Thermal sensing mechanism in micro-electro mechanical systems (MEMS) has an inherent potential to realize various flexible physical MEMS sensors due to the absence of resonating structure. In such thermal sensors, it is crucial to limit the thermal mass around the sensing structure to speed-up the time response. The absence of rigid body presents technical challenges to fabricate free-standing sensing elements in flexible MEMS sensors. In such sensors, the thermal mass has been generally limited by the utilization of thin substrates. However, there is a limit when switching to thinner substrate become challenging, mainly due to handling and installation constraints. As a solution, here we propose a laser ablation process to locally remove the unnecessary substrate parts in flexible MEMS thermal sensors. The concept was validated experimentally through thermal analysis and response time measurements under operation as airflow sensors. Time response suppression of up to 50% was confirmed.

We study identical semi-passive systems coupled pairwise by heterogeneous, linear or nonlinear passive systems, including systems with memory such as memristive systems. The semi-passive systems are assumed to satisfy a certain input-output symmetry and can for instance be oscillators with an asymptotically stable limit cycle or systems with asymptotically stable equilibrium, but also chaotic systems or neuronal oscillators. We represent the system of coupled semi-passive systems by a graph. The semi-passive systems are associated with the nodes of that graph and the nonlinear passive systems are associated with the graphs edges. To each such system we associate a weight defined as the largest value w.r.t. which the system is input-or output-passive. We derive a sufficient condition for synchronization that is phrased in terms of the algebraic connectivity of the weighted coupling graph, a generalized one-sided Lipschitz-condition associated to the semi-passive system and a constant related to its linear input-output-behavior. We also derive a sufficient condition for output synchronization in a borderline case where our main condition is not applicable.

Edge computing has emerged as a pivotal solution to meet the growing demand for real-time data processing and reduced latency in modern distributed systems. However, its decentralized nature increases the attack surface, making it vulnerable to Distributed Denial of Service (DDoS) attacks. Traditional security models, which rely on perimeter-based defenses, struggle to address these threats due to their inability to effectively manage insider threats, dynamic traffic, and sophisticated attack vectors. This paper explores the integration of Zero Trust Security Models (ZTSM) with behavioral analytics to enhance DDoS detection and mitigation in edge computing environments. Zero Trust principles, such as "never trust, always verify," enforce strict access control, continuous authentication, and micro-segmentation, reducing unauthorized access to critical resources. Behavioral DDoS detection complements this by analyzing patterns in network traffic, user behavior, and resource utilization to identify anomalies indicative of potential DDoS attacks. The proposed framework leverages machine learning algorithms to create adaptive and context-aware detection mechanisms that are scalable across edge nodes. By combining ZTSM with behavioral detection, the model offers proactive mitigation strategies, ensuring minimal disruption to edge services. This integration also addresses challenges like scalability, real-time decision-making, and false positive reduction. The study demonstrates the effectiveness of this approach through simulated DDoS scenarios in edge environments. Results indicate significant improvements in detection accuracy, response time, and overall resilience of the edge infrastructure. These findings underscore the importance of adopting Zero Trust principles and advanced behavioral analytics to safeguard edge computing systems against evolving DDoS threats. Future work will focus on enhancing model interoperability, extending the solution to heterogeneous edge environments, and incorporating predictive capabilities to anticipate and mitigate threats proactively.

With rapid digitization and digitalization, drawing a fine line between the digital and the physical world has become nearly impossible. It has become essential more than ever to integrate all spheres of life into a single Digital Thread to address pressing challenges of modern society – accessible and inclusive healthcare in terms of equality, and equity. The techno-social advancements and mutual acceptance have enabled the infusion of digital models to simulate social settings with minimum resource utilization to make effective decisions. However, there is a significant gap in feeding back the models with appropriate changes in real-time. In other words, an active behavioral modeling of modern society is lacking, that influences community healthcare as a “whole”. By creating virtual replicas of physical systems, digital twins can enable real-time monitoring, simulation, and optimization of urban dynamics. This paper explores the potential of digital twins to promote inclusive healthcare for evolving smart cities. We argue that digital twins can be used to:Identify and address disparities in access to healthcare servicesFacilitate community participationSimulate the impact of urban policies and interventions on different groups of peopleAid policy making bodies for better access to healthcareThis paper proposes several proposals for stitching the actual and virtual societies using digital twins. Several discussed concepts within this framework envision an active, integrated, and synchronized community aware of data privacy and security. The proposal also provides high- level step-wise transitions that will enable this transformation.

Passive synthetic aperture radar (SAR) has attracted research in recent years because of its advantages in exploiting opportunistic signals for imaging. Hence, it minimizes the need for active radar transmitters, significantly reducing system costs. In addition, the massive proliferation of telecommunication satellites in recent years motivates the idea of using the signal for the integrated sensing and communication (ISAC) concept. This paper explores the feasibility and opportunity of exploiting communication signals, specifically the common waveform of orthogonal frequency division multiplexing (OFDM), as a source of SAR illumination. The reflected signal is captured using a flying uncrewed aerial vehicle (UAV) at a much lower altitude than the satellite. To perform this evaluation, we developed an extensive signal-level emulator that considers many realistic aspects, such as orbital parameters, pulse repetition frequency (PRF), RF radio bandwidth, and antenna beamwidth. Point targets were placed within the imaging swath using regular spacing, and then the backscattered signal was collected using the UAV. Accordingly, the SAR image was formed using the range-Doppler (RDA) processing algorithm. The integrated sidelobe ratio (ISLR) was evaluated for ground reference point (GRP) cross-sections in order to provide a numerical assessment for performance. The results indicate the feasibility of exploring the direction of ISAC in passive SAR image formation.

This paper presents the application of Infrared Thermography to measure distribution transformer efficiency. The method is very intuitive, practical, and easy to use. The results obtained with the proposed method are compared to those obtained by the standard procedure, showing excellent agreement with them. The paper presents the nature of the losses in energy transformation, the theoretical basis of the proposed method, and the comparison of its results with those obtained from the standard procedure.

The IPv6 Routing Protocol for LLNs (RPL) is fundamental to the Internet of Things (IoT), providing essential routing capabilities for devices operating in constrained environments. At the heart of RPL's efficiency is the Trickle Algorithm (TA), designed to optimize the dissemination of control messages across networks, balancing timely updates with the imperative to conserve bandwidth and energy. This paper focuses on validating the implementation of the TA within the Latest version of Contiki-OS (Contiki-NG), a leading experimental platform for IoT networks. By comparing the results of theoretical models with practical simulations of Contiki-NG, we aim to determine the reliability of the Contiki-NG representation of the TA behaviour. Our analysis reveals a high degree of agreement between theoretical expectations and simulated results, underscoring Contiki-NG's robustness as a simulation tool for IoT research. The findings affirm the precision of Contiki-NG implementation and highlight the platform's value in facilitating the development and testing of IoT protocols. By ensuring the reliability of these simulations, our work strengthens the confidence of the IoT research community in using Contiki-NG as a foundation for exploring innovative solutions to the unique challenges of IoT networks.

The present study investigates the stability and in vitro bioavailability of an Acyclovir-loaded Ophthalmic Lyophilisate Carrier System (OLCS) designed for enhanced ocular drug delivery. Acyclovir, an antiviral drug commonly used for treating herpes simplex keratitis, faces challenges such as low bioavailability and limited corneal permeability in conventional formulations. The OLCS approach leverages lyophilized carriers to provide improved stability, controlled drug release, and targeted delivery to ocular tissues. The research evaluated the long-term stability of OLCS under different environmental conditions (accelerated and real-time) in terms of drug content, physical integrity, and reconstitution properties. Stability was assessed using analytical techniques such as high-performance liquid chromatography (HPLC) and differential scanning calorimetry (DSC). The in vitro bioavailability was studied through corneal permeation assays using excised corneal tissues, with comparison to standard acyclovir eye drops. Results showed that OLCS exhibited excellent stability over 12 months, with negligible drug degradation and preserved lyophilized structure. Reconstituted formulations displayed consistent physicochemical properties, including pH and osmolarity. In vitro bioavailability testing demonstrated significantly higher drug permeation and retention in corneal tissues compared to conventional formulations, attributed to the enhanced mucoadhesive and controlled release properties of the OLCS. The findings suggest that Acyclovir-loaded OLCS is a promising alternative to conventional ocular delivery systems, offering improved therapeutic performance and long-term efficacy. Further in vivo studies are warranted to validate its clinical applicability for managing ocular viral infections.

The definition of the rated voltage of a hydropower generator is a difficult task that depends on several electrical, mechanical, and construction features. Naturally, the trade-off between influence variables must be solved to reach a minimum cost. This work contributes to analyzing the most critical constraints, exposing the authors' experience in solving this trade-off, and proposing an expedited formulation for defining the rated voltage of a generator based on successfully constructed generators deployed worldwide.

Orthogonal frequency-division multiplexing (OFDM) has drawn a lot of attention in the study of integrated sensing and communications (ISAC). However, the fundamental drawback of Doppler intolerance limits its applicability in highly dynamic scenarios such as vehicular applications. In addition, the delay and Doppler shift that are non-integer times the resolution cause the off-grid effect, leading to scalloping loss and increasing the sidelobes. In this work, a super-resolution range-doppler map (RDM) modeling algorithm with low complexity is proposed to suppress these impacts. The proposed algorithm models the RDM contributed by the detected targets jointly via 2D surface fitting and then reconstructs and subtracts the corresponding signal components from the original received signal. Consequently, the weaker targets hiding under the inter-carrier interference (ICI) and off-grid effect become detectable. The communications capability is not impacted since the algorithm is only deployed at the radar receiver. In addition, two variants of the algorithm are illustrated, showing a trade-off between accuracy and complexity. Simulation results demonstrate that the proposed algorithms can effectively suppress ICI and mitigate off-grid effects, while simultaneously enhancing the accuracy of delay and Doppler estimation. It is indicated that the proposed algorithms can provide sufficient improvements in multi-target scenarios.

Abnormal joint rigidity is a common symptom of many neurological disorders like Parkinson's disease and stroke. Joint rigidity is clinically described as increased resistance during passive joint movement and is quantitatively related to the static passive component of joint impedance, i.e., passive stiffness. Here, we introduce a novel approach to estimate the passive stiffness of the wrist in humans across two coupled Degrees of Freedom (DoFs): Flexo-Extension (FE) and Radio-Ulnar Deviation (RUD). This method employs a subject-specific kinematic model of the human wrist, treated as a universal joint with two skew-oblique axes (FE and RUD), not intersecting and not necessarily perpendicular one to each other. We tested this methodology on ten healthy volunteers using infrared cameras and a hand-held device equipped with a 6-axis load cell for manual wrist perturbations. We used motion and force/torque data to determine angles and torques at the wrist DoFs, and applied multiple linear regression to calculate the 2-by-2 stiffness matrix. Our findings align with existing literature in terms of stiffness behavior, showing stiffness anisotropy with the highest value predominantly along the RUD direction. However, compared to a simplified wrist model with intersecting, orthogonal axes and to prior studies assuming alignment between human joints and robotic ones, our method demonstrates significant differences, particularly in the magnitude of the stiffness ellipse. By providing a more anatomically plausible representation of wrist kinematics through relaxing the constraints of simplified methods, our results show, for the first time, an accurate subject-specific estimation of wrist stiffness.

The present contribution investigates Index Modulation Aided Non-Orthogonal Multiple Access (IM-NOMA) in simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS)-assisted networks. In the proposed IM-NOMA scheme, the information for a specific user (IM user) is spatially modulated across STAR-RIS subsurfaces according to a predefined pattern. Then, the IM user detects its signal using an energy-based maximum likelihood (EML) detection method, which technically leverages the energy of the received signals to identify the active subsurface indices. In this context, the performance of the proposed IM-NOMA scheme is quantified over Beaulieu-Xie (BX) fading channels in terms of pairwise error probability (PEP), bit error rate union bound, and achievable rate. The obtained PEP expressions are then used to derive a tight upper bound on the bit error rate (BER), which is subsequently utilized in quantifying the overall system performance in terms of upper bounded BER and the achievable rate. Finally, we validate the derived analytic expressions with respective results from extensive Monte Carlo simulations that provides valuable insights of the theoretical and practical importance on the achievable performance for all users in the system.

The objective of this study was to develop and evaluate an acyclovir-loaded ophthalmic lyophilisate carrier system, aimed at enhancing ocular drug delivery for the treatment of viral eye infections. Acyclovir, an antiviral agent, is often prescribed for ocular herpes simplex virus infections, but its ocular bioavailability is limited due to rapid drainage and poor permeability. To address these limitations, a novel lyophilized formulation was designed using biocompatible and biodegradable polymers as carriers to prolong the retention time and facilitate sustained release of the drug at the site of infection. The acyclovir-loaded lyophilisates were prepared using a freeze-drying technique, with optimization of the formulation parameters including polymer concentration, cryoprotectants, and lyophilization conditions. The characterization of the lyophilized systems included evaluation of physical properties, drug content, reconstitution behavior, morphology, and in vitro release profiles. The formulations exhibited high drug entrapment efficiency, desirable rehydration properties, and sustained drug release over an extended period. In vivo ocular tolerability of the developed system was assessed in a rabbit model. The formulations showed good ocular compatibility with minimal irritation or inflammation, indicating their potential for safe and effective use in ocular therapy. The pharmacokinetic study revealed enhanced corneal retention and prolonged drug release compared to conventional aqueous solutions. This study demonstrates the potential of acyclovir-loaded ophthalmic lyophilisate carrier systems for improved ocular drug delivery. The developed formulation provides a promising alternative to conventional therapies, offering prolonged ocular residence time, improved drug bioavailability, and minimal side effects, thereby enhancing the therapeutic efficacy of acyclovir in treating viral ocular infections. Further clinical studies are warranted to confirm these findings and explore the potential for broader clinical application.