Abstract: Background: Stigma in health can result in a broad range of vulnerabilities and risk for patients and healthcare providers. The media plays a role in people’s understanding of health and stigma is socially constructed through many communication channels including via media framing. Among health issues that were affected by stigma recently were the Monkeypox and Covid-19. Objectives: This research aims to examine how The Washington Post framed stigma around mon-keypox and COVID-19. Guided by the framing theory and stigma theory, online news coverage for monkeypox and COVID-19 were analyzed to understand the construction of social stigma through the media reporting. Methods: This research employed a qualitative content analysis to compare news framing in The Washington Post online news regarding monkeypox and COVID-19. Results: Based on endemic, reassurance, and sexual transmission frames, the The Washington Post predominantly defined Africa as the source of the disease, blames gay communities, and empha-sizes no need to worry about the spread of the monkeypox virus. For the COVID-19 coverage, The Washington Post described China as the source of the coronavirus and constructs the image of panic towards the spread of the virus. Conclusions: The shifts in stigma discourse essentially manifest racism, xenophobia, and sexism in public health. This research affirms that the media reinforces stigma phenomenon in health through framing and offers constructive suggestions for mitigating this issue.

During the response of plants to adverse stresses, aquaporin (AQP) plays a prominent role in membrane water transport based on received upstream signals. In addition, they have various physical parts for dealing with environmental stresses. Due to the importance of the AQP gene family, studies have been conducted investigating the function and regulatory system of these genes. However, many of their molecular aspects are still unknown. This study aims to carry out a genomic-wide investigation of the AQP gene family in durum wheat using bioinformatics tools and to investigate the expression patterns of some members in response to salt stress. Our results showed that there are 80 TtAQP genes in durum wheat, which are classified into four main groups based on phylogenetic analysis. Many duplications were observed between the members of the TtAQP gene family, and high diversity in response to post-translational modifications was observed between TtAQP family members. The expression pattern of TtAQP genes disclosed that these genes are primarily upregulated in response to salt stress. Besides, qPCR data revealed that TtAQPs are more induced in delayed responses to salinity stress. Overall, our findings illustrate that TtAQP members are diverse in terms of their structure, regulatory systems and expression levels.

An adaptor signature can be viewed as a signature concealed with a secret value and, by design, any two of the trio yield the other. In a multiparty setting, an initial adaptor signature allows each party create additional adaptor signatures without the original secret. Adaptor signatures help address scalability and interoperabity issues in blockchain. They can also bring some important advantages to cryptocurrencies, such as low on-chain cost, improved transaction fungibility, and less limitations of a blockchain’s scripting language. In this paper, we propose a new two-party adaptor signature scheme that relies on quantum-safe hard problems in coding theory. The proposed scheme uses a hash-and-sign code-based signature scheme introduced by Debris-Alazard et al. and a code-based hard relation defined from the well-known syndrome decoding problem. To achieve all the basic properties of adaptor signatures formalized by Aumayr et al., we introduce further modifications to the aforementioned signature scheme. We also give a security analysis of our scheme and its application to the atomic swap. After providing a set of parameters for our scheme, we show that it has the smallest pre-signature size compared to existing post-quantum adaptor signatures.

Deep Learning is developing interesting tools which are of great interest for inverse imaging applications. In this work, we consider a medical imaging reconstruction task from subsampled measurements, which is an active research field where Convolutional Neural Networks have already revealed their great potential. However, the commonly used architectures are very deep and, hence, prone to overfitting and unfeasible for clinical usages. Inspired by the ideas of the green-AI literature, we here propose a shallow neural network to perform an efficient Learned Post-Processing on images roughly reconstructed by the filtered backprojection algorithm. The results obtained on images from the training set and on unseen images, using both the non-expensive network and the widely used very deep ResUNet show that the proposed network computes images of comparable or higher quality in about one fourth of time.

Total 219 countries and territories globally suffering from the recent pandemic COVID-19 is now in its second wave with more brutality, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) . It has several symptoms like as persistent fever; respiratory illnesses; cough; fatigue; shortness of breath; loss of appetite; persistent pain or pressure in the chest; dysgeusia; acute respiratory distress syndrome (ARDS) etc., and here the things to worry about is the development of pulmonary fibrosis after COVID-19 in both peoples who had died of due to acute respiratory distress syndrome (ARDS) or those who survived. Due to COVID-19, dysregulated immune response and wound repair mainly in elderly patients causes this secondary pulmonary fibrosis. Thus using anti-fibrotic agents could be meaningful in these circumstances although their efficacy in treating COVID-19 is subject to more detailed laboratory research works. In this review article you will get to know about the lung fibrosis generation due to COVID-19 infection, about anti-fibrotic agents and the currents challenges of this field.

Cryptography has been used from time immemorial for preserving the confidentiality of data/information in storage or in transit. Thus, cryptography research has also been evolving from the classical Caesar cipher to the modern cryptosystems based on modular arithmetic to the contemporary cryptosystems based on quantum computing. The emergence of quantum computing imposes a major threat on the modern cryptosystems based on modular arithmetic whereby, even the computationally hard problems which constitute for the strength of the modular arithmetic ciphers could be solved in deterministic time. This threat triggered post-quantum cryptography research in order to design and develop post-quantum algorithms that can withstand quantum computing attacks. This paper provides a review of the various post-quantum cryptography and, in specific, code-based cryptography research dimensions. The research directions that are yet to be explored in code-based cryptography research is another key contribution of this paper.

In this paper, we introduce a computational procedure that enables autonomous LEO laser trackers endowed with INSs to increase the current accuracy when shooting at middle distant medium-size LEO debris targets. The code is designed for the trackers to throw the targets into the Atmosphere by means of ablations. In case that the targets are eclipsed to the trackers by the Earth, the motions of the trackers and targets are modeled by equations that contain post-Newtonian terms accounting for the curvature of space. Otherwise, when the approaching targets become visible for the trackers, we additionally use more accurate equations, which allow to account for the local bending of the laser beams aimed at the targets. We observe that under certain circumstances the correct shooting configurations that allow to safely and efficiently shoot down the targets, differ from the current estimations by distances that may be larger than the size of many targets. In short, this procedure enables to estimate the optimal shooting instants for any middle distant medium-size LEO debris target.

This study investigated the prevalence and severity of depression, anxiety, and stress and determined the association between various factors, social support, and depression and anxiety among university healthcare workers in Malaysia after the government lifted the movement control order (MCO) put in place to curb the coronavirus disease 2019 (COVID-19) pandemic. This online, cross-sectional survey recruited 399 participants from two university hospitals, and they were administered a self-reported questionnaire on demographic, personal, and clinical characteristics; COVID-19-related stressors; and coping. In addition, they completed the Multidimensional Scale of Perceived Social Support (MSPSS) to measure perceived social support, as well as the 21-item Depression, Anxiety, and Stress Scale (DASS-21) to assess depression, anxiety, and stress. We found that the prevalence of depression, anxiety, and stress were 21.8%, 31.6%, and 29.1%, respectively. Participants with moderate to extremely severe depression, anxiety, and stress made up 13.3%, 25.8%, and 8.1% of the sample, respectively. Being single or divorced, fear of frequent exposure to COVID-19 patients, those who agreed that their area of living had a high prevalence of COVID-19 cases, and uncertainty regarding the prevalence of COVID-19 cases in the area of living were associated with higher odds of depression and anxiety. Conversely, having more than three children and greater perceived friend support were associated with lower odds of depression and anxiety. The prevalence of depression, anxiety, and stress remained elevated even after the MCO was lifted.

The “tubulin code” combines different α/β-tubulin isotypes with several post-translational modifications (PTMs) to generate microtubule diversity in cells. During cell division, specific microtubule populations in the mitotic spindle are differentially modified, but only recently has the functional significance of these modifications started to be elucidated. In particular, α-tubulin detyrosination of stable microtubules in the spindle was shown to guide chromosomes during congression to the metaphase plate and allow the discrimination of mitotic errors, whose correction is required to prevent chromosomal instability (CIN), a hallmark of human cancers. Although alterations in certain tubulin PTMs have been reported in human cancers, it remains unclear whether and how tubulin PTMs have any functional implications for cancer cell properties. Here we review the role of the tubulin code in chromosome segregation during mitosis, together with the emerging cancer tubulin code and discuss possible links, as well as the respective diagnostic, prognostic and therapeutic implications for human cancers.

This conceptual article is aimed at evaluating the COVID19 impacts on the global economy and to postulating an ‘economic paradigm shift’. We argue that the existing economic, political, and trade principles have been challenged due to the global pandemic issue. We also compare the COVID19 impact on Western countries and Eastern countries with their control mechanisms. The emerging of new economies is due to the travel restrictions and disrupt of the global economies are discussed that pave a gateway to the economic paradigm shift. The article finally assesses the advent of 5G technology and its implications to face any future health threats that will lead to the next economic paradigm shift in the modern world.

Since January, the disease caused by the Sars Cov-2 virus has spread and following pandemic. In a few months, the virus is seriously affecting the health systems of the various countries of the world and placing people in difficult psychological conditions. Few scientific resources on mental health have been published but still no one has proposed an action plan to cope the future psychological problems.This manuscript provides a plan to easily frame the priorities of mental health area related to COVID-19 to be taken into consideration which should be disseminated and that should be known by all health professionals and also by the major administrators of public health.

The incidence of cancer in organ transplant patients is higher than in the non-transplanted population. The incidence increases with increasing age. The use of Fludeoxyglucose positron emission tomography/CT (PET/CT) is sensitive and specific to detect PTLD LD compared with conventional CT imaging. We analyzed the medical data of 127 patients aged over 60 years, who underwent heart transplantation in the above period, who have been practicing early CT-PET for diagnostic purposes between February 2007 and October 2018. Of 127 consecutive patients who underwent CT-PET, SUVs up than 4, were found in 84 patients of which 20 were affected by PTLDs, seven patients were affected by chronic non neoplastic inflammatory diseases, the remainder were affected by other neoplasms. The favorable effect of physical activity programs on cardiorespiratory and psychomotor function occur in all patients. In conclusion, CT-PET at the first doubts about the possibility of the development of neoplasms has allowed a diagnosis and then a treatment more quickly. Education in behavioral norms that improve the patient's quality of life is necessary.

The maintenance of high quality standards for prolonging shelf life of fruit and maintaining sensory and nutritional quality is a priority the horticultural products. The aim of this work was to test the effectiveness of a single treatment of edible coating based on Aloe arborescens and a combined treatment of 1-Methylcycyclopropene and edible coating to prolong the shelf-life of "Settembrina" white flesh peach fruit. White flesh peach fruit were harvested at the commercial ripening stage and treated with edible coating (EC) or 1-MCP +EC and stored for 28 days at 1°C. After 7, 14, 21 and 28 days fruits were removed from cold storage, transferred at 20°C and then analyzed immediately (cold out) and after 6 days (shelf life) to evaluate the combined effect of cold storage and room temperature. Weight loss, physical, chemical and sensory parameters were measured. Fruit treated with EC and 1-MCP +EC kept their marketing values better than CTR after 14 days of storage and 6 days of simulated shelf life, in terms of flesh firmness, total soluble solids and titratable acidity as well as sensory parameters. After 21 days of storage, all treatments showed a deterioration of the quality parameters. The single and combined application of Aloe-based coating (with 1-MCP) slowed down the maturation processes of the fruit, limited the weight loss and preserved its organoleptic characteristics.

Background: Many randomized controlled trials (RCTs) and systematic reviews (SRs) on acupuncture treatment for post-acute stroke dysphagia have been published. Due to conflicting results an overview of SRs to summarize and assess the quality of this evidence to determine whether acupuncture is effective for this disease was conducted. Methods: Seven databases were searched for SRs and/or Meta-analysis of RCTs and quasi-RCTs on acupuncture for post-acute stroke dysphagia. Two authors independently identified SRs and meta-analyses, collected data to assess the quality of included SRs and meta analyses according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and the revised Assessment of Multiple Systematic Reviews (AMSTAR 2). Results: 31 SRs were identified. Quality of 22 SRs was critically low, 5 SRs were low, and 4 Cochrane SRs were moderate when evaluated by AMSTAR2. 17 SRs reported 85.2-96.3% items of PRISMA. Five SRs included explanatory RCTs, 16 SRs included pragmatic RCTs, and 10 SRs included both. Conclusion: Currently evidence on the effectiveness of acupuncture on post-acute stroke dysphagia is low quality. Type of study appeared to have no direct influence on the result, but the primary outcome measures showed a relationship with the quality of SRs. High quality trials with large sample sizes should be the focus of future research. PROSPERO REGISTRATION NUMBER: CRD42019134163

Understanding the role of risk regions identified by genome-wide association studies (GWAS) have made considerable progress lately referred to the post-GWAS era. Annotation of the genes to the GWAS and fine-mapped functional variants, and understanding their biological pathway/gene networks enrichments is expected to give rich dividend by elucidating the mechanisms underlying prostate cancer. To this aim, we compiled and analysed currently available post-GWAS data on genes identified through GWAS and validated through experimental studies in prostate cancer to investigate molecular biological pathways enriched for assigned functional genes. The results highlight some well-known cancer signalling pathways, antigen presentation process and enrichment in cell growth and development gene networks suggesting prostate cancer may result from the accumulation of the effects of functional variants through multiple gene sets and pathways. The upstream analysis identifies critical transcription factors, which supplements the results regarding the regulatory role of the post-GWAS genes. We also identified the common genes between post-GWAS and three well-annotated prostate cancer Oncomine data in patient samples in order to uncover possible main genes in prostate cancer development/progression. Post-GWAS generated knowledge of gene networks and pathways, if analysed further and targeted appropriately, will have an important impact on clinical management of the disease.

The type of tail of sheep is an important economic trait. However, the candidate genes associated with the tail type are uncertain. The objective of this study was to identify the genetic region and genotype responsible for the tail type phenotype. Here we perform a genome-wide association study (GWAS) in 40 large tailed Han sheep and 40 Altay sheep as case and 40 Tibetan sheep as control. The results indicated that a total 31 genome-wide significant SNPs associated with type of tail traits were detected. For significant SNPS loci, determine its physical location, and screening of candidate genes within section. By combining information of previously reported and annotated biological functional genes, we identified SPAG17, Tbx15, VRTN, NPC2, BMP2 and PDGFD as the most promising candidate genes for type of tail traits. Based on the above identified candidate genes on type of tail traits, we selected BMP2 and PDGFD to conduct the genetic effect analysis in a large Altay sheep and Tibetan sheep population. Rs119 T>C in the exon1 of BMP2 gene and 1 SNPs in the exon4 (rs69 C>A) of PDGFD gene were detected, rs119 that located on exon1 of BMP2 gene was TT genotype in Altay sheep, while with CC genotype in Tibetan sheep. On rs69 of PDGFD gene, Altay sheep with CC genotype, however, Tibetan sheep with AA genotype. These results indicated that the significant associations of SNPs detected in GWAS were indirectly caused by the genetic effects of BMP2 and PDGFD on sheep tail fat deposition.

Several researchers have contemplated deep learning-based post-filters to increase the quality of statistical parametric speech synthesis, which perform a mapping of the synthetic speech to the natural speech, considering the different parameters separately and trying to reduce the gap between them. The Long Short-term Memory (LSTM) Neural Networks have been applied successfully in this purpose, but there are still many aspects to improve in the results and in the process itself. In this paper, we introduce a new pre-training approach for the LSTM, with the objective of enhancing the quality of the synthesized speech, particularly in the spectrum, in a more efficient manner. Our approach begins with an auto-associative training of one LSTM network, which is used as an initialization for the post-filters. We show the advantages of this initialization for the enhancing of the Mel-Frequency Cepstral parameters of synthetic speech. Results show that the initialization succeeds in achieving better results in enhancing the statistical parametric speech spectrum in most cases when compared to the common random initialization approach of the networks.

In this paper the development process of a deployable modular sandwich panelized system for rapid assembly building construction is presented, and its structural performance under some different action effects is investigated. This system, which includes an innovative sandwich panel and its integrated connections, can be used as structural walls and floors in quickly assembled post-disaster housing, as well as load bearing panels for pre-fabricated modular construction and semi-permanent buildings. Panels and connections are composed of a pneumatic fabric formwork, and two 3-D high-density polyethylene (HDPE) sheets as the skins, filled with high-density rigid Polyurethane (PU) foam as the core. HDPE sheets manufactured with a studded surface considerably enhance the stress distribution, buckling performance and delamination strength of the sandwich panel under various loading conditions. The load-carrying behaviour of the system in accordance with some ASTM standards is presented here. The results show the system satisfies the codes criteria regarding semi-permanent housing.

Building a social security system to ensure Singapore residents have peace of mind in funding for retirement has been at the top of Singapore Government’s policy agenda over the last decade. Implementation of the Lifelong Income For the Elderly (LIFE) scheme in 2009 clearly shows that the government spares no effort in improving its pension scheme to boost its residents’ income after retirement. Despite the recent modifications to the LIFE scheme, Singapore residents must still choose between two plans: the Standard and Basic plans. To enhance the flexibility of the LIFE scheme while maintaining its simplicity, we propose some plan modifications such that scheme members do not face a dichotomy of plan choices. Instead, they select two age parameters: the Payout Age and the Life-annuity Age. This paper provides an actuarial framework for determining members’ payouts and bequests based on the proposed age parameters. We analyze the net cash receipts and internal rate of return (IRR) for various plan-parameter configurations. This information helps members make their plan choices. To address cost-of-living increases we propose to extend the plan to accommodate an annual step-up of monthly payouts. By deferring the Payout Age from 65 to 68, members can enjoy an annual increase of about 2% of the payouts for the same first-year monthly benefits.

The authors argue that despite significant numbers of foreign workers present in the Russian labor market Russia remains unattractive for highly skilled foreign workers. The economic crisis the country has faced since 2014 has resulted in a further outflow of foreign nationals from OECD countries. So Russia has to look for employees among newcomers from the former Soviet states, the majority of whom come from Central Asia. Russian politics with regard to immigration is characterized by the collision between “geopolitical” and domestic policy rationales. On one hand, seeking to maximize its influence in the post-Soviet space Russia provides preferential conditions of employment for citizens of partner countries in the framework of the Eurasian Economic Union. On the other hand, the Kremlin is afraid to liberalize its approach to immigration and the integration of immigrants through fear of losing popularity among the electorate. The second substantial feature of Russian immigration politics is an extreme level of securitization of migration issues. The government considers these issues primarily through a policing lens. One cannot claim that the Russian state does not take any steps to liberalize “migratory regulation”; however, the effect of these measures is being vastly reduced by new restrictions. In addition, liberally designed laws do not bring the expected results due to the corrupt practices that pervade the whole of migration regulation.

The African society is one of the societies with rich culture and traditions. Apart from the indigenous religion of Africa, Christianity and Islam are worshiped as the major religions of the African society. Literature reflects a great amount of influence of religions on the existing societies, people and cultures. African literature often mirrors the clash of indigenous religion with Christianity. In the writings of African authors one can find the elements of Christian beliefs and practices. The present paper, however, is focused on the African woman novelist Buchi Emecheta’s selected four novels: Second-Class Citizen (1974), The Bride Price (1976), The Slave-Girl (1977) and The Joys of Motherhood (1979). The paper attempts to discuss the impact of Christianity on the social and cultural aspects of the African society with special focus on African women. The findings reveal the positive as well as negative impacts of the new religion on African people and on the position of African women through the characters present in the selected novels. With the medium of writing and through Christianity, Emecheta seek to educate her society and improve upon the position of the African women.

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a frequent, debilitating and still enigmatic disease. There is a broad overlap in the symptomatology of ME/CFS and the Post-COVID Syndrome (PCS). A fraction of the PCS patients develops the full clinical picture of ME/CFS. New observations in microvessels and blood from patients suffering from PCS have appeared and include microclots and malformed pathological blood cells. Capillary blood flow is impaired not only by pathological blood components but also by prothrombotic changes in the vascular wall, endothelial dysfunction, and expression of adhesion molecules in the capillaries. These disturbances can finally cause a low capillary flow and even capillary stasis. A low cardiac stroke volume due to hypovolemia and the inability of the capacitance vessels to adequately constrict to deliver the necessary cardiac preload generate an unfavorable low precapillary perfusion pressure. Furthermore, a predominance of vasoconstrictor over vasodilator influences exists, in which sympathetic hyperactivity and endothelial dysfunction play a strong role, causing constriction of resistance vessels and of precapillary sphincters which leads to a fall in capillary pressure behind the sphincters. The interaction of these two precapillary cardiovascular mechanisms causing a low capillary perfusion pressure is hemodynamically highly unfavorable in the presence of a primary capillary stasis already caused by the pathological blood components and their interaction with the capillary wall, to severely impair organ perfusion. The detrimental coincidence of the microcirculatory with the precapillary cardiovascular disturbances may constitute the key disturbance of the Post-COVID-19 syndrome and finally lead to ME/CFS in pre-disposed patients because the interaction causes a particular kind of perfusion disturbance - capillary ischemia-reperfusion - which has a high potential of causing mitochondrial dysfunction by inducing sodium- and calcium-overload in skeletal muscles. The latter in turns worsens the vascular situation by the generation of reactive oxygen species to close a vicious cycle from which the patient can hardly escape.

Post-stroke depressive disorders (PSD) and post-stroke cognitive impairments (PCI) are frequent consequences of ischemic stroke (IS). The study was focused on exploring possible associations between relative volumes of cortical and limbic brain structures during the acute period of IS, and changes in biochemical indices of hypothalamic-pituitary-adrenal, sympathoadrenal medullary and inflammatory systems, with the development of PSD or PCI after mild or moderate IS. Patients developing PSD later on had significantly smaller relative volumes of the hippocampus, entorhinal cortex, and temporal pole versus patients without depressive symptoms. PCI development was associated with significantly smaller volumes of temporal pole and supramarginal gyrus versus patients without cognitive changes. Multiple logistic regression analysis showed higher likelihood of developing PSD in patients with smaller temporal pole volume (β0=10.9; β=-4.27; p=0.04) and increased salivary α-amylase activity (β0=-3.55; β=2.68e-05; p=0.02,). PCI likelihood was higher in patients with smaller supramarginal gyrus volume (β0=3.41; β=-0.99; p=0.047), smaller temporal pole volume (β0=3.41; β=-3.12; p=0. 06), and increased hair cortisol concentration at admission (index of accumulated stress load within a month before IS; β0=3.41; β=-0.05; p=0.08). The data support the hypothesis suggesting predisposition to PSD and PCI and multi hit scenarios of their pathogenesis with IS providing a final hit.

This study aimed to identify changes in visitor behavior and visitor interest in healing forests before and after the COVID-19 pandemic. The study used text mining analysis techniques to identify changes in visitation behavior over time, divided into three periods: pre-COVID-19 (January 1 to December 31, 2019), during the COVID-19 pandemic (November 1, 2020 to October 31, 2022), and post-COVID-19 (November 1, 2022 to October 31, 2023 ). After the COVID-19 outbreak, healing forest use behavior did not regress to pre-COVID-19 levels. Activity-based keywords such as "hiking," "trekking," and "walking" stood out as the main drivers of this change in behavior. Therefore, related authorities must examine the scalability of the functions, services, and programs of healing forests from a general healing space to a space for leisure and tourism. These findings will contribute to the development of future marketing strategies and programs for healing forests.

This comprehensive review delves into gold market business management, covering historical perspectives, market dynamics, mining models, investment strategies, technology, regulations, global trends, and sustainability. Starting with a historical overview, it traces the gold market's evolution and analyzes supply-demand dynamics, price fluctuations, and geopolitical influences. The article scrutinizes gold mining models, evaluating environmental and social responsibilities, and explores financial management strategies, emphasizing risk and gold's role in portfolios. Examining technology's role, it highlights innovations in exploration, mining, and processing for improved efficiency and sustainability. The regulatory landscape analysis encompasses compliance challenges, environmental regulations, and ethical considerations, emphasizing global impacts. Global market trends are explored, identifying potential opportunities and challenges, offering strategic insights. The review concludes by evaluating sustainability practices, emphasizing environmental conservation, community engagement, and ethical sourcing.

The meticulously engineered powertrain mounting system of hybrid-electric vehicles plays a critical role in minimizing vehicle vibrations and noise, thereby enhancing the longevity of vital powertrain components. However, developing and designing such a system demands substantial time and financial investments due to intricate analysis and modeling requirements. To tackle this challenge, this study integrates data-mining technology into the design and optimization processes of the powertrain mount system. The research focuses on the powertrain mounting system of a transverse four-cylinder hybrid-electric vehicle, employing the CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology to establish a data-mining prediction model for mounting stiffness. This model utilizes three data mining algorithms—Multi-SVR, MRTs, and MLPR—assessing their predictive accuracy concerning mounting system stiffness estimation. A comparative analysis reveals that the MRTs algorithm outperforms others as the most effective prediction model. The proposed predictive model elucidates the quantifiable correlation between vibration isolation performance and installation stiffness, overcoming complexities associated with traditional modeling approaches. Applying this model in powertrain mounting system design showcases the efficacy of the CRISP-DM-based approach, significantly enhancing design efficiency without compromising prediction accuracy.

Intelligent Tutoring Systems (ITS) are increasingly popular for online learning. These systems use adaptive algorithms to recommend relevant content based on students' profiles. However, instructors need to periodically assess students' performance to ensure learning outcomes and adjust strategies accordingly. Our objective is to predict students' progress in advance, enabling teachers to make quicker decisions and facilitating the iterative process of adaptive algorithms. For this study, we collected a dataset from ALIN, an online learning platform, consisting of over 5,000 students' learning records and test results. Using this dataset, we conducted experiments employing various machine learning algorithms. The results indicate that learning behavior contributes to improving forecast performance, while students' progress strongly correlates with their previous test results. Additionally, we discovered that students' progress can be indirectly predicted by forecasting their scores. Furthermore, by breaking down overall scores into several distinct components and predicting individual scores for each component, the accuracy of the forecasts can be improved.

Data mining techniques have garnered significant attention within the realm of education. However, the procurement of ample student data poses a formidable challenge. In response to this challenge, we present a student dataset characterized by its size and distinctive attributes. This dataset encompasses various task-related topics interconnected through a learning pathway, thereby enabling researchers to delve into the data from novel perspectives. Moreover, it encompasses extensive longitudinal student behavioral data, a rarity that adds substantial value. Spanning the years from 2010 to 2021, our dataset comprises a cohort of 7,933 students, 64,344 test scores, and 183,390 behavior records, solidifying its status as a valuable resource for educational research. In our experiments, we achieved successful predictions of students' test outcomes based on behavioral learning data. The strengths of our dataset render it apt for analyzing the nexus between student conduct and academic performance, crafting personalized learning recommendations, and pursuing various other research pursuits.

Carbon nanomaterial is widely used in structural health monitoring due to the advantage of sensitivity and good mechanical properties. This study presents a novel approach employing carbon nanocomposite materials (CNM) to characterize deformation and damage evolution in physical modelling. As the primary measurement method, the CNM is used to investigate the deformation characteristics of a 200-400 m thick sandstone bed at a 1 km deep longwall mine. The sandstone unit is identified as an ultra-thick key stratum (UTKS), with its thicknesses varying across different mining panels of the UTKS. The results of CNM monitoring show that the UTKS remains stable even after a consecutive excavation of 900 m in width. This stability impedes the upward propagation of overlying strata failure, leading to minimal surface subsidence. The study demonstrates the huge potential of CNM in the mining area, which can be useful for investigating material damage in physical modelling studies. The findings suggest that the cumulative extraction width in individual mining areas of the mine should be controlled to avoid a sudden collapse of the UTKS, and that special attention should be paid to where the UTKS's thickness changes substantially. The substantial variation in UTKS thickness significantly impacts the pattern of overburden subsidence.

The capability of extracting information and analyze it into a common format is essential for performing predictions, comparing projects through cost benchmarking, and for having a deeper understanding of the project costs. However, the lack of standardization and the manual inclusion of the data makes this process very time-consuming, unreliable, and inefficient. To tackle this problem, a novel approach with a big impact is presented combining the benefits of data mining, statistics, and machine learning to extract and analyze the information related to railway costs infrastructure data. To validate the suggested approach, data from 23 real historical projects from the client network rail was extracted, allowing their costs to be comparable. Finally, some machine learning and data analytics methods were implemented to identify the most relevant factors allowing for costs benchmarking. The presented method proves the benefits of data extraction being able to gather, analyze and benchmark each project in an efficient manner, and deeply understand the relationships and the relevant factors that matter in infrastructure costs.

The environmental legacy of post-closure mine sites poses a significant risk to the sustainability of mining operations and natural resource development. This study aims to advance the understanding of sustainable mine site reclamation behavior in Canada by using multi-temporal Landsat satellite images to examine the long-term land cover changes at post-closure mine sites. Six representative post-closure mine sites were selected for the evaluation and comparison. The Normalized Difference Vegetation Index (NDVI) analysis, Landsat image classification, post-classification change detection, and Regrowth Index (RI) analysis were conducted to assess the speed and extent of landscape and vegetation recovery at the target mine sites. A significant vegetation recovery was quantified for the mine sites that have experienced active reclamation activities. In contrast, the post-closure mine area undergoing only the passive revegetation typically demonstrated a slow and minor increase in vegetation over time. The actively revegetated mine sites can typically be restored to a level that equals or better than the pre-mining situation. This work confirms that active reclamation and revegetation at post-closure mine sites is critically important in sustainable mining. The quantified mine site reclamation behavior and the relevant sustainable practices would be useful for evidence-based sustainable resource management in Canada.

This paper investigates if Corporate Social Responsibility (CSR) reports published by a selected group of Nordic companies are aligned with the Global Reporting Initiative (GRI) standards. To achieve this goal, several natural language processing, and text mining techniques were implemented and tested. We extracted strings, corpus, and hybrid semantic similarities from the reports and evaluated the models through the intrinsic assessment methodology. A quantitative ranking score based on index matching was developed to complement the semantic valuation. The final results show that Latent Semantic Analysis (LSA) and Global Vectors for Word Representation (GloVE) are the best methods for our study. Our findings will open the door to the automatic evaluation of sustainability reports which could have a strong impact on the environment.

Natural cycles underpin the very stuff of life. In this commentary we consider unnatural cycles: that is, anthropogenic activities which have a circularity, but whose nature is to have a detrimental effect on human health, exacerbating existing problems. Natural cycles have feedback loops, some of which have recently come to light, with an understanding that everything is connected in some way. In health, feedback loops are imperative in homeostatic mechanisms. However, in the unnatural cycle the feedback loops serve to reinforce (and in some cases amplify) negative problems. We offer a commentary on an unnatural cycle moving from air quality to lung function and back to air quality; we call this the lung disease unnatural cycle. We suggest where links occur, and where wider consideration of interactions between various disciplines can lead to breaking this unnatural (or vicious) cycle, changing it to a healthy cycle where individual health can be improved, along with better global scale outcomes. We suggest that many activities within this unnatural cycle occur within silos. However, the improved cycle incorporates joint activities at geological, health, and financial levels, to the mutual benefit of all, breaking the unnatural cycle, and improving health, life and financial costs.

Ethnopharmacology experts face several challenges when identifying and retrieving documents and resources related to their scientific focus. The volume of sources that need to be monitored, the variety of formats utilized, the different quality of language use across sources, present some of what we call “big data” challenges in the analysis of this data. This study aims to understand if and how experts can be supported effectively through intelligent tools in the task of ethnopharmacological literature research. To this end, we utilize a real case study of ethnopharmacology research, aimed at the Southern Balkans and Coastal zone of Asia Minor. Thus, we propose a methodology for more efficient research in ethnopharmacology. Our work follows an “Expert-Apprentice” paradigm in an automatic URL extraction process, through crawling, where the apprentice is a Machine Learning (ML) algorithm, utilizing a combination of Active Learning (AL) and Reinforcement Learning (RL), and the Expert is the human researcher. ML-powered research improved 3.1 times the effectiveness and 5.14 times the efficiency of the domain expert, fetching a total number of 420 relevant ethnopharmacological documents in only 7 hours versus an estimated 36-hour human-expert effort. Therefore, utilizing Artificial Intelligence (AI) tools to support the researcher can boost the efficiency and effectiveness of the identification and retrieval of appropriate documents.

Followed by the aspect of respect for rights 20, the social and environmental issue has a score of 19.9, the dialogue issue is 19. In short, the proximity plan indicates that the policies used regarding the issue of generating employment and economic growth (Empresa Mantaro Perú SAC), continue to be the strategic variables in the first instance, but they are far from the social actors (rural communities of Aco, Vico, Cruz Pampa and others) and environmental (Junín environmental dialogue table) and Ombudsman's Office (rights) that are currently closer together. The actor from the Archbishop of Huancayo referred to communication is one of the driving variables of power. The proximity plan shows us that the policies used regarding the issue of generating employment and economic growth (Empresa Mantaro Perú SAC), continue to be the strategic variables in the first instance, but that they are far from the social actors (Rural Communities of Aco , Vico, Cruz Pampa and others) and environmental (Junín Environmental Dialogue Table) and Ombudsman's Office (rights) that are currently closer together. The actor from the Archbishop of Huancayo referred to communication is one of the driving variables of power. The proximity plan shows us that the policies used regarding the issue of generating employment and economic growth (Empresa Mantaro Perú SAC), continue to be the strategic variables in the first instance, but that they are far from the social actors (Rural Communities of Aco , Vico, Cruz Pampa and others) and environmental (Junín Environmental Dialogue Table) and Ombudsman's Office (rights) that are currently closer together. The actor from the Archbishop of Huancayo referred to communication is one of the driving variables of power. but they are far from the social actors (rural communities of Aco, Vico, Cruz Pampa and others) and environmental (Junín Environmental Dialogue Table) and the Ombudsman's Office (rights) that are closer together today. The actor from the Archbishopric of Huancayo referred to communication, is within the driving variables of power. but they are far from the social actors (rural communities of Aco, Vico, Cruz Pampa and others) and environmental (Junín Environmental Dialogue Table) and the Ombudsman's Office (rights) that are currently closer together. The actor of the Archbishopric of Huancayo referred to communication, is within the driving variables of power.

The fact is quite transparent that almost everybody around the world is using android apps. Half of the population of this planet is associated with messaging, social media, gaming, and browsers. This online marketplace provides free and paid access to users. On the Google Play store, users are encouraged to download countless of applications belonging to predefined categories. In this research paper, we have scrapped thousands of users reviews and app ratings. We have scrapped 148 apps’ reviews from 14 categories. We have collected 506259 reviews from Google play store and subsequently checked the semantics of reviews about some applications form users to determine whether reviews are positive, negative, or neutral. We have evaluated the results by using different machine learning algorithms like Naïve Bayes, Random Forest, and Logistic Regression algorithm. we have calculated Term Frequency (TF) and Inverse Document Frequency (IDF) with different parameters like accuracy, precision, recall, and F1 and compared the statistical result of these algorithms. We have visualized these statistical results in the form of a bar chart. In this paper, the analysis of each algorithm is performed one by one, and the results have been compared. Eventually, We've discovered that Logistic Regression is the best algorithm for a review-analysis of all Google play store. We have proved that Logistic Regression gets the speed of precision, accuracy, recall, and F1 in both after preprocessing and data collection of this dataset.

An increase in the use of smartphones has laid to the use of the internet and social media platforms. The most commonly used social media platforms are Twitter, Facebook, WhatsApp and Instagram. People are sharing their personal experiences, reviews, feedbacks on the web. The information which is available on the web is unstructured and enormous. Hence, there is a huge scope of research on understanding the sentiment of the data available on the web. Sentiment Analysis (SA) can be carried out on the reviews, feedbacks, discussions available on the web. There has been extensive research carried out on SA in the English language, but data on the web also contains different other languages which should be analyzed. This paper aims to analyze, review and discuss the approaches, algorithms, challenges faced by the researchers while carrying out the SA on Indigenous languages.

Syanyonja village in the gold district of Busia, South East of the Republic of Uganda contain geologically epigenetic gold quartz vein deposits in carbonate-altered mafic metavolcanic rocks, deposited as quartz reefs in mineralized shear zones. In supracrustal rocks, alluvial gold is obtained from weathered auriferous quartz veins, which are of late orogenic granitic activity. The Syanyonja gold deposits have long been subjected to artisanal and small-scale gold mining (ASGM) by the locals for livelihood. This study determined the amount of mercury discharged with tailings into Namukombe stream, a major water body in Syanyonja village and investigated the impacts of ASGM on the mining population and the environment. Quantitation of mercury discharged with tailings was done by mass balance method. Field survey at the mining sites was done followed by administration of questionnaires to 50 stampeders in the village. The study indicates that about 8% of mercury mixed with auriferous materials are lost in tailings, accounting for an annual mercury release of about 1.757 kg into the environment. Socio-demographic results indicate that the majority of the mining population (64%) are male and ASGM have left health and environmental footprints, which directly or indirectly affects the population. The most common health problems among miners are malaria (36%) and abdominal pain (20%). The standard of living of the miners are evidently low, and most mines are characterized by school dropouts, prostitutes and thieves. Mining sites have deplorably poor hygiene, with evident burning of amalgams to recover gold. ASGM have been accompanied by wanton mowing down of vegetation, land degradation as well as mercuric pollution of water, air, land and aquatic ecosystems. It is suggested that the Ugandan government should re-enforce committees to follow up on ASGM activities, train artisans on sustainable gold mining using borax, magnets, sluice boxes as well as take up farming actively as an alternative.

Recently natural gas (NG) global market attracted much attention in case it is cleaner than oil, and simultaneously in most regions is cheaper than renewable energy sources. However, price fluctuations, environmental concerns, technological development, emerging unconventional resources, energy security challenges, and shipment are some of the forces that made the NG market more dynamic and complex. From a policy-making perspective, it is vital to uncover demand-side future trends. This paper proposed an intelligent forecasting model to forecast NG global demand, however investigating a multi-dimensional purified input vector. The model starts with a data mining (DM) step to purify input features, identify the best time lags, and to pre-process selected input vector. Then a hybrid artificial neural network (ANN) which equipped with genetic optimizer is applied to set up ANN’s characteristics. Among 13 available input features, six features (e.g. Alternative and Nuclear Energy, CO2 Emissions, GDP per Capita, Urban Population, Natural Gas Production, Oil Consumption) selected as the most critical feature via the DM step. Then, the hybrid prediction model is designed to extrapolate the consumption of future trends. The proposed model overcomes competitive models refer to different error based evaluation statistics. Besides, as the model proposed the best input feature set, results compared to the model which used the raw input set, with no DM purification process.

Bike sharing systems are a key element of a smart city as they have the potential for reducing pollutant emissions and traffic congestion thus substantially improving citizens’ quality of life. In these systems, bicycles are made available for shared use to individuals on a very short-term basis. They are rented in a station and returned in any other station with free docks. However, to achieve a satisfactory user experience, all the stations in the system must be neither overloaded nor empty. The occupancy level of the stations can be constantly monitored through IoT-based services. The goal of this work is to analyze occupancy level data acquired from real systems to discover situations of dock overload in multiple stations which could lead to service disruption. The proposed methodology relies on a pattern mining approach. A new pattern type, called Occupancy Monitoring Pattern (OMPs), is proposed to characterize situations of dock overload in multiple stations. Since stations are geo-referenced and their occupancy levels are periodically monitored, OMPs can be filtered and evaluated by considering also the spatial and temporal correlation of the acquired measurements. The results achieved on real Smart City data highlight the potential of these techniques in supporting domain experts in maintenance activities, such as periodic re-balancing of the occupancy levels of the stations, as well as in improving the user experience, such as suggesting alternative stations in the neighborhood.

Frass generated during the production of black soldier fly larvae is taking the interest of scientists and horticultural producers because it is a material from the biotransformation of organic waste, it has several nutrients that can be used by plants, and its recent focus on its biostimulant capacity. The thermal-composting process is a stabilization that improves physical and chemical properties of treated wastes, allowing better performance in plants compared to its fresh state. In this research, thermocomposted frass was evaluated as a germination substrate for kale seeds (Brassica olerasea). To achieve this, it was evaluated the phytotoxicity in different concentrations on the seeds, and seedlings were grown in a germination substrates mixed with 20, 40, 60, 80 and 100% of frass. The treatment with 20% frass showed the highest values of seedling height, stem diameter, number of leaves, length, and width of first true leaf and length and width of cotyledons, reduced the content of phenols and tannins antioxidants but the content of flavonoids increased compared to the control and the rest of mixtures.

After the end of colonial rule in 1997, the Hong Kong education system has been experiencing a policy reform on Senior Secondary Education (NSS) Curriculum commenced in September 2009 aiming to educate students as globalized Hong Kong citizens for the Western. Although previous scholars argued that it is hard to differentiate between domestic values and the Western values in account of curriculum development (Sewpaul, 2007), this study demonstrates an analysis using the dichotomy to investigate how local curriculum, especially in the post-colonial areas, embrace both the Western values and domestic values. Using content analysis on the NSS curriculum guides, this article evaluates the influence of the Western values and Chinese values in the NSS curriculum through investigating: (1) how the Western values and Chinese values was incorporated into the curriculum throughout its development (2) why some values are more significantly highlighted in the curriculum guide. Findings include that more Chinese values are involved in the curriculum documents. This may be because of the upholding of domestic values as a post-colonial city. The Western values which are academically related are greatly incorporated into the local curriculum.

Research indicates that painful experiences can significantly affect the fear of cancer recurrence among cancer survivors, which is a distressing concern that influences both physiological and psychological recovery. Yet, the role of potential factors and mechanisms in these relationships is not fully known. This cross-sectional study aimed to advance our comprehension of the associa-tions between total pain and the fear of recurrence in post-treatment cancer patients by examining two potential mediators: psychological flexibility and mentalization. Three hundred and thirty-five participants (aged 22 to 88, 49.1% female) who had finished their cancer treatment completed self-report assessments of total pain, fear of recurrence, psychological flexibility, and mentalization. The serial mediation analysis showed that all dimensions of total pain were positively related to the fear of recurrence indirectly through psychological flexibility and mentalization, in serial. Additionally, gender moderated these serial mediational effects. In line with the psychological flexibility model, personal capacities to face difficult internal/external problems and interpret one’s behaviour in motivational terms can counterbalance patients’ negative emotions and feelings related to the illness.

Mitochondria are semiautonomous organelles, which are pivotal for the energy metabolism of eukaryotic cells. These organelles contain their own genetic system (mtDNA, mitogenome) that encodes to some structural RNAs and organellar proteins. The biogenesis and function of mitochondria is tightly regulated by nuclear-encoded protein cofactors. The mtDNAs of land plants are characteristically large, with a complex mode of gene organization and expression, in particularly at the post-transcriptional level. To become functional, the primary organellar transcripts undergo extensive maturation steps, which include endo- and exo-nucleolytic cleavage, RNA-base editing, and both ‘cis’- and ‘trans’-splicing events. These essential processing steps rely on the activities of a large set of nuclear-encoded factors. Among these, the RNA helicases serve as key players in organellar (mt-)RNA metabolism. This review summarizes the important roles played by these factors in regulating the highly dynamic processes of transcription, RNA processing, and translation within the mitochondria of land plants. We further discuss recent advancements in understanding how dysregulation of mitochondrial RNA helicases affects the organellar gene expression, respiratory functions, and hence the growth, development and physiology of land plants.

P. palmata is a viable source of nutrients with bioactive properties. The present study determined the potential role of post-extraction ultrasonication on some compositional features and antioxidant properties of resulting extracts after sequential enzymatic/alkaline treatment. No significant difference was detected in terms of protein content and recovery as well as amino acid composition of the extracts. The nitrogen-to-protein conversion factor of 5 was found to be too high for the seaweed and its enzymatic/alkaline extracts. The extracts sonicated by bath for 10 minutes and not sonicated showed the highest and lowest total phenolic contents (p<0.05), respectively. The highest radical scavenging and lowest metal chelating activities were observed for the non-sonicated sample, as evidenced by IC50 values. The extract sonicated by bath for 10 minutes showed the most favorable in vitro antioxidant properties since its radical scavenging was not significantly different from that of not sonicated sample (p>0.05), whereas its metal chelating activity was significantly higher (p<0.05). To conclude, post-extraction ultrasonication by an ultrasonic bath for 10 minutes is recommended to increase phenolic content and improve antioxidant properties of enzymatic-alkaline extracts from P. palmata.

Kiribati is a Pacific Island nation with a widely dispersed population and one of the highest rates of leprosy worldwide. Single dose rifampicin post exposure prophylaxis (SDR-PEP) of leprosy contacts has reduced new case detection rates in controlled trials. In 2018, a SDR-PEP programme was introduced in Kiribati that included screening and chemoprophylaxis of household contacts of leprosy cases both retrospectively (2010-2017) and prospectively (2018 onwards). We conducted a retrospective audit to determine comprehensiveness, timeliness and feasibility of the SDR-PEP programme. Overall, 13,643 household contacts were identified (9,791 in the retrospective and 3,850 in the prospective cohort). In the retrospective cohort, 1,044 (11%) contacts were absent, 403 (4%) were ineligible for SDR and 42 new cases were detected (0.4%) Overall SDR coverage was 84.7%. In the prospective cohort, 164 (4%) contacts were absent, 251 (7%) were ineligible for SDR and 23 new cases were diagnosed (0.6%). Overall, SDR coverage was 88.1%. Across both cohorts, there were a total of 23 SDR refusals. The median time to SDR ad-ministration was 220 days (IQR 162-468) and 120 days (IQR 36-283) for the retrospective and prospective cohorts respectively. SDR was readily accepted in both cohorts. The new case detec-tion rate (0.5%) is consistent with that seen in other studies. Overall SDR coverage in both the retrospective and prospective phases met programmatic expectations.

Post-earthquake fire is considered as a catastrophic secondary disaster to personal and property safety. In this article, an evacuation assessment of a complex rail transit station under the post-earthquake fire for sustainable buildings was conducted from the internal environment and external rescue based on Building Information Modeling (BIM) and Fire Dynamic Simulation (FDS). The internal environment evacuation assessment simulation experiments were conducted in six hypothetical high-risk scenarios. Besides, the external rescue assessment was based on investigation of the route and the rescue time required during different periods of holidays and workdays. The results show that: (1) The influence caused by different size of fire area in the power distribution room is smaller than those in the train at the platform floor. (2) In the case of fire scenarios with the same fire area but different fire locations, the impact on temperature is greater than that on CO concentration in power distribution room. (3) It shows slight differences between single-floor fire and double-floor fire on evacuation of small area fire in power distribu-tion room. Meanwhile, optimized design recommendations are proposed to reduce the risk of emergency evacuation in both internal and external environments of rail transit stations for sustainable future buildings.

Non-coding RNAs, specifically microRNAs (miRNAs), exhibit altered expression and contribute significantly to the pathological processes observed in numerous diseases, encompassing various types of human cancers. Recent discoveries regarding miRNAs underscore their crucial roles in tumor pathogenesis and their responses to diverse therapeutic interventions. miRNAs are a subset of non-coding RNAs that regulate the expression of a multitude of genes post-transcriptionally and thus are potential diagnostic, prognostic, and predictive biomarkers and have also emerged as potential therapeutics. Because miRNAs are involved in the post-transcriptional regulation of their target mRNAs via repressing gene expression, defects in the miRNA biogenesis pathway and miRNA expression perturb the expression of a multitude of oncogenic or tumor-suppressive target genes that are involved in the pathogenesis of various cancers. As such, numerous miRNAs have been identified to be downregulated or upregulated in many types of cancers functioning as either oncomiRs or oncosuppressor miRs. Moreover, dysregulation of miRNA biogenesis pathways can also change miRNA expression and function in cancer. Profiling deregulated miRNAs in many cancer types has been shown to correlate with disease diagnosis, indicate optimal treatment options, and predict response to a specific therapy. Specific miRNA signatures can track all stages of disease including many cancer types and hold potential as biomarkers and therapeutic targets as well as therapeutics as miRNA mimics and inhibitors (antagomirs). As such, identifying specific miRNAs and mRNAs they regulate in many types of cancer along with downstream pathways can be used as potential therapeutic targets. Because a single miRNA can regulate a pool of targets involved in similar cellular processes and pathways, thereby amplifying the cellular response, this potentially makes miRNAs powerful therapeutics to restore cell functions that are altered as part of a disease phenotype. However, because a single miRNA regulates multiple gene expressions their cellular effects are numerous leading to potential off-target effects. Besides their intended targeting issues, there is a need to reduce the immunogenic reactions and determine the minimal dosing to achieve the desired effect while minimizing side effects. As such, a careful risk evaluation of miRNA therapeutics is needed to minimize off-target effects and to avoid overdosing of miRNA drugs. Furthermore, a limited understanding and validation of the specific roles of miRNAs limits their clinical application. In addition, there are many challenges concerning the delivery, sensitivity, specificity, toxicity, and applicability of the potential utility of miRNAs as therapeutic targets or therapeutics. Thus, future work will warrant if miRNAs can be used as cancer biomarkers as well as therapeutics for clinical application.

This academic article examines the effectiveness of international humanitarian intervention in conflict zones. Humanitarian intervention refers to the use of military, diplomatic, or economic measures by external actors to protect civilians, promote human rights, and alleviate suffering in areas affected by armed conflict. The research employs a comprehensive approach, incorporating content analysis, case study, and historical analysis in its methodology. It explores the key challenges faced by international actors in implementing humanitarian interventions and evaluates their impact on conflict resolution and post-conflict reconstruction. Through this thorough analysis of case studies and scholarly literature, this article contributes to the ongoing debate surrounding the efficacy of international humanitarian intervention.

Background There is a considerable overlap between clinical presentation of post-COVID-19 condition (PCC) and myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) . Many of their common symptoms can be linked to dysregulation of the autonomic nervous system (dysautonomia). This study aimed to objectively assess autonomic function in patients with PCC and in patients with ME/CFS whose disease was not related to COVID-19. Methods Synchronous recordings of an electrocardiogram, continuous dynamics of blood pressure in the digital artery using the Penaz method and ultrasound pneumotachography with the spirography function were obtained with spiroarteriocardiorhythmography method in 34 patients diagnosed with ME/CFS, in whom the onset of the disease was not associated with COVID-19, 29 patients meeting PCC definition and 32 healthy controls. Heart rate variability (HRV), systolic and diastolic blood pressure variability (RV), respiration variability were assessed at rest and in tests with fixed respiratory rates. At rest, indicators of baroreflex regulation were additionally determined (baroreflex effectiveness index and baroreflex sensitivity). Results The total power, power of very low frequency, low-frequency and high-frequency of RR interval variability at rest as well as baroreflex effectiveness index in up-ramps of arterial blood pressure and baroreflex sensitivity were significantly lower both in PCC and ME/CFS patients compared to HC. Several diagnostic prediction models for ME/CFS were developed based on HRV parameters. During slow breathing HRV parameters return to normal in PCC, but not in ME/CFS. Correlation analysis revealed a close relationship of HRV, RV parameters and baroreflex sensitivity with fatigue, but not with HADS depressive/anxiety symptoms in ME/CFS and PCC. Conclusion A similar pattern of HRV and baroreflex failure with signs of a pathological acceleration of age-dependant dysautonomia was identified in ME/CFS and PCC. The clinical, diagnostic and therapeutic implications of these findings are discussed, in light of previously described relationship between inflammation, vascular pathology, atherosclerotic cardiovascular disease and autonomic dysfunction.

This paper delves into the essential role of local communities in mitigating climate change in the post-pandemic landscape, with a specific focus on Barangay Acmac, Iligan City, Philippines. Employing a qualitative research approach, the study integrates in-depth literature reviews, informal interviews with participants (B-Part 1 to B-Part 20), meticulous observations, and thematic analysis. The research has uncovered crucial insights, including variations in community climate change awareness that emphasize the necessity of comprehensive education and awareness campaigns. It highlights the significance of community-driven initiatives, adaptation, and resilience as key elements in addressing climate change. Collaborative efforts between local government officials and the community are showcased as effective instruments of climate change mitigation, yet challenges persist, including addressing the perceived powerlessness of some community members. The methodology, participants, and theoretical framework offer a robust foundation for exploring community responses to climate change. Drawing from a diverse theoretical framework, this study presents a comprehensive understanding of the climate change and pandemic intersection. The findings firmly establish that local communities are pivotal players in the global endeavor to mitigate climate change, underscoring the need for collective action, extensive education, and shared responsibility. The post-pandemic era presents a unique opportunity for local communities, policymakers, barangay officials, and residents to champion climate education, foster community-driven initiatives, and empower individuals to enact change at the grassroots level. The collaborative spirit observed in Barangay Acmac in 2022 serves as a model for communities worldwide, offering a blueprint for addressing similar challenges. Overall, this paper concludes with a resounding call to action, advocating for the recognition of local communities as the driving force in climate change mitigation, stressing that the power to combat climate change resides within these communities. The time for action is now, and local communities are the catalysts of this essential mission.

In the ever-evolving landscape of higher education, the shift to hybrid teaching approaches has introduced novel challenges and opportunities for both educators and students. This qualitative study, grounded in a robust theoretical framework, combines in-depth literature reviews, informal interviews, observations, and thematic analysis to explore the multifaceted dynamics of hybrid language learning within a College of Arts and Sciences. By investigating the experiences of twenty students (referred to as 'S-Part 1' to 'S-Part 20'), the research delves into the core theme of 'Teacher-Student Interaction in Higher Education Institutions' and unpacks nine additional themes: 'Adaptability and Resilience,' 'Flexibility and Autonomy,' 'Connectivity Challenges,' 'Feelings of Isolation,' 'Technology Competence,' 'Personalized Learning,' 'Resource Accessibility,' 'Support Mechanisms,' and 'Positive Learning Experiences.' In this study, these themes encapsulate the diverse experiences and challenges encountered by teachers and college students as they navigate the evolving educational landscape. The insights derived from the study are underpinned by a robust theoretical framework and complemented by existing scholarly literature. As we delve into these themes, it becomes evident that they shed light on not only the intricacies of hybrid language learning but also the broader educational context, offering valuable guidance for educators, institutions, students, and researchers. Based on the ample results, recommendations are presented for school administrators, teachers, students, and future researchers to enhance the quality of hybrid teaching and learning, aligning it with the demands of the post-academic era. Overall, this study contributes to the ongoing dialogue on innovative pedagogical strategies and responsive educational environments, underlining the significance of teacher-student interaction in the new normal of higher education.

Background: Post-Covid condition can reduce activity and quality of life, resulting in a significant socioeconomic and health burden. Understanding its impact on patients' health is important for the development of personalized rehabilitation interventions. An independent association between obesity and post-covid condition was found because of complications and comorbidities. Methods: Sixteen patients with obesity and post-COVID symptoms (i.e., dyspnea, pain, poor sleep quality, muscle fatigue) admitted to the Istituto Auxologico Italiano, Piancavallo (VB), Italy, were recruited for a four-week rehabilitation program including conventional exercise therapy, nutritional intervention, psychological support and whole-body cryostimulation (WBC). Results: All participants attended all sessions of the program. Anthropometric data showed statistically significant changes in weight, waist circumference and body mass index. Biochemical analyses showed significant reductions in lipid and inflammatory profiles. There was a significant improvement in physical performance, reduction in pain and improvement in psychological well-being. Conclusion: A multidisciplinary rehabilitation protocol including WBC designed for patients with obesity and post-covid condition is safe and feasible. The overall improvements demonstrate that multidisciplinary rehabilitation was effective on post COVID patients and suggest that the use of WBC is safe and could play a role as a booster in rehabilitation programs.

As the oil industry increasingly turns to unconventional low-permeability and low-porosity reservoirs, precise post-fracture production prediction is a pivotal instrument in investment de-cision-making, formulation of energy policies, and promotion of environmental assessments. Nevertheless, despite extensive research spanning decades, the precise post-fracture production prediction based on logging parameters remains an intricate task. In this study, we gathered ex-tensive logging data and segmented the post-frac gas production during well testing on the first day to enrich the dataset. Nine pipelines were then architected using various techniques of data preprocessing, feature extraction, and advanced machine learning models. Hyperparameter op-timization was executed via the GridsearchCV. To assess the efficacy of diverse models, metrics including the coefficient of determination (R2), standard deviation (SD), and root mean square error (RMSE) were invoked. Among the several pipelines explored, the PS-NN exhibited excellent predictive capability in specific reservoir contexts (an R2 value of 0.94 and an RMSE of 48.15). In essence, integrating machine learning with logging parameters effectively assesses reservoir productivity at multi-meter formation scales. This strategy not only mitigates uncertainties en-demic to reservoir exploration but also equips petroleum engineers with monitoring on reservoir dynamics, thereby facilitating reservoir development. Additionally, this approach provides res-ervoir engineers with an efficient way of reservoir performance oversight.

The measures related to climate change and international technology transfer have been closely intervened as most international environmental instruments, including the United Nations Framework Convention on Climate Change and Kyoto Protocol as well as Paris Agreement which contain the provisions concerning environmentally friendly technology transfer. However, technologies have not been transferred adequately owing to the flaws in the relevant provisions and due to the fact that most technologies are possessed by transnational corporations and other private sectors. In order to undertake the commitment by the governments of developed countries and at the same time, to conform the commercial activities of their respective private sectors to the level of promised international obligation, an uniform international technology transfer agreement needs to be established separately, particularly under the World Trade Organization regime in Post Pairs-Agreement era. This paper first explores the defects and challenges found in provisions related to technology transfer in existing environmental agreements, and examines the possibilities of concluding an international technology transfer agreement both from theoretical and practical perspectives. climate change;environmentally sound technology;international technology transfer;Post Paris-Agreement Era

Myalgic Encephalomyelitis / Chronic Fatigue Syndrome (ME/CFS) is an acquired disease with significant morbidity that affects both children and adults. Effective tools to assess functional capacity (FC) are severely lacking which has significant consequences for timely diagnosis, assessments for patient disability benefits and assessing the impact and effectiveness of interventions. In interventional research the inability to assess FC can result in an incomplete assessment of the potential effect of the intervention. Specifically of concern is that if an intervention is effective in reducing symptom load, patients may increase their activity level to reach a pre-intervention symptom load. Thus, if FC is not accurately assessed, beneficial treatment outcomes may be missed. To address this issue, using extensive, repeated patient feedback we have developed a new questionnaire, FUNCAP, to achieve optimal FC assessment in ME/CFS patients. The questionnaire covers eight domains and activity types: A. Personal hygiene / basic functions, B. Walking / movement, C. Being upright, D. Activities in the home, E. Communication, F. Activities outside the home, G. Reactions to light and sound, and H. Concentration. Through five rounds of anonymous web-based surveys and a further test - retest validation round, two versions of the questionnaire were developed; a longer version comprising 55 questions (FUNCAP55) to improve diagnostic and disability benefit/ insurance FC assessments and a shorter version (FUNCAP27) for interventional research and less extensive FC assessments. FUNCAP may also be useful in other conditions where fatigue and PEM is present, such as Long Covid.

Earnings management and financial distress are two critical facets of financial management in corporations that have lately been the focus of thorough research from both scholars and practitioners. The purpose of the research is to evaluate the influence of financial distress on the earnings management of companies listed at the NSE during the pre- and post-COVID eras. Multiple linear regression was applied in this study to examine the causal association between earnings management and financial distress. To assess the effect of financial distress on earnings management, the study analyzed data from 37 listed companies during the pre-COVID and post-COVID periods. The methodology also takes into account control factors like firm size, GDP, and inflation. The pre-COVID research indicates a significant positive correlation between financial strain and earnings management. In contrast, the correlation between financial distress and earnings management is not statistically significant in the post-COVID analysis. However, research shows that firm size has a statistically significant negative influence on managing earnings. The results of this study show that smaller firms are more inclined to engage in earnings management during the post-COVID era than bigger ones.

The present review aimed to identify through what means neurologic injury can predispose individuals to Post-Traumatic Stress Disorder (PTSD). In recent years, comprehensive studies have helped to clarify which structures in the central nervous system (CNS) that, when damaged, lead to distinct PTSD symptoms–namely dissociative reactions or flashbacks. Our review narrowed its focus to three common neurologic injuries including traumatic brain injury (TBI), subarachnoid hemorrhage (SAH), and stroke. Beyond discussing the potential mechanisms by which neurotrauma may lead to PTSD, we summarized our current understanding of the pathophysiology of the disorder and discussed predicted associations between the limbic system and PTSD. In particular, the role of noradrenergic neuromodulatory signaling on the HPA axis as it pertains to fear memory recall needs to be further explored to better understand its effects on limbic structures in PTSD patients. This paper also described how damage to the CNS can alter the function of the limbic system and result in characteristic symptoms of PTSD like intrusive memories and acute psychological distress. Further, our review intended to draw attention to a lack of PTSD prognosis in TBI, SAH, and stroke patients who could benefit from early treatment. PTSD symptoms often compound with pre-existing issues further deteriorating health outcomes for these patients. It is ultimately our goal to clarify the relationship between neurotrauma and PTSD such that earlier diagnoses and appropriate treatment are observed in clinic.

(1) Background: COVID-19 had devastating effects on both physical and mental well-being, prompting the need for interventions. This study aimed to explore the impact of mindfulness-based stress reduction on mental health, mindfulness, and sleep disorders in COVID-19 survivors.; (2) Methods: In this interventional parallel study, the sample was selected university students in nursing and midwifery faculties using total population sampling. The intervention included providing counseling on mindfulness methods by trained experts. General health, mindfulness and sleep quality questionnaires were used for data collection. Data were analyzed using SPSS22, descriptive and interferential statistic. A significance level of 0.05 was considered; (3) Results: In this study, the mean age of the students was 20.29 ± 2.03 years. 69.5% of the participants were female and 96% were single. 71.3% were studying nursing and 67% had a history of Corona infection. The intervention was only effective on general health questionnaire and the mean score of this questionnaire was 1.7 higher than the control group; (4) Conclusions: the study indicated that MBSR intervention improves mindfulness, mental health, and sleep quality in COVID-19 survivors. Regular assessment and use of this intervention can help address long-term challenges and improve overall well-being.

(1) Background: Treatment of male stress incontinence in patients with prostate cancer treated with radical prostatectomy and adjuvant pelvic radiation is a therapeutic challenge. The efficacy and safety of the adjustable trans-obturator male system (ATOMS) in these patients is not well established, despite the general belief that outcomes are worse than in patients without radiation. (2) Methods: Retrospective multicenter study evaluating patients treated with silicone-covered scrotal port (SSP) ATOMS implant after radical prostatectomy and radiotherapy in nine different institutions between 2016 and 2022. The primary endpoint was dry patient rate, defined as pad-test ≤20 mL/day. The secondary endpoints were complication rate (defined using Clavien-Dindo classification), device removal and self-perceived satisfaction using the Patient Global Impression of Improvement (PGI-I) scale. Wilcoxon rank sum test, Fisher’s exact test and logistic regression were performed using stepwise method with a 0.15 entry and 0.1 stay criteria. (3) Results: 223 patients fulfilled criteria for inclusion, 12 (5.4%) had received salvage prostatectomy after radiation and 27 (12.1%) previous devices for stress incontinence. After ATOMS adjustment, 95 patients (42.6%) were dry and 36 (16.1%) had complications of any grade (grade I, n=20; grade II, n=11; grade III, n=5) during the first 3 months postoperatively. At a mean 36 ± 21 months follow-up the device was explanted in 26 (11.7%) patients. Regarding self-perceived satisfaction with the implant, 105 of 125 patients (84%) considered themselves satisfied (PGI-I 1 to 3). In univariate analysis dryness was associated to younger age (p=.06), primary prostatectomy (p=.08), no previous incontinence surgery (p=.02), absence of overactive bladder symptoms (p=.04), absence of bladder neck stricture (p=.001), no need of surgical revision (p=.008) and lower baseline incontinence severity (p=.0003). Multivariate analysis identified absence of surgical revision (p=.018), absence of bladder neck stricture (p=.05), primary prostatectomy (p=.07) and baseline incontinence severity (p<.0001) were independent predictors of dryness. A logistic regression model was proposed and internally validated. (4) Conclusions: ATOMS is an efficacious and safe alternative to treat male incontinence after radical prostatectomy and adjuvant radiotherapy. Factors predictive of dryness are identified in this complex scenario to allow better patient selection.

Informal caregivers are essential in supporting stroke survivors' recovery, but it is unclear whether community interventions can promote healthy eating behaviors and social participation among stroke survivors and their caregivers. The Train-Your-Brain (TYB) program's impact on adherence to national dietary guidelines and social participation among informal caregivers and stroke survivors were assessed in a quasi-experimental study. The TYB program had nine sessions, with one focusing on healthy lifestyle. Participants were prompted to discuss the topics covered, leading to social participation. Dietary habits were assessed using fruits and vegetables consumption questions, while social participation was measured using the Social Impact Measurement Framework. Mann-Whitney U-test and Wilcoxon Signed Rank Test were utilized to analyze the data. Eighteen informal caregivers and 27 stroke survivors participated in the program. Results showed a significant increase in fruit and vegetable intake and social participation in informal caregivers after the program, but there were no significant changes in stroke survivors. Informal caregivers play a crucial role in stroke survivors' lifestyle habits and in encouraging social connections with other stroke survivors. Thus, healthcare professionals should prioritize caregiver support and education to facilitate long-term dietary and social changes that can positively impact stroke survivor's recovery and quality of life.

A Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a debilitating chronic disease of unknown aetiology under growing interest now in view of the increasingly recognized post-COVID syndrome as a new entity with similar clinical presentation. We performed the first cross-sectional study of ME/CFS in community population in Russia and then described and compared some clinical and pathophysiological characteristics of ME/CFS and post-COVID syndrome as neuroimmune disorders. Of the cohort of 76 individuals who suggested themselves suffering from ME/CFS 56 subsequently were confirmed as having CFS/ME according to ≥1 of the 4 most commonly used case definition. Of the cohort of 14 individuals with post-COVID-19 syndrome 14 met diagnostic criteria for ME/CFS. The prevalence of clinically expressed and subclinical anxiety and depression in ME / CFS and post-COVID ME/CFS did not differ significantly from that in healthy individuals. Severity of anxiety / depressive symptoms did not correlate with the severity of fatigue neigther in ME / CFS nor in post-COVID ME/CFS, but the positive correlation was found between the severity of fatigue and 20 other symptoms of ME / CFS related to the domains of “post-exertional exhaustion”, “immune dysfunction”, “sleep disturbances”, "dysfunction of the autonomic nervous system", "neurological sensory / motor disorders" and "pain syndromes". Immunological abnormalities were identified in 12/12 patients with ME / CFS according to the results of laboratory testing. The prevalence of postural orthostatic tachycardia assessed by the active standing test was 37.5% in ME / CFS and 75.0% in post-COVID ME/CFS (the latter was higher than in healthy controls, p = 0.02) There was a more pronounced increase in heart rate starting from the 6th minute of the test in post-COVID ME/CFS compared with the control group. Assessment of the functional characteristics of microcirculation by laser doppler flowmetry revealed obvious and very similar changes in ME/CFS and post-COVID ME/CFS compared to the healthy controls. The identified pattern corresponded to the hyperemic form of microcirculation disorders, usually observed in acute inflammatory processes or in deficiency of systemic vasoconstriction influences.

Autonomic dysfunction is an increasingly recognised complication in chronic neurological conditions such as Parkinson’s disease , and other medical conditions, including diabetes mellitus, chronic fatigue syndrome, postural tachycardia syndrome with and without Ehlers-Danlos syndrome, fibromyalgia and recently Long Covid. Despite laboratory-based tests to evaluate normal and abnormal autonomic function, there are no home-based tests to record neuro-cardiovascular autonomic responses to common stimuli in daily life that are dependent on the normal functioning of the autonomic nervous system. We have developed an adapted blood pressure/heart Autonomic Profile (aAP) that can be used by an individual independently and repeatedly in a domiciliary setting to determine the physiological and symptomatic response to standing, food, and physical and mental (cognitive, emotional) activities. The aAP aids separating autonomic failure (often irreversible) from autonomic dysfunction. This helps the individual and attending healthcare professional understand the relationship between symptoms and common triggers in daily life and informs on self-management in debilitating conditions such as the postural tachycardia syndrome (PoTS) and Long Covid.

The importance of statistics in biological research is inevitable. The appliance of statistics is the most powerful tool to support the scientific hypothesis and to give credibility to biological research methodology, to interpret convoluted explanations and conclusions based on the research findings. The evidences from literatures point the persistent statistical errors, selection of non-proper statistical test with the consequence of misinterpretation of the scientific results which are published in international journals. One of the most extensively used statistical tests in the field of biology (preclinical and clinical) is analysis of variance (ANOVA). ANOVA test is in place of multiple T-tests to compare the means of more than two groups at a time; and there are some important points which biologists should be aware of them to avoid possible misinterpretation of results with the consequence of wrong conclusion. Accordingly, the aim of the current review is to help biologists to understand the basic concepts of ANOVA test and reach to a more valid interpretation of achieved results.

The prevalence of corona virus and the novel COVID-19 disease in the entire globe has exacerbated different impact on socioeconomic spectrum in the world, including water use pattern. Thus a research was conducted to examine the comparative use of water during pre- and post-COVID-19 lockdown pattern among post-primary schools in Iwo, Osun State, Nigeria. A survey was conducted among fifteen schools which were randomly selected, but with eight public and seven private schools for the investigation. Both descriptive and inferential statistical techniques were used in data analysis. The results revealed that the major source of water to the schools investigated is ground water which is obtained through hand-dug wells and boreholes. It was further discovered that there was increase in water use during post-COVID-19 lockdown era as a result of the directive by the government that clean water should be provided for hand-washing by all schools regardless of the owner to curtail the spread of COVID-19 disease in the country. One sample t-test also revealed that there was a significant difference in water use at (p<0.01) level. It is recommended that the government and other stakeholders in water sector to ensure that all-time and non-seasonal dependent source of water be provided rather than ground water source which is susceptible to variations in water yields from seasonal variations. This will enable continuous clean water supply, for all purposes, including COVID-19 protocols.

Here we report the effect of substrate, sonication process, and post-annealing on the structural, morphological, and optical properties of ZnO thin films grown in the presence of isopropyl alcohol (IPA) at temperature 30 – 65 ℃ by successive ionic layer adsorption and reaction (SILAR) method on both soda lime glass (SLG) and Cu foil. The X-ray diffraction (XRD) patterns confirmed the preferential growth thin films along (002) and (101) plane of wurtzite ZnO structure while grown on SLG and Cu foil substrate respectively. Both XRD and Raman spectra confirmed the ZnO and Cu-oxide phases of the deposited films. Scanning electron microscope (SEM) image of the deposited films shows compact and uniformly distributed grains for samples grown without sonication while using IPA at temperature 50 and 65 ℃. The post-annealing treatment improves the crystallinity of the films, further evident by XRD and transmission and reflection results. The estimated optical bandgaps are in the range of 3.37-3.48 eV for as-grown samples. Results revealed that high-quality ZnO thin films could be grown without sonication using IPA dispersant at 50 ℃, which is much lower than the reported results using the SILAR method. This study suggests that in the presence of IPA, the SLG substrate results in better c-axis oriented ZnO thin films than that of DI water, ethylene glycol, propylene glycol at the optimum temperature of 50 ℃. Air-annealing of the samples grown on Cu foils induced the formation of Cu_xO/ZnO junctions which is evident from the characteristic I-V curve including the structural and optical data.

The Ras family of proteins is tethered to the inner leaflet of the cell membranes which play an essential role in signal transduction pathways that promote cellular proliferation, survival, growth, and differentiation. KRas-4B, the most mutated Ras isoform in different cancers, has been under extensive study for more than two decades. Here we have focused our interest on the influence of cholesterol on the orientations that KRas-4B adopts with respect to the plane of the anionic model membranes. How cholesterol in the bilayer might modulate preferences for specific orientation states is far from clear. Herein, after analyzing data from in total 4000 ns-long MD simulations for four KRas-4B systems, properties such as the area per lipid and thickness of the membrane as well as selected radial distribution functions, penetration of different moieties of KRas-4B, and internal conformational fluctuations of flexible moieties in KRas-4B have been calculated. It has been shown that high cholesterol content in the PM favors OS1, exposing the effector-binding loop for signal transduction in the cell from the atomic level. We confirm that high cholesterol in the PM helps KRas-4B mutant stay in its constitutively active state, which suggests that high cholesterol intake can increase mortality and may promote cancer progression for cancer patients. We propose that during the treatment of KRas-4B-related cancers, reducing the cholesterol level in the PM and sustaining cancer progression by controlling the plasma cholesterol intake might be taken into account in anti-cancer therapies.

In this experimental report the biocompatibility of elastomeric scaffold structures made via stereolithography employing table-top 3D printer (Ember, Autodesk) and commercial resin FormLabs Flexible (FormLabs) was studied. The samples were manufactured using standard printing and development protocol, which is known to inherit cytotoxicity due to remaining non-polymerized remaining monomers, despite the polymerized material being fully biocompatible. Additional steps were taken to remedy this problem: the fabricated structures were soaked in isopropanol and methanol for different conditions (temperature, duration) in order to leach out the non-polymerized monomers. Also printed structures were UV exposed to assure maximum polymerization degree of the material. Post-processed structures were seeded with myogenic stem cells and the number of live cells was evaluated as an indicator for the material biocompatibility. The straightforward post-processing protocol enhances the biocompatibility by 7 times after 7 days soaking in isopropanol and methanol and is comparable to control (glass and polystyrene) samples. This proposes the approach as a novel and simple method to be widely applicable for dramatic cytotoxicity reduction of optically 3D printed micro-/nano-scaffolds for biomedical applications.

Clean production of renewable fuels is a great challenge of our scientific community. Iridium complexes have demonstrated a superior catalytic activity in the water oxidation (WO) reaction, which is a crucial step in water splitting process. Herein we have used a defective zirconium MOF with UiO-66 structure as support of a highly active Ir complex based on EDTA with formula [Ir(HEDTA)Cl]Na. The defects are induced by the partial substitution of tereftalic acid with smaller formiate groups. Anchoring of the complex occurs through a post-synthetic exchange of formiate anions, coordinated at the zirconium clusters of the MOF, with the free carboxylate group of the [Ir(HEDTA)Cl]-complex. The modified material was tested as heterogenous catalyst for the WO reaction by using Cerium Ammonium Nitrate as sacrificial agent. Although TOF and TON values are comparable to those of other iridium heterogenized catalysts, the MOF exhibits iridium leaching not limited at the first catalytic run, as usually observed, suggesting a lack of stability of the hybrid system under strong oxidative conditions.

Sustainable agriculture is essential to provide food security for a growing world population without further sacrificing the integrity of the environment. To make progress towards agricultural sustainability we must consider ecological and socioeconomic processes within the agricultural socio-ecosystem and involve stakeholders in the research process. We propose an innovative experimental approach for examining how natural regulation of ecosystems may provide an alternative to increasing external inputs in agriculture while improving the socio-economic welfare of farmers. These “social-ecological experiments” go further to participatory action research by not only involving stakeholders in the research process but also by manipulating simultaneously socioeconomic and ecological processes under real field conditions to give a faster route to sustainability. Social-ecological experiments are undertaken in real field conditions, explicitly involving stakeholders, and help untangle the drivers of social-ecological dynamics under various land management and farming practices. Social-ecological experiments are distinct from adaptive management and scenario-planning approaches as they highlight the interactions between ecological and social processes, manipulate the social and ecological processes shaping the system and show causal links between patterns and processes. As an example, we describe a social-ecological experiment for reducing herbicide use. Social-ecological experiments offer great opportunities for increasing stakeholders’ acceptance of environmental policies implemented through adaptive management. These experiments may help to identify management practices that optimize multiple objectives, deliver a portfolio of ecosystem services and satisfy key stakeholders.

As shown earlier, the algorithmic complexity, like Shannon information and Boltzmann entropy, tends to increase in accordance with the general law of complification. However, the algorithmic complexity of most material systems does not reach its maximum, i.e. chaotic state, due to the various laws of nature that create certain structures. The complexity of such structures is very different from the algorithmic complexity, and we intuitively feel that its maximal value should be somewhere between order and chaos. I propose a formula for calculation such structural complexity, which can be called - structuredness. The structuredness of any material system is determined by structures of three main types: stable, dissipative, and post-dissipative. The latter are defined as stable structures created by dissipative ones, directly or indirectly. Post-dissipative structures, as well as stable, can exist for an unlimited time, but at the micro level only, without energy influx. The appearance of such structures leads to the “ratchet” process, which determines the structuregenesis in non-living and, especially, in living systems. This process allows systems with post-dissipative structures to develop in the direction of maximum structuring due to the gradual accumulation of these structures, even when such structuring contradicts the general law of complification.

Concrete is more stable than filler as a false roof in a downward approach type infill quarry, but the mechanical model of a concrete false roof is not clear. As a result, there has been limited application. Therefore, it is necessary to establish the mechanical model of a concrete artificial false roof to enhance the stability of the downward approach infill quarry. This paper combines theoretical analysis, model derivation, finite element analysis and field application methods. Firstly, the basic assumptions of thin plate theory and elasticity mechanics are used as the premise to establish the differential equations of the deflection curve of the concrete thin plate false roof. Then, the three types of bending moment generation of the concrete thin plate false roof are explored. Secondly, the relationship between the location of dangerous surface and dangerous point in hard-supported weak-slab structure and soft-supported weak-slab structure is analyzed, and the type of uniform load and calculation model of concrete thin-slab false roof are improved. Finally, according to the established mechanical model of thin slab false roof, finite element analysis and field application were carried out. The results show that there are three types of deflection curve differential equations in the concrete thin slab false roof perpendicular to the strike, and three extreme points exist in the middle section of the thin slab and on both sides of the surrounding rock. The hazardous surface of the hard-supported weak-slab structure is present at the intersection of the encircling rock and the thin-slab fake ceiling, with the perilous point situated on the top of this surface. The hazardous surface of the soft-supported weak-slab structure can be found in the centre section of the thin-slab fake ceiling, with the perilous point being located at the bottom of this surface. Moreover, the damage mode for both structures is that of maximum tensile stress damage. Upon conducting a finite element analysis, the results indicate that the concrete's maximum tensile stress is significantly high.

Text annotation is an essential element of the natural language processing approaches. The manual annotation process performed by humans has several drawbacks, such as subjectivity, slowness, fatigue, and possibly carelessness. In addition, annotators may annotate ambiguous data. So, we developed the concept of automated annotation to get the best annotations using several machine-learning approaches. The proposed approach is based on an ensemble algorithm of meta-learners and meta-vectorizer techniques. The approach employs a semi-supervised learning technique for automated annotation, aimed at detecting hate speech. This involves leveraging various machine learning algorithms, including Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbors (KNN), and Naive Bayes (NB), in conjunction with Word2Vec and TF-IDF text extraction methods. The annotation process is performed using 13,169 Indonesian YouTube comments data. The proposed model used a Stemming approach using data from Sastrawi and also new data of 2,245 words. Semi-supervised learning uses 5%, 10%, and 20% of labeled data as compared to performing labeling based on 80% of the datasets. In semi-supervised learning, the model learns from the labeled data, which provides explicit information, and the unlabeled data, which offers implicit insights. This hybrid approach enables the model to generalize and make informed predictions even when limited labeled data is available, ultimately enhancing its ability to handle real-world scenarios with scarce annotated information. In addition, the proposed method uses a variety of thresholds for matching words labeled with hate speech ranging from 0.6, 0.7, 0.8, and 0.9. The experiment showed that the KNN-Word2ec model has the best accuracy value of 96.9% with a scenario of 5%:80%:0.9. However, several other methods have also accuracy above 90%, such as SVM and DT based on both text extraction methods in several test scenarios.

Sensor fusion algorithms and models have been widely used in recent times. Although research evidence has informed the use of sensor fusion models in diverse applications, there is room for improvement, especially in home-based health monitoring applications which require less supervision and technical knowledge of users. The present work compares data mining-based fusion software packages such as RapidMiner Studio, Anaconda, Weka, and Orange, and proposes a data fusion framework suitable for in-home applications. 574 privacy-friendly (binary) images and 1,722 datasets gleaned from thermal and Radar sensing solutions respectively, were fused using the software packages on instances of homogeneous and heterogeneous data aggregation. Experimental results indicated that the proposed fusion framework achieved an average Classification Accuracy of 84.7% and 95.7% on homogeneous and heterogeneous datasets respectively, with the help of data mining and machine learning models such as Naïve Bayes, Decision Tree, Neural Network, Random Forest, Stochastic Gradient Descent, Support Vector Machine, K-Nearest Neighbours and CN2 induction. Further evaluation of the sensor data fusion framework based on cross validation of features indicated average values of 94.4% for Classification Accuracy, 95.7% Precision and 96.4% for Recall.

children is challenging. An important advance is the 15-questions Pictorial Personality Traits Questionnaire for Children. A study on students from 10 to 13 years old in Poland validated the questionnaire but with some observations. Thus, there is a need for replication and stronger evi-dence in this age group. In Chile, we replicated the study with 3,423 4th-graders (9 to 12 years old). Teachers, in regular sessions, applied the questionnaire to their entire classes. We found similar results, including that asexual pictograms worked well in both genders. We also found positive relationships between conscientiousness, openness, and extraversion with mathematical performance. Furthermore, a combination of these three traits has a relationship with math performance twice as big as each trait alone. Moreover, students with the lowest scores in this combination of personality traits (6.6% of students) have 0.27 standard deviations less in mathematical performance than those with the highest score, which is 74.3% of students. To the best of our knowledge, this is the first time that a study finds a strong relationship between a combination of personality traits gathered with a 15-question questionnaire and fourth-graders´ math perfor-mances.

Bone metastasis detection and quantification on bone scintigraphy is challenging and clinical important for treatment and patient’s life quality. To develop a CNN based diagnostic system for automated segmentation on bone metastasis regions is non-trivial, especially for a small dataset. A dataset in house comprising 100 breast cancer patients and 100 prostate cancer patients is utilized for this research. The Double U-Net model is adapted through the integration of background removal, adding negative samples, and transfer learning methods for bone metastasis detection. The performance is investigated via 10-fold cross-validation and computed in pixel-wise scale. The best model we achieved has precision of 63.08%, sensitivity of 70.82%, and F1-score of 66.72%. The developed system has the potential to provide pre-diagnostic reports for physicians in final decisions and the calculation of the bone scan index (BSI) with the combination with bone skeleton segmentation.

Election outcomes have been predicted in the past with the help of various state-of-the-art language models. Sentiment analysis helps in establishing the opinions of the public about a particular subject, a popular experiment known as opinion mining. Twitter has grown in popularity and proven to be a key tool in mining people’s sentiments concerning election and other trending subjects of interest. The outcome of the just concluded Presidential election in Nigeria shifts the focus on Lagos State governorship election. In this study, we propose a Bidirectional Encoder Representations from Transformers (BERT) model for the sentiment analysis of governorship election in Lagos State Nigeria using Twitter data. A total of 800,000 personal and public tweets were scraped from twitter concerning the three prominent contesting candidates using carefully selected search queries. The tweets were preprocessed to avoid noise and inconsistencies. The preprocessed tweets were passed into the pretrained and finetuned BERT model. The result was analyzed to establish the sentiments of the public about the candidates. The social networks of the candidates were also analyzed. The parameter-tuning yield different results with different learning rates (LR). Results showed that the learning rate at 1e-7 gave the best performance and that the smaller the learning rate, the higher the accuracy but the larger the epoch size, the higher the accuracy.

Introduction: Social media platforms such as Facebook, LinkedIn, Twitter, among others have been used as a tool for staging protests, opinion polls, campaign strategy, medium of agitation and a place of interest expression especially during elections. Past studies have established people’s opinion elections using social media posts. The advent of state-of-the-art algorithms for unstructured text processing implies tremendous progress in natural language processing and understanding. Aim: In this work, a Natural Language framework is designed to understand Nigeria 2023 presidential election based on public opinion using Twitter dataset. Methods: Raw datasets concerning discourse around Nigeria 2023 elections from Twitter of 2,059,113 18 dimensions were collected. Sentiment analysis was performed on the preprocessed dataset using three different machine learning models namely: Long Short-Term Memory (LSTM) Recurrent Neural Network, Bidirectional Encoder Representations from Transformers (BERT) and Linear Support Vector Classifier (LSVC) models. Personal tweet analysis of the three candidates provided insight on their campaign strategies and personalities while public tweet analysis established the public’s opinion about them. The performance of the models was also compared using accuracy, recall, false positive rate, precision and F-measure. Results: LSTM model gave an accuracy, precision, recall, AUC and f-measure of 88%, 82.7%, 87.2% , 87.6% and 82.9% respectively; the BERT model gave an accuracy, precision, recall, AUC and f-measure of 94%, 88.5%, 92.5%, 94.7% and 91.7% respectively while the LSVC model gave an accuracy, precision, recall, AUC and f-measure of 73%, 81.4%, 76.4%, 81.2% and 79.2% respectively. Conclusion: The experimental results show that sentiment analysis and other Natural Language Processing tasks can aid in the understanding of the social media space. Results also revealed the leverage of each aspirant towards winning the election. We conclude that sentiment analysis can form a general basis for generating insights for election and modeling election outcomes.

Chronic venous disease (CVD) occurs in a substantial proportion of the world's population. If the onset of CVD looks like a cosmetic defect, then over time, it can develop into serious problems that require surgical intervention. The aim of the work is to use deep learning (DL) methods for automatic classification of the stage of CVD for self-diagnosis of a patient by using the image of the patient’s legs. The required for DL algorithms images of legs with CVD were obtained by using Internet Data Mining. For images preprocessing, the binary classification problem “legs - no legs” was solved based on Resnet50 with accuracy 0.998. The application of this filter made it possible to collect a data set of 11,118 good quality leg images with various stages of CVD. For classification of various stages of CVD according to CEAP classification, the multi classification problem was set and resolved by using two neural networks with completely different architecture - Resnet50 and DeiT. The model based on DeiT without any tuning shows better results than the model based on Resnet50 (precision = 0.770 (DeiT) and 0.615 (Resnet50)). To demonstrate the results of the work, a telegram bot was developed, in which fully functioning DL algorithms are implemented. This bot allows evaluating the condition of the patient's legs with a fairly good accuracy for the CVD classification.

The world is currently facing an outbreak of the monkeypox virus and confirmed cases have been reported from 74 countries. Following a recent “emergency meeting”, the World Health Organization just declared monkeypox a global health emergency. As a result, people from all over the world are using social media platforms, such as Twitter, for information seeking and sharing related to the outbreak, as well as for familiarizing themselves with the guidelines and protocols that are being recommended by various policy-making bodies to reduce the spread of the virus. This is resulting in the generation of tremendous amounts of Big Data related to such paradigms of social media behavior. Mining this Big Data and compiling it in the form of a dataset can serve a wide range of use-cases and applications such as analysis of public opinions, interests, views, perspectives, attitudes, and sentiment towards this outbreak. Therefore, this work presents MonkeyPox2022Tweets, an open-access dataset of more than 255,000 Tweets related to the 2022 monkeypox outbreak that were posted on Twitter since the first detected case of this outbreak on May 7, 2022. The dataset is compliant with the privacy policy, developer agreement, and guidelines for content redistribution of Twitter, as well as with the FAIR principles (Findability, Accessibility, Interoperability, and Reusability) principles for scientific data management.

The emergence of the Covid-19 pandemic has created both negative and positive changes, including implementing the circular economy across the globe. This Systematic Review follows the PRISMA statement and employs the Text Mining (Voyant Tools) technique to visualize and analyze the impacts of the Covid-19 on three aspects of the circular economy: economic, social, and environmental. The research employs Latent Dirichlet Allocation (LDA) to identify five major topics: (1) Shortage of medical equipment but high medical waste during Covid-19 due to the high demand in healthcare; (2) The long term negative impacts of lockdown on economic and social activities because of Covid-19 pandemic; (3) The reports on impacts of Covid-19 pandemic on the manufacturing globally, and their coping strategies and new opportunities; (4) The impacts of international restriction on the tourism, trade, shipping, and aviation industry, causing billion-dollar losses; (5) The reduction of pollution with health environment improvements with example cases from China and EU. The research identifies current literature gaps in the circular economy and Covid- 19 topics and encourages the application of text mining tools into researching to stimulate the research process and assist in communicating with the public.

Google play store allow the user to download a mobile application (app) and user get inspired by the rating and reviews of the mobile app. A recent study analyzes that user preferences, user opinion for improvement, user sentiment about particular feature and detail with descriptions of experiences are very useful for an application developer. However, many application reviews are very large and difficult to process manually. Star rating is given of the whole application and the developer cannot analyze the single feature. In this research, we have scrapped 282,231 user reviews through different data scraping techniques. We have applied the text classification on these user reviews. We have applied different algorithms and find the precision, accuracy, F1 score and recall. In evaluated results, we have to also find the best algorithm.

Causative agent of the viral pneumonia outbreak in the World identified as SARS-CoV2 leads to a severe respiratory illness like SARS and MERS. The pathogen spreading has turned into a pandemic dissemination and increased the mortality rate. Therefore, useful information is urgently necessary for effective control of the disease. Our study shows the existence of unvarying sequence with no mutation, including ORF1ab regions in 134 high-quality filtered genome sequences of SARS-CoV2 downloaded from the GISAID database. We have detected this sequence stability by using MAUVE analysis and pairwise alignment with Global Needleman Wunsch algorithm for each two different sequences, reciprocally. They also confirmed all these results were also with the Clustal W analysis. The first 6500 bp including ORF1ab region is an unvarying sequence. According to the highest TM-score of predicted protein structure analysis, the results showed it is very similar to spike protein of feline infectious peritonitis virus strain UU4 (PDB 6JX7) depending on amino acid sequences encoded by this unvarying region, and N-acetyl-D-glucosamine is the ligand of this protein. These results have confirmed that N-acetyl-D-glucosamine could play an important role in controlling of SARS-CoV-2. Also, our molecular docking analysis data supports a strong protein-ligand interaction of N-acetyl-D-glucosamine with spike receptor-binding domain bound with ACE2 (PDB 6M0J) and RNA-binding domain of nucleocapsid phosphoprotein (PDB 6WKP) from SARS CoV-2. Therefore, binding of N-acetyl-D-glucosamine to these proteins could inhibit SARS CoV-2’s replication. In the present work, we have suggested providing a repurposing compound for further in vitro and in vivo studies and new insights for ongoing clinical treatments as a new strategy to control of SARS-CoV-2 infections.

Background & Objectives: The global pandemic caused by novel coronavirus SARS-CoV-2 has claimed several lives worldwide. With the virus gathering rapid spread, the world has witnessed increasing number of confirmed cases and mortality rate, India is not far behind with approximately 37,000 affected individuals as on May 2, 2020. The ongoing pandemic has raised several questions which need to be answered by analysis of transmission of the infection. The data has been collected on daily basis from WHO and other sites. We have represented the data collated graphically using statistical packages, R and other online softwares. The present study provides a holistic overview of the spread of COVID-19 infection in India. Methods: Real-time data query was done based on daily observations using publicly available data from reference websites for COVID-19 and other government official reports for the period (15^th February, 2020 to April 28^th, 2020). Statistical analysis was performed to draw important inferences regarding COVID-19 trend in India. Results: A decrease in growth rate of cases due to COVID-19 in India post lockdown and improvement in recovery rate during the month of April was identified. The case fatality rate was estimated to be 3.22% of the total reported cases. State-wise analysis revealed a deteriorating situation in states of Maharashtra and Gujarat among others as cases continued to increase rapidly there. A positive linear correlation between the number of deaths and total cases and exponential relation between population density and number of cases reported per square km was established. Interpretation & Conclusions: Despite early preventive measures taken up by the Government of India, the increasing number of cases in India is a concern. This study compiles state-wise and district-wise data to report the daily confirmed cases, case fatalities and strategies adopted in the form of case studies. Understanding the transmission spread of SARS-CoV-2 in a diverse and populated country like India will be crucial in assessing the effectiveness of control policies towards the spread of COVID-19 infection.

Process (or business process) management systems fulfill defining, executing, monitoring and managing process models deployed on process-aware enterprises. Accordingly, the functional formation of the systems is made up of three subsystems such as modeling subsystem, enacting subsystem and mining subsystem. In recent times, the mining subsystem has been becoming an essential subsystem. Many enterprises have successfully completed the introduction and application of the process automation technology through the modeling subsystem and the enacting subsystem. According as the time has come to the phase of redesigning and reengineering the deployed process models, from now on it is important for the mining subsystem to cooperate with the analyzing subsystem; the essential cooperation capability is to provide seamless integrations between the designing works with the modeling subsystem and the redesigning work with the mining subsystem. In other words, we need to seamlessly integrate the discovery functionality of the mining subsystem and the analyzing functionality of the modeling subsystem. This integrated approach might be suitable very well when those deployed process models discovered by the mining subsystem are complex and very large-scaled, in particular. In this paper, we propose an integrated approach for seamlessly as well as effectively providing the mining and the analyzing functionalities to the redesigning work on very large-scale and massively parallel process models that are discovered from their enactment event logs. The integrated approach especially aims at analyzing not only their structural complexity and correctness but also their animation-based behavioral properness, and becomes concretized to a sophisticated analyzer. The core function of the analyzer is to discover a very large-scale and massively parallel process model from a process log dataset and to validate the structural complexity and the syntactical and behavioral properness of the discovered process model. Finally, this paper writes up the detailed description of the system architecture with its functional integration of process mining and process analyzing. And more precisely, we excogitate a series of functional algorithms for extracting the structural constructs as well as for visualizing the behavioral properness on those discovered very large-scale and massively parallel process models. As experimental validation, we apply the proposed approach and analyzer to a couple of process enactment event log datasets available on the website of the 4TU.Centre for Research Data.

Deep behavioral covariates (DBCs) introduced in this perspective form a new class of covariates that have the potential to enhance the performance of predictive models and improve analytics in clinical decision support applications. DBCs can measure how engaged a patient tends to be and how he or she tends to respond to events, and they may be highly predictive of the patient’s outcomes for a planned treatment. DBCs may potentially serve as a standard to measure patient engagement and activation and may form highly efficient mechanisms for improving patient outcomes.

Failure characteristics induced by unloading disturbance and the corresponding mechanical mechanism of the coal seam floor are important theoretical bases for water-bursting prevention from the floor of the coal seam and rock burst alarm in deep mining. However, the existing two-dimensional ground-pressure-control theory based on shallow mining cannot sufficiently guide deep-mining practices. In this study, the redistribution of mining-induced stress field in rocks surrounding the longwall face and mechanical behaviors of strata in deep mining are investigated through a combination of numerical simulation, physical simulation, and field measurement. Results demonstrate that mining-induced stress fields in the floor of the longwall face differ in space and time. Vertical stress unloading from top to bottom of the floor and horizontal stress unloading are relatively low. A concentration zone of high horizontal stress exists at stope boundaries. The critical yield load of rock stratum in the floor is determined through thin plate yield theory. Under the combined effect of concentrated high horizontal and vertical resilience stresses, strata in the floor fracture from seam to seam if the load increases to the minimum critical buckling value. Fractured strata slide along the fracture surface, which leads to floor heave. The stope floor shows evident time-delay progressive failure characteristics. The stress shell in the stope floor in deep mining is found to be a sensitive mechanical parameter that produces three-dimensional ground-pressure behavior in the floor. This ground-pressure behavior in the stope floor is controlled by the existence of the corresponding stress shell and effects induced by its space–time evolution. This study provides theoretical basis for the dynamic control of a hazard-inducing environment in engineering and minimizing or altering disaster-occurrence conditions during the construction engineering of the coal seam floor.

Strata and surface movement induced by mining under open-pit final slope is a huge threat to mine safety. Physical model test is an important method to study mining-induced strata and surface movement laws. Because of rock joints predominantly control rock mass deformation and failure, thus physical model test leaving out of consideration of rock joints is difficult to reflect the influence of rock joints on rock mass deformation. Therefore, this paper presents a three-dimensional physical model test considering simplified dominant rock joints. This test process includes the design of testing equipment, the construction of physical model with dominant rock joint sets, conduction of mining and deformation monitoring. And mining under eastern final slope of Yanqianshan iron mine was selected as a case to study the behavior of mining-induced strata and surface movement.

N-methyl-D-aspartate receptors (NMDARs) are the main class of ionotropic receptor for the ex-citatory neurotransmitter glutamate. It plays a crucial role in the permeability of Ca2+ ions and excitatory neurotransmission in the brain. Being heteromeric receptors, they are composed of sev-eral subunits, including two obligatory GluN1 subunits (eight splice variants) and regulatory GluN2 (GluN2A~D) and/or GluN3 (GluN3A~B) subunits. Widely distributed in the brain, they regulate other neurotransmission systems and are therefore involved in essential functions such as synaptic transmission, learning and memory, plasticity, excitotoxicity. The present review will discuss the physiopathological impacts of NMDAR and particularly GluN2A and GluN2B subu-nits in cognitive processes and neurodegenerative diseases (Alzheimer's disease, Huntington's disease, Parkinson's disease). The pharmacology of different NMDAR antagonists and their po-tentialities will be presented. In particular, a focus will be given on fluoroethylnormemantine (FENM), an investigational drug with very promising developments as a tomography radiotracer for NMDARs, an anxiolytic in post-traumatic stress disorder and a neuroprotective agent in Alzheimer's disease.

In recent decades, large-scale forest disturbances such as windthrow and bark beetle infestations have significantly impacted the carbon balance of Earth and forest ecosystems, mainly by altering soil respiration (SR) in addition to decreased gross primary productivity. SR is considered the second highest flux on Earth, emitting 78-98 Gt C yr-1 to the atmosphere, thus contributing to global warming. Post-disturbance development is marked by changes in the size of SR. To investigate the impact of bark beetle (Ips typographus) infestations in mountain spruce forests on SR, we measured SR at infested sites by bark beetles and adjacent undisturbed stands in the High Tatra Mountains (Slovakia). The measurements were conducted during the vegetation period (May-September) in 2016 and 2017, five to six years after the initial attack by bark beetles, at an altitudinal gradient (1100-1400 m a.s.l.). SR varied throughout different months and altitudes, and we observed that SR at infested sites reached higher values (in most cases) than at uninfested stands. Average monthly and altitude SR rates during both vegetation periods showed higher values at infested sites, but not all of them were statistically significant. We observed an insignificant change in SR rate between 2016 and 2017 at both infested and uninfested sites. The highest rates of SR were observed in 2016 from July to August at both infested and undisturbed sites. On the other hand, in 2017 the highest SR rates were observed in June and July at undisturbed sites and from June to August at infested sites. However, yearly SR from May-September at infested sites showed significantly higher rates than uninfested ones in both years. SR showed a decreasing pattern with elevation gain in 2016 at infested sites. However, this pattern was not observed in 2017. Based on our observation and results we postulate that SR rates at infested sites are boosted by needle fall and debris from dead and dying trees, which increases heterotrophic SR and compensates the decrease of autotrophic SR from tree roots. This study provides important insights into the impact of bark beetle infestations on SR and highlights the need for further research on the long-term effects of forest disturbances on carbon cycling.

The success of Toxoplasma gondii (intracellular parasite) host cell invasion relies on the apical complex, a specialized microtubule cytoskeleton structure associated with secretory organelles. The genome encodes three isoforms of both α- and β-tubulin which are altered by specific post-translational modifications (PTMs), changing the biochemical/biophysical proprieties of microtubules, and modulating their interaction with associated proteins. Tubulin PTMs are a powerful and evolutionarily conserved mechanism to generate tubulin diversity, forming a biochemical ‘tubulin code’ that can be ‘read’ by microtubule-interacting factors. The T. gondii tubulin PTMs are: α-tubulin acetylation, α-tubulin detyrosination, Δ5α-tubulin, Δ2α-tubulin, α- and β-tubulin polyglutamylation, and α- and α-tubulin methylation. Tubulin glutamylation is a key candidate to assist microtubule remodeling in Toxoplasma, being involved in the regulation of microtubule stability, dynamics, interaction with motor proteins, and severing enzymes. The correct balance of tubulin glutamylation is achieved by the coordinated action of polyglutamylases and deglutamylating enzymes. In this work we will review and discuss the current knowledge on T. gondii tubulin glutamylation. By in silico identification of mammalian protein orthologs we explored and updated the identification of putative proteins related to glutamylation, contributing to a better understanding of the role of tubulin glutamylation in T. gondii.

In this inclusive review article, the focus is on navigating the dynamic landscape of literature education, particularly the profound pedagogical shifts observed in the aftermath of the pandemic. The article synthesizes global insights, delving into how educators have adapted to hybrid models and embraced digital tools, fundamentally transforming the reading, analysis, and engagement with literary works. Exploring the integration of digital tools in literature education, the review uncovers the transformative impact of technology on traditional pedagogies. It analyzes the challenges and opportunities inherent in adapting literature education to the digital era, emphasizing the need for inclusive practices and adaptability. The article concludes by suggesting future research directions, encompassing AI integration, inclusive practices, cultural variations, long-term effects of hybrid models, and effective pedagogical strategies for technology integration. With a comprehensive approach, this review not only summarizes the current state of literature education but also discusses its implications, offering a holistic understanding of the contemporary educational landscape and setting the stage for future research endeavors.

The coronavirus disease 2019 (COVID-19), instigated by the zoonotic Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), rapidly transformed from an outbreak in Wuhan, China, into a widespread global pandemic. A significant post-infection condition, known as ‘long- COVID’, emerges in a substantial subset of patients, manifesting with a constellation of over 200 reported symptoms that span multiple organ systems. This condition, also known as 'post-acute sequelae of SARS-CoV-2 infection' (PASC), presents a perplexing clinical picture with far-reaching implications, often persisting long after the acute phase. While initial research focused on the immediate pulmonary impact of the virus, the recognition of COVID-19 as a multiorgan disruptor has unveiled a gamut of protracted and severe health issues. This review provides a summary of the primary effects of long COVID on the cardiovascular, nervous, and respiratory systems. It also delves into the mechanisms underlying these impacts and underscores the critical need for a comprehensive understanding of long COVID's pathogenesis.

Post-operative pain is the unpleasant sensory and emotional experience after surgery, its origin being both the inflammatory reaction induced by the surgical trauma on the abdominal wall, and the splanchnic pain, induced by the activation of nociceptors of the viscera, which are highly sensi-tive to distension, ischemia and inflammation. Nowadays, it is well recognized that there is a close relationship between the gut microbiome and pain perception, and that microbiome is highly af-fected by both anaesthesia and surgical manipulation. Thus, efforts to restore the disturbed micro-biome by supplementation with beneficial bacteria, namely probiotics, seems to be effective. In this article the knowledge gained mainly from experimental research on this topic is analyzed, the con-cluding message being that each probiotic strain works in its own way towards pain relief.

Post-harvest handling and ripening techniques have an impact on peach quality and shelf life, which has a big impact on consumer satisfaction and market competitiveness. This review paper examines recent advancements in ripening techniques and post-harvest technologies with the goal of improving peach fruit quality and sustainability. The factors impacting fruit quality after harvest and the physiological changes that occur throughout peach ripening are fully explained. For maintaining peach freshness and reducing losses, novel handling methods like modified atmosphere packaging (MAP) and controlled atmosphere storage (CAS) have been investigated. The study explores the possibilities of nanotechnology applications and low-temperature storage for prolonging shelf life while maintaining texture, flavor, and aroma. Examining the effectiveness and waste reduction potential of automation and mechanization in post-harvest activities. The paper also discusses ethylene-based and non-ethylene-based ripening agents, as well as innovative techniques including gene editing and RNAi technology for controlled and delayed ripening. Analyses are done on how these technologies affect the sensory qualities and nutrient profiles of peaches. The study emphasizes the significance of sustainable practices in the peach industry by focusing on waste reduction, resource efficiency, and circular economy integration. Post-harvest technologies' potential environmental consequences have been taken into consideration and the paper encourages more study and cooperation to increase sustainability.

Background: As the world tends to recover from the crisis created by the COVID-19 pandemic, vaccines have served as one of the most valuable tools of combat and large-scale immunization has helped to tide over the catastrophe. However, several reports of post -vaccination neurological complications around the globe have surfaced up in recent literature. This study was conducted with an aim to describe the spectrum of neurological ailments requiring hospitalization following COVID-19 vaccination.Methods: An ambispective observational study was carried out from March 2021 to February 2022 at a tertiary care institute with 25 patients who satisfied the inclusion criteria of a new onset neurological illness requiring hospitalization within 4 days to 42 days of vaccination. The patients were duly followed upto months after discharge.Results: The median age of the sample was 36 years and the median latency of onset of neurological events was 14 days. Guillain-Barré syndrome was documented in 11 patients; majority were acute motor axonal neuropathy (45.45%) followed by acute inflammatory demyelinating polyradiculoneuropathy (36.36%) and acute motor sensory axonal neuropathy (18.18%). Central nervous system demyelination was reported in 9 patients; two patients had acute disseminated encephalomyelitis, 2 patients were diagnosed with myelin oligodendrocyte glycoprotein antibody-associated disease, one patient with aquaporin-4 positive neuromyelitis optica spectrum disorder , and the rest were labelled as seronegative central nervous system demyelination. Vascular events were noted in 5 patients comprising of 2 cases each of cerebral venous sinus thrombosis and intracerebral hemorrhage and a case of Moyamoya angiopathy. All patients were conservatively managed and had a complete to partial recovery suggestive of a favorable outcome after 3 months. Conclusion: The pathomechanisms of post -vaccination neurological complications is yet to be elucidatedand any causal association attributable to vaccination warrants further research. Compared to the enormity of the vaccinated population, the adverse effects are miniscule to hinder and discourage vaccination.

The menopausal transition often accompanies distressing manifestations, including vasomotor symptoms, sleep disruptions, and depressive syndrome. Omega-3 fatty acids have emerged as a potential intervention to alleviate these symptoms. This review aimed to comprehensively assess the impact of omega-3 supplementation on vasomotor symptoms, sleep quality, and depression among postmenopausal women. We conducted a systematic search focusing on randomized control trials across the Cochrane Library, Web of Science, PubMed, CINAHL, EMBASE, and SCOPUS databases from inception to August 2023. Among the initial pool of 163 studies, 9 studies met the inclusion criteria and were incorporated into this review. Four studies suggested potential benefits of omega-3 intake for improving hot flashes and night sweats. Sleep quality outcomes displayed heterogeneity across the studies. Incorporating diverse scales such as the Hamilton Depression Rating Scale-21, the Patient Health Questionnaire depression scale, and Generalized Anxiety Disorder-7 for depression outcomes, the review found inconclusive evidence on omega-3's impact on depression. Overall, the combined analysis of these studies did not provide substantial evidence to support the efficacy of omega-3 fatty acids in improving vasomotor symptoms, sleep quality, and depression. Further well-designed RCTs with larger participant groups are crucial to validate and generalize these results.

Coronary artery disease (CAD) is a prevalent cardiovascular condition characterized by the accumulation of plaque within coronary arteries. This plaque accumulation obstructs blood flow to the heart, resulting in a heart attack. While distinct features of CAD have been identified, its causes remain largely unclear, with the exception of environmental and nutritional factors. This study aimed to investigate the connection between genetic factors and CAD, focusing on the thymidylate synthase (TS) gene, a gene involved in one-carbon metabolism. Therefore, our research targeted single nucleotide polymorphisms that could potentially impact TS gene expression and lead to dysfunction. Our findings strongly associate the TS 1100T&gt;C and 1170A&gt;G genotypes with CAD susceptibility. We observed that TS 1100T&gt;C polymorphisms increased disease susceptibility in several groups, while the TS 1170A&gt;G polymorphism displayed a decreasing trend for disease risk when interacting with clinical factors. Furthermore, our results demonstrate the potential contribution of the TS 1100/1170 haplotypes to disease susceptibility, indicating a synergistic interaction with clinical factors in disease occurrence. Based on these findings, we propose that polymorphisms in the 3'-UTR miRNA binding site of the TS gene could serve as clinically useful biomarkers for the prevention, prognosis, and management of CAD.

The blackening of cut carrots decreases their shelf life and causes severe economic losses but the molecular and metabolic mechanisms that underpin this phenomenon remain poorly characterized. Studies were therefore undertaken to determine the molecular and metabolic causes of the blackening. The susceptibility of blackening was dependent on the period of time that the crop was stored underground prior to harvest. The structure of the cell walls in the black regions was substantially changed compared to the orange regions. The black regions of carrot batons had decreased immunodetection of xyloglucan, HG-pectin, RG-I pectin, galactan and arabinan but had higher levels of lignin and phenolic compounds compared to the orange regions. Transcript profiling analysis revealed that phytohormone signalling processes were activated in the black regions. Transcripts associated with auxin signalling and ethylene-responsive transcription factors were increased in the black regions. In contrast, the levels of transcripts encoding proteins associated with secondary metabolism were decreased in the black regions. These findings implicate ethylene and auxin-related processes in the control of the primary to secondary metabolism shift that results in lignification and cell wall disruption that underpin the blackening process.

A developing energy-saving approach of cold sintering in a pure aqueous medium was applied to the preparation of α-Al2O3 ceramics and performed on spark plasma sintering equipment. The initial γ-Al(OH)3 and γ-AlOOH powders and the cold-sintered ceramics were studied by X-ray diffraction analysis, infrared spectroscopy, thermal analysis, and scanning electron microscopy to reveal the chemical and structural transformations they experienced during the cold sintering. At 450°C and 70 MPa, initial γ-AlOOH transformed into a fragile α-Al2O3 material. Porous α-Al2O3 ceramics with about 60% porosity was obtained after cold sintering of γ-Al(OH)3 in the same conditions combined with subsequent annealing at 1250°C for 3 h. The role of water molecules in the studied processes was considered as the enhancement of structural mobility in the cold-sintered material due to its reversible hydroxylation similar to earlier investigated supercritical water action on the precursors during α-Al2O3 formation. Further improvement of the cold sintering setup and regimens would open prospects in α-Al2O3 ceramics manufacturing by an ecologically benign route.