This letter presents the design, fabrication, and characterization of an ultra-wideband (UWB) microstrip patch antenna engineered for the early detection of breast tumors. The proposed design offers a nearly constant radiation pattern, high gain, and compact size through the utilization of an artificial magnetic conductor (AMC) structure comprising four different unit cells. This design feature ensures uniform coverage and reception across breast tissue, critical for early-stage tumor detection. Operating in the 2.9 GHz to 7.0 GHz frequency band, the antenna integrates a back-plane reflector that helps increasing the gain by 3 dB while enhancing bandwidth at low frequencies, thereby improving directivity and front-to-back ratio. The proposed antenna achieves a peak gain of ~8.1 dBi with the AMC. Furthermore, our antenna exhibits a nearly constant radiation pattern across 93% of its operating bandwidth. These characteristics highlight the antenna suitability for applications such as diagnosis of breast carcinoma and microwave imaging.(This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.)

Early detection of sepsis is crucial for timely intervention and improved patient outcomes. Traditional diagnostic methods often fail to identify sepsis in its initial stages, leading to delays in treatment. This study investigates the potential of machine learning techniques, particularly multilayer perceptron (MLP) models, for early sepsis prediction using physiological data from intensive care unit (ICU) patients. A large dataset comprising approximately 40,000 ICU patient records from two hospital systems was analyzed. Various machine learning algorithms were implemented and compared, with a focus on optimizing an MLP model for early sepsis detection. The MLP model was trained on physiological data collected up to six hours before the clinical manifestation of sepsis symptoms. The findings demonstrated that the MLP model outperformed traditional methods, accurately predicting sepsis onset up to six hours before clinical symptoms became apparent. The MLP model exhibited significant improvements in accuracy compared to conventional models used for sepsis diagnosis. The ability to predict sepsis development at an early stage holds immense clinical value. Early detection facilitated by the MLP model can potentially lead to prompt administration of appropriate treatments, thereby improving patient outcomes and reducing mortality rates associated with sepsis. Moreover, early identification of sepsis cases can optimize resource allocation and improve operational efficiency in critical care environments. This study highlights the efficacy of MLP models in leveraging physiological data for early sepsis prediction. The proposed approach offers a promising solution for enhancing sepsis management protocols, ultimately contributing to improved patient care and resource utilization in intensive care settings.

We experimentally characterize and numerically model the amplification characteristics of a sub-threshold opto-electronic oscillator subject to external continuous wave radio frequency injection, as well as the transient temporal characteristics when subject to a pulsed radio frequency injection. The opto-electronic oscillator demonstrates enhanced sensitivity and amplification as threshold is approached. A radio frequency gain of 27.5 dB is demonstrated at an optical power of 0.989 times the threshold optical power. Furthermore, the transient behavior shows signatures of both the intrinsic time-delay of the opto-electronic oscillator and the finite bandwidth of the electronic radio frequency filter. Approximating higher-order group delay contributions of the experimental band-pass filter as an external time-delay allows the system to be modeled with a well-known opto-electronic oscillator rate equation model. The nonlinear, time-delayed differential equation model provides numerical agreement with experimental results. The model is approximately solved in respective analytical and transcendental regimes, giving reliable predictions compared to the experiment. It is demonstrated that small time-delay variation yields precise and predictable control over the frequency selectivity of the opto-electronic oscillator sensor.

This paper presents a cost-effective Unmanned Aerial Vehicle (UAV) payload, intended for coastal area remote sensing applications. The payload consists of a compact pushbroom imaging spectrometer built in-house with commercial offthe-shelf (COTS) components, and it contains navigation sensors that allow for direct georeferencing. The payload furthermore incorporates an upward-facing spectrometer, enabling direct computation of remote sensing reflectance ρRS (λ) from imaging spectrometer data in different lighting and atmospheric conditions. In contrast to existing micro-imaging spectrometer payloads, the presented UAV payload offers enhanced versatility and flexibility. While specifically tailored to coastal ocean color mapping, the payload can easily be adapted to a wide range of remote sensing applications. The presented payload has been tested in the field. The results presented in this work, show that remote sensing payloads, built with COTS components, allow retrieving of remote sensing reflectance ρRS (λ) datacubes eligible for scientific use.

High dimension data increasingly emerged in many application areas. Feature selection methods are widely studied to solve the issues, playing a crucial role in eliminating redundant or irrelevent feature. Previous feature selection algorithms seldom consider the fact that each label might be determinded by its specific features,which has its own characteristics. Meanwhile, they often neglect mutual exclusion among labels, named "negative label correlations" and assume labels share global correlation in exploring label correlation. To address the above problem, we propose a novel algorithm called "Multi-label learning with label-specific feature selection via local positive and negative correlation" (LLCPN-MIFS). Multiple datasets manifest that the effectiveness of the proposed method.

Travel Website is an innovative web-based application designed to revolutionize the field of travel planning, which was implemented using a combination of HTML, CSS, JavaScript, and PHP. The uniqueness of the project lies in the fact that the application provides customers with the most relevant information on fares, a vast array of reviews for each unit, and the ability to book without any additional difficulty and hassle. As a result, taking into account the focus on real-time systems and user-generated content, Travel Website undoubtedly surpasses its competitors in terms of modernity and business. The main distinctive characteristic of the application is the ability to collect information on the cost of services from transportation and housing, which allows customers to choose the optimal, profitable journey. On the other hand, the system is algorithm-based to gather the fare information and update it instantly. Thus, users get live notifications whenever fares change are new special offers are available. Finally, the Travel Website's critical feature is proved based on user-generated content. Users can share their real travel experience, review, and rate transportation. Thus, all users enjoy valuables tips and hints recommended.

6th generation (6G) cellular networks are expected to enable various safety-critical use cases, e.g., in the industrial domain, which require flawless operation of the network. Thus, resilience is one of the key requirements for 6G. A particularly critical point is that 6G, as any other wireless technology, is based on the open radio medium, making it susceptible to interference. Especially intentional interference, i.e., jamming, can severely degrade the network operability. Therefore, a new approach for detecting anomalies in the radio spectrum using a digital twin (DT) of the radio environment is presented in this work. This allows the integration of contextual awareness in the anomaly detection process and is thereby superior to state-of-the-art methods  for spectrum anomaly detection. We propose a suitable system architecture and discuss the tasks of machine learning (ML) therein, particularly for reducing the computational complexity and to detect anomalies in an unsupervised manner. The feasibility of the approach is demonstrated by ray tracing simulations. The results indicate a strong detection capability in case of an accurate DT and thereby illustrate the potential of DTs to enhance monitoring of  wireless networks in the future.

This paper presents a sophisticated centralized secondary controller tailored for inverter-based, islanded Microgrids (MGs) that are prone to unforeseen network partitions triggered by faults activating protective devices. In typical radialconfigured MGs, a line fault can cause protective devices to isolate the faulted line, thereby splitting the MG into two electrically independent sub-microgrids (SMGs), while retaining the existing communication and control framework. Diverging from both centralized and distributed traditional secondary controllers, which often fail to normalize the frequency in one of the split SMGs, the proposed controller exhibits exceptional performance. Through simulation studies on 6-bus and 13-bus islanded MG setups, our controller has demonstrated not only its ability to swiftly restore nominal frequency in both SMGs within a few seconds, but also to ensure fair power distribution among the distributed generators (DGs) serving the SMGs. This rapid frequency stabilization underscores the controller's effectiveness in maintaining stable frequency levels immediately following a fault.

The sintered Nd-Fe-B magnets with various rare earth (RE) contents were prepared using strip casting technique, and the dependence of the microstructures and coercivity on rare earth content was investigated. It is found that an appropriate higher rare earth content in magnets is beneficial to the formation of uniform distribution of the RE-rich phases in grain boundaries, which leads to a rapid growth in the coercivity of magnets with increasing the rare earth content from the range of 29 wt.% to 32 wt.%. Moreover, continuous thin layers of RE-rich phase were confirmed by scanning electron microscopy in details. We further demonstrate that the coercivity of magnets becomes stronger with an appropriate higher RE content using micromagnetic simulation.

In recent years, the concept of the digital divide has gained significant attention across various sectors, including agriculture. The digital divide refers to the gap between those who have access to digital technologies and those who do not, and this divide is particularly pronounced in rural communities. This paper explores the digital divide in agriculture, identifying its key factors and discussing strategies to bridge this gap, thereby ensuring equitable access to technology in rural areas.

Digital sovereignty in education can be described as the ability of the educational community to exercise their agency and self-governance to guarantee that the use of technology is aligned with the purpose of education. The lack of familiarity of educators with critical digital literacy studies often hinders productivity on the debate and practice of its principles, such as ethics, environmental impact, or digital agency. Novice learners, as are often average educators with respect to technology, often fail to represent the problems using domain principles, as experts do. Besides, self-efficacy is known to influence student learning, enabling the transition from motivation to action when positive. Previous studies suggest that in-service teachers commonly lack confidence with respect to their digital competence. Effective analogies can assist educators in critically interpreting technological innovations by activating relevant prior knowledge from their contexts, which facilitates the representational change toward more abstract understandings of technological impact. This paper proposes the analogy with the food sovereignty principles, broadly accepted within educators, and presents an initial qualitative evaluation. The results suggest its potential to address the barriers of misrepresentation and lack of confidence. On the one hand, the analogy is easily understood and allows teachers to identify structural similarities at the level of their educational values, facilitating more abstract representations than their initial encoding. In addition, it is anticipated that, by situating the discussion within domains where teachers feel more confident than in technical topics, they seem more confident to generate new knowledge that facilitates this learning.

Proportional Integral Derivative (PID) controllers have become commonplace in various industries, finding applications in diverse fields such as industrial automation, robotics, and process control. Despite their widespread use, the challenge of parameter tuning remains a significant bottlenec k in realizing optimal controller performance. While methods like the Ziegler-Nichols method have been traditionally employed for PID parameter tuning, they often require significant expertise and may not always yield satisfactory results. Consequently, trial and error remains a prevalent albeit laborious approach to tuning PID controllers. This paper investigates the utilization of a novel Particle Swarm Optimization (PSO) algorithm as an alternative method for tuning PID controllers. PSO is a metaheuristic optimization technique inspired by the social behavior of birds flocking and fish schooling. It operates by iteratively updating a population of candidate solutions (particles) based on their individual and collective performance, with the aim of finding the optimal solution to a given optimization problem. By integrating PSO with PID controller tuning, this study seeks to overcome the limitations of traditional tuning methods and improve controller performance. The proposed approach involves formulating the PID controller parameters as optimization variables and defining an objective function that quantifies the controller's performance in terms of desired control objectives such as stability, overshoot, and se ttling time. Through a series of simulations, the effectiveness of PSO-based PID tuning is evaluated across different objective functions. The results demonstrate the capability of our algorithm to efficiently search the parameter space and converge to optimal or near-optimal PID settings, thereby enhancing control system performance while reducing the need for extensive manual tuning. Millonas' adaptability principle [2] of swarm intelligence states that the population must be able to change its behavior mode when it is worth the computational price. There has been no particle swarm optimization algorithm that decreases the number of particles as the distance to the global optimum is decreased and that is what makes our approach unique. The number of particles should be proportional to the search space, and this has not been the case historically. In PSO, the computational cost is directly proportional to the number of particles and as particles get closer to the global optimum, less of them are needed to converge on the target.

In recent years, the robotics field has witnessed an unprecedented surge in the development of humanoid robots, which bear an increasingly close resemblance to human beings in appearance and functionality. This evolution has presented researchers with complex challenges, particularly in the domain of controlling the increasing number of robotic motors that animate these lifelike figures. This paper focuses on a novel approach to managing the intricate facial expressions of a humanoid face endowed with 22 degrees of freedom. We introduce a groundbreaking inverse kinematic model that leverages deep learning regression techniques to bridge the gap between the visual representation of human facial expressions and the corresponding servo motor configurations required to replicate these expressions. By mapping image space to servo motor space, our model enables precise, dynamic control over facial expressions, enhancing the robot's ability to engage in more nuanced and human-like interactions. Our methodology not only addresses the technical complexities associated with the fine-tuned control of facial motor servos but also contributes to the broader discourse on improving humanoid robots' social adaptability and interaction capabilities. Through extensive experimentation and validation, we demonstrate the efficacy and robustness of our approach, marking a significant advancement in humanoid robotics control systems.

Residual generation and hypothesis test are two important components in fault detection techniques. Recent studies mainly focused on enhancing residual generation algorithms, but often overlook the Gaussian distribution assumption that necessary for hypothesis test. Based on the conditional invertible neural network (CINN), this study proposes a novel approach for mapping residual signals into near-Gaussian distributed latent variables, thereby enhancing the reliability and effectiveness of the hypothesis test approaches used for fault detection. With the specially designed architecture using CINN, the proposed mapping from residual signals to latent variables has no information loss, thus guaranteeing the accuracy of the proposed fault detection method. The main contributions of this study are two-fold: 1) To ensure that the latent variables are distributed similarly to an ideal Gaussian distribution, a novel CINN training approach is proposed; 2) historical process information is incorporated into the residual-to-latent variable mapping, dynamically refining the mapping procedures in response to the system behavior. This approach is primarily used to tackle the challenges posed by non-additive and non-Gaussian noises in industrial systems. A DC speed control system and a waste water treatment system are adopted to verify the effectiveness of the proposed fault detection approach.

This research offers a thorough analysis of the dynamic behavior of 1-, 2-, and 3-DoF mechanical systems under a sinusoidal force, examining both mechanical and dq coordinates. By utilizing standardized initial conditions, the 1-DoF system displays fascinating oscillatory patterns with dual frequency components, highlighting the significance of low damping. The adaptation to dq coordinates simplifies the analysis and highlights the system's nuanced behavior. In contrast, the 2-degree-offreedom system exhibits intricate interactions, oscillation phenomena, and multiple frequency components in mechanical coordinates. The contribution of masses that do not experience external forces in dq coordinates is minimal. On the other hand, the 3-degree-of-freedom system shows diverse interactions and frequency components that are different from the dq transformations. The observed dynamics not only enhance comprehension of these systems but also provide valuable insights for refining analytical approaches in the analysis of dynamic systems. This study sets the stage for future investigations and urges the development of streamlined analytical frameworks for a more focused exploration of externally influenced variables in dynamic mechanical systems.

This study addresses concerns surrounding the inadvertent promotion of tobacco-related products on TikTok by introducing an efficient deep learning-based video analysis system. Our approach focuses on categorizing TikTok videos based on tobacco-related cues, including content related to e-cigarettes, vapes, cigarettes, various tobacco flavors, and accessories that may bypass tobacco control policies. The proposed two-stage process begins with the extraction of essential cues using speech-to-text, Optical Character Recognition (OCR), and video classification techniques. This initial phase comprehensively captures textual and visual information associated with tobacco products, forming the foundation for understanding video content. Subsequently, in the second stage, the extracted cues are integrated into a vision-language model alongside the input video. This stage trains the model to analyze contextual nuances, achieving a detailed understanding of the nuanced elements associated with tobacco promotion on TikTok. The system classifies input videos into predefined classes (cigarette, e-cigarette/vapes, pouches, or others) and provides detailed analyses. This capability enables a granular examination of diverse tobacco-related content on TikTok, proving valuable for regulatory agencies like the FDA in quickly identifying potential illegal promotion and sales of non-compliant tobacco products and accessories.

The integration of artificial intelligence (AI) into energy networks significantly advanced short-term forecasting, particularly in smart meter applications. However, as distributed energy resources proliferated and energy systems grew in complexity, traditional centralized approaches to data analysis became insufficient in addressing privacy-preserving challenges. Federated learning (FL) emerged as a promising solution, leveraging distributed data sources while safeguarding user privacy. Nonetheless, FL encountered inherent vulnerabilities to adversarial attacks during model training, undermining its reliability and effectiveness. Existing techniques to eliminate these attacks often required additional frameworks for detection, imposing an added burden on devices. To address this issue, we proposed a novel method called federated random layer aggregation (FedRLA). It aggregated only one randomly chosen neural network layer on the server in a privacy-aware manner, leaving the remaining layers unchanged. FedRLA exhibited superior resilience against adversarial attacks by confining attackers to a single neural network layer. Our simulations, focusing on household energy consumption, demonstrated that FedRLA achieved 3.56 times less data transmission compared to FedAvg during global model training. This enhanced efficiency translated to improved energy usage and resource conservation. Furthermore, FedRLA performed better in the presence of differential privacy under attack and no attack conditions.

The use of Internet-of-Things (IoT) technology in electric power distribution is growing, specially with smart meters, automated controls, demand response (DR), and renewable distributed energy resource (DER) integration. These advancements have made it easier to monitor, control, and optimize DER-rich cyber-physical distribution systems (CPDS). However, as IoT devices become more widespread, they also increase the risk of cyber-attacks, which makes ensuring system resiliency critical. This work presents a novel resiliency-driven load restoration technique leveraging demand response through IoT devices to enhance the resiliency of a three-phase unbalanced power distribution systems. A hierarchical optimization framework is proposed, featuring a continuous linear program for primary optimization and a binary linear program for secondary optimization, aimed at achieving load-source energy balance and connection of secondary-level house/building loads based on their criticality. To address the limitations of traditional theoretical methods in analyzing distribution system losses across multiple devices and configurations, this paper proposes a novel deep neural network-based approach for system loss calculation. Validation of the proposed methodology is conducted using the IEEE 123-node system modeled in the HELICS co-simulation testbed, to demonstrate optimally configuring switch status, supplying critical loads, balancing load generation, and maintaining feeder voltages.