Miracle-dz/UniFAR-FTUs huggingface dataset:
Incidence and Comparison of Suicide in Various Phases of the Menstrual Cycle: a Systematic Review and Meta-analysis

Background: Suicide is a global health concern. There are reproductive health-related factors that are responsible for increasing the risk of female suicide. There are a number of studies examining the association between suicide and the menstrual cycle, but still, there are no conclusive findings. Aim: We aimed to pool data from all the studies reporting data on suicides and the menstrual cycle phase to report the following outcomes: incidence of suicidal deaths in the menstrual, secretory, and proliferative phases, and to find out whether the burden of suicide in the menstrual phase in particular, was more at a young age (18-35 years) or middle age (36-50years). Methods: The PubMed database was extensively searched from inception till 12th April 2022. The data for the number of events occurring for each outcome were pooled using random-effects model and forest plots were created. Results: Five articles were shortlisted for inclusion in our analysis. Incidence of suicide in the secretory phase was highest at 45.2% [95% CI, 0.367–0.537]. The incidence of suicide, when occurring in the menstrual phase, was reported to be 68.4% (95 CI, 0.317–1.052) and 31.6% (95 CI, -0.052.3–0.68) for young-aged and middle-aged victims, respectively. Conclusion: Our results demonstrate that the menstrual phase has a lower risk of mortality due to suicide when compared to the other two phases of the menstrual cycle. Nevertheless, when suicide occurred in the menstrual phase, the incidence of suicide among the younger age-group was higher than for those in the middle age-group.
Comparative Analysis of Different Attack on Web Log Data using Machine Learning Technique

: It is proposed in this research that a multi-stage filter be designed based on the analysis and distribution of various types of network assaults in web log datasets. An extended GOA algorithm with a decision tree algorithm is used in the first stage of the filter to detect frequent attacks, and an enhanced GOA algorithm with a genetic algorithm is used in the second stage to detect moderate attacks. An upgraded GOA algorithm using Nave Bayes as a base learner has been utilized in the final stage of the filter to detect the rare attacks. Many features are included in benchmark datasets used to test and evaluate intrusion detection systems. These massive datasets, on the other hand, necessitate more computational power and time. Intrusion detection relies heavily on the identification of relevant and irrelevant features in high-dimensional datasets. This research provides a strategy for reducing the number of features in an ensemble in order to better classify web-attacks. Information security policy breaches are known as intrusions. Intrusion detection (ID) is a set of actions for detecting and recognizing suspicious behaviors that make the expedient acceptance of standards of secrecy, quality, consistency, and availability of a computer-based network system more difficult. Using the GOA technique, we describe a new approach to feature selection and classification for the NSL-KDD cup 99 intrusion detection dataset. As a primary goal, it is to reduce the number of features used in training data for intrusion classification. Features are selected and eliminated in supervised learning in order to improve classification accuracy by focusing on the most significant input training features and eliminating those that are less important. Several input feature subsets of the training dataset of NSL-KDD cup 99 dataset were used in the experiment to test the classifier.
Simulated responses of soil carbon to climate change in CMIP6 Earth system models: the role of false priming

Abstract. Reliable estimates of soil carbon change are required to determine the carbon budgets consistent with the Paris Agreement climate targets. This study evaluates projections of soil carbon during the 21st century in Coupled Model Intercomparison Project Phase 6 (CMIP6) Earth system models (ESMs) under a range of atmospheric composition scenarios. In general, we find a reduced spread of changes in global soil carbon (ΔCs) in CMIP6 compared to the previous CMIP5 model generation. However, similar reductions were not seen in the derived contributions to ΔCs due to both increases in plant net primary productivity (NPP, named ΔCs,NPP) and reductions in the effective soil carbon turnover time (τs, named ΔCs,τ). Instead, we find a strong relationship across the CMIP6 models between these NPP and τs components of ΔCs, with more positive values of ΔCs,NPP being correlated with more negative values of ΔCs,τ. We show that the concept of “false priming” is likely to be contributing to this emergent relationship, which leads to a decrease in the effective soil carbon turnover time as a direct result of NPP increase and occurs when the rate of increase in NPP is relatively fast compared to the slower timescales of a multi-pool soil carbon model. This finding suggests that the structure of soil carbon models within ESMs in CMIP6 has likely contributed towards the reduction in the overall model spread in future soil carbon projections since CMIP5.
Automated Multi-Dimensional Analysis of Peer Feedback in Middle School Mathematics

: Peer review, a commonly-used pedagogy in contemporary education has been found to positively influence student learning, benefitting both feedback provider and recipient. However, the quality of the feedback may vary, and lower-quality feedback (e.g., lacking specificity), is less likely to be implemented by the recipient, leading to suboptimal outcomes. Although recent work has used criteria to scaffold feedback to ensure quality, it is often difficult to monitor whether students follow these criteria. In this study, we develop models that automatically detect the attributes of student feedback, reflecting the presence of three pedagogically relevant constructs: 1) commenting on the process, 2) commenting on the answer, and 3) relating to self. We find models employing sentence embeddings produce the best results, with AUC ROCs ranging from . 90-. 96, and are robust to algorithmic bias.
HEART DISEASE IDENTIFICATION AND NOTIFICATION SYSTEM USING MACHINE LEARNING

— The most serious health problem is heart disease (HD), which has affected many people all over the world. Shortness of breath, muscular weakness, and swollen feet are prominent signs of HD. Due to a number of factors, including accuracy and execution time, present heart disease diagnosis techniques are not very effective in early time identification. Researchers are working to develop an effective method for the detection of heart disease. In this study, we suggested a machine learning-based system that can quickly and accurately diagnose cardiac problems. The categorization techniques used in the system's development include support vector machines, decision trees, and random forests. Then, in order to increase accuracy, we combined all of the algorithms mentioned earlier to create a hybrid algorithm. The classifiers' performances are evaluated using the performance measurement metrics. On the features chosen via features selection algorithms, the classifiers' performances have been evaluated. The flask framework website provides the final forecast. The suggested system can also recommend food to patients who test positive.
Observational benchmarks inform representation of soil organic carbon dynamics in land surface models

1 Representing soil organic carbon (SOC) dynamics in Earth system models (ESMs) is a key 2 source of uncertainty in predicting carbon climate feedbacks. Machine learning models can help 3 identify dominant environmental controllers and their functional relationships with SOC stocks. 4 The resulting knowledge can be implemented in ESMs to reduce uncertainty and better predict 5 SOC dynamics over space and time. In this study, we used a large number of SOC field 6 observations (n = 54,000), geospatial datasets of environmental factors (n = 46), and two 7 machine learning approaches (Random Forest (RF) and Generalized Additive Modeling (GAM)) 8 to: (1) identify dominant environmental controllers of global and biome-specific SOC stocks, (2) 9 derive functional relationships between environmental controllers and SOC stocks, and (3) 10 compare the identified environmental controllers and predictive relationships with those in 11 Coupled Model Intercomparison Project phase six (CMIP6) models. Our results showed that 12 diurnal temperature, drought index, cation exchange capacity, and precipitation were important 13 observed environmental controllers of SOC stocks. RF model predictions of global-scale SOC 14 stocks were relatively accurate (R 2 = 0.61, RMSE = 0.46 kg m -2 ). In contrast, precipitation, 15 temperature, and net primary productivity explained >96% of ESM-modeled SOC stock 16 variability. We also found very different functional relationships between environmental factors 17 and SOC stocks in observations and ESMs. SOC predictions in ESMs may be improved 18 significantly by including additional environmental controls (e.g., cation exchange capacity) and 19 representing the functional relationships of environmental controllers consistent with 20 observations. 21
Sea ice concentration satellite retrievals influenced by 1 surface changes due to warm air intrusions: A case study 2 from the MOSAiC expedition

21 Warm air intrusions over Arctic sea ice can rapidly change the snow and ice sur- 22 face conditions and can alter sea ice concentration (SIC) estimates derived from 23 satellite-based microwave radiometry without altering the true SIC. Here we focus 24 on two warm moist air intrusions that produced surface glazing during the Mul- 25 tidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) 26 expedition that reached the research vessel Polarstern in mid-April 2020. After 27 the events, we observe increased SIC deviations between different satellite prod- 28 ucts, including climate data records, and especially an underestimation of SIC 29 for algorithms based on polarization difference. To examine the causes of this 30 underestimation, we use the extensive MOSAiC snow and ice measurements
The contribution of residential wood combustion to the PM2.5 concentrations in the Helsinki metropolitan area

Abstract. This article has investigated the contribution of residential wood combustion (RWC) to the fine particulate matter (PM2.5) concentrations in the Helsinki metropolitan area (HMA) for 6 years, from 2009 to 2014. We have used the PM2.5 concentrations measured at eight air quality measurement stations. The dispersion of emissions on an urban scale was evaluated with multiple-source Gaussian dispersion models, UDM-FMI (urban dispersion model of the Finnish Meteorological Institute) and CAR-FMI (Contaminants in the Air from a Road model of the Finnish Meteorological Institute), and on a regional scale using the chemical transport model SILAM (System for Integrated modeLling of Atmospheric coMposition). The overall agreement of the predicted concentrations with measurements of PM2.5 was good or fairly good for all stations and years; e.g. at the permanent residential station the daily average values of the index of agreement ranged from 0.69 to 0.81, and the fractional bias values ranged from −0.08 to 0.11, for the considered 6 years. Both the measured and predicted daily averaged concentrations showed increasing trends towards the lower-temperature values. The highest predicted annual averaged concentrations in the region occurred in the vicinity of major roads and streets and in the suburban residential areas to the northwest, north, and northeast of the city centre. The average concentrations of PM2.5 attributed to RWC in winter were up to 10- or 15-fold, compared to the corresponding concentrations in summer. During the considered 6-yearly period, the spatially highest predicted fractions of RWC of the annual PM2.5 concentrations ranged from 12 % to 14 %. In winter, the corresponding contributions ranged from 16 % to 21 %. The RWC contribution was higher than the corresponding urban vehicular traffic contribution at all the residential stations during all years. The study has highlighted new research needs for the future, in particular (i) the modelling of the RWC emissions that would be explicitly based on the actual ambient temperatures and (ii) the modelling of the impacts of the most important holiday periods on the emissions from RWC.
The emergence of pathology in gynaecology1

In this address I propose to recount some gleanings I have made whilst probing the literature and the ideas that were held in past years in the context of pathology and gynaecology. Inevitably, people have to be considered alongside the work for which they are noted in order to do justice to my purpose, and it is advantageous to try to bring together presentday knowledge and those elements from former times that have stood the test of experience.
Multi-phase Biogeochemical Model for Microbially Induced Desaturation and Precipitation

A next-generation biogeochemical model was developed to explore the impact of the native water source on microbially induced desaturation and precipitation (MIDP) via denitrification. MIDP is a non-disruptive, nature-based ground improvement technique that offers the promise of cost-effective mitigation of earthquake-induced soil liquefaction under and adjacent to existing structures. MIDP leverages native soil bacteria to reduce the potential for liquefaction triggering in the short term through biogenic gas generation (treatment completed within hours to days) and over a longer term through calcium 20 carbonate precipitation (treatment completed in weeks to months). This next-generation biogeochemical model expands earlier modeling to consider multi-phase speciation, bacterial competition, inhibition, and precipitation. This biogeochemical model was used to explore the impact of varying treatment recipes on MIDP products and by-products in a natural seawater environment. The case study presented herein demonstrates the importance of optimizing treatment recipes to minimize unwanted by-products (e.g., H 2 S production) or incomplete denitrification (e.g., nitrate and nitrite accumulation)
Anticipating False Beliefs and Planning Pertinent Reactions in Human-Aware Task Planning with Models of Theory of Mind

It is essential for a collaborative robot to consider Theory of Mind (ToM) when interacting with humans. Indeed, performing an action in the absence of another agent may create false beliefs like in the well-known Sally & Anne Task (Wimmer and Perner 1983). The robot should be able to detect, react to, and even anticipate false beliefs of other agents with a detrimental impact on the task to achieve. Currently, ToM is mainly used to control the task execution and resolve in a reactive way the detrimental false beliefs. Some works introduce ToM at the planning level by considering distinct beliefs, and we are in this context. This work proposes an extension of an existing human-aware task planner and effectively allows the robot to anticipate a false human belief ensuring a smooth collaboration through an implicitly coordinated plan. First, we propose to capture the observability properties of the environment in the state description using two observability types and the notion of co-presence . They allow us to maintain distinct agent beliefs by reasoning directly on what agents can observe through specifically modeled Situation Assessment processes, instead of reasoning of action effects. Then, thanks to the better estimated human beliefs, we can predict if a false belief with adverse impact will occur. If that is the case then, first, the robot’s plan can be to communicate minimally and proactively. Second, if this false belief is due to a non-observed robot action, the robot’s plan can be to postpone this action until it can be observed by the human, avoiding the creation of the false belief. We implemented our new conceptual approach, prove its soundness and completeness, discuss its effectiveness qualitatively, and show experimental results on three novel domains.
Arm Morello Programme: Architectural security goals and known limitations

Arm’s Morello prototype incorporates a ﬁrst-generation CHERI-enabled Armv8-A CPU prototype. We have developed Morello to enable CHERI-based research by a growing community of researchers seeking access to potentially transformative architectural security improvement. This includes supporting experimentation, evaluation, and demonstration across microarchitecture and software. Morello is an exciting opportunity to work with – and improve – CHERI, and we seek your help and collaboration in preparing CHERI for mainstream use. The purpose of this document is to lay out the speciﬁc architectural security objectives of the Arm Morello prototype, as well as areas that fell out of scope for the project. We invite not only your feedback, but also your collaboration, in helping us to create a future class of CHERI-extended processors that dramatically enhance software security.
The Re-Emergence of Hepatitis E Virus in Europe and Vaccine Development

Hepatitis E virus (HEV) is one of the leading causes of acute viral hepatitis. Transmission of HEV mainly occurs via the fecal-oral route (ingesting contaminated water or food) or by contact with infected animals and their raw meat products. Some animals, such as pigs, wild boars, sheep, goats, rabbits, camels, rats, etc., are natural reservoirs of HEV, which places people in close contact with them at increased risk of HEV disease. Although hepatitis E is a self-limiting infection, it could also lead to severe illness, particularly among pregnant women, or chronic infection in immunocompromised people. A growing number of studies point out that HEV can be classified as a re-emerging virus in developed countries. Preventative efforts are needed to reduce the incidence of acute and chronic hepatitis E in non-endemic and endemic countries. There is a recombinant HEV vaccine, but it is approved for use and commercially available only in China and Pakistan. However, further studies are needed to demonstrate the necessity of applying a preventive vaccine and to create conditions for reducing the spread of HEV. This review emphasizes the hepatitis E virus and its importance for public health in Europe, the methods of virus transmission and treatment, and summarizes the latest studies on HEV vaccine development.
Projection of snowfall extremes in the French Alps as a function of elevation and global warming level

Abstract. Following the projected increase in extreme precipitation, an increase in extreme snowfall may be expected in cold regions, e.g., for high latitudes or at high elevations. By contrast, in low- to medium-elevation areas, the probability of experiencing rainfall instead of snowfall is generally projected to increase due to warming conditions. Yet, in mountainous areas, despite the likely existence of these contrasted trends according to elevation, changes in extreme snowfall with warming remain poorly quantified. This paper assesses projected changes in heavy and extreme snowfall, i.e., in mean annual maxima and 100-year return levels, in the French Alps as a function of elevation and global warming level. We apply a recent methodology, based on the analysis of annual maxima with non-stationary extreme value models, to an ensemble of 20 adjusted general circulation model–regional climate model (GCM–RCM) pairs from the EURO-CORDEX experiment under the Representative Concentration Pathway 8.5 (RCP8.5) scenario. For each of the 23 massifs of the French Alps, maxima in the hydrological sense (1 August to 31 July) are provided from 1951 to 2100 and every 300 m of elevations between 900 and 3600 m. Results rely on relative or absolute changes computed with respect to current climate conditions (corresponding here to +1 ∘C global warming level) at the massif scale and averaged over all massifs. Overall, daily mean annual maxima of snowfall are projected to decrease below 3000 m and increase above 3600 m, while 100-year return levels are projected to decrease below 2400 m and increase above 3300 m. At elevations in between, values are on average projected to increase until +3 ∘C of global warming and then decrease. At +4 ∘C, average relative changes in mean annual maxima and 100-year return levels, respectively, vary from −26 % and −15 % at 900 m to +3 % and +8 % at 3600 m. Finally, for each global warming level between +1.5 and +4 ∘C, we compute the elevation threshold that separates contrasted trends, i.e., where the average relative change equals zero. This elevation threshold is shown to be lower for higher return periods, and it is projected to rise from 3000 m at +1.5 ∘C to 3350 m at +4 ∘C for mean annual maxima and from 2600 to 3000 m for 100-year return levels. These results have implications for the management of risks related to extreme snowfall.
Soil-biodegradable plastic films do not decompose in a lake sediment over 9 months of incubation

Abstract. Agriculture relies heavily on the use of plastic mulch films, which increase crop yields and can lower water demands. In recent years, soil-biodegradable mulch films have been marketed to replace the non-biodegradable, conventional polyethylene-based mulch films. These biodegradable mulch films are designed to be ploughed into the soil after use to be biodegraded in situ by soil microorganisms. However, research has shown that part of the mulch film material may be transported from the fields to neighboring environments, including aquatic ecosystems. Research on potential biodegradation of soil-biodegradable plastics in freshwater habitats is lacking. Here, we investigated the mineralization of soil-biodegradable agricultural mulch films in freshwater lake sediments of Lake Lucerne, Switzerland. Two types of commercial soil-biodegradable mulch films were incubated within lake sediment cores, along with traditional polyethylene (PE) plastic, and the production of CO2 and CH4 was followed over time relative to non-plastic-containing control sediments. After the 40-week incubation period, the films were visually intact and showed no signs of mineralization. Gas analyses showed no additional production of either CO2 or CH4 in the degradable mulch film incubations, compared to the control or PE plastic incubations. We conclude that these two used soil-biodegradable mulch films have a low biodegradability in lake sediments, likely reflecting that the microbial community structure in the lake sediment lacks active microbial degraders. Our results highlight the importance of preventing transport of soil-biodegradable mulch films from agricultural soils to surrounding aquatic environments.
GAIN: On the Generalization of Instructional Action Understanding

Despite the great success achieved in instructional action understanding by deep learning and mountainous data, deploying trained models to the unseen environment still remains a great challenge, since it requires strong generalizability of models from in-distribution training data to out-of-distribution (OOD) data. In this paper, we introduce a benchmark, named GAIN, to analyze the GeneralizAbility of INstructional action understanding models. In GAIN, we reassemble steps of existing instructional video training datasets to construct the OOD tasks and then collect the corresponding videos. We evaluate the generalizability of models trained on in-distribution datasets with the performance on OOD videos and observe a significant performance drop. We further propose a simple yet effective approach, which cuts off the excessive contextual dependency of action steps by performing causal inference, to provide a potential direction for enhancing the OOD generalizability. In the experiments, we show that this simple approach can improve several baselines on both instructional action segmentation and detection tasks. We expect the introduction of the GAIN dataset will promote future in-depth research on the generalization of instructional video understanding. The project page is https://jun-long-li.github. io/GAIN.