This research bridges the gap between claimed and actual ethical behavior by analyzing subtle "hidden honest signals" in language using AI and Natural Language Processing (NLP). It categorizes individuals into four "tribes": "doves" (ethical, collaborative), "wolves" (loyal, diligent), "sharks" (competitive, aggressive), and "parasites" (self-serving, exploitative). Using data from migrant workers in Taiwan, the study examines how ethical behavior correlates with productivity in healthcare and manufacturing sectors. Results show that ethical behavior influences work outcomes, though not always in expected ways. The findings suggest that developing ethical training programs could improve productivity, particularly for migrant workers in these settings.

In this paper, we present a method to compute the hysteresis loss in synchronous machines through a hysteresis model and along a single magnetostatic field simulation, which is substantially faster than a transient simulation. The methodology is applied to a permanent magnet synchronous machine. The results are compared to those obtained by existing models and are in the range of data sheet values, and demonstrates an improvement in accuracy at an affordable computational cost.

Chronic kidney disease (CKD) affects millions of people worldwide, making early diagnosis critical for effective management and healthcare cost reduction. Monitoring blood urea levels is essential for this purpose. In this paper, we present a microwave sensor capable of detecting urea concentrations within the clinical range required for CKD diagnosis, a sensitivity not achieved by previous microwave (MW) based approaches. The proposed device operates without reagents or enzymes, requires no specialized personnel and offers an extended lifespan, ease of use, and sensitivity comparable to conventional optical sensors. Our work consists of two main steps. First, we conducted a dielectric characterization of artificial blood plasma (ABPS) and urea-enriched ABPS (ABPSU, 320 mg/dL) over a frequency range of 500 MHz to 10 GHz. The results revealed that the most significant differentiation between samples occurs at low frequencies, particularly in the loss tangent rather than in the real part of the dielectric constant, thereby identifying the optimal frequency range for sensor design. In the second step, we designed a double concentric circular split-ring resonator (DCCSRR) on a 49 mm × 45 mm Rogers 5880™ substrate. A dedicated optimization protocol enhanced both the resonance peak amplitude and the quality factor of the sensor’s fundamental mode. After fabrication, the DCCSRR was tested for urea concentrations ranging from 10 to 320 mg/dL, exhibiting measurable variations in S21 (dB) and frequency shifts around 575 MHz. This study establishes the first microwave sensor operating within the urea concentration range necessary for CKD diagnosis, paving the way for practical microwave-based urea monitoring.

Traumatic brain injury (TBI) is a major public health concern that often leads to mortality or long-term disability. Accurate prediction of outcomes, such as mortality and Functional Status Scale (FSS) scores, improves treatment strategies and guides clinical decisions. This study applies supervised machine learning (ML) techniques to predict mortality and FSS score in a real-world dataset of 300 pediatric TBI patients compiled by the University of Colorado School of Medicine. The dataset includes detailed clinical information, such as demographics, injury mechanisms, and hospitalization outcomes. Eighteen ML models predict mortality, while thirteen models forecast FSS scores. Performance metrics, including accuracy, ROC AUC, F1-score, and mean squared error, evaluate the models. Logistic regression and Extra Trees show high precision in mortality prediction, while linear regression achieves the best FSS score prediction with an R² score of 0.62. Feature selection reduces 103 clinical variables to the most relevant ones, improving model efficiency and interpretability. The study highlights how ML models help clinicians identify high-risk cases and support personalized interventions. This research demonstrates the potential of ML-driven predictive analytics to integrate into clinical workflows, providing healthcare professionals with data-driven tools to improve TBI care.

This paper presents a cloud-based surface electromyography (sEMG) gesture classification system that leverages personalized, small-scale Convolutional Neural Networks (CNNs), addressing the variability in muscle signals that hinders the adoption of sEMG-based systems. Unlike existing one-size-fits-all sEMG systems, our approach incorporates user-specific sensor and gesture calibration that fine-tunes the model based on individual muscle signal characteristics. This personalization enables the system to adapt to unique signal patterns, making it highly robust to userspecific differences. The system employs a user-dependent number of sEMG sensors connected to a Wi-Fi-enabled device, transmitting real-time muscle activation data to a centralized server for model training and gesture inference. The system creates a personalized model for each new user that allows custom gesture sets. All signal processing, model inference and training happens on the server side in real time. This cloud-based approach reduces the computational complexity required on the device, enabling lightweight and cost-effective hardware. Moreover, the system easily scales to accommodate a large number of users, providing scalability and flexibility.

Assessing urban environments using intelligent tools helps decision-makers improve citizen quality of life. Our research introduces a Geospatial Augmented Reality (AR) System for Aesthetic and Environmental Assessment of the University. This framework integrates user presence from geospatial analysis, greenery evaluated through image processing, user sentiments derived from biosensor data using deep learning, and contextual information. These factors are synthesized via Multi-Criteria Decision Making (MCDM) to generate scores. Subsequently, the system utilizes voice recognition to identify user-selected areas, overlaying scores, and relevant contextual data through AR technology. This approach empowers decision-makers to enhance urban spaces effectively.

Task-oriented digital semantic communication systems utilize neural networks to extract and transmit taskrelevant features, which significantly improves the communication efficiency compared to traditional communication systems. These systems also ensure compatibility with modern digital communication frameworks, and thus attract much attention in emerging resource-constrained communication scenarios. However, existing research has limitations in optimizing the effectiveness of transmitted features, which hinders further improvements in system performance. In this paper, two regularization terms are proposed to address this limitation. First, a transmission effectiveness regularization term is introduced to enhance the downstream task performance by reducing the redundant information in the transmitted features. Second, a semi-orthogonal regularization term is proposed to optimize the vector quantization module, which improves quantization efficiency and model generalization by minimizing the similarity between codewords. These proposed regularization terms are then incorporated into the objective function of an existing robust digital semantic communication system. Furthermore, a new implementation scheme and a corresponding optimization objective is presented. Experimental results demonstrate that, the presented scheme achieves better inference performance with low latency and exhibits stronger robustness in the case of mismatched training and testing channels compared to the baseline schemes.

Hierarchical contextual embedding is a novel approach to encoding semantic relationships within complex linguistic data, emphasizing the integration of token interactions across multiple levels of abstraction. By leveraging a multi-layered structure, the proposed framework successfully captures both local dependencies and global contextual patterns, addressing challenges associated with long-range dependencies and extended textual inputs. Extensive experiments reveal substantial performance improvements in tasks requiring deep contextual comprehension, supported through robust scalability and efficient handling of large input sequences. The embedding mechanism demonstrates adaptability across diverse linguistic domains, including tasks with zero-shot transfer learning scenarios, while maintaining competitive accuracy. Comparative analyses highlight the framework's advantages over baseline models, particularly in representing syntactic hierarchies and semantic relationships in a computationally efficient manner. Visualization of embedding spaces illustrates coherent clustering of related tokens, affirming the framework's ability to encode hierarchical linguistic structures effectively. Error analysis uncovers specific challenges, such as resolving nested clauses and ambiguous references, which demonstrate the complexity of achieving comprehensive linguistic modeling. Scalability tests validate the model's capacity to manage varying input sizes while maintaining computational efficiency. Results demonstrate the framework's robustness against noisy inputs, with consistent accuracy across moderate error rates. Theoretical principles underpinning the hierarchical approach align closely with observed outcomes, reinforcing the framework’s conceptual soundness. With its innovative design, the framework contributes significantly to the advancement of adaptive language modeling architectures and their practical applications. Comprehensive evaluations across tasks and domains affirm the framework’s broad applicability and potential to inspire future research in language modeling and contextual representation.

由于太赫兹无线通信拥有广阔的未开发频谱，它正在成为未来 6G 网络的关键技术。使用模拟相控天线阵列对于缓解太赫兹信号的显著衰减特别有效，从而确保高质量的通信。通过采用精心设计的移相器，这些阵列可以实现波束成形，将信号增益集中在空间中以补偿信号损失。然而，在超宽带情况下，相控阵系统的性能会受到影响，因为移相器通常是为中心频率设计的，但应用于整个频带。当子载波频率与中心频率不同时，这将导致标准波束成形方法中出现明显的波束偏差和散焦。在本文中，我们提出了模拟波束成形方法来增强视距太赫兹超宽带相控发射机阵列系统的性能。利用空间啁啾特性，我们开发了移相器设计方案，以在整个频带上实现更均匀的等效信道增益，以适应各种应用场景。我们通过模拟结果证明了所提出方法的有效性和优势，与最先进的模拟波束形成方案相比，结果显示出显着的改进，包括提高吞吐量和降低误码率。

Cybersecurity threats have escalated rapidly in recent years, with ransomware posing one of the most critical challenges to digital infrastructures globally. Traditional detection mechanisms, while effective against known threats, often lack the adaptability needed to address the fast-evolving tactics employed by ransomware attackers. The RansomGuard framework introduces a transformative, multi-layered approach to ransomware detection, integrating Dynamic Encryption Pattern Analysis (DEPA), Real-Time Behavior Profiling (RBP), and Heuristic Risk Scoring (HRS) to achieve unprecedented levels of accuracy and reliability. By employing adaptive analytical techniques, RansomGuard effectively distinguishes between benign and malicious activities, minimizing false positives and optimizing detection speed. Extensive evaluation across diverse ransomware families demonstrates RansomGuard's ability to achieve high detection rates while maintaining computational efficiency, ensuring its suitability for real-time deployment in dynamic and resourceconstrained environments. This study not only showcases a novel solution to the challenges associated with ransomware detection but also sets a new standard for cybersecurity frameworks aimed at proactive threat mitigation, positioning RansomGuard as a critical advancement in ransomware defense strategies.

Dynamic Neural Embedding (DNE) introduces a novel approach to language modeling by dynamically adjusting word embeddings based on contextual information, thereby enhancing the model's ability to capture semantic nuances and improving performance across various natural language processing tasks. Comprehensive empirical evaluations demonstrate that DNE achieves higher accuracy and F1 scores on benchmarks such as GLUE and SQuAD compared to established baseline models. Additionally, DNE exhibits robust adaptability across diverse domains, maintaining high performance with minimal fine-tuning, and effectively leverages larger datasets to enhance performance, indicating strong scalability with increasing data volumes. These findings underscore the potential of DNE to advance the field of language modeling, offering a promising direction for future research and development in natural language processing.

Mixed-precision quantization (MPQ) is gaining momentum in academia and industry as a way to improve the trade-off between accuracy and latency of Deep Neural Networks (DNNs) in edge applications. MPQ requires dedicated hardware to support different bit-widths. One approach uses Precision-Scalable MAC units (PSMACs) based on multipliers operating in Sum-Together (ST) mode. These can be configured to compute N = 1, 2, 4 multiplications/dot-products in parallel with operands at 16/N bits. We contribute to the State of the Art (SoA) in three directions: we compare for the first time the SoA ST multipliers architectures in performance, power and area; compared to previous work, we contribute to the portfolio of STbased accelerators proposing three designs for the most common DNN algorithms: 2D-Convolution, Depth-wise Convolution and Fully-Connected; we show how these accelerators can be obtained with a High-Level Synthesis (HLS) flow. In particular, we perform a design-space exploration (DSE) in area, latency, power, varying many knobs, including PSMAC units parallelism, clock frequency and ST multipliers type. From the DSE on a 28-nm technology we observe that both at multiplier level and at accelerator level there is no one-fits-all solution for each possible scenario. Our findings allow accelerators' designers to choose, out of a rich variety, the best combination of ST multiplier and HLS knobs depending on the target, either high performance, low area, or low power.

Domino logic circuits inherent speed and area efficiency benefits make them essential for high-speed and low-power digital applications. However, particularly in contemporary, massively scaled technologies, traditional domino logic architectures typically encounter issues such as excessive power waste, higher delay, and lower noise immunity. To overcome these constraints, we suggest two to three improved circuit designs that offer new designs on traditional domino logic. To meet the demands of diverse high-performance applications, each design concentrates on certain performance improvements, such as lowering power consumption, enhancing noise margins, or optimizing delay characteristics. The newly designed domino circuits presented in this work are carefully examined and contrasted with traditional domino logic in several performance parameters. Important features including noise margin, area efficiency, power-delay product (PDP), and process variability tolerance are all included in a thorough comparison table. According to simulation data in ideal conditions, the suggested circuits perform better than conventional domino designs in controlled environments, showing notable gains in speed, delay, and energy while preserving a high level of operational reliability. The results of our study demonstrate how domino logic circuits can improve the performance of high-speed digital systems. The proposed designs give practical solutions to some of the fundamental difficulties we face in existing domino logic such as power and delay.

Structural Health Monitoring (SHM) is vital to guarantee the early identification of any deterioration in bridge structures and to prevent unexpected collapses. As structures degrade, one of the most efficient techniques in SHM is vibrationbased damage diagnosis, which evaluates damage through analysing the dynamic features of structures. Identification of bridge deterioration may be done by measuring the vibration response caused by cars crossing a bridge. This paper proposes a unique Vehicle-Bridge Interaction (VBI)-based damage detection method to assess the bridge's condition.First, the method's foundations of theory are studied. A half-car dynamic model represents the vehicle and its suspension system, while the Finite Element Method is used to describe the bridge superstructure. The equations of motion for the bridge and the vehicle are obtained, respectively, using the mode superposition technique, D'Alembert's principle, and the finite element method. After that, the Newmark-beta approach is used to solve the linked dynamic issue. The roughness of the bridge road surface is taken into consideration during the examination.The proposed method for determining bridge damage is based on an analysis of the pitching rotation of a passing vehicle. The three main theoretical elements of this response are the bridge natural frequency, the vehicle frequency, and a pseudo-frequency component related to the vehicle speed. The Empirical Mode Decomposition (EMD) approach dissects the signal into its constituent parts, or intrinsic mode functions. A recommended damage detection method makes use of the intrinsic mode functions (IMFs) corresponding to the vehicle speed component of the response obtained from the passing vehicle. Through the study, it is observed that the proposed technique is capable of effectively identifying various damage cases considered in this research.

This paper considers the optimal control problem of steering a Dubins vehicle with a decaying speed that depends linearly on the turn-rate to a terminal point and heading with maximum kinetic energy. Pontryagin’s minimum principle is applied and the extremals are identified as sequences of straight segments and spiral-shaped turns described by involutes of a circle. A method is proposed to exactly transcribe the optimal control problem into a series of finite dimensional optimizations. Each of these optimizations determines a locally optimal candidate path that reaches the terminal state. The lowest cost path among the candidate paths is then identified. The approach is demonstrated through several numerical examples.

Numerical methods are essential for simulating transient physical phenomena, providing crucial support to engineers and researchers in fields such as defense, space, and the natural sciences. By modeling these phenomena with partial differential equations (PDEs), deeper insights can be gained. Among the various PDE-solving methods, the Crank-Nicolson technique stands out for its efficiency in handling time-dependent PDEs, offering precise and stable solutions across diverse applications. In this work, we present a 3D GPU-accelerated code optimized for parallel execution in three-dimensional space-time domains. The PDE solver is applied to quantum wave function information processing, specifically in the context of the circular double-slit experiment. Additionally, we utilize the solver to simulate a dual Mach-Zehnder interferometer to model the Fenna-Matthews-Olson (FMO) complex, developing a natureinspired algorithm based on its quantum dynamics. This simulation demonstrates near-perfect intelligence in a photosynthetic system without any neural network. This work may help in the design of solar cells, potentially increasing their efficiency to a greater extent.

The design of dual-band bandpass filters consisting of series short-circuited wire-bonded multiconductor transmission lines and shunt open stubs is thoroughly carried out in this paper. Two different configurations are studied and closed-form analytical design equations are derived to synthesize Chebyshev filtering functions. A novel, simplified and time-saving synthesis procedure to design dual-band filters with narrow or moderate broad bandwidths is finally presented, attending to the required final specifications and the physical and manufacturing limitations. The usefulness and validity of the proposed analytical equations are illustrated by designing, manufacturing, and measuring three different prototypes, showing an excellent agreement between analytical and measured results.

This work encompasses some relevant issues that arise when using a broadband method to characterize the even and odd mode propagation constants of symmetric microstrip coupled lines using four-port pure-mode differential and common mode S-Parameters. Correct excitations of differential and common modes play a significant role in experimental results. From these 4-port measurements, parameters corresponding to the even and odd modes can be extracted from the multi-mode generalized S-parameters obtained by the vector network analyzer. Differential and common mode sub-matrices are considered as two independent equivalent 2-port transmission lines. The method will be applied to extract the propagation constants of even and odd modes of coupled microstrip transmission lines. A discussion is included on ensuring the correct excitation of the modes in the structure and avoiding possible unwanted effects such as radiation, that negatively affects the final estimation, both in phase and attenuation. Two figures of merit are defined to evaluate how transitions affect the performance of the method. These figures of merit allow a reasonable estimation of how the final experiment is affected by selected excitation of modes. To assess the theory presented, two sets of seven lines of different lengths are manufactured. The experimental results show an excellent agreement, both with electromagnetic simulations and with the analytical model used, for the phase constant in the improved case of 30 • bends, and an improvement in the useful bandwidth of the attenuation constant. This fact verifies the correct functioning of the method. In addition, the measurements are presented in the range of 0.01-67 GHz, which represents a significant advance in bandwidth for this type of structure.