Silicon photonics is the most viable and cost-effective platform for next generation optical interconnects. One of the key optical devices in these interconnects is an optical modulator which, along with photodetectors, effectively defines performance of the whole optical link. However, due to its centrosymmetric structure, silicon does not offer linear electro-optic effects, leaving plasma dispersion effect as the only feasible modulation mechanism for high speed applications. The performance of plasma dispersion-based modulators is affected by several design variables, affecting various modulator figures of merit (FOMs) where performance trade-off exists. This necessitates design optimization, requiring reasonably accurate mathematical predictive models for all the FOMs. This paper presents a rigorous full 3D model for the junction capacitance of silicon-on-insulator interleaved junction optical phase modulators with submicron dimensions. Junction capacitance is instrumental in calculating power consumption per bit, modulation speed and efficiency of optical modulators. Solution of Drift-Diffusion and Poisson's equations were carried out on 3D finite-element-mesh and Maxwell's equations were solved using Finite-Difference-Frequency-Domain (FDFD) method on non-uniform rectangular grid to calculate confinement factor. Whole of the modeling process has been detailed and all the coefficients required in the model are presented. Model validation suggests < 10 % RMS error.

This paper develops a power-based microscopic energy consumption model for battery electric vehicles (BEVs) entitled Virginia Tech Comprehensive Power-based Energy Consumption Model 2.0 (VT-CPEM 2.0). This model avoids bang-bang control, is calibrated using US Environmental Protection Agency (EPA) city and highway energy consumption ratings; along with publicly available vehicle and roadway pavement parameters; and captures regenerative braking. The model validation considers three BEVs and three drive cycles. The test results show that the estimated energy consumption rates by the proposed model are consistent with in-laboratory data collected by the Argonne National Laboratory producing an average error of 3.9% and a maximum error of less than 7.5% for three vehicles across three drive cycles. Additionally, the model demonstrates that the optimal speed of BEVs increases with an increase in the auxiliary power.

This research is in the domain of Natural Language Processing and delves into statistical methods that are intrinsic to the input text, that is not needing any external corpus or training. The methods are used to calculate similarity of sentences in Hindi. The study is performed on sentence pairs in English and Hindi and the differences are analysed with the structural and grammar attributes of the languages. The methods chosen mathematically operate at different levels from character to phrase. The purpose is to identify methods at which a language performs differently than the other, and how different they are with the actual truth. The conclusions can be elemental when considering building larger applications that may need to use one of these methods or hybrid of these methods. An analysis on various methods applied on a sentence pair dataset with both the languages Hindi and English is conducted using visualisation of the produced output of these methods against their actual scores.

Differentiating between a Relaxed State and a Physical Stress Stage is a useful technology for wearable devices. Currently, an accurate physical stress detection system utilizing the Electrodermal Activity Signal is not available. Using the Electrodermal Activity physiological signal, we develop a novel approach to this problem. Assessing physical changes with Electrodermal Activity/physiological signals is a field that has seen significant progress. The Electrodermal Activity signal tries to observe the changes in the electrical output of the eccrine sweat glands. These changes are performed by the sudomotor nerves, part of the nervous system, and by assessing the changes in these nerves (through Electrodermal Activity), it would be possible to develop a technology like that described above. We have used a dataset from a previous experiment, which has 5-minute stages, to provide the data for our experiment. The data for this experiment was taken with a wearable device. The data were pre-processed using a bandpass butterworth filter and a minmax normalizer. After this, feature extraction and selection were performed. For the model training, multiple models were trained for the stratified and non-stratified dataset. Multiple of them had excellent performance, but the Random Forest Classifier especially stood out for the non-stratified dataset, with a macroaverage cross-validated F-1 score of 0.80. After establishing that the best model was the Random Forest Classifier, Shapley values were used to understand and explain the feature importance of the model.

Rainfall forecasting has recently attained the highest level of academic relevance, due to its intricacies and enduring uses, including flood forecasting and pollutant concentration level monitoring. It is difficult to predict rainfall due to its complicated nature, which is impacted by a number of meteorological, oceanic, and geographical elements. In order to get over these limitations, methods that combine time-series data with machine learning techniques are being investigated. The goal of this project is to apply machine learning algorithms to reliably forecast rainfall. Current models rely on intricate statistical models that are frequently excessively expensive, both financially and computationally, or aren't used in downstream applications. Using simple rainfall estimating models based on traditional machine learning methods and Deep Learning architectures that are effective for various downstream applications, this work offers a comparative examination. In order to capture both short-term and long-term rainfall trends, we test a variety of machine learning models, such as Random Forest, Support Vector Machines, and Long Short-Term Memory (LSTM) networks. Additionally, the study investigates how data preprocessing methods and hyperparameter adjustment can improve the model's performance. Real-time meteorological data from several geographical locations was used to validate the model, and the findings showed that it could make predictions more accurately than conventional statistical techniques.

Island energy systems face significant challenges in delivering reliable and sustainable energy to remote and isolated regions, which often remain heavily reliant on costly and environmentally damaging diesel generators. To address such issues, this paper introduces a market-driven, multi-energy optimal dispatch framework for energy hubs (EHs), validated through real-time simulations. The framework is designed to optimize decentralized energy systems by enhancing economic efficiency, energy security, and decarbonization efforts. The proposed framework, formulated as a bi-level programming model, enables a typical EH at the upper level to strategically participate in a developed local energy market structured at the lower level. This approach minimizes the EH’s operational cost while ensuring operational flexibility and compliance with grid codes. To demonstrate the model’s efficiency and practicality, the real-world case of Kundur Island, Indonesia, is considered as a study site. The findings show that strategic bidding and consumer flexibility can significantly reduce the opration cost of the EH. Moreover, the real-time validation confirms the framework’s practical applicability in maintaining system resilience and adhering to regulatory requirements. Indeed, this study provides a scalable solution for decentralized energy markets, offering actionable insights for rural electrification, market design, and energy policy in geographically isolated regions.  

This paper explores the integration of WordPress and associated technologies in developing a custom web-based HR system, focused on recruitment, staff deployment, and document management. Using Delorecarescreeningapp.com as a case study, we highlight WordPress's role in delivering scalable, secure, and user-friendly HR solutions. By incorporating middleware, API integrations, and automation tools, WordPress proves to be an effective solution for streamlining HR operations and optimizing the employee lifecycle from recruitment to document management.Purpose of the Research: This research aims to introduce a web-based solution for optimizing HR processes-focusing on recruitment, staff deployment, and document management-using modern technologies. By leveraging WordPress and middleware integrations, the system automates HR workflows, enhancing efficiency, scalability, and compliance in organizations with large, dynamic workforces.

A robust ray-tracing (RT) based single-site 3D localization method is proposed for urban non-line-of-sight (NLOS) scenarios. RT is utilized to model channel propagation in complex environments, with NLOS paths transformed into LOS paths through a generalized source (GS) approach. The weights of GS are determined using a multipath residual weighting scheme based on an assignment optimization algorithm. To address irregular NLOS noise, a robust iteratively reweighted least squares with initial weight (W-IRLS) method is developed. Simulation results demonstrate that the method achieves near-Cramer-Rao lower bound (CRLB) accuracy under severe NLOS conditions and maintains robustness against geometric model errors, providing critical support for integrated sensing and communications (ISAC) applications in complex urban environments.

Omega-type bianisotropic Huygens' metasurfaces offer a novel approach for controlling the aperture field distribution of leaky-wave antennas. This paper presents a methodology utilizing a parallel-plate waveguide with a metasurface as its top plate. Previous limitations on constant leakage factor are addressed using a slowly varying amplitude approximation in order to satisfy Maxwell's wave equation, enabling the design of the radiation pattern. A semi-analytical algorithm is employed to obtain the required multi-layer unit-cell geometries. Here, the inter-layer coupling is taken into account, enabling an efficient synthesis of these antennas. Several designs presenting different pointing angles and aperture field distributions are carried out, showing very good agreement between theory and realistic simulations without further full-wave optimization. Finally, the design process is experimentally validated through several prototypes, whose measurements are shown and discussed. These results, demonstrating straightforward semi-analytical synthesis of versatile aperture profiles, would significantly broaden the applicability of such antennas in next-generation wireless systems.

Detection and prediction of the onset of seizures are among the most challenging problems in epilepsy diagnostics and treatment. Small electronic devices capable of doing that will improve the quality of life for epilepsy patients while also open new opportunities for pharmacological intervention. This paper presents a novel approach using machine learning techniques to detect seizures onset using intracranial electroencephalography (EEG) signals. The proposed approach was tested on intracranial EEG data recorded in rats with pilocarpine model of temporal lobe epilepsy. A principal component analysis was applied for feature selection before using a support vector machine for the detection of seizures. Hjorth's parameters and Daubechies discrete wavelet transform coefficients were found to be the most informative features of EEG data. We found that the support vector machine approach had a classification sensitivity of 90% and a specificity of 74% for detecting ictal episodes. Changing the epoch parameter from one to twenty-one seconds results in changing the redistribution of principal components" values to 10% but does not affect the classification result. Support vector machines are accessible and convenient methods for classification that have achieved promising classification quality, and are rather lightweight compared to other machine learning methods. So we suggest their future use in mobile devices for early epileptic seizure and preictal episodes detection.

Traditional computed tomography (CT) reconstruction methods, such as Filtered Backprojection Techniques (FBPT), haveM long been valued for their speed. However, advancements in modern imaging present new challenges: achieving high-quality reconstructions with limited data, managing noise, and enhancing computational efficiency. For full projection sets, challenges include addressing projection data inconsistencies like noise. In sparse data scenarios, challenges shift to managing unobservable object patterns and mitigating the evolving estimation errors caused by uncontrolled prediction inaccuracies. We present a novel MENT-based reconstruction approach that delivers exceptional speed and accuracy, addressing image quality, noise resilience, and sparse data processing challenges. Our method, the Extended High Efficiency Computed Tomography (eHECTOR), incorporates MENT within an Extended Kalman Filter-Type (eKFT) feedback loop, resolving lack of observability and effectively managing data inconsistencies. This approach enables faster, more precise reconstructions under sparse and noisy data conditions, marking a significant advancement over traditional methods and paving the way for next-generation diagnostic imaging.

The escalating frequency and sophistication of malicious encryption attacks have required the development of more effective detection mechanisms. The Dynamic Encryption Pattern Analysis (DEPA) framework introduces a novel approach that focuses on real-time monitoring and analysis of encryption behaviors, enabling the identification of ransomware activities with high accuracy and minimal false positives. Through a modular architecture, DEPA seamlessly integrates into existing cybersecurity infrastructures, offering scalability and adaptability across various organizational environments. Empirical evaluations demonstrate DEPA's efficacy in detecting a wide range of ransomware variants, including those employing advanced obfuscation techniques, while maintaining efficient resource utilization. The findings demonstrate the potential of behavior-based detection methodologies in enhancing defenses against evolving ransomware threats.

Recursive mechanisms have become essential in bridging the gap between linear processing capabilities and the intricate demands of extended textual coherence. Yet, traditional models often struggle to retain semantic and contextual clarity across evolving dialogues and multi-layered interactions, highlighting a pressing need for adaptive context handling frameworks. Introducing Recursive Context Layering (RCL), a novel approach that recursively structures context in Large Language Models (LLMs), this study provides a transformative methodology for retaining thematic alignment and managing complex discourse across extended text. Through recursive layering, RCL establishes a cumulative context system where each layer incrementally refines semantic depth, dynamically adapting to shifts in conversational focus while mitigating the challenges of semantic drift. The RCL framework allows LLMs to seamlessly adjust to varying levels of contextual granularity, maintaining response coherence in both structured and fluid language environments. Experimental findings substantiate the efficacy of RCL, demonstrating substantial improvements in semantic accuracy, contextual consistency, and retention metrics over baseline models, marking a significant leap in LLM design for tasks requiring sustained contextual depth and responsiveness. Overall, Recursive Context Layering not only enhances contextual fidelity in language processing applications but also sets a robust foundation for recursive strategies, positioning it as a crucial advancement for models handling complex and extensive discourse.

This article presents a novel design methodology for bandpass filters applied to rectangular waveguide (RW) technology using TE 201 singlets with inductive irises. The conventional circuit model for an inductive iris interconnecting two RW sections, which typically handles only the TE 10 mode, is extended to include the propagation of the TE 20 mode through a wider RW section. The impact of the reactances at the source and load terminals of the circuit model is analyzed to develop a unique solution for the synthesis of the singlet coupling matrix. The proposed methodology improves design flexibility, allowing for filters with both cascaded singlet and cascaded triplet topologies. This is demonstrated through two illustrative designs. The first design employs propagating RW sections as phase shifters to interconnect TE 201 singlets, which is the most common approach. The second design, however, replaces these sections with TE 101 resonant cavities. This substitution offers advantages in spuriousfree range, volume, and mass. Both prototypes were successfully manufactured and measured to validate the approach experimentally. In addition, the proposed method simplifies electromagnetic simulations by focusing on the irises at a single frequency point, thus avoiding the need to analyze entire singlet structures over full frequency sweeps.

Essential proteins are group of proteins that are indispensable to survival and development of cells. Prediction and analysis of essential genes/proteins are crucial for uncovering the mechanisms of cells. Using bioinformatics and high-throughput technologies, forecasting essential genes/proteins by protein-protein interaction (PPI) networks have become more efficient than traditional approaches which use expensive and time-consuming experimental methods. Previous studies have found that the essentiality of genes closely relates to their properties in PPI network. In this work, we propose a supervised deep model for predicting human essential genes using neighboring details of genes/proteins in the PPI network. Our approach implements a weight-biased random walk on PPI network to get the node network context. Then, some different measures are used to get some feature vectors for each node (gene/protein) that preserve the network structure as well as the gene's properties in the PPI network. These feature vectors are then fed to a Relational AutoEncoder to embed the genes' features into latent space. At last, these embedded features are put into a trained classifier to predict the human essential genes. The prediction results on two human PPI networks show that our model achieves better performance than those that only refer to genes' centrality properties in the network.

The article presents the study of a reflection symmetric modal filter (MF) and meander lines (ML) based on a four-layer printed circuit board (PCB). Their prototypes were made, and the transmission coefficients were measured in the frequency range from 10 MHz to 10 GHz. At the output of the structure, the response to an ultra-wideband (UWB) pulse was analyzed. The results of post-processing showed four decomposed pulses at the output of the MF and the ML with two-turns, and six decomposed pulses in the ML with one-turn, which is in complete agreement with the modal theory and analytical models. The N-norms were used to characterize the output voltage waveforms. Both MLs demonstrated better attenuation of the UWB pulse than MFs of the same PCB size. More specifically, the value of N1 decreases by at least 2 times, N2 by at least 1.6 times, and N5 by at least 1.36 times. The values of N3 and N4 remained unchanged. The post-processing method for detecting combinational pulses in the time responses of ML is presented.

In this study, seven types of Chameleon Cloud compute nodes (ARM64, Haswell, Skylake, Cascade Lake, Ice Lake R650, Ice Lake R750, and Zen 3) are benchmarked using an HPC software suite that is meant to study polymorphic computing architectures. This benchmark is sensitive to the compute nodes' integer, memory, and threading performance. Each of the compute nodes is evaluated for execution time, speedup, and efficiency across a sweep of worker thread counts. The ARM64 compute node is comparatively the least performing compute node by a wide margin. The best performing compute nodes were the Ice Lake and Zen 3 systems. These nodes performed roughly equally.

This paper presents two units of Y-band (325-500 GHz) and two units of D-band (110-170 GHz) all-pole inline Chebyshev band-pass filters (BPFs) using high-precision computer numerically controlled milling. The filters are based on six-coupled resonant cavities and are designed for a 20 dB return loss in a 10 GHz bandwidth centered around 470 GHz and 160 GHz, respectively. The filter sections are designed using the standard waveguides of the next frequency band to guarantee single-mode propagation, and taper sections are placed for the transition. Statistical analyses of the design parameters are performed to estimate the manufacturing tolerance. Microscope measurements are done and it is seen that machine tolerance on cavity lengths of about 3.5 microns causes a 2 GHz center frequency shift in Y-band. Measurements validate the analyses. The fabrication repeatability in the D-band is more consistent than in the Y-band. Indeed, two Y-band BPF units have a center frequency shift difference of 6 GHz while the the D-band BPF units have almost identical responses.