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3D-MRI brain glioma intelligent segmentation based on improved 3D U-net network

Purpose To enhance glioma segmentation, a 3D-MRI intelligent glioma segmentation method based on deep learning is introduced. This method offers significant guidance for medical diagnosis, grading, and treatment strategy selection. Methods Glioma case data were sourced from the BraTS2023 public dataset. Firstly, we preprocess the dataset, including 3D clipping, resampling, artifact elimination and normalization. Secondly, in order to enhance the perception ability of the network to different scale features, we introduce the space pyramid pool module. Then, by making the model focus on glioma details and suppressing irrelevant background information, we propose a multi-scale fusion attention mechanism; And finally, to address class imbalance and enhance learning of misclassified voxels, a combination of Dice and Focal loss functions was employed, creating a loss function, this method not only maintains the accuracy of segmentation, It also improves the recognition of challenge samples, thus improving the accuracy and generalization of the model in glioma segmentation. Results Experimental findings reveal that the enhanced 3D U-Net network model stabilizes training loss at 0.1 after 150 training iterations. The refined model demonstrates superior performance with the highest DSC, Recall, and Precision values of 0.7512, 0.7064, and 0.77451, respectively. In Whole Tumor (WT) segmentation, the Dice Similarity Coefficient (DSC), Recall, and Precision scores are 0.9168, 0.9426, and 0.9375, respectively. For Core Tumor (TC) segmentation, these scores are 0.8954, 0.9014, and 0.9369, respectively. In Enhanced Tumor (ET) segmentation, the method achieves DSC, Recall, and Precision values of 0.8674, 0.9045, and 0.9011, respectively. Conclusions The DSC, Recall, and Precision indices in the WT, TC, and ET segments using this method are the highest recorded, significantly enhancing glioma segmentation. This improvement bolsters the accuracy and reliability of diagnoses, ultimately providing a scientific foundation for clinical diagnosis and treatment.

Correction: The evolution of bone-eating worm diversity in the Upper Cretaceous Chalk Group of the United Kingdom

Correction: Monitoring the appropriate prescription of low molecular weight heparins and Fondaparinux through administrative data. A retrospective observational study in the Tuscany region

Exploring knowledge sharing intention of digitalization of rural intangible cultural heritage (DRICH): Integrating stimulus-organism-response (SOR) theory and social exchange theory (SET)

In the digital age, knowledge sharing is gaining increasing significance, but its contribution in promoting the protection and transmission of rural intangible cultural heritage is still not fully appreciated. Based on the stimulus-organism-response (SOR) theory and the social exchange theory (SET), this study developed a theoretical model to understand the knowledge sharing intention in the digitalization of rural intangible heritage (DRICH). We examined the influence of external stimuli (i.e., heritage rewards and heritage image) on organism feedback (i.e., cultural identity, responsibility, trust, and outcome expectation) and knowledge sharing intention. A total of 363 valid samples were obtained by means of questionnaire survey and analyzed by structural equation model. The results show that: (1) Heritage rewards and heritage image, as important external stimuli, have a positive impact on cultural identity; (2) Outcome expectation, trust and responsibility positively influence knowledge sharing intention, among which outcome expectation has the strongest effect; (3) Heritage-related self-efficacy (SE) positively moderates the relationship between cultural identity and responsibility. By exploring the influence mechanism of the sharing intention in DRICH, this study provides an empirical basis for the theory and practice of the protection and inheritance of rural intangible cultural heritage.

Hundreds of physicists on a remote island: we visit the ultimate quantum party

On the effect of flexible adjustment of the p value significance threshold on the reproducibility of randomized clinical trials

Background Reproducibility crisis is among major concerns of many scientists worldwide. Some researchers believe that the crisis is mostly attributed to the conventional p significance threshold value arbitrarily chosen to be 0.05 and propose to lower the cut-off to 0.005. Reducing the cut-off, although decreases the false-positive rate, is associated with an increase in false-negative rate. Recently, a flexible p significance threshold that minimizes the weighted sum of errors in statistical inference tests of hypothesis was proposed. Methods The current in silico study was conducted to compare the error rates under different conditions assumed for the p significance threshold—0.05, 0.005, and a flexible threshold. Using a Monte Carlo simulation, the false-positive rate (when the null hypothesis was true) and false-negative rate (when the alternative hypothesis was true) were calculated in a hypothetical randomized clinical trial. Results Increasing the study sample size was associated with a reduction in the false-negative rate, however, the false-positive rate occurred at a fixed value regardless of the sample size when fixed significance thresholds were used; the rate decreased, however, when the flexible threshold was employed. While employing the flexible threshold abolished the reproducibility crisis to a large extent, the method uncovered an inherent conflict in the frequentist statistical inference framework. Calculation of the flexible p significance threshold is only possible a posteriori, after the results are obtained. The threshold would thus be different even for replicas, which is in contradiction to the common sense. Conclusions It seems that relying on frequentist statistical inference and the p value is no longer a viable approach. Emphasis should be shifted toward alternative approaches for data analysis, Bayesian statistical methods, for example.

Daily briefing: Breathing patterns might be as unique as a fingerprint

The effectiveness of care bundles for reducing caesarean section safely: A systematic review and meta-analysis

Care bundles, which consist of three or more interventions implemented together, may help address the global rise in caesarean births. The aim of this systematic review is to evaluate the effectiveness of care bundles designed to reduce caesarean section (CS). MEDLINE, CINAHL, CENTRAL and Embase were searched from January 2000 to June 2024 using terms related to CS and care bundles. Grey literature and professional body websites were also searched. Randomised or non-randomised studies reporting on pregnant or labouring women who received a care bundle designed to reduce CS safely were eligible. Data were extracted by two reviewers independently. Pre-specified outcomes included CS (overall, elective, and emergency), assisted vaginal birth, neonatal admission to intensive care, and care bundle compliance. Meta-analyses were undertaken using Review Manager 5.4 and a random effects model. Odds Ratio (OR) with 95% Confidence Interval (CI) were calculated. The certainty of the evidence was assessed using the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach. Ten non-randomised studies were included. Care bundles reduced CS overall (OR 0.83, 95% CI 0.72 to 0.97) and emergency CS (OR 0.82, 95% CI 0.68 to 0.98). No differences were observed in assisted vaginal birth (OR 1.10, 95% CI 0.91 to 1.33) or neonatal admission to intensive care (OR 1.06, 95% CI 0.56 to 2.02). Compliance to the care bundles ranged from 50% to 92%. The certainty of the evidence for all outcomes was very low. Randomised trial research is required to better assess care bundle use in reducing CS safely.

Recording my research led to a photojournalism career

Complexity analysis of a closed-loop supply chain for power battery recycling under government subsidies

Considering government subsidies for new energy electric vehicle manufacturers, the study will construct a static game model to analyze the optimal strategies of decision-makers from the perspectives of manufacturers and retailers. It will analysis the impact of key model parameters such as government subsidy criteria, the fund returns rate and market size on optimal strategy selection. Additionally, a dynamic game model will be developed to investigate the complex behavior characteristics of decision-makers, as well as the influence of different behaviors on their complex benefits. The research results show: First, government subsidy criteria, the fund return rate and market size importantly influence the optimal pricing strategies and recycling services. Second, the stability region of the equilibrium strategy is given. Moreover, when the decision-makers ‘s price adjustment speed exceeds a certain threshold, the equilibrium strategy undergoes complex behaviors such as bifurcation and chaos. The time-delay feedback control method is adopted to regulate the chaos. Ultimately, based on the findings, a series of targeted management insights and recommendations are proposed, indicating that while short-term government subsidies can lead to increased profits for members, excessive subsidies in the long run can result in system chaos and profit decline.

Why science recruiters struggle to find high-calibre candidates

ZFD-Net: Zinc flower defect detection model of galvanized steel surface based on improved YOLOV5

Due to the complex factory environment, zinc flower defects and galvanized sheet background are difficult to distinguish, and the production line running speed is fast, the existing detection methods are difficult to meet the needs of real-time detection in terms of accuracy and speed. We propose ZFD-Net, a zinc flower defect detection model on the surface of galvanized sheet based on improved you only look once (YOLO)v5. Firstly, the model combined the YOLOV5 model with our proposed cross stage partial transformer (CSTR) module in this paper to increase the model receptive field and improve the global feature extraction (FE) capability. Secondly, we use bi-directional feature pyramid network (Bi-FPN) weighted bidirectional feature pyramid network to fuse defect details of different levels and scales to improve them. Then we propose a cross resnet simam fasternet (CRSFN) module to improve the reasoning speed of ZFD-Net and ensure the detection effect of zinc flower defects. Finally, we construct a high-quality dataset of zinc flower defect (ZFD) detection on galvanized sheet surface, which solves the problem that no public dataset is available at present. ZFD-Net is compared with state-of-the-art (SOTA) methods on the self-built data set, and its performance indicators are better than all methods.

Secure energy outage analysis of UAV swarm-based network

In the pursuit of enhancing energy-efficient and secure communications, this study explores secure energy outage probability (SEP) within unmanned aerial vehicle (UAV) swarm-based energy harvesting networks. UAVs, often deployed as relay nodes to facilitate communication between a source and a destination, face significant security and energy efficiency challenges, particularly when relying on battery-free configurations. These UAVs must simultaneously harvest energy from ambient sources and thwart eavesdropping attempts from external interceptors, demanding secure solutions for efficient operation. To address these challenges, we propose two energy-efficient secure UAV selection methods aimed at optimizing the communication network’s physical layer security. The first method prioritizes maximizing the communication link quality between the source and the selected UAV, while the second method focuses on minimizing the link quality between the UAV and the eavesdropper. Through these strategies, the study targets improved SEP performance without compromising energy efficiency, ensuring sustainable network operation. The study evaluates the effectiveness of these methods through rigorous mathematical frameworks in both approximate and asymptotic forms. Through numerical analyses, our proposed methods demonstrate notable improvements in SEP performance. Specifically, we observe that SEP increases significantly when the transmit signal-to-noise ratio (SNR) is varied from low to medium values; however, the benefits taper off at higher SNR ranges, indicating a performance plateau. Moreover, introducing a larger number of UAVs in the swarm enhances SEP, showcasing the scalability advantage of the proposed methods. Further insights reveal that configuring a higher power-splitting coefficient enhances SEP at lower SNR values, although it yields diminishing returns at higher SNR levels. Additionally, our findings highlight the influence of UAV placement, with closer proximity to the destination improving SEP performance. However, there exists an optimal flying altitude for UAVs that minimizes SEP, offering a balance between security enhancement and energy efficiency. These results underscore the superiority of the proposed methods in addressing the dual challenges of physical layer security and energy conservation.

Impact of predicted microbiota tryptophanase activity on Cryptosporidium parvum proliferation

Protozoa in the genus Cryptosporidium infect intestinal epithelial cells. The profile of the fecal microbiota has been shown to impact the proliferation of Cryptosporidium parvum in a mouse model of cryptosporidiosis and a reverse effect of the parasite on the microbiota has also been described. The mechanisms underlying this interaction are unknown. The lack of effective drugs and vaccines is motivating the search for pro- or prebiotics capable of increasing resistance to parasite proliferation in the gastrointestinal tract. To understand if and how the intestinal microbiota could be harnessed for this purpose, we tested if C. parvum proliferation in the mouse responds to oral administration of Escherichia coli. This bacterium was chosen because of its reported importance in mediating colonization resistance, because it encodes tryptophanase, an enzyme which converts tryptophan into indole, and because of the availability of an ampicillin-resistant strain expressing green fluorescent protein. Excretion of GFP+ E. coli in the feces was highly variable among mice, a phenomenon which is also observed with C. parvum. A positive correlation between fecal output of probiotic E. coli and C. parvum was observed. This finding may indicate that intestinal colonization with two microorganisms as different as E. coli and C. parvum responds to the same conditions in the GI tract. Consistent with an effect of the microbiota on cryptosporidiosis, the pre-infection microbiota taxonomic profile was predictive of mouse susceptibility to C. parvum. Contrary to the reported inhibitory effect of indole on C. parvum, microbiota indole production potential was positively correlated with C. parvum fecal output. The effect of cryptosporidiosis on the microbiota was characterized by an expansion of facultative anaerobes, particularly Gammaproteobacteria. This study is a first attempt to assess the proliferation in the mouse of a defined probiotic and quantify its effect on C. parvum development.

Eco-friendly degreasing adsorbent derived from oily scum and walnut shells for oilfield sewage treatment and industrial oils adsorption

Oily scum (OS) and walnut shells (WS) were employed to produce an eco-friendly degreasing adsorbent (DA) for the treatment of oilfield sewage and the adsorption of industrial oils. The optimal conditions for preparing DA were established as follows: a mass ratio of OS to WS of 1:2, a pyrolysis temperature of 600°C, a heating rate of 10°C/min, and a pyrolysis duration of 2 hours. The carbon mass fraction in DA was determined to be 90.72%, with a BET specific surface area of 563.31 m2/g, a pore volume of 0.794 cm3/g, and an average pore size of 5.618 nm. The pores were predominantly mesoporous, centered around a size of 3 nm. In oilfield sewage treatment tests, DA achieved a remarkable reduction of 92.64% in the petroleum content of oilfield sewage, outperforming activated carbon (AC) treatment by 13.52% under identical conditions. In tests for industrial oil adsorption, DA demonstrated initial adsorption rates of 138.55 mg/(g·min) for diesel and 189.52 mg/(g·min) for crude oil, achieving cumulative adsorption capacities of 973.3 mg/g and 1017 mg/g for diesel and crude oil, respectively. Thermal regeneration of DA significantly enhanced its oil affinity, markedly improving its initial adsorption rates. Furthermore, the cumulative adsorption capacity of regenerated DA for diesel and crude oil reached 966.88 mg/g and 946.15 mg/g respectively, surpassing that of regenerated AC for diesel (585.47 mg/g) and crude oil (849.21 mg/g). Additionally, DA showed a shorter penetration adsorption time, positioning it as a feasible option for recycling and as an eco-friendly emergency adsorbent in the context of industrial oil spills.

Relationships between numerical score and free text comments in student evaluations of teaching: A sentiment topic analysis reveals the influence of gender and culture

Student evaluations of teaching (SET) have been widely used by university staff to inform decisions on hiring and promotion. In recent years, an increasing body of research has revealed that student evaluations may be systemically affected by students’ own conscious or unconscious biases. In this article, we study a data set from an Australian university, where both numerical and text survey responses were available in large quantities. Our study directly linked comments to numerical ratings, we developed approaches to convert text to quantitative data in the form of topics and sentiment scores, and make use of Bayesian ordinal regression techniques to identify drivers of SET scores. Our analysis of text identified 6 teaching dimensions that students discuss in their comments. Our findings suggest that students’ SET ratings were correlated primarily with the personal characteristics of the lecturer (such as approachability, and being nice) than measures related to teaching dimensions such as course content and assessment. We found a positive gender effect towards the majority gender in a faculty, possibly reflecting students’ gendered expectations. Finally we found that lecturers with a non-English language background were consistently rated lower by the student population, and this effect manifests strongly in local students.

How does digital transformation affect corporate sustainability performance? Evidence from listed energy companies in China

Promoting sustainable growth in the energy sector is key to China’s high-quality development under socialist modernization. Based on dynamic capability theory and the resource-based view, this study uses unbalanced panel data (2011–2023) of Chinese listed energy enterprises to examine how digital transformation (DT) impacts sustainable development performance (SDP), including its subdimensions: financial performance, environmental‒social governance (ESG), and innovation. Using two-way fixed effects models and mediation analysis, we find that (1) DT significantly enhances SDP; (2) this improvement operates through alleviating financing constraints and optimizing resource allocation efficiency; and (3) the benefits are more pronounced in small-scale enterprises and downstream segments of the energy value chain. These findings deepen the understanding of the role of DTs in sustainable development and provide practical guidance for energy firms.

Theories used to explain care-leavers’ journey out of care: A scoping review

This study was motivated by Mike Stein’s 2006 critique of care-leaving research as reflecting a ‘poverty of theory’. A scoping review of care-leaving journal articles was conducted for the nine-year period from 2015 to 2023. 252 articles met the inclusion criteria, including that theory must be explicitly mentioned. 133 theories were used across these publications, with resilience theory being used in 24% of articles, life course in 10%, emerging adulthood and attachment in 8% each, social capital in 6% and ecological theory in 4%. Over half of the publications were driven by theory (from conceptualisation, through research design, to interpretation of findings), while a fifth were informed in parts by theory and a quarter alluded only briefly to theory. Three quarters of the articles utilised theory to formulate practice recommendations for care-leaving services. Only two theories could be identified that were constructed to explain care-leaving. Although most theories considered the care-leaver within their social environment, there was little use of structural, systemic, critical and rights-oriented theories. The study concludes that Stein’s original concern has been somewhat addressed over the past several years. Other studies find that around a quarter to half of care-leaving publications use theory. This study confirms that, of those studies that make at least some use of theory, most weave theory firmly into the study and mobilise that theory to make recommendations for practice. Nevertheless, research should become yet more theory-driven, contextual, systemic, rights-oriented and critical theories should be used more frequently, and more needs to be done to build theory for care-leaving.

Spectroscopic measurement of near-infrared soil pH parameters based on GhostNet-CBAM

Soil pH is an important parameter that affects plant nutrient uptake and biological activity and has received extensive attention and research. In this paper, we propose a neural network algorithm using Ghostnet combined with Convolutional Block Attention Module (CABM) to realize the near-infrared (NIR) PH spectral measurement of soil. The method firstly utilizes Monte Carlo Cross Validation (MCCV) method to reject the anomalous samples in the data, and then uses GhostNet combined with CBAM algorithm to train and predict the PH values of the four Lucas soil spectral data measured by the two different methods, and compares the prediction results with those of PLSR and VGGNet-16 methods. The results showed that the R2 of the GhostNet-CBAM method could reach up to 0.9447, and the RMSE reached as low as 0.3267, and the scatter density plots of the predicted and true values further confirmed that the method could quickly and accurately obtain the soil pH parameters.

Supervisor support and virtual leadership moderate the association between technostress creators and strain in remote work: Evidence based on hair cortisol and occupational physician’s hetero-evaluations

We investigated the association between technostress creators (TCs) and the strain response among smart workers, who are defined as individuals who make extensive use of information and communication technologies for work-related tasks and in interpersonal relationships with colleagues and supervisors. The moderating role of supervisor support and virtual leadership was a specific focus of our study. We conducted two studies, in each of which we complemented the self-report measures on the TCs and supervisor support and virtual leadership with more objective measures of stress and strain. In Study 1, we investigated the association between perceived TCs and hair cortisol concentration (HCC) as a biomarker of stress. In Study 2, we examined the association between perceived TCs and stress-related psychophysical symptoms (i.e., psychophysical strain) as assessed by the occupational physician (i.e., hetero-evaluation). In Study 1, 102 smart workers from different organizations completed a self-report questionnaire and we collected a strand of hair. Study 1 provided little support for the association between TCs and HCC, but supervisor support did affect the association between techno-uncertainty (one of five TCs) and HCC, which was positive when support was low, but negative—albeit marginally significant—when support was high. In Study 2, 105 smart workers from a company in the service sector completed a self-report questionnaire and underwent an occupational health examination. Techno-invasion (another TC) was positively related to psychophysical strain as assessed by the occupational physician. In addition, virtual leadership attenuated the association between three TCs (techno-overload, techno-invasion, techno-insecurity) and psychophysical strain. Based on these nuanced pattern of results, our studies suggest that TCs lead to a strain response among smart workers, and that positive relationships with one’s supervisor—in terms of supervisor support and virtual leadership—can attenuate the negative consequences of technostress.