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.

Recent revolutionary advancements in the services as observed with the use cases of Industry 5.0, consumer electronics 2.0/smart devices 2.0, digital healthcare ecosystem, Internet-of-Things (IoT), advanced digital finance/currency, and Non-Terrestrial Network (NTN) expansion, to name a few, have resulted in a spectacular growth in the number of wireless-connected devices. Subsequently, this has drastically increased the demands for network capacity, channel capacity, reliability, privacy, and security provisions. Despite that, the 5 th Generation (5G) of wireless communication networks has introduced various innovative services such as Ultra-Reliable Low Latency Communication (URLLCs), Massive Machine Type Communication (mMTCs), and Enhanced Mobile Broadband (eMBB). These services only support isolated operations and the requisite reliable service delivery remains a challenge. The Beyond 5G (B5G)/6 th Generation (6G) wireless networks aim at simultaneously providing multiple integrated services through intelligent network operations with ultra-high speed and reliability supporting integrated NTN and terrestrial networks. However, the prospect of such an extensively connected decentralized 3D wireless network also foresees security concerns, underscoring the necessity for seamless and infrastructurefree (decentralized) security solutions. The conventional security mechanisms are considered inadequate to ensure the security provisions of such extensive, decentralized, and heterogeneous networks. Physical Layer Security (PLS) is a promising technique to extend seamless and infrastructure-less security solutions, ensuring the availability, confidentiality, and integrity of legitimate transmissions. This paper provides a comprehensive overview with tutorials and presents the state-of-the-art of PLS, focusing mainly on NTN wireless communications. Furthermore, current research challenges, open issues, and future research directions are also thoroughly discussed in an amalgamation of various emerging 6G technologies. Finally, we provide an overview of implementation challenges in NTN and potential solutions to support the standardization progression of NTN in upcoming releases of 3 rd Generation Partnership Project (3GPP).

Microservice applications, composed of many independent containerized components, are well-suited for hybrid deployments spanning cloud and edge datacenters. The decision regarding which microservices should run on the edge and which in the cloud is a classic optimization problem with various dimensions. In this paper, we address the deployment of synchronous microservice applications in a cloud-edge infrastructure with theoretically infinite resources and a pay-per-use cost model for CPU, memory, and network. Our objective is to devise a strategy that leverages telemetry data and loadbalancing capabilities provided by service mesh technologies to ensure an average user delay below a configurable threshold at minimal cost. We propose a greedy algorithm that utilizes an analytical model of user delay and evaluate its effectiveness through both simulations and deployment within a novel opensource Kubernetes Controller, named Geographical Microservice Autoplacer (GMA). The proposed controller has a high level of automation and can be used with any application able to export telemetry data to the Istio service mesh.

This paper investigates the impact of various architectural modifications and hyperparameter tuning on the performance of Convolutional Neural Networks (CNNs) in image classification tasks. We conducted multiple tests, varying the number of kernels in the convolutional layer and the output size of the fully connected (FC) layer across different architectures. Despite these variations, the performance on the tuning set consistently surpassed that on the training set, indicating that increased complexity did not enhance model performance. Our experiments revealed that adding dropout layers improved accuracy on the tuning set, with CNN1 achieving the best performance. However, increasing the number of convolutional layers in CNN2 and CNN3 led to overfitting. Comparatively, the Bag of Visual Words (BOWs) method showed the highest accuracy but required significant computational time. The Multilayer Perceptron (MLP) classifier demonstrated substantial overfitting. Future improvements include data augmentation and refined Region of Interest (ROI) segmentation. Additionally, employing pre-trained models like VGG16 for fine-tuning could further enhance performance. This study underscores the importance of balancing architectural complexity and computational efficiency in CNN-based image classification.

This paper proposes a control system to guide drones in delivering packages autonomously. The objective is to guide the quadrotor to take off from a ground vehicle, go to the delivery point, return to the ground vehicle, and land on it. The drone should disregard the ground vehicle as its leader when flying toward the delivery point and resume its leadership after delivering the parcel. To ensure this, the leader-follower formation control paradigm is adopted, allowing controlling the follower to track the leader's movement as a reference trajectory. To fully accomplish the delivery task, the delivery drone will follow two leaders at two different moments: the delivery point in the delivery step and the ground vehicle when returning to it. The delivery drone should also avoid obstacles, such as buildings and trees, to which the well-known potential field theory is adopted, thus completing the controller design. Results of an experiment on a lab scale are presented and discussed, which validates the proposed control system. Consequently, the correctness of the underlying hypothesis that the leader-follower control paradigm is a feasible solution to control a delivery drone in accomplishing package delivery is confirmed. This is the main contribution of the study. Moreover, a possible generalization of the proposed control system for a set of delivery drones is outlined.

Blockchain has emerged as a foundational element in establishing trust relationships within networks, demonstrating its reliability and efficacy across diverse applications. It can coordinate all nodes within the network independently of thirdparty entities for unified decision-making and consistency, and is traceable and immutable, making blockchain particularly attractive for communication networks. Wireless networks are an important part of network and communication systems, their flexibility significantly enhances the coverage of communication systems, making their integration with blockchain undeniably promising. This synergy between wireless communication and blockchain has culminated in the development of Wireless Blockchain Networks (WBNs), which offers a more trustworthy communication paradigm for the forthcoming sixth-generation (6G) wireless networks. This paper serves as a comprehensive tutorial on the integration of WBN and 6G, to establish trustworthy wireless networks. We begin by defining the WBN and exploring its advantages, underscoring its broad applicability in various 6G scenarios. Furthermore, we present the key technologies underpinning WBN and its critical performance metrics. Subsequently, we provide a series of case studies that illustrate the integration of WBN with 6G use cases, which underscore the utility and effectiveness of WBN in practical communication settings, indicating potential benefits for future networks. Finally, we summarize the current practical blockchain cases deployed by network operators and discuss the future direction of WBN. This tutorial is expected to provide an in-depth exploration of the fundamental principles, technological architectures, and practical applications on the integration of blockchain with 6G.

The 3D-Flow OPRA Level-1 Trigger system is capable of extracting ALL valuable information from radiation, which can save taxpayers millions of dollars in HEP experiments. Unlike the traditional Level-1 Trigger algorithm implementation in “cabled-logic”, or the CERN-CMS approach of executing 128 algorithms, or the costly lower performant FPGA approach, the 3D-Flow architecture allows the execution of billions of different algorithms like a generic processor but has the advantage over any Pentium, Hypercube, or other processor/architecture in that it can execute specialized instructions (or “OPRA steps” for an optimized Object Pattern Real-Time Recognition Algorithm) to identify particles with the capability to execute at each “step” up to 26 operations in less than 3 ns. The 3D-Flow performance is further increased by its bypass switch and North East West South communication channels with neighbors. The 3D-Flow pyramid provides reduction from thousands of input channels to one or a few output channels. A 3D-Flow system with 10,485 Gbps input bandwidth, 8,192 electronic input channels, 43,000 x 3D-Flow processors and the capability to execute up to 40 “OPRA steps” on each processor is technology-independent, feasible in a 36 cm cube of electronics consuming less than 5 kW at a cost of less than $10.70 per channel. The 3D-Flow conceptual design allows the input data rate and the complexity of the real-time algorithm to increase satisfying the most stringent requirements, and can be used for tracking applications. It was proven feasible and functional in hardware using two FPGA chips in 2001 and two modular electronic boards in 2003, suitable to build 3D-Flow systems for detectors of any size. The inventor has faced many adversities and suppressions that have delayed its implementation. This caused a monetary burden on taxpayers and curbed the advancement of science that calls for an investigation to fix science as stated in the October 2018 issue of Scientific American.

Quantum annealing has emerged as a transformative approach in solving combinatorial optimization problems by leveraging the principles of quantum mechanics. A comprehensive survey of quantum annealing is presented, delineating its foundational concepts, state-of-the-art advancements, and diverse applications across domains such as logistics, finance, machine learning, and energy optimization. Notable contributions include the introduction of a systematic benchmarking framework to evaluate quantum annealing against classical optimization techniques across metrics like time-to-target, scalability, and energy efficiency. Additionally, novel hybrid quantum-classical paradigms and hardware advancements are explored, addressing challenges in qubit connectivity, coherence, and noise resilience. Optimization problems are mapped to suitable quantum annealing platforms, offering practical guidelines for leveraging quantum technologies in real-world scenarios. Critical gaps in scalability and integration with machine learning are identified, with future research directions proposed to bridge these challenges. These findings provide a holistic understanding of quantum annealing's potential, offering a foundational resource for advancing quantum computing in optimization.

Traditional drug discovery is time-intensive and costly, often spanning over a decade and incurring billions in expenses. This study introduces a novel machine learning pipeline tailored to predict and optimize inhibitors for Enhancer of Zeste Homolog 2 (EZH2), a critical epigenetic target implicated in cancer progression. Leveraging curated datasets from repositories like the Protein Data Bank, PubChem, and ChEMBL, the pipeline integrates feature selection using Lipinski's Rule of Five with advanced regression algorithms, achieving predictive metrics of R² = 0.75 and RMSE = 0.8 for inhibitory potency (pIC50 values). These results highlight the pipeline's strong predictive accuracy and reliability in identifying potent inhibitors. Unique to this approach is the focus on biologically interpretable descriptors, such as molecular weight and LogP, which enhance model transparency and relevance to pharmacokinetics. Validation through molecular docking (SwissDock) and RDKit reinforced robustness, with the model demonstrating a threefold improvement in efficiency by narrowing chemical libraries and reducing experimental burdens. By combining machine learning with pharmacological insights, this study addresses key bottlenecks in early-stage drug discovery, providing a scalable and adaptable framework for EZH2-targeted cancer therapeutics. While experimental validation remains indispensable, this computational approach significantly accelerates the prioritization of candidate compounds, contributing to cost-effective and efficient oncological drug development.

This letter presents an innovative single-site angle of arrival (AOA) and time difference of arrival (TDOA) hybrid localization algorithm for non-line-of-sight (NLOS) scenarios. An advanced ray-tracing (RT) method is employed to evaluate channel propagation in complex environments, transforming NLOS paths between the base station (BS) and mobile station (MS) into equivalent LOS paths by leveraging multipath propagation. This process identifies generalized sources (GSs) in the BS's environment, including LOS, reflective, diffractive, and transmissive sources. A novel weighting algorithm based on an assignment optimization approach is proposed to determine the locations of TDOA reference stations and assign weights to the TDOA/AOA hybrid equations. Furthermore, considering the significant NLOS noise in the hybrid TDOA/AOA equations, the proposed method integrates a weighting-enhanced iterative reweighted least squares (W-IRLS) algorithm to enhance the robustness of the localization process. Simulation results demonstrate that the proposed algorithm achieves Cramer-Rao lower bound (CRLB)-level accuracy under severe NLOS multipath conditions.

Vehicle-to-vehicle (V2V) communications require highly accurate and real-time channel modeling techniques. The dynamic nature of V2V scenarios demands continuous scene updates while ray-tracing (RT) algorithms typically require substantial computational time to achieve accurate predictions. This letter proposes an OptiX-based GPU-parallel dynamic shooting and bouncing ray tracing (DSRT) method that combines the advantages of shooting and bouncing ray (SBR) and RT approaches. The method employs an enhanced adaptive ray tube splitting mechanism to improve accuracy. A level of detail (LOD)-based dynamic scene update method further optimizes performance, while OptiX and CUDA acceleration enable efficient RT. Channel measurements in urban environments demonstrate the high accuracy of our algorithm. Performance evaluation results show that DSRT achieves a parallelization degree exceeding 98.8% and delivers exceptional speedup ratios.

This paper presents novel observations on the impact of ground-bounce noise (GBN) on logic-HIGH levels of the output response of Complementary Metal Oxide Semiconductor (CMOS) inverter circuits. While the impact of power supply noise on logic-HIGH is undeniable and comprehensively studied in the literature, the impact of ground bounce is perceived, and the analysis is majorly focused on logic-LOW. This work demonstrates as well as analyses the significant impact of GBN on logic-HIGH and the observations are supported by a theoretical investigation using both the analytical and small-signal approaches. Several examples are considered to validate the novel observations considering both the simulation and measurement-based case studies. The simulation-based case studies are performed using several CMOS technologies, and measurement-based case studies are performed using standard HEX inverter Integrated Circuits (ICs). There is a close correlation observed between the practical observations and the corresponding theoretical foundation.

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.