The biosynthesis of zinc oxide nanoparticles (ZnO NPs) via an aqueous peel extract of Citrus sinensis represents a green and sustainable alternative to conventional synthesis methods, eliminating the need for toxic chemicals and diminishing energy consumption. Bioactive compounds present in the extract, such as flavonoids and polyphenols, serve as natural reducing and stabilizing agents during the nanoparticle formation process. Characterization via UV–VIS spectrophotometry confirmed the formation of crystalline and stable ZnO NPs. In this study, an aqueous orange-peel extract was employed as the biological reducing agent for the synthesis of ZnO nanoparticles from zinc nitrate as the precursor, and the influence of the precursor/extract ratio on the properties of the obtained particles was investigated. The antimicrobial activity of the synthesized ZnO NPs was tested against Escherichia coli and Staphylococcus aureus, with significant growth inhibition observed/particularly in the case of E. coli. The results obtained underscore the potential application of biosynthesized ZnO NPs in medicine, food preservation, and biodegradable packaging. This study confirms an efficient, eco-friendly and straightforward approach to the green synthesis of ZnO nanoparticles using orange peel extract.

Recently there has been a lot of progress in the development of economic nonlinear model predictive control (NMPC) schemes for multistage optimal power flow (OPF) problems. However, the additional inclusion of discrete decision variables to model generator runtimes and generator startup costs can amount to large scale mixed-integer nonlinear programs (MINLPs) that are computationally very challenging. This work investigates the practical approach that replaces the nonlinear AC power flow equations by convex quadratic approximations. In combination with the discrete generator dynamics this leads to a mixed-integer quadratically constrained program (MIQCP) which is of significantly lower complexity and can be solved in reasonable time by off-the-shelf solvers such as CPLEX. We further show that simple terminal constraints are not sufficient to guarantee recursive feasibility of the NMPC scheme if constraints on generator runtime and on the number of generator startup events are present. To address this challenge we propose the use of additional time-coupled constraints and prove the resulting recursive feasibility property. Based on the assumption of periodic dissipativity of the underlying system we can prove stability of the proposed controller. To illustrate our results, we present simulations of a realistic 6-bus microgrid under different demand scenarios.

Phasianus chaphamaparvovirus 1 is a newly recognized pathogen associated with high mortality in young pheasant chicks with significant losses for pheasant breeders. Here we present an outbreak of necrotizing hepatitis with high mortality in a flock of young pheasant chicks in Bosnia and Herzegovina associated with Phasianus chaphamaparvovirus 1 and coccidiosis coinfection. Severe necrotizing hepatitis with characteristic intranuclear inclusion bodies in affected hepatocytes was observed on microscopic examination. In addition, multifocal areas of intestinal mucosal necrosis with numerous developing stages (meronts) of coccidia were present. Phasianus chaphamaparvovirus 1 was detected with qPCR from the livers of diseased birds. Our report represents the first data on the presence of this new virus in pheasant flocks in the Balkan region and suggests that the virus is widespread across Europe. It further supports the inclusion of Chaphamaparvovirus as a differential diagnosis of pheasant mortality outbreaks both in breeding facilities and in the wild.

Wildlife–vehicle collisions (WVCs) represent a growing safety, ecological, and economic challenge, with direct consequences for human lives, material damage, and biodiversity conservation. In the Federation of Bosnia and Herzegovina, systematic analyses that link traffic accidents caused by collisions with wildlife are lacking. This research identifies high-risk locations of WVCs and applies geospatial analysis to the main roads of the Federation of Bosnia and Herzegovina. The analysis is based on official police reports documenting 14,169 traffic accidents between 2021 and 2023, of which 104 cases (0.73%) were classified as animal-related. Although species were not specified in the reports, these accidents predominantly occurred in areas where wildlife crossings are expected, and thus are treated as potential wildlife–vehicle collisions. The results indicate a concentration of WVCs in nine municipalities, with eight critical road segments identified on main roads. Additional analyses explored the relationship between collisions, road infrastructure (bridges, tunnels), and ecological features of habitats (Emerald Network, Natura 2000, Red List of FBiH, IUCN). Based on the findings, it can be concluded that spatially targeted prevention is essential, with priority given to infrastructural measures (wildlife overpasses, fencing, signage) and strategic measures (improved databases, continuous monitoring, and integration into spatial planning). The obtained results provide a foundation for policies that simultaneously enhance traffic safety and contribute to the protection of wildlife populations.

Immersive virtual reality (IVR) can heighten presence and enable active, embodied interaction in realistic 3D environments, which has been associated with potential benefits in physics education. This article presents a comprehensive review of IVR implementation in physics education at both school and university levels. The analysis included 34 studies indexed in Scopus and ERIC, published between 1 January 2018 and 1 June 2025. Methodologically, this review followed a preferred reporting items for systematic reviews and meta-analyses approach. IVR implementations for physics learning were assessed, topic-aligned activity types were summarized, and associated opportunities and challenges were documented. Findings indicate that IVR can support physics learning by providing complementary visualizations, optimizing cognitive load, enabling haptic learning, saving time, and fostering collaborative inquiry. Conversely, the most frequent potential IVR challenges include discomfort, unreliable interaction, orchestration issues, relatively high costs, and significant extraneous cognitive load.

BACKGROUND Acute hyperglycemia is frequently observed in patients presenting with acute coronary syndromes and is considered a marker of metabolic and neurohormonal stress. However, its prognostic significance relative to chronic glycemic status remains incompletely understood, particularly in patients with non-ST-segment elevation myocardial infarction (NSTEMI). Glycated hemoglobin (HbA1c) reflects long-term glycemic control but may not adequately capture acute metabolic derangements occurring during myocardial ischemia. Stress hyperglycemia reflects a transient metabolic response to acute illness mediated by counter-regulatory hormones, systemic inflammation, and increased hepatic gluconeogenesis, and does not necessarily indicate pre-existing insulin resistance or chronic dysglycemia. Recent studies suggest that stress-related hyperglycemia indices may better reflect short-term risk, yet comparative data in NSTEMI populations remain limited. AIM To determine whether admission stress hyperglycemia indices are associated with early mortality in patients with non-ST elevation myocardial infarction. METHODS This prospective, single-center observational study consecutively enrolled 171 patients admitted with confirmed NSTEMI. Stress hyperglycemia was assessed using the stress hyperglycemia ratio (SHR) and the admission glucose-to-chronic glycemia ratio (ACGR), calculated from admission plasma glucose and HbA1c values obtained at hospital presentation. Patients were categorized according to established HbA1c thresholds. Clinical, laboratory, and echocardiographic data were systematically collected. All patients were followed for three months after discharge. The primary endpoint was the occurrence of major adverse cardiovascular events (MACE), defined as a composite of cardiovascular death, non-fatal myocardial infarction, or urgent coronary revascularization. The secondary endpoint was all-cause mortality. Discriminatory performance was evaluated using receiver operating characteristic (ROC) curve analysis. Multivariable logistic regression models were constructed to assess the independent and incremental prognostic value of stress hyperglycemia indices before and after adjustment for established clinical and echocardiographic predictors. RESULTS During the three-month follow-up period, 88 MACE and 25 deaths were recorded. HbA1c categories were not significantly associated with all-cause mortality or MACE. In contrast, admission glucose levels, SHR, and ACGR were significantly higher in non-survivors than in survivors. No significant differences in HbA1c were observed between outcome groups. Stress hyperglycemia indices demonstrated modest discriminatory ability for predicting mortality and showed greater discrimination than HbA1c in ROC analyses. In multivariable models, both SHR and ACGR remained independently associated with early mortality after adjustment for demographic, clinical, and echocardiographic variables, whereas no independent association with the composite MACE endpoint was observed. ROC-derived thresholds used for survival analyses were exploratory and have not been externally validated. CONCLUSION In patients with NSTEMI, stress hyperglycemia indices assessed at hospital admission are independently associated with early mortality, whereas chronic glycemic status shows limited prognostic relevance. These indices appear to reflect acute systemic stress and metabolic instability and may provide clinically useful information for early risk stratification during the initial phase of hospitalization, particularly when comprehensive echocardiographic assessment is not yet available.

This paper presents a reflective analysis of the International Teacher Education (ITE) eTwinning Lab 2025-26, a transnational collaborative project involving pre-service teachers at the University of Cagliari (Italy) in partnership with Aix-Marseille Université (France), the University of Castilla-La Mancha (Spain), and the University of Tuzla (Bosnia and Herzegovina). Framed by the eTwinning annual theme “Citizenship education: celebrating what unites us”, the 12-hour laboratory engaged pre-service primary teachers (Pinter, 2017) in designing and simulating transnational, project-based learning experiences using English as a Lingua Franca. Grounded in the principles of Content and Language Integrated Learning (CLIL) (Coyle, Hood, & Marsh, 2010) and digital pedagogy, the lab aimed to develop participants’ soft skills and transversal competences: digital literacy, intercultural communication, collaborative project design, pedagogical innovation, and professional discourse community building (Swales, 1990). Employing a mixed-methods approach, data were analysed from post-project questionnaires (N=95 [Cagliari], N=10 [Tuzla], N=10 [Aix-Marseille], N=8 [Castilla-La Mancha]), ongoing formative assessments (through the usage of several tools as Mentimeter, AnswerGarden, and Padlet), and analysis of collaborative outputs (Canva projects). Findings reveal significant improvement in digital tool mastery, particularly with AI chatbots and collaborative platforms, and, most of all, enhanced intercultural awareness and learners’ self-confidence. It is argued here that this experience, with both its products and processes, can contribute to the growing literature on online teacher education and can provide a replicable model for integrating eTwinning into university-based training programs across Europe.

A frequency-locked loop synchronization with an inherent orthogonal signal generator is one of the key enablers of reliable synchronization for grid-connected power converters. An accurate and robust grid synchronization is essential for stable control and power regulation in those applications. Conventional structures, commonly based on second-order generalized integrators, may operate irregularly in the presence of measurement noise, harmonic distortion, frequency changes and a DC-offset component. To address this issue, a modified Kalman-based generator with the DC-offset immunity is integrated into the conventional frequency-locked loop. The proposed approach utilizes a state-space oscillator model equipped with an additional state for DC-offset component estimation, enabling generation of orthogonal signal with DC-offset rejection. The effectiveness of the proposed loop is verified in the MATLAB/Simulink environment under numerous tests such as DC-offset introduction, sudden amplitude and frequency changes. The shown results demonstrate accurate grid voltage estimation and ripple-free frequency estimation with providing robustness and accuracy against conventional structures.

Future 6G edge-intelligent radios require neural networks that satisfy strict latency and energy constraints, yet the practical behaviour of quantization-aware Neural Architecture Search (NAS) models on real Field-Programmable Gate Array (FPGA) hardware remains insufficiently explored. This work evaluates a set of Pareto-optimal architectures generated by a quantization-aware NAS MONAS-LQ, together with state-of-the-art reference models, when deployed on an FPGA using the Brevitas-FINN-Vivado toolchain. The results show that all models found by MONAS-LQ maintain accuracy within 0.30% of server-side execution, with several exhibiting slight improvements, while energy consumption remains below 20 mJ/sample and latency is dominated by early convolutional layers rather than overall model size. Compared to existing quantized architectures, the MONAS-LQ models achieve more favourable accuracy–efficiency trade-offs. These findings highlight the relevance of hardware-aware NAS for deriving deployable and energy-efficient Deep Learning (DL) models tailored to the resource constraints of future edge-intelligent radio systems.

We present LITE, a lightweight trajectory-unaware CSI estimation framework implemented as an O-RAN xApp and integrated within a CF-MaMIMO emulator. LITE compresses high-dimensional CSI at the O-DU, transports a compact latent representation over the midhaul, and predicts short-horizon channel gains at the Near-RT RIC using a compact SE-BiLSTM model. The demo highlights end-to-end real-time operation, interactive visualization, fault injection, and fallback mechanisms under realistic impairments such as missing or delayed measurements. Results show stable and accurate per-AP predictions while meeting Near-RT latency constraints, demonstrating the feasibility of embedding bandwidth-aware intelligence in O-RAN-compliant RAN loops.

With the stringent requirements of evolving vertical services, there is a growing need for more flexible, ultra low-latency Beyond 5G (B5G)/6G architectures. One way to achieve this is by disaggregating core network functions and deploying them closer to the end users. The distribution and optimal placement of both Control Plane (CP) and User Plane (UP) functions at the network edge, along with the collocated orchestrated Application Functions (AFs), can significantly improve End-to-End (E2E) latency and network reliability by avoiding unnecessary traffic flows to the centralized cloud servers. Furthermore, as 5G evolves into 6G, automated and intelligent network management solutions will be needed in the distributed communication compute continuum. This paper presents an early-stage PhD research direction that focuses on the optimal quality-aware disaggregation of 5G core functions, aiming to reduce E2E latency and improve the throughput of 6G vertical services. The focus is on the optimal User Plane Function (UPF) placement, as well as the other CP functions and AFs, which altogether interact with the UPF. As UPF is directly involved in user data forwarding, it is crucial for handling user traffic over 5G/B5G network and, as such, it significantly impacts E2E latency. This paper provides i) an overview of theoretical concepts and State of the Art (SotA) UPF placement methodologies, and ii) future directions for optimized placement of B5G Core and application functions within the edge cloud continuum, leveraging intelligent network and service orchestration solutions.

Teleoperation is increasingly used to complement Level-4 autonomous driving, but it depends on strict network requirements i.e., latency below 5 ms, uplink throughput exceeding 25 Mbps, and reliability of 99.999%. To meet such requirements Network Slicing has emerged as a key paradigm to allocate and isolate network resources. However, in current 5G Standalone (5G SA)networks, Network Slicing faces fragmented orchestration. 3GPP mechanisms govern the 5G Core (5GC), O-RAN manages Radio Access Network (RAN) resources, while Transport Network (TN) operates separately, limiting end-to-end Quality of Service (QoS) guarantees.In this work, we propose a Cross-Domain Controller (CDC) that establishes synergy between network domains i.e., 5GC, TN, and RAN, through policy-driven coordination. The CDC implements a closed-loop, threshold-based mechanism that monitors slice Key Performance Indicators (KPIs) and triggers runtime network resource reconfigurations when the Teleoperation slice requires additional capacity to meet its QoS targets. Experimental validation on a real-world 5G SA testbed demonstrates that the CDC maintains strict slice isolation while dynamically expanding allocated network resources.

ABSTRACT Enhancing crop tolerance to multiple abiotic stresses is critical for achieving sustainable agriculture. Targeted seed‐stage interventions using natural signaling compounds (e.g., melatonin) provide a unique opportunity to establish early stress tolerance that can persist through the critical seed‐to‐seedling transition. Melatonin seed priming (MSP) is rapidly emerging as a green and climate‐smart strategy for enhancing plant stress tolerance. MSP triggers defensive molecular, biochemical, and physiological reprogramming during germination, thereby improving plant performance under subsequent stress conditions. This review synthesizes recent mechanistic insights into how MSP confers stress tolerance across diverse species by modulating redox signaling, hormonal homeostasis, and stress‐related gene networks. We elucidate the synergistic potential of MSP when combined with nanoformulations, other priming agents, or beneficial microbes. We also discuss its crosstalk with key signaling pathways to better understand the tolerance mechanisms. Furthermore, we propose a forward‐looking strategy that integrates omics, genome editing, speed breeding, and molecular phenotyping methods to improve MSP applications for the development of stress‐smart crops. Despite its potential, MSP still faces multiple challenges, including species‐specific responses, dosage variability, limited post‐priming seed storage stability, and a lack of field‐scale validation. Addressing these bottlenecks through high‐throughput screening, epigenetic memory assessment, and optimized delivery systems will be essential to fully harness the practical potential of MSP as a sustainable and green approach for future agriculture.