Semantic Role Labeling (SRL) serves as the foundational and pivotal technology for semantic analysis. However, methods relying on pre-trained language models are constrained by issues such as semantic ambiguity and training complexity. Addressing this concern, this paper proposes a Chinese SRL approach that integrates pre-trained language models with Biaffine technology, aiming to enhance the model’s capability in processing semantic information from long sentences while reducing training complexity. By incorporating pooling techniques and part-of-speech features, the model exhibits significant improvements in capturing semantic role boundary relationships. Experimental results demonstrate that the RoBERTa-MPBF model employing maximum pooling achieves an F1 score of 90.89% on the CPB dataset, outperforming models solely based on conditional random fields. Moreover, the introduction of part-of-speech tagging results in an average F1 score improvement of approximately 1.5%. Despite the increased computational burden, considering the performance enhancement, this additional time cost is deemed acceptable. In convolutional kernel size testing, the model maintains F1 scores between 88.6% and 88.8% when the kernel size is 2 or 3. However, as the kernel size increases to 4, the F1 score drops to 80.37%, and further increases to 5 result in an F1 score reduction to 69.51%.

This study elucidates the poorly known phenomenon of bulbil formation observed in three species of Sonerila viz., Sonerila tenella, S. ponmudiana and S. nairii. These bulbils represent clusters of 5 to 20 fleshy scales, developing adventitiously from roots or lower stem nodes, connected by a common stalk or premature stem, and predominantly occurring during early winter when plants are in fruiting or late flowering stages. Initially, these scales are white or vinaceous, but later turn into green, ciliate structures resembling leaves. The bulbils undergo rapid growth into independent plantlets, typically within a few weeks, with clusters up to 10 cm from the parent individual. While commonly associated with vivipary, Sonerila bulbils differ by their developmental origin and lack of connection to reproductive structures. Instead, their occurrence may contribute significantly to short-distance dispersal and population persistence in fragmented habitats. This study generates hypotheses on the ecological significance of dispersal mechanisms in response to environmental heterogeneity, highlighting the unique adaptive features of Sonerila bulbil formation within the broader context of plant reproductive strategies.

This study presents a portable spectroscopic system compatible with ambient light to assess hepatic steatosis (HS) and macrovesicular steatosis (MaS) in human liver specimens. Traditional assessment methods for MaS are limited, prompting the need for non-invasive alternatives. The study utilized a two-stage approach on thawed snap-frozen liver specimens. Biochemical validation compared fat content from Raman and reflectance intensities with triglyceride (TG) quantifications, while histopathological validation contrasted Raman-derived fat content with pathologist evaluations and an algorithm. Analysis of 16 specimens showed a positive correlation between spectroscopic data and TG quantifications. The Raman system differentiated various degrees of global HS and MaS in an additional 66 specimens. A dual-variable prediction algorithm classified significant discrepancies (≥10%) between algorithm-estimated global HS and pathologist-estimated MaS. This study demonstrates the viability of a portable spectroscopic system for non-invasive HS and MaS assessment to enhance real-time donor liver assessments during recovery to improve transplantation outcomes.

1. Marine sponges as natural samplers of environmental DNA (eDNA) are receiving growing attention as an untapped source of biodiversity data. However, little is known about the state of the DNA that is represented by these samples and how this compares to conventional aquatic eDNA samples. 2. Here, we present an artificial spiking experiment where DNA in cellular and extracellular states was added into tanks containing two sponge species. Aquatic eDNA samples and sponge (natural sampler DNA (nsDNA)) samples were collected over 7 days and DNA from the two states was quantified using qPCR. 3. We found that there was a significant interaction between DNA state and sample type (eDNA and nsDNA), with lower detection and concentration of extracellular DNA, compared to cellular DNA, found in nsDNA samples. We also found that detection probability and concentration of DNA were significantly lower in nsDNA than eDNA overall. 4. During methodological testing, PCR inhibition was observed in both sponge species; this was prohibitive in one of the species. Further work to investigate the degree of PCR inhibition during nsDNA metabarcoding is important to understand it’s impact on the communities resolved using these nsDNA methods. 5. Synthesis and applications. We show that nsDNA may originate from a subset of the DNA present in environmental media, potentially resolving a more stable picture of local communities. Natural samplers provide a promising option for hard-to-reach environments and for analysing archived samples; however, further work and optimisation is required to understand what is and isn’t well represented by this sample type compared to widely applied aquatic eDNA approaches.

The snow leopard (Panthera uncia) inhabits mountainous areas of Central and South Asia, including the northern region of Nepal, sharing borders with both India and China. For low-density, far-ranging species inhabiting inaccessible terrain, GPS collars are effective, given the volume of data, the accuracy of locations, and the ability to track numerous individuals simultaneously. For the first time, we analyzed spatiotemporal dynamics using satellite telemetry data to understand differences in movement patterns, time budgeting, and home range utilization between male and female snow leopards, satellite-collared in the northeastern Himalayas of Nepal. The ecological behaviour and time budgeting of snow leopards were modeled by the hidden Markov model (HMM) whereas home ranges were estimated and compared by various methods, such as the minimum convex polygon (MCP) and Kernel Density Estimation (KDE) methods with href and the Brownian bridge movement model. This research showed clear sex differences in movement patterns and home range sizes, which indicate different ecological needs and resource-use techniques. Furthermore, this study provides reliable information on snow leopards from the telemetry data and links it to conservation implications in eastern Nepal to ensure their long-term survival, promote coexistence, and foster cross-border collaboration.

Abstract: Climate change and elevated CO2 concentration have significantly influenced on the Spatiotemporal distribution of global and regional grassland carbon storage. To detect the effects of present and future climate scenarios on spatiotemporal distribution of NEP and analysis the main factors of grassland NEP, this study used the CENTURY model after parameter calibration to estimate the values of net ecosystem production (NEP) from 2000 to 2014. The one-way ANOVA and error analysis were used to verify the model. The SSP126, SSP245 and SSP585 as the future climate scenarios also be used to simulate the spatiotemporal change of the NEP from 2015 to 2100. The results show that there was a good agreement between the observed and predicted NEP; The inter-annual variation of NEP decreased year by year under different scenarios. The northwest and eastern of Gansu province are sensitive areas to NEP spatial change. Precipitation, longitude and temperature were significantly positively correlated with NEP, which is significantly negatively correlated with temperature, but weakly correlated with precipitation. In addition, increased CO2 concentration was positively correlated with NEP. The annual average changes of grassland NEP in Gansu under the SSP126, SSP245 and SSP585 climate scenarios are 595.95 gC·m-2·a-1、604.53 gC·m-2·a-1 and 679.17 gC·m-2·a-1, respectively. During 2019-2048, grassland NEP decreased at a rate of -0.69 gC·m-2·a-1 (P<0.01) in the SSP126 climate scenario, no significant changes in the SSP245 climate scenario at a rate of -0.2 gC·m-2·a-1, and increased significantly at a rate of 1.36 gC·m-2·a-1 (P<0.01) in the SSP585 climate scenario. On average, grassland in Gansu is a carbon sink under different[ climate scenarios, and the potential carbon sequestration capacity of grassland will gradually increase under future climate change.

not-yet-known not-yet-known not-yet-known unknown Silicon heterojunction technology (HJT) and tunnel oxide passivated contacts (TOPCon) solar cell technologies are expected to dominate the photovoltaic market in the coming years. However, there are still some concerns about the long-term stability of these technologies. This work examines the effects of two widely used commercial soldering fluxes (Flux A and Flux B) on the stability of commercial silicon HJT and TOPCon solar cells. The soldering flux was applied to the solar cells, and the solar cells were annealed at 85 oC under low relative humidity. TOPCon solar cells were found to be stable; however, significant degradation was observed in the HJT solar cells after only 50 hrs. The efficiency of the HJT cells decreased by ~ 61% rel with Flux A and ~ 55% rel with Flux B, respectively. We attribute part of the observed degradation to holes present in the HJT cell metalisation after printing, which allow the soldering flux to easily penetrate the contact and subsequently react with the paste constituents. In addition, we find that the indium tin oxide (ITO) layer is very sensitive to soldering flux, showing major cracks and significant peeling after 50 hrs of annealing. Consequently, this work shows that some soldering flux can react with the ITO layer, without requiring the presence of water. This suggests that certain types of soldering flux can harm HJT solar cells even after encapsulation without the need for moisture ingress. Therefore, paying more attention to the choice of soldering flux is essential, especially when working with HJT cells. It is strongly recommended that users perform comprehensive component analysis testing on soldering fluxes before their official use rather than solely relying on datasheets provided by suppliers.

A document by Aline Kirsten Vidal de Oliveira. Click on the document to view its contents.

We investigate rapid thermal processing (RTP) as alternative to the prolonged thermal annealing process used to form tunnel-oxide passivating contacts for silicon solar cells. The thermal treatment is generally followed by hydrogenation to passivate defects at the Si/SiOx interface. Whereas industrial manufacturing generally uses Cz wafers, research is often carried out with FZ wafers. Both types of wafers are prone to the formation of thermal defects in the bulk. To disentangle effects of the interface and the bulk, we assess the lifetime at different steps of the process sequence for both wafer types. We find that the initial bulk lifetime of our p-type FZ material is maintained for RTP up to temperatures of about 450°C, followed by a severe decay and eventually a moderate extent of recovery at temperatures above 800°C. Compared to FZ material, the initial bulk lifetimes in our p-type Cz material are slightly lower, but they are maintained on that level up to about 600°C. Beyond that temperature, the lifetimes also decay, but to a lesser extent than in the FZ material, and there is no curing at higher temperatures. Hydrogenation can partially passivate the bulk defects in FZ material, but the initial state is not recovered. In Cz material, it appears that RTP creates two different types of defects; for those created up to 800°C the initial state can be recovered by hydrogenation whereas those created at higher temperature cannot be passivated by hydrogenation. We also investigate the formation of n-type passivating contacts by RTP, and we fabricate solar cell precursors with a single RTP step and the same hydrogenation for both contact polarities. After sputtering a transparent conducting ITO layer on the full area and an Ag metallization, we achieve solar cells efficiencies up to 20.5%.

Enterovirus D68 (EV-D68) has gained attention as a respiratory and neuropathogen, particularly affecting children and causing outbreaks of severe respiratory illness and acute flaccid myelitis. This review explores the current state of knowledge regarding the immunology of EV-D68, delving into the intricate interactions between the virus and the host immune system. From the initial encounter of the virus with the innate immune response to the development of adaptive immunity, we navigate through key aspects of EV-D68 immunology. 1. Innate Immune Response: The journey begins with the recognition of EV-D68 by the innate immune system. Pattern recognition receptors (PRRs) identify viral components, triggering a cascade of antiviral responses. The role of interferons, natural killer cells, and other innate immune effectors in controlling early viral replication is highlighted. 2. Adaptive Immunity: The adaptive immune system orchestrates a more targeted and specific defense against EV-D68. B cells produce antibodies that recognize and neutralize the virus, while T cells play a crucial role in coordinating and regulating the immune response. This section explores the dynamics of antibody-mediated immunity and the cellular immune response in the context of EV-D68 infection. 3. Viral Evasion Mechanisms: EV-D68 employs various strategies to evade the host immune response, including antigenic variation and interference with host antiviral pathways. Understanding these mechanisms is vital for developing effective countermeasures. 4. Vaccine and Therapeutic Developments: A critical aspect of EV-D68 immunology is the ongoing efforts to develop vaccines and antiviral therapies. This section provides an overview of current research and potential strategies for preventing and treating EV-D68 infections. 5. Future Perspectives: The review concludes by highlighting the gaps in our understanding of EV-D68 immunology and proposing avenues for future research. The importance of a comprehensive approach to tackle this evolving respiratory pathogen is emphasized.

Recent advancements in immunotherapy spotlight the pivotal role of macrophages in disease treatment. The polarization of macrophages, wherein these immune cells differentiate into either pro-inflammatory (M1) or anti-inflammatory (M2) states, plays a vital role in the therapeutic modulation of a spectrum of diseases. Oligonucleotides have emerged as potent tools for modulating macrophage polarization with high specificity and potential cost-effectiveness compared to traditional therapies. In this review, we delve into the application of oligonucleotides for steering macrophage polarization, introducing how macrophage is polarized and how 4 different kinds of oligonucleotide (ASO, siRNA, agomir/antagomir and aptamer) works, discusses their therapeutic potentials and challenges associated with these strategies, emphasizing the importance of precise, disease-specific interventions by presenting actual examples. While the application of oligonucleotides in clinical faces challenges, we also highlights some recent, significant advances in current trials and the future potential in stimulating macrophages for therapeutic advantages in immunotherapy, signaling a frontier in disease treatment and management.

Long-term excessive use of fertilizers decreases soil productivity, which is detrimental to sustainable agricultural development. The purpose of this paper is to identify organic amendments suitable for winter wheat growth in the North China Plain by studying the effects of organic amendments on the economic benefit and carbon sequestration of winter wheat fields, and to provide a theoretical basis for the wide application of organic amendments. The nitrogen rates were N0 (0 kg/ha) and N240 (240 kg/ha), and the organic amendments were straw, manure, mushroom residue and biochar. The result showed that compared to N0, N240 significantly increased the yield by 244.1%-318.4% and the organic carbon sequestration by 16.7-30.5%, respectively, but increased the carbon emission by 29.3-45.5%. In addition, soil carbon sequestrations increased with three types of organic amendments compared to straw, with the biochar treatment being the largest, increasing carbon sequestration by 13.3-33.6%. In terms of yield and economic benefits, compared to straw, manure and biochar increased yield by 0.0-1.5% and 4.0-13.3%, respectively, and mushroom residue slightly decreased yield, but only the economic benefit of mushroom residue was greater than that of straw, with an increase in economic benefit of 1.3% and 8.2% in the 2021-2022 and 2022-2023, respectively. Furthermore, Net ecosystem productivity showed that N0 and N240 was the source and sink of CO 2, respectively. The TOPSIS results showed that N240 with mushroom residue could be recommended for increasing soil carbon sequestration and economic benefits for winter wheat in the NCP. Low-cost mushroom residue can increase farmer motivation and improve SOC, making a big step forward in the spread of organic amendments.

Acute ischemic stroke (AIS) is one of the leading causes of death and disabilities, and as such, it is of utmost importance to identify novel treatment options. Current acute treatments for AIS are limited to either thrombectomy or thrombolysis, both of which must be initiated within 4.5-6 hours of symptom onset. Remote ischemic conditioning (RIC) is a promising non-invasive treatment that is thought to activate the body’s own protective mechanisms against damaging ischemia through circulating microRNAs (miRNAs). Here, we investigate the transcriptional changes in human brain microvascular endothelial cells (HBMECs) transfected with four selected RIC-upregulated miRNAs (RIC-miRNAs), miR-16-5p, miR-144-3p, miR-182-5p, and miR-451a, under oxygen and glucose deprivation (OGD) - mimicking the initial stages of AIS. Pronounced transcriptional changes were present after RIC-miRNA transfection, with 149 unique downregulated and 212 upregulated differentially expressed genes in HBMECs after OGD and RIC-miRNA transfection compared to all other conditions. These genes were involved in cell cycle regulation, DNA replication and pathways of energy metabolism. However, we saw no direct effect on cell viability after RIC-miRNA transfection and OGD. In conclusion, our study suggests that the selected RIC-miRNAs regulate pathways that may facilitate endothelial cell recovery and remodeling events from ischemic damage, offering new therapeutic avenues for AIS.

Eating disorders (ED) and personality disorders (PD) co-occur together frequently, and the latter need to be considered by the treating clinicians as they can hinder treatment effectiveness or, if left unaddressed, can lead to relapse. These persons present with a combination of ED symptoms and behaviours, interpersonal problems and global suffering and often have difficulties forming and sustaining a therapeutic alliance. How can we address this condition so as to make ED treatment more effective? We describe the story of Silvia, a 34 years old woman with Binge Eating Disorder (BED) and PD treated with Metacognitive Interpersonal Therapy. She entered treatment with frequent binges and was seriously overweight (Body Mass Index= 36.7). She had difficulties describing her inner states and understanding why she binged, so she could not control them. During therapy, starting from the information coming from the food diary, she gradually becomes aware of how she processes underlying symptoms and interpersonal behaviour. The therapist used a combination of CBT techniques and experiential work, such as guided imagery, to foster change in both the ED and PD domains. By treatment termination, Sivia no longer suffered from either BED or PD and lost 10kg. This case is an example of how it is possible to combine aspects of treatment targeting ED with attention to PD features in an effective way. It also invites researchers to consider ED/PD comorbidity so as to reduce dropouts and increase treatment effectiveness

Plant-microbe associations are ubiquitous, but parsing the contributions of dispersal, host filtering, competition, and the environment on microbial community composition is challenging. Floral nectar-inhabiting microbes offer a tractable system to disentangle community assembly processes. We inoculated a synthetic community of yeasts and bacteria into nectars of 31 phylogenetically diverse plant species while excluding pollinators. We monitored weather conditions and, after 24 hours, collected and cultured communities. We found a strong signature of plant species on resulting microbial abundance and community composition, in part explained by plant phylogeny and nectar peroxide content, but not measured floral morphological traits. Higher maximum and minimum temperatures increased microbial growth overall and favored certain microbes over others. Our work supports the roles of plant identity and abiotic conditions in assembly and growth of plant-associated microbial communities and finds evidence for diversity-productivity relationships within plant-associated microbiomes.

https://www.researchgate.net/publication/382952222_Artificial_Intelligence-Based_Cloud_Planning_and_Migration_to_Cut_the_Cost_of_Cloud_Sasibhushan_Rao_Chanthatihttps://journals.e-palli.com/home/index.php/ajsts/article/view/3210The paper titled “Artificial Intelligence-Based Cloud Planning and Migration to Cut the Cost of Cloud” aims to examine how AI can be implemented to improve cloud planning and migration in a bid to reduce their costs. The proposal is concerned with the utilization of multiple AI techniques, such as machine learning models, natural language processing, and reinforcement learning, to manage the migration process in the cloud. In incorporating AI within the transitions, the paper establishes how organizations improve productivity, stability, and security during Cloud transitions. It provides a detailed pseudocode of the scenario, making the content sufficiently intelligible to the IT professionals who wish to implement these AI algorithms. In this regard, this paper helps to fill the gap that has been demonstrated in the current literature regarding the link between theoretical uses of AI and its application in cloud migration towards enhancing the deployment efficacy and cost-efficiency of cloud services. The article was first completed in 2021 and later I have modified the article with latest updates till date 2024.

https://www.researchgate.net/publication/382955327_Product_Colour_Variation_Management_with_Artificial_Intelligencehttps://journals.e-palli.com/home/index.php/ajet/article/view/3213This research focuses on the topic of using AI in color variant management in products to enhance the appeal and performance of the products in the marketplace by incorporating artificial intelligence, deep learning, and neural network systems. Real-time consumer and product information, preferences, buying history, and sales history; I created an AI model to predict and change product colors in real-time. The complete workflow used comprises data gathering, processing, and feature extraction, model training, integration of the color adjustment software tools, and finally, testing and validation. The efficiency of such AI-driven interventions was analyzed through the consumer satisfaction indices, the sales results, and the engagement data based on the consumption of digital platforms. This study demonstrates valuable potential of AI to improve product design application and development while providing valuable suggestions for Businesses adapting and improving market outcomes according to the changing consumer trends. Such an application of AI implements a new best practice in ways of enhancing futuristic consumer-oriented marketing approaches. The paper was first completed in 2021 and later I have modified the paper with latest updates till date 2024.

This paper discusses the potential of machine learning, data science, and natural language processing (NLP) in mitigating the incidence of spoofing and financial risks hinged on cyber threats. Another one is spoofing; it is the act of impersonating legitimate entities to gain unauthorized information and it is indeed a threat to the public and companies to some extent. The research introduces two primary methodologies to combat spoofing: an email filtering system using a machine learning algorithm and an encryption and decryption system using a Caesar Cipher and Python programming language. It distinguishes between approved domains and unapproved domains by using machine learning and successfully filters out phishing emails from reaching the intended clients. This study also illustrates how to conduct email domain verification using MongoDB Atlas, which a database is containing approved vendors’ domains, to reduce spoofing. Specifically, incorporating NLP helps the system analyze raw data to categorize it and identify patterns potentially leading to a spoofing attempt, enhancing the spoofing detection and prevention of the system. The paper also presents arguments that require awareness and integration of new technologies in the security frameworks. Hence, incorporating machine learning, data science, and NLP presents robust, versatile, and cost-effective solutions to enhance cybersecurity and ultimately protect vital information and organizations’ monetary loss due to cybercrimes.