Abstract
The growth in cloud computing, Big Data, AI and high-performance computing (HPC) necessitate the deployment of additional data centers (DC's) with high energy demands. The unprecedented increase in the Thermal Design Power (TDP) of the computing chips will require innovative cooling techniques. Furthermore, DC's are increasingly limited in their ability to add powerful GPU servers by power capacity constraints. As cooling energy use accounts for up to 40% of DC energy consumption, creative cooling solutions are urgently needed to allow deployment of additional servers, enhance sustainability and increase energy efficiency of DC's. The information in this study is provided from Start Campus' Sines facility supported by Alfa Laval for the heat exchanger and CO2 emission calculations. The study evaluates the performance and sustainability impact of various data center cooling strategies including an air-only deployment and a subsequent hybrid air/water cooling solution all utilizing sea water as the cooling source. We evaluate scenarios from 3 MW to 15+1 MW of IT load in 3 MW increments which correspond to the size of heat exchangers used in the Start Campus' modular system design. This study also evaluates the CO2 emissions compared to a conventional chiller system for all the presented scenarios. Results indicate that the effective use of the sea water cooled system combined with liquid cooled systems improve the efficiency of the DC, plays a role in decreasing the CO2 emissions and supports in achieving sustainability goals.

Abstract
The fast global expansion of online public services has transformed how governments interact with citizens, offering convenience and efficiency. However, this digital transformation also introduces significant security risks, as sensitive data exchanged between users and service providers over public networks are exposed to cyber threats. Thus, ensuring the security and trustworthiness of these services is critical to the success of Electronic Government (EGOV) initiatives. This study evaluates the information security posture of 3068 public service platforms across all 193 UN Member States through non-intrusive assessments conducted in 2023 and 2024. The evaluation focuses on three key dimensions: (i) the adoption of secure end-to-end communication protocols, (ii) the trustworthiness of digital certificate chains, and (iii) the exposure of hosting servers to known vulnerabilities. The findings reveal that while some progress has been made in securing online public services, substantial gaps remain in the implementation of international security standards and best practices. Many platforms continue to rely on outdated cryptographic protocols, misconfigured certificates, and unpatched vulnerabilities, leaving citizens and services vulnerable to cyber threats due to weaknesses that malicious actors can easily and inconspicuously identify. These insights emphasize the need for effective implementation of more comprehensive cybersecurity policies, proactive security assessments, and improved regulatory compliance checks. Additionally, this work provides actionable guidance for governments and system administrators to enhance the security of EGOV infrastructures by addressing persistent vulnerabilities and adopting robust cybersecurity practices.

Abstract
Rendering on conventional computers is capable of generating realistic imagery, but the computational complexity of these light transport algorithms is a limiting factor of image synthesis. Quantum computers have the potential to significantly improve rendering performance through reducing the underlying complexity of the algorithms behind light transport. This article investigates hybrid quantum-classical algorithms for ray tracing, a core component of most rendering techniques. Through a practical implementation of quantum ray tracing in a 3D environment, we show quantum approaches provide a quadratic improvement in query complexity compared to the equivalent classical approach. Based on domain specific knowledge, we then propose algorithms to significantly reduce the computation required for quantum ray tracing through exploiting image space coherence and a principled termination criteria for quantum searching. We show results obtained using a simulator for both Whitted style ray tracing, and for accelerating ray tracing operations when performing classical Monte Carlo integration for area lights and indirect illumination.

Abstract
Adaptive protocols enable the construction of more efficient state preparation circuits in variational quantum algorithms (VQAs) by utilizing data obtained from the quantum processor during the execution of the algorithm. This idea originated with Adaptive Derivative-Assembled Problem-Tailored variational quantum eigensolver (ADAPT-VQE), an algorithm that iteratively grows the state preparation circuit operator by operator, with each new operator accompanied by a new variational parameter, and where all parameters acquired thus far are optimized in each iteration. In ADAPT-VQE and other adaptive VQAs that followed it, it has been shown that initializing parameters to their optimal values from the previous iteration speeds up convergence and avoids shallow local traps in the parameter landscape. However, no other data from the optimization performed at one iteration is carried over to the next. In this work, we propose an improved quasi-Newton optimization protocol specifically tailored to adaptive VQAs. The distinctive feature in our proposal is that approximate second derivatives of the cost function are recycled across iterations in addition to optimal parameter values. We implement a quasi-Newton optimizer where an approximation to the inverse Hessian matrix is continuously built and grown across the iterations of an adaptive VQA. The resulting algorithm has the flavor of a continuous optimization where the dimension of the search space is augmented when the gradient norm falls below a given threshold. We show that this inter-optimization exchange of second-order information leads the approximate Hessian in the state of the optimizer to be consistently closer to the exact Hessian. As a result, our method achieves a superlinear convergence rate even in situations where the typical implementation of a quasi-Newton optimizer converges only linearly. Our protocol decreases the measurement costs in implementing adaptive VQAs on quantum hardware as well as the runtime of their classical simulation.

Abstract
Contracts and assertions are effective methods to enhance software quality by enforcing preconditions, postconditions, and invariants. Previous research has demonstrated the value of contracts in traditional software development. However, the adoption and impact of contracts in the context of mobile app development, particularly of Android apps, remain unexplored. To address this, we present the first large-scale empirical study on the use of contracts in Android apps, written in Java or Kotlin. We consider contract elements divided into five categories: conditional runtime exceptions, APIs, annotations, assertions, and other. We analyzed 2,390 Android apps from the F-Droid repository and processed 52,977 KLOC to determine 1) how and to what extent contracts are used, 2) which language features are used to denote contracts, 3) how contract usage evolves from the first to the last version, and 4) whether contracts are used safely in the context of program evolution and inheritance. Our findings include: 1) although most apps do not specify contracts, annotation-based approaches are the most popular; 2) apps that use contracts continue to use them in later versions, but the number of methods increases at a higher rate than the number of contracts; and 3) there are potentially unsafe specification changes when apps evolve and in subtyping relationships, which indicates a lack of specification stability. Finally, we present a qualitative study that gathers challenges faced by practitioners when using contracts and that validates our recommendations. © David R. Ferreira, Alexandra Mendes, João F. Ferreira, and Carolina Carreira.

Abstract
Software testing plays a fundamental role in software engineering, involving the systematic evaluation of software to identify defects, errors, and vulnerabilities from the early stages of the development process. Education in software testing is essential for students and professionals, as it promotes quality and favours the construction of reliable software solutions. However, motivating students to learn software testing may be a challenge. To overcome this, educators may incorporate some strategies into the teaching and learning process, such as real-world examples, interactive learning, and gamification. Gamification aims to make learning software testing more engaging for students by creating a more enjoyable experience. One approach that has proven effective is to use serious games. This paper presents a novel serious game to teach white-box testing test case design techniques, named GAMFLEW (GAMe For LEarning White-box testing). It describes the design, game mechanics, and its implementation. It also presents a preliminary evaluation experiment with students to assess the usability, learnability, and perceived problems, among other aspects. The results obtained are encouraging.