Search Results (1,961)

In analyzing the decoupling of emissions from economic growth, current literature foregoes the nonlinear complexities of macroeconomic systems, leading to ineffective energy transition policies, specifically for developing countries. This study focuses on the Indian energy–economy–emission nexus to establish a control system that internalizes inflation, trade openness, and fossil fuel imports with economic growth and macro-emissions to visualize the complex pathways of decoupling. Through long-term cointegration and vector error correction modeling, it was found that GDP and energy affect capital, inflation and energy imports, which are locked in a long-run negative feedback loop that ultimately increases emissions. Capital growth enables decoupling at 0.7% CO₂ emissions reduction for every 1% capital growth, while 1% inflation growth inhibits decoupling by increasing CO₂ emissions by 0.8%. A cybernetic fractional circuit of R-C elements and operational amplifiers was utilized to examine the delay of pulses from GDP to the loop elements, which revealed that capital is periodic with GDP pulses. However, inflation, being aperiodic with the clock pulses of GDP, causes the pulse-width of capital to decrease and fossil fuel imports to increase. Through the circuital model, it was possible to determine the exact policy intervention schedule in business cycle growth and recession phases that could build clean energy capital and limit inflation-induced recoupling. Full article

Human CMV, regularly reactivated by simple triggers, results in asymptomatic viral shedding, powerful cellular immune responses, and memory inflation. Immunocompetent individuals benefit from a robust immune response, which aids in viral management without causing clinically significant illness; however, immunodeficient individuals are always at a higher risk of CMV reactivation and disease. Hematopoietic stem cell transplant (HSCT) recipients are consistently at higher risk of CMV reactivation and clinically significant CMV illness due to primary disease, immunosuppression, and graft vs. host disease. Early recovery of CMV-CMI responses may mitigate effects of viral reactivation in HSCT recipients. Immune reconstitution following transplantation occurs spontaneously and is mediated initially by donor-derived T cells, followed by clonal growth of T cells produced from graft progenitors. CMV-specific immune reconstitution post-transplant is related to spontaneous clearance of CMV reactivation and may eliminate the need for prophylactic or pre-emptive medication, making it a potential predictive marker for monitoring CMV reactivation. This review highlights current thoughts and therapeutic options for CMV reactivation in HSCT, with focus on CMV immune reconstitution and post-HSCT monitoring. Immune monitoring aids in risk stratification of transplant recipients who may progress from CMV reactivation to clinically significant CMV infection. Implementing this approach in clinical practice reduces the need for periodic viral surveillance and antiviral therapy in recipients who have a high CMV-CMI and thus may experience self-limited reactivation. Therefore, in the age of precision medicine, it is critical to incorporate CMV-specific cellular immune surveillance into conventional procedures and algorithms for the management of transplant recipients. Full article

Abstract: The paper examined the impact of macroeconomic uncertainty on the ten largest subsectors of the Nigerian economy using quarterly data from Q1 1981 to Q4 2023. The rationale behind selecting the subsectors is that these sectors constitute about 89 percent of the entire productive activities in the economy. To achieve the objectives, the paper created an index for macroeconomic uncertainty using exchange rate uncertainty, interest rate uncertainty, inflation uncertainty, and real gross domestic product (GDP) uncertainty to create this index. Furthermore, the paper explored the impacts of macroeconomic uncertainty and these individual economic uncertainty indexes on sector output. The study employed the novel dynamic autoregressive distributed lag (novel dynamic ARDL) technique to estimate the results and used the canonical cointegrating regression (CCR) and fully modified ordinary least square (FMOLS) techniques as robustness on the main findings. The findings demonstrated that during periods of recession, macroeconomic uncertainty tends to heighten or reach its peak in Nigeria. Furthermore, the paper showed that the sectors react homogenously to macroeconomic uncertainty. In addition, the impulse response results from the novel dynamic ARDL estimation show that macroeconomic uncertainty can predict robust negative movements in sector output for Nigeria. Indeed, these findings are insightful as they show the importance of macroeconomic uncertainties as key drivers of sector output in Nigeria. The paper argues that the policy authorities should improve their efforts to reduce macroeconomic uncertainty and foster a stable real sector/sectoral output to enhance the macroeconomic environment for Nigeria to aim for higher levels of growth. Full article

We present a review of the Semi-Symmetric Metric Gravity (SSMG) theory, representing a geometric extension of standard general relativity, based on a connection introduced by Friedmann and Schouten in 1924. The semi-symmetric connection is a connection that generalizes the Levi-Civita one by allowing for the presence of a simple form of the torsion, described in terms of a torsion vector. The Einstein field equations are postulated to have the same form as in standard general relativity, thus relating the Einstein tensor constructed with the help of the semi-symmetric connection, with the energy–momentum tensor tensor. The inclusion of the torsion contributions in the field equations has intriguing cosmological implications, particularly during the late-time evolution of the Universe. Presumably, these effects also dominate under high-energy conditions, and thus SSMG could potentially address unresolved issues in general relativity and cosmology, such as the initial singularity, inflation, or the ⁷Li problem of the Big-Bang Nucleosynthesis. The explicit presence of torsion in the field equations leads to the non-conservation of the energy–momentum tensor tensor, which can be interpreted within the irreversible thermodynamics of open systems as describing particle creation processes. We also review in detail the cosmological applications of the theory, and investigate the statistical tests for several models, by constraining the model parameters via comparison with several observational datasets. Full article

This meta-analysis examined the efficacy of low-load resistance training with blood flow restriction (LL-BFR) versus high-load resistance training (HL-RT) on muscle strength and hypertrophy, exploring factors affecting outcomes. We searched Embase, CNKI, Wanfang, PubMed, Ovid Medline, ProQuest, Cochrane Library, Embase, and Scopus from inception to July 2024. After assessing the risk of bias using the Cochrane tool, a meta-analysis was conducted to calculate the overall effect size. Subgroup analyses were performed to explore the impact of different modulating factors on training effects. LL-BFR was found to be inferior to HL-RT with regard to muscle strength gains (SMD = −0.33, 95% CI: −0.49 to −0.18, p < 0.0001). However, subgroup analyses revealed that LL-BFR achieved muscle strength gains comparable to HL-RT under individualized pressure (SMD = −0.07, p = 0.56), intermittent cuff inflation (SMD = −0.07, p = 0.65), and a higher number of training sessions (SMD = −0.12, p = 0.30). No significant difference in muscle mass gains was observed between LL-BFR and HL-RT (SMD = 0.01, p = 0.94), and this conclusion remained consistent after controlling for modulating variables. HL-RT is superior to LL-BFR in enhancing muscle strength gains. Nevertheless, under appropriate conditions, including individualized pressure prescription, intermittent cuff inflation, and a higher number of training sessions, LL-BFR can achieve muscle strength gains comparable to HL-RT, emphasizing the importance of tailored training programs. Both methods exhibit similar effects on muscle mass gains, indicating that LL-BFR serves as an effective alternative for individuals who cannot perform HL-RT because of physical limitations or injury concerns. Full article

Primordial black holes (PBH), if survive to the present time, can be a fraction, or even the dominant form of dark matter of the Universe. If PBH evaporate before the present time, rare forms of dark matter like superweakly interacting or supermassive particles can be produced in the course of their evaporation. Stable remnants of PBH evaporation can also play the role of dark matter candidates. In the context of the modern standard cosmology, based on inflationary models with baryosynthesis and dark matter, which find their physical grounds beyond the Standard models of elementary particles (BSM), primordial black holes acquire the important role of sensitive probes for BSM models and their parameters. It makes PBHs a profound messenger of physics of Dark Universe. Full article

In recent years, fiscal policy in the Eurozone (EZ) has faced challenges posed by the strong and rapid increase in inflation as a consequence of the COVID-19 pandemic and other geo-political crises. Due to the fear of “fiscal inflation” present during episodes of fiscal stimulus during the pandemic crisis, this paper assesses the relationship between discretionary fiscal policy and inflation in developed EZ economies, taking into consideration the rise in energy prices as a control variable. This study considers the econometric framework of heterogeneous, non-stationary panels (Pooled Mean Group (PMG) and Common Correlated Effects Mean Group (CCEMG) estimators). Using quarterly panel data for the period 2015q1–2024q1, the results show that, in the long run, the effects of fiscal policy on inflation are insignificant. However, covering only the pandemic and other geo-political crises (2020q1–2024q1), research shows a significant negative long-run relationship between fiscal expenditure and inflation and heterogeneous short-run fiscal adjustments due to the lack of a fiscal union in the EU economies. Hence, accompanied by monetary policy, the discretionary response of fiscal policy to inflationary shock was oriented in the same direction—the reduction in inflationary pressures during a geo-political crisis. Fiscal policy mitigated inflationary pressures in these recent crises, while in the long run, it did not affect nominal variables, indicating that there is no evidence of fiscal inflation in the sample of EZ economies during a stabilization period or under crisis conditions. Full article

Object detection architectures struggle to detect small objects across applications including remote sensing and autonomous vehicles. Specifically, for unmanned aerial vehicles, poor detection of small objects directly limits this technology’s applicability. Objects both appear smaller than they are in large-scale images captured in aerial imagery and are represented by reduced information in high-altitude imagery. This paper presents a new architecture, CR-CNN, which predicts independent regions of interest from two unique prediction branches within the first stage of the network: a conventional R-CNN convolutional backbone and an hourglass backbone. Utilizing two independent sources within the first stage, our approach leads to an increase in successful predictions of regions that contain smaller objects. Anchor-based methods such as R-CNNs also utilize less than half the number of small objects compared to larger ones during training due to the poor intersection over union (IoU) scores between the generated anchors and the groundtruth—further reducing their performance on small objects. Therefore, we also propose artificially inflating the IoU of smaller objects during training using a simple, size-based Gaussian multiplier—leading to an increase in the quantity of small objects seen per training cycle based on an increase in the number of anchor–object pairs during training. This architecture and training strategy led to improved detection overall on two challenging aerial-based datasets heavily composed of small objects while predicting fewer false positives compared to Mask R-CNN. These results suggest that while new and unique architectures will continue to play a part in advancing the field of object detection, the training methodologies and strategies used will also play a valuable role. Full article

Patents contain detailed information of researched and developed technologies. We analyzed patent documents to understand the technology in a given domain. For the patent data analysis, we extracted the keywords from the patent documents using text mining techniques. Next, we built a patent document–keyword matrix using the patent keywords and analyzed the matrix data using statistical methods. Each element of the matrix represents the frequency of a keyword that occurs in a patent document. In general, most of the elements were zero because the keyword becomes a column of the matrix even if it occurs in only one document. Due to this zero-inflated problem, we experienced difficulty in analyzing patent keywords using existing statistical methods such as linear regression analysis. The purpose of this paper is to build a statistical model to solve the zero-inflated problem. In this paper, we propose a regression model based on quantile cumulative distribution function to solve this problem that occurs in patent keyword analysis. We perform experiments to show the performance of our proposed method using patent documents related to blockchain technology. We compare regression modeling based on a quantile cumulative distribution function with convenient models such as linear regression modeling. We expect that this paper will contribute to overcoming the zero-inflated problem in patent keyword analysis performed in various technology fields. Full article

Additive manufacturing (AM) has emerged as one of the core components of the fourth industrial revolution, Industry 4.0. Among others, the extrusion AM (EAM) of thermoplastic materials has been named as the most widely adopted technology. Fused filament fabrication (FFF) relies on the commercial availability of expensive filaments; hence, pellet extruder-based EAM techniques are desired. Large-format EAM systems would benefit from the ability to print lightweight objects with less materials and lower power consumption, which is possible with the use of hollow extrudates rather than solid extrudates to print objects. In this work, we designed a custom extruder head and developed an EAM system that allows the extrusion of inflatable hollow extrudates of a relatively wide material choice. By incorporating a co-axial nozzle–needle system, a thermoplastic shell was extruded while the hollow core was generated by using pressurized nitrogen gas. The ability to print using hollow extrudates with controllable inflation allows us to print objects with gradient part density with different degrees of mechanical properties. In this article, the effect of different process parameters, namely, extrusion temperature, extrusion speed, and gas pressure, were studied using poly-lactic acid (PLA) pellets. Initially, a set of preliminary tests was conducted to identify the maximum and minimum ranges of these parameters that result in consistent hollow extrudates. Finally, the parameters were varied to understand how they affect the core diameter and shell thickness of the hollow extrudates. These findings were supported by analyses of microscopic images taken under an optical microscope. Full article

This study examines the impact of exchange rate stability on the economic growth of South Africa from 2000 to 2023, a period characterised by significant political and economic changes. Exchange rate stability is critical for developing countries, affecting key macroeconomic variables such as trade balances, foreign direct investment (FDI), and inflation. For emerging economies like South Africa, maintaining a stable exchange rate can reduce uncertainty in international transactions, foster investor confidence, and support sustainable economic development. This research explores whether consistent exchange rate management has positively influenced South Africa’s economic trajectory, particularly by mitigating the adverse effects of global shocks and domestic volatility. Using the EasyData online database, which contains yearly time series data, the method of analysis adopted by the research is the ordinary least squares (OLS) regression method. The findings show that while exchange rate stability positively impacts GDP, the influence of FDI and political risk is more substantial. These results underscore the importance of fostering a stable economic environment through sound exchange rate policies, political stability, and efforts to attract foreign investments to ensure long-term economic growth. Full article

The largehead hairtail (Trichiurus japonicus) is the most productive fish caught in China. In order to understand the seasonal distribution of T. japonicus in the East China Sea and the central and southern parts of the Yellow Sea, three species distribution models were used in this study, namely the random-forest model, K-nearest-neighbor algorithm, and gradient-ascending decision-tree model, based on the data of trawling surveys in the East China Sea and central and southern parts of the Yellow Sea from 2008 to 2009. Combined with a variance inflation factor and cross-check, a distribution model of T. japonicus was screened and constructed to analyze the influence of environmental factors on the distribution of T. japonicus in the East China Sea and central and southern parts of the Yellow Sea. The results showed that the random-forest model had the advantages of fitting effect and prediction ability among the three models. The analysis of this model showed that the water depth, bottom water temperature, and surface salinity had a great influence on the habitat distribution of T. japonicus. The relative resources of T. japonicus increased with the increase of bottom water temperature, reached the maximum at 23.8 °C, and first increased and then decreased with the increase of water depth and surface salinity, reaching the maximum when water depth is 72 m and surface salinity is 31.2%. This study also used the random-forest model to predict the spatial distribution of T. japonicus in the central and southern waters of the East China Sea and south-central Yellow Sea from 2008 to 2009, and the results showed that the predicted results were close to the actual situation. The research results can provide a reference for the exploitation and protection of T. japonicus resources in the East China Sea and the south-central Yellow Sea. Full article

In this work, we focus on two important aspects of modern cosmology: reheating and Hubble constant tension within the framework of a unified cosmic theory, namely the quintessential inflation connecting the early inflationary era and late-time cosmic acceleration. In the context of reheating, we use instant preheating and gravitational reheating, two viable reheating mechanisms when the evolution of the universe is not affected by an oscillating regime. After obtaining the reheating temperature, we analyze the number of e-folds and establish its relationship with the reheating temperature. This allows us to connect, for different quintessential inflation models (in particular for models coming from super-symmetric theories such as $\alpha$-attractors), the reheating temperature with the spectral index of scalar perturbations, thereby enabling us to constrain its values. In the second part of this article, we explore various alternatives to address the $H_0$ tension. From our perspective, this tension suggests that the simple Λ-Cold Dark Matter model, used as the baseline by the Planck team, needs to be refined in order to reconcile its results with the late-time measurements of the Hubble constant. Initially, we establish that quintessential inflation alone cannot mitigate the Hubble tension by solely deviating from the concordance model at low redshifts. The introduction of a phantom fluid, capable of increasing the Hubble rate at the present time, becomes a crucial element in alleviating the Hubble tension, resulting in a deviation from the Λ-Cold Dark Matter model only at low redshifts. On a different note, by utilizing quintessential inflation as a source of early dark energy, thereby diminishing the physical size of the sound horizon close to the baryon–photon decoupling redshift, we observe a reduction in the Hubble tension. This alternative avenue, which has the same effect of a cosmological constant changing its scale close to the recombination, sheds light on the nuanced interplay between the quintessential inflation and the Hubble tension, offering a distinct perspective on addressing this cosmological challenge. Full article

High-dimensional sparse matrix data frequently arise in various applications. A notable example is the weighted word–word co-occurrence count data, which summarizes the weighted frequency of word pairs appearing within the same context window. This type of data typically contains highly skewed non-negative values with an abundance of zeros. Another example is the co-occurrence of item–item or user–item pairs in e-commerce, which also generates high-dimensional data. The objective is to utilize these data to predict the relevance between items or users. In this paper, we assume that items or users can be represented by unknown dense vectors. The model treats the co-occurrence counts as arising from zero-inflated Gamma random variables and employs cosine similarity between the unknown vectors to summarize item–item relevance. The unknown values are estimated using the shared parameter alternating zero-inflated Gamma regression models (SA-ZIG). Both canonical link and log link models are considered. Two parameter updating schemes are proposed, along with an algorithm to estimate the unknown parameters. Convergence analysis is presented analytically. Numerical studies demonstrate that the SA-ZIG using Fisher scoring without learning rate adjustment may fail to find the maximum likelihood estimate. However, the SA-ZIG with learning rate adjustment performs satisfactorily in our simulation studies. Full article