With the dynamic nature of modern software development and operations environments and the increasing complexity of cloud-based software systems, traditional monitoring practices are often insufficient to timely identify and handle unexpected operational failures. To address these challenges, this paper presents the findings from a quantitative industry survey focused on the application of Machine Learning (ML) to enhance software monitoring and alert management strategies. The survey targets industry professionals, aiming to understand the current challenges and future trends in ML-driven software monitoring. We analyze 25 responses from 11 different software companies to conclude if and how ML is being integrated into their monitoring systems. Key findings revealed a growing but still limited reliance on ML to intelligently filter raw monitoring data, prioritize issues, and respond to system alerts, thereby improving operational efficiency and system reliability. The paper also discusses the barriers to adopting ML-based solutions and provides insights into the future direction of software monitoring.

While control barrier functions are employed in addressing safety, control synthesis methods based on them generally rely on accurate system dynamics. This is a critical limitation, since the dynamics of complex systems are often not fully known. Supervised machine learning techniques hold great promise for alleviating this weakness by inferring models from data. We propose a novel control barrier function-based framework for safe control through event-triggered learning, which switches between prioritizing control performance and improving model accuracy based on the uncertainty of the learned model. By updating a Gaussian process model with training points gathered online, the approach guarantees the feasibility of control barrier function conditions with high probability, such that safety can be ensured in a data-efficient manner. Furthermore, we establish the absence of Zeno behavior in the triggering scheme, and extend the algorithm to sampled-data realizations by accounting for inter-sampling effects. The effectiveness of the proposed approach and theory is demonstrated in simulations.

Organic materials have found widespread applications but require doping to overcome their intrinsically low carrier concentration. Doping injects free carriers into the polymer, moving the position of the Fermi level, and creates coulombic traps, changing the shape of the electronic density of states (DOS). We develop equations to explicitly map the DOS parameters to the Seebeck vs conductivity relationship. At low carrier concentrations, this relationship is a universal slope -kB/q\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$-{k}_{B}/q$$\end{document}, while at higher carrier concentrations, the slope becomes dependent on the shape of the DOS. We conclude that, at high doping, a heavy-tailed DOS leads to higher thermoelectric power factors.

The Rasch‐Built Pompe‐Specific Activity (R‐PAct) scale is a patient‐reported outcome measure specifically designed to quantify the effects of Pompe disease on daily life activities, developed for use in Dutch‐ and English‐speaking countries. This study aimed to validate the R‐PAct for use in other countries.

This paper is the first to analyse the role of women authors in fostering justice-relevant topics in climate adaptation research. As representation, citation and payment patterns remain gender-biased across scientific disciplines, we explore the case of climate science, particularly adaptation, as its most human-oriented facet. In climate research and policy, there has been a recent surge of interest in climate justice topics: mentions of justice have increased almost tenfold in Intergovernmental Panel on Climate Change Working Group 2 reports between the latest assessment cycles (AR5 and AR6). We conduct a systematic examination of the topic space in the adaptation policy scholarship. As it is a vast and rapidly growing field, we use topic modelling, an unsupervised machine learning method, to identify the literature on climate justice and related fields, as well as to examine the relationship between topic prevalence and the gender of the authors. We find climate change adaptation policy research to be male dominated, with women holding 38.8% of first and 28.8% of last authorships. However, we observe topic-specific variability, whereby the share of female authors is higher among publications on justice-relevant topics. Female authorship is highly linked to topics such as Community, Local Knowledge, and Governance, but less to Food Security and Climate Finance. Our findings corroborate the evidence that female authors play a significant role in advancing the research and dialogue on the relationship between climate change and areas that have meaningful impact on lives of women and other marginalised groups.

While control barrier functions (CBFs) are employed in addressing safety, control synthesis methods based on them generally rely on accurate system dynamics. This is a critical limitation, since the dynamics of complex systems are often not fully known. Supervised machine learning techniques hold great promise for alleviating this weakness by inferring models from data. We propose a novel \revision{approach for safe event-triggered learning of Gaussian process models in CBF-based continuous-time control for unknown control-affine systems. By applying a finite excitation at triggering times, our approach ensures a sufficient information gain to maintain the feasibility of the CBF-based safety condition with high probability. Our approach probabilistically guarantees safety based on a suitable GP prior and rules out} Zeno behavior in the triggering scheme. The effectiveness of the proposed approach and theory is demonstrated in simulations.

This study investigates the use of neural network and their ability to predict disease progression based on clinical data and biomarkers. Using deep neural networks, a model was developed that efficiently analyzes the complex relationship between various factors and predict the probability of disease. The model was validated using retrospective analysis which indicated a good predictive ability that could be further utilized in better diagnostics and personalized treatment methods. More importantly, reserch detected specific pattern in the data, which enabled a more accurate prediction of disease at different stages. The study tried to improve a model by fine-tuned neural networks and tested other frameworks to gain the highets precision. This research also provides a basic for future work in directing the development of personalized therapeutic approaches based on individual patient characteristics.

Abstract The aim of this study was to evaluate the phytotoxic, genotoxic, cytotoxic and antimicrobial effects of the Mentha arvensis L. essential oil (EO). The biological activity of M. arvensis EO depended on the analyzed variable and the tested oil concentration. Higher concentrations of EO (20 and 30 µg mL−1) showed a moderate inhibitory effect on the germination and growth of seedlings of tested weed species (Bellis perennis, Cyanus segetum, Daucus carota, Leucanthemum vulgare, Matricaria chamomilla, Nepeta cataria, Taraxacum officinale, Trifolium repens and Verbena × hybrida). The results obtained also indicate that the EO of M. arvensis has some genotoxic, cytotoxic and proliferative potential in both plant and human in vitro systems. Similar results were obtained for antimicrobial activity against eight bacteria, including multidrug-resistant (MDR) strains [Bacillus subtilis, Enterococcus faecalis, Staphylococcus aureus, methicillin-resistant S. aureus (MRSA), Escherichia coli, extended-spectrum beta-lactamase-producing (ESBL) E. coli, Pseudomonas aeruginosa and Salmonella enterica subsp. enterica serovar Enteritidis], with the effect on multidrug-resistant bacterial strains. Research indicates that the EO of M. arvensis shows phytotoxic, genotoxic, cytotoxic and antimicrobial effects, as well as its potential application as a herbicide and against various human diseases.

Using the strong-field-approximation theory beyond the dipole approximation we investigate above-threshold ionization induced by the monochromatic and bichromatic laser fields. Particular emphasis is on the approach based on the saddle-point method and the quantum-orbit theory which provides an intuitive picture of the underlying process. In particular, we investigate how the solutions of the saddle-point equations and the corresponding quantum orbits and velocities are affected by the nondipole effects. The photoelectron trajectories are two dimensional for linearly polarized field and three dimensional for two-component tailored fields, and the electron motion in the propagation direction appears due to the nondipole corrections. We show that the influence of these corrections is not the same for all contributions of different saddle-point solutions. For a linearly polarized driving field, we focus our attention only on the rescattered electrons. On the other hand, for the tailored driving field, exemplified by the ω–2ω orthogonally polarized two-color field, which is of the current interest in the strong-field community, we devote our attention to both the direct and the rescattered electrons. In this case, we quantitatively investigate the shift which appears in the photoelectron momentum distribution due to the nondipole effects and explain how these corrections affect the quantum orbits and velocities which correspond to the saddle-point solutions. Published by the American Physical Society 2024

Data-driven models that act as surrogates for computationally costly 3D topology optimization techniques are very popular because they help alleviate multiple time-consuming 3D finite element analyses during optimization. In this study, one such 3D CNN-based surrogate model for the topology optimization of Schoen’s gyroid triply periodic minimal surface unit cell is investigated. Gyroid-like unit cells are designed using a voxel algorithm and homogenization-based topology optimization codes in MATLAB. A few such optimization data are used as input–output for supervised learning of the topology-optimization process via the 3D CNN model in Python code. These models could then be used to instantaneously predict the optimized unit cell geometry for any topology parameters. The high accuracy of the model was demonstrated by a low mean square error metric and a high Dice coefficient metric. The model has the major disadvantage of running numerous costly topology optimization runs but has the advantages that the trained model can be reused for different cases of TO and that the methodology of the accelerated design of 3D metamaterials can be extended for designing any complex, computationally costly problems of metamaterials with multi-objective properties or multiscale applications. The main purpose of this paper is to provide the complete associated MATLAB and PYTHON codes for optimizing the topology of any cellular structure and predicting new topologies using deep learning for educational purposes.

The emergence of bacteria with antibiotic resistance and multiple resistance is characteristic of animal and human pathogens. It is wide known that bee products, which have been used in alternative medicine since ancient times, have antimicrobial potential. Application of bee products for therapeutic purposes is defined as apitherapy. The study aimed to evaluate the antimicrobial activity of commercial chestnut honey, pollen and propolis produced in western Bosnia and Herzegovina (Sanski Most) individually and in five combinations (apimixtures). The antimicrobial properties of samples were investigated using the agar well diffusion method against three Gram-positive bacteria (Bacillus subtilis subsp. spizizenii ATCC 6633, Methicillin-resistant Staphylococcus aureus ATCC 33591, Enterococcus faecalis ATCC 29212); three Gram-negative bacteria (ESBL producing Escherichia coli ATCC 35218, Salmonella enterica subsp. enterica serovar Enteritidis ATCC 13076, Pseudomonas aeruginosa ATCC 9027) and one fungal species (Candida albicans ATCC 10231). Pure bee pollen inhibited the growth of only Gram-negative bacteria, concentrated chestnut honey was active against all Gram-negative and Gram-positivebacteria, while 20% propolis extract and apimixtures A2 (80% honey and 20% propolis) and A3 (60% honey, 20% pollen and 20% propolis extract) inhibited the growth of all tested microorganisms. Chestnut honey andthree apimixtures (A1, A2 and A3) showed the highest antibacterial action against all tested Gram-negative bacteria and MRSA compared to other investigated samples. In this study, examined honeybee products from Bosnia and Herzegovina and their mixtures had significant activity against tested bacteria, including strains with proven resistance to conventional antibiotics, MRSA and ESBL producing E. coli.

Effective preprocessing of electroencephalography (EEG) data is fundamental for deriving meaningful insights. Independent component analysis (ICA) serves as an important step in this process by aiming to eliminate undesirable artifacts from EEG data. However, the decision on which and how many components to be removed remains somewhat arbitrary, despite the availability of both automatic and manual artifact rejection methods based on ICA. This study investigates the influence of different ICA-based artifact rejection strategies on EEG-based auditory attention decoding (AAD) analysis. We employ multiple ICA-based artifact rejection approaches, ranging from manual to automatic versions, and assess their effects on conventional AAD methods. The comparison aims to uncover potential variations in analysis results due to different artifact rejection choices within pipelines, and whether such variations differ across different AAD methods. Although our study finds no large difference in performance of linear AAD models between artifact rejection methods, two exeptions were found. When predicting EEG responses, the manual artifact rejection method appeared to perform better in frontal channel groups. Conversely, when reconstructing speech envelopes from EEG, not using artifact rejection outperformed other approaches.

elevated nocturnal BP clinic BP monitoring alone is inadequate. ABPM should become golden standard to confirm adequate BP control in patients with kidney disease.

This paper introduces a "lifecycle perspective" on social robot design and human-robot interaction, and explores the practices of maintenance, repair, and letting go of social robots. Drawing on interviews with robot owners and representatives of robot development and repair companies, we argue that these previously disregarded aspects of everyday use provide a context for negotiating the social value and meaning of interactions with robots. We discuss owner concerns about robot obsolescence, as well as company support for long term human-robot interaction through repair, reuse, and giving owners closure in letting go of robots they can no longer use. Our work expands the purview of HRI study and design beyond the common focus on initial design and adoption and to perceptions and practices that can foster more enduring relationships with social robots, support sustainability in robot design, and address owners’ emotional attachment to robots.