Browse Articles

Characterization of Limeira intrusion stones for aggregate use in engineering applications

Component mechanisms of Gaia: Biogeochemical cycles are special

Multi objective optimization algorithm for hybrid quantum harmonic oscillator and its application in rotor system optimization

3A or not 3A: Cytidine deaminases in the etiology of the CAG-repeat expansion diseases

The effects of social media abstinence on affective well-being and life satisfaction: a systematic review and meta-analysis

Reply to Trollmann et al.: Perspective on LNP structure and simulation

A hybrid framework: singular value decomposition and kernel ridge regression optimized using mathematical-based fine-tuning for enhancing river water level forecasting

Abstract The precise monitoring and timely alerting of river water levels represent critical measures aimed at safeguarding the well-being and assets of residents in river basins. Achieving this objective necessitates the development of highly accurate river water level forecasts. Hence, a novel hybrid model is provided, incorporating singular value decomposition (SVD) in conjunction with kernel-based ridge regression (SKRidge), multivariate variational mode decomposition (MVMD), and the light gradient boosting machine (LGBM) as a feature selection method, along with the Runge–Kutta optimization (RUN) algorithm for parameter optimization. The L-SKRidge model combines the advantages of both the SKRidge and ridge regression techniques, resulting in a more robust and accurate forecasting tool. By incorporating the linear relationship and regularization techniques of ridge regression with the flexibility and adaptability of the SKRidge algorithm, the L-SKRidge model is able to capture complex patterns in the data while also preventing overfitting. The L-SKRidge method is applied to forecast water levels in the Brook and Dunk Rivers in Canada for two distinct time horizons, specifically one- and three days ahead. Statistical criteria and data visualization tools indicates that the L-SKRidge model has superior efficiency in both the Brook (achieving R = 0.970 and RMSE = 0.051) and Dunk (with R = 0.958 and RMSE = 0.039) Rivers, surpassing the performance of other hybrid and standalone frameworks. The results show that the L-SKRidge method has an acceptable ability to provide accurate water level predictions. This capability can be of significant use to academics and policymakers as they develop innovative approaches for hydraulic control and advance sustainable water resource management.

Association of health locus of control with anxiety and depression and mediating roles of health risk behaviors among college students

Abstract We aimed to assess the association of health locus of control with anxiety and depression, and explore the mediating effects of health risk behaviors. A multi-stage cluster random sampling method was used among Chinese college students. Logistic regression models were used to explore the associations of health locus of control with anxiety and depression. Structural equation models were used to explore the mediation roles of health risk behaviors in the associations of health locus of control with anxiety and depression. A total of 3,951 college students were included in this study. Internality was associated with lower prevalence of depression (OR = 0.94, 95% CI, 0.91–0.97), powerful others externality was also associated with lower prevalence of anxiety and depression (0.92, 0.88–0.96; 0.93, 0.89–0.96), while chance externality was associated with higher risk of anxiety and depression (1.13, 1.08–1.18; 1.24, 1.20–1.28). The mediated proportion of health risk behaviors in associations of internality, powerful others externality, chance externality with anxiety was 7.55%, 2.37% and 2.18%, respectively. The mediated proportion of health risk behaviors in associations of powerful others externality, chance externality with depression was 10.48% and 2.14%, respectively. Health locus of control is associated with anxiety and depression that are mediated by health risk behaviors.

Rethinking postdoc careers through the science of science

Synergistic effects of titanium dioxide and graphene nanofillers on delamination and thrust forces in machining glass fiber reinforced nanocomposites

Abrupt changes in algal biomass of thousands of US lakes are related to climate and are more likely in low-disturbance watersheds

Climate change is predicted to intensify lake algal blooms globally and result in regime shifts. However, observed increases in algal biomass do not consistently correlate with air temperature or precipitation, and evidence is lacking for a causal effect of climate or the nonlinear dynamics needed to demonstrate regime shifts. We modeled the causal effects of climate on annual lake chlorophyll (a measure of algal biomass) over 34 y for 24,452 lakes across broad ecoclimatic zones of the United States and evaluated the potential for regime shifts. We found that algal biomass was causally related to climate in 34% of lakes. In these cases, 71% exhibited abrupt but mostly temporary shifts as opposed to persistent changes, 13% had the potential for regime shifts. Climate was causally related to algal biomass in lakes experiencing all levels of human disturbance, but with different likelihood. Climate causality was most likely to be observed in lakes with minimal human disturbance and cooler summer temperatures that have increased over the 34 y studied. Climate causality was variable in lakes with low to moderate human disturbance, and least likely in lakes with high human disturbance, which may mask climate causality. Our results explain some of the previously observed heterogeneous climate responses of lake algal biomass globally and they can be used to predict future climate effects on lakes.

Regulation of lanthanide supramolecular nanoreactors via a bimetallic cluster cutting strategy to boost aza-Darzens reactions

Explainable artificial intelligence for botnet detection in internet of things

Abstract The proliferation of internet of things (IoT) devices has led to unprecedented connectivity and convenience. However, this increased interconnectivity has also introduced significant security challenges, particularly concerning the detection and mitigation of botnet attacks. Detecting botnet activities in IoT environments is challenging due to the diverse nature of IoT devices and the large-scale data generated. Artificial intelligence and machine learning based approaches showed great potential in IoT botnet detection. However, as these approaches continue to advance and become more complex, new questions are opened about how decisions are made using such technologies. Integrating an explainability layer into these models can increase trustworthy and transparency. This paper proposes the utilization of explainable artificial intelligence (XAI) techniques for improving the interpretability and transparency of the botnet detection process. It analyzes the impact of incorporating XAI in the botnet detection process, including enhanced model interpretability, trustworthiness, and potential for early detection of emerging botnet attack patterns. Three different XAI based techniques are presented i.e. rule extraction and distillation, local interpretable model-agnostic explanations (LIME), Shapley additive explanations (SHAP). The experimental results demonstrate the effectiveness of the proposed approach, providing valuable insights into the inner workings of the detection model and facilitating the development of robust defense mechanisms against IoT botnet attacks. The findings of this study contribute to the growing body of research on XAI in cybersecurity and offer practical guidance for securing IoT ecosystems against botnet threats.

The AaFoxA factor regulates female reproduction through chromatin remodeling in the mosquito vector Aedes aegypti

Female mosquitoes are vectors of many devastating human diseases because they require blood feeding to initiate reproduction. Thus, elucidation of molecular mechanisms managing female mosquito reproduction is essential. Although the regulation of gene expression during the mosquito gonadotrophic cycle has been studied in detail, how this process is controlled at the chromatin level remains unclear. Chromatin must be accessible for transcription factors (TFs) governing gene expression. A specialized class of TFs, called pioneer factors (PFs), binds and remodels closed chromatin, permitting other TFs to bind DNA and activate the gene expression. Here, we identified a homolog of the vertebrate PF FoxA in the mosquito Aedes aegypti and used the CRISPR-Cas9 system to generate mosquitoes deficient in AaFoxA . We found that ovary development was severely retarded in mutant females. Multiomics and molecular biology analyses have shown that AaFoxA increased histone acetylation and decreased methylation of H3K27 by controlling the chromatin accessibility of histone modification enzymes and chromatin remodelers. AaFoxA is bound to the loci of chromatin remodelers, changing their chromatin accessibility and modulating their temporal expression patterns. AaFoxA increased the accessibility of the ecdysone receptor (EcR) and E74 loci, indicating the important role of AaFoxA in the hormonal regulation of mosquito reproductive events. Further, the CUT&RUN and ATAC-seq analyses revealed that AaFoxA temporarily bound closed chromatin, making it differentially accessible during the mosquito gonadotrophic cycle. Hence, this study demonstrates that AaFoxA modulates chromatin dynamics throughout female mosquito reproduction.

Selective transformation of propargylic ester towards tunable polymerization pathways

Intelligent fault diagnosis and operation condition monitoring of transformer based on multi-source data fusion and mining

Abstract Transformers are important equipment in the power system and their reliable and safe operation is an important guarantee for the high-efficiency operation of the power system. In order to achieve the prognostics and health management of the transformer, a novel intelligent fault diagnosis of the transformer based on multi-source data fusion and correlation analysis is proposed. Firstly, data fusion for multiple components of transformer dissolved gases is performed by an improved entropy weighting method. Then, the combination of bidirectional long short-term memory network, attention mechanism, and convolution neural network is employed to predict the load rate, upper oil temperature, winding temperature data, and the fusion indices of dissolved gas components in the transformer. Furthermore, Apriori correlation analysis is performed on the transformer load rate and upper oil layer, winding temperature, and fusion indices of gas components by support and confidence levels to achieve a predictive assessment of the transformer state. Finally, the validity of the algorithm is verified by applying actual data from a power system monitoring platform. The results show that in the vicinity of sample point 88, the dissolved gas, upper oil temperature, and winding temperature data are not within the normal range of intervals, and it is presumed that the arc discharge phenomenon. Furthermore, the average correct fault diagnosis rate of 100 diagnoses of the transformer fault diagnosis model proposed in this paper is 0.917, and the mean square error of the correct rate is 0.018. The proposed model can achieve the prediction of the accident early warning, to prevent further expansion of the accident.

No evidence for Peto’s paradox in terrestrial vertebrates

Larger, longer-lived species are expected to have a higher cancer prevalence compared to smaller, shorter-lived species owing to the greater number of cell divisions that occur during their lifespan. Yet, to date, no evidence has been found to support this expectation, and no association has been found between cancer prevalence and body size across species—a phenomenon known as Peto’s paradox. Specifically, while anticancer mechanisms have been identified for individual species, wider phylogenetic evidence has remained elusive. Here, we show that there is no evidence for Peto’s paradox across amphibians, birds, mammals, and squamate reptiles: Larger species do in fact have a higher cancer prevalence compared to smaller species. Moreover, we demonstrate that the accumulation of repeated instances of accelerated body size evolution in mammals and birds is associated with a reduction in the prevalence of neoplasia and malignancy, suggesting that increased rates of body size evolution are associated with the evolution of improved cellular growth control. These results represent empirical evidence showing that larger body size is related to higher cancer prevalence, thus rejecting Peto’s paradox, and demonstrating the importance of heterogenous routes of body size evolution in shaping anticancer defenses.

A long road ahead to reliable and complete medicinal plant genomes

Exploration of ureolytic airborne bacteria for biocementation applications from different climate zones in Japan

Limited and biased global conservation funding means most threatened species remain unsupported

The conservation of biodiversity represents a global challenge as the world experiences its sixth mass extinction. Understanding how conservation efforts are allocated is paramount to effectively protect threatened species. We analyzed ~14,600 conservation projects over a 25-y period, revealing substantial taxonomic biases in funding. When matched with formal assessments of species’ threat status, several highly threatened groups such as amphibians receive little and ever-decreasing support. Within particular groups (e.g., Mammalia, Reptilia), funding is directed to a very narrow selection of taxa, leaving the majority of their threatened species with limited or no support. More attention is urgently needed to assess the extinction risks of neglected taxa, especially smaller species. Paradoxically, while approximately 6% of species identified as threatened were supported by conservation funds, 29% of the funding was allocated to species of “least concern”. A more holistic distribution of conservation funding is, therefore, urgently needed if we are to protect biodiversity efficiently. We suggest avenues and mechanisms for a more balanced coverage of threatened species within conservation programs and highlight some of the benefits that could be derived from such an approach.