Abstract Many different polymers and polymer composites are used for engineering applications in which friction and wear are critical issues. This article briefs (a) the importance of polymer tribology in general, (b) the special design principles of polymer composites for low friction and wear under sliding against smooth metallic counterparts, and (c) synergistic effects of nano-particles and traditional fillers and fibers for an optimal tribological performance. Based on these fundamental aspects, the article reviews traditional applications of polymeric tribo-components in mechanical and automotive engineering, including slide elements in textile machines, filament wound bushings for harsh environments, cages of high-precision ball bearings in dental turbines, and hybrid bushings in Diesel fuel injection pumps. A following chapter on special developments of tribo-components outlines (a) ways to achieve electrical conductivity of polymer bearings, (b) the enhancement of self-lubrication and self-healing potential by the incorporation of micro-capsules into the polymer matrix, (c) modern additive manufacturing methods for friction and wear loaded polymer parts, (d) the application and properties of high temperature polymer coatings, and (e) the composition and use of polymer composites under friction at cryogenic temperature conditions.
This study provides a comprehensive characterization of various isolated single and half-bridge gate drivers over the entire temperature range from room temperature down to -194 °C. Unlike previous studies, which primarily focused on electrical output parameters such as rise/fall times and propagation delays, this paper also explores critical functionalities like undervoltage lockout (UVLO) and common-mode transient immunity (CMTI). In general, most drivers demonstrate a trend toward reduced rise/fall times and propagation delays as temperatures decreased. The UVLO threshold of most gate drivers tested was found to be quite stable down to low temperatures, but with exceptions. The first comprehensive characterization of the power-up and -down behavior of gate drivers identified critical operating states for practical use. In addition, CMTI testing revealed premature functional failures of some drivers at low temperatures.
Mikio Nakano, Hironori Takeuchi, Sadahiro Yoshikawa
et al.
This paper proposes to refer to the field of software engineering related to the life cycle of dialogue systems as Dialogue Systems Engineering, and surveys this field while also discussing its future directions. With the advancement of large language models, the core technologies underlying dialogue systems have significantly progressed. As a result, dialogue system technology is now expected to be applied to solving various societal issues and in business contexts. To achieve this, it is important to build, operate, and continuously improve dialogue systems correctly and efficiently. Accordingly, in addition to applying existing software engineering knowledge, it is becoming increasingly important to evolve software engineering tailored specifically to dialogue systems. In this paper, we enumerate the knowledge areas of dialogue systems engineering based on those of software engineering, as defined in the Software Engineering Body of Knowledge (SWEBOK) Version 4.0, and survey each area. Based on this survey, we identify unexplored topics in each area and discuss the future direction of dialogue systems engineering.
Although it has been more than four decades that the first components-based software development (CBSD) studies were conducted, there is still no standard method or tool for component selection which is widely accepted by the industry. The gulf between industry and academia contributes to the lack of an accepted tool. We conducted a mixed methods survey of nearly 100 people engaged in component-based software engineering practice or research to better understand the problems facing industry, how these needs could be addressed, and current best practices employed in component selection. We also sought to identify and prioritize quality criteria for component selection from an industry perspective. In response to the call for CBSD component selection tools to incorporate recent technical advances, we also explored the perceptions of professionals about AI-driven tools, present and envisioned.
Advancements in large language models (LLMs) have led to a surge of prompt engineering (PE) techniques that can enhance various requirements engineering (RE) tasks. However, current LLMs are often characterized by significant uncertainty and a lack of controllability. This absence of clear guidance on how to effectively prompt LLMs acts as a barrier to their trustworthy implementation in the RE field. We present the first roadmap-oriented systematic literature review of Prompt Engineering for RE (PE4RE). Following Kitchenham's and Petersen's secondary-study protocol, we searched six digital libraries, screened 867 records, and analyzed 35 primary studies. To bring order to a fragmented landscape, we propose a hybrid taxonomy that links technique-oriented patterns (e.g., few-shot, Chain-of-Thought) to task-oriented RE roles (elicitation, validation, traceability). Two research questions, with five sub-questions, map the tasks addressed, LLM families used, and prompt types adopted, and expose current limitations and research gaps. Finally, we outline a step-by-step roadmap showing how today's ad-hoc PE prototypes can evolve into reproducible, practitioner-friendly workflows.
Traditional drought monitoring primarily relies on ground observations, which are often limited in coverage. Consequently, many earlier drought analysis studies were limited to a narrow geographic area and a single drought index. This study aims to use multiple remote sensing parameters to provide a more comprehensive analysis of droughts in the Padiyathalawa catchment area, a dry zone river basin in Sri Lanka. Three satellite-derived indices, namely the Vegetation Condition Index (VCI), Temperature Condition Index (TCI), and Standardized Precipitation (SP), were integrated using the Principal Component Analysis (PCA) technique to derive a Combined Drought Index (CDI). The impact of spatially distributed drought on variations in river flow was further evaluated by employing the Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS) rainfall-runoff model, configured as a semi-distributed model with five (5) sub-catchments. The CDI analysis shows dry conditions from June to August and wetter periods influenced by the northeast monsoon. The developed CDI displayed a correlation with streamflow but requires refinement to reflect streamflow dynamics accurately during high rainfall events. While the HEC-HMS model effectively simulated streamflow (NSE = 0.78-0.92), limitations emerged in low-flow simulation, particularly when the discharge was below $0.1 \mathrm{~m}^{3} / \mathrm{sec}$. The drought-prone areas identified by the CDI were further analyzed using the HEC-HMS with a set of hypothetical drought scenarios. It was found that river flow reduces with increased drought severity and its effect reduces when the sub-catchment is further from the catchment outlet. This study highlights the potential of integrating remote sensing data, PCA, and hydrological modeling for drought assessment. The methodology and the results of this study can be used in formulating adaptation measures to reduce drought impacts.
In this contribution, the simulation-based digital twin of a high-speed turbomachine, i.e. a fan with a nominal speed of 16500 rpm, that has been developed for the aerospace industry with multiple uses in various platforms, and the corresponding findings are presented. The relevant simulation-based digital twin is created by first running various multi-physics engineering simulations, i.e. computational solid mechanics, computational fluid dynamics, and low-frequency electromagnetic analyses with Ansys software. Subsequent dynamic reduced order models (ROM) are formed and integrated together to create the digital twin that are bidirectionally connected with the asset. The digital twin is also validated via experimental data that are obtained with various setups for several parameters read at discrete locations via sensors, based on fidelity comparisons both between experiments and simulations, and between simulations and ROMs. Several operational scenarios including those with stress tests are run and the asset is checked against fatigue and thermal requirements for the rotor-stator assembly and the motor circuit respectively. This is done with an inhouse program that incrementally reads time, temperature, and rotational speed data and in which the relevant fatigue and thermal criteria are defined. This program is also used to simulate and analyse what-if scenarios. It is seen that vibration fatigue dominates all possible sources of failure in most cases. As a novel aspect, the fatigue induced by starts and stops of the fan is expressed as equivalent operating hours (EOH) depending on time and temperature parameters of the corresponding scenarios, as opposed to be taken as a constant value for all scenarios. Using this novelty, it is also conceptually demonstrated that simulation-based digital twins can play a significant role in operation and maintenance (O&M) when combined with conventional data-driven predictive maintenance techniques, not only for O&M teams but also asset owners and original equipment manufacturers (OEMs) particularly with the use of what-if analyses. Approaches to simulation-based digital twins in context with multi-physics and ROM fidelity are also discussed based on several digital twin maturity models.
Transcritical CO<sub>2</sub> thermal systems have emerged as leading solutions to addressing challenges such as "range anxiety in winter" and pronounced greenhouse effects associated with the working fluid in electric vehicle thermal systems. Nevertheless, the intricate interplay of transcritical cycles in the varied scenarios of electric vehicles introduces complexity, with performance and operational stability intricately linked to the refrigerant charge. This study conducts simulations to investigate the variability in refrigerant charging requirements for transcritical CO<sub>2</sub> thermal management systems under diverse operating conditions. We specifically examined the impact of three critical factors, namely ambient temperature, indoor airflow rate, and outdoor air velocity, on refrigerant requirements in different modes and their underlying mechanisms. In the heat pump mode, the demand for refrigerant charge increases with ambient temperatures and wind speed and decreases with cabinet air flow rate, with changes of 18.6%, 18.9%, and 6.16%, respectively. In the cooling mode, the refrigerant charge requirement decreases with ambient temperatures and cabinet air flow rate and increases with outdoor wind speeds, with changes of 7.03%, 7.85%, and 2.27%, respectively. In situations of nonoptimal charging, potential alterations in the interaction between system control variables and target variables contribute to system instability. This necessitates adjustments to refrigerant distribution to mitigate instability under specific operating conditions. The research outcomes hold substantial reference value for the optimization of electric vehicle air-conditioning accumulator designs, enhancement of energy efficiency, and improvement of overall thermal comfort.
Heating and ventilation. Air conditioning, Low temperature engineering. Cryogenic engineering. Refrigeration
This paper investigated the performance of Vedic multipliers in a 32-bit Multiplier-Accumulator Unit (MAC) by comparing Urdhva Tiryakbhyam and Nikhilam Sutras with various adder architectures. The goal was to identify the optimal combination of speed and resource efficiency. Urdhva Tiryakbhyam with CLA emerged as the fastest option, achieving a minimal delay of 0.709 ns. However, this came at the cost of higher resource utilization, measured in Logic Look-Up Tables (LUTs). Conversely, Nikhilam implementations generally required fewer LUTs, making them more resource-efficient, but they exhibited slightly slower performance. CLA consistently delivered the best delay for both Vedic multiplier types among the adder architectures. All the explored configurations are viable for practical implementation on Xilinx ISE 14.7. The key takeaway is that the choice between Urdhva Tiryakbhyam and Nikhilam and the specific adder architecture hinges on the application’s priorities.
Renan Lima Baima, Tiago Miguel Barao Caetano, Ana Carolina Oliveira Lima
et al.
The primary objective is to emphasize the merits of active methodologies and cross-disciplinary curricula in Requirement Engineering. This direction promises a holistic and applied trajectory for Computer Engineering education, supported by the outcomes of our case study, where artifact-centric learning proved effective, with 73% of students achieving the highest grade. Self-assessments further corroborated academic excellence, emphasizing students' engagement in skill enhancement and knowledge acquisition.
Transportation construction on the Qinghai-Tibet Plateau has always attracted much attention, and the completion of the Qinghai-Tibet Plateau Railway, one of the four major projects in the new century, is bound to accelerate the economic development of the plateau region and the completion of more infrastructure. Therefore, concrete materials are widely used in the construction of the plateau region, and the long-term performance of its materials and structures in this environment is worthy of attention. The plateau climate has three main characteristics: large temperature difference, low air pressure, and low humidity, which makes the concrete withstand different degrees of damage from preparation to use, thereby reducing the overall carrying capacity and safety of the building structure, reducing its service life, and increasing the risk of building erosion by the environment. This paper mainly summarizes and analyzes the factors affecting the performance of concrete in the plateau environment, that is, temperature, humidity and air pressure, and on this basis, explores their comprehensive influence. The comprehensive study shows that temperature could cause a freeze-thaw cycle of concrete, resulting in continuous accumulation of internal damage of concrete; Low temperature could cause water migration in concrete, easily causing concrete shrinkage and carbonization; Low air pressure could reduce the shrinkage and durability of concrete structures. Based on a series of theoretical studies, this paper further discusses the relevant solutions applicable to various practical engineering problems.
The "Information Retrieval in Software Engineering (IRSE)" at FIRE 2023 shared task introduces code comment classification, a challenging task that pairs a code snippet with a comment that should be evaluated as either useful or not useful to the understanding of the relevant code. We answer the code comment classification shared task challenge by providing a two-fold evaluation: from an algorithmic perspective, we compare the performance of classical machine learning systems and complement our evaluations from a data-driven perspective by generating additional data with the help of large language model (LLM) prompting to measure the potential increase in performance. Our best model, which took second place in the shared task, is a Neural Network with a Macro-F1 score of 88.401% on the provided seed data and a 1.5% overall increase in performance on the data generated by the LLM.
Cloud compute adoption has been growing since its inception in the early 2000's with estimates that the size of this market in terms of worldwide spend will increase from \$700 billion in 2021 to \$1.3 trillion in 2025. While there is a significant research activity in many areas of cloud computing technologies, we see little attention being paid to advancing software engineering practices needed to support the current and next generation of cloud native applications. By cloud native, we mean software that is designed and built specifically for deployment to a modern cloud platform. This paper frames the landscape of Cloud Native Software Engineering from a practitioners standpoint, and identifies several software engineering research opportunities that should be investigated. We cover specific engineering challenges associated with software architectures commonly used in cloud applications along with incremental challenges that are expected with emerging IoT/Edge computing use cases.
Jocelyn Ahmed Mazari, Antoine Reverberi, Pierre Yser
et al.
In this work, we propose a built-in Deep Learning Physics Optimization (DLPO) framework to set up a shape optimization study of the Duisburg Test Case (DTC) container vessel. We present two different applications: (1) sensitivity analysis to detect the most promising generic basis hull shapes, and (2) multi-objective optimization to quantify the trade-off between optimal hull forms. DLPO framework allows for the evaluation of design iterations automatically in an end-to-end manner. We achieved these results by coupling Extrality's Deep Learning Physics (DLP) model to a CAD engine and an optimizer. Our proposed DLP model is trained on full 3D volume data coming from RANS simulations, and it can provide accurate and high-quality 3D flow predictions in real-time, which makes it a good evaluator to perform optimization of new container vessel designs w.r.t the hydrodynamic efficiency. In particular, it is able to recover the forces acting on the vessel by integration on the hull surface with a mean relative error of 3.84\% \pm 2.179\% on the total resistance. Each iteration takes only 20 seconds, thus leading to a drastic saving of time and engineering efforts, while delivering valuable insight into the performance of the vessel, including RANS-like detailed flow information. We conclude that DLPO framework is a promising tool to accelerate the ship design process and lead to more efficient ships with better hydrodynamic performance.