This paper presents the development of an embedded communication interface that integrates devices using different protocols: I²C, which is widely adopted in sensors and embedded systems, and Modbus TCP/RTU, which is widely used in industrial programmable logic controllers (PLCs). This study focuses on an automation scenario involving SMT (surface mount technology) lines where industrial robots handle printed circuit boards (PCBs). In this application, TF-Luna LiDAR sensors operate via the I²C protocol to provide accurate distance measurements that guide the movements of the robotic arms. However, these sensors are not natively compatible with most PLC models, which communicate via Modbus or other protocols.
To address this incompatibility, this study proposes developing an embedded interface board that can act as a bridge between the two protocols by converting and forwarding the collected information. The interface can read multiple I²C sensors, organize the data, and make it available to the PLC via Modbus RTU or TCP, depending on the industrial network configuration. This solution contributes to precise synchronization between sensors and actuators, improves production process efficiency, and reinforces the interoperability and flexibility principles required by Industry 4.0. It also makes device interconnectivity more accessible. The proposed approach also enables modular expansion and use in various architectures, as well as greater reliability in real-time communication in industrial environments.

Traceability, the ability to follow and interpret system behavior from design through operation, is well-established in software engineering through formal standards and structured workflows. In contrast, electronic system design often lacks comparable mechanisms, particularly at the level of physical components, signal pathways, and runtime system behavior.

This paper presents a standards-oriented gap analysis focusing on traceability practices across three key layers of electronic system design: the circuit/component level, the embedded firmware level, and the system-level behavior. Drawing from published standards and documentation guidelines in both software and electronics disciplines, the study identifies critical areas where electronics workflows provide limited support for runtime observability and post-deployment diagnosis.

The analysis highlights that, while validation and testing protocols exist for electronic hardware, comprehensive traceability frameworks—spanning from schematic design to deployed system behavior—are not sufficiently standardized. This gap becomes particularly relevant in safety-critical and sensor-integrated applications.

As the research methodology, relevant standards from software engineering and electrical and electronics engineering will be selected, specifically those addressing traceability practices. Traceability-related content will be extracted and analyzed to provide a structured, comparative discussion across the two domains.

The paper concludes by outlining future research directions for integrating traceability-aware design principles into electronic system development, using cross-disciplinary insights inspired by traceability in software engineering.

This paper presents a practical, efficient, and automated approach for determining the Hall–Huray Surface Ratio parameter, which is essential for accurately modeling conductor surface roughness losses in high-frequency PCB transmission lines manufactured with FR4 substrates. The growing complexity of modern digital systems and the demand for precise signal integrity analysis require advanced electromagnetic models to predict interconnect behavior. Among several roughness models, the Huray model is widely recognized for its ability to represent conductive losses associated with copper surface roughness in a physically meaningful way. The proposed methodology combines automated parametric sweeps in ANSYS HFSS with experimental validation using a WavePulser 40iX vector network analyzer (VNA), applying time-domain gating techniques to isolate multiple reflection effects. The 3D simulation setup adopts the Wideband Debye model for the dielectric and the snowball (Huray) model for surface roughness characterization. The Hall–Huray Surface Ratio variable was systematically adjusted from 1 to 9 to achieve close agreement with the experimental data. The results demonstrated a deviation of less than 0.04 dB at 10 GHz in insertion loss and a maximum variation of 1.05 Ω in the impedance profile obtained via TDR. Although minor mismatches in the reflection parameter (S11) were observed, the outcomes validate the accuracy, robustness, and practical applicability of the proposed method. This technique offers an effective balance between cost, complexity, and precision for high-frequency structure characterization up to 20 GHz, with promising potential for future extensions in electromagnetic compatibility (EMC) studies and advanced interconnect modeling.

Introduction and objectives:

Three-phase induction motors are widely used in industry for their reliability and low maintenance. However, faults such as broken rotor bars (BRBs) can disrupt performance and increase costs. Early detection is therefore necessary. This work presents a non-invasive method that combines phase current signals with deep learning to detect and classify BRB faults in squirrel-cage induction motors.

Methods:

Signals were generated through FEMM simulations for rotors with 22, 24, 26, and 28 bars—typical configurations in industrial motors. Zero to six broken bars were considered, and the resulting phase current signals were transformed into two-dimensional images using Gramian Angular Summation Fields (GASFs) to highlight fault-related patterns. Two datasets were used: dataset A (79,086 GASF images, 11,298 per class) for training, and dataset B (22,488 time signals, with class sizes from 2,459 to 3,212) for testing. Several convolutional neural networks with residual connections (ResNet18 to ResNet152) were evaluated. ResNet152 was selected for its superior performance, achieving 95.13% accuracy and 96.77% sensitivity on dataset A.

Results:

Given the class imbalance in dataset B, metrics that are suited for imbalanced multiclass classification were used. The model achieved a sensitivity of 0.95, macro F1-score of 0.87, Matthews correlation coefficient of 0.82, and Jaccard index of 0.78, showing good generalization.

Conclusion:

The proposed system offers a non-invasive, data-driven approach to BRB fault diagnosis, which is capable of operating under real conditions without interrupting the motor. Its industrial applicability is reinforced by a graphical interface, allowing users to upload raw signals and obtain reliable predictions easily, supporting predictive maintenance strategies.

This work explores an autonomous navigation strategy for mobile robots that combines the coordinated integration of two complementary approaches: the A* algorithm, renowned for its efficiency in global path planning, and the Dynamic Window Approach (DWA), which is well-suited for reactive local control. The aim is to ensure robust navigation in semi-structured environments by combining long-term planning with real-time adaptability. The performance of the method was evaluated in a simulated environment with fixed obstacles, through two representative scenarios. The first scenario, characterized by a high risk of blockage (dead ends, narrow passages), revealed the limitations of using DWA alone, which often becomes trapped in complex configurations. In contrast, the combined approach leverages the predictive capabilities of A* to effectively bypass problematic areas. The second, less constrained scenario aimed to compare trajectory quality under favorable conditions. Results demonstrate that the proposed integration not only enables the robot to reach the goal but also improves overall performance, as evidenced by a 1.76% reduction in average distance traveled and a 1.89% decrease in navigation time compared to DWA alone. The presented approach stands out due to its ability to dynamically adjust the robot's behavior while maintaining a global view of the environment. This contributes to increased reliability and efficiency of autonomous navigation, especially in complex or cluttered contexts.

High-gain antennas play an important role in long-range wireless communication systems like radar, satellite communication, and space exploration. In such applications, Microstrip array antennas and phased array antennas require complicated feeding networks to realize beamforming and directional radiation, which increases the complexity of antenna design as well as the cost. In contrast, reflective metasurface and transmissive metasurface have high gain property with low cost due to easy fabrication . Compared with reflective, transmissive metasurface offers several advantages such as no aperture blockage from the feed source and less sensitivity to fabricating tolerances . And planar transmissive metasurface shows miniaturized size and lighter weight than traditional three-dimensional (3-D) lens antennas .Despite various advantages, normal transmissive metasurface usually suffers from limited bandwidth. In this submission, a transmissive metasurface with wide gain bandwidth is proposed. The used element is realized by a four-layer rectangular slot, showing wide phase-shifting coverage and low transmission loss within a broad bandwidth. By decreasing the design frequency of the transmission elements, the realized gain of the metasurface becomes flatter. Based on low-cost PCB technology, the metasurface with the dimension of 204 mm × 204 mm is designed and fabricated. The measured results are agreed with the simulated ones well, showing good radiation characteristics with a measured gain of 28.2 dBi at 18 GHz and aperture efficiency of 35.1%. Bandwidths of 26.4% for 1-dB gain and 47.9% for 3-dB gain are achieved as well.

This study utilizes the Analytic Hierarchy Process (AHP) to assess the relative impact of various Radio Frequency Identification (RFID) application subtypes within the domains of Civil Engineering and general Engineering outputs. By normalizing and assigning weighted scores to data, a structured comparison was made across four publication categories: Civil Engineering Journal Articles, Civil Engineering Conference Papers, Engineering Journal Articles, and Engineering Conference Papers. The AHP method provided a robust framework for evaluating the significance of each application type, allowing for objective prioritization. The analysis revealed that RFID applications focused on Safety and Security consistently ranked highest across all categories, highlighting their essential role in improving infrastructure management, operational reliability, and project safety. Conversely, subtypes such as Environmental Monitoring and Water Resource Management demonstrated relatively low relevance, suggesting limited impact or underutilization in current engineering literature and practice. This research is significant as it introduces a quantitative and systematic method to evaluate the effectiveness of RFID technologies in engineering contexts. The findings offer valuable insights for researchers, engineers, and policymakers, guiding future efforts in adopting RFID systems to optimize civil infrastructure, streamline construction processes, and enhance the overall efficiency and safety of engineering projects. The study thus supports evidence-based decision-making in technology adoption.

Zinc oxide is a highly studied material due to its versatile properties and widespread use in multiple fields of application. Therefore, it is important to develop methods to synthesize zinc oxide particles, capable of achieving homogeneous morphology and size, to maximize their applicability as well as improve and control their properties.

This study focuses on the production of Fe-doped zinc nanoparticles via one-pot hydrothermal synthesis, since although there is currently a wide variety of well-established procedures for their production, these methods tend to require many steps and highly specialized equipment to achieve adequate control of the nanoparticles produced.

Therefore, hydrothermal synthesis is proposed as a simpler method to achieve better control of crystallized nanoparticles via a single step without requiring any further purification or refinement of the crystalline structures.

To enhance the properties of the ZnO nanoparticles, doping of the wurtzite structure with Fe ³⁺ ions in quantities from 1 to 10% was proposed, and experiments were carried out on Teflon-lined stainless-steel autoclaves at 160 °C during various reaction intervals (1–12 h) using Zn(NO₃)₂ 6H₂O, FeCl₃·6H₂O and NaOH solutions as hydrothermal media.

All the obtained powders analyzed by XRD displayed defined peaks coinciding with the ZnO in wurtzite phase, indicating that no secondary phases crystallized during the treatment. In addition, displacement was concurrent with the decreasing size of the crystallite as the Fe ³⁺ content increased (32 to 28 nm). Finally, FT-IR and UV-Vis spectroscopy indicated the formation of ZnO nanoparticles doped with high Fe content.

Chlorinated derivatives, including pesticide fungicides and insecticides, are extensively applied in agriculture, disinfection, and industry, yet their persistence leads to severe contamination of aquatic environments[1-2]. Conventional bioremediation methods are insufficient for removing these pollutants, which has stimulated the development of catalytic approaches to degrade stable chloro-organic compounds effectively[3-5]. In the current study a range of catalytic strategies has been designed to address the degradation of persistent organic pollutants. In this investigation, the formation of metal-coordinated chitosan gels were analyzed via rheological measurements (G′ and G″). using Medusa modeling mechanism of gel formation was proposed. Ionic gels were further transformed into covalently cross-linked macroporous cryogels containing in situ immobilized Pd or Pt nanoparticles through redox-driven reactions. The catalytic efficiency of these cryogels for degrading chloro-organic contaminants in continuous water treatment was evaluated, with PdNPs and PtNPs uniformly dispersed as nanosized particles within the porous polymer structure. The degradation kinetics of o-chlorophenol, p-chlorophenol, and 2,4-dichloro-phenol were investigated using catalysts with varying Pd and Pt loadings. Conversion increased with higher formic acid excess and elevated temperatures, reaching 80–90% at 80 °C. The CHI–GA–PdNPs cryogel demonstrated superior hydrogenation activity at pH 6 compared to CHI–GA–PtNPs, though no marked difference was observed at pH 3. Batch-mode termination studies and control experiments were conducted to explore catalyst deactivation, with silver nitrate addition showing limited benefit. Overall, this catalytic platform holds promise for application in flow-through systems and for broader use in the synthesis of valuable chemicals.

Propolis is a resinous substance known for its bioactive compounds, such as phenolics and flavonoids, which exhibit antioxidant, antibacterial, and anti-inflammatory benefits. While this can be harvested from sources globally, it can also be derived from the native stingless Philippine kiwot bees. This study used microwave-assisted extraction (MAE) using ethanol, changing the solvent-to-sample ratio (5mL/g, 10mL/g, and 15mL/g), solvent concentration (35%v/v, 65%v/v, and 95%v/v), and extraction time (60s, 180s, and 300s), to determine their effects on the yield and phenolic and flavonoid contents. The extracted propolis was concentrated using rotary evaporator and freeze-dried. Phenolic content was determined through Folin–Ciocalteu method, while flavonoid content was identified through aluminum chloride complex formation in methanol solution. Interactions between solvent-to-sample ratio and solvent concentration significantly affected yield, while extraction time did not. Results show that using MAE with ethanol as a solvent successfully extracted propolis. Lower solvent-to-sample ratios with increasing solvent concentration resulted in higher yields. Conversely, high solvent concentrations with increasing solvent-to-sample ratio decreases yield. The highest yield (0.144 g/g) was obtained with 5mL/g solvent-to-sample ratio, 95%v/v solvent concentration, and 300s extraction time. Highest phenolic content (180.145 mgGAE/g propolis) was observed with 5mL/g solvent-to-sample ratio, 95%v/v solvent concentration, and 60s extraction time, while highest flavonoid content (124.190 mg QE/g propolis) was determined with 10mL/g solvent-to-sample ratio, 35%v/v solvent concentration, and 180s extraction time. Effects of extraction parameters were determined, which can further optimize the quantity and quality of propolis.