Explore the vast range of titles available.

Green finance is inextricably linked to investment risk, particularly in emerging and developing economies (EMDE). This study uses the difference in differences (DID) method to evaluate the mean causal effects of a treatment on an outcome of the determinants of scaling up green financing and climate change mitigation in the N-11 countries from 2005 to 2019. After analyzing with a dummy for the treated countries, it was confirmed that the outcome covariates: rescon (renewable energy sources consumption), population, FDI, CO 2 , inflation, technical corporation grants, domestic credit to the private sector, and research and development are very significant in promoting green financing and climate change mitigation in the study countries. The probit regression results give a different outcome, as rescon, FID, CO 2 , Human Development Index (HDI), and investment in the energy sector by the private sector that will likely have an impact on the green financing and climate change mitigation of the study countries. Furthermore, after matching the analysis through the nearest neighbor matching, kernel matching, and radius matching, it produced mixed results for both the treated and the untreated countries. Either group experienced an improvement in green financing and climate change mitigation or a decrease. Overall, the DID showed no significant difference among the countries.

A bstract Non-trivial gravitational saddles have played a key role in the island proposal for the black hole information paradox. It is worth asking if non-trivial saddles exist in microscopic descriptions of black holes. We show this to be the case for 1/8 BPS black holes in N = 8 String Theory in a duality frame, where all charges are Ramond Ramond. The saddles are in the Coulomb branch, where they describe marginally stable bound states of the constituent branes, and correspond to vacua of the BFSS model. The non-perturbative suppression scale is determined by the binding energy. We comment on possible relevance of our results for developing string theoretic parallels of astrophysical black holes.

Let S denote the class of functions analytic and univalent (i.e. one-to-one) in the unit disk D=z∈C:|z|<1 normalized by f(0)=0=f′(0)-1 . The logarithmic coefficients γn of f∈S are defined by logf(z)z=2∑n=1∞γnzn. Let F1(F2andF3resp.) denote the class of functions f∈A such that Re(1-z)f′(z)>0(Re(1-z2)f′(z)>0andRe(1-z+z2)f′(z)>0resp.)inD. The classes F1,F2 and F3 are subclasses of the class of close-to-convex functions. In the present paper, we determine the sharp upper bound for |γ1| , |γ2| and |γ3| for functions f in the classes F1,F2 and F3 .

Primitive remote sensing (RS) image classification algorithms primarily rely on labeled images to train the model. However, acquiring labeled data in remote sensing is often expensive and labor-intensive, requiring extensive domain expertise, especially when large and diverse datasets are used. Traditional methods, such as maximum likelihood classification and k-nearest neighbors, depend on manually crafted features and struggle to handle large-scale, high-dimensional data, underperforming in these scenarios. These limitations highlight the need for domain adaptation techniques, which can transfer knowledge from labeled datasets to new, unlabeled domains. Domain adaptation approaches have been developed to address this scenario, utilizing existing labeled images for training and classifying unknown images from different scenes. The distribution variability issue can arise due to variations in acquisition environment conditions, scenes, times, or changing sensors. Existing domain adaptation approaches consider one or more types of losses, such as primary losses (e.g., center and triplet losses), secondary losses (e.g., Maximum Mean Discrepancy (MMD), CORAL, and entropy), by extracting features from backbone networks like VGG or ResNet. However, none of the existing work incorporates an attention mechanism alongside all these losses within a unified framework. In this framework, we integrate primary, secondary, and entropy losses along with a self-attention mechanism. We systematically review the performance of these losses on state-of-the-art Neural Network models, including VGG, ResNet, AlexNet, GoogLeNet, EfficientNet, MobileNet, and ViT. Extensive experiments conducted on the RSSCN7, NWPU-RESISC45, AID, and UCMerced datasets, including a comparison of features extracted from the classification layer and the penultimate layer of the fully connected network, validate the effectiveness of the proposed methodology, paving the way for a more robust and accurate remote sensing system capable of handling domain shifts.

Recently in China, a novel coronavirus outbreak took place which caused pneumonia-like symptoms. This coronavirus belongs to the family of SARS and MERS and causes respiratory system disease known as COVID-19. At present we use polymerase chain reaction (PCR) based molecular biology methods for the detection of coronavirus. Other than these PCR based methods, some improved methods also exist such as microarray-based techniques, Real time-quantitative PCR, CRISPR-Cas13 based tools but almost all of the available methods have advantages and disadvantages. There are many limitations associated with this method and hence there is a need for a fast, more sensitive, and specific diagnostic tool which can detect a greater number of samples in less time. Here we have summarised currently available nucleic acid-based diagnostic methods for the detection of coronavirus and the need for developing a better technique for a fast and sensitive detection of coronavirus infections. Graphic abstract Nucleic acid based detection tool for SARS-CoV-2.

A bstract Understanding the loss landscape is an important problem in machine learning. One key feature of the loss function, common to many neural network architectures, is the presence of exponentially many low lying local minima. Physical systems with similar energy landscapes may provide useful insights. In this work, we point out that black holes naturally give rise to such landscapes, owing to the existence of black hole entropy. For definiteness, we consider 1/8 BPS black holes in N = 8 string theory. These provide an infinite family of potential landscapes arising in the microscopic descriptions of corresponding black holes. The counting of minima amounts to black hole microstate counting. Moreover, the exact numbers of the minima for these landscapes are a priori known from dualities in string theory. Some of the minima are connected by paths of low loss values, resembling mode connectivity. We estimate the number of runs needed to find all the solutions. Initial explorations suggest that Stochastic Gradient Descent can find a significant fraction of the minima.

Huanglongbing (HLB, aka citrus greening), one of the most devastating diseases of citrus, has wreaked havoc on the global citrus industry in recent decades. The culprit behind such a gloomy scenario is the phloem-limited bacteria “Candidatus Liberibacter asiaticus” (CLas), which are transmitted via psyllid. To date, there are no effective long-termcommercialized control measures for HLB, making it increasingly difficult to prevent the disease spread. To combat HLB effectively, introduction of multipronged management strategies towards controlling CLas population within the phloem system is deemed necessary. This article presents a comprehensive review of up-to-date scientific information about HLB, including currently available management practices and unprecedented challenges associated with the disease control. Additionally, a triangular disease management approach has been introduced targeting pathogen, host, and vector. Pathogen-targeting approaches include (i) inhibition of important proteins of CLas, (ii) use of the most efficient antimicrobial or immunity-inducing compounds to suppress the growth of CLas, and (iii) use of tools to suppress or kill the CLas. Approaches for targeting the host include (i) improvement of the host immune system, (ii) effective use of transgenic variety to build the host’s resistance against CLas, and (iii) induction of systemic acquired resistance. Strategies for targeting the vector include (i) chemical and biological control and (ii) eradication of HLB-affected trees. Finally, a hypothetical model for integrated disease management has been discussed to mitigate the HLB pandemic.

Huanglongbing (HLB, also known as citrus greening) is considered to be the most devastating disease that has significantly damaged the citrus industry globally. HLB is caused by the Candidatus Liberibacter asiaticus (CLas), the fastidious phloem-restricted gram-negative bacterium, vectored by the asian citrus psyllid. To date, there is no effective control available against CLas. To alleviate the effects of HLB on the industry and protect citrus farmers, there is an urgent need to identify or develop inhibitor molecules to suppress or eradicate CLas from infected citrus plant. In this paper, we demonstrate for the first time an in planta efficacy of two antimicrobial compounds against CLas viz. 2S albumin (a plant based protein; ~12.5 kDa), Nano-Zinc Oxide (Nano-ZnO; ~ 4.0 nm diameter) and their combinations. Aqueous formulations of these compounds were trunk-injected to HLB affected Mosambi plants (Citrus sinensis) grafted on 3-year old rough lemon (C. jambhiri) rootstock with known CLas titer maintained inside an insect-free screen house. The effective concentration of 2S albumin (330 ppm) coupled with the Nano-ZnO (330 ppm) at 1:1 ratio was used. The dynamics of CLas pathogen load of treated Mosambi plants was assessed using TaqMan-qPCR assay every 30 days after treatment (DAT) and monitored till 120 days. We observed that 2S albumin-Nano-ZnO formulation performed the best among all the treatments decreasing CLas population by 96.2%, 97.6%, 95.6%, and 97% of the initial bacterial load (per 12.5 ng of genomic DNA) at 30, 60, 90, and 120 DAT, respectively. Our studies demonstrated the potency of 2S albumin-Nano-ZnO formulation as an antimicrobial treatment for suppressing CLas in planta and could potentially be developed as a novel anti CLas therapeutics to mitigate the HLB severity affecting the citrus industry worldwide.

The incidence of delayed wound healing associated with diabetes is increasing globally. Synthetic drugs for wound management often carry adverse effects, underscoring the need for safer and more effective alternatives. Sinapic acid, a phytochemical found in edible plants such as spices, citrus fruits, and berries, has drawn attention for its potential in addressing diabetic wounds. This study investigates the protective effects of sinapic acid at two pharmacological doses in mitigating delayed wound healing and oxidative stress linked to type 2 diabetes. In-vitro, the effects of sinapic acid on cell toxicity, migration, and antioxidant activity were evaluated under high-glucose conditions using L929 murine fibroblast cells and human umbilical vein endothelial cells (HUVEC). In-vivo, diabetic wounds were induced in male Sprague Dawley rats fed with high-fat diet and treated with streptozotocin. Sinapic acid was administered orally at 20 mg/kg and 40 mg/kg, and its efficacy was assessed using an excision wound model. Key markers, including hepatic, and lipid profiles, were analyzed. The findings revealed that sinapic acid significantly improved blood glucose levels and oxidative stress markers in diabetic wound rats. Enhanced angiogenesis, re-epithelialization, wound contraction, cell migration, and SIRT1 levels were observed. This is the first report demonstrating that oral sinapic acid at 20 mg/kg and 40 mg/kg mitigates oxidative stress and promotes wound healing in streptozotocin/high-fat diet (STZ/HFD)-induced diabetes, with lower doses showing greater efficacy.

Background Amyotrophic Lateral Sclerosis (ALS) is a progressive neurodegenerative disorder affecting nerve cells in the brain and spinal cord. With a global incidence of 1.9 to 6 per 100,000 people, ALS is slightly more common in men and prevalent in individuals over 60. However, this review provides a concise update on the regulatory landscape and therapeutic advancements in managing ALS, focusing on the recent approval of Tofersen, the first gene therapy specifically targeting SOD1 mutation-related ALS. Results It highlights Tofersen unique role as an orphan drug approved by the US FDA, emphasizing its mechanism of action, gene silencing and its impact on reducing neurodegeneration. Additionally, the review synthesizes data from ongoing clinical trials, pharmacovigilance reports, and case studies to comprehensively understand Tofersen’s safety, efficacy and market exclusivity. Beyond this, it explores the emerging potential of nanotherapeutic approaches to ALS treatment, identifying critical research gaps and future directions. Conclusion Integrating regulatory updates, clinical evidence, and innovative therapeutic strategies, the review uniquely contributes to the ALS literature by bridging current treatment realities with potential future therapies, aiming to inform researchers, clinicians, and policymakers on optimizing ALS management. Graphical Abstract Article Highlights This review provides a concurrent approach to nanotherapeutics and updates clinical trials for the therapeutic management of amyotrophic lateral sclerosis (ALS). It offers the latest regulatory updates on Tofersen, which is categorised as an orphan drug. The review also provides comprehensive information on the disease pathophysiology of ALS, single-cell molecular dynamics, case study reports, clinical trials, the physicochemical profile, and the regulatory status of Tofersen. Furthermore, it highlights major gaps in current nanotherapeutics approaches, offering valuable insights for future research.