Browse Articles
Discover research articles across all indexed journals
Design and realization of compressor data abnormality safety monitoring and inducement traceability expert system
Centrifugal compressors are widely used in the oil and natural gas industry for gas compression, reinjection, and transportation. Fault diagnosis and identification of centrifugal compressors are crucial. To promptly monitor abnormal changes in compressor data and trace the causes leading to these data anomalies, this paper proposes a security monitoring and root cause tracing method for compressor data anomalies. Additionally, it presents an intelligent system design method for fault tracing in compressors and localization of faults from different sources. This method starts from petrochemical big data and consists of three parts: fault dynamic knowledge graph construction, instrument data sliding fault-tolerant filtering, and the fusion and reasoning of fault dynamic knowledge graph and instrument data variation monitoring. The results show that this method effectively overcomes the problems of false alarms and missed alarms based on fixed threshold alarm methods, and achieves 100% classification of two types of faults: non starting of the drive machine and low oil pressure by constructing a PCA (Principal Component Analysis)—SPE (Square Prediction Error)—CNN (Convolutional Neural Network) classifier. Combined with dynamic knowledge graph and NLP (Natural Language Processing) inference, it achieves good diagnostic results.
Moroccan natural products for multitarget-based treatment of Alzheimer’s disease: A computational study
Alzheimer’s disease is a neurodegenerative disorder that impairs neurocognitive functions. Acetylcholinesterase, Butyrylcholinesterase, Monoamine Oxidase B, Beta-Secretase, and Glycogen Synthase Kinase Beta play central roles in its pathogenesis. Current medications primarily inhibit AChE but fail to halt or reverse disease progression due to the multifactorial nature of Alzheimer’s. This underscores the necessity of developing multi-target ligands for effective treatment. This study investigates the potential of phytochemical compounds from Moroccan medicinal plants as multi-target agents against Alzheimer’s disease, employing computational approaches. A virtual screening of 386 phytochemical compounds, followed by an assessment of pharmacokinetic properties and ADMET profiles, led to the identification of two promising compounds, naringenin (C23) and hesperetin (C24), derived from Anabasis aretioides. These compounds exhibit favourable pharmacokinetic profiles and strong binding affinities for the five key targets associated with the disease. Density functional theory, molecular dynamics simulations, and MM-GBSA calculations further confirmed their structural stability, with a slight preference for C24, exhibiting superior intermolecular interactions and overall stability. These findings provide a strong basis for further experimental research, including in vitro and in vivo studies, to substantiate their potential efficacy in Alzheimer’s disease.
The astonishing scientists who starved to protect plants during the Second World War
Universal parent-focused child sexual abuse prevention: A quasi-experimental protocol
Background Child sexual abuse (CSA) is a significant public health concern, and there is a lack of universal, evidence-based primary prevention interventions that extend beyond a focus solely on children. Parents remain a consistently underutilized target for primary prevention efforts aimed at mitigating CSA despite their unique relationship and close proximity to their children. CSA risk is not confined to any specific demographic, and its effects on affected children are well-documented, significantly impacting numerous dimensions of their wellbeing. Thus, there is a clear and urgent need to address this gap in prevention strategies. Methods This study will use a quasi-experimental design (target N = 412) to examine potential gains in CSA-related awareness and intentions to use protective behaviors among parents who participate in a universal parent-focused CSA prevention workshop, Smarter Parents. Safer Kids., compared to those who do not. Participants in both the control (n = 206) and experimental group (n = 206) will complete 3 survey assessments: Survey 0 (baseline), Survey 1 (1-month), and Survey 2 (3-month follow-up). The experimental group will participate in a Smarter Parents. Safer Kids. workshop between the Surveys 0 and 1. We will use data collected from the baseline to measure potential mediators of CSA-related awareness and intention to use protective and preventive behaviors. In adjacent efforts to enhance the curriculum’s reach with future dissemination and implementation, we will also explore the impact of recruitment materials and strategies on parental engagement. Conclusion Results of this study will advance efforts to implement parent-focused CSA prevention with a universal audience.
Affective polarization in a word: Open-ended and self-coded evaluations of partisan affect
The literature finds that partisanship drives negative emotional evaluations of out-partisans. Yet, scholars base these insights on measures–like thermometers, candidate evaluations, and social-distance measures–that discount the sentiment attached to individuals’ negative attitudes. We introduce a unique measure of affect capturing the motivation underpinning partisans’ attitudes. Our measure asks respondents for one-word to describe voters in their party and the opposing party. Then respondents code the sentiment behind their word choice themselves. Together, our measure produces qualitative and quantitative measures of respondents’ affect. We find that our self-coded open-ended measure has strong face validity and correlates strongly with existing affect measures. This measure advances our understating of partisan affect by allowing scholars a window into respondents’ state of mind. Scholars can easily apply our measure’s procedure beyond partisanship to other groups of interest.
Impacts of host phylogeny, diet, and geography on the gut microbiome of rodents
Mammalian gut microbial communities are thought to play a variety of important roles in health and fitness, including digestion, metabolism, nutrition, immune response, behavior, and pathogen protection. Gut microbiota diversity among hosts is strongly shaped by diet as well as phylogenetic relationships among hosts. Although various host factors may influence microbial community structure, the relative contribution may vary depending on several variables, such as taxonomic scales of the species studied, dietary patterns, geographic location, and gut physiology. The present study focused on 12 species of rodents representing 3 rodent families and 3 dietary guilds (herbivores, granivores, and omnivores) to evaluate the influence of host phylogeny, dietary guild and geography on microbial diversity and community composition. Colon samples were examined from rodents that were collected from 7 different localities in Texas and Oklahoma which were characterized using 16S rRNA gene amplicon sequencing targeting the V1-V3 variable regions. The microbiota of colon samples was largely dominated by the family Porphyromonadaceae (Parabacteriodes, Coprobacter) and herbivorous hosts harbored richer gut microbial communities than granivores and omnivores. Differential abundance analysis showed significant trends in the abundance of several bacterial families when comparing herbivores and granivores to omnivores, however, there were no significant differences observed between herbivores and granivores. The gut microbiotas displayed patterns consistent with phylosymbiosis as host phylogeny explained more variation in gut microbiotas (34%) than host dietary guilds (10%), and geography (3%). Overall, results indicate that among this rodent assemblage, evolutionary relatedness is the major determinant of microbiome compositional variation, but diet and to a lesser extent geographic provenance are also influential.
The impact mechanism of geopolitical risks on ESG performance: The moderating effects of investor attention and government subsidies
The impact of geopolitical risks (GPR) on enterprises is significant, yet the existing literature lacks a comprehensive understanding of how GPR affects environmental, social, and governance (ESG) performance. This study addresses this gap by analysing data from Chinese enterprises over the period 2009 to 2021. It empirically examines the impact of GPR on ESG performance and explores the underlying mechanisms. Specifically, the analysis considers the roles of investor attention and government subsidies as moderating factors. The results indicate that GPR inhibits corporate ESG performance. State-owned enterprises are found to mitigate these adverse effects, while privately-owned enterprises tend to exacerbate them. Mechanism tests reveal that GPR negatively impacts ESG performance by increasing financing constraints and reducing financial performance. Furthermore, increased investor attention and government subsidies can alleviate the negative effects of GPR on ESG performance. These findings offer valuable insights for organisations, governments, and stakeholders, enabling them to better respond to GPR and achieve sustainable development.
Air pollution and brain damage: what the science says
Prevalence of keratoconus and keratoconus suspect, and their characteristics on corneal tomography in a population-based study
Keratoconus (KC) is a progressive corneal disorder resulting in severe visual impairment. We aimed to determine the prevalence and corneal tomographic characteristics of KC and keratoconus suspect (KCS) in a population-based study, and to construct discrimination models with or without corneal tomography. A total of 1,544 eyes (822 participants aged ≥35 years) were evaluated using data from the Yamagata Study (2015–2017). Systemic and ophthalmological examinations including corneal tomography with swept-source anterior segment optical coherence tomography (AS-OCT) were conducted to determine the prevalence and corneal tomographic characteristics of KC and KCS. In addition, data on 766 eyes were used to construct discrimination models with or without corneal tomography. In results, KC was diagnosed in six (0.85%) participants, and KCS was diagnosed in 27 (1.46%) participants. The values including corneal power, keratometric cylinder, corneal central and thinnest thickness, corneal asymmetry, higher-order irregularity, and their inter-eye differences were associated with KC and KCS. The areas under the receiver operating characteristic curves for the three multivariate discrimination models (without corneal tomography, with corneal tomography, and without corneal tomography + inter-eye difference models) for participants with KC or KCS were 0.848, 1.000, and 0.930, respectively. When corneal tomography is unavailable, inter-eye differences in corneal parameters may be useful screening tools for KC and KCS.
Remove subsidies to solve India’s fertilizer-overuse problem
A new STI in the city: MPOX in Barcelona. First outbreak (5/2022-5/2023) and subsequent resurgence
Background In May 2022, after the suspension of the mobility restrictions due to the COVID-19 pandemic, the first outbreak of MPOX virus, transmitted from human to human, was detected outside of Africa, affecting mostly sexually active men who have sex with men. Our aim is to report the first outbreak of MPOX in Barcelona city in the period from 5/2022 to 5/2023 and the subsequent surge of cases in 8/2023. Methods We performed a descriptive study of all notified cases in city residents, obtained through epidemiological surveys. The analyses are presented for the hospitalized cases and the key population of men who have sex with men. Results Of 2037 notified cases, 82.6% were confirmed. The cumulative incidence in the general population was 1.03 (95%CI 1.00–1.06) per 1000 inhabitants and 2.13 (2.01–2.17) per 1000 in men. Men were older than women, with a median age of 37 years (interquartile range 31–43). While 84.5% of men reported having sex with partners of the same gender, 70.9% of women only reported sex with partners of the opposite gender. Complications occurred in 4.1% of infected persons, 1.6% required hospitalization, and no deaths were registered. Georeferencing was highly associated with gay venues. Gay, bisexual and other men who have sex with men (GBMSM) accounted for most cases and severe cases, and were associated with attending public sex venues and not providing contact tracing information. Digital and printed prevention campaign materials were developed for GBMSM. Discussion The 2022 MPOX outbreak posed a major challenge to surveillance and sexual health services worldwide. With new cases and re-infections on the rise, MPOX may become a regular infection to be incorporated in STI testing and management guidelines. Barcelona has some characteristics that may facilitate the occurrence and spread of emergencies related to sexual health.
“Many old people taking care of old people”: Experiences of older adults after Hurricane María in Puerto Rico
The Puerto Rican population has presented demographic changes resulting in a greater proportion of older adults than almost any other country in the world, with an estimated 28% of the total population being over 60 years of age. A key public health issue in Puerto Rico (PR) is older adults’ mental health and wellbeing. Located in the Caribbean, PR is prone to natural hazards such as hurricanes, which are a known threat to older adults’ overall health and wellbeing. However, the needs of older adults in PR and their mental health have been largely neglected, especially in less visible and marginalized rural areas. Therefore, the main objective of this study is to examine the challenges and needs of older adults following Hurricane María in the rural towns of Adjuntas and Castañer, PR. We implemented an exploratory qualitative research design informed by the principles of Liming methodology. We used semi-structured interviews to gather data and analyzed it using thematic analysis. A total of 25 participants engaged in our interview process including older adults (n = 15) and community caregivers and leaders that provide services to older adults (n = 10). We present three main themes regarding older adults’ experiences after Hurricane María: 1) Challenges to wellbeing (i.e, loneliness, lack of resources), 2) Relational solidarity (i.e., communal support, equitable resource sharing) and 3) La brega (i.e., adaption and autogestión). Older adults in Adjuntas and Castañer addressed the emergent challenges after Hurricane María such as loneliness and lack of resources by relying on their communities. These findings highlight the role of community support as a key component to understand and foster older adults’ wellbeing following a natural hazard.
Optimizing antibiotic stewardship and reducing antimicrobial resistance in Central Asia: A study protocol for evidence-based practice and policy
Antimicrobial resistance (AMR) poses a significant global health challenge, prompting the World Health Organization (WHO) to stress the importance of monitoring antibiotic consumption and sales to address AMR effectively. This study protocol aims to optimize antibiotic stewardship and combat AMR in Central Asia through evidence-based practices and policies. The protocol includes objectives such as conducting systematic reviews of interventions to promote judicious antibiotic use, assessing antibiotic consumption trends, and investigating antibiotic overuse practices among healthcare providers. The study aims to raise awareness among stakeholders to enhance appropriate antibiotic prescribing practices. By establishing regulatory frameworks, monitoring policies’ effectiveness, and providing training programs for healthcare professionals, this study seeks to contribute to the global efforts in combating AMR and promoting prudent antibiotic use.
How science recruiters and job applicants can get on the same page
Utilizing a novel fecal sampling method to examine resistance of the honey bee (Apis mellifera) gut microbiome to a low dose of tetracycline
Disruption of host-associated microbial communities can have detrimental impacts on host health. However, the capacity of individual host-associated microbial communities to resist disturbance has not been well defined. Using a novel fecal sampling method for honey bees (Apis mellifera), we examined the resistance of the honey bee gut microbiome to disruption from a low dose of the antibiotic, tetracycline (4.5 μg). Prior to the experiment, bacterial communities from fecal samples were compared to communities from dissected whole guts of the same individuals to ensure fecal samples accurately represented the gut microbiome. Fecal samples were collected from lab-caged honey bees prior to, and five days after, tetracycline exposure to assess how antibiotic disturbance affected the communities of individuals. We used metrics of alpha and beta diversity calculated from 16S rRNA gene amplicon sequences to compare gut community structure. Low dose tetracycline exposure did not consistently change honey bee gut microbiome structure, but there was individual variation in response to exposure and specific taxa (one ASV assigned to Lactobacillus kunkeei and one ASV in the genus Bombella) were differentially abundant following tetracycline treatment. To assess whether individual variation could be influenced by the presence of tetracycline resistance genes, we quantified the abundance of tet(B) and tet(M) with qPCR. The abundance of tet(M) prior to tetracycline treatment was negatively correlated with change in community membership, assessed by difference in Jaccard dissimilarity over the five-day experiment. Our results suggest that the honey bee gut microbiome has some ability to resist or recover from antibiotic-induced change, specific taxa may vary in their susceptibility to tetracycline exposure, and antibiotic resistance genes may contribute to gut microbiome resistance.
CohortDiagnostics: Phenotype evaluation across a network of observational data sources using population-level characterization
Objective This paper introduces a novel framework for evaluating phenotype algorithms (PAs) using the open-source tool, Cohort Diagnostics. Materials and methods The method is based on several diagnostic criteria to evaluate a patient cohort returned by a PA. Diagnostics include estimates of incidence rate, index date entry code breakdown, and prevalence of all observed clinical events prior to, on, and after index date. We test our framework by evaluating one PA for systemic lupus erythematosus (SLE) and two PAs for Alzheimer’s disease (AD) across 10 different observational data sources. Results By utilizing CohortDiagnostics, we found that the population-level characteristics of individuals in the cohort of SLE closely matched the disease’s anticipated clinical profile. Specifically, the incidence rate of SLE was consistently higher in occurrence among females. Moreover, expected clinical events like laboratory tests, treatments, and repeated diagnoses were also observed. For AD, although one PA identified considerably fewer patients, absence of notable differences in clinical characteristics between the two cohorts suggested similar specificity. Discussion We provide a practical and data-driven approach to evaluate PAs, using two clinical diseases as examples, across a network of OMOP data sources. Cohort Diagnostics can ensure the subjects identified by a specific PA align with those intended for inclusion in a research study. Conclusion Diagnostics based on large-scale population-level characterization can offer insights into the misclassification errors of PAs.
Social-media bans won’t work — there are better ways to keep kids safe
A comparison of disseminated intravascular coagulation scoring systems and their performance to predict mortality in sepsis patients: A systematic review and meta-analysis
Background Disseminated intravascular coagulation (DIC) is a common complication in sepsis patients which exacerbates patient outcomes. The prevalence and outcomes of DIC in sepsis is wide-ranging and highly depends on the severity of the disease and diagnostic approaches utilized. Varied diagnostic criteria of DIC have been developed and their performance in diagnosis and prognosis is not consistent. Therefore, this study aimed to determine the score positivity rate and performance of different DIC scoring systems in predicting mortality in sepsis patients. Methods Four databases, including Medline (through PubMed), Scopus, Embase, and Web of Science were searched for studies that determined DIC in sepsis patients using the three scoring systems namely: the International Society on Thrombosis and Hemostasis DIC (ISTH-DIC) criteria, the Japanese association for acute medicine DIC (JAAM-DIC) criteria, and the sepsis-induced coagulopathy (SIC) criteria. A random-effect meta-analysis was performed with a 95% confidence interval (CI). Subgroup analysis was conducted in view of geographic region and sepsis stages. the protocol was submitted to the Prospective Register for Systematic Reviews (PROSPERO) with an identifier (CRD42023409614). Results Twenty-one studies, published between 2009 and 2024, comprising 9319 sepsis patients were included. The pooled proportion of cases diagnosed as positive using ISTH-DIC criteria, JAAM-DIC criteria, and SIC were 28% (95% CI: 24–34%), 55% (95% CI:42–70%), and 57% (95% CI: 52–78%), respectively. The pooled mortality rates were 44% (95% CI:33–53%), 37% (95% CI: 29–46%), and 35% (95% CI: 29–41%), respectively. The pooled sensitivity and specificity of ISTH-DIC to predict mortality were 0.43 (95% CI: 0.34–0.52), and 0.81 (95% CI: 0.74–0.87), respectively, while for JAAM-DIC it was 0.73 (95% CI: 0.57–0.85) and 0.46 (95% CI: 0.28–0.65), respectively. Pooled sensitivity and specificity for SIC were 0.71 (95% CI: 0.57–0.82) and 0.49 (95% CI: 0.31–0.66), respectively. Conclusion The SIC and JAAM-DIC scores exhibited higher sensitivity to identify patients with coagulopathy and predict patient outcomes, and thus are valuable to identify patients for possible treatment at an early stage. The ISTH-DIC score perhaps identified patients at later stages and demonstrated better specificity to predict disease outcomes. Thus, early identification of patients using the SIC and JAAM-DIC scores and later confirmation using the ISTH-DIC score would be beneficial approach for improved management of patients with sepsis.
Elevated few-shot network intrusion detection via self-attention mechanisms and iterative refinement
The network intrusion detection system (NIDS) plays a critical role in maintaining network security. However, traditional NIDS relies on a large volume of samples for training, which exhibits insufficient adaptability in rapidly changing network environments and complex attack methods, especially when facing novel and rare attacks. As attack strategies evolve, there is often a lack of sufficient samples to train models, making it difficult for traditional methods to respond quickly and effectively to new threats. Although existing few-shot network intrusion detection systems have begun to address sample scarcity, these systems often fail to effectively capture long-range dependencies within the network environment due to limited observational scope. To overcome these challenges, this paper proposes a novel elevated few-shot network intrusion detection method based on self-attention mechanisms and iterative refinement. This approach leverages the advantages of self-attention to effectively extract key features from network traffic and capture long-range dependencies. Additionally, the introduction of positional encoding ensures the temporal sequence of traffic is preserved during processing, enhancing the model’s ability to capture temporal dynamics. By combining multiple update strategies in meta-learning, the model is initially trained on a general foundation during the training phase, followed by fine-tuning with few-shot data during the testing phase, significantly reducing sample dependency while improving the model’s adaptability and prediction accuracy. Experimental results indicate that this method achieved detection rates of 99.90% and 98.23% on the CICIDS2017 and CICIDS2018 datasets, respectively, using only 10 samples.