The full realization of smart city technology is dependent on the secure and honest collaboration between IoT applications and edge-computing. In particular, resource constrained IoT devices may rely on fog-computing to alleviate the computing load of IoT tasks. Mutual authentication is needed between IoT and fog to preserve IoT data security, and monetization of fog services is needed to promote the fog service ecosystem. However, there is no guarantee that fog nodes will always respond to IoT requests correctly, either intentionally or accidentally. In the public decentralized IoT-fog environment, it is crucial to enforce integrity among fog nodes. In this paper, we propose a blockchain-based system that 1) streamlines the mutual authentication service monetization between IoT and fog, 2) verifies the integrity of fog nodes via service audits, and 3) discourages malicious activity and promotes honesty among fog nodes through incentives and penalties.

This paper presents a residential, round-trip, mixed ownership, electric car-sharing scheme with an optimized charging schedule, expressed as a mixed integer linear programming model. Representing the given travel demand as a graph, a mixed (shared and owned cars) ownership fleet configuration, with different trip fulfillment possibilities, is determined using maximal cliques. Next, a car-to-trip allocation model for feasible trip plans, with considerations for equal car utilization and charging price, is used to generate charging profiles for the fleet. The model and the outputs can be variously integrated into existing energy system modeling frameworks. A case study, using data from a test building with a residential car-sharing fleet in Munich, demonstrates the potential of the proposed models to derive meaningful operational insights for fleet and energy system planners. 92% of the recorded trips at the test building could have been feasibly accomplished with only 60% of the available cars.

Predicting user-item interactions in e-commerce platforms plays an important role in improving recommendation systems and optimizing user experience. This paper presents an in-depth exploration of using temporal graph networks (TGNs) for user-item interaction prediction. Using TGN on datasets, Amazon, LastFM, and RetailMarket, we experiment node classification and edge prediction tasks. Our approach achieves an accuracy of 80% and an AUC score of 0.93. Our findings prove the effectiveness of TGNs in identifying patterns and predicting interactions, thus advocating for their integration into e-commerce platforms to improve recommendation systems and user satisfaction.

Mandarin Chinese, a widely spoken language globally, has abundant, regularly updated short news texts online. Generating precise summaries of these texts is vital for effective information transmission and comprehension. This article introduces DMSeqNet-mBART, an enhanced mBART-based model, as a state-of-the-art solution for Chinese short news text summarization. This model incorporates Adaptive-DropMessage technology, a novel approach that intelligently discards or retains information based on the attention mechanism's output. This paper demonstrates that DMSeqNet-mBART excels across several benchmarks, including BERTScore, BLEU, and ROUGE metrics, surpassing other advanced models like GPT-4, T5, and MLC. The paper outlines the Adaptive-DropMessage mechanism, enhanced dynamic convolutional layers, gated residual connections, custom feed-forward networks with batch normalization, and improvements to self-attention and cross-attention. Results from comparative experiments on six recognized Chinese short news text summary datasets indicate that the model's performance in terms of fluency, completeness, robustness, and accuracy significantly outperforms leading industry models. The DMSeqNet-mBART's success is attributed to its unique combination of architectural and methodological enhancements, suggesting its suitability for various complex text data processing tasks. The model provides novel insights and methods for processing similar complex text data.

A simple numerical solution for general relativity in the Newtonian weak field regime is provided -- no tensor calculus is required. The topics under consideration are the deflection of photons and neutrinos by the Sun, the relativistic rotation of Mercury's orbit plane, the Shapiro time delay, and the gravitational redshift -- namely, the four Solar System tests of general relativity. To simplify the calculations, Euler integration is used. Hopefully, this numerical solution can be found to be an important tool, primarily due to this simplicity.

Millimeter-wave (mmWave) communication systems utilize phased-array antennas to generate highly directional beams, effectively reducing the signal footprint. Nonetheless, eavesdropping, particularly within the main-lobe, remains a significant concern. This paper introduces BeamSec, a novel beam hopping approach to maximize absolute secrecy rates with no information about the Channel State Information (CSI) or location of the eavesdroppers. Methodologically, BeamSec identifies diverse beam-pairs between transceivers by analyzing signal characteristics, such as Angle of Departure (AoD) and Angle of Arrival (AoA). To prevent the complete message from being eavesdropped, BeamSec splits and encodes data between selected beams. Depending on the availability of full/partial or transmitter (TX)/receiver (RX)-only channel knowledge, Beam-Sec optimizes time allocation using analytical models. Our experimental validation using an 802.11ad-compatible 60 GHz phased-array testbed demonstrates a significant increase in absolute secrecy rate showcasing resilience against at least four colluding eavesdroppers. Specifically, BeamSec demonstrates a non-zero absolute secrecy rate even for the simplistic uniform time allocation approach. Radio map (partial channel knowledge) and known room geometry (instantaneous TX/RX) based schemes provide further improvement of 124.8% and 58.13%, respectively, as compared to uniform time allocation.

Contribution: This study uses a qualitative research method to analyze interviews where participants simplified an electric circuit while explaining their thought process. Background: Rearranging circuit diagrams is a fundamental skill in electrical and electronic engineering, yet students can struggle with unfamiliar configurations. Current research in the discipline is often quantitative, centered on conceptual understanding. By using a qualitative method, the process of 'how' students interact with circuit diagrams is investigated. Research Question: How do students approach circuit diagram simplifications? Methodology: 15-minute individual discussions with 10 participants (undergraduate Years 1-4) simplifying an unconventionally presented circuit diagram were recorded. Reflexive thematic analysis was used to identify common themes. Findings: 1. Participants initially rely upon pattern recognition to solve circuit problems before applying other analysis techniques. 2. Two rearrangement methods were identified: 'component focused', where combinations of components are grouped and then connected together, and 'ground focused' where components in the circuit are related to ground and then connected together. 3. Students using a ground focused strategy were less hesitant in their circuit rearrangement process. 4. Students broadly used mechanicalistic methods of error checking, selecting software tools rather than applying conceptual understanding.

Heart failure is a leading cause of death among Diabetic and Obese patients globally, contributing to 8.5% of all heart disease deaths and potentially 36% of cardiovascular disease deaths. Early detection is crucial for timely intervention, reducing symptoms, lowering hospitalizations, and improving patient outcomes through personalized management. This paper presents the use of Semi-Parametric and Accelerated Failure Time survival models (AFT) on Heart failure prediction. Cox Proportional Hazard model from the family of Semi-Parametric survival models and Weibull's Accelerated Failure Time models from the family of Accelerated Failure Time models have been compared and contrasted. The Cox PH model excels in its ability to adapt to various survival time distributions avoiding any assumptions on survival time distribution. The Cox proportional hazard allows for the examination of covariate effects on the hazard function, making it a widely used survival model. Weibull AFT model follows a parametric approach, directly estimating the distribution of the survival time. Medical records data of close to 299 patients, who had heart failure, collected during their clinical followup period is used for building and evaluating the model. The final evaluation of model performance was conducted, focusing on their capacity to predict the probability of patient survival beyond 250 days from the clinical visit. Among the Cox proportional hazard model and Weibull's AFT model, the Weibull's AFT model demonstrated superior performance compared to the Cox model. Remarkably, Weibull's model exhibited consistently exceptional performance in both train and test validations.

The 1D inversion method is widely used in airborne transient electromagnetic method (ATEM) but often encounter inaccurate imaging and overlook important geological information. The three-dimensional inversion is relatively robust and provide more detailed information on subsurface resistivity distributions but limited by significant computational burdens. In this study, we develop a rapid 3D imaging scheme based on deep learning for subsurface resistivity structures with lateral heterogeneity, which performs better than traditional 1D inversion but slightly inferior to 3D inversion, but its efficiency makes it more practical than traditional 3D inversion. To create a reasonably large training set of different resistivity models that generalizes well. Here we propose three strategies that focus on training datasets, to improve the performance of DL models, including divide-and-conquer strategy, random models generating and depth constraints. Through a large of reasonable structural models, appropriate network setups, a generalized result can be obtained through our proposed UNet framework, which has been demonstrated to be effective on both synthetic and field data. The results of gradient-based 3D and 1D inversion method are compared and analyzed, respectively, which demonstrates the reliability of the proposed method. To flexibly apply the deep learning model to field data, we propose a model scaling scheme to make pre-trained UNet model compatible with different inversion specifications and survey configurations, without retraining a new DL model. The inverted structures from field data clearly delineate the geometries of the lakes, faults and surrounding mountains. The inversion operator can support instantaneous 3D subsurface resistivity imaging for ATEM observations.

This research investigates the potential of web scraping techniques to optimize decision-making within the e-commerce landscape. By leveraging advanced scraping methods, the project aims to develop a framework that extracts and analyses data from various e-commerce domains. This data will encompass crucial elements like product information and pricing strategies. Through innovative data analysis techniques, the framework will uncover valuable insights and trends, empowering e-commerce businesses to make informed decisions in several key areas. These areas may include optimizing product selection, implementing dynamic pricing strategies, and crafting targeted marketing campaigns. Ultimately, this research seeks to demonstrate how web scraping, when employed ethically and responsibly, can become a powerful tool for e-commerce businesses to gain a competitive edge and enhance their overall decision-making processes.

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.

6G is 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, e.g., through 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 simulations using ray tracing. 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.