The development of flexible bioelectronics remains a complex, multidisciplinary process that demands specialized expertise and labor-intensive efforts, limiting scalability, adaptability and accessibility. Here, we introduce DeviceAgent, an autonomous multimodal AI agent that integrates large language models (LLMs), vision-language models (VLMs), and domain-specific computational tools into a unified framework for bioelectronics research. Leveraging the emergent reasoning abilities of LLMs and VLMs, DeviceAgent enables zero and few-shot generalization, contextual learning, and flexible task execution across modalities. A multimodal context memory system orchestrates these capabilities, providing end-to-end support across the experimental pipeline–from high-level design objectives to fabrication protocol generation, visual defect inspection, and electrophysiological signal analysis, while maintaining human oversight at critical decision points. We demonstrate its capabilities through the development of stretchable mesh electronics for interfacing with human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs), a representative application involving complex device architectures, heterogeneous material nanofabrication, and electrophysiology analysis. DeviceAgent autonomously (1) generates customized bioelectronic layouts; (2) creates comprehensive fabrication protocols tailored to specific materials and processes; (3) identifies microscopic defects using visual reasoning; and (4) analyzes cardiac electrophysiological recordings in an interpretable manner. By embedding LLMs and VLMs within a structured, tool-augmented architecture, DeviceAgent establishes a scalable and accessible paradigm for AI-scientist collaboration in nanofabrication and bioelectronics research.

Authors in this paper investigates the relationship between the business environment and the operating performance of the plastic products manufacturing sector intended for the construction industry in Bosnia and Herzegovina (BiH). The focus is placed on how regulatory and institutional conditions impact economic indicators such as sales revenue growth, profit, export earnings, total assets, and employment. Since empirical studies in this area are scarce, this research contributes to filling an important academic gap. The study relies on both secondary and primary data sources. First, the business environment of BiH is evaluated using reports. Second, financial and operational data were collected for the top 20 companies in the plastic products manufacturing sector for the construction industry in BiH. The analysis covers the period 2015–2024, using indicators of revenue, profit, exports, total assets, and number of employees. A trend analysis was conducted to identify growth patterns, while a qualitative assessment was applied to connect these trends with the conditions of the business environment. The analysis of company performance reveals a positive growth trend in all major economic categories. Sales revenue steadily increased between 2015 and 2024, reflecting growing demand in the markets. Profitability improved despite regulatory barriers, indicating a strong adaptive capacity of firms. Export earnings expanded, showing that plastic product manufacturers. Total assets of the companies grew significantly, suggesting reinvestment and capacity expansion. Employment levels also increased, underscoring the sector’s contribution to job creation. These results support the hypothesis of a significant impact of the plastic products manufacturing industry on economic performance indicators in BiH.

Connected and automated vehicles rely on data from various entities to support safety-critical applications such as Cooperative Adaptive Cruise Control (CACC). However, unauthorized data manipulation through, for example, data injection attacks can compromise vehicle safety and lead to incidents. Existing vehicular security mechanisms, such as Misbehavior Detection System (MBD), have limitations in detecting and mitigating all types of threats on their own. To address these limitations, our prior work has proposed the concept of a Trust Assessment Framework (TAF), which assesses data trustworthiness by combining evidence from multiple security systems operating as trust sources. However, TAF as a concept has not been extensively evaluated in safety-critical Cooperative Driving (CD) applications. In this work, we refine the architecture of the TAF and implement a software prototype based on it. We integrate the TAF prototype with a CACC simulation environment and implement three types of data injection attacks. We demonstrate that by incorporating multiple security mechanisms as trust sources, the TAF significantly improves attack detection performance and reduces the number of crashes by 86% compared to using a single security mechanism, such as MBD.

Wearable electrochemical biosensors offer a promising alternative to conventional invasive blood‐based methods for monitoring biomarkers in diagnostic or therapeutic applications. Microneedle (MN)‐based technology provides direct access to the skin's interstitial fluid (ISF), enabling real‐time monitoring of biomarkers. Nevertheless, current micro‐ and nanofabrication techniques do not adequately support the development of MN‐based wearable technology that can utilize soft hybrid conductive inks, limiting its use in transdermal biosensing. Herein, an MN‐based biosensing platform is developed by integrating 3D printing, soft lithography, and hybrid conductive ink technology, featuring a fenestrated MN shell (FMNS) that serves as a protective layer for the inner hybrid conductive ink coating and prevents delamination during skin application. This FMNS patch demonstrates a wide pH monitoring range, high selectivity and accurate detection of subtle ISF pH changes, safe integration of hybrid conductive inks, and reduced fabrication time and cost when compared to other microfabrication methods such as lithography and deep reactive ion etching. The biosensor excels in protecting the biosensing layer and demonstrates excellent analytical performance in monitoring changes in pH levels of the skin ISF. This micro‐ and nanofabrication approach has great potential in integrating hybrid conductive ink technology into transdermal wearable devices for health monitoring and diagnostics.

In computational cardiology, a paradigm shift has occurred with the transition from static cardiovascular risk assessment to dynamic, customized modeling. A hybrid conceptual framework for AI-based digital twins is presented in this paper, which combines simulation models informed by physics and datadriven perception models in a synergistic way. For conditions like myocardial infarction and stroke, this strategy seeks to provide previously unheard-of possibilities for disease prediction, real-time cardiovascular monitoring, and customized treatment optimization. Key elements of the framework include graph neural networks (GNNs) for modeling vascular topology, physicsinformed neural networks (PINNs) for hemodynamic analysis, and multi-scale mathematical underpinnings. We illustrate a crucial first step toward the realization of a comprehensive digital twin that is based on physiological first $p$ rinciples a nd responsive to real-time data by validating the data-driven perception module.

This study aims to determine the antioxidant and antibacterial potential of aqueous extracts of six different plant species, which are widely used in traditional medicine: Salvia officinalis L., Mentha longifolia (L.) Huds., Urtica dioica L., Rosmarinus officinalis L., Rubus fruticosus L. and Achillea millefolium L. The samples were collected in the Tuzla region between March and July 2025. All of the mentioned plants are important in traditional medicine and have been previously reported to have biological activity. The antioxidant activity was tested by the DPPH and FRAP methods, while the antibacterial potential was tested by the diffusion technique on reference bacterial strains from the WDCM collection. The extracts showed high antioxidant activity, with nettle and sage extracts showing the most potent in neutralizing DPPH radicals. Weak to moderate antibacterial activity was recorded in the case of nettle, sage, mint and rosemary extracts, and a complete absence of activity in the case of yarrow and blackberry leaf extracts.

We chart new-physics models that produce exotic, high-multiplicity muon decays featuring prompt or displaced $e^+e^-$ pairs and/or photons, with or without missing energy, such as $\mu \to 5e$, $\mu \to 7e$, etc. Starting from an effective-field-theory perspective, we estimate the reach on the ultraviolet scale and identify conditions under which lower-multiplicity modes are suppressed or occur at comparable rates. We then construct explicit realizations in minimal dark-sector models with light, feebly interacting particles, such as flavor-protected scalars, dark photons, inelastic dark matter, and axion-like particles. The predicted novel signatures can be probed at MEG II and Mu3e, as well as during calibration runs of COMET and Mu2e. A future discovery would provide valuable insights into short-distance dynamics and the mechanism of lepton-flavor symmetry breaking.

This paper presents the development and validation of a computation model of a cascaded multilevel power inverter as an initial step toward creating a high-fidelity digital twin. A preliminary overview of the cascaded multilevel inverter topology is provided, followed by introducing a mathematical modeling approach that uses graph theory to derive the system's nodal equations. The computation model is implemented in a programmable environment, and its output is compared to the MATALB/Simulink-based simulation, which serves as a reference for performance evaluation. The evaluation is performed using the mean square error (MSE) metric to quantify the accuracy of the proposed model. Results indicate a close agreement between the computation model and simulation data, confirming the model's potential for integration into future real-time digital twin architectures for power converter control and diagnostics.

BACKGROUND Inflammatory bowel disease (IBD) may negatively affect health-related physical fitness. However, the development of interventions to improve health-related physical fitness and thereby disease outcomes is hindered by insufficient evidence. This study compared health-related physical fitness between patients with IBD and healthy control subjects, examined associations with disease and treatment characteristics, and explored patients' perspectives. METHODS In this cross-sectional study, 105 patients with IBD and 102 age- and sex-matched healthy control subjects performed validated tests for body fat (4-site skinfold thickness), cardiorespiratory fitness (steep ramp test), muscular strength (steep ramp test, 60-second sit-to-stand test, hand-held dynamometry), muscular endurance (isokinetic dynamometry), and flexibility (sit-and-reach test). Data on disease and treatment characteristics, fatigue, physical activity, and patients' perspectives were collected. RESULTS Patients with IBD had higher body fat (29.5% vs 26.9%; P = .012), lower steep ramp test performance (peak work rate 4.2 W/kg vs 4.8 W/kg; P < .001), fewer sit-to-stand repetitions (42 vs 47; P = .002), and reduced hamstring strength (3.0 N/kg vs 3.2 N/kg; P = .011) compared with healthy control subjects. This was associated with higher age, female sex, higher body mass index, fatigue, arthritis, and multiple biologicals used. Most patients considered physical fitness important and beneficial for their symptoms, and the majority expressed interest in professional support. CONCLUSIONS Patients with IBD have higher body fat and reduced cardiorespiratory fitness and muscular strength compared with healthy control subjects. Especially, patients with a higher age, female sex, higher body mass index, fatigue, arthritis, or multiple biologicals used are at risk for such impairments and may benefit from physical exercise interventions.

Abstract Objective In underdeveloped countries, allergic disease prevalence is low but rising in developing countries, while in developed countries, it remains high and stable. This study assessed prevalence and risk factors among schoolchildren in southern region of Bosnia and Herzegovina (BiH). Methods A cross-sectional study (2020) included 1851 children: 937 aged 6–8 years and 914 aged 11–13 years. Data related to asthma, allergic rhinitis, atopic dermatitis, and associated risk factors were collected using the International Study of Asthma and Allergies in Childhood (ISAAC) questionnaire. Results Diagnosed asthma prevalence was 1.8% in children aged 6–8 years and 2.4% in 11–13 years. Allergic rhinitis was diagnosed in 5.5 and 10.4%, respectively, significantly higher in older children (p < 0.001). Atopic dermatitis prevalence was 16.6% in 6–8 years and 11.8% in 11–13 years, higher in younger children (p = 0.003). Asthma and allergic rhinitis were more common in boys, while atopic dermatitis was more frequent in girls. Asthma was negatively associated with egg consumption and daycare attendance, while furry animal contact, synthetic pillows, and maternal allergic rhinitis increased risk. Allergic rhinitis was associated with maternal smoking during pregnancy, parental allergic rhinitis, and bird contact. Atopic dermatitis was positively related to maternal atopic dermatitis, bird contact, air conditioning, and daycare attendance, whereas sponge pillow use, room sharing, and dog contact appeared protective. Conclusion In this region, asthma prevalence was low, allergic rhinitis moderate, and atopic dermatitis high. Broader studies across BiH are needed to monitor trends and guide prevention.

Neural operators have emerged as powerful tools for learning nonlinear mappings between function spaces, enabling real-time prediction of complex dynamics in diverse scientific and engineering applications. With their growing adoption in engineering design evaluation, a wide range of neural operator architectures have been proposed for various problem settings. However, model selection remains challenging due to the absence of fair and comprehensive comparisons. To address this, we propose and standardize six representative 3D industry-scale engineering design datasets spanning thermal analysis, linear elasticity, elasto-plasticity, time-dependent plastic problems, and computational fluid dynamics. All datasets include fully preprocessed inputs and outputs for model training, making them directly usable across diverse neural operator architectures. Using these datasets, we conduct a systematic comparison of four types of neural operator variants, including Branch-Trunk-based Neural Operators inspired by DeepONet, Graph-based Neural Operators inspired by Graph Neural Networks, Grid-based Neural Operators inspired by Fourier Neural Operators, and Point-based Neural Operators inspired by PointNet. We further introduce practical enhancements to adapt these models to different engineering settings, improving the fairness of the comparison. Our benchmarking study evaluates each model strengths and limitations in terms of predictive performance, computational efficiency, memory usage, and deployment complexity. The findings provide actionable insights to guide future neural operator development.

This paper presents the formation of construction costs and the risk factors influencing price changes in the construction of a residential-commercial building for the market, "Lamela B1," in Banovići. The building consists of a basement, ground floor, and five floors, with a planned construction period of 16 months.The study includes an analysis of labor and material costs, as well as risk factors that affect construction price fluctuations, in accordance with market demands and the surrounding conditions specific to the construction site. All relevant influences on market price formation have been identified, along with potential price changes and a risk analysis of cost variations during construction.The most significant challenges arise in aligning planned construction costs with actual expenditures and assessing their overall impact on the project. The aim of this research is to demonstrate, through a specific case study and within the context of the surrounding conditions, the importance of improving existing planning and project management processes in high-rise construction by applying scientific and professional methodologies. Keywords: cost, construction, planning, risk, environment

This paper presents a detailed planning process for the construction of the residential-commercial building "Lamela B1" in Banovići, with a structure consisting of a basement, ground floor, and five upper floors. The planned construction period for this project is a total of sixteen months.Within the study, an assessment of the average monthly workforce requirements has been conducted, along with an analysis of the diagram depicting the average daily workforce needs for each month. Additionally, a static material plan has been developed, correlating with dynamic construction schedules created using network planning methods and Gantt charts.The duration of individual construction operations has been determined based on applicable norms and standards in the construction industry. Special attention has been given to the gradual introduction of machinery and labor, as well as the procurement of necessary materials. This planning approach ensures the continuous operation of essential machinery and workforce while also securing the periodic procurement of required construction materials. During the development of the network plan, Gantt chart, and static material plan, careful consideration was given to the logical sequence of operations, ensuring that the necessary conditions for starting a specific operation were met, as well as identifying opportunities for parallel execution of multiple operations.Based on the developed dynamic schedules and workforce requirements, a diagram representing the average monthly workforce demand has been constructed. This study contributes to a better understanding of construction planning methods and resource optimization in the context of project dynamics and time management. Keywords: building, planning, execution, workforce, network planning, work dynamics

The negative impacts of the old mining operations both in our country and throughout the world represent one of the biggest environmental problems. The analysis of the previous practice in Bosnia and Herzegovina regarding the closure of mines in the past decades lead to the conclusion that very few were closed in accordance with the regulations. The problem was further complicated by the war that took place in our country from 1992 to 1995 which in certain number of cases led to the sudden stoppage of mine operations, many of which did not continue to operate even after the cessation of war activities. In the period 2013-2014 the Japanese Agency for International Cooperation (JICA) did an initial analysis of black industrial spots (Hot spots) in the Federation of Bosnia and Herzegovina and 4 such locations were determined , one of which was the former mine in Vareš. The mine consisted of several units at different locations where various types of raw mineral materials were exploited , which made reasearch even more difficult. This primarily refers to the location of surface mine (SM) „Smreka“, the ore processing and packing plant at SM „Veovača“ and the water accumulation with flotation tailings. For the mentioned sites, a field inspection and analyses of the existing condition were carried out to obtain results required to serve as a basis for proposal of measures to reduce the impact on the environment as well as the rough estimate of the relating remediation costs, which were done separately for each of the research polygon sites. It should be emphasized that Bosnia and Herzegovina's accession to European Union implies significantly stricter environmental protection practices in the mining industry as well. Today, environmental protection in mining is an international standard and obligation. At the same time, the need should be pointed out to the contribution of each country in reducing the negative impact on the environment and climate changes. Key words: abandoned mines, environmental risks, measures to reduce environmental impacts, remediation