Revisiting the dynamic interactions between economic growth and environmental pollution in Italy: evidence from a gradient descent algorithm

Although the literature on the relationship between economic growth and CO2 emissions is extensive, the use of machine learning (ML) tools remains seminal. In this paper, we assess this nexus for Italy using innovative algorithms, with yearly data for the 1960–2017 period. We develop three distinct models: the batch gradient descent (BGD), the stochastic gradient descent (SGD), and the multilayer perceptron (MLP). Despite the phase of low Italian economic growth, results reveal that CO2 emissions increased in the predicting model. Compared to the observed statistical data, the algorithm shows a correlation between low growth and higher CO2 increase, which contradicts the main strand of literature. Based on this outcome, adequate policy recommendations are provided.

Artificial Intelligence and Bank Soundness: A Done Deal? - Part 1

Banks soundness plays a crucial role in determining economic prosperity. As such, banks are under intense scrutiny to make wise decisions that enhances bank stability. Artificial Intelligence (AI) plays a significant role in changing the way banks operate and service their customers. Banks are becoming more modern and relevant in people’s life as a result. The most significant contribution of AI is it provides a lifeline for bank’s survival. The chapter provides a taxonomy of bank soundness in the face of AI through the lens of CAMELS where C (Capital), A(Asset), M(Management), E(Earnings), L(Liquidity), S(Sensitivity). The taxonomy partitions opportunities from the main strand of CAMELS into distinct categories of 1 (C), 6(A), 17(M), 16 (E), 3(L), 6(S). It is highly evident that banks will soon extinct if they do not embed AI into their operations. As such, AI is a done deal for banks. Yet will AI contribute to bank soundness remains to be seen.

Artificial Intelligence and Bank Soundness: Between the Devil and the Deep Blue Sea - Part 2

Banks have experienced chronic weaknesses as well as frequent crisis over the years. As bank failures are costly and affect global economies, banks are constantly under intense scrutiny by regulators. This makes banks the most highly regulated industry in the world today. As banks grow into the 21st century framework, banks are in need to embrace Artificial Intelligence (AI) to not only to provide personalized world class service to its large database of customers but most importantly to survive. The chapter provides a taxonomy of bank soundness in the face of AI through the lens of CAMELS where C (Capital), A(Asset), M(Management), E(Earnings), L(Liquidity), S(Sensitivity). The taxonomy partitions challenges from the main strand of CAMELS into distinct categories of AI into 1(C), 4(A), 17(M), 8 (E), 1(L), 2(S) categories that banks and regulatory teams need to consider in evaluating AI use in banks. Although AI offers numerous opportunities to enable banks to operate more efficiently and effectively, at the same time banks also need to give assurance that AI ‘do no harm’ to stakeholders. Posing many unresolved questions, it seems that banks are trapped between the devil and the deep blue sea for now.

Experimental study on the formation of core-spun yarn manufactured on a modified vortex spinning system

Aiming at achieving online monitoring of the yarn formation process of vortex spinning, this paper presents a direct observation method that adopts a charge-coupled device camera fitted with an industrial endoscope that can reach into the nozzle chamber through a small hole drilled on the nozzle wall. Local pressure distortion in the vortex chamber due to the mounting of the observation apparatus was found experimentally. However, the yarn quality was not significantly affected, indicating that the proposed method has the potential to find industrial applications. Based on this method, the formation process of core-spun yarn containing a copper wire manufactured on a modified vortex spinning system is successfully observed. The results show that the core wire is covered by the main strand (core fibers), while the main strand is false-twisted during the yarn formation process. The level of false twist in the main strand fluctuates with time. The level of false twist in the main strand is higher in the upstream region, while it gradually decreases toward the downstream. Mostly, the trailing ends of some fibers are separated from the main strand and expand over the spindle tip to wrap around both the core fibers and core wire to form wrapper fibers in the yarn, while the leading ends of a smaller number of fibers are separated to form wrapper-wild fibers in the yarn. The proposed method can be extended to the study of fiber movement in confined spaces in other textile manufacturing processes.

Origins

Although unprecedented in scale and ambition, Stalin’s offensive biological warfare program was not an isolated phenomenon. It can instead be viewed as a response to, and extension of, the biological sabotage programs pursued during the First World War by Germany. During the nearly three-decade period of Stalin’s leadership (1924-1953), two distinct, and highly compartmentalized, components of the Soviet Union’s offensive biological warfare program are in evidence. The main strand was launched by the Red Army in Moscow in 1926 and is very well-documented with numerous archival and secondary sources available. There is in addition a second, earlier and much more ephemeral strand, which is based in Leningrad, which was mainly concealed within the RSFSR People’s Commissariat of Health (RSFSR Narkomzdrav) and the Red Army’s Military-Medical Academy.

Slip on the Elmore Ranch fault during the past 330 years and its relation to slip on the Superstition Hills fault

The Elmore Ranch fault is a left-lateral fault that strikes northeast within the right-lateral transform boundary that strikes northwest through southern California. It lies transverse and adjacent to the segment of the Superstition Hills fault that ruptured in 1987. Rupture of the Elmore Ranch fault (MS = 6.2) preceded rupture of the Superstition Hills fault (MS = 6.6) by about 11.4 hr. The Elmore Ranch fault slipped at the surface in 1987 with left-lateral displacements of up to 130 mm. Geological data indicate that it had slipped prehistorically, sometime after about 1660 A.D., probably in a single event. Excavations at three sites enable the following comparisons: November 1987 (mm) ~1660-October 1987 (mm) Main Strand 70 ± 5 230 ± 20 West Strand 30 ± 5 90 ± 10 East Strand 50 ± 10 < 25(?) Total 150 ± 20 320 ± 30 At these sites, the only significant component of dip slip (down to the southeast) was found on the west strand for slip previous to 1987. The Superstition Hills fault has also been documented to have experienced one slip event between ∼1660 and 1987. Thus these slip events on the Elmore Ranch and Superstition Hills faults may have occurred in a sequence similar to that in 1987. Neither the main fault nor the cross-fault, however, appear to have exactly duplicated their previous surficial slip. Previous slip was probably smaller on the Superstition Hills fault and larger on the Elmore Ranch fault zone than in the 1987 event. Because the temporal correlation between previous slip events is not proven, rupture sequences other than a doublet in which main fault rupture follows cross-fault rupture are possible.