynamic correlations and linkages with metal (silicon, lithium, and rare earth) markets, particularly during economic events which is essential for hedging effectiveness and asset allocation. This study investigates the nexus between metal markets, oil price volatility (OVX), market sentiments (VIX), and solar energy markets using DCC, ADCC models, and the quantile regression approach. The results show both symmetric and asymmetric shock spillover between metals markets, VIX, OVX, and solar energy markets which are more prominent during COVID-19 pandemic, US-China trade frictions, and Russian invasion of Ukraine. For portfolio management, the hedging effectiveness of lithium stocks is highest, followed by silicon and rare earth metals. However, the hedge ratios are time-varying, and the variability is highest during US-China trade frictions. The quantile regression estimates reveal that lithium market is the most persistent determinant of solar energy stocks followed by silicon market even after segregating the periods into Paris Agreement and COVID-19 pandemic. Thus, lithium and silicon are driving markets of solar energy markets and can be a cause of omitted variable bias if stay unobserved. Nonetheless, there is little influence of VIX, rare earth metals, and OVX on solar energy stocks. Lastly, the estimations of threshold regression suggest that market sentiments change the association between metal markets and solar energy markets after the VIX reaches a certain threshold level. © 2023

Entrance monitoring and authentication IoT based system

Crime cases involving house breaks and theft are on the rise yearly. It was reported that during the Covid 19 breakout, house breaks cases were rampant. Due to this situation, this project focused on developing gate monitoring and authentication system for landed houses in Malaysia. The proposed system in this paper used the IoT and embedded image processing technique, where Raspberry Pi acted as the main brain of the system combined with embedded face recognition technique. OpenCV and Python are used for the face recognition module with Raspbian OS. The system works when a camera detects and capture an image at the entrance, the system searches the database while a message is sent to the owner through Telegram and the captured image is sent to Gmail, if the captured image is not in the database, no entry is granted and otherwise. The prototype system objectives are to notify owner when there is a visitor, identify the identities of the visitor and authenticate the authority for admission. © 2022 Author(s).

Pre-trained Deep Learning Models for COVID19 Classification: CNNs vs. Vision Transformer

The fast proliferation of the coronavirus disease 2019 (COVID19) has pushed many countries' healthcare systems to the brink of disaster. It has become a necessity to automate the screening procedures to reduce the ongoing cost to the healthcare systems. Although the use of the Convolutional Neural Networks (CNNs) is gaining attention in the field of COVID19 diagnosis based on medical images, these models have disadvantages due to their image-specific inductive bias, which contradict to the Vision Transformer (ViT). This paper conducts comparative study of the use of the three most established CNN models and a ViT to deal with the classification of COVID19 and Non-COVID19 cases. This study uses 2481 computed tomography (CT) images of 1252 COVID19 and 1229 Non-COVID19 patients. Confusion metrics and performance metrics were used to analyze the models. The experimental results show all the pre-trained CNNs (VGG16, ResNet50, and IncetionV3)outperformed the pre-trained ViT model, with InceptionV3 as the best performing model (99.20% of accuracy). © 2022 IEEE.

MALAYSIA SPONDYLARTHRITIS ACCELERATED MANAGEMENT (SAM) MODEL: EXPEDITING AXSPA PATIENT JOURNEY from EARLY REFERRAL, DIAGNOSIS and ACCESS to OPTIMAL CARE

Background: Axial spondyloarthritis (axSpA) is an important cause of infam-matory back pain (IBP). It is under-recognized, leading to signifcant delays in diagnosis. Early recognition and diagnosis are crucial to achieve the best outcomes for patients and in Malaysia, signifcant gaps in the clinical management of axSpA remain. Therefore, we sought to implement a strategy to improve the time to diagnosis and management of axSpA in Malaysia by collaborating and adopting guidance from an international axSpA expert. Objectives: The objectives were to improve disease recognition among healthcare practitioners (HCPs), reducing time to specialist referral and diagnosis whilst improving disease management by developing and implementing a new patient care model called the Spondyloarthritis Accelerated Management (SAM) and measure its effectiveness in 3 Rheumatology centers in Malaysia. Methods: The SAM initiative was developed by the Malaysian SpA Consortium Working Group involving 8 Malaysian rheumatologists from 3 local centers and 1 international axSpA expert from the UK as part of the steering committee. Selections were based on clinical expertise. The frst local alignment meeting on model structure was held in July 2020 with subsequent meetings held to address key barriers to early axSpA diagnosis and timely access to quality care. A care model with feasible key performance indicators (KPIs) was established, adapted and tracked monthly in the 3 rheumatology centers (Figure 1). Referral tools were developed to facilitate early referrals to rheumatologists. These included a QR-coded '3-R' referral guide1 and a patient self-screening tool with a patient self-referral letter all hosted on the Malaysian Society of Rheumatology (MSR) website, educational talks to HCPs and public awareness forums on IBP and axSpA. Data were collected on referral source, duration of referrals, knowledge on IBP in HCPs by surveys and imaging accessibility at baseline and at 1 year after the initiative was launched. Baseline data collected were from August to October 2020 and 1 year data were from November 2020 to November 2021. Results: At 1 year, the SAM initiative showed a 44.4% (Median: 1.33 [IQR 1-1.7] vs 1.92 [IQR 1.6-2.1]) increase in IBP referrals, a reducing trend from 9.5 (IQR 8-11.1) to 5.9 (IQR 5.1-6.8) weeks of waiting time to a frst Rheumatology visit and an increase of 37.2% (34% vs 71%) in IBP patients who were seen at the rheumatology clinic within 6 weeks. All patients with IBP had X-rays (sacroiliac joints or pelvis). MRI requests in X-ray negative patients suspected of axSpA was increased by 13.9% (77.8% vs 91.7%) and waiting time for MRI was reduced by 3.1 weeks (12 vs 8.9 weeks). The IBP knowledge among 224 HCPs improved by 40.6% (45.7% vs 86.3%). The number of patients newly diagnosed with axSpA increased by 40% (Median: 5 [IQR 4-9.5] vs 7 [IQR 6.5-7]) despite the COVID-19 pandemic. Conclusion: The SAM initiative has shown promising initial results in improving referrals of patients with IBP, promoting earlier diagnosis and establishing the importance of having timely access to optimal care. A nationwide implementation is being planned to improve the recognition of the axSpA in Malaysia.

EmergencyRoomNurseSchedulingataPublicHospitalinJakarta during Covid-19 Pandemic, Using TPB Algorithm Method

Introduction: The emergency room (ER) is a department that has a high potential risk of exposure to the Covid-19 viruses. Nursing management must anticipate the ratio of nurses and patients in the ER is maintained at a minimum ratio of 1:4. Nurse scheduling is not an easy job to provide satisfaction to the nurses by distributing schedules evenly but operating costs can be kept to a minimum. This study is aimed to meet the demands of nurses in line with the current patient growth with two days off per week and to determine the individual correct shift pattern to achieve 40 hours per week during the Covid-19 Pandemic. Method: the modified Tribrewala, Phillipe, and Browne (TPB) algorithm. Results: The current ER nurse ratio to the patient is 8 nurses for 9 patients (0.8>0.25). The required ER nurses based on TPB algorithm calculations are 35 people per day. The current ER Nurses’ shift patterns do not match with the calculated shift pattern based on the TPB methodwhich meets the government regulations that each nurse works 40 hours per day. The number of nurses who are on vacation based on vacation optimization is 5 nurses per day. Conclusion: The ER of a Public Hospital in Jakarta has enough workforces during the Covid-19 pandemic;however, the shift patterns and vacation optimization still need improvement. © 2022 UPM Press. All rights reserved.

COVID-19 prediction analysis using artificial intelligence procedures and GIS spatial analyst: a case study for Iraq

The prediction of diseases caused by viral infections is a complex medical task where many real data that consists of different variables must be employed. As known, COVID-19 is the most dangerous disease worldwide;nowhere, an effective drug has been found yet. To limit its spread, it is essential to find a rational method that shows the spread of this virus by relying on many infected people's data. A model consisting of three artificial neural networks' (ANN) functions was developed to predict COVID-19 separation in Iraq based on real infection data supplied by the public health department at the Iraqi Ministry of Health. The performance efficiency of this model was evaluated, where its performance efficiency reached 81.6% when employed four statistical error criteria as mean absolute percentage error (MAPE), root mean square error (RMSE), coefficient of determination (R-2), and Nash-Sutcliffe coefficient (NC). The severity of the virus's spread across Iraq was assessed in a short term (in the next 6 months), where the results show that the spread severity will intensify in this short term by 17.1%, and the average death cases will increase by 8.3%. These results clarified by creating spatial distribution maps for virus spread are simulated by employing a Geographic Information System (GIS) environment to be used as a useful database for developing plans for combating viruses in Iraq.

Covid-induced investor sentiments and market reaction under extreme meteorological conditions: Evidence from clean energy sector of asia-pacific

The unprecedented global economic and social crisis caused by the coronavirus outbreak has not spared the energy sector. Using a dynamic model, we investigated the effect of COVID-19 cases on investor sentiments and stock returns of clean energy in the Asian-Pacific region. The results show that coronavirus cases negatively affect stock returns using investor sentiments as a transmission channel. We also find a negative effect of air pollution on stock returns. Since COVID-19 restricted trade and plummeted the oil prices, economies relied on non-renewable sources to meet energy demands. Nevertheless, the investor’s optimism and high sentiment level may deteriorate this link. On the other hand, we do not find any significant effect of low-high temperature on either investor sentiments or clean energy stock returns. Clean energy stocks were viewed as more sustainable and less vulnerable to external shocks, however, the fear and pessimism among investors induced by coronavirus are spilled over the renewable energy sector. © 2020, Politechnika Lubelska. All rights reserved.