Browse Articles

Enduring CO Electrolysis with Ampere-Level Reaction Rates Using Nickel-Doped Iridium Catalysts

In silico exploration of anticancer plant phytochemicals for EGFR-targeted lung cancer therapy

Cobalt-Catalyzed Enantioselective and Diastereodivergent Construction of C–N Atropisomers

In-vitro assessment of anti-breast cancer potential of pumpkin seed oil nanoemulsion with mechanistic insights into cytotoxicity and apoptosis

Robust High-Spin State in One-Dimensional CrX₂ (X = Cl, Br, I) at the Single-Chain Limit

A data-driven approach to prioritize MITRE ATT&CK techniques for active directory adversary emulation

Abstract Advanced Persistent Threats (APTs) continue to evolve, employing sophisticated and evasive techniques that pose significant challenges to modern defense mechanisms, particularly in Active Directory (AD) environments. Adversary emulation serves as a proactive security strategy, enabling organizations to replicate real-world adversary behaviors to assess and enhance detection, response, and mitigation capabilities. However, existing frameworks often lack a structured approach to prioritizing techniques based on impact, feasibility, and security control gaps, leading to suboptimal resource allocation. This study proposes a Multi-Criteria Decision-Making (MCDM) approach that integrates Operational Threat Intelligence (OTI) and structured datasets from MITRE ATT&CK to systematically prioritize adversary techniques. The methodology evaluates techniques across three key dimensions: Active Directory Impact, Threat Score, and Security Control Gap, employing entropy-based weighting to ensure an objective and data-driven prioritization process. To validate the proposed framework, a real-world case study based on the APT3 threat group is presented, demonstrating the applicability and effectiveness of the prioritization strategy in aligning adversary emulation with real-world attack scenarios. By focusing on high-impact and difficult-to-detect techniques, this framework enhances the effectiveness of adversary emulation and strengthens security postures in AD environments.

Magnitude of atopic dermatitis and associated factors among children in northwest Ethiopia

Response characteristics of shale permeability to cyclic loading under different gas pressures

Abiotic reactions drive post-wetting soil emissions of N2O and NO and contribute partially to CO2 emissions

Toxicity, health risks, environmental effects of trace metals in the sediments of the Middle Black Sea (Samsun) and bioaccumulation in Hyallella azteca amphipods

A modified perturb and observe MPPT algorithm for PEMFC with rapid convergence and low power oscillation

Abstract Proton Exchange Membrane Fuel Cells (PEMFCs) enable continuous energy production regardless of environmental conditions due to the storability of hydrogen. When examining the current–power (I–P) curve of a PEMFC under steady-state operating conditions, maximum power is observed at a specific current level. To extract this power, Maximum Power Point Tracking (MPPT) algorithms are employed. These algorithms should feature a simple structure and rapidly track the maximum power point. However, intelligent and optimization-based methods in the literature often involve high computational complexity. In this study, a modified Perturb and Observe (P&O)-based MPPT algorithm is developed to achieve a fast steady-state response under varying PEMFC operating conditions. The proposed algorithm also minimizes power oscillations in the steady state. Its performance is evaluated in a MATLAB/Simulink environment under five different scenarios. A comparative analysis is conducted against the conventional P&O and optimization-based MPPT algorithms, including Particle Swarm Optimization (PSO), Cuckoo Search Algorithm (CSA), and Genetic Algorithm (GA). The results, presented graphically, demonstrate the advantages of the proposed approach.

Exploring the crop suitability of first-contact tribal lands in the eastern United States: impacts of past and near-future climate conditions

Multistage fall detection framework via 3D pose sequences and TCN integration

Direct somatic embryogenesis induction in Aspilia Africana (Pers.) C. D. Adams, and assessment of genetic homogeneity and physiology of regenerants

Complete mitochondrial genome of a novel black soldier fly isolate from Jordans

The phosphatases TCPTP, PTPN22, and SHP1 play unique roles in T cell phosphotyrosine maintenance and feedback regulation of the TCR

Circular q-rung orthopair fuzzy modeling using Schweizer-Sklar weighted aggregation for water purification strategy selection

Early European evidence of artificial cranial modification from the Italian Late Upper Palaeolithic Arene Candide Cave

Characterizing a highly excited and sustained brain response activity during gaming: P300-CE

Inspired by the high engagement and sustained behavioral excitement observed in video game players, we hypothesized that distinct brain activity patterns occur during gaming compared to a generic nongame setting. Using electroencephalography (EEG), we characterized the brain’s response to critical in-game events, focusing on the response magnitude and adaptation. Data from a large participant cohort (n = 140 for game tasks, n = 200 for nongame tasks) revealed a clear, definite, and consistent temporospatial structure of brain response patterns triggered by critical in-game events. Most notably, this response displayed unusually large magnitudes and a lack of adaptation over repeated exposures—markedly different from the observed neural responses to typical events in lab-based tasks (e.g., regarding novelty, unexpected events or errors). The identification of this distinctive response component in gaming contexts may inspire further research into fundamental cognitive systems, such as motivation, reward, emotional engagement, and their dynamic interplay in gaming environments.

Influence of professional background on assessment of simulated cardiopulmonary resuscitation videos in an observational study

Abstract Video-assisted dispatcher support in cardiopulmonary resuscitation (VA-CPR) has shown beneficial effects on CPR quality. This study examines the influence of professional background on the ability to identify typical errors in CPR performance using video-based assessments. Within this simulation-based observational study, 61 participants (31 EMS personnel, 30 emergency physicians) evaluated nine video sequences showing simulated CPR or ventilation. Participants were grouped by profession, not randomly. The primary endpoint was the correct identification of expert-defined errors in the presented videos, analyzed in relation to professional background. Evaluation accuracy for CPR and ventilation videos was examined in relation to participant characteristics. Overall, n = 427 CPR videos were correctly classified in 73.3% of cases by EMS personnel and 75.7% by emergency physicians (β = 0.370, SE = 0.297, 95% CI: −0.21 to 0.95, p = 0.213). Ventilation scenarios (n = 122) were correctly classified in 93.5% (EMS) and 98.3%, (EP) (β = 4.50, SE = 6.73, 95% CI: −8.82 to 17.82, p = 0.505). The models assessing classification accuracy for CPR and ventilation did not reach statistical significance (pCPR=0.869 and pventilation=0.183), and none of the tested predictors were significantly associated with evaluation accuracy. No significant differences in evaluation accuracy for CPR and ventilation videos were observed between professional groups or across tested participant characteristics.