Institut Mihajlo Pupin

This study investigated the influence of fabric variant and fabric state on the structural and dimensional behavior of flax double weft knitted fabrics. Two fabric variants were examined: variant I, an unbalanced structure with a 1 × 1 rib with a single miss stitch; and variant II, a balanced structure with a 1 × 1 rib with a 1 × 2 rib. Key structural characteristics, including stitch density, stitch length, weight, and thickness, were analyzed in dry-relaxed (DR) and wet-relaxed (WR) states. Additionally, the dimensional changes following washing were studied. Results showed that variant I had higher stitch density on the back, longer average stitch length, and greater weight and thickness than variant II. These differences reflect the effect of single miss stitches on fabric structure versus the 1 × 2 rib in fabric variant II. Washing caused structural relaxation and partial recovery in both variants, with variant I showing smaller dimensional changes and greater overall stability. Both fabrics shrank in length and expanded in width; variant I demonstrated notably reduced widthwise expansion. Statistical analysis confirmed that the fabric variant significantly influenced stitch formation, dimensional changes, and overall stability. These findings suggest that the structure with a 1 × 1 rib with single miss stitches (variant I) provides enhanced dimensional control and predictable performance during following washing, making it especially suitable for applications requiring high fabric stability.

This paper examines the influence of barrel length on pressure and the initial velocity of the projectile. The theoretical analysis is conducted using the Drozdov analytical solution. An experimental method is used to investigate the ballistic characteristics of different barrel lengths from 720 mm to 600 mm, and also to validate the analytical results. The obtained deviations between analytical and measured values are less than 3.5%, 2%, and 6% for barrel maximum pressure, initial velocity, and firing time, respectively. The study findings show the reliability of the used model in determining internal ballistic characteristics for small-caliber weapons.

This study examines the stretch properties of nine variants of single-tuck weft-knitted fabrics made from 31 tex ×2 wool/PAN yarn and compares them with a plain fabric. The research emphasizes how variations in the arrangement and percentage of tuck loops within the stitch repeat affect the fabrics' stretch characteristics. Results show that the presence and percentage of tuck loops at the width repeat significantly influence stretch properties. The plain fabric displayed apparent anisotropy, with widthwise deformation roughly four times greater than lengthwise deformation. This behaviour was largely dominated by the elastic component, indicating strong immediate recovery and dimensional stability. In contrast, tuck stitch variants showed more balanced deformation between directions, reflecting the moderating influence of tuck loops on fabric anisotropy. Increasing the percentage of tuck loops improved lengthwise extensibility while decreasing widthwise recovery, thereby altering elastic and residual deformation behaviour. The analysis of deformation components revealed that tuck loops decrease the elastic deformation ratio and increase delayed and residual deformation, suggesting greater stress relaxation and a higher permanent set. These results highlight the sensitive interplay between the presence and combination of knit and tuck loops and their effects on loop configuration and fabric mechanics. The results thus confirm that the controlled use of tuck stitches provides a practical approach to optimizing fabric performance in terms of stretch, recovery and stability, thereby offering valuable insights for designing functional and high-performance knitted textiles.

Abstract Vagus nerve stimulation shows promise for treating heart failure, epilepsy, diabetes, psychiatric disorders, but its clinical use is yet limited, possibly due to the poor spatial selectivity of cuff electrodes presently used. Existing intraneural electrodes offer higher selectivity but are invasive and complex to implant. An electrode with a unique feature of combining epineural and intraneural contacts was developed and optimized via in silico modeling for selective stimulation, rapid implantation, and minimal invasiveness. A tridimentional fabrication process integrating photolithography techniques with 2‐photon polymerization was engineered to create penetrating, narrow spikes on a flexible polyimide substrate. In vitro tests demonstrate mechanical robustness and enhance electrochemical properties thanks to customized titanium nitride coating. In vivo trials in rats and pigs showcase that the optimized design enables lower‐threshold stimulation with precise function targeting and fewer off‐target effects. This framework, including model‐driven design and novel fabrication process, enables precise tailoring of neural interfaces to diverse clinical targets, overcoming the selectivity–invasiveness trade‐off and advancing bioelectronic therapies toward closed‐loop control and chronic use.

Smart water management is turning into one of the key elements for a sustainable water infrastructure in the Balkans. Systems based on collected data can solve major problems, such as leakage, pollution and inefficient water distribution. This paper explores the path from data to action using artificial intelligence and machine learning in regional smart water management projects. In the paper, a comparative analysis of the implementation of smart systems in Bosnia and Herzegovina, Croatia, Serbia and Montenegro was carried out, with a focus on predictive analytics, the application of edge AI technology and real-time decision support systems.This study introduces hybrid technologies that combine IoT with machine learning algorithms such as Random Forest,XGBoost, and Deep Q-Learning with the goal of adaptive water distribution.Results indicate that localized AI models can reduce network losses by up to 30 % and improve anomaly detection accuracy by 25 %. Despite financial and infrastructural limitations, the adoption of AI-powered smart water systems demonstrates a measurable impact on sustainability and operational efficiency. The paper concludes with a roadmap for regional collaboration and the standardization of AI-driven water management practices in the Balkans.

This paper explores cost-effective solutions for automated guided vehicle (AGV) through the design and implementation of a low-cost, hoverboard-based line-following AGV tailored for textile manufacturing environments, specifically within sewing plants. The designed AGV leverages the capability of a commercial hoverboard as its mobility platform, significantly reducing development costs while maintaining effective operational performance. Utilizing affordable sensors such as infrared line detectors and ultrasonic sensors, the AGV autonomously navigates pre-defined pathways marked on the factory floor. Its primary function is transporting materials such as fabric bundles and partially or finished products between workstations, addressing common logistical challenges in dynamic and labor-intensive textile production settings. The system is designed for easy integration with both existing plant layouts and information and communication environment, requiring minimal infrastructural changes. Field testing demonstrated the AGV’s reliability, maneuverability, and responsiveness in real-world sewing plant conditions. The proposed solution underscores the potential of retrofitting existing consumer electronics for industrial automation, offering a scalable and economically viable alternative for small- to medium-sized textile enterprises seeking to enhance productivity and workflow efficiency.

This study examines the key factors influencing the quality of distance learning in higher education during the Covid-19 pandemic, when online learning became the dominant mode of education. Using a descriptive method and a 26-item questionnaire, data were collected from a representative sample of 360 students in Vojvodina, Serbia. The factors analysed include computer literacy and technology access (Ph1), students’ ability to balance life obligations with study demands (Ph2), and their motivation for distance learning (Ph3). The results show that 89% of students had adequate IT access, 47% were able to reconcile study and personal obligations, and 70% reported strong motivation. Correlation analysis confirmed a statistically significant positive relation-ship between all three factors and students’ perceptions of well-organized distance learning, thus supporting the main research hypothesis. Beyond these findings, the study interprets digital literacy as adaptability, time management as resilience, and motivation as values orientation and futures thinking—core dimensions of sustainability competences outlined in the European Green Comp framework. Distance learning is therefore positioned not only as an emergency response but also as a transformative pedagogy that integrates brain (knowledge), hands (skills), heart (values), and spirit (purpose), contributing to sustainable and resilient higher education.

Summer savory ( Satureja hortensis L.) was sonicated to obtain extracts rich in highly desirable phytochemicals. Despite this, the plant is still not investigated thoroughly. Therefore, this study aimed to use and optimize ultrasound-assisted extraction for isolation of bioactive compounds for summer savory, and to study chemical profile and thermal properties of the extract obtained under optimal conditions. The box-Behnken experimental design with 17 experiments was created for process optimization in combination with response surface methodology. Investigated responses were total phenolic (TPC) and flavonoid (TFC) contents and two antioxidant assays, DPPH (IC 50 ) and ABTS (EC 50 ) assays. Independent variables were ethanol concentration (40–80 %), temperature (40–80 °C), and liquid-solid ratio (20–40 mL/g). Experimentally obtained results were adapted to the polynomial model with multiple regression using analysis of variance to evaluate the obtained model and assess optimal values for both independent and dependent variables. Predicted values for extraction parameters were ethanol concentration of 40 %, temperature 80 °C, and LSR 31.3 mL/g. Estimated values were 135.55 mg GAE/g, 23.97 mg RU/g, 27.66 µg/mL, and 29.62 µg/mL for TPC, TFC, IC 50 , and EC 50 , respectively. UHPLC-DAD-MS/MS analysis showed that the dominant compound in the investigated extract was rosmarinic acid (52.25 µg/mL), followed by chlorogenic acid (1.82 µg/mL). The analyzed sample showed a glass transition at 50 °C and thermal degradation onset at 245 °C. Decomposition occurred in five stages between room temperature and 800 °C, with major mass losses due to organic compounds’ degradation and polymerization, leaving a 38.3 % residue. • Summer savory extracts were prepared using ultrasound-assisted extraction technique. • Optimization was performed with response surface methodology and Box-Behnken design. • Investigated parameters were total phenolic and total flavonoids contents, DPPH, and ABTS assays. • Extract obtained under optimal conditions was analyzed for presence of polyphenolic compounds. • Thermal analysis indicated complex nature of the optimized extract with five-stage decomposition.