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Introduction In recent years, there has been a concerning increase in suicidal thoughts and, in some countries, completed suicides, amplified by the COVID-19 pandemic. Screening for suicidal ideation (SI) in the general population is limited due to ethical, effectiveness, and feasibility concerns. Identifying individuals at risk of suicide remains a complex challenge. Our study aimed to develop a predictive model using COVID-19 data, gathering psychometric information from 1790 respondents in Slovenia via an online survey conducted between July 2020 and December 2020, with a second wave of data (ne=1200) collected from January 2022 to February 2022. Objectives With 9.7% of respondents reporting recent SI in the first wave of data, our primary goal was to estimate SI indirectly using SIDAS. We examined changes in habits, demographics, coping strategies, and satisfaction in key life aspects to discreetly identify potential risk factors. Methods We employed four machine learning algorithms (logistic regression, random forest, XGBoost, and support vector machines) and assessed model performance using the area under the receiver operating characteristic curve (AUC). Initial assessment used a held-out dataset, followed by validation with a new cohort of 1,200 users from the late COVID-19 period. Results Logistic regression, random forest, and XGBoost achieved comparable AUCs, reaching 0.83 on unseen data. Our analysis revealed significant associations between Brief-COPE subscales and SI. Self-Blame emerged as a strong SI indicator, followed by increased Substance Use, reduced Positive Reframing, Behavioral Disengagement, dissatisfaction with relationships, and younger age, in both 2020 and 2022 models. The model consistently performed well, even with varying population characteristics. Conclusions These results suggest that SI presence can be reasonably estimated using selected indicators, offering promise for developing an indirect screening tool without explicit questioning about suicidal thoughts. However, individuals flagged as at-risk should undergo clinical examination, as this model serves as an initial step in identifying SI risk factors in the context of the stressful event’s (COVID-19 pandemic) impact on mental health. Disclosure of Interest None Declared

Gathered from a real-world discrete manufacturing floor, this dataset features measurements of pneumatic pressure and electrical current during production. Spanning 7 days and encompassing approximately 150 processed units, the data is organized into time series sampled at 100 Hz. The observed machine performs 24 steps to process each unit. Each measurement in the time series, is annotated, linking it to one of the 24 processing steps performed by the machine for processing of a single piece. Segmenting the time series into contiguous regions of constant processing step labels results in 3674 labeled segments, each encompassing one part of the production process. The dataset enriched with labels facilitates the use of supervised learning techniques, like time series classification, and supports the testing of unsupervised methods, such as clustering of time series data.The focus of this dataset is on an end-of-line testing machine for small consumer-grade electric drive assemblies (device under test – DUT). The machine performs multiple actions in the process of evaluating each DUT, with the dataset capturing the pneumatic pressures and electrical currents involved. These measurements are segmented in alignment with the testing machine's internal state transitions, each corresponding to a distinct action undertaken in manipulating the device under observation.The included segments offer distinct signatures of pressure and current for each action, making the dataset valuable for developing algorithms for the non-invasive monitoring of industrial (specifically discrete) processes.

We report on a quantum critical behavior in the quasi-1D spin-1/2 zigzag frustrated chain antiferromagnet CaCoV_{2}O_{7}, induced by an applied magnetic field. Below T_{N}=3.3 K our zero-field neutron diffraction studies revealed the up-up-down-down spin structure, stabilized by an order-by-disorder phenomenon. At base temperature, the magnetic order is suppressed by an applied magnetic field (B), inducing a transition into a quantum paramagnetic state at B_{c}=3 T, as revealed by both neutron diffraction and ESR data. The transition exhibits an unusually sharp phase boundary with the critical exponent ϕ=0.164(3)≈1/6, in contrast to the earlier experimental observations for uniform spin-1/2 chain systems. Such a sharp QPT is anticipated due to a rare combination of spin-orbit coupling and competing NN and NNN exchange interactions J_{1} and J_{2} of the zigzag spin chain.