The Source Code Control System (SCCS) was first introduced in 1975 [1]. It controlled computer program source code by tracking versions and recording who made changes, when, and why. The present retrospective paper assesses the strengths and weaknesses of SCCS and traces its influence on software engineering over the past fifty years.

With rapid digitization and digitalization, drawing a fine line between the digital and the physical world has become nearly impossible. It has become essential more than ever to integrate all spheres of life into a single Digital Thread to address pressing challenges of modern society – accessible and inclusive healthcare in terms of equality, and equity. The techno-social advancements and mutual acceptance have enabled the infusion of digital models to simulate social settings with minimum resource utilization to make effective decisions. However, there is a significant gap in feeding back the models with appropriate changes in real-time. In other words, an active behavioral modeling of modern society is lacking, that influences community healthcare as a “whole”. By creating virtual replicas of physical systems, digital twins can enable real-time monitoring, simulation, and optimization of urban dynamics. This paper explores the potential of digital twins to promote inclusive healthcare for evolving smart cities. We argue that digital twins can be used to:Identify and address disparities in access to healthcare servicesFacilitate community participationSimulate the impact of urban policies and interventions on different groups of peopleAid policy making bodies for better access to healthcareThis paper proposes several proposals for stitching the actual and virtual societies using digital twins. Several discussed concepts within this framework envision an active, integrated, and synchronized community aware of data privacy and security. The proposal also provides high- level step-wise transitions that will enable this transformation.

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.

Windows represents the most common platform found in seized computers due to its widespread presence. This disparity has become worse due to the introduction of Microsoft's Windows. Post Cyber Incident analysis of Microsoft Windows machines has become increasingly challenging due to the everevolving nature of digital threats. Traditional digital forensics methods often struggle to keep pace with modern cybercrime activities' volume, sophistication, and complexity, which either target or originate from Windows machines. In response to these challenges, this research introduces WinRegRL, a framework that combines Reinforcement Learning (RL) and Rule-Based Artificial Intelligence (RB-AI) to enhance the efficiency, effectiveness and accuracy of digital investigations in the context of Windows Operating Systems. WinRegRL fully captures key information, elaborates the MDP environment, solves the RL problem and extracts expertise for later use. Implementation and testing of WinRegRL validated the research hypothesis by enabling optimised analysis and correlation of Registry forensics. Results prove that the proposed RL model out-performs all previous approaches including bling automation and human expert performance in terms of time, the number of artefacts explored, and the accuracy of results. Another advantage of the proposed framework is the ease of repetition, especially in this context, more than one machine of the same configuration is under investigation, a context often faced in real DFIR practice.

The increasing sophistication of modern cyber threats, particularly file-less malware relying on "living off the land" techniques, poses significant challenges to traditional detection mechanisms. Memory forensics has emerged as a critical approach to detecting such threats by analysing dynamic changes in system memory. This research introduces SPECTRE (Snapshot Processing, Emulation, Comparison, and Threat Reporting Engine), a modular Cyber incident response system designed to enhance threat detection, investigation, and visualization. By adopting Volatility's JSON format as an intermediate output, SPECTRE ensures compatibility with widely used Digital Forensics and Response (DFIR) tools, minimizing manual data transformations and enabling seamless integration into established workflows. Its emulation capabilities safely replicate realistic attack scenarios, such as credential dumping and malicious process injections, for controlled experimentation and validation. The anomaly detection module addresses critical attack vectors, including RunDLL32 abuse and malicious IP detection, while the IP forensics module enhances threat intelligence by integrating tools like Virus Total and geolocation APIs. SPECTRE's advanced visualization techniques transform raw memory data into actionable insights, aiding Red, Blue, and Purple teams in refining their strategies and responding more effectively to emerging threats. Bridging gaps between memory and network forensics, SPECTRE offers a scalable, robust platform for advancing threat detection, team training, and forensic research in combating sophisticated cyber threats.

The global prevalence of diabetes is increasing at a rapid rate, with projections indicating that the number of people with diabetes will surpass 1.31 billion by 2050. Diabetic retinopathy (DR) is a common vision complication that arises from diabetes and is a leading cause of preventable blindness worldwide. The magnitude of this disease's impact calls for modern and more efficient solutions for DR detection; early detection of the disease can lessen the risk of vision loss substantially for those inflicted with it: with quick intervention, the risk of severe vision loss can be reduced by as much as 90%. In this paper, we utilize and finetune the Segment Anything Model(SAM) to segment images for feature extraction in the Indian Diabetic Retinopathy Image Dataset (IDRiD), an open-access dataset of retinal images structured for retinopathy screening research. We then employed a Convolutional Neural Network (CNN) architecture, specifically a Keras model, to classify the key features within these segmented images and grade the severity of DR using ETDRS Classification, a semi-qualitative grading scale that assigns a severity score of 0 to 4 to patients. Our successful implementation of SAM in conjunction with CNN architecture demonstrates the promise of this approach in diabetic retinopathy and disease classification as a whole.

Optoelectronic thin films play a critical role across various high-tech industries, including new materials, energy storage sectors, chip manufacturing, and biomedicine. This paper details the enhancement of optoelectronic thin film properties through the innovative use of Sum Frequency Generation (SFG) spectroscopy. This non-linear spectroscopic technique is uniquely suited to studying film surfaces and interfaces without damaging the samples, offering detailed insights into molecular arrangements and chemical states at these critical junctures. Further, this study introduces a novel Python based application developed using the PyQt5 framework, which is designed to efficiently handle and analyze spectroscopic data. The application incorporates advanced data processing functions such as data denoising, Fourier transformation, square wave matrix extraction, inverse Fourier transformation, and data integration, providing a comprehensive tool for researchers. Our results demonstrate significant improvements in the precision and efficiency of data analysis, leading to enhanced performance and quality of optoelectronic films. The integration of interdisciplinary technological approaches with advanced programming techniques and mathematical analysis through SFG spectroscopy underscores its potential to revolutionize the field by providing a more precise characterization of the material's microstructural features and advancing the development and optimization processes of optoelectronic thin film technology.

Variable-speed generation units have widely been used in pumped storage hydropower plants. Nevertheless, conventional hydropower plants can also benefit from variable speed operation to compensate for efficiency loss when the hydro turbines work at a water head different from the nominal. When the units are connected to the grid through a DC link, some flexibility in the speed of operation is expected, as the generated AC power will be converted into DC power. This work presents the initial considerations for this operation, including estimating the efficiency achieved for revenue evaluation purposes. The case of the Itaipu Power Plant's 50 Hz units connected to a 60 Hz power system through a DC link will be presented as an example of an application.

The Sliding Window technique is a versatile approach frequently applied in domains ranging from genomics and electronics to weather forecasting and algorithm development. While Matplotlib facilitates the visualization of sequential continuous data, KitikiPlot, our Python library, is dedicated to complementing this by focusing on the visualization of sequential categorical data using sliding windows. By offering intuitive and customizable visualizations, it empowers researchers, data scientists, and analysts across diverse fields to derive rapid insights into categorical trends, promoting informed decision-making. Built on top of Matplotlib, it seamlessly integrates with various research projects and demonstrates significant potential across multiple fields. This library is open-source and available on GitHub: https://github.com/BodduSriPavan-111/kitikiplot.

Contributions: This paper presents an innovative course curriculum designed to bridge the gap between electric circuit knowledge and control engineering principles. The curriculum emphasizes hands-on learning through op-amp circuit design projects, fostering a deeper understanding of both theoretical concepts and practical applications. Background: Traditional educational approaches often treat electric circuits and control systems as separate disciplines, hindering students' ability to grasp the interconnectedness of these fields. This disconnect can impede the holistic understanding required for effective circuit design and feedback control system development. Intended Outcomes: The proposed course aims to equip students with a comprehensive understanding of op-amp circuit design principles, proficiency in analyzing and optimizing frequency responses and bandwidth using techniques like zero-pole placement, the ability to translate theoretical knowledge into practical applications in industrial settings, and a stronger appreciation for the synergistic relationship between electric circuits and control engineering. Application Design:The course features a series of three illustrative examples that progressively introduce students to selecting resistor and capacitor values to achieve desired DC gains and frequency responses, analyzing feedback loops using open-loop transfer functions and zero-pole placement techniques for bandwidth optimization, and designing controller architectures for industrial applications by applying the principles learned in previous exercises. Findings: Through student engagement and project implementation, the course demonstrates a significant improvement in students' understanding of both circuit design and control engineering concepts. The hands-on approach fosters deeper comprehension, practical skills, and a more integrated perspective on these interconnected disciplines.

The Caesar cipher is one of the most basic cryptographic methods that was invented by Julius Caesar himself. This encryption method was used by him to safely communicate with soldiers. While simple, analysis and techniques to crack the cipher have been questions people have pondering since the time of the emperor. One such technique is letter frequency analysis, which uses the distribution of letters in English text to make predictions. In this paper, we will be exploring how letter frequency and machine learning regression models can be used to crack the Caesar cipher. We will be analyzing the performance of several models, optimize the best ones and create our final key guessing models. Our end result are multiple regression models that reliably predict the key of a Caesar cipher.

In industry NLP application, our dataset by prompting large language models (LLMs) has a certain number of noise data. We present a simple method to find the noise data and remove them. We retain the data that contains certain common tokens between the LLMs data and the prediction results of a generative model trained on the LLMs data. We remove the data that does not contain certain common tokens between the LLMs data and the prediction results of a generative model trained on the LLMs data. We adopt T5-Base as our generative model. The experiment result shows our method is highly effective and does not require any manual annotation. For industry deep learning application, our method improves the NLP tasks accuracy from 88% to 98% under human evaluation, meanwhile the LLMs data source is sufficiently abundant.

Long-term cuffless monitoring of blood pressure (BP) is of immense value in the diagnosis and prevention of cardiovascular diseases (CVD). While there are critical needs for wearable cuffless BP monitoring devices in personalized CVD care and hypertension management, current technologies face challenges in long-term accuracy which limits significantly its clinical applicability. To maintain the accuracy of cuffless blood pressure forecasting, frequent calibration is a vital necessity. However, conventional BP calibration approaches are inherently inconvenient and time-consuming. In this work, we address the challenge by developing a novel auto-calibration system enabling long-term BP estimation. The innovation of this approach combines depth-resolved vascular characteristics extracted from multi-wavelength photoplethysmographic (MW-PPG) measurements at fingertip, with an adaptive cross-modal knowledge distillation algorithm for continuous refinement of blood pressure estimation. We have evaluated the system on the CAS-BP database of over a thousand subjects using various calibration strategies. The results have shown that frequent calibration can effectively enhance the accuracy of long-term blood pressure estimation. Compared to traditional manual calibration, the proposed auto-calibration method can perform frequent calibration of the model without requiring the true BP values. Furthermore, under the condition of daily autocalibration, the system attains an optimal Mean Absolute Error (MAE) of 3.77 mmHg and 2.62 mmHg for systolic BP (SBP) and diastolic BP (DBP) respectively, significantly outperforming the performance achieved through traditional calibration methods. This work provides a novel conceptual framework to guide the calibration of cuffless wearable devices for BP monitoring.

This technical solution presents an innovative port concept based on the marine systems technology developed by Jakob Sadighi. 1 The goal is to develop a future-proof port that is economically profitable and ecologically sustainable. The concept integrates various technologies for power generation, flood protection, and efficient ship handling.

The advent of multi-unmanned aerial vehicle (multi-UAV) networks in mobile edge computing (MEC) introduces dynamic computational topologies where UAVs, acting as mobile edge servers, are tasked with processing data from ground-based user equipment (UE). This paper addresses the dual challenges of optimizing both UAV deployment and task offloading within such networks to minimize communication latency and efficiently utilize UAV resources, which are limited by battery life and processing capabilities. We propose a bi-level optimization framework that simultaneously tackles the placement of UAVs and the distribution of computational tasks among them. At the higher level, UAV deployment is optimized to ensure minimal distance to the UEs, thereby reducing latency and energy consumption during data transmission. At the lower level, task offloading is optimized to balance the computational load across the UAV network, considering each UAV's capacity and battery constraints. We demonstrate through extensive simulations the significant improvements in system efficiency, latency, and resilience. This approach not only enhances the performance of UAV-assisted MEC networks but also provides scalable solutions adaptable to various operational scenarios.

Intrusion Detection Systems (IDSs) are vital for protecting computer networks against unauthorized access and evolving cyber threats. Traditional signature-based IDSs, while effective for known attacks, struggle to identify novel and sophisticated threats. Machine Learning-based Intrusion Detection Systems (ML-IDSs) have emerged as a promising solution, offering enhanced capabilities for threat detection, adaptability, and response. This chapter explores the promising capabilities of ML-IDSs in cybersecurity, focusing on five key strengths. First, machine learning (ML) algorithms enable the detection of zero-day attacks by identifying anomalous patterns in network traffic without the need for predefined signatures. Second, explainable AI (XAI) integrated with ML models enhances understanding and trust by elucidating the decision-making process behind alerts. Third, ML methods facilitate the detection of intrusions in encrypted traffic, maintaining data privacy while ensuring security. Fourth, the use of Graph Neural Networks (GNNs) introduces context-aware and provenancebased detection, providing structured analysis of relationships within network data. Finally, Large Language Models (LLMs) enhance IDSs by understanding network traffic as human language, detecting complex intrusions, and processing sequential network data for intrusion detection. This paper highlights these capabilities and presents an organized analysis of the stateof-the-art contributions in each area, followed by a detailed discussion of open research challenges and future directions in this important domain. This comprehensive review underscores the transformative role of ML in IDSs in addressing emerging and increasingly powerful cyber threats.

This project investigates the effects of weight quantization on deep neural network performance, focusing on image datasets. Weight quantization reduces the precision of model weights which leads to reduction in model size and computational requirements, making them suitable for resource-constrained devices. The project explores the impact of these techniques on model accuracy, training efficiency, and generalization capabilities. The research is motivated by the need to develop efficient and effective deep learning models for image classification tasks that can be easily deployed in real-world applications.

In this paper, we study a secure integrated sensing and communication (ISAC) system employing a full-duplex base station with sensing capabilities against a mobile proactive adversarial target-a malicious unmanned aerial vehicle (M-UAV). We develop a game-theoretic model to enhance communication security, radar sensing accuracy, and power efficiency. The interaction between the legitimate network and the mobile adversary is formulated as a non-cooperative Stackelberg game (NSG), where the M-UAV acts as the leader and strategically adjusts its trajectory to improve its eavesdropping ability while conserving power and avoiding obstacles. In response, the legitimate network, acting as the follower, dynamically allocates resources to minimize network power usage while ensuring required secrecy rates and sensing performance. To address this challenging problem, we propose a low-complexity successive convex approximation (SCA) method for network resource optimization combined with a deep reinforcement learning (DRL) algorithm for adaptive M-UAV trajectory planning through sequential interactions and learning. Simulation results demonstrate the efficacy of the proposed method in addressing security challenges of dynamic ISAC systems in 6G, i.e., achieving a Stackelberg equilibrium with robust performance while mitigating the adversary's ability to intercept network signals.