dataesr/scientific-paragraphs-categorization huggingface dataset:
Security and Privacy in AIoT As AIoT becomes more prevalent in various domains, security and privacy concerns become increasingly important. There is a need to develop robust security measures to protect data and ensure the privacy of users. Several research directions have been proposed to address this issue. One direction is to examine the tunneled session by obtaining the key in the security analysis of the key distribution step and understanding the encryption algorithm through the decompilation of the android package  [70]. This approach aims to identify vulnerabilities in the encryption algorithm and the key distribution process to improve security. Another promising direction is to explore the use of blockchain as a service, trust management, and security by design approach  [66]. Blockchain technology can provide a decentralized and tamperproof method of managing transactions and data.
By using blockchain, trust management can be improved, and security can be built into the design of the AIoT system. In addition, encryption on the communication link and optimizing the current management system, such as load balance for data read and stored, can improve system security  [66]. This approach focuses on ensuring the confidentiality and integrity of data transmission between devices and storage systems. Furthermore, a study on the security of the Advanced Metering Infrastructure (AMI), which is a key element of the Smart Grid, is needed. The study will focus on how to securely integrate energy-aware smart homes, equipped with smart meters and smart appliances  [99]. This approach aims to enhance the security of the Smart Grid, which is critical for maintaining the stability and reliability of the power grid.
-Pedro Hilaro is partly funded by the Mobility Grant 185AG0B210029 of the PhD program of the French Embassy in Angola. -Fatma Outay is supported by the Research Cluster R19098 of Zayed University (Dubai, United Arab Emirates). • Conflict of interest/Competing interests: No conflict of interest • Ethics approval: Not applicable • Consent to participate: Not applicable • Consent for publication: All authors and funding schemes have accepted to publish this article. • Availability of data and materials: Not applicable • Code availability: Not applicable • Authors' contributions:.
Background In the older general population, neurodegenerative diseases (NDs) are associated with increased disability, decreased physical and cognitive function. Detecting risk factors can help implement prevention measures. Using deep neural networks (DNNs), a machine-learning algorithm could be an alternative to Cox regression in tabular datasets with many predictive features. We aimed to compare the performance of different types of DNNs with regularized Cox proportional hazards models to predict NDs in the older general population.
Identifying these risk factors is crucial for early intervention and can help delay disease onset. It exists already research on the prediction of neurodegenerative diseases. For example, in a large cohort study, researchers have reported results on the prediction of NDs using traditional statistical analyses (hypothesisdriven approaches)  [9]. Another cohort study assessed 14,066 older participants free of cognitive decline with a follow-up of 4. 5 years. Using Cox models, they found that subjective cognitive decline and anxiety were independently associated with mild cognitive impairment and dementia  [10]. Cohort studies based on samples from the general population can providing information with less selection and recall bias than case-control studies  [4]. Reinke et al. studied dementia risk in a population with German claims data in 117,895 individuals during a 10-year follow-up.
Participants, inclusion criteria and study design We analysed participants of the English Longitudinal Study of Ageing (ELSA)  [19] , an ongoing cohort study representative of the general population over 50 years of age living in England. ELSA collects health data, including socio-economic, cognitive, behavioural, psychological, and lifestyle information. Participants are assessed every 2 years (waves) with computer-assisted interviews and self-reported questionnaires. Each biennial assessment is called a "wave". In addition, every 4 years, participants undertake a physical exam and provide blood samples. The data collection goes from wave 1 (baseline for the ELSA study, performed in 2002-2003) to wave 9 (2018/-2019). Ethical approval was obtained from the Multicentre Research and Ethics Committee  [20].
Features Based on the literature  [7, 8] , we chose possible predictors (features) of NDs that were available in the ELSA Wave 2 dataset. We selected 95 baseline variables (features) associated with the occurrence of NDs or expected outcomes. We identified 27 comorbidities, 15 psychosocial, 11 biomarkers, 9 symptoms, 7 lifestyle, seven environmental, 6 physical functioning tests, 6 disability, 4 cognition tests and three demographic variables (Supplementary Table  1 ). Eleven of the 13 risk factors for dementia reported by the Lancet commission for dementia prevention  [21]  are among the 91 features. The two not included features (traumatic brain injury and air pollution) are unavailable in wave 2 of the ELSA study. Among the 95 selected features, four variables with variance inflation factor (VIF) > 10 were excluded from the analysis.
Statistical analysis Missing data We checked missingness in every variable of interest. Assuming a missing at-random mechanism, a completecase analysis would introduce bias  [22]. Consequently, we applied multiple imputations to deal with the missing data issue. We imputed only the baseline predictor variables and not the outcome. We built the imputation model with the full dataset by selecting the best missing data predictors. The function "Quickpred" from the "mice" R package allows a selection of predictors according to correlations and usable cases. We selected the best predictors among the available variables with the function "Quickpred" and included the outcome and possible confounders such as age and sex  [23]. To decide the number of imputations, we used the maximum percentage of observed missing data  [24].
Data pre-processing Categorical predictors were dichotomized into 0, and 1. To deal with numerous continuous predictors with a skewed distribution, we transformed them with logarithms to the base 2 + 1 with the following formula: y = log 2 (x) + 1. y = transformed predictor; x = original non-transformed predictor. We used logarithms to the base 2 because of its binary nature, which makes the computation of machine learning more performant. Nested cross-validation was carried out to reduce the risk of model overfitting. In the nested cross-validation, we used two repeated 5-fold cross-validation in each of the datasets obtained from the multiple imputation stage to have ten datasets to train (80% of data) the model and ten datasets to test (20% of data) the model. Then, we performed feature selection and hyper-parameter tuning only on the training datasets.
Cox models with selected features (CoxSf) We applied Elastic Net regularisation in 10 randomly chosen training datasets from each of the 40-imputed datasets. We kept the features selected (i. e. having coefficients not equal to 0) at least eight times from the 10 training datasets of each imputed dataset. We selected the variables according to how often each variable appeared in the imputed datasets. We computed the number of imputed datasets that a variable appeared. We wanted to use the variables that occurred in at least 30 imputed datasets in a Cox model. For example, a variable "x" appeared in all 40 imputed datasets. In consequence, it was kept for the Cox model. In contrast, a variable "y", which occurred only in 24 imputed datasets, was excluded from the model. Using the variables that occurred in 30 to 40 imputed datasets, we applied Cox regression models to each imputed dataset and pooled them with Rubin's rules (pool function from the "mice" R package)  [23].
Model evaluation The output of the three DNNs is the predicted values of new events of NDs for each participant. Then we used these values to calculate the assessment measures. C-statistics (or C-index) measures a model's goodness of fit, giving the probability that an individual that experienced the event has a higher score than an individual that did not experience the event. Harrell's C-statistics is a type of c-statistics with a rank correlation method for censored data. Uno's C-statistics has an advantage over Harrell's C-statistics as it does not depend on the studyspecific censoring distribution  [35]. We evaluated the performance of the models in the test datasets by measuring Uno's C-statistics with 95% confidence intervals calculated with 100 replications of bootstrapping. We assessed the following time-dependent measures: AUC, balanced accuracy, sensitivity and specificity.
We assessed the overfitting of DNNs by plotting the loss function over epochs in each of the imputed training and test datasets. The stability of the models was assessed by calculating confidence intervals with bootstrapping with the Uno's C-statistic in each of the 40 imputed datasets. Finally, we calculated the power of our sample size for the categorical CoxSf model final predictors. We assessed the feature importance of the three DNNs using Shapley additive explanations (SHAP) analysis, showing the top ten most important features for each model  [36]. We analysed the possible shared features among the three DNNs and the Cox model using visual methods. Data analyses were performed in R version 4. 0. 0 using R packages "mice", "survival", "glmnet", "c060" and, "sur-vivalROC". Sample split and Uno C-statistics calculations were performed with Python.
In the imputation model, we included age, sex, time-to-event and outcomes. We created 40 imputed datasets with 20 iterations using chained equations  [24]. The imputations were values considered as plausible and with a low percentage of bias (Supplementary Table  2 ). We also observed a distribution of imputed data similar to the original non-imputed values (Supplementary Figs. 2 to 4 ). All models had as initial input the 91 pre-selected variables described in the methods section. After having verified the proportional hazard assumption was not violated, we generated the CoxEn models with Elastic Net regularization. We used alpha = 0. 84 and lambda = 0. 0093, which gave the lowest partial likelihood deviance. To find the best list of features for the final CoxSf, we generated models on 40 imputed datasets using 7, 8 and 9 selected variables, respectively and pooled them by following Rubin's rules.
Nine variables appeared in at least 30 imputed datasets, eight in at least 31 imputed datasets and seven in all 40 imputed datasets (Fig. 2 Panel A ). Using the Wald test, the model with eight variables had , vigorous physical activity (0. 7, (0. 6, 0. 9)) and higher BMI (0. 4 (0. 2, 0. 8)) were associated with a lower ND risk (Fig. 2 Panel C ). The DNNs models were generated with the described methodology. The performance of the models from the highest to the lowest Uno's C-statistic was (mean (95% confidence intervals) 0. 757 (0. 702, 0. 805), 0. 734 (0. 694, 0. 772), 0. 732 (0. 689, 0. 771), 0. 706 (0. 651, 0. 752), 0. 708 (0. 653, 0. 754) for TabTransformer, CoxSf, CoxEn, Densenet and FeedForward respectively (Fig. 3  and Supplementary Table  3 ). Uno's C-index from the TabTransformer model was significantly higher than the other models (Fig.
3 , Tukey's test adjusted p < 0. 001). Uno's C-index was not significantly different between CoxEn and CoxSf models (p = 0. 07) and between Densenet and Feedforward (p = 0. 13). Figure  4  and Supplementary Tables  4 to 7  (in each imputed dataset) show the evolution of the timedependent measures over time (time-dependent AUC, balanced-accuracy, sensitivity and specificity at 4, 8, 10 and 12 years of follow-up). TabTransformer shows better balance accuracy, specificity and much better sensitivity after 8 years than the other models. Using time-dependent AUC the best model was the CoxSf in the 8th year of follow-up, All models showed the highest AUC in the 8th year of follow-up and were (mean (95% confidence intervals)) 0. 894 (0. 874, 0. 913), 0. 892 (0. 872, 0. 912), 0. 870 (0. 845, 0. 894), 0.
Therefore, it was not oversampled with cases. Another issue of many studies was the poor assumption of the Cox models. Again, our analysis verified these assumptions. Finally, they described the lack of external validation. In this latter case, this study was not validated in a different dataset. The feature importance results showed features in common with the three DNNs and Cox models. In all models (DNNs and CoxSf ), memory function and vigorous physical activity were the most crucial variables to predict NDs after age. In the case of memory function index, the best association with the outcome was with a lower memory function index associated with a higher risk of NDs rather than a high value associated with a lower risk, which suggests the importance of reporting lower memory index values in the population at risk of NDs.
Acknowledgments We thank  Anna Schritz  for her contribution to this study. We gratefully acknowledge the  UK Data Archive  for supplying the ELSA data. ELSA was developed by a team of researchers based at  University College London , the  Institute of Fiscal Studies , and the  National Centre for Social Research. The data creators or the funders of the data collections and the UK Data Archive do not bear any responsibility for the analyses or interpretations presented here.
Funding This work was supported by the  National Centre of Excellence in Research on Parkinson's Disease (NCER-PD) , funded by the  Luxembourg National Research Fund  ( FNR/NCER13/BM/11264123 ). The data creators or the funders of the data collection and the UK Data Archive do not bear any responsibility for the analyses or interpretations presented here. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
5 5 Figure 4 and Supplementary Tables4 to 7(in each imputed dataset) show the evolution of the timedependent measures over time (time-dependent AUC, balanced-accuracy, sensitivity and specificity at 4, 8, 10 and 12 years of follow-up). TabTransformer shows better balance accuracy, specificity and much better sensitivity after 8 years than the other models. Using time-dependent AUC the best model was the CoxSf in the 8th year of follow-up, All models showed the highest AUC in the 8th year of follow-up and were (mean (95% confidence intervals)) 0. 894 (0. 874, 0. 913), 0. 892 (0. 872, 0. 912), 0. 870 (0. 845, 0. 894), 0. 874 (0. 849, 0. 898), and 0. 884 (0. 863, 0. 905) for CoxSf, CoxEn, Densenet, FeedForward and TabTransformer respectively (Supplementary Table4and Fig. 4). In the 12th year, all models showed a decrease in AUC values (mean decrease between 10 and 12th year: 10.
3% of terrestrial surface on Earth. Based on this dataset, the first extensive map of fungal alpha-diversity was drawn and evidenced a heterogeneous and spatially structured distribution in large biogeographical patterns of 231 km radius for richness (Hill number q=0) and smaller patterns of 36 km radius for dominant fungi (Hill number q=2). As related to other environmental parameters, the spatial distribution of fungal diversity was mainly influenced by local filters such as soil characteristics and land management, but also by global filters such as climate conditions. The spatial distribution of abundant and rare fungi was determined by distinct or similar filters with various relative influences. Interestingly, cropped soils exhibited the highest pool of fungal diversity relatively to forest and vineyard soils.
lambda function and applied the transformation with the BoxCox function. Outliers were tracked using the grubbs. test function in the "outliers" package  (Komsta and Komsta, 2011)  for each Hill number dataset. Once the outliers were removed, to estimate the multiple comparisons across the modalities (land uses or climate types), analyses of variance (ANOVAs) were used, and we verified the normality assumption of residuals. If it was satisfied, we used a Least Significant Difference (LSD) test with adjusted p-value < 0. 05; if it was not satisfied, we used a nonparametric test with kruskal function from "agricolae" package and a correction by the Bonferroni method for the multiple comparisons. The details of the samples removed in the different land uses are available in Supplementary Figure  1 , as recommended by  (Dini-Andreote et al.
, 2021). The environmental data that did not follow a Gaussian distribution were log-transformed. Moreover, the soil pH covaries with land uses, with a majority of acidic soils in forests and a majority of neutral and basic soils in croplands and vineyards/orchards (Supplementary Figure  2 ). For all these reasons, we transformed the soil pH variable with a polynomial transformation of degree 2. Relationship between fungal alpha-diversity and environmental filters was assessed using variance partitioning. Briefly, the first step consisted in reducing the effect of model collinearity to obtain the most parsimonious models. We used the vif function (the variance inflation factors (VIF)) in the "usdm" package  (Naimi, 2015)  and kept the explanatory variables with VIF ≤ 5. A second filtering step was performed to determine the best environmental variables using the regsubset function ("leaps" package  (Lumley and Lumley, 2013) ) and based on the Bayesian information criterion (BIC) and adjusted R 2.
Lastly, we conducted a redundancy analysis  (Legendre, 2018)  to model variation of the overall environmental filters using the rda and ordiR2step functions ("vegan" package  (Oksanen et al. , 2013) ). To select the best variables, we performed a forward selection to build a model maximizing the adjusted R 2. We used the ordiR2step function with 10,000 permutations maximum and the anova. cca function ("vegan" package) to evaluate the variance explained by the best explanatory environmental filtering variables. Geostatistical modeling was used to assess the alpha-diversity spatial variations. We followed a standard approach as proposed in  (Granger et al. , 2015). First, a variogram model was fitted to the experimental variogram computed using alpha-diversity observed at the sample sites. Then, we predicted the unsampled positions by kriging method using a local neighborhood.
We implemented this approach with the "gstat" package  (Bivand et al. , 2015). We tried to fit various authorized variogram models and kept the one that minimized the objective function. Then, we used the results of leave-one-out cross-validation (LOOCV) to evaluate the performance of the best fitted geostatistical model by computing the standardized squared prediction errors  (Lark, 2002). To obtain the information of the putative OTU richness across France, we computed the rarefaction (interpolation) and prediction (extrapolation) curves for fungal richness (q=0) using the R package iNEXT  (Hsieh et al. , 2016).
To describe the topology of the networks, a set of metrics was calculated using the "statnet" package  (Handcock et al. , 2019) , including the number of connected nodes, the proportion of connected nodes, the number of links, and the connectance. These metrics are defined in  (Karimi et al. , 2017a). We used a kruskal test with a correction by the Bonferroni method for the multiple comparisons of the fungal networks based on land uses, using 100 repetitions. The networks were mapped using Cytoscape version 3. 9. 1.
Conclusions Our study confirms that a nation-wide survey is relevant to deeply investigate the spatial change. To go further, it will be important to explore fungal beta-diversity and fungal taxonomy at the scale of France in order to reach a more comprehensive understanding of spatial distribution, ecological processes and environmental filters. Finally, it will also be important to investigate the ecological and functional traits assignment of soil fungal communities, using recent tools and databases developed to better predict the shift in soil functioning according to land management intensity  (Djemiel et al. , 2022). Figures.
Stage #2: Coarse data retrieving First, a coarse estimation of carbon footprint was carried out for each element of the measuring system. Embodied footprints were evaluated per year with the only exception of the pen-drive. On the other hand, operational footprints were evaluated mostly per hour, with the only exceptions of internet consumption (per gigabyte) and car (per kilometer). The values are reported in the column pair ''coarse CO 2,eq '' of Table  1  while avoiding multiple accounting. In detail, some table entries appear blank because resistors consumption is already taken into account in the generator consumption, while the consumption of the instrument control software and of the pen-drive are already taken into account in the laptops' one.
Conclusion This work addresses the environmental sustainability of measurements, and constitutes a first methodological step for investigating the relationship between sustainability and measurement uncertainty. Indeed, a less-uncertain measurement may be less environmentally sustainable because it requires more material and energetic resources. On the other hand, by highlighting relevant factors, one could act on reducing specific impacts without compromising the uncertainty itself. To assess the carbon footprint of a measurement, life cycle assessment was recalled from ITU recommendations concerning the environmental impacts of ICT. Then, an iterative approach was proposed to mitigate the issue of data retrieving, namely the lack of a complete and exhaustive database with impacts associated with the items of interest.
Bioinformatic analyses The 16S rRNA gene amplicons were analyzed following the structure of the workflow AmpliconTagger, as previously described in detail  (Tremblay and Yergeau, 2019). This workflow includes the generation of OTU counts and determination of alpha diversity based on observed OTUs and the Shannon/Simpson indexes  (Tremblay and Yergeau, 2019). The AmpliconTagger code is publicly available at https://bitbu cket. org/jtremblay514/nrc_pipeline_public/src/1. 1. In addition we utilized the R package "ranacapa" for visualizing rarefaction curves  (Kandlikar et al. , 2018). Metagenomic reads were trimmed with Trimmomatic v0. 39 (settings LEADING:3, TRAILING:3, SLI-DINGWINDOW:4:15, MINLEN:36  (Bolger et al. , 2014) ). Trimmed reads were co-assembled with Megahit v1. 2. 9 (-presets meta-sensitive)  (Li et al.
, 2015). Reads were mapped back to contigs using BBMap v38. 73 (minid = 0. 95)  (Bushnell, 2014). Assembled contigs were binned with MetaBAT2 v2. 15  (Kang et al. , 2019)  and annotation was performed using the MetaErg pipeline v1. 2. 2  (Dong and Strous, 2019). To investigate the effect of diesel treatment on gene and pathway abundances across the samples, a Pearson's correlation coefficient was calculated using MicrobiomeAnalyst pattern search with total sum scaling normalization  (Chong et al. , 2020)  with genes annotated using the TIGRFAM database and KEGG pathways in the previous step with MetaErg. CheckM v1. 0. 18 was used to assess completeness and contamination of each bin  (Parks et al. , 2015). The metagenomeassembled genomes (MAGs) and bins were then manually refined as well as using RefineM  (Parks et al.