As greenhouse gas emissions increasingly become a central issue in global sustainability, it is imperative to develop solutions that effectively reduce avoidable emissions. One significant contributor to these emissions is the high percentage of empty trucks on the road at any given time. This paper aims to investigate a full truckload marketplace, specifically designed to utilize the empty cargo space of trucks, thereby minimizing empty backhauls by facilitating container transportation between two terminals. The study examines two levels of planning: operational and tactical. At the operational level, we present a lower bound for the marketplace's costs and propose an online just-in-time policy. This policy involves the marketplace's decision to accept or reject a truck offer, aiming to synchronize the incoming rate of truck offers with that of shipment requests, ultimately maximizing profitability. Moving to the tactical planning problem, our objective is to identify optimal shipping prices and truck payments that maximize the objective function of the marketplace. Two scenarios determine this objective function: the marketplace can operate as a profit-maximizing firm or as a social planner. In the latter scenario, the goal is to maximize the volume of transportation services provided, consequently minimizing the additional non-productive miles driven by empty trucks. To address the tactical planning problem, we propose algorithms that leverage the problem structure. Furthermore, we conduct simulation experiments for both planning levels. The results reveal several key findings, including a pooling effect triggered by higher rates of drivers logging into the marketplace. Additionally, we observe an increase in optimal truck costs and shipping prices in response to an escalation in the delay penalty. Furthermore, we demonstrate the greater social value generated by the social planner approach. Overall, the experimental results offer valuable insights and practical guidelines for decision-makers regarding market design and pricing decisions.

This study presents a novel chipless complementary split-ring resonator (CSRR) loaded substrate integrated waveguide (SIW) tag, coupled with an ultra-wideband coplanar antenna, for real-time monitoring of lubricant evaporation on lubricant-infused surfaces (LISs). The sensor introduces a new method for assessing the stability of the lubricant layer, a critical factor for the efficient operation of these coatings. Its design and functionality have been validated through simulations and measurements of the evaporation rate of two different lubricants with different vapor pressures, demonstrating high sensitivity in detecting lubricant evaporation. This sensor represents a significant advancement in biomaterial development and testing, providing a real-time solution to ensure the effectiveness of lubricant-infused coatings, which is essential for minimizing complications like non-specific adhesion of blood components and bacteria.

This paper proposes a system architecture for deploying AI-driven interactive systems within the context of a Cyber-Physical-Social System framework, specifically designed for applications in the emerging Metaverse, applicable to a plethora of domains. The proposed architecture integrates advanced technologies to facilitate immersive user experiences and intelligent system interactions based on user actions and realtime data inputs from the real and the digital world(s), digital twins, artificial agents and elements. The integration of the proposed architecture components aim to support sophisticated interactions between physical, digital entities, and humans within the Metaverse. The paper presents the theoretical underpinnings and system description of the proposed architecture, contributing to future developments in Metaverse systems and technologies. This article has been accepted for publication in IEEE International Symposium on Emerging Metaverse (ISEMV 2024). This is the author's version which has not been fully edited and content may change prior to final publication. Citation information: L. Nisiotis, "A System Architecture for AI-Driven Interactive Systems Within a CPSS Framework for Metaverse Applications," 2024 IEEE International Symposium on Emerging Metaverse (ISEMV), Bellevue, WA, USA, 2024, pp. 9-12, doi: 10.1109/ISEMV63338.2024.00016.  

Gait analysis contributes to the identification of structural and functional limitations, as well as to the development of treatment plans and the evaluation of their effect on the patient. For this purpose, spatial and temporal characteristics of the patient's gait are studied and compared with reference values taken from control subjects. This paper examines the use of trajectories described by the instantaneous center of rotation (ICR) of the lower extremity segments as a biomechanical measure for assessing human gait. Gait trials were performed in which subjects walked on the ground and on a treadmill using a wearable inertial motion capture system (MVN Awinda, Xsens), while sports cameras recorded motion in the sagittal plane. Kinematic data from gait trials were acquired by video processing using an open source sports analysis tool (Kinovea) and directly through the Xsens Analyze software. The position of the ICRs of the lower extremity segments in the sagittal plane was calculated for each time sample and the determined trajectories were analyzed in time, frequency, and state space.

An important part of training simple neural networks for students and researchers alike is choosing an adequate activation function. Activation functions serve a number of important purposes, including but not limited to: preventing dead neurons, eliminating the vanishing gradient problem, and allowing networks to learn complex patterns. Currently, students are taught to use the "guess-and-check" method in choosing the best activation functions for their neural networks. Although literature and knowns exist for choosing activation functions for specific tasks, not all literature applies. The purpose of this presentation is to report an algorithm that can help choose the best activation function by eliminating human-handed checking and increasing time efficiency.

Various applications in maritime scenarios, including offshore operation, navigation, and search and rescue, require reliable high-speed communication. However, wireless maritime communication remains challenging due to limited available radio frequency (RF) bandwidth. Additional challenges are imposed by ducting and water surface reflections that lead to high packet loss rates. This work aims to provide a freespace optical (FSO) communication-based solution to address the limited bandwidth issues. An unlicensed 6 GHz RF link is used as a backup for the FSO link during outage events. The hybrid link deployment was conducted on an island located 19 km offshore at the Red Sea shore. The location was chosen based on the need for construction operations led by Red Sea Global (RSG). In addition, the deployment area is considered a challenging environment for FSO due to its high temperatures, humidity, wind speed, and sand/dust storm events. Our findings indicate that the FSO solution successfully achieves a 20 Gbps full duplex with an availability of 98.71%. The overall connectivity remained at 99.999% with the RF as a backup mode. These results highlight the potential of using FSO for high-speed maritime communication and thus connecting remote and unconnected areas.

According to the World Health Organization [1], cancer is the leading cause of death globally, with nearly 10 million deaths reported in 2020. Each year, around 400,000 children are diagnosed with cancer. The most common types of cancer varying by region.Laryngeal cancer is a type of head and neck cancer. According to Cancer.net, over 184,000 cases of laryngeal cancer were diagnosed worldwide in 2020 [3]. While it may not be as prevalent as breast, lung, or prostate cancer, laryngeal cancer presents unique opportunities for early diagnosis using audio and image data. Approximately 60% of laryngeal cancers are found in the glottis, and about 35% are in the supraglottis. For cancers confined to the larynx, the five-year survival rates are 84% for glottis cancers and 61% for supraglottis cancers [3]. Figure 1, provided by the American Society of Clinical Oncology, illustrates the medical anatomy of the glottis and supraglottis in the human throat.

Suspended microstrip antenna (SMSA) suspended in air offers higher gain and wider bandwidth (BW). However, cross-polarization level (CPL) increases and impedance becomes inductive resulting in mismatch due to increased probe feed length. Slots in SMSA (E-SMSA) decrease the inductive reactance, increase the capacitive reactance and thus improve impedance BW. Orthogonal surface currents in opposite directions around two rectangular slots in E-SMSA cancel the cross-polar radiations and reduce CPL. Meta-surfaces consisting of an array of square patches of dimensions and periodicity <0.1λ0, fabricated around slotted SMSA and on the other side of the substrate, around a rectangular parasitic patch (PP) designed on a superstrate and placed above slotted SMSA, increase the inductance of structure. To decrease the inductive reactance, substrate height is decreased which decreases CPL and improves radiation characteristics. The optimized structure consisting of SMSA with slots, PP and meta-surfaces offers 8.8dBi peak gain with side lobe level (SLL) and CPL 20dB over the entire 3.3-3.8 GHz, sub-6GHz band used for 5G applications. The measured results of the prototype structure agree closely with the simulation results.

In the rapidly evolving landscape of wireless communication, securing transmitted information remains a critical challenge. This study provides a comprehensive review of secret key generation (SKG) techniques designed to secure physical layer wireless communication channels. We focus on key generation methods that exploit physical layer characteristics such as channel state information, received signal strength, and bit error rate. The computational complexity of these techniques is also examined, emphasizing the trade-offs between security and efficiency for real-world implementations. The review identifies open challenges such as developing low-cost, resource-efficient systems, managing artificial noise, and addressing the impact of mobility and multiuser massive MIMO systems on SKG rates. Our findings aim to guide the development of more efficient, scalable SKG methods for next-generation wireless networks. Additionally, we propose a method for securely generating identical secret keys between nodes using Cuckoo filters and a Map-Reduce model for frame hashing, ensuring long-term security in IoT environments.

The release of Segment Anything Models (SAM1 and SAM2) by Meta has significantly influenced various domains, including medical imaging. However, existing implementations of SAM primarily focus on standalone tools that require local installation and configuration, limiting accessibility and ease of use. In this work, we present the web-based extension of SAM2 integrated into the Open Health Imaging Foundation (OHIF) viewer. Our solution supports all SAM2 prompt types, including points and bounding boxes, and enables multi-label predictions. This web-based integration eliminates installation requirements, offering a more user-friendly interface. The implementation is open-source and available at https://github.com/CCI-Bonn/OHIF-SAM2.

Federated learning (FL) is crucial in edge computing for next-generation wireless networks because it enables collaborative learning among devices while protecting data privacy. However, marine edge networks encounter complex issues, including frequent network disruptions, highly dynamic network topologies, and stringent bandwidth limitations, compared to traditional FL environments. Additionally, the heterogeneity of devices and unconventional data distribution complicate model training further. Moreover, due to the broadcast nature and dynamic vulnerability of wireless medium, edge nodes are susceptible to interference or malicious attacks, such as information theft and poisoning attacks, leading to inaccurate or even failed learning outcomes. Many distributed learning algorithms assume stable node environments, overlooking security and communication bottlenecks, which reduces learning efficiency and model performance. Therefore, researching privacy protection and defenses against poisoning attacks in federated learning within edge computing networks is of significant importance and value. To address this, we propose the Differential Evolution Edge-Partially Federated Learning framework, which aims to secure gradient communication by leveraging the characteristics of heterogeneous devices and non-independent and identically distributed (Non-IID) data, thereby reducing the risk of information leakage during gradient communication and effectively mitigating the impact of data heterogeneity in edge environments. Additionally, we have designed a Partial Layer Mean Detection method based on this framework to detect and defend against poisoning attacks. This algorithm protects the security of gradient information without significantly increasing computational and communication overhead. Experimental results demonstrate that, in marine edge computing environments, this framework improves privacy protection performance by 20% and increases poisoning attack defense rates by 10% without adding extra computational costs.

With the evolution of 6G networks, the convergence of the Metaverse and video streaming has opened new avenues for immersive user experiences. However, delivering high-quality video streaming in such an interactive and virtual environment poses significant challenges, particularly with regards to Quality of Experience (QoE). This paper investigates the impact of user interaction behaviors on video streaming performance in the Metaverse, specifically over 6G networks. Through a comprehensive analysis of the factors affecting QoE-such as latency, bandwidth, and interactive response times-this study proposes a novel QoE optimization framework. The framework integrates user interaction data with network resource allocation strategies, leveraging the capabilities of 6G, including ultralow latency and high-speed communication. Experimental results demonstrate improved QoE for Metaverse video streaming when user interaction behaviors are taken into account, providing insights for future developments in immersive media over 6G.

Interpretable facial expression recognition (FER) is crucial for developing AI systems that are not only technically advanced but also ethically responsible and socially accepted. In CNN-based FER methods, visual attention regions—derived from the network's intermediate layers—are typically employed to highlight the facial areas most influential in determining the recognized expression. However, these methods often highlight facial regions unrelated to expressions or overlook critical expressive zones, thus significantly diminishing their interpretability. In this work, we explore the incorporation of human expertise knowledge and domain-specific insights, like action unit-guided facial expression priors, to focus learning efforts on pertinent expression-related features, thereby mitigating the influence of extraneous non-expression factors and bolstering the interpretability of FER methods. To this end, we propose a novel Directing Visual Attention (DVA) method to focus the visual attention regions of the trained CNN classifier on the expression-related parts of the input face image, ensuring the accurate and full capture of pertinent expression features. Specifically, DVA leverages prior knowledge related to facial expressions to automatically construct plausible attention maps for guiding the visual attention regions within the CNN network. Through the meticulously designed attention guidance loss, our method effectively directs the model's focus for each expression label towards the facial regions most indicative of that expression. The proposed DVA can seamlessly integrate with any CNN-based FER model, enhancing training supervision without incurring additional inference costs. Extensive qualitative and quantitative experiments demonstrate that our method not only boosts the recognition performance of both baseline and state-of-the-art methods with a decent margin but, more critically, dramatically improves their interpretability. We believe that the proposed DVA offers a new and viable approach towards interpretable FER, making the FER models more transparent and explainable.

 Reconfigurable Intelligent Surfaces (RIS) represent a transformative technology for achieving the ambitious performance requirements of 6G wireless networks. This comprehensive review examines advanced beamforming techniques incorporating RIS technology, presenting a systematic analysis of theoretical foundations, implementation challenges, and future directions. We develop a rigorous analytical framework that characterizes the fundamental performance limits of RIS-assisted beamforming, demonstrating that system capacity scales as O(log N) with the number of reflecting elements while maintaining energy efficiency that follows O(log N/N). Our analysis reveals that hybrid approaches combining traditional optimization with machine learning techniques can achieve 85-90% of theoretical performance bounds while reducing computational complexity by approximately 80%. We present a detailed examination of practical implementation challenges, including hardware impairments, channel estimation overhead, and real-time optimization requirements. The study establishes that RIS-assisted beamforming can enable a 300% increase in spectral efficiency and 60-70% reduction in power consumption compared to conventional massive MIMO systems. Additionally, we identify critical research directions, including distributed RIS optimization, multi-user scheduling, and integration with artificial intelligence frameworks. This review provides a comprehensive foundation for researchers and practitioners working toward the realization of RIS-enabled 6G networks, offering both theoretical insights and practical implementation guidelines.

This study presents two main aims: (i) to assess functionally-calibrated musculoskeletal models (FCMs) in both tracking and predictive simulations of human motion, against non-linearly scaled models (NSMs), and (ii) to examine the effects of three different variations of our baseline functional calibration approach on the results of tracking and predictive simulations. Methods: Motion capture experiments of six functional activities were performed with three healthy subjects. The musculotendon (MT) parameters of 18 muscles per leg were estimated using an optimal control problem. A baseline problem formulation and three variations were developed to generate four different FCMs per subject. Then, the FCMs were compared against NSMs in tracking simulations of the motions excluded from the calibration and fully-predictive simulations of gait. Results: In the tracking simulations, the FCMs led to more accurate joint torques estimations. Including gait in the calibration problems improved the knee torques accuracy (normalised root mean square error: 0.31+/-0.11), compared to the baseline calibration (normalised root mean square error: 0.70+/-0.21). Regarding the gait predictive simulations, the NSMs consistently yielded more accurate subtalar inversion/eversion torques and knee flexion angles, compared to the FCMs. The accuracy of the predicted muscle excitations was generally consistent between NSMs and FCMs. Conclusion: The results suggest that, while the FCMs led to more accurate joint torques estimations in the tracking simulations, they did not outperform the NSMs in the fully-predictive gait simulations.

This study presents a novel application of a Probabilistic Bayesian Neural Network (PBNN) for estimating vocal function variables and enhancing non-invasive ambulatory voice monitoring by addressing aleatoric and epistemic uncertainties in regression tasks. The proposed PBNN allows for estimating key physiological parameters including subglottal pressure, vocal fold contact pressure, thyroaritenoid and cricothyroid muscle activations, from seven aerodynamic and acoustic features. The PBNN is trained on the Triangular Body-Cover Model (TBCM) of the vocal folds to produce a non-linear inverse mapping between its inputs and outputs. Furthermore, the selected aerodynamic and acoustic features can be obtained in ambulatory settings, thus enhancing the practical applicability of the proposed method. Transfer Learning is then applied to integrate real voice data into the initially synthetic-trained network to refine subglottal pressure estimations. The confidence intervals generated by the PBNN illustrate its ability to identify uncertain estimations, as the results show correlations between prediction errors and the estimated aleatoric and epistemic uncertainties. This correlation is advantageous because it shows that the network can effectively predict potential inaccuracies in its estimations. Increased uncertainty is mainly observed at operating points where the TBCM is likely to exhibit non-linear behaviors, at higher subglottal pressures. This suggests that the selected input features may not be robust enough for capturing the nonlinear effects in the TBCM. These results highlight the potential for future research to assess the viability of incorporating new features and additional measurements that could better capture non-linear responses.

Despite advancements in deep learning, conversational translation still faces challenges, including ambiguity in short sentences and limited data for colloquial language, as well as the need for culturally-situated and commonsense-based translations. However, Large Language Models (LLMs), using their vast background knowledge, can bring about a fundamental change in conversational translation. Therefore, in this paper, we demonstrate that LLMs, with appropriate prompts, could potentially solve most challenges identified in the last two decades of conversational translation research.

The release of the Citizens Broadband Radio Service (CBRS) spectrum has enabled private network deployments, but signal strength in this band is highly sensitive to weather conditions. While traditional signal propagation models are often constrained by their assumptions and lack adaptability to varying environments, Machine Learning (ML) models offer a flexible alternative for predicting signal strength under complex conditions. In this work, we develop a Neural Network (NN) based predictive model for CBRS signal strength, leveraging a comprehensive set of weather parameters, collected over a 24month period from a live CBRS network in Buffalo, NY. Our model demonstrates a Mean Absolute Error (MAE) of 1.83 dB in predicting hourly signal strength, and we further evaluate its generalization capability by applying a transfer learning approach to another CBRS network in Elmira, NY, achieving a MAE of 2.13 dB. This study highlights the potential of ML methods in modeling weather-induced signal variability and their applicability across different network environments.