Search Results (312)

This study introduces an energy-aware hybrid security framework that safeguards embedded systems against code theft, closing a critical gap. The approach integrates bitstream encryption, dynamic key generation, and Dynamic Function eXchange (DFX)-based memory obfuscation, yielding a layered hardware–software countermeasure to Read-Only Memory (ROM) scraping, side-channel attacks, and Man-in-the-Middle (MITM) intrusions by eavesdropping on communications on pins, cables, or Printed Circuit Board (PCB) routes. Prototyped on a Xilinx Zynq-7020 System-on-Chip (SoC) and applicable to MicroBlaze-based designs, it derives a fresh Authenticated Encryption with Associated Data (AEAD) key for each record via an Ascon-eXtendable-Output Function (XOF)–based Key Derivation Function (KDF) bound to a device identifier and a rotating slice from a secret pool, while relocating both the pool and selected Block RAM (BRAM)-resident code pages via Dynamic Function eXchange (DFX). This moving-target strategy frustrates ROM scraping, probing, and communication-line eavesdropping, while cryptographic confidentiality and integrity are provided by a lightweight AEAD (Ascon). Hardware evaluation reports cycles/byte, end-to-end latency, and per-packet energy under identical conditions across lightweight AEAD baselines; the framework’s key-derivation and DFX layers are orthogonal to the chosen AEAD. The threat model, field layouts (Nonce/AAD), receiver-side acceptance checks, and quantitative bounds are specified to enable reproducibility. By avoiding online key exchange and keeping long-lived secrets off Programmable Logic (PL)-based external memories while continuously relocating their physical locus, the framework provides a deployable, energy-aware defense in depth against code-theft vectors in FPGA-based systems. Overall, the work provides an original and deployable solution for strengthening the security of commercial products against code theft in embedded environments. Full article

The study investigates how agriculture can serve as a driver of sustainable tourism and local development within the Vesuvius National Park under the European Charter for Sustainable Tourism (ECST) framework. Based on 14 semi-structured interviews with farmers, tourism operators, cultural institutions, and producer consortia, the findings reveal that agriculture plays a central role not only as a productive sector but also as a custodian of biodiversity, identity, and territorial resilience. Stakeholders emphasised the economic and symbolic value of traditional crops, highlighting how farm-based experiences, product certifications, and civil-society networks strengthen community cohesion and diversify visitor flows. Nevertheless, tourism remains predominantly concentrated in the vicinity of the volcano’s crater, thereby excluding the park’s other trails, limiting the positive impacts on rural and peripheral areas. Practical implications point to the need for improved mobility infrastructure, cross-sector coordination, and targeted incentives to link agrotourism circuits with regional branding and EU sustainability policies. Overall, the study shows that integrating agriculture into tourism governance can foster more inclusive, resilient, and territorially embedded forms of rural development in protected areas. Full article

This study introduces Internal Induction Heating (In-IH) as an efficient method for local mold temperature control in thin-walled polypropylene (PP) injection molding. Unlike conventional systems that are slow and energy-intensive, the insert is integrated directly into the induction circuit in the In-IH system, generating eddy currents for rapid and localized heating. Numerical and experimental analyses were performed to examine the effects of insert geometry and heating parameters; it was found that thinner inserts achieved higher surface temperatures—the 0.5 mm insert reached ~550 °C, while the 2.0 mm insert reached only ~80 °C—confirming an inverse relationship between thickness and temperature. Narrower inserts (25 mm) concentrated heat more effectively, whereas wider ones yielded better temperature uniformity. The cooling conditions strongly affected the temperature gradients. Mold-filling experiments demonstrated that In-IH significantly improved the flowability of PP: at 180 °C, the 0.4 mm specimen achieved a flow length of 85.33 mm, compared with 43.66 mm for the 0.2 mm specimen. At 250–300 °C, all samples approached full filling (~100 mm). The simulation and experimental results agreed, with a maximum deviation of 10%, confirming that In-IH provides rapid, energy-efficient, and precise temperature control, thus enhancing melt flow and product quality for thin-walled PP components. Full article

Artificial sweeteners, or non-caloric sweeteners (NCSs), are widely consumed as sugar substitutes to reduce energy intake and manage obesity. Once considered inert, accumulating evidence now shows that NCSs interact with host physiology, altering gut microbiota composition and neural circuits that regulate feeding. This review synthesizes current knowledge on how NCSs disrupt the gut–brain axis (GBA), with particular focus on microbiota-mediated effects and neural reward processing. In homeostatic regulation, NCS-induced dysbiosis reduces beneficial taxa such as Akkermansia muciniphila and Faecalibacterium prausnitzii, diminishes short-chain fatty acid production, impairs gut barrier integrity, and promotes systemic inflammation. These changes blunt satiety signaling and favor appetite-promoting pathways. Beyond homeostasis, NCSs also rewire hedonic circuits: unlike caloric sugars, which couple sweet taste with caloric reinforcement to robustly activate dopaminergic and hypothalamic pathways, NCSs provide sensory sweetness without energy, weakening reward prediction error signaling and altering neuropeptidergic modulation by orexin, neurotensin, and oxytocin. Microbial disruption further exacerbates dopaminergic instability by reducing precursors and metabolites critical for reward regulation. Together, these top-down (neural) and bottom-up (microbial) mechanisms converge to foster maladaptive food seeking, metabolic dysregulation, and increased vulnerability to overeating. Identifying whether microbiome-targeted interventions can counteract these effects is a key research priority for mitigating the impact of NCSs on human health. Full article

Drought is a leading constraint on plant productivity and will intensify with climate change. Plant acclimation emerges from a multilayered regulatory system that integrates signaling, transcriptional reprogramming, RNA-based control, and chromatin dynamics. Within this hierarchy, non-coding RNAs (ncRNAs) provide a unifying regulatory layer; microRNAs (miRNAs) modulate abscisic acid and auxin circuits, oxidative stress defenses, and root architecture. This balances growth with survival under water-deficient conditions. Small interfering RNAs (siRNAs) include 24-nucleotide heterochromatic populations that operate through RNA-directed DNA methylation, which positions ncRNA control at the transcription–chromatin interface. Long non-coding RNAs (lncRNAs) act in cis and trans, interact with small RNA pathways, and can serve as chromatin-associated scaffolds. Circular RNAs (circRNAs) are increasingly being detected as responsive to drought. Functional studies in Arabidopsis and maize (e.g., ath-circ032768 and circMED16) underscore their regulatory potential. This review consolidates ncRNA biogenesis and function, catalogs drought-responsive modules across model and crop species, especially cereals, and outlines methodological priorities, such as long-read support for isoforms and back-splice junctions, stringent validation, and integrative multiomics. The evidence suggests that ncRNAs are tractable entry points for enhancing drought resilience while managing growth–stress trade-offs. Full article

The integration of high-pressure grinding roller (HPGR) with pre-concentration techniques and stirred mills is recognized for its energy efficiency. Studies have suggested that the feed with a P₈₀ around 1 mm is acceptable for stirred mills or coarse particle flotation. Nonetheless, published experimental data characterizing the comminution behavior of single-stage HPGR circuits configured with a 1 mm screen aperture remain scarce. Moreover, extant research remains confined to laboratory scale. Consequently, critical performance metrics, including production capacity, screening efficiency, and process continuity, have not been substantively documented in the literature. In this paper, the HPGR performance in an industrial-scale HPGR/tower mill comminution circuit was assessed and optimized by laboratory and industrial tests. The research meticulously analyzed the impact of feed rate on the industrial-scale flip-flow screen and HPGR performance and found that the HPGR featuring two studded rolls with a diameter of 800 mm and a width of 400 mm, operating in a reverse classification circuit with a scalped feed by a 14.64 m² flip-flow screen while running continuously 24 h per day, is capable of producing a −1 mm comminution product suitable for tower mill feed. Under the optimal operating conditions identified, it achieved a specific energy consumption of 4.57 kWh/t with a feed rate of 27.08 t/h. Full article

Small surface defects in printed circuit boards (PCBs) severely affect the reliability of electronic devices, making PCB surface defect detection crucial for ensuring the quality of electronic products. However, the existing detection methods often struggle with insufficient accuracy and the inherent trade-off between detection precision and inference speed. To address these problems, we propose a novel ESDM-HNN-YOLO (EH-YOLO) network based on the improved YOLOv10 for efficient detection of small PCB defects. Firstly, an enhanced spatial-depth module (ESDM) is designed, which transforms spatial-dimensional features into depth-dimensional representations while integrating spatial attention module (SAM) and channel attention module (CAM) to highlight critical features. This dual mechanism not only effectively suppresses feature loss in micro-defects but also significantly enhances detection accuracy. Secondly, a hybrid neck network (HNN) is designed, which optimizes the speed–accuracy balance through hierarchical architecture. The hierarchical structure uses a computationally efficient weighted bidirectional feature pyramid network (BiFPN) to enhance multi-scale feature fusion of small objects in the shallow layer and uses a path aggregation network (PAN) to prevent feature loss in the deeper layer. Comprehensive evaluations on benchmark datasets (PCB_DATASET and DeepPCB) demonstrate the superior performance of EH-YOLO, achieving mAP@50-95 scores of 45.3% and 78.8% with inference speeds of 166.67 FPS and 158.73 FPS, respectively. These results significantly outperform existing approaches in both accuracy and processing efficiency. Full article

The integration of renewable energy sources (RES) into energy systems is becoming increasingly widespread around the world, driven by various factors, the most relevant of which is the high environmental friendliness of these types of energy resources and the possibility of creating stable generation systems that are independent of the economic and geopolitical situation. The large-scale involvement of green energy leads to the creation of distributed energy networks that combine several different methods of generation, each with its own characteristics. As a result, the issues of data collection and processing necessary for optimizing the operation of such energy systems are becoming increasingly relevant. The first stage of renewable energy integration involves building models to assess theoretical potential, allowing the feasibility of using a particular type of resource in specific geographical conditions to be determined. The second stage of assessment involves determining the technical potential, which allows the actual energy values that can be obtained by the consumer to be determined. The paper discusses a method for assessing the technical potential of solar energy using the example of a private consumer’s energy system. For this purpose, a generator circuit with load models was implemented in the SimInTech dynamic simulation environment, accepting various sets of parameters as input, which were obtained using an intelligent information search procedure and intelligent forecasting methods. This approach makes it possible to forecast the amount of incoming solar insolation in the short term, whose values are then fed into the simulation model, allowing the forecast values of the technical potential of solar energy for the energy system configuration under consideration to be determined. The implementation of such a hybrid assessment system allows not only the technical potential of RES to be determined based on historical datasets but also provides the opportunity to obtain forecast values for energy production volumes. This allows for flexible configuration of the parameters of the elements used, which makes it possible to scale the solution to the specific configuration of the energy system in use. The proposed solution can be used as one of the elements of distributed energy systems with RES, where the concept of demand distribution and management plays an important role. Its implementation is impossible without predictive models. Full article

The global energy crisis demands efficient electricity production solutions, especially for isolated communities where hydraulic energy can be harnessed sustainably. This paper presents a case study analyzing the efficiency of a 13,330 kW hydrogenerator, consisting of a bulb-type hydro-aggregate using the calorimetric method—a viable alternative when testing at nominal load is not feasible due to technical limitations. The method involves measuring the thermal energy absorbed by the cooling water under three operating conditions: no-load unexcited, no-load excited, and symmetric three-phase short-circuit. Measurements followed IEC standards and were conducted with high-precision instruments for temperature, flow, voltage, and current. The results quantify mechanical, ventilation, iron, and copper losses, as well as additional losses via radiation and convection. Thermal analysis revealed significant heat accumulation in the rotor and stator windings, indicating the need for improved cooling solutions. The calorimetric method enables efficiency evaluation without interrupting generator operation, offering a valuable tool for diagnostics, predictive maintenance, and informed decisions on modernization. Furthermore, integrating an intelligent operational control system could enhance efficiency and improve the quality of the supplied energy, supporting long-term sustainability in hydroelectric power generation. Full article

This article presents a design solution for feeding cylindrical parts with axial orientation. A working algorithm was developed to control and synchronize the main components, which was verified via a simulation. The pneumatic and electrical circuits were designed using a software platform for engineering purposes. Based on the CAD project created, a real prototype was built. The energy consumption of the system was tested and evaluated. The results from the prototype verified the solution. This article emphasizes the use of specific sensors for detecting part orientation and their role in improving process reliability. The system is suitable for industrial implementation due to its functionality, stable operation, low energy consumption, and ability to be integrated into automated production systems. Full article

With the rapid advances of the electronics industry, a large amount of acidic etching waste solutions (AEWS) for etching Printed Circuit Board (PCB) are generated, which require complete remediation and sustainable recycling to avoid environmental pollution and wasting of resources. Herein, the novel purification technology for the acidic copper-containing etching waste solution was exploited via integrated alkali gradient pH control (3.0, 3.2, and 3.5). At pH 3.0, the system demonstrated selective metal removal with 94.02% efficiency for Fe and 82.60% for Mn. Elevating the pH to 3.2 enabled effective elimination of Zn (59.32%), Cr (59.46%), and Al (33.24%), while maintaining minimal copper loss (8.16%). Further pH adjustment to 3.5 achieved enhanced removal efficiencies of 97.86% (Fe), 91.30% (Mn), 59.38% (Zn), 62.10% (Cr), 21.66% (Ca), 34.05% (Al), and 26.66% (Co), with copper retention remaining high at 70.83% (29.17% loss). Furthermore, using the purified AEWS (pH 3.2) as precursor, high-purity nano-CuO was successfully synthesized through a Cu₂(OH)₃Cl conversion-calcination process, exhibiting 99.20% CuO purity with 0.0012% chlorine content and <0.1% metallic impurities. The development and application of the purification technology for AEWS containing copper, along with the production methodology for high-purity CuO, were significant to the fields of electronic information industry, environmental engineering, green industry and sustainable development of the ecological environment. Full article

Given the decline in fossil energy reserves and the need for less pollution, achieving carbon zero is challenging in major industrial sectors. However, the emergence of large-scale hydrogen production systems powered by renewable energy sources offers an achievable option for carbon neutrality in specific applications. When combined with energy storage systems, static power converters are crucial in these production systems. This paper offers a comprehensive review of various power converter topologies, focusing on AC– and DC–bus architectures that interface battery storage units, electrolyzers, and fuel cells. The evaluation of DC/AC, AC/DC, and DC/DC converter topologies, considering cost, energy efficiency, control complexity, power level suitability, and power quality, represents a significant advancement in the field. Furthermore, the subsequent exploration of battery aging behavioral modeling, characterization methods, and real-time parameter estimation of the battery’s equivalent electrical circuit model enhances our understanding of these systems. Large-scale hydrogen production systems most often use an AC–bus architecture. However, DC–bus configuration offers advantages over AC–bus architecture, including high efficiency, simpler energy management, and lower system costs. In addition, MVDC or HVDC DC/DC converters, including isolated and non-isolated designs based on multiple cascaded DABs and MMC-type topologies, have also been studied to adapt the DC–bus to loads. Finally, this work summarizes several battery energy storage projects in the European Union, specifically supporting the large-scale integration of renewable energy sources. It also provides recommendations, discussion results, and future research perspectives from this study. Full article

Optical phase modulation is essential for a wide range of silicon photonic integrated circuits used in communication applications. In this study, an optical phase shifter utilizing photo-elastic effects is proposed, where mechanical stress is induced by electrostatic micro-electro-mechanical systems (MEMS) with actuators arranged in a comb drive configuration. The design incorporates suspended serpentine silicon nitride (SiN) optical waveguides. Through extensive numerical simulations, it is shown that the change in the effective refractive index (n_eff) of the optical waveguide is a function of the voltage applied to the electrostatic actuators and that such n_eff tuning can be achieved for a broad range of wavelengths. Implemented within one arm of an unbalanced Mach–Zehnder interferometer (MZI), the phase shifter achieves a phase change of π when the stressed optical path measures 4.7 mm, and the actuators are supplied with 80 V DC and consume almost no power. This results in a half-wave voltage-length product (V_πL) of 37.6 V·cm. Comparative analysis with contemporary optical phase shifters highlights the proposed design’s superior power efficiency, compact footprint, and simplified fabrication process, making it a highly efficient component for reconfigurable MEMS-based silicon nitride photonic integrated circuits. Full article

Model predictive control offers high-performance regulation for multiphase drives but is critically dependent on the accuracy of mathematical models for prediction, making it vulnerable to parameter mismatches and uncertainties. To achieve parameter-independent control across both healthy and faulty operations, this paper proposes a novel dynamic mode decomposition with control (DMDc)-based model predictive current control (MPCC) scheme for five-phase permanent magnet synchronous motors. The core innovation lies in constructing discrete-time state-space models directly from operational data via the open-loop DMDc identification, completely eliminating reliance on explicit motor parameters. Furthermore, an improved fault-tolerant strategy is developed to mitigate the torque ripple induced by inter-turn short-circuit (ITSC) faults. This strategy estimates the key fault characteristic, the product of the short-circuit ratio and current, through a spectral decomposition of the AC component in the q-axis current variations, bypassing the need for complex parameter-dependent observers. The derived compensation currents are seamlessly integrated into the predictive control loop. Experimental results comprehensively validate the effectiveness of the proposed framework, demonstrating a performance comparable to a conventional MPCC under healthy conditions and a significant reduction in torque ripple under ITSC fault conditions, all achieved without any prior knowledge of motor parameters or the retuning of controller gains. Full article

This review analyzes the Electronic Digital Twin (EDT) tools characterizing the industrial transformation phase from Industry 4.0 to Industry 5.0. The goal is to provide innovative research EDT solutions to integrate in manufacturing production processes. Specifically, this research is focused on the possibility of combining the advanced technologies and electronics and mechatronics of industrial machines with Artificial Intelligence (AI) algorithms. Furthermore, this review provides important elements about possible future implementations of AI-EDTs and some circuital examples to support the understanding of the concept of circuit simulation in EDT models. EDTs are useful to comprehend the modeling concepts functional to the AI application using the output of the circuit simulations. The output of the circuit is used to train the AI model, thus strengthening the capability to classify and predict the real behavior of production machines with a good accuracy. This review discusses perspectives, limits, and advantages of EDTs and is useful to define new research patterns integrating structured EDTs in advanced industrial environments. The focus of this paper is the definition of possible perspectives of EDT implementations, including AI, in data-driven processes in specific strategic areas of industrial research by classifying the scientific topics in six main pillars. This paper is also suitable for the researcher to develop innovative topics for projects scaled into different work packages based on EDT facilities. Full article