dataesr/scientific-paragraphs-categorization huggingface dataset:
Objective: Despite the availability of thousands of mental health applications, the extent to which they are used and the factors associated with their use remain largely unknown. The present study aims to (a) assess in a representative US-based population sample the use of smartphone apps for mental health and wellbeing (SAMHW), (b) determine the variables predicting the use of SAMHW, and (c) explore how a set of variables related to mental health, smartphone use, and smartphone "addiction" may be associated with the use of SAMHW. Methods: Data was collected via online questionnaire from 1989 adults. The data gathered included information on smartphone use behavior, mental health, and the use of SAMHW. Latent class analysis was used to categorize participants. Machine learning and logistic regression analyses were used to determine the most important predictors of SAMHW use and associations between predictors and outcome variables.
Data collection material The online questionnaire included items on two sociodemographic characteristics in participants (age and sex); smartphone operation system (OS); smartphone use in the 12 months preceding the study (e. g. daily use duration, average daily use duration for non-essential activities, social problems due to smartphone use); use of smartphone apps in the 12 months preceding the study to diagnose a mental health problem and/or to assess wellbeing (Yes/No); use of smartphone apps in the 12 months preceding the study to treat a mental health problem and/or to improve wellbeing (Yes/ No); psychiatric diagnoses, including alcohol use disorder, over the course of the participant's lifetime (Yes/No); and smartphone use during the COVID-19 pandemic (7-point response scale). Participants also completed the Smartphone Application-Based Addiction Scale,  22  which is comprised of 6 items and a 6-point response scale ranging from strongly disagree to strongly agree; the DSM (Diagnostic and Statistical Manual of Mental Disorders)-5 Self-Rated Level 1 Cross-Cutting Symptom Measure 23 (24 items, 5-point response scale ranging from Not at all to Nearly every day), assessing symptoms, during the 2 weeks preceding the survey completion, across 14 domains: anxiety, depression, anger, personality functioning, mania, sleep problems, somatic symptoms, psychosis, suicidal ideation, memory, dissociation, repetitive thoughts and behaviors, and substance use.
Ethics The survey was conducted in accordance with the Swiss Human Research Act (Chapter 1, Section 1, Article 2, Scope: 2c) as it relates to research involving anonymously collected health-related personal data (https://www. fedlex. admin. ch/eli/cc/2013/617/en). Additionally, the company who collected the data in the United States obtained the participants digital informed consent for participation in the survey. The data collection was conducted in compliance with the US law and ethical norms. Participation was voluntary and restricted to those aged 18 years and over. All data was collected anonymously.
Data analysis To answer RQ1, we constructed a crosstab with the smartphone health app variables × the participants' sociodemographic characteristics and conducted a Pearson's chi-square of independence. To answer RQ2, we conducted a latent class analysis (LCA) using the "poLCA" R package on the relevant data. Using participants' item responses, LCA algorithms divide participants into subgroups based on unobservable constructs (latent variables). For a more detailed description of LCA algorithms, see Hagenaars and McCutcheon  24  and Weller et al. 25  In total, we ran 6 LCA models, with first model nclass = 2, second model nclass = 3, and so on. In each model, the other poLCA function parameter settings were as follows: nrep = 10, na. rm = F, graphs = T, maxiter = 100,000. After making sure that the six models built were well identified (through maximum likelihood estimation), we proceeded to the comparison and model selection.
We selected the model with three latent classes because it was the one with best performance metrics (Bayesian Information Criterion [BIC] metrics (where the lowest value is considered better), entropy metrics (where the highest value is considered better)) and interpretability. To answer RQ3, we computed a crosstab with age × sex × latent-class and calculated the Pearson's X 2 of independence. To answer RQ4, we used the "randomForest" package for R to build a machine learning classification model. For more information on the random forest (RF) algorithm, see Breiman et al. 26  and Vera Cruz et al. 27  The RF classification models yield, among other outputs, the relative importance of each predictor variable by relying on the mean decrease accuracy (MDA) measure. More specifically, the MDA metric expresses how much accuracy the model would lose by excluding each variable, thus indicating the importance of each variable for the success of the classification.
RF are nonparametric and as such are able to capture nonlinear relationships, including interactions among predictors. 26  To select the best option, we built three classification models with different fitting parameters. In machine learning, the original dataset has to be split into at least two sets: one to train the model; the other to estimate the performance of the model in making predictions. In the current study, the dataset was randomly subdivided as follows: train-set = 70% of the sample; test-set = 30% of the sample. The fitting parameters for the selected model were as follows: "ntree" = 500, "mtry" = 4. The performance metrics of the selected model on the test-set data were as follows: accuracy score = 0. 71 (95% confidence interval 0. 65-0. 77); No-Information Rate [NIR] = 75; p value (Accuracy [ACC]>NIR) = <0.
Availability of data and materials: The material used in this study and data supporting this finding is available at: https:// gitlab. huma-num. fr/gveracruz/smartphone_problematic_use_ behavior/-/tree/main/Material%20and%20data Guarantor: YK. ORCID iD: Germano Vera Cruz https://orcid. org/0000-0002-8297-6933.
Tools that fit the above specification include Cetus, a compiler infrastructure for the source-to-source transformation; Clava, a C/C++ source-to-source tool based on Clang; Par4All, an automatic parallelizing and optimizing compiler; Pluto, an automatic parallelizer and locality optimizer for affine loop nests; ROSE, a compiler-based infrastructure for building source-to-source program transformations and analysis tools; Intel's C++ compiler (icc), and TRACO, an automatic parallelizing and optimizing compiler, based on the transitive closure of dependence graphs. While these tools perform various automatic transformations and optimizations, only ROSE and icc perform loop fission  [35, Section 3. 1]. Based on our assessment, most of these tools process only canonical loops: Definition 15 (Canonical Loop  [25, 4.
Evaluation We performed an experimental evaluation of our loop fission technique on a suite of parallel benchmarks. Taking the sequential baseline, we applied the loop fission transformation and parallelization. We compared the result of our technique to the baseline and to an alternative loop fission method implemented in ROSE. We conducted this experiment in C programming language because it naturally maps to the syntax of the imperative while language presented in Sect. 2. We implement the parallel command as OpenMP directives. For instance, the sequential baseline program on the left of Fig. 5  becomes the parallel version on right,  12  after applying our loop fission transformation and parallelization. j = 0; while (j < M ) { s [ j ] += r [ j ]* A [ j ]; q [ j ] += A [ j ]* p [ j ]; j ++; } # pragma omp parallel private ( j ) { // Each " pragma " block below // have its own copy of j.
g. , linear algebra, data mining and stencils; and commonly used for measuring various loop optimizations. NAS Parallel Benchmarks are designed for performance evaluation of parallel supercomputers, derived from computational fluid dynamics applications. MiBench is an embedded benchmark suite, with everyday programming applications e. g. , image-processing libraries, telecommunication, security and office equipment routines. From these suites, we extracted problems that offered potential for loop fission, or already assumed expected form, resulting in 12 benchmarks. We detail these benchmarks in Table  4. Because these three suites are not mutually compatible, we leveraged the timing utilities from PolyBench/C to establish a common and comparable measurement strategy. To assess performance of other kinds of loops that our algorithm can distribute, but which do not occur prevalently in these benchmarks, we converted a portion of problems to use while loops.
Comparison target We compared our approach to ROSE Compiler. It is a rich compiler architecture that offers various program transformations and automatic parallelization, and supports multiple compilation targets. ROSE's built-in LoopProcessor tool supports loop fission for C-to-C programs. This input/output specification was necessary to allow observation of the transformation results and fit with the measurement strategy we defined previously. To our knowledge, ROSE is the only tool that satisfies these evaluation requirements. Experimental setup We ran the benchmarks using a Linux 5. 10. 0-18-amd64 #1 SMP Debian 5. 10. 140-1 (2022-09-02) x86 64 GNU/Linux machine, with 4 Intel(R) Core(TM) i5-6300U CPU @ 2. 40GHz processors, and gcc compiler version 7. 5. 0. The evaluation was performed in a containerized environment on Docker version 20.
10. 18, build b40c2f6. For each benchmark, we recorded the clock time 5 times, excluded min and max, and averaged the remaining 3 times to obtain the result. We constrained variance between recorded times not to exceed 5%. We ran experiments on 5 input data sizes, as defined in PolyBench/C: MINI, SMALL, MEDIUM, LARGE and EXTRALARGE (abbr. XS, S, M, L, XL). We also tested 4 gcc compiler optimization levels -O0 through -O3. Speedup is the ratio of sequential and parallel executions, S = T Seq /T Par , where a value greater than 1 indicates parallel is outperforming the sequential execution. In presentation of these results, the sequential benchmarks are always considered the baseline, and speedup is reported in relation to the transformed versions. Our open source benchmarks, and instructions for reproducing the results, are available online  [8].
In terms of transformation results, we observed relatively similar results for both techniques. We discovered one interesting transformation difference, with benchmark gemm, which ROSE handles differently from our technique. After transformation, the program must be parallelized by inserting OpenMP directives. This parallelization step can be fully automatic and performed with e. g. , ROSE or Clava, demonstrating that pipelining the transformed programs is feasible. For evaluations, we used manual parallelization for our technique and automatic approach for ROSE. However, we also noted that the automatic insertion of parallelization directives yielded, in some cases, suboptimal choices, such as parallelization of loop nests. This added unnecessary overhead to execution time, and negatively impacted the results obtained for ROSE, e.
g. , for benchmarks Fig. 6. Speedup of selected benchmarks implemented using while loops. Note the influence of various compiler optimization levels, -O0 to -O3 on each problem, and how parallelization overhead tends to decrease as input data size grows from MINI to EXTRALARGE. The gain is lower for mvt because it assumes fissioned form in the original benchmark. bicg and gesummv obtain higher gain from applied loop distribution. fdtd-2d and gemm, as observable in the results. It is possible this issue could be mitigated by providing annotations and more detailed instructions for applying the parallelization directives. In other experiments with alternative parallelization tools [7, Sect. 4. 3], we have been successful at finding optimal parallelization directives automatically, and therefore conclude it is achievable.
Irrespective of the railway use case, the operation of GNSS, IMU, Camera, LIDAR sensors to provide accurate and reliable information is a complex task. On one side, each of the technologies or systems must be properly characterized. Individual and common errors and fault modes must be identified, modelled and monitored. Then, the combination of the sensor's measurements requires the design of advanced signal processing algorithms to provide not only a final localization or positioning information but also associated confidence metrics. All this can only be achieved by a combination of analytical as well as real data analysis methods  (GPS. gov 2021). One of the first goals of RAILGAP is therefore the derivation and validation of the necessary error models, fault detection methods and sensor fusion solutions based on multiple technologies, for accurate-precise and robust/trustable railway positioning, trajectory determination and environmental Digital Map construction.
The methodology for building the Digital Map also includes techniques for performing the continuous monitoring and control of the Trackside Digital Map in order to detect critical deviations with respect to the version assumed to be the reference Map. The use of Digital Maps in On-Board Signaling Subsystems, like innovative Automatic Train Protections (ATP) that use new technologies such as GNSS, IMU, LIDAR for train positioning or Automatic Train Operations (ATO), has been recognized as a key requirement. However, specific relevant challenges in the adoption of Digital Maps will be represented by the cost of building and maintenance of the Maps According to the approach above, the building processes for Ground Truth and Digital Map are based on the postprocessing of measurement data recorded, by using not only specific diagnostic/test trains but also commercial trains, provided that they are equipped with the RAILGAP measurement system.
Onboard Sensors Characterization Methodology Overview The railway environment presents many hazards for the EGNSS technology and EGNSS performance is strongly dependent on the number of visible satellites that can be reduced in urban or forest canyons, or even none in tunnels. The railway specific scenarios complicate the chances of a unique EGNSS solution to fulfil the stringent railway requirements. In order to guarantee the required reliability, safety and availability, any railway localization or trajectory determination function must consider the use of different sensor data in addition to the odometric system already existing on board the train. Furthermore, the construction of enhanced digital maps that not only provide topographic information of the railway tracks but also key elements of the surrounding essential for signalling purposes require the use of camera or LIDAR sensors.
Global Navigation Satellite System Satellite-based navigation solution are now widely spread in every day's life as they offer an absolute continuous position everywhere and anytime. GNSS solutions are based on time of propagation measurement that characterize the distance between satellites and the receiver. Mass market receivers rely on code measurement and provides accuracy of couple of meters. For RAILGAP needs of generating GT and creating Digital maps, a more accurate solution is required. For that reason, carrier-phase measurement will also be exploited, that can improve accuracy up to an order of less than a meter. However, both pseudoranges and carrier-phase measurements are affected by a number of systematic errors or biases and random noises. Those error sources, by the point of origin, are divided into: satellite related errors (clock bias, orbit errors…), propagation medium related errors (ionospheric and tropospheric delays) and receiver related errors (hardware noise, clock bias and multipath).
In other conditions of reception, local effects will be added. First results show the impact of the hardware installation on the error characterization. The data used for the following analysis was collected during the ERSAT GGC project. This dataset was used because the antenna installation in the RAILGAP project is going to be the same as the one used in the Sardinia Trial Site of the ERSAT GGC project in Italy. The accuracy of characterization of the nominal case, directly affects the accuracy of fault detection and the contribution of the GNSS receiver in the multi-sensor final solution. Additionally, time-correlated error models are also going to be considered for the implementation of sequential-estimators-based GNSS/INS integration algorithms in order to achieve the level of accuracy RAILGAP requires.
The random bias, also known as turn-on bias, changes every time the sensor is switched on. This bias is small in high quality sensors and significant in lower-cost IMUs. It can be partially estimated by an initial alignment procedure or must be estimated when the INS is coupled with other sensors like GNSS. The noise error cannot be known a priori and it evolves over time with certain stochastic behavior. Its characterization is the main focus in RAILGAP so that the derived models can be used for the design of rigorous GNSS/INS estimators. The first step consists on the identification of the types of noises. This is done traditionally by analyzing static IMU time-series data and the use of Allan Deviation (AD) estimators, that provide information of the dominant noises at different time intervals.
Conclusions The paper provided an overview of the main goals of the EU project RAILGAP and how a proper characterization of different sensors will enable building robust and accurate ground truth and digital map, filling an important gap for the development and assessment of new solutions in railway applications. During the RAILGAP project, the collection of a large measurement dataset in diverse railway scenarios will be carried out with trains under commercial operation that will allow, for the first time, to perform a rigorous error modelling and statistical assessment of the different technologies under railway environment. Next contributions of the project will be the development of the Ground Truth and Digital map relying on these characterizations.
Modelling details CenW ecosystem model overview. We used the CenW ecosystem model  [34]  for our model-based study. CenW is a dynamic process-based model, combining the major carbon, energy, nutrient and water fluxes in an ecosystem  [34]. CenW uses site characteristics, climate data and management input to predict how different pools, such as leaves, roots and different fractions of organic carbon, will change over time under the influence of any external factors, such as weather variables or management choices. For the present work, we used CenW version 4. 2, which runs on a daily time step and dynamically simulates canopy photosynthesis and vegetation growth through the allocation of fixed carbon to leaves and roots. The carbon and nitrogen cycles are completed through litter fall, decomposition, autotrophic and heterotrophic respiration, and their relationships to climatic drivers, allowing the simulation of the behavior of ecosystems over time.
RCP 4. 5 is a stabilization scenario, where total radiative forcing is stabilized by 2100 using a range of strategies for reducing anthropogenic GHG emissions. The most pessimistic scenario is RCP 8. 5, corresponding to high GHG concentrations and characterized by continuous increases of GHG emissions beyond 2100. For this study, we selected data from the CNRM2014 experiment because all meteorological variables needed for running CenW were available at a fine spatial resolution of 8 km for all of France  [44]. The CNRM2014 experiment consisted of the dynamic downscaling of modelled climate variables under different climate change scenarios from the global to regional (France) scales. At first, atmospheric GHG concentrations from the different RCP scenarios were used to run the CNRM-CM5 v5.
1 and ARPEGE-Climat v5. 1 global circulation models. Then, results from these models were used to inform a regional climate model called ALADIN-Climat v5. 2 that delivered continuous daily meteorological variables at a 12-km resolution across France. Finally, daily meteorological variables were projected onto an 8-km grid and bias corrected. The dataset was accessed through the DRIAS portal (https://drias-prod. meteo. fr/okapi/ accueil/okapiWebDrias/index. jsp?lang=en) and included projections of daily precipitation (Figs 1 and 2e, 2f in S1 File), minimum and maximum air temperatures, air humidity and global solar radiation for the reference period , and for the period 2006-2100 for the RCP 2. 6, RCP 4. 5 and RCP 8. 5 scenarios. The atmospheric CO 2 concentrations data under the different RCP scenarios were downloaded from the RCP Database version 2.
pcic" version 1. 1-9. 1 was applied to the ensemble of meteorological data used to run the CenW model for the different RCP scenarios with the 1975-2005 period selected as the reference. The first three selected indices relate to extreme hot temperatures and the last three to precipitation. The number of hot days (NHD) is calculated as the number of days in a year with air temperatures above 25˚C, the second (TX90P) correspond to the proportion of days with temperatures above the 90th percentile of the reference period. The warm spell duration index (WSDI) characterizes the length of heat waves and is defined as the number of days in a year with at least 6 consecutive days with max temperature above the 90 th percentile of the reference period. The maximum length of the dry spell index (MLDS) represents the annual maximum number of consecutive days where daily precipitation is less than 1 mm d -1.
AC received funding principally by the " New Zealand Government -Livestock Research for milk production. It highlights the importance of tailoring farming practices to achieve the dual goals of maintaining agricultural production while safeguarding soil C stocks. Group of the  Global Research Alliance on Agricultural Greenhouse Gases  under grants  SOW12-GPLER-LCR-PM (Proposal ID 16949-15 LCR ), the  AnaEE France  ( ANR , grant number  11-INBS-0001 ), the  SOILWARM  ( ANR , grant number  21-CE01-0025 ), and the  CarSolEI ADEME  project  2252 , (grant number  30001461 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Complementary foliar measurements Area, mass, and K-deficiency symptom development of individual leaves were measured for the studied sites to parameterise the new leaf cohort sub-model and the K-deficiencyhttps://doi. org/10. 5194/bg-20-  3093-2023 3093- Biogeosciences, 20, 3093-3117, 2023  symptom area sub-model described below. To this aim, we used the scanned pictures (tabletop scanner device model HP ScanJet G4050; 300 dpi) of leaves collected during the biomass samplings at both sites (every 6 months at EU-CFLUX and annually at Itatinga), on at least six trees per date and treatment, and at three crown levels. Individual leaf areas as well as the proportion of anthocyanic symptoms on individual leaves were automatically computed from the images based on a colour threshold calibrated by photointerpretation and automatised in a MATLAB ® script (le Maire, 2023).
The leaf-scale metrics of each canopy third were upscaled to stand averages using linear regressions with individual tree D 2 H (i. e. the product of squared diameter with tree height). Regressions were done using the scikit-learn Python library  (Pedregosa et al. , 2011). The resulting parameters and functions were then applied to the D 2 H of trees using inventories of diameter and height of plots. This allowed the upscaling of leaf individual area and symptomatic leaf area in order to compute their plot averages.