The development of smart grids poses great challenges to the scientific and professional community. Increasingly strict requirements from regulators and consumers require appropriate actions from the Distribution System Operator (DSO), infrastructure development, and large investments in the modernization and digitalization of electrical distribution systems. The connection of a large number of electricity sources to the existing distribution grid causes problems that are reflected in unauthorized voltage changes or overloads in the network, as well as compromised power quality. Communication infrastructure, as well as the technologies themselves, are often not satisfactory for the requirements that arise in real networks, and the development of smart grids requires appropriate/advanced information and communication infrastructure. The development of smart grids requires an interdisciplinary approach, experts of different profiles, and clearly defined long-term strategies. This paper provides an overview of existing AI technologies which are proposed for application in power systems, as well as an overview of areas where AI can be implemented to support the operation of power systems in the future (such as maintenance, forecasting, optimization, protection, etc.). In a separate section, a simulation of the production of small PV systems connected to consumer households in weak low-voltage grids (LVG) is presented as an illustrative example. An overview of proposed AI applications in LVGs is provided, along with a discussion of possible improvements and overcoming issues that arise in existing grids with prosumers.

The rapid growth of clinical explainable AI (XAI) models raised concerns over unclear purposes and false hope regarding explanations. Currently, no standardised metrics exist for XAI evaluation. We developed a clinician-informed, 14-item checklist including clinical, machine and decision attributes. This is the first step toward XAI standardisation and transparent reporting XAI methods to enhance trust, reduce risks, foster AI adoption, and improve decisions to determine the true clinical potential of applied XAI.

Introductory programming courses are widely known for their difficulty among students. Success in courses is commonly measured in the form of final grades, which might not capture the challenges students face during their learning process. In this paper, we predict students’ success and their future compiler errors based on previously made errors. Furthermore, we examine the effect of applying two clustering techniques before making the predictions and identify key weeks and errors that have the greatest impact on predictions. Experimental results show that students’ compiler errors observed through the semester are an important predictor of students’ achievement and future struggles. Predictions are further improved using sentence encoder-generated embeddings with K-Means algorithm. Our study suggests that students’ errors, particularly the most recent ones, enable meaningful clustering that enhances performance prediction after only three weeks of the semester.

Introductory programming courses present significant challenges for novice learners, often leading to frustration and difficulty in identifying learning gaps. This research aims to develop an AI-driven tool that provides personalized guidance, moving beyond traditional "one-size-fits-all" approaches. Recognizing the limitations of relying solely on digital interaction logs in the era of generative AI, we explore the integration of student personal characteristics and fine-grained programming interactions to predict learning behavior and performance. We will investigate how to accurately predict student outcomes early in the semester, analyze the dynamics of learning behaviors, and design an AI-assisted tool to recommend tailored learning materials and feedback. Our goal is to foster effective learning and mitigate the risks associated with over-reliance on general-purpose AI, ultimately enhancing knowledge retention and problem-solving skills.

Introduction: Tumor-infiltrating lymphocytes (TIL) are linked to responses to chemotherapy and immunotherapy and clinical outcomes, especially in high-risk breast carcinomas. MammaPrint® (MP) and BluePrint® (BP) are genomic tests designed to provide risk stratification and molecular classification for early-stage hormone receptor (HR)-positive breast carcinomas, which could include tumors with HER2-low expression. We investigated correlations between TIL measurements, HER2 status, and MP/BP assays in early-stage HR-positive breast carcinomas. Materials and Methods: 167 early-stage HR-positive breast carcinomas with known MP/BP risk categorization were evaluated for TIL using whole slide scanned images according to the International TILs Working Group 2014 guidelines. HER2-low breast cancers were identified by IHC scores of 1+ and 2+ without HER2 amplification. A subset of high-TIL, high-risk cases underwent TSO500 (Illumina) next-generation sequencing (NGS). Results: The patients had a mean age of 51 years, ranging from 26 to 75 years. Among the profiled cases, 97% were either luminal A (96/167) or luminal B (66/167) breast carcinomas, with only five cases classified as HER2-enriched (n = 2) or basal-like (n = 3) carcinomas. Tumor grade was strongly associated with recurrence risk (p<0.001). The prevalence of the HER2-low phenotype was 65%, including 46/69 (67%) high-risk cases. TIL levels ranged from 0 to 70% and were low (≤10%) in the majority (75%) of cases in the cohort. However, high TIL levels were more frequently observed in cases with high recurrence risk (56% vs. 39%, p = 0.03). Additionally, TIL-enriched high-recurrence risk carcinomas contained targetable genomic alterations, including PIK3CA, BRCA1, BRCA2, and HER2 mutations. Conclusions: TIL levels are higher in early-stage HR-positive breast carcinomas with a high recurrence risk. These tumors also harbor targetable genomic alterations, suggesting that TIL measurement and genomic profiling could enhance risk stratification and identify patients who might benefit from targeted therapies. Her-2 low expression in high-risk patients provides a consideration for including novel ADC therapies in this subset of patients. Citation Format: Zoran Gatalica, Inga Rose, Faruk Skenderi, Nataliya Kuzmova, Semir Beslija, Timur Ceric, Inga Marijanovic, Ilir Kurtishi, Semir Vranic. High Tumor-Infiltrating Lymphocyte Levels Correlate with High MammaPrint® Recurrence Risk in Early-Stage Breast Carcinomas [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2024; 2024 Dec 10-13; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(12 Suppl):Abstract nr P1-11-17.

This paper’s primary aim is to examine the impact of managerialcompetencies on the performance of healthcareorganizations in Bosnia and Herzegovina, with a particularfocus on the role of middle management.Research Methodology: A quantitative research approachwas employed, and data were collected through a structuredquestionnaire designed to measure six key dimensionsof managerial competencies: leadership, strategicthinking, communication, decision-making, teamwork, andchange management. The construction of the questionnairewas based on previous relevant research and theoretical models of managerial competencies, with particularattention given to the models developed by Boyatzis (1982)and later expanded by Whetten and Cameron (2011), as wellas findings from research on healthcare management, suchas Calhoun et al. (2008) on competencies for healthcareleaders. The items were adapted to the specific context ofhealthcare institutions in Bosnia and Herzegovina, and eachitem was rated on a 5-point Likert scale (1- Strongly Disagree;2-Disagree; 3 – Neutral; 4- Agree; 5- Strongly Agree).The questionnaire was distributed to a purposive sample of120 middle managers working in various healthcare institutionsacross Bosnia and Herzegovina. Data were collectedduring three months from January to April 2025. Descriptivestatistics were used for data analysis.Conclusion: The results indicate that communication andteamwork competencies were rated most positively and significantlycorrelated with organizational outcomes. In contrast,strategic thinking and change management receivedlower ratings. The instrument’s reliability was confirmedthrough high internal consistency (Cronbach α > 0.70).

Introduction: Quality health services is a priority in thehealth system. However, after the outbreak of the COVID-19 pandemic, the goals of the health system had tobe adapted to the changed circumstances, in order tomeet the health needs of patients and the expectationsof health workers related to ensuring safe working conditionsin a crisis.Aim: To examine the attitudes and opinions of patientson the quality of services provided during treatment forthe infection of COVID-19Research material and methods: The study includedpatients aged 18 and over at the Sarajevo Canton HealthCenter who were infected with the COVID-19 virus in thesecond (autumn 2020) and third (spring 2021) waves ofthe pandemic and who used primary health care servicesduring their treatment. To conduct the research, we surveyed a total of 524 patients in the period from15 September 2022 – 30 November 2022.Results: By the recommendations of the Crisis Staff ofthe Ministry of Health and the rules of the profession,the majority of respondents contacted the doctor onthe third day after the onset of symptoms, 193 of them(36.8%). During the second and third wave, the majorityof patients, 287 (54.8%) believed that there were nohealth workers during that period who did not behaveprofessionally during the treatment. During treatmentand visits to healthcare institutions, 72 (13.7%) patientsfelt that they always felt that healthcare professionalstreated them differently. During treatment for COVID-19, 78 (14.89%) patients believed that there werealways enough health workers during their treatment.Conclusion: a large number of respondents think thatthe healthcare professionals treated them professionallyduring the treatment, and that they had enough informationabout the situation, and that they acted in accordancewith the instructions published by the profession.

Abstract Background/Objectives Molar-Incisor Hypomineralisation (MIH) affects 14% of the global population, often leading to compromised first permanent molars (FPM). Early extraction of severely affected FPMs may temporarily affect proper eruption and alignment of second permanent molars (SPM) and second premolars (SP). This study aimed to evaluate the eruption patterns of SPMs and SPs, and the overeruption of opposing FPMs, after early FPM extraction using panoramic radiographs in 11-year-old patients. A secondary aim was to assess radiographic quality for these evaluations. Subjects and Methods This split-mouth trial included patients aged 6–9 with severe MIH requiring FPM extraction. Panoramic radiographs were taken pre-extraction (T0) and at age 11 (T1) to measure eruption length and angulation of SPMs and SPs. Radiographs were analysed using Facad software, and imaging errors were recorded. Paired t-tests compared extraction and non-extraction sides. Results Among 47 patients, 31 had maxillary and 25 mandibular FPM extractions. At T0, eruption length and angulation of SPMs and SPs were similar between sides. At T1, maxillary SPMs erupted faster (13.5mm vs. 10.8mm, p < 0.001) and more upright (72.9° vs. 62.1°, p < 0.001) on the extraction side, while SPs showed increased mesial angulation (82.5° vs. 89.3°, p < 0.05). Mandibular SPMs and SPs showed no differences. No overeruption of opposing FPMs was observed. Measurement reliability was excellent (ICC: 0.997–0.999), despite 75 of 94 radiographic contained errors. Limitations The three-year follow-up limits long-term insights, and radiographic distortions may affect reliability. Conclusions Early FPM extraction impacts maxillary but not mandibular SPM and SP eruption patterns without causing overeruption of opposing FPMs by age 11. Radiographic techniques are essential to minimize incorrect patient positioning, as such factors may impact measurement reliability.

Group Recommender Systems aim to support groups in making collective decisions, and research has consistently shown that the more we understand about group members and their interactions, the better support such systems can provide. In this work, we propose a conceptual framework for modeling group dynamics from group chat interactions, with a particular focus on decision-making scenarios. The framework is designed to support the development of intelligent agents that provide advanced forms of decision support to groups. It consists of modular, loosely coupled components that process and analyze textual and multimedia content, which is shared in group interactions, to extract user preferences, emotional states, interpersonal relationships, and behavioral patterns. By incorporating sentiment analysis, summarization, dialogue state tracking, and conflict resolution profiling, the framework captures both individual and collective aspects of group behavior. Unlike existing approaches, our model is intended to operate dynamically and adaptively during live group interactions, offering a novel foundation for group recommender and decision support systems.

Understanding how students perceive and utilize Large Language Models (LLMs) and how these interactions relate to their learning behavior and individual differences is crucial for optimizing educational process and outcomes. This paper introduces a novel dataset comprising weekly self-reported data from students in an introductory programming course, i.e., students’ AI tool usage, perceived difficulty of weekly subject areas, personality traits, preferred learning styles, and general attitudes toward AI. We present a descriptive overview of the collected data and conduct a correlation analysis to gain first insights into the students’ individual differences and their learning outcomes, frequency of AI tools usage, as well as their attitudes toward AI. The findings reveal that while individual student characteristics did not show significant correlations with final performance or frequency of AI tool usage, the combination of students’ expectations for success and their perceived value of the task (constructs of expectancy theory) were significantly associated with both course outcomes and how often they used the AI tool. Additionally, motivational factors may be the key to fostering positive attitudes toward AI, while personality traits, particularly those related to negative emotionality, may play a more significant role in shaping resistance. This initial analysis lays the groundwork for future investigations on the prospects of AI in support of the students’ learning process.