Month: April 2024

Abstract

The study explores the nexus between crude oil prices (COP), financial risk index (FRI), political risk index (PRI), green innovation (GIN) and information globalization index (ING) for Shanghai stock exchange (SSE) in China from 1997/M9 to 2021/M12 by utilizes novel wavelet methodologies to handle co-movement dynamics of multivariate time series via moving weighted regression also wavelet-based causality employed to identify the causality. The finding of Wavelet regression indicates the highest multiple correlation from a linear combination of the and ING at each scale, which indicates these variables are impacted by the stock market index. While FRI relates investor confidence declining stock prices may be detrimental to the SSE. However, any changes in PRI government rules or regulations could influence SSE-listed enterprises. GIN develops and employs eco-friendly technologies and procedures. As global awareness of sustainability grows, pioneering green entrepreneurs may attract investors and increase corporate valuations. Investors may find green innovation-driven companies less enticing, resulting in lower stock prices. Yet, ING may propose improved access to global markets and information, hence increasing the value of SSE's shares. According to the findings of wavelet-based Granger Causality, COP, FRI, GIN, ING, PRI, and SSE show significant causal interconnections at several scales. The study offers policy insights based on these findings.

Abstract

The study explores the nexus between crude oil prices (COP), financial risk index (FRI), political risk index (PRI), green innovation (GIN) and information globalization index (ING) for Shanghai stock exchange (SSE) in China from 1997/M9 to 2021/M12 by utilizes novel wavelet methodologies to handle co-movement dynamics of multivariate time series via moving weighted regression also wavelet-based causality employed to identify the causality. The finding of Wavelet regression indicates the highest multiple correlation from a linear combination of the and ING at each scale, which indicates these variables are impacted by the stock market index. While FRI relates investor confidence declining stock prices may be detrimental to the SSE. However, any changes in PRI government rules or regulations could influence SSE-listed enterprises. GIN develops and employs eco-friendly technologies and procedures. As global awareness of sustainability grows, pioneering green entrepreneurs may attract investors and increase corporate valuations. Investors may find green innovation-driven companies less enticing, resulting in lower stock prices. Yet, ING may propose improved access to global markets and information, hence increasing the value of SSE's shares. According to the findings of wavelet-based Granger Causality, COP, FRI, GIN, ING, PRI, and SSE show significant causal interconnections at several scales. The study offers policy insights based on these findings.

Abstract

This study presents the petrographical and petrophysical characteristics of the Cambro-Ordovician clastic reservoirs from the Risha field, northeastern Jordan. Routine core analysis, wireline logs, petrographic thin sections, scanning electron microscopy, and X-ray diffraction were integrated to characterize the gas reservoirs of the Risha, Dubeidib, and Umm Sahm formations (the equivalent of Sarah, Qasim, and Upper Saq formations of northern Saudi Arabia). These reservoirs are variably micro- and mesoporous, with permeability < 1 mD and dominantly < 6% porosity. Wireline log-based assessment exhibits low shale volume (< 30%) and high hydrocarbon saturation (45–95%) in these tight reservoirs. Petrographic investigation reveals that these reservoirs are fine-grained sandstones, moderately sorted with high mineralogical maturity. The Risha and Dubeidib reservoirs are subarkose, while the Umm Sahm reservoir is composed of quartz arenite. The late diagenetic silica cementation is inferred as reservoir quality-reducing diagenetic factor, with quartz overgrowth of > 10% corresponding to < 4% porosity. SEM images exhibit the presence of grain-coating, pore-filling, and pore-lining chlorite, and illite phases which hinder quartz overgrowth and had a positive effect in retaining the primary porosity. The sandstones with > 20% clay-coating coverage corresponds to a lower quartz overgrowth (< 5%) and therefore higher intergranular porosity (> 5%). Locally sutured grain contacts and stylolites are observed which indicate intense chemical compaction. The feldspar grains are observed to be partially dissolved, which generated minor secondary porosity. Micropore-dominated pore systems and rare secondary macroporosity are typically isolated by abundant cement and/or pore throats choked by clay minerals.

Abstract

This study presents the petrographical and petrophysical characteristics of the Cambro-Ordovician clastic reservoirs from the Risha field, northeastern Jordan. Routine core analysis, wireline logs, petrographic thin sections, scanning electron microscopy, and X-ray diffraction were integrated to characterize the gas reservoirs of the Risha, Dubeidib, and Umm Sahm formations (the equivalent of Sarah, Qasim, and Upper Saq formations of northern Saudi Arabia). These reservoirs are variably micro- and mesoporous, with permeability < 1 mD and dominantly < 6% porosity. Wireline log-based assessment exhibits low shale volume (< 30%) and high hydrocarbon saturation (45–95%) in these tight reservoirs. Petrographic investigation reveals that these reservoirs are fine-grained sandstones, moderately sorted with high mineralogical maturity. The Risha and Dubeidib reservoirs are subarkose, while the Umm Sahm reservoir is composed of quartz arenite. The late diagenetic silica cementation is inferred as reservoir quality-reducing diagenetic factor, with quartz overgrowth of > 10% corresponding to < 4% porosity. SEM images exhibit the presence of grain-coating, pore-filling, and pore-lining chlorite, and illite phases which hinder quartz overgrowth and had a positive effect in retaining the primary porosity. The sandstones with > 20% clay-coating coverage corresponds to a lower quartz overgrowth (< 5%) and therefore higher intergranular porosity (> 5%). Locally sutured grain contacts and stylolites are observed which indicate intense chemical compaction. The feldspar grains are observed to be partially dissolved, which generated minor secondary porosity. Micropore-dominated pore systems and rare secondary macroporosity are typically isolated by abundant cement and/or pore throats choked by clay minerals.

Abstract

The percentage of people affected by skin cancer has been rising in recent years. Melanoma is identified as the most dangerous and life-threatening among the three types of skin cancer since it causes more deaths than the other two over time. According to the expertise, if the melanoma case is discovered at an early stage, the death rate may be decreased. Due to the skin lesion’s complexity, it is challenging to identify melanoma at an early stage. In this work, an automated assistant system is suggested to help doctors in identifying melanoma effectively at an early stage. Because pixel intensity values include distinctive and useful features in an image, hence the pixel intensity value estimation (IVE) model is embedded with a transfer learning network for efficient Melanoma detection. Four popular transfer learning models have been analyzed to derive the best-performed model in Melanoma detection (MD). Finally, data sensitivity is analyzed on the best model. The experiment shows that overall the best performance in Recall (98.8%), F1-score (99.0%), Accuracy (99.18%), and AUC-ROC curve (97.8%) is achieved by the VGG-16 transfer learning model for 4056 data; we denoted the model as IVE-MDNet model. In the model, the network consists of 13 convolutional layers and five max-pooling layers and the learning weights are obtained using the ‘ImageNet’ dataset. A new sub-layer model is formed, which is combined with the pre-trained network to design the proposed transfer learning approach. Before feeding the image to the model, the artifacts were removed using a pre-processing technique which uses a series of precise procedures.

Abstract

The percentage of people affected by skin cancer has been rising in recent years. Melanoma is identified as the most dangerous and life-threatening among the three types of skin cancer since it causes more deaths than the other two over time. According to the expertise, if the melanoma case is discovered at an early stage, the death rate may be decreased. Due to the skin lesion’s complexity, it is challenging to identify melanoma at an early stage. In this work, an automated assistant system is suggested to help doctors in identifying melanoma effectively at an early stage. Because pixel intensity values include distinctive and useful features in an image, hence the pixel intensity value estimation (IVE) model is embedded with a transfer learning network for efficient Melanoma detection. Four popular transfer learning models have been analyzed to derive the best-performed model in Melanoma detection (MD). Finally, data sensitivity is analyzed on the best model. The experiment shows that overall the best performance in Recall (98.8%), F1-score (99.0%), Accuracy (99.18%), and AUC-ROC curve (97.8%) is achieved by the VGG-16 transfer learning model for 4056 data; we denoted the model as IVE-MDNet model. In the model, the network consists of 13 convolutional layers and five max-pooling layers and the learning weights are obtained using the ‘ImageNet’ dataset. A new sub-layer model is formed, which is combined with the pre-trained network to design the proposed transfer learning approach. Before feeding the image to the model, the artifacts were removed using a pre-processing technique which uses a series of precise procedures.

Abstract

The design of optimized non-pharmaceutical interventions (NPIs) is critical to the effective control of emergent outbreaks of infectious diseases such as SARS, A/H1N1 and COVID-19 and to ensure that numbers of hospitalized cases do not exceed the carrying capacity of medical resources. To address this issue, we formulated a classic SIR model to include a close contact tracing strategy and structured prevention and control interruptions (SPCIs). The impact of the timing of SPCIs on the maximum number of non-isolated infected individuals and on the duration of an infectious disease outside quarantined areas (i.e. implementing a dynamic zero-case policy) were analyzed numerically and theoretically. These analyses revealed that to minimize the maximum number of non-isolated infected individuals, the optimal time to initiate SPCIs is when they can control the peak value of a second rebound of the epidemic to be equal to the first peak value. More individuals may be infected at the peak of the second wave with a stronger intervention during SPCIs. The longer the duration of the intervention and the stronger the contact tracing intensity during SPCIs, the more effective they are in shortening the duration of an infectious disease outside quarantined areas. The dynamic evolution of the number of isolated and non-isolated individuals, including two peaks and long tail patterns, have been confirmed by various real data sets of multiple-wave COVID-19 epidemics in China. Our results provide important theoretical support for the adjustment of NPI strategies in relation to a given carrying capacity of medical resources.

Abstract

The design of optimized non-pharmaceutical interventions (NPIs) is critical to the effective control of emergent outbreaks of infectious diseases such as SARS, A/H1N1 and COVID-19 and to ensure that numbers of hospitalized cases do not exceed the carrying capacity of medical resources. To address this issue, we formulated a classic SIR model to include a close contact tracing strategy and structured prevention and control interruptions (SPCIs). The impact of the timing of SPCIs on the maximum number of non-isolated infected individuals and on the duration of an infectious disease outside quarantined areas (i.e. implementing a dynamic zero-case policy) were analyzed numerically and theoretically. These analyses revealed that to minimize the maximum number of non-isolated infected individuals, the optimal time to initiate SPCIs is when they can control the peak value of a second rebound of the epidemic to be equal to the first peak value. More individuals may be infected at the peak of the second wave with a stronger intervention during SPCIs. The longer the duration of the intervention and the stronger the contact tracing intensity during SPCIs, the more effective they are in shortening the duration of an infectious disease outside quarantined areas. The dynamic evolution of the number of isolated and non-isolated individuals, including two peaks and long tail patterns, have been confirmed by various real data sets of multiple-wave COVID-19 epidemics in China. Our results provide important theoretical support for the adjustment of NPI strategies in relation to a given carrying capacity of medical resources.